diff --git a/README.md b/README.md index 19d1e4f..ad09d3b 100644 --- a/README.md +++ b/README.md @@ -81,28 +81,6 @@ If you are mainly interested in how getML performs compared to other approaches, | [SFScores: Predicting health check scores][sfscoresnb] | featuretools | R-squared (getML 29.1%, featuretools 26.5%) | | [Stats: Predicting users' reputation][statsnb] | featuretools | R-squared (getML 98.1%, featuretools 96.6%) | -### Propositionalization - -In particular, we have benchmarked getML's _FastProp_ (short for fast propositionalization) against other implementations of the propositionalization algorithm. - -

- -

- -As we can see, _FastProp_ is true to its name: It achieves similar or slightly better performance than _featuretools_ or _tsfresh_, but generates features between 11x to 65x faster than these implementations. - -If you want to reproduce these results, please refer to the following notebooks: - -| | Results | Remarks | -| ------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| [Air pollution][airpollutionnb_prop] | ~65x faster than featuretools, ~33x faster than tsfresh | The predictive accuracy can be significantly improved by using RelMT instead of propositionalization approaches, please refer to [this notebook][airpollutionnb]. | -| [Dodgers][dodgersnb_prop] | ~42x faster than featuretools, ~75x faster than tsfresh | The predictive accuracy can be significantly improved by using the mapping preprocessor and/or more advanced feature learning algorithms, please refer to [this notebook][dodgersnb]. | -| [Interstate94][interstate94nb_prop] | ~55x faster than featuretools | | -| [Occupancy][occupancynb_prop] | ~87x faster than featuretools, ~41x faster than tsfresh | | -| [Robot][robotnb_prop] | ~162x faster than featuretools, ~77x faster than tsfresh | | - -These results are very hardware-dependent and may be different on your machine. However, we have no doubt that you will find that getML's _FastProp_ is significantly faster than _featuretools_ and _tsfresh_ while consuming considerably less memory. - ### Relational Dataset Repository Some benchmarks are also featured on the [Relational Dataset Repository](https://relational.fit.cvut.cz/): @@ -139,10 +117,5 @@ Some benchmarks are also featured on the [Relational Dataset Repository](https:/ [sfscoresnb]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/sfscores.ipynb [statsnb]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/stats.ipynb -[airpollutionnb_prop]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/propositionalization/air_pollution_prop.ipynb -[dodgersnb_prop]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/propositionalization/dodgers_prop.ipynb -[interstate94nb_prop]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/propositionalization/interstate94_prop.ipynb -[occupancynb_prop]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/propositionalization/occupancy_prop.ipynb -[robotnb_prop]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/propositionalization/robot_prop.ipynb diff --git a/adventure_works.ipynb b/adventure_works.ipynb index 438e4e8..d42fb27 100644 --- a/adventure_works.ipynb +++ b/adventure_works.ipynb @@ -102,27 +102,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220319231543.log.\n", + "getML engine is already running.\n", "\n", "\n", - "Loading pipelines...\n", - "[========================================] 100%\n", - "\n", "\n", - "Connected to project 'adventure_works'\n" + "Connected to project 'adventure_works'\n", + "http://localhost:1709/#/listprojects/adventure_works/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/adventure_works/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -293,55 +279,55 @@ " \n", " \n", " \n", - " ProductID\n", + " ProductID\n", " \n", " \n", " \n", - " MakeFlag\n", + " MakeFlag \n", " \n", " \n", " \n", - " FinishedGoodsFlag\n", + " ProductSubcategoryID\n", " \n", " \n", " \n", - " SafetyStockLevel\n", + " ProductModelID\n", " \n", " \n", " \n", - " ReorderPoint\n", + " SafetyStockLevel\n", " \n", " \n", " \n", - " DaysToManufacture\n", + " ReorderPoint\n", " \n", " \n", " \n", - " ProductSubcategoryID\n", + " StandardCost\n", " \n", " \n", " \n", - " ProductModelID\n", + " ListPrice\n", " \n", " \n", " \n", - " Name \n", + " FinishedGoodsFlag\n", " \n", " \n", " \n", - " ProductNumber\n", + " DaysToManufacture\n", " \n", " \n", " \n", - " Color \n", + " Name \n", " \n", " \n", " \n", - " StandardCost \n", + " ProductNumber\n", " \n", " \n", " \n", - " ListPrice \n", + " Color \n", " \n", " \n", " \n", @@ -401,47 +387,47 @@ " \n", " \n", " \n", - " unused_float\n", + " join_key\n", " \n", " \n", " \n", - " unused_float\n", + " categorical\n", " \n", " \n", " \n", - " unused_float\n", + " categorical \n", " \n", " \n", " \n", - " unused_float\n", + " categorical \n", " \n", " \n", " \n", - " unused_float\n", + " numerical\n", " \n", " \n", " \n", - " unused_float\n", + " numerical\n", " \n", " \n", " \n", - " unused_float\n", + " numerical\n", " \n", " \n", " \n", - " unused_float\n", + " numerical\n", " \n", " \n", " \n", - " unused_string \n", + " unused_float\n", " \n", " \n", " \n", - " unused_string\n", + " unused_float\n", " \n", " \n", " \n", - " unused_string\n", + " unused_string \n", " \n", " \n", " \n", @@ -509,29 +495,19 @@ " 0\n", " \n", " \n", - " \n", - " \n", - " \n", + " 1\n", " \n", - " \n", - " 1 \n", - " \n", + " \n", + " \n", + " 0\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " nan\n", " \n", - " \n", - " 0 \n", - " \n", + " \n", + " \n", + " nan\n", " \n", " \n", " \n", @@ -539,8 +515,8 @@ " \n", " \n", " \n", - " \n", - " 0\n", + " 1000 \n", @@ -552,8 +528,8 @@ " \n", " \n", " \n", - " \n", - " 1000\n", + " 750 \n", @@ -565,9 +541,9 @@ " \n", " \n", " \n", - " \n", - " 750\n", + " 0 \n", " \n", @@ -578,9 +554,9 @@ " \n", " \n", " \n", - " \n", + " \n", " 0 \n", " \n", @@ -592,7 +568,7 @@ " \n", " \n", " \n", - " nan0 \n", @@ -605,7 +581,7 @@ " \n", " \n", " \n", - " nan0 \n", @@ -625,14 +601,6 @@ " \n", " \n", " \n", - " 0.0000\n", - " \n", - " \n", - " \n", - " 0.0000\n", - " \n", - " \n", - " \n", " NULL\n", " \n", " \n", @@ -686,29 +654,19 @@ " 1\n", " \n", " \n", - " \n", - " \n", - " \n", + " 2\n", " \n", - " \n", - " 2 \n", - " \n", + " \n", + " \n", + " 0\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " nan\n", " \n", - " \n", - " 0 \n", - " \n", + " \n", + " \n", + " nan\n", " \n", " \n", " \n", @@ -716,8 +674,8 @@ " \n", " \n", " \n", - " \n", - " 0\n", + " 1000 \n", @@ -729,8 +687,8 @@ " \n", " \n", " \n", - " \n", - " 1000\n", + " 750 \n", @@ -742,9 +700,9 @@ " \n", " \n", " \n", - " \n", - " 750\n", + " 0 \n", " \n", @@ -755,9 +713,9 @@ " \n", " \n", " \n", - " \n", + " \n", " 0 \n", " \n", @@ -769,7 +727,7 @@ " \n", " \n", " \n", - " nan0 \n", @@ -782,7 +740,7 @@ " \n", " \n", " \n", - " nan0 \n", @@ -802,14 +760,6 @@ " \n", " \n", " \n", - " 0.0000\n", - " \n", - " \n", - " \n", - " 0.0000\n", - " \n", - " \n", - " \n", " NULL\n", " \n", " \n", @@ -863,29 +813,19 @@ " 2\n", " \n", " \n", - " \n", - " \n", - " \n", + " 3\n", " \n", - " \n", - " 3 \n", - " \n", + " \n", + " \n", + " 1\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " nan\n", " \n", - " \n", - " 1 \n", - " \n", + " \n", + " \n", + " nan\n", " \n", " \n", " \n", @@ -893,8 +833,8 @@ " \n", " \n", " \n", - " \n", - " 0\n", + " 800 \n", @@ -906,8 +846,8 @@ " \n", " \n", " \n", - " \n", - " 800\n", + " 600 \n", @@ -919,9 +859,9 @@ " \n", " \n", " \n", - " \n", - " 600\n", + " 0 \n", " \n", @@ -932,9 +872,9 @@ " \n", " \n", " \n", - " \n", - " 1\n", + " 0 \n", " \n", @@ -946,7 +886,7 @@ " \n", " \n", " \n", - " nan0 \n", @@ -959,7 +899,7 @@ " \n", " \n", " \n", - " nan1 \n", @@ -979,14 +919,6 @@ " \n", " \n", " \n", - " 0.0000\n", - " \n", - " \n", - " \n", - " 0.0000\n", - " \n", - " \n", - " \n", " NULL\n", " \n", " \n", @@ -1040,29 +972,19 @@ " 3\n", " \n", " \n", - " \n", - " \n", - " \n", + " 4\n", " \n", - " \n", - " 4 \n", - " \n", + " \n", + " \n", + " 0\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " nan\n", " \n", - " \n", - " 0 \n", - " \n", + " \n", + " \n", + " nan\n", " \n", " \n", " \n", @@ -1070,8 +992,8 @@ " \n", " \n", " \n", - " \n", - " 0\n", + " 800 \n", @@ -1083,8 +1005,8 @@ " \n", " \n", " \n", - " \n", - " 800\n", + " 600 \n", @@ -1096,9 +1018,9 @@ " \n", " \n", " \n", - " \n", - " 600\n", + " 0 \n", " \n", @@ -1109,9 +1031,9 @@ " \n", " \n", " \n", - " \n", + " \n", " 0 \n", " \n", @@ -1123,7 +1045,7 @@ " \n", " \n", " \n", - " nan0 \n", @@ -1136,7 +1058,7 @@ " \n", " \n", " \n", - " nan0 \n", @@ -1156,15 +1078,7 @@ " \n", " \n", " \n", - " 0.0000\n", - " \n", - " \n", - " \n", - " 0.0000\n", - " \n", - " \n", - " \n", - " NULL\n", + " NULL\n", " \n", " \n", " \n", @@ -1217,29 +1131,19 @@ " 4\n", " \n", " \n", - " \n", - " \n", - " \n", + " 316\n", " \n", - " \n", - " 316 \n", - " \n", + " \n", + " \n", + " 1\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " nan\n", " \n", - " \n", - " 1 \n", - " \n", + " \n", + " \n", + " nan\n", " \n", " \n", " \n", @@ -1247,8 +1151,8 @@ " \n", " \n", " \n", - " \n", - " 0\n", + " 800 \n", @@ -1260,8 +1164,8 @@ " \n", " \n", " \n", - " \n", - " 800\n", + " 600 \n", @@ -1273,9 +1177,9 @@ " \n", " \n", " \n", - " \n", - " 600\n", + " 0 \n", " \n", @@ -1286,9 +1190,9 @@ " \n", " \n", " \n", - " \n", - " 1\n", + " 0 \n", " \n", @@ -1300,7 +1204,7 @@ " \n", " \n", " \n", - " nan0 \n", @@ -1313,7 +1217,7 @@ " \n", " \n", " \n", - " nan1 \n", @@ -1333,14 +1237,6 @@ " \n", " \n", " \n", - " 0.0000\n", - " \n", - " \n", - " \n", - " 0.0000\n", - " \n", - " \n", - " \n", " NULL\n", " \n", " \n", @@ -1394,29 +1290,19 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " ...\n", " \n", - " \n", - " ... \n", - " \n", + " \n", + " \n", + " ...\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " ...\n", " \n", - " \n", - " ... \n", - " \n", + " \n", + " \n", + " ...\n", " \n", " \n", " \n", @@ -1424,7 +1310,7 @@ " \n", " \n", " \n", - " \n", + " \n", " ... \n", + " \n", " ... \n", + " \n", " ... \n", " \n", @@ -1463,9 +1349,9 @@ " \n", " \n", " \n", - " \n", + " \n", " ... \n", " \n", @@ -1557,43 +1443,25 @@ " ...\n", " \n", " \n", - " \n", - " ...\n", - " \n", - " \n", - " \n", - " ...\n", - " \n", - " \n", " \n", " \n", " \n", " 499\n", " \n", " \n", - " \n", - " \n", - " \n", + " 995\n", " \n", - " \n", - " 995 \n", - " \n", + " \n", + " \n", + " 1\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 5\n", " \n", - " \n", - " 1 \n", - " \n", + " \n", + " \n", + " 96\n", " \n", " \n", " \n", @@ -1601,8 +1469,8 @@ " \n", " \n", " \n", - " \n", - " 1\n", + " 500 \n", @@ -1614,8 +1482,8 @@ " \n", " \n", " \n", - " \n", - " 500\n", + " 375 \n", @@ -1626,11 +1494,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 375 \n", + " 44.9506\n", " \n", " \n", @@ -1639,11 +1507,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 101.24\n", " \n", " \n", @@ -1654,7 +1522,7 @@ " \n", " \n", " \n", - " 51 \n", @@ -1667,7 +1535,7 @@ " \n", " \n", " \n", - " 961 \n", @@ -1687,14 +1555,6 @@ " \n", " \n", " \n", - " 44.9506\n", - " \n", - " \n", - " \n", - " 101.2400\n", - " \n", - " \n", - " \n", " NULL\n", " \n", " \n", @@ -1748,29 +1608,19 @@ " 500\n", " \n", " \n", - " \n", - " \n", - " \n", + " 996\n", " \n", - " \n", - " 996 \n", - " \n", + " \n", + " \n", + " 1\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 5\n", " \n", - " \n", - " 1 \n", - " \n", + " \n", + " \n", + " 97\n", " \n", " \n", " \n", @@ -1778,8 +1628,8 @@ " \n", " \n", " \n", - " \n", - " 1\n", + " 500 \n", @@ -1791,8 +1641,8 @@ " \n", " \n", " \n", - " \n", - " 500\n", + " 375 \n", @@ -1803,11 +1653,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 375 \n", + " 53.9416\n", " \n", " \n", @@ -1816,11 +1666,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 121.49\n", " \n", " \n", @@ -1831,7 +1681,7 @@ " \n", " \n", " \n", - " 51 \n", @@ -1844,7 +1694,7 @@ " \n", " \n", " \n", - " 971 \n", @@ -1864,14 +1714,6 @@ " \n", " \n", " \n", - " 53.9416\n", - " \n", - " \n", - " \n", - " 121.4900\n", - " \n", - " \n", - " \n", " NULL\n", " \n", " \n", @@ -1925,29 +1767,19 @@ " 501\n", " \n", " \n", - " \n", - " \n", - " \n", + " 997\n", " \n", - " \n", - " 997 \n", - " \n", + " \n", + " \n", + " 1\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 2\n", " \n", - " \n", - " 1 \n", - " \n", + " \n", + " \n", + " 31\n", " \n", " \n", " \n", @@ -1955,8 +1787,8 @@ " \n", " \n", " \n", - " \n", - " 1\n", + " 100 \n", @@ -1968,8 +1800,8 @@ " \n", " \n", " \n", - " \n", - " 100\n", + " 75 \n", @@ -1980,11 +1812,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 75 \n", + " 343.6496\n", " \n", " \n", @@ -1993,11 +1825,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 4 \n", + " 539.99\n", " \n", " \n", @@ -2008,7 +1840,7 @@ " \n", " \n", " \n", - " 21 \n", @@ -2021,7 +1853,7 @@ " \n", " \n", " \n", - " 314 \n", @@ -2041,14 +1873,6 @@ " \n", " \n", " \n", - " 343.6496\n", - " \n", - " \n", - " \n", - " 539.9900\n", - " \n", - " \n", - " \n", " 44\n", " \n", " \n", @@ -2102,29 +1926,19 @@ " 502\n", " \n", " \n", - " \n", - " \n", - " \n", + " 998\n", " \n", - " \n", - " 998 \n", - " \n", + " \n", + " \n", + " 1\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 2\n", " \n", - " \n", - " 1 \n", - " \n", + " \n", + " \n", + " 31\n", " \n", " \n", " \n", @@ -2132,8 +1946,8 @@ " \n", " \n", " \n", - " \n", - " 1\n", + " 100 \n", @@ -2145,8 +1959,8 @@ " \n", " \n", " \n", - " \n", - " 100\n", + " 75 \n", @@ -2157,11 +1971,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 75 \n", + " 343.6496\n", " \n", " \n", @@ -2170,11 +1984,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 4 \n", + " 539.99\n", " \n", " \n", @@ -2185,7 +1999,7 @@ " \n", " \n", " \n", - " 21 \n", @@ -2198,7 +2012,7 @@ " \n", " \n", " \n", - " 314 \n", @@ -2218,14 +2032,6 @@ " \n", " \n", " \n", - " 343.6496\n", - " \n", - " \n", - " \n", - " 539.9900\n", - " \n", - " \n", - " \n", " 48\n", " \n", " \n", @@ -2279,29 +2085,19 @@ " 503\n", " \n", " \n", - " \n", - " \n", - " \n", + " 999\n", " \n", - " \n", - " 999 \n", - " \n", + " \n", + " \n", + " 1\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 2\n", " \n", - " \n", - " 1 \n", - " \n", + " \n", + " \n", + " 31\n", " \n", " \n", " \n", @@ -2309,8 +2105,8 @@ " \n", " \n", " \n", - " \n", - " 1\n", + " 100 \n", @@ -2322,8 +2118,8 @@ " \n", " \n", " \n", - " \n", - " 100\n", + " 75 \n", @@ -2334,11 +2130,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 75 \n", + " 343.6496\n", " \n", " \n", @@ -2347,11 +2143,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 4 \n", + " 539.99\n", " \n", " \n", @@ -2362,7 +2158,7 @@ " \n", " \n", " \n", - " 21 \n", @@ -2375,7 +2171,7 @@ " \n", " \n", " \n", - " 314 \n", @@ -2395,14 +2191,6 @@ " \n", " \n", " \n", - " 343.6496\n", - " \n", - " \n", - " \n", - " 539.9900\n", - " \n", - " \n", - " \n", " 52\n", " \n", " \n", @@ -2457,26 +2245,26 @@ "\n", "

\n", " 504 rows x 25 columns
\n", - " memory usage: 0.19 MB
\n", + " memory usage: 0.16 MB
\n", " name: Product
\n", " type: getml.DataFrame
\n", " url: http://localhost:1709/#/getdataframe/adventure_works/Product/\n", "

\n" ], "text/plain": [ - "name ProductID MakeFlag FinishedGoodsFlag SafetyStockLevel ... SellStartDate SellEndDate \n", - "role unused_float unused_float unused_float unused_float ... unused_string unused_string\n", - " 0 1 0 0 1000 ... 2008-04-30 00:00:00 NULL \n", - " 1 2 0 0 1000 ... 2008-04-30 00:00:00 NULL \n", - " 2 3 1 0 800 ... 2008-04-30 00:00:00 NULL \n", - " 3 4 0 0 800 ... 2008-04-30 00:00:00 NULL \n", - " 4 316 1 0 800 ... 2008-04-30 00:00:00 NULL \n", - " ... ... ... ... ... ... \n", - " 499 995 1 1 500 ... 2013-05-30 00:00:00 NULL \n", - " 500 996 1 1 500 ... 2013-05-30 00:00:00 NULL \n", - " 501 997 1 1 100 ... 2013-05-30 00:00:00 NULL \n", - " 502 998 1 1 100 ... 2013-05-30 00:00:00 NULL \n", - " 503 999 1 1 100 ... 2013-05-30 00:00:00 NULL \n", + "name ProductID MakeFlag ProductSubcategoryID ProductModelID ... SellStartDate SellEndDate \n", + "role join_key categorical categorical categorical ... unused_string unused_string\n", + " 0 1 0 nan nan ... 2008-04-30 00:00:00 NULL \n", + " 1 2 0 nan nan ... 2008-04-30 00:00:00 NULL \n", + " 2 3 1 nan nan ... 2008-04-30 00:00:00 NULL \n", + " 3 4 0 nan nan ... 2008-04-30 00:00:00 NULL \n", + " 4 316 1 nan nan ... 2008-04-30 00:00:00 NULL \n", + " ... ... ... ... ... ... \n", + " 499 995 1 5 96 ... 2013-05-30 00:00:00 NULL \n", + " 500 996 1 5 97 ... 2013-05-30 00:00:00 NULL \n", + " 501 997 1 2 31 ... 2013-05-30 00:00:00 NULL \n", + " 502 998 1 2 31 ... 2013-05-30 00:00:00 NULL \n", + " 503 999 1 2 31 ... 2013-05-30 00:00:00 NULL \n", "\n", "name DiscontinuedDate rowguid ModifiedDate \n", "role unused_string unused_string unused_string \n", @@ -2494,7 +2282,7 @@ "\n", "\n", "504 rows x 25 columns\n", - "memory usage: 0.19 MB\n", + "memory usage: 0.16 MB\n", "name: Product\n", "type: getml.DataFrame\n", "url: http://localhost:1709/#/getdataframe/adventure_works/Product/" @@ -2565,47 +2353,47 @@ " \n", " \n", " \n", - " SalesOrderID\n", + " ModifiedDate\n", " \n", " \n", " \n", - " SalesOrderDetailID\n", + " SalesOrderID\n", " \n", " \n", " \n", - " OrderQty\n", + " SalesOrderDetailID\n", " \n", " \n", " \n", - " ProductID\n", + " ProductID\n", " \n", " \n", " \n", - " SpecialOfferID\n", + " SpecialOfferID\n", " \n", " \n", " \n", - " CarrierTrackingNumber\n", + " OrderQty\n", " \n", " \n", " \n", - " UnitPrice \n", + " UnitPrice\n", " \n", " \n", " \n", - " UnitPriceDiscount\n", + " UnitPriceDiscount\n", " \n", " \n", " \n", - " LineTotal \n", + " LineTotal\n", " \n", " \n", " \n", - " rowguid \n", + " CarrierTrackingNumber\n", " \n", " \n", " \n", - " ModifiedDate \n", + " rowguid \n", " \n", " \n", " \n", @@ -2617,47 +2405,99 @@ " \n", " \n", " \n", - " unused_float\n", + " time_stamp\n", " \n", " \n", " \n", - " unused_float\n", + " join_key\n", " \n", " \n", " \n", - " unused_float\n", + " join_key\n", " \n", " \n", " \n", - " unused_float\n", + " join_key\n", " \n", " \n", " \n", - " unused_float\n", + " join_key\n", " \n", " \n", " \n", - " unused_string \n", + " numerical\n", " \n", " \n", " \n", - " unused_string\n", + " numerical\n", " \n", " \n", " \n", - " unused_string \n", + " numerical\n", " \n", " \n", " \n", - " unused_string\n", + " numerical\n", + " \n", + " \n", + " \n", + " unused_string \n", " \n", " \n", " \n", " unused_string \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " unused_string \n", + " unit\n", + " \n", + " \n", + " \n", + " time stamp, comparison only\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2669,16 +2509,23 @@ " 0\n", " \n", " \n", - " \n", - " \n", - " \n", + " 2011-05-31\n", " \n", - " \n", - " 43659 \n", - " \n", + " \n", + " \n", + " 43659\n", + " \n", + " \n", + " \n", + " 1\n", + " \n", + " \n", + " \n", + " 776\n", + " \n", + " \n", + " \n", + " 1\n", " \n", " \n", " \n", @@ -2686,7 +2533,7 @@ " \n", " \n", " \n", - " \n", + " \n", " 1 \n", - " 1 \n", + " 2024.994\n", " \n", " \n", @@ -2712,8 +2559,8 @@ " \n", " \n", " \n", - " \n", - " 776\n", + " 0 \n", @@ -2724,11 +2571,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 2024.994\n", " \n", " \n", @@ -2738,41 +2585,32 @@ " \n", " \n", " \n", - " 2024.9940\n", + " B207C96D-D9E6-402B-8470-2CC176C4...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 1\n", + " \n", " \n", - " 0.0000\n", + " 2011-05-31\n", " \n", " \n", " \n", - " 2024.994000\n", + " 43659\n", " \n", " \n", " \n", - " B207C96D-D9E6-402B-8470-2CC176C4...\n", + " 2\n", " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " 777\n", " \n", " \n", - " \n", - " \n", - " \n", - " 1\n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " 43659 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -2780,8 +2618,8 @@ " \n", " \n", " \n", - " \n", - " 2\n", + " 3 \n", @@ -2792,11 +2630,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 3 \n", + " 2024.994\n", " \n", " \n", @@ -2806,8 +2644,8 @@ " \n", " \n", " \n", - " \n", - " 777\n", + " 0 \n", @@ -2818,11 +2656,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 6074.982\n", " \n", " \n", @@ -2832,41 +2670,32 @@ " \n", " \n", " \n", - " 2024.9940\n", + " 7ABB600D-1E77-41BE-9FE5-B9142CFC...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 2\n", + " \n", " \n", - " 0.0000\n", + " 2011-05-31\n", " \n", " \n", " \n", - " 6074.982000\n", + " 43659\n", " \n", " \n", " \n", - " 7ABB600D-1E77-41BE-9FE5-B9142CFC...\n", + " 3\n", " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " 778\n", " \n", " \n", - " \n", - " \n", - " \n", - " 2\n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " 43659 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -2874,8 +2703,8 @@ " \n", " \n", " \n", - " \n", - " 3\n", + " 1 \n", @@ -2886,11 +2715,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 2024.994\n", " \n", " \n", @@ -2900,8 +2729,8 @@ " \n", " \n", " \n", - " \n", - " 778\n", + " 0 \n", @@ -2912,11 +2741,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 2024.994\n", " \n", " \n", @@ -2926,41 +2755,32 @@ " \n", " \n", " \n", - " 2024.9940\n", + " 475CF8C6-49F6-486E-B0AD-AFC6A50C...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 3\n", + " \n", " \n", - " 0.0000\n", + " 2011-05-31\n", " \n", " \n", " \n", - " 2024.994000\n", + " 43659\n", " \n", " \n", " \n", - " 475CF8C6-49F6-486E-B0AD-AFC6A50C...\n", + " 4\n", " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " 771\n", " \n", " \n", - " \n", - " \n", - " \n", - " 3\n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 43659 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -2968,8 +2788,8 @@ " \n", " \n", " \n", - " \n", - " 4\n", + " 1 \n", @@ -2980,11 +2800,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 2039.994\n", " \n", " \n", @@ -2994,8 +2814,8 @@ " \n", " \n", " \n", - " \n", - " 771\n", + " 0 \n", @@ -3006,11 +2826,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 2039.994\n", " \n", " \n", @@ -3020,41 +2840,32 @@ " \n", " \n", " \n", - " 2039.9940\n", + " 04C4DE91-5815-45D6-8670-F462719F...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 4\n", + " \n", " \n", - " 0.0000\n", + " 2011-05-31\n", " \n", " \n", " \n", - " 2039.994000\n", + " 43659\n", " \n", " \n", " \n", - " 04C4DE91-5815-45D6-8670-F462719F...\n", + " 5\n", " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " 772\n", " \n", " \n", - " \n", - " \n", - " \n", - " 4\n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " 43659 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -3062,8 +2873,8 @@ " \n", " \n", " \n", - " \n", - " 5\n", + " 1 \n", @@ -3074,11 +2885,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 2039.994\n", " \n", " \n", @@ -3088,8 +2899,8 @@ " \n", " \n", " \n", - " \n", - " 772\n", + " 0 \n", @@ -3100,11 +2911,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 2039.994\n", " \n", " \n", @@ -3114,36 +2925,40 @@ " \n", " \n", " \n", - " 2039.9940\n", + " 5A74C7D2-E641-438E-A7AC-37BF2328...\n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " 0.0000\n", + " ...\n", " \n", " \n", " \n", - " 2039.994000\n", + " ...\n", " \n", " \n", " \n", - " 5A74C7D2-E641-438E-A7AC-37BF2328...\n", + " ...\n", " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " ...\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " ...\n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " ... \n", + " \n", " ... \n", " \n", @@ -3169,7 +2984,7 @@ " \n", " \n", " \n", - " \n", + " \n", " ... \n", + " \n", " ... \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " ... \n", " \n", @@ -3211,38 +3013,29 @@ " ...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 121312\n", + " \n", " \n", - " ...\n", + " 2014-06-30\n", " \n", " \n", " \n", - " ...\n", + " 75122\n", " \n", " \n", " \n", - " ...\n", + " 121313\n", " \n", " \n", " \n", - " ...\n", + " 878\n", " \n", " \n", - " \n", - " \n", - " \n", - " 121312\n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 75122 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -3250,8 +3043,8 @@ " \n", " \n", " \n", - " \n", - " 121313\n", + " 1 \n", @@ -3262,11 +3055,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 21.98\n", " \n", " \n", @@ -3276,8 +3069,8 @@ " \n", " \n", " \n", - " \n", - " 878\n", + " 0 \n", @@ -3288,11 +3081,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 21.98\n", " \n", " \n", @@ -3302,41 +3095,32 @@ " \n", " \n", " \n", - " 21.9800\n", + " 8CAD6675-18CC-4F47-8287-97B41A8E...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 121313\n", + " \n", " \n", - " 0.0000\n", + " 2014-06-30\n", " \n", " \n", " \n", - " 21.980000\n", + " 75122\n", " \n", " \n", " \n", - " 8CAD6675-18CC-4F47-8287-97B41A8E...\n", + " 121314\n", " \n", " \n", " \n", - " 2014-06-30 00:00:00\n", + " 712\n", " \n", " \n", - " \n", - " \n", - " \n", - " 121313\n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 75122 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -3344,8 +3128,8 @@ " \n", " \n", " \n", - " \n", - " 121314\n", + " 1 \n", @@ -3356,11 +3140,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 8.99\n", " \n", " \n", @@ -3370,8 +3154,8 @@ " \n", " \n", " \n", - " \n", - " 712\n", + " 0 \n", @@ -3382,11 +3166,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 8.99\n", " \n", " \n", @@ -3396,41 +3180,32 @@ " \n", " \n", " \n", - " 8.9900\n", + " 84F1C363-1C50-4442-BE16-541C59B6...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 121314\n", + " \n", " \n", - " 0.0000\n", + " 2014-06-30\n", " \n", " \n", " \n", - " 8.990000\n", + " 75123\n", " \n", " \n", " \n", - " 84F1C363-1C50-4442-BE16-541C59B6...\n", + " 121315\n", " \n", " \n", " \n", - " 2014-06-30 00:00:00\n", + " 878\n", " \n", " \n", - " \n", - " \n", - " \n", - " 121314\n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 75123 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -3438,8 +3213,8 @@ " \n", " \n", " \n", - " \n", - " 121315\n", + " 1 \n", @@ -3450,11 +3225,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 21.98\n", " \n", " \n", @@ -3464,8 +3239,8 @@ " \n", " \n", " \n", - " \n", - " 878\n", + " 0 \n", @@ -3476,11 +3251,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 21.98\n", " \n", " \n", @@ -3490,41 +3265,32 @@ " \n", " \n", " \n", - " 21.9800\n", + " C18B6476-429F-4BB1-828E-2BE5F82A...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 121315\n", + " \n", " \n", - " 0.0000\n", + " 2014-06-30\n", " \n", " \n", " \n", - " 21.980000\n", + " 75123\n", " \n", " \n", " \n", - " C18B6476-429F-4BB1-828E-2BE5F82A...\n", + " 121316\n", " \n", " \n", " \n", - " 2014-06-30 00:00:00\n", + " 879\n", " \n", " \n", - " \n", - " \n", - " \n", - " 121315\n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 75123 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -3532,8 +3298,8 @@ " \n", " \n", " \n", - " \n", - " 121316\n", + " 1 \n", @@ -3545,9 +3311,9 @@ " \n", " \n", " \n", - " \n", - " 1\n", + " 159 \n", " \n", @@ -3558,8 +3324,8 @@ " \n", " \n", " \n", - " \n", - " 879\n", + " 0 \n", @@ -3571,9 +3337,9 @@ " \n", " \n", " \n", - " \n", - " 1\n", + " 159 \n", " \n", @@ -3584,41 +3350,32 @@ " \n", " \n", " \n", - " 159.0000\n", + " 75A89C6A-C60A-47EA-8A52-B52A9C43...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 121316\n", + " \n", " \n", - " 0.0000\n", + " 2014-06-30\n", " \n", " \n", " \n", - " 159.000000\n", + " 75123\n", " \n", " \n", " \n", - " 75A89C6A-C60A-47EA-8A52-B52A9C43...\n", + " 121317\n", " \n", " \n", " \n", - " 2014-06-30 00:00:00\n", + " 712\n", " \n", " \n", - " \n", - " \n", - " \n", - " 121316\n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 75123 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -3626,8 +3383,8 @@ " \n", " \n", " \n", - " \n", - " 121317\n", + " 1 \n", @@ -3638,11 +3395,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 8.99\n", " \n", " \n", @@ -3652,8 +3409,8 @@ " \n", " \n", " \n", - " \n", - " 712\n", + " 0 \n", @@ -3664,11 +3421,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 8.99\n", " \n", " \n", @@ -3678,25 +3435,9 @@ " \n", " \n", " \n", - " 8.9900\n", - " \n", - " \n", - " \n", - " 0.0000\n", - " \n", - " \n", - " \n", - " 8.990000\n", - " \n", - " \n", - " \n", " 73646D26-0461-450D-8019-2C6C8586...\n", " \n", " \n", - " \n", - " 2014-06-30 00:00:00\n", - " \n", - " \n", " \n", " \n", " \n", @@ -3704,44 +3445,46 @@ "\n", "

\n", " 121317 rows x 11 columns
\n", - " memory usage: 21.84 MB
\n", + " memory usage: 14.08 MB
\n", " name: SalesOrderDetail
\n", " type: getml.DataFrame
\n", " url: http://localhost:1709/#/getdataframe/adventure_works/SalesOrderDetail/\n", "

\n" ], "text/plain": [ - " name SalesOrderID SalesOrderDetailID OrderQty ProductID ... UnitPrice UnitPriceDiscount\n", - " role unused_float unused_float unused_float unused_float ... unused_string unused_string \n", - " 0 43659 1 1 776 ... 2024.9940 0.0000 \n", - " 1 43659 2 3 777 ... 2024.9940 0.0000 \n", - " 2 43659 3 1 778 ... 2024.9940 0.0000 \n", - " 3 43659 4 1 771 ... 2039.9940 0.0000 \n", - " 4 43659 5 1 772 ... 2039.9940 0.0000 \n", - " ... ... ... ... ... ... \n", - "121312 75122 121313 1 878 ... 21.9800 0.0000 \n", - "121313 75122 121314 1 712 ... 8.9900 0.0000 \n", - "121314 75123 121315 1 878 ... 21.9800 0.0000 \n", - "121315 75123 121316 1 879 ... 159.0000 0.0000 \n", - "121316 75123 121317 1 712 ... 8.9900 0.0000 \n", + " name ModifiedDate SalesOrderID SalesOrderDetailID ProductID ... UnitPrice UnitPriceDiscount\n", + " role time_stamp join_key join_key join_key ... numerical numerical\n", + " unit time stamp, comparison only ... \n", + " 0 2011-05-31 43659 1 776 ... 2024.994 0\n", + " 1 2011-05-31 43659 2 777 ... 2024.994 0\n", + " 2 2011-05-31 43659 3 778 ... 2024.994 0\n", + " 3 2011-05-31 43659 4 771 ... 2039.994 0\n", + " 4 2011-05-31 43659 5 772 ... 2039.994 0\n", + " ... ... ... ... ... ...\n", + "121312 2014-06-30 75122 121313 878 ... 21.98 0\n", + "121313 2014-06-30 75122 121314 712 ... 8.99 0\n", + "121314 2014-06-30 75123 121315 878 ... 21.98 0\n", + "121315 2014-06-30 75123 121316 879 ... 159 0\n", + "121316 2014-06-30 75123 121317 712 ... 8.99 0\n", "\n", - " name LineTotal rowguid ModifiedDate \n", - " role unused_string unused_string unused_string \n", - " 0 2024.994000 B207C96D-D9E6-402B-8470-2CC176C4... 2011-05-31 00:00:00\n", - " 1 6074.982000 7ABB600D-1E77-41BE-9FE5-B9142CFC... 2011-05-31 00:00:00\n", - " 2 2024.994000 475CF8C6-49F6-486E-B0AD-AFC6A50C... 2011-05-31 00:00:00\n", - " 3 2039.994000 04C4DE91-5815-45D6-8670-F462719F... 2011-05-31 00:00:00\n", - " 4 2039.994000 5A74C7D2-E641-438E-A7AC-37BF2328... 2011-05-31 00:00:00\n", - " ... ... ... \n", - "121312 21.980000 8CAD6675-18CC-4F47-8287-97B41A8E... 2014-06-30 00:00:00\n", - "121313 8.990000 84F1C363-1C50-4442-BE16-541C59B6... 2014-06-30 00:00:00\n", - "121314 21.980000 C18B6476-429F-4BB1-828E-2BE5F82A... 2014-06-30 00:00:00\n", - "121315 159.000000 75A89C6A-C60A-47EA-8A52-B52A9C43... 2014-06-30 00:00:00\n", - "121316 8.990000 73646D26-0461-450D-8019-2C6C8586... 2014-06-30 00:00:00\n", + " name LineTotal CarrierTrackingNumber rowguid \n", + " role numerical unused_string unused_string \n", + " unit \n", + " 0 2024.994 4911-403C-98 B207C96D-D9E6-402B-8470-2CC176C4...\n", + " 1 6074.982 4911-403C-98 7ABB600D-1E77-41BE-9FE5-B9142CFC...\n", + " 2 2024.994 4911-403C-98 475CF8C6-49F6-486E-B0AD-AFC6A50C...\n", + " 3 2039.994 4911-403C-98 04C4DE91-5815-45D6-8670-F462719F...\n", + " 4 2039.994 4911-403C-98 5A74C7D2-E641-438E-A7AC-37BF2328...\n", + " ... ... ... \n", + "121312 21.98 NULL 8CAD6675-18CC-4F47-8287-97B41A8E...\n", + "121313 8.99 NULL 84F1C363-1C50-4442-BE16-541C59B6...\n", + "121314 21.98 NULL C18B6476-429F-4BB1-828E-2BE5F82A...\n", + "121315 159 NULL 75A89C6A-C60A-47EA-8A52-B52A9C43...\n", + "121316 8.99 NULL 73646D26-0461-450D-8019-2C6C8586...\n", "\n", "\n", "121317 rows x 11 columns\n", - "memory usage: 21.84 MB\n", + "memory usage: 14.08 MB\n", "name: SalesOrderDetail\n", "type: getml.DataFrame\n", "url: http://localhost:1709/#/getdataframe/adventure_works/SalesOrderDetail/" @@ -3812,107 +3555,119 @@ " \n", " \n", " \n", - " SalesOrderID\n", + " OrderDate\n", " \n", " \n", " \n", - " RevisionNumber\n", + " DueDate\n", " \n", " \n", " \n", - " Status\n", + " ShipDate\n", " \n", " \n", " \n", - " OnlineOrderFlag\n", + " ModifiedDate\n", " \n", " \n", " \n", - " CustomerID\n", + " CustomerID\n", " \n", " \n", " \n", - " SalesPersonID\n", + " SalesOrderID\n", " \n", " \n", " \n", - " TerritoryID\n", + " SalesPersonID\n", " \n", " \n", " \n", - " BillToAddressID\n", + " TerritoryID\n", " \n", " \n", " \n", - " ShipToAddressID\n", + " RevisionNumber\n", " \n", " \n", " \n", - " ShipMethodID\n", + " OnlineOrderFlag\n", " \n", " \n", " \n", - " CreditCardID\n", + " SalesPersonIDCat\n", " \n", " \n", " \n", - " CurrencyRateID\n", + " TerritoryIDCat\n", " \n", " \n", " \n", - " OrderDate \n", + " ShipMethodID\n", " \n", " \n", " \n", - " DueDate \n", + " SubTotal\n", " \n", " \n", " \n", - " ShipDate \n", + " TaxAmt\n", " \n", " \n", " \n", - " SalesOrderNumber\n", + " Freight\n", " \n", " \n", " \n", - " PurchaseOrderNumber\n", + " TotalDue\n", " \n", " \n", " \n", - " AccountNumber \n", + " Status\n", " \n", " \n", " \n", - " CreditCardApprovalCode\n", + " BillToAddressID\n", " \n", " \n", " \n", - " SubTotal \n", + " ShipToAddressID\n", " \n", " \n", " \n", - " TaxAmt \n", + " CreditCardID\n", " \n", " \n", " \n", - " Freight \n", + " CurrencyRateID\n", " \n", " \n", " \n", - " TotalDue \n", + " churn\n", " \n", " \n", " \n", - " Comment \n", + " SalesOrderNumber\n", " \n", " \n", " \n", - " rowguid \n", + " PurchaseOrderNumber\n", " \n", " \n", " \n", - " ModifiedDate \n", + " AccountNumber \n", + " \n", + " \n", + " \n", + " CreditCardApprovalCode\n", + " \n", + " \n", + " \n", + " Comment \n", + " \n", + " \n", + " \n", + " rowguid \n", " \n", " \n", " \n", @@ -3924,43 +3679,71 @@ " \n", " \n", " \n", - " unused_float\n", + " time_stamp\n", " \n", " \n", " \n", - " unused_float\n", + " time_stamp\n", " \n", " \n", " \n", - " unused_float\n", + " time_stamp\n", " \n", " \n", " \n", - " unused_float\n", + " time_stamp\n", " \n", " \n", " \n", - " unused_float\n", + " join_key\n", " \n", " \n", " \n", - " unused_float\n", + " join_key\n", " \n", " \n", " \n", - " unused_float\n", + " join_key\n", " \n", " \n", " \n", - " unused_float\n", + " join_key\n", " \n", " \n", " \n", - " unused_float\n", + " categorical \n", " \n", " \n", " \n", - " unused_float\n", + " categorical \n", + " \n", + " \n", + " \n", + " categorical \n", + " \n", + " \n", + " \n", + " categorical \n", + " \n", + " \n", + " \n", + " categorical \n", + " \n", + " \n", + " \n", + " numerical\n", + " \n", + " \n", + " \n", + " numerical\n", + " \n", + " \n", + " \n", + " numerical\n", + " \n", + " \n", + " \n", + " numerical\n", " \n", " \n", " \n", @@ -3968,19 +3751,23 @@ " \n", " \n", " \n", - " unused_float\n", + " unused_float\n", " \n", " \n", " \n", - " unused_string \n", + " unused_float\n", " \n", " \n", " \n", - " unused_string \n", + " unused_float\n", " \n", " \n", " \n", - " unused_string \n", + " unused_float\n", + " \n", + " \n", + " \n", + " unused_float\n", " \n", " \n", " \n", @@ -4004,27 +3791,131 @@ " \n", " \n", " \n", - " unused_string\n", + " unused_string \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " unused_string\n", + " unit\n", " \n", " \n", " \n", - " unused_string\n", + " time stamp, comparison only\n", " \n", " \n", " \n", - " unused_string\n", + " time stamp, comparison only\n", " \n", " \n", " \n", - " unused_string \n", + " time stamp, comparison only\n", " \n", " \n", " \n", - " unused_string \n", + " time stamp, comparison only\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4036,40 +3927,66 @@ " 0\n", " \n", " \n", - " \n", - " \n", - " \n", + " 2011-05-31\n", " \n", - " \n", - " 43659 \n", - " \n", + " \n", + " \n", + " 2011-06-12\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 2011-06-07\n", " \n", - " \n", - " 8 \n", - " \n", + " \n", + " \n", + " 2011-06-07\n", + " \n", + " \n", + " \n", + " 29825\n", + " \n", + " \n", + " \n", + " 43659\n", + " \n", + " \n", + " \n", + " 279\n", + " \n", + " \n", + " \n", + " 5\n", + " \n", + " \n", + " \n", + " 8\n", + " \n", + " \n", + " \n", + " 0\n", + " \n", + " \n", + " \n", + " 279\n", + " \n", + " \n", + " \n", + " 5\n", + " \n", + " \n", + " \n", + " 5\n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " 5 \n", + " 20565.6206\n", " \n", " \n", @@ -4078,11 +3995,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 0 \n", + " 1971.5149\n", " \n", " \n", @@ -4091,11 +4008,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 29825 \n", + " 616.0984\n", " \n", " \n", @@ -4104,11 +4021,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 279 \n", + " 23153.2339\n", " \n", " \n", @@ -4158,7 +4075,7 @@ " \n", " \n", " \n", - " 516281 \n", @@ -4171,7 +4088,7 @@ " \n", " \n", " \n", - " 16281nan \n", @@ -4184,7 +4101,7 @@ " \n", " \n", " \n", - " nan0 \n", @@ -4192,101 +4109,95 @@ " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " SO43659\n", " \n", " \n", " \n", - " 2011-06-12 00:00:00\n", + " PO522145787\n", " \n", " \n", " \n", - " 2011-06-07 00:00:00\n", + " 10-4020-000676\n", " \n", " \n", " \n", - " SO43659\n", + " 105041Vi84182\n", " \n", " \n", " \n", - " PO522145787\n", + " NULL\n", " \n", " \n", " \n", - " 10-4020-000676\n", + " 79B65321-39CA-4115-9CBA-8FE0903E...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 1\n", + " \n", " \n", - " 105041Vi84182\n", + " 2011-05-31\n", " \n", " \n", " \n", - " 20565.6206\n", + " 2011-06-12\n", " \n", " \n", " \n", - " 1971.5149\n", + " 2011-06-07\n", " \n", " \n", " \n", - " 616.0984\n", + " 2011-06-07\n", " \n", " \n", " \n", - " 23153.2339\n", + " 29672\n", " \n", " \n", " \n", - " NULL\n", + " 43660\n", " \n", " \n", " \n", - " 79B65321-39CA-4115-9CBA-8FE0903E...\n", + " 279\n", " \n", " \n", " \n", - " 2011-06-07 00:00:00\n", + " 5\n", " \n", " \n", - " \n", - " \n", - " \n", - " 1\n", + " \n", + " 8\n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 0\n", " \n", - " \n", - " 43660 \n", - " \n", + " \n", + " \n", + " 279\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 5\n", " \n", - " \n", - " 8 \n", - " \n", + " \n", + " \n", + " 5\n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " 5 \n", + " 1294.2529\n", " \n", " \n", @@ -4295,11 +4206,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 0 \n", + " 124.2483\n", " \n", " \n", @@ -4308,11 +4219,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 29672 \n", + " 38.8276\n", " \n", " \n", @@ -4321,11 +4232,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 279 \n", + " 1457.3288\n", " \n", " \n", @@ -4375,7 +4286,7 @@ " \n", " \n", " \n", - " 55618 \n", @@ -4388,7 +4299,7 @@ " \n", " \n", " \n", - " 5618nan \n", @@ -4401,7 +4312,7 @@ " \n", " \n", " \n", - " nan0 \n", @@ -4409,101 +4320,95 @@ " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " SO43660\n", " \n", " \n", " \n", - " 2011-06-12 00:00:00\n", + " PO18850127500\n", " \n", " \n", " \n", - " 2011-06-07 00:00:00\n", + " 10-4020-000117\n", " \n", " \n", " \n", - " SO43660\n", + " 115213Vi29411\n", " \n", " \n", " \n", - " PO18850127500\n", + " NULL\n", " \n", " \n", " \n", - " 10-4020-000117\n", + " 738DC42D-D03B-48A1-9822-F95A67EA...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 2\n", + " \n", " \n", - " 115213Vi29411\n", + " 2011-05-31\n", " \n", " \n", " \n", - " 1294.2529\n", + " 2011-06-12\n", " \n", " \n", " \n", - " 124.2483\n", + " 2011-06-07\n", " \n", " \n", " \n", - " 38.8276\n", + " 2011-06-07\n", " \n", " \n", " \n", - " 1457.3288\n", + " 29734\n", " \n", " \n", " \n", - " NULL\n", + " 43661\n", " \n", " \n", " \n", - " 738DC42D-D03B-48A1-9822-F95A67EA...\n", + " 282\n", " \n", " \n", " \n", - " 2011-06-07 00:00:00\n", + " 6\n", " \n", " \n", - " \n", - " \n", - " \n", - " 2\n", + " \n", + " 8\n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 0\n", " \n", - " \n", - " 43661 \n", - " \n", + " \n", + " \n", + " 282\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 6\n", " \n", - " \n", - " 8 \n", - " \n", + " \n", + " \n", + " 5\n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " 5 \n", + " 32726.4786\n", " \n", " \n", @@ -4512,11 +4417,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 0 \n", + " 3153.7696\n", " \n", " \n", @@ -4525,11 +4430,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 29734 \n", + " 985.553\n", " \n", " \n", @@ -4538,11 +4443,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 282 \n", + " 36865.8012\n", " \n", " \n", @@ -4553,7 +4458,7 @@ " \n", " \n", " \n", - " 65 \n", @@ -4592,7 +4497,7 @@ " \n", " \n", " \n", - " 51346 \n", @@ -4605,7 +4510,7 @@ " \n", " \n", " \n", - " 13464 \n", @@ -4618,7 +4523,7 @@ " \n", " \n", " \n", - " 40 \n", @@ -4626,101 +4531,95 @@ " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " SO43661\n", " \n", " \n", " \n", - " 2011-06-12 00:00:00\n", + " PO18473189620\n", " \n", " \n", " \n", - " 2011-06-07 00:00:00\n", + " 10-4020-000442\n", " \n", " \n", " \n", - " SO43661\n", + " 85274Vi6854\n", " \n", " \n", " \n", - " PO18473189620\n", + " NULL\n", " \n", " \n", " \n", - " 10-4020-000442\n", + " D91B9131-18A4-4A11-BC3A-90B6F53E...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 3\n", + " \n", " \n", - " 85274Vi6854\n", + " 2011-05-31\n", " \n", " \n", " \n", - " 32726.4786\n", + " 2011-06-12\n", " \n", " \n", " \n", - " 3153.7696\n", + " 2011-06-07\n", " \n", " \n", " \n", - " 985.5530\n", + " 2011-06-07\n", " \n", " \n", " \n", - " 36865.8012\n", + " 29994\n", " \n", " \n", " \n", - " NULL\n", + " 43662\n", " \n", " \n", " \n", - " D91B9131-18A4-4A11-BC3A-90B6F53E...\n", + " 282\n", " \n", " \n", " \n", - " 2011-06-07 00:00:00\n", + " 6\n", " \n", " \n", - " \n", - " \n", - " \n", - " 3\n", + " \n", + " 8\n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 0\n", " \n", - " \n", - " 43662 \n", - " \n", + " \n", + " \n", + " 282\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 6\n", " \n", - " \n", - " 8 \n", - " \n", + " \n", + " \n", + " 5\n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " 5 \n", + " 28832.5289\n", " \n", " \n", @@ -4729,11 +4628,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 0 \n", + " 2775.1646\n", " \n", " \n", @@ -4742,11 +4641,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 29994 \n", + " 867.2389\n", " \n", " \n", @@ -4755,11 +4654,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 282 \n", + " 32474.9324\n", " \n", " \n", @@ -4770,7 +4669,7 @@ " \n", " \n", " \n", - " 65 \n", @@ -4809,7 +4708,7 @@ " \n", " \n", " \n", - " 510456 \n", @@ -4822,7 +4721,7 @@ " \n", " \n", " \n", - " 104564 \n", @@ -4835,7 +4734,7 @@ " \n", " \n", " \n", - " 40 \n", @@ -4843,101 +4742,95 @@ " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " SO43662\n", " \n", " \n", " \n", - " 2011-06-12 00:00:00\n", + " PO18444174044\n", " \n", " \n", " \n", - " 2011-06-07 00:00:00\n", + " 10-4020-000227\n", " \n", " \n", " \n", - " SO43662\n", + " 125295Vi53935\n", " \n", " \n", " \n", - " PO18444174044\n", + " NULL\n", " \n", " \n", " \n", - " 10-4020-000227\n", + " 4A1ECFC0-CC3A-4740-B028-1C50BB48...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 4\n", + " \n", " \n", - " 125295Vi53935\n", + " 2011-05-31\n", " \n", " \n", " \n", - " 28832.5289\n", + " 2011-06-12\n", " \n", " \n", " \n", - " 2775.1646\n", + " 2011-06-07\n", " \n", " \n", " \n", - " 867.2389\n", + " 2011-06-07\n", " \n", " \n", " \n", - " 32474.9324\n", + " 29565\n", " \n", " \n", " \n", - " NULL\n", + " 43663\n", " \n", " \n", " \n", - " 4A1ECFC0-CC3A-4740-B028-1C50BB48...\n", + " 276\n", " \n", " \n", " \n", - " 2011-06-07 00:00:00\n", + " 4\n", " \n", " \n", - " \n", - " \n", - " \n", - " 4\n", + " \n", + " 8\n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 0\n", " \n", - " \n", - " 43663 \n", - " \n", + " \n", + " \n", + " 276\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 4\n", " \n", - " \n", - " 8 \n", - " \n", + " \n", + " \n", + " 5\n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " 5 \n", + " 419.4589\n", " \n", " \n", @@ -4946,11 +4839,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 0 \n", + " 40.2681\n", " \n", " \n", @@ -4959,11 +4852,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 29565 \n", + " 12.5838\n", " \n", " \n", @@ -4972,11 +4865,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 276 \n", + " 472.3108\n", " \n", " \n", @@ -4987,7 +4880,7 @@ " \n", " \n", " \n", - " 45 \n", @@ -5026,7 +4919,7 @@ " \n", " \n", " \n", - " 54322 \n", @@ -5039,7 +4932,7 @@ " \n", " \n", " \n", - " 4322nan \n", @@ -5052,7 +4945,7 @@ " \n", " \n", " \n", - " nan0 \n", @@ -5060,90 +4953,84 @@ " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " SO43663\n", " \n", " \n", " \n", - " 2011-06-12 00:00:00\n", + " PO18009186470\n", " \n", " \n", " \n", - " 2011-06-07 00:00:00\n", + " 10-4020-000510\n", " \n", " \n", " \n", - " SO43663\n", + " 45303Vi22691\n", " \n", " \n", " \n", - " PO18009186470\n", + " NULL\n", " \n", " \n", " \n", - " 10-4020-000510\n", + " 9B1E7A40-6AE0-4AD3-811C-A6495185...\n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " 45303Vi22691\n", + " ...\n", " \n", " \n", " \n", - " 419.4589\n", + " ...\n", " \n", " \n", " \n", - " 40.2681\n", + " ...\n", " \n", " \n", " \n", - " 12.5838\n", + " ...\n", " \n", " \n", " \n", - " 472.3108\n", + " ...\n", " \n", " \n", " \n", - " NULL\n", + " ...\n", " \n", " \n", " \n", - " 9B1E7A40-6AE0-4AD3-811C-A6495185...\n", + " ...\n", " \n", " \n", " \n", - " 2011-06-07 00:00:00\n", + " ...\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " ...\n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " ...\n", " \n", - " \n", - " ... \n", - " \n", + " \n", + " \n", + " ...\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " ...\n", " \n", - " \n", - " ... \n", - " \n", + " \n", + " \n", + " ...\n", " \n", " \n", " \n", @@ -5151,9 +5038,9 @@ " \n", " \n", " \n", - " \n", + " \n", " ... \n", " \n", @@ -5164,9 +5051,9 @@ " \n", " \n", " \n", - " \n", + " \n", " ... \n", " \n", @@ -5177,9 +5064,9 @@ " \n", " \n", " \n", - " \n", + " \n", " ... \n", " \n", @@ -5190,9 +5077,9 @@ " \n", " \n", " \n", - " \n", + " \n", " ... \n", " \n", @@ -5300,78 +5187,72 @@ " ...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 31460\n", + " \n", " \n", - " ...\n", + " 2014-06-30\n", " \n", " \n", " \n", - " ...\n", + " 2014-07-12\n", " \n", " \n", " \n", - " ...\n", + " 2014-07-07\n", " \n", " \n", " \n", - " ...\n", + " 2014-07-07\n", " \n", " \n", " \n", - " ...\n", + " 11981\n", " \n", " \n", " \n", - " ...\n", + " 75119\n", " \n", " \n", " \n", - " ...\n", + " nan\n", " \n", " \n", " \n", - " ...\n", + " 1\n", " \n", " \n", - " \n", - " \n", - " \n", - " 31460\n", + " \n", + " 8\n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 1\n", " \n", - " \n", - " 75119 \n", - " \n", + " \n", + " \n", + " nan\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 1\n", " \n", - " \n", - " 8 \n", - " \n", + " \n", + " \n", + " 1\n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " 5 \n", + " 42.28\n", " \n", " \n", @@ -5380,11 +5261,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 3.3824\n", " \n", " \n", @@ -5393,11 +5274,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 11981 \n", + " 1.057\n", " \n", " \n", @@ -5406,11 +5287,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " nan \n", + " 46.7194\n", " \n", " \n", @@ -5421,7 +5302,7 @@ " \n", " \n", " \n", - " 15 \n", @@ -5460,7 +5341,7 @@ " \n", " \n", " \n", - " 16761 \n", @@ -5473,7 +5354,7 @@ " \n", " \n", " \n", - " 6761nan \n", @@ -5494,19 +5375,19 @@ " \n", " \n", " \n", - " 2014-06-30 00:00:00\n", + " SO75119\n", " \n", " \n", " \n", - " 2014-07-12 00:00:00\n", + " NULL\n", " \n", " \n", " \n", - " 2014-07-07 00:00:00\n", + " 10-4030-011981\n", " \n", " \n", " \n", - " SO75119\n", + " 429826Vi35166\n", " \n", " \n", " \n", @@ -5514,81 +5395,75 @@ " \n", " \n", " \n", - " 10-4030-011981\n", + " 9382F1C9-383A-435F-9449-0EECEA21...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 31461\n", + " \n", " \n", - " 429826Vi35166\n", + " 2014-06-30\n", " \n", " \n", " \n", - " 42.2800\n", + " 2014-07-12\n", " \n", " \n", " \n", - " 3.3824\n", + " 2014-07-07\n", " \n", " \n", " \n", - " 1.0570\n", + " 2014-07-07\n", " \n", " \n", " \n", - " 46.7194\n", + " 18749\n", " \n", " \n", " \n", - " NULL\n", + " 75120\n", " \n", " \n", " \n", - " 9382F1C9-383A-435F-9449-0EECEA21...\n", + " nan\n", " \n", " \n", " \n", - " 2014-07-07 00:00:00\n", + " 6\n", " \n", " \n", - " \n", - " \n", - " \n", - " 31461\n", + " \n", + " 8\n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 1\n", " \n", - " \n", - " 75120 \n", - " \n", + " \n", + " \n", + " nan\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 6\n", " \n", - " \n", - " 8 \n", - " \n", + " \n", + " \n", + " 1\n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " 5 \n", + " 84.96\n", " \n", " \n", @@ -5597,11 +5472,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 6.7968\n", " \n", " \n", @@ -5610,11 +5485,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 18749 \n", + " 2.124\n", " \n", " \n", @@ -5623,11 +5498,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " nan \n", + " 93.8808\n", " \n", " \n", @@ -5638,7 +5513,7 @@ " \n", " \n", " \n", - " 65 \n", @@ -5677,7 +5552,7 @@ " \n", " \n", " \n", - " 18925 \n", @@ -5690,7 +5565,7 @@ " \n", " \n", " \n", - " 8925nan \n", @@ -5711,19 +5586,19 @@ " \n", " \n", " \n", - " 2014-06-30 00:00:00\n", + " SO75120\n", " \n", " \n", " \n", - " 2014-07-12 00:00:00\n", + " NULL\n", " \n", " \n", " \n", - " 2014-07-07 00:00:00\n", + " 10-4030-018749\n", " \n", " \n", " \n", - " SO75120\n", + " 929849Vi46003\n", " \n", " \n", " \n", @@ -5731,81 +5606,75 @@ " \n", " \n", " \n", - " 10-4030-018749\n", + " AE6A4FCF-FF73-4CD4-AF2C-5993D00D...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 31462\n", + " \n", " \n", - " 929849Vi46003\n", + " 2014-06-30\n", " \n", " \n", " \n", - " 84.9600\n", + " 2014-07-12\n", " \n", " \n", " \n", - " 6.7968\n", + " 2014-07-07\n", " \n", " \n", " \n", - " 2.1240\n", + " 2014-07-07\n", " \n", " \n", " \n", - " 93.8808\n", + " 15251\n", " \n", " \n", " \n", - " NULL\n", + " 75121\n", " \n", " \n", " \n", - " AE6A4FCF-FF73-4CD4-AF2C-5993D00D...\n", + " nan\n", " \n", " \n", " \n", - " 2014-07-07 00:00:00\n", + " 6\n", " \n", " \n", - " \n", - " \n", - " \n", - " 31462\n", + " \n", + " 8\n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 1\n", " \n", - " \n", - " 75121 \n", - " \n", + " \n", + " \n", + " nan\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 6\n", " \n", - " \n", - " 8 \n", - " \n", + " \n", + " \n", + " 1\n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " 5 \n", + " 74.98\n", " \n", " \n", @@ -5814,11 +5683,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 5.9984\n", " \n", " \n", @@ -5827,11 +5696,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 15251 \n", + " 1.8745\n", " \n", " \n", @@ -5840,11 +5709,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " nan \n", + " 82.8529\n", " \n", " \n", @@ -5855,7 +5724,7 @@ " \n", " \n", " \n", - " 65 \n", @@ -5894,7 +5763,7 @@ " \n", " \n", " \n", - " 114220 \n", @@ -5907,7 +5776,7 @@ " \n", " \n", " \n", - " 14220nan \n", @@ -5928,19 +5797,19 @@ " \n", " \n", " \n", - " 2014-06-30 00:00:00\n", + " SO75121\n", " \n", " \n", " \n", - " 2014-07-12 00:00:00\n", + " NULL\n", " \n", " \n", " \n", - " 2014-07-07 00:00:00\n", + " 10-4030-015251\n", " \n", " \n", " \n", - " SO75121\n", + " 529864Vi73738\n", " \n", " \n", " \n", @@ -5948,81 +5817,75 @@ " \n", " \n", " \n", - " 10-4030-015251\n", + " D7395C0E-00CB-4BFA-A238-0D6A9F49...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 31463\n", + " \n", " \n", - " 529864Vi73738\n", + " 2014-06-30\n", " \n", " \n", " \n", - " 74.9800\n", + " 2014-07-12\n", " \n", " \n", " \n", - " 5.9984\n", + " 2014-07-07\n", " \n", " \n", " \n", - " 1.8745\n", + " 2014-07-07\n", " \n", " \n", " \n", - " 82.8529\n", + " 15868\n", " \n", " \n", " \n", - " NULL\n", + " 75122\n", " \n", " \n", " \n", - " D7395C0E-00CB-4BFA-A238-0D6A9F49...\n", + " nan\n", " \n", " \n", " \n", - " 2014-07-07 00:00:00\n", + " 6\n", " \n", " \n", - " \n", - " \n", - " \n", - " 31463\n", + " \n", + " 8\n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 1\n", " \n", - " \n", - " 75122 \n", - " \n", + " \n", + " \n", + " nan\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 6\n", " \n", - " \n", - " 8 \n", - " \n", + " \n", + " \n", + " 1\n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " 5 \n", + " 30.97\n", " \n", " \n", @@ -6031,11 +5894,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 2.4776\n", " \n", " \n", @@ -6044,11 +5907,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 15868 \n", + " 0.7743\n", " \n", " \n", @@ -6057,11 +5920,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " nan \n", + " 34.2219\n", " \n", " \n", @@ -6072,7 +5935,7 @@ " \n", " \n", " \n", - " 65 \n", @@ -6111,7 +5974,7 @@ " \n", " \n", " \n", - " 118719 \n", @@ -6124,7 +5987,7 @@ " \n", " \n", " \n", - " 18719nan \n", @@ -6145,19 +6008,19 @@ " \n", " \n", " \n", - " 2014-06-30 00:00:00\n", + " SO75122\n", " \n", " \n", " \n", - " 2014-07-12 00:00:00\n", + " NULL\n", " \n", " \n", " \n", - " 2014-07-07 00:00:00\n", + " 10-4030-015868\n", " \n", " \n", " \n", - " SO75122\n", + " 330022Vi97312\n", " \n", " \n", " \n", @@ -6165,81 +6028,75 @@ " \n", " \n", " \n", - " 10-4030-015868\n", + " 4221035A-4159-492F-AF40-4363A64F...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 31464\n", + " \n", " \n", - " 330022Vi97312\n", + " 2014-06-30\n", " \n", " \n", " \n", - " 30.9700\n", + " 2014-07-12\n", " \n", " \n", " \n", - " 2.4776\n", + " 2014-07-07\n", " \n", " \n", " \n", - " 0.7743\n", + " 2014-07-07\n", " \n", " \n", " \n", - " 34.2219\n", + " 18759\n", " \n", " \n", " \n", - " NULL\n", + " 75123\n", " \n", " \n", " \n", - " 4221035A-4159-492F-AF40-4363A64F...\n", + " nan\n", " \n", " \n", " \n", - " 2014-07-07 00:00:00\n", + " 6\n", " \n", " \n", - " \n", - " \n", - " \n", - " 31464\n", + " \n", + " 8\n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 1\n", " \n", - " \n", - " 75123 \n", - " \n", + " \n", + " \n", + " nan\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " 6\n", " \n", - " \n", - " 8 \n", - " \n", + " \n", + " \n", + " 1\n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " 5 \n", + " 189.97\n", " \n", " \n", @@ -6248,11 +6105,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 1 \n", + " 15.1976\n", " \n", " \n", @@ -6261,11 +6118,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 18759 \n", + " 4.7493\n", " \n", " \n", @@ -6274,11 +6131,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " nan \n", + " 209.9169\n", " \n", " \n", @@ -6289,7 +6146,7 @@ " \n", " \n", " \n", - " 65 \n", @@ -6328,7 +6185,7 @@ " \n", " \n", " \n", - " 110084 \n", @@ -6341,7 +6198,7 @@ " \n", " \n", " \n", - " 10084nan \n", @@ -6362,18 +6219,6 @@ " \n", " \n", " \n", - " 2014-06-30 00:00:00\n", - " \n", - " \n", - " \n", - " 2014-07-12 00:00:00\n", - " \n", - " \n", - " \n", - " 2014-07-07 00:00:00\n", - " \n", - " \n", - " \n", " SO75123\n", " \n", " \n", @@ -6390,22 +6235,6 @@ " \n", " \n", " \n", - " 189.9700\n", - " \n", - " \n", - " \n", - " 15.1976\n", - " \n", - " \n", - " \n", - " 4.7493\n", - " \n", - " \n", - " \n", - " 209.9169\n", - " \n", - " \n", - " \n", " NULL\n", " \n", " \n", @@ -6413,55 +6242,64 @@ " D54752FF-2B54-4BE5-95EA-3B72289C...\n", " \n", " \n", - " \n", - " 2014-07-07 00:00:00\n", - " \n", - " \n", " \n", " \n", " \n", "\n", "\n", "

\n", - " 31465 rows x 26 columns
\n", - " memory usage: 12.80 MB
\n", + " 31465 rows x 29 columns
\n", + " memory usage: 8.59 MB
\n", " name: SalesOrderHeader
\n", " type: getml.DataFrame
\n", " url: http://localhost:1709/#/getdataframe/adventure_works/SalesOrderHeader/\n", "

\n" ], "text/plain": [ - " name SalesOrderID RevisionNumber Status OnlineOrderFlag ... Freight TotalDue Comment \n", - " role unused_float unused_float unused_float unused_float ... unused_string unused_string unused_string\n", - " 0 43659 8 5 0 ... 616.0984 23153.2339 NULL \n", - " 1 43660 8 5 0 ... 38.8276 1457.3288 NULL \n", - " 2 43661 8 5 0 ... 985.5530 36865.8012 NULL \n", - " 3 43662 8 5 0 ... 867.2389 32474.9324 NULL \n", - " 4 43663 8 5 0 ... 12.5838 472.3108 NULL \n", - " ... ... ... ... ... ... ... \n", - "31460 75119 8 5 1 ... 1.0570 46.7194 NULL \n", - "31461 75120 8 5 1 ... 2.1240 93.8808 NULL \n", - "31462 75121 8 5 1 ... 1.8745 82.8529 NULL \n", - "31463 75122 8 5 1 ... 0.7743 34.2219 NULL \n", - "31464 75123 8 5 1 ... 4.7493 209.9169 NULL \n", + " name OrderDate DueDate ShipDate ModifiedDate\n", + " role time_stamp time_stamp time_stamp time_stamp\n", + " unit time stamp, comparison only time stamp, comparison only time stamp, comparison only time stamp, comparison only\n", + " 0 2011-05-31 2011-06-12 2011-06-07 2011-06-07\n", + " 1 2011-05-31 2011-06-12 2011-06-07 2011-06-07\n", + " 2 2011-05-31 2011-06-12 2011-06-07 2011-06-07\n", + " 3 2011-05-31 2011-06-12 2011-06-07 2011-06-07\n", + " 4 2011-05-31 2011-06-12 2011-06-07 2011-06-07\n", + " ... ... ... ...\n", + "31460 2014-06-30 2014-07-12 2014-07-07 2014-07-07\n", + "31461 2014-06-30 2014-07-12 2014-07-07 2014-07-07\n", + "31462 2014-06-30 2014-07-12 2014-07-07 2014-07-07\n", + "31463 2014-06-30 2014-07-12 2014-07-07 2014-07-07\n", + "31464 2014-06-30 2014-07-12 2014-07-07 2014-07-07\n", "\n", - " name rowguid ModifiedDate \n", - " role unused_string unused_string \n", - " 0 79B65321-39CA-4115-9CBA-8FE0903E... 2011-06-07 00:00:00\n", - " 1 738DC42D-D03B-48A1-9822-F95A67EA... 2011-06-07 00:00:00\n", - " 2 D91B9131-18A4-4A11-BC3A-90B6F53E... 2011-06-07 00:00:00\n", - " 3 4A1ECFC0-CC3A-4740-B028-1C50BB48... 2011-06-07 00:00:00\n", - " 4 9B1E7A40-6AE0-4AD3-811C-A6495185... 2011-06-07 00:00:00\n", - " ... ... \n", - "31460 9382F1C9-383A-435F-9449-0EECEA21... 2014-07-07 00:00:00\n", - "31461 AE6A4FCF-FF73-4CD4-AF2C-5993D00D... 2014-07-07 00:00:00\n", - "31462 D7395C0E-00CB-4BFA-A238-0D6A9F49... 2014-07-07 00:00:00\n", - "31463 4221035A-4159-492F-AF40-4363A64F... 2014-07-07 00:00:00\n", - "31464 D54752FF-2B54-4BE5-95EA-3B72289C... 2014-07-07 00:00:00\n", + " name ... PurchaseOrderNumber AccountNumber CreditCardApprovalCode Comment rowguid \n", + " role ... unused_string unused_string unused_string unused_string unused_string \n", + " unit ... \n", + " 0 ... PO522145787 10-4020-000676 105041Vi84182 NULL \n", + " 1 ... PO18850127500 10-4020-000117 115213Vi29411 NULL \n", + " 2 ... PO18473189620 10-4020-000442 85274Vi6854 NULL \n", + " 3 ... PO18444174044 10-4020-000227 125295Vi53935 NULL \n", + " 4 ... PO18009186470 10-4020-000510 45303Vi22691 NULL \n", + " ... ... ... ... ... \n", + "31460 ... NULL 10-4030-011981 429826Vi35166 NULL \n", + "31461 ... NULL 10-4030-018749 929849Vi46003 NULL \n", + "31462 ... NULL 10-4030-015251 529864Vi73738 NULL \n", + "31463 ... NULL 10-4030-015868 330022Vi97312 NULL \n", + "31464 ... NULL 10-4030-018759 230370Vi51970 NULL \n", + "\n", + " 0 79B65321-39CA-4115-9CBA-8FE0903E...\n", + " 1 738DC42D-D03B-48A1-9822-F95A67EA...\n", + " 2 D91B9131-18A4-4A11-BC3A-90B6F53E...\n", + " 3 4A1ECFC0-CC3A-4740-B028-1C50BB48...\n", + " 4 9B1E7A40-6AE0-4AD3-811C-A6495185...\n", + "31460 9382F1C9-383A-435F-9449-0EECEA21...\n", + "31461 AE6A4FCF-FF73-4CD4-AF2C-5993D00D...\n", + "31462 D7395C0E-00CB-4BFA-A238-0D6A9F49...\n", + "31463 4221035A-4159-492F-AF40-4363A64F...\n", + "31464 D54752FF-2B54-4BE5-95EA-3B72289C...\n", "\n", "\n", - "31465 rows x 26 columns\n", - "memory usage: 12.80 MB\n", + "31465 rows x 29 columns\n", + "memory usage: 8.59 MB\n", "name: SalesOrderHeader\n", "type: getml.DataFrame\n", "url: http://localhost:1709/#/getdataframe/adventure_works/SalesOrderHeader/" @@ -6532,11 +6370,11 @@ " \n", " \n", " \n", - " SalesOrderID\n", + " SalesOrderID\n", " \n", " \n", " \n", - " SalesReasonID\n", + " SalesReasonID\n", " \n", " \n", " \n", @@ -6552,11 +6390,11 @@ " \n", " \n", " \n", - " unused_float\n", + " join_key\n", " \n", " \n", " \n", - " unused_float\n", + " categorical \n", " \n", " \n", " \n", @@ -6572,29 +6410,11 @@ " 0\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 43697 \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " 43697\n", " \n", - " \n", - " 5 \n", - " \n", + " \n", + " \n", + " 5\n", " \n", " \n", " \n", @@ -6607,29 +6427,11 @@ " 1\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 43697 \n", - " \n", + " 43697\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 9 \n", - " \n", + " 9\n", " \n", " \n", " \n", @@ -6642,29 +6444,11 @@ " 2\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 43702 \n", - " \n", + " 43702\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 5 \n", - " \n", + " 5\n", " \n", " \n", " \n", @@ -6677,29 +6461,11 @@ " 3\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 43702 \n", - " \n", + " 43702\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 9 \n", - " \n", + " 9\n", " \n", " \n", " \n", @@ -6712,29 +6478,11 @@ " 4\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 43703 \n", - " \n", + " 43703\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 5 \n", - " \n", + " 5\n", " \n", " \n", " \n", @@ -6747,29 +6495,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " ... \n", - " \n", + " ...\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " ... \n", - " \n", + " ...\n", " \n", " \n", " \n", @@ -6782,29 +6512,11 @@ " 27642\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 75119 \n", - " \n", + " 75119\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 1 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -6817,29 +6529,11 @@ " 27643\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 75120 \n", - " \n", + " 75120\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 1 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -6852,29 +6546,11 @@ " 27644\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 75121 \n", - " \n", + " 75121\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 1 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -6887,29 +6563,11 @@ " 27645\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 75122 \n", - " \n", + " 75122\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 1 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -6922,29 +6580,11 @@ " 27646\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 75123 \n", - " \n", + " 75123\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 1 \n", - " \n", + " 1\n", " \n", " \n", " \n", @@ -6958,7 +6598,7 @@ "\n", "

\n", " 27647 rows x 3 columns
\n", - " memory usage: 1.22 MB
\n", + " memory usage: 1.00 MB
\n", " name: SalesOrderHeaderSalesReason
\n", " type: getml.DataFrame
\n", " url: http://localhost:1709/#/getdataframe/adventure_works/SalesOrderHeaderSalesReason/\n", @@ -6966,22 +6606,22 @@ ], "text/plain": [ " name SalesOrderID SalesReasonID ModifiedDate \n", - " role unused_float unused_float unused_string \n", - " 0 43697 5 2011-05-31 00:00:00\n", - " 1 43697 9 2011-05-31 00:00:00\n", - " 2 43702 5 2011-06-01 00:00:00\n", - " 3 43702 9 2011-06-01 00:00:00\n", - " 4 43703 5 2011-06-01 00:00:00\n", - " ... ... ... \n", - "27642 75119 1 2014-06-30 00:00:00\n", - "27643 75120 1 2014-06-30 00:00:00\n", - "27644 75121 1 2014-06-30 00:00:00\n", - "27645 75122 1 2014-06-30 00:00:00\n", - "27646 75123 1 2014-06-30 00:00:00\n", + " role join_key categorical unused_string \n", + " 0 43697 5 2011-05-31 00:00:00\n", + " 1 43697 9 2011-05-31 00:00:00\n", + " 2 43702 5 2011-06-01 00:00:00\n", + " 3 43702 9 2011-06-01 00:00:00\n", + " 4 43703 5 2011-06-01 00:00:00\n", + " ... ... ... \n", + "27642 75119 1 2014-06-30 00:00:00\n", + "27643 75120 1 2014-06-30 00:00:00\n", + "27644 75121 1 2014-06-30 00:00:00\n", + "27645 75122 1 2014-06-30 00:00:00\n", + "27646 75123 1 2014-06-30 00:00:00\n", "\n", "\n", "27647 rows x 3 columns\n", - "memory usage: 1.22 MB\n", + "memory usage: 1.00 MB\n", "name: SalesOrderHeaderSalesReason\n", "type: getml.DataFrame\n", "url: http://localhost:1709/#/getdataframe/adventure_works/SalesOrderHeaderSalesReason/" @@ -7052,39 +6692,39 @@ " \n", " \n", " \n", - " SpecialOfferID\n", + " StartDate\n", " \n", " \n", " \n", - " MinQty\n", + " EndDate\n", " \n", " \n", " \n", - " MaxQty\n", + " SpecialOfferID\n", " \n", " \n", " \n", - " Description \n", + " Category \n", " \n", " \n", " \n", - " DiscountPct \n", + " Description \n", " \n", " \n", " \n", - " Type \n", + " Type \n", " \n", " \n", " \n", - " Category \n", + " MinQty\n", " \n", " \n", " \n", - " StartDate \n", + " DiscountPct\n", " \n", " \n", " \n", - " EndDate \n", + " MaxQty\n", " \n", " \n", " \n", @@ -7104,39 +6744,39 @@ " \n", " \n", " \n", - " unused_float\n", + " time_stamp\n", " \n", " \n", " \n", - " unused_float\n", + " time_stamp\n", " \n", " \n", " \n", - " unused_float\n", + " join_key\n", " \n", " \n", " \n", - " unused_string \n", + " categorical\n", " \n", " \n", " \n", - " unused_string\n", + " categorical \n", " \n", " \n", " \n", - " unused_string \n", + " categorical \n", " \n", " \n", " \n", - " unused_string\n", + " numerical\n", " \n", " \n", " \n", - " unused_string \n", + " numerical\n", " \n", " \n", " \n", - " unused_string \n", + " unused_float\n", " \n", " \n", " \n", @@ -7149,6 +6789,58 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " unit\n", + " \n", + " \n", + " \n", + " time stamp, comparison only\n", + " \n", + " \n", + " \n", + " time stamp, comparison only\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7156,12 +6848,36 @@ " 0\n", " \n", " \n", + " 2011-05-01\n", + " \n", + " \n", + " \n", + " 2014-11-30\n", + " \n", + " \n", + " \n", + " 1\n", + " \n", + " \n", + " \n", + " No Discount\n", + " \n", + " \n", + " \n", + " No Discount\n", + " \n", + " \n", + " \n", + " No Discount\n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " 1\n", + " 0 \n", @@ -7173,9 +6889,9 @@ " \n", " \n", " \n", - " \n", + " \n", " 0 \n", " \n", @@ -7195,49 +6911,49 @@ " \n", " \n", " \n", - " No Discount\n", + " 0290C4F5-191F-4337-AB6B-0A2DDE03...\n", " \n", " \n", " \n", - " 0.0000\n", + " 2011-04-01 00:00:00\n", " \n", " \n", + " \n", + " \n", + " \n", + " 1\n", + " \n", " \n", - " No Discount\n", + " 2011-05-31\n", " \n", " \n", " \n", - " No Discount\n", + " 2014-05-30\n", " \n", " \n", " \n", - " 2011-05-01 00:00:00\n", + " 2\n", " \n", " \n", " \n", - " 2014-11-30 00:00:00\n", + " Reseller\n", " \n", " \n", " \n", - " 0290C4F5-191F-4337-AB6B-0A2DDE03...\n", + " Volume Discount 11 to 14\n", " \n", " \n", " \n", - " 2011-04-01 00:00:00\n", + " Volume Discount\n", " \n", " \n", - " \n", - " \n", - " \n", - " 1\n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " 2\n", + " 11 \n", @@ -7248,11 +6964,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 11 \n", + " 0.02\n", " \n", " \n", @@ -7271,49 +6987,49 @@ " \n", " \n", " \n", - " Volume Discount 11 to 14\n", + " D7542EE7-15DB-4541-985C-5CC27AEF...\n", " \n", " \n", " \n", - " 0.0200\n", + " 2011-05-01 00:00:00\n", " \n", " \n", + " \n", + " \n", + " \n", + " 2\n", + " \n", " \n", - " Volume Discount\n", + " 2011-05-31\n", " \n", " \n", " \n", - " Reseller\n", + " 2014-05-30\n", " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " 3\n", " \n", " \n", " \n", - " 2014-05-30 00:00:00\n", + " Reseller\n", " \n", " \n", " \n", - " D7542EE7-15DB-4541-985C-5CC27AEF...\n", + " Volume Discount 15 to 24\n", " \n", " \n", " \n", - " 2011-05-01 00:00:00\n", + " Volume Discount\n", " \n", " \n", - " \n", - " \n", - " \n", - " 2\n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " 3\n", + " 15 \n", @@ -7324,11 +7040,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 15 \n", + " 0.05\n", " \n", " \n", @@ -7347,49 +7063,49 @@ " \n", " \n", " \n", - " Volume Discount 15 to 24\n", + " 4BDBCC01-8CF7-40A9-B643-40EC5B71...\n", " \n", " \n", " \n", - " 0.0500\n", + " 2011-05-01 00:00:00\n", " \n", " \n", + " \n", + " \n", + " \n", + " 3\n", + " \n", " \n", - " Volume Discount\n", + " 2011-05-31\n", " \n", " \n", " \n", - " Reseller\n", + " 2014-05-30\n", " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " 4\n", " \n", " \n", " \n", - " 2014-05-30 00:00:00\n", + " Reseller\n", " \n", " \n", " \n", - " 4BDBCC01-8CF7-40A9-B643-40EC5B71...\n", + " Volume Discount 25 to 40\n", " \n", " \n", " \n", - " 2011-05-01 00:00:00\n", + " Volume Discount\n", " \n", " \n", - " \n", - " \n", - " \n", - " 3\n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " 4\n", + " 25 \n", @@ -7400,11 +7116,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 25 \n", + " 0.1\n", " \n", " \n", @@ -7423,49 +7139,49 @@ " \n", " \n", " \n", - " Volume Discount 25 to 40\n", + " 504B5E85-8F3F-4EBC-9E1D-C1BC5DEA...\n", " \n", " \n", " \n", - " 0.1000\n", + " 2011-05-01 00:00:00\n", " \n", " \n", + " \n", + " \n", + " \n", + " 4\n", + " \n", " \n", - " Volume Discount\n", + " 2011-05-31\n", " \n", " \n", " \n", - " Reseller\n", + " 2014-05-30\n", " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " 5\n", " \n", " \n", " \n", - " 2014-05-30 00:00:00\n", + " Reseller\n", " \n", " \n", " \n", - " 504B5E85-8F3F-4EBC-9E1D-C1BC5DEA...\n", + " Volume Discount 41 to 60\n", " \n", " \n", " \n", - " 2011-05-01 00:00:00\n", + " Volume Discount\n", " \n", " \n", - " \n", - " \n", - " \n", - " 4\n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " 5\n", + " 41 \n", @@ -7476,11 +7192,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " 41 \n", + " 0.15\n", " \n", " \n", @@ -7499,48 +7215,48 @@ " \n", " \n", " \n", - " Volume Discount 41 to 60\n", + " 677E1D9D-944F-4E81-90E8-47EB0A82...\n", " \n", " \n", " \n", - " 0.1500\n", + " 2011-05-01 00:00:00\n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " Volume Discount\n", + " ...\n", " \n", " \n", " \n", - " Reseller\n", + " ...\n", " \n", " \n", " \n", - " 2011-05-31 00:00:00\n", + " ...\n", " \n", " \n", " \n", - " 2014-05-30 00:00:00\n", + " ...\n", " \n", " \n", " \n", - " 677E1D9D-944F-4E81-90E8-47EB0A82...\n", + " ...\n", " \n", " \n", " \n", - " 2011-05-01 00:00:00\n", + " ...\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " ... \n", + " \n", " ... \n", " \n", @@ -7582,42 +7298,42 @@ " ...\n", " \n", " \n", + " \n", + " \n", + " \n", + " 11\n", + " \n", " \n", - " ...\n", + " 2013-05-30\n", " \n", " \n", " \n", - " ...\n", + " 2013-07-14\n", " \n", " \n", " \n", - " ...\n", + " 12\n", " \n", " \n", " \n", - " ...\n", + " Reseller\n", " \n", " \n", " \n", - " ...\n", + " LL Road Frame Sale\n", " \n", " \n", " \n", - " ...\n", + " Excess Inventory\n", " \n", " \n", - " \n", - " \n", - " \n", - " 11\n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " 12\n", + " 0 \n", @@ -7628,11 +7344,11 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " 0 .35\n", " \n", " \n", @@ -7651,49 +7367,49 @@ " \n", " \n", " \n", - " LL Road Frame Sale\n", + " C0AF1C89-9722-4235-9248-3FBA4D9E...\n", " \n", " \n", " \n", - " 0.3500\n", + " 2013-04-30 00:00:00\n", " \n", " \n", + " \n", + " \n", + " \n", + " 12\n", + " \n", " \n", - " Excess Inventory\n", + " 2013-05-30\n", " \n", " \n", " \n", - " Reseller\n", + " 2013-08-29\n", " \n", " \n", " \n", - " 2013-05-30 00:00:00\n", + " 13\n", " \n", " \n", " \n", - " 2013-07-14 00:00:00\n", + " Reseller\n", " \n", " \n", " \n", - " C0AF1C89-9722-4235-9248-3FBA4D9E...\n", + " Touring-3000 Promotion\n", " \n", " \n", " \n", - " 2013-04-30 00:00:00\n", + " New Product\n", " \n", " \n", - " \n", - " \n", - " \n", - " 12\n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " 13\n", + " 0 \n", @@ -7704,11 +7420,11 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " 0 .15\n", " \n", " \n", @@ -7727,49 +7443,49 @@ " \n", " \n", " \n", - " Touring-3000 Promotion\n", + " 5061CCE4-E021-45A8-9A75-DFB36CBB...\n", " \n", " \n", " \n", - " 0.1500\n", + " 2013-04-30 00:00:00\n", " \n", " \n", + " \n", + " \n", + " \n", + " 13\n", + " \n", " \n", - " New Product\n", + " 2013-05-30\n", " \n", " \n", " \n", - " Reseller\n", + " 2013-08-29\n", " \n", " \n", " \n", - " 2013-05-30 00:00:00\n", + " 14\n", " \n", " \n", " \n", - " 2013-08-29 00:00:00\n", + " Reseller\n", " \n", " \n", " \n", - " 5061CCE4-E021-45A8-9A75-DFB36CBB...\n", + " Touring-1000 Promotion\n", " \n", " \n", " \n", - " 2013-04-30 00:00:00\n", + " New Product\n", " \n", " \n", - " \n", - " \n", - " \n", - " 13\n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " 14\n", + " 0 \n", @@ -7780,11 +7496,11 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " 0 .2\n", " \n", " \n", @@ -7803,49 +7519,49 @@ " \n", " \n", " \n", - " Touring-1000 Promotion\n", + " 1AF84A9E-A98C-4BD9-B48F-DC2B8B6B...\n", " \n", " \n", " \n", - " 0.2000\n", + " 2013-04-30 00:00:00\n", " \n", " \n", + " \n", + " \n", + " \n", + " 14\n", + " \n", " \n", - " New Product\n", + " 2013-07-14\n", " \n", " \n", " \n", - " Reseller\n", + " 2013-08-14\n", " \n", " \n", " \n", - " 2013-05-30 00:00:00\n", + " 15\n", " \n", " \n", " \n", - " 2013-08-29 00:00:00\n", + " Customer\n", " \n", " \n", " \n", - " 1AF84A9E-A98C-4BD9-B48F-DC2B8B6B...\n", + " Half-Price Pedal Sale\n", " \n", " \n", " \n", - " 2013-04-30 00:00:00\n", + " Seasonal Discount\n", " \n", " \n", - " \n", - " \n", - " \n", - " 14\n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " 15\n", + " 0 \n", @@ -7856,11 +7572,11 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " 0 .5\n", " \n", " \n", @@ -7879,49 +7595,49 @@ " \n", " \n", " \n", - " Half-Price Pedal Sale\n", + " 03E3594D-6EBB-46A6-B8EE-A9289C0C...\n", " \n", " \n", " \n", - " 0.5000\n", + " 2013-06-14 00:00:00\n", " \n", " \n", + " \n", + " \n", + " \n", + " 15\n", + " \n", " \n", - " Seasonal Discount\n", + " 2014-03-31\n", " \n", " \n", " \n", - " Customer\n", + " 2014-05-30\n", " \n", " \n", " \n", - " 2013-07-14 00:00:00\n", + " 16\n", " \n", " \n", " \n", - " 2013-08-14 00:00:00\n", + " Reseller\n", " \n", " \n", " \n", - " 03E3594D-6EBB-46A6-B8EE-A9289C0C...\n", + " Mountain-500 Silver Clearance Sa...\n", " \n", " \n", " \n", - " 2013-06-14 00:00:00\n", + " Discontinued Product\n", " \n", " \n", - " \n", - " \n", - " \n", - " 15\n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " 16\n", + " 0 \n", @@ -7932,11 +7648,11 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " 0 .4\n", " \n", " \n", @@ -7955,30 +7671,6 @@ " \n", " \n", " \n", - " Mountain-500 Silver Clearance Sa...\n", - " \n", - " \n", - " \n", - " 0.4000\n", - " \n", - " \n", - " \n", - " Discontinued Product\n", - " \n", - " \n", - " \n", - " Reseller\n", - " \n", - " \n", - " \n", - " 2014-03-31 00:00:00\n", - " \n", - " \n", - " \n", - " 2014-05-30 00:00:00\n", - " \n", - " \n", - " \n", " EB7CB484-BCCF-4D2D-BF73-521B2001...\n", " \n", " \n", @@ -8000,33 +7692,35 @@ "

\n" ], "text/plain": [ - "name SpecialOfferID MinQty MaxQty Description ... Category \n", - "role unused_float unused_float unused_float unused_string ... unused_string\n", - " 0 1 0 nan No Discount ... No Discount \n", - " 1 2 11 14 Volume Discount 11 to 14 ... Reseller \n", - " 2 3 15 24 Volume Discount 15 to 24 ... Reseller \n", - " 3 4 25 40 Volume Discount 25 to 40 ... Reseller \n", - " 4 5 41 60 Volume Discount 41 to 60 ... Reseller \n", - " ... ... ... ... ... \n", - " 11 12 0 nan LL Road Frame Sale ... Reseller \n", - " 12 13 0 nan Touring-3000 Promotion ... Reseller \n", - " 13 14 0 nan Touring-1000 Promotion ... Reseller \n", - " 14 15 0 nan Half-Price Pedal Sale ... Customer \n", - " 15 16 0 nan Mountain-500 Silver Clearance Sa... ... Reseller \n", + "name StartDate EndDate SpecialOfferID Category ... MinQty\n", + "role time_stamp time_stamp join_key categorical ... numerical\n", + "unit time stamp, comparison only time stamp, comparison only ... \n", + " 0 2011-05-01 2014-11-30 1 No Discount ... 0\n", + " 1 2011-05-31 2014-05-30 2 Reseller ... 11\n", + " 2 2011-05-31 2014-05-30 3 Reseller ... 15\n", + " 3 2011-05-31 2014-05-30 4 Reseller ... 25\n", + " 4 2011-05-31 2014-05-30 5 Reseller ... 41\n", + " ... ... ... ... ...\n", + " 11 2013-05-30 2013-07-14 12 Reseller ... 0\n", + " 12 2013-05-30 2013-08-29 13 Reseller ... 0\n", + " 13 2013-05-30 2013-08-29 14 Reseller ... 0\n", + " 14 2013-07-14 2013-08-14 15 Customer ... 0\n", + " 15 2014-03-31 2014-05-30 16 Reseller ... 0\n", "\n", - "name StartDate EndDate rowguid ModifiedDate \n", - "role unused_string unused_string unused_string unused_string \n", - " 0 2011-05-01 00:00:00 2014-11-30 00:00:00 0290C4F5-191F-4337-AB6B-0A2DDE03... 2011-04-01 00:00:00\n", - " 1 2011-05-31 00:00:00 2014-05-30 00:00:00 D7542EE7-15DB-4541-985C-5CC27AEF... 2011-05-01 00:00:00\n", - " 2 2011-05-31 00:00:00 2014-05-30 00:00:00 4BDBCC01-8CF7-40A9-B643-40EC5B71... 2011-05-01 00:00:00\n", - " 3 2011-05-31 00:00:00 2014-05-30 00:00:00 504B5E85-8F3F-4EBC-9E1D-C1BC5DEA... 2011-05-01 00:00:00\n", - " 4 2011-05-31 00:00:00 2014-05-30 00:00:00 677E1D9D-944F-4E81-90E8-47EB0A82... 2011-05-01 00:00:00\n", - " ... ... ... ... \n", - " 11 2013-05-30 00:00:00 2013-07-14 00:00:00 C0AF1C89-9722-4235-9248-3FBA4D9E... 2013-04-30 00:00:00\n", - " 12 2013-05-30 00:00:00 2013-08-29 00:00:00 5061CCE4-E021-45A8-9A75-DFB36CBB... 2013-04-30 00:00:00\n", - " 13 2013-05-30 00:00:00 2013-08-29 00:00:00 1AF84A9E-A98C-4BD9-B48F-DC2B8B6B... 2013-04-30 00:00:00\n", - " 14 2013-07-14 00:00:00 2013-08-14 00:00:00 03E3594D-6EBB-46A6-B8EE-A9289C0C... 2013-06-14 00:00:00\n", - " 15 2014-03-31 00:00:00 2014-05-30 00:00:00 EB7CB484-BCCF-4D2D-BF73-521B2001... 2014-03-01 00:00:00\n", + "name DiscountPct MaxQty rowguid ModifiedDate \n", + "role numerical unused_float unused_string unused_string \n", + "unit \n", + " 0 0 nan 0290C4F5-191F-4337-AB6B-0A2DDE03... 2011-04-01 00:00:00\n", + " 1 0.02 14 D7542EE7-15DB-4541-985C-5CC27AEF... 2011-05-01 00:00:00\n", + " 2 0.05 24 4BDBCC01-8CF7-40A9-B643-40EC5B71... 2011-05-01 00:00:00\n", + " 3 0.1 40 504B5E85-8F3F-4EBC-9E1D-C1BC5DEA... 2011-05-01 00:00:00\n", + " 4 0.15 60 677E1D9D-944F-4E81-90E8-47EB0A82... 2011-05-01 00:00:00\n", + " ... ... ... ... \n", + " 11 0.35 nan C0AF1C89-9722-4235-9248-3FBA4D9E... 2013-04-30 00:00:00\n", + " 12 0.15 nan 5061CCE4-E021-45A8-9A75-DFB36CBB... 2013-04-30 00:00:00\n", + " 13 0.2 nan 1AF84A9E-A98C-4BD9-B48F-DC2B8B6B... 2013-04-30 00:00:00\n", + " 14 0.5 nan 03E3594D-6EBB-46A6-B8EE-A9289C0C... 2013-06-14 00:00:00\n", + " 15 0.4 nan EB7CB484-BCCF-4D2D-BF73-521B2001... 2014-03-01 00:00:00\n", "\n", "\n", "16 rows x 11 columns\n", @@ -8101,11 +7795,15 @@ " \n", " \n", " \n", - " BusinessEntityID\n", + " test\n", " \n", " \n", " \n", - " SalesPersonID\n", + " SalesPersonID\n", + " \n", + " \n", + " \n", + " BusinessEntityID\n", " \n", " \n", " \n", @@ -8133,11 +7831,15 @@ " \n", " \n", " \n", - " unused_float\n", + " time_stamp\n", " \n", " \n", " \n", - " unused_float\n", + " join_key\n", + " \n", + " \n", + " \n", + " unused_float\n", " \n", " \n", " \n", @@ -8158,6 +7860,42 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " unit\n", + " \n", + " \n", + " \n", + " time stamp, comparison only\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8165,16 +7903,11 @@ " 0\n", " \n", " \n", - " \n", - " \n", - " \n", + " 2014-09-12 11:15:07\n", " \n", - " \n", - " 292 \n", - " \n", + " \n", + " \n", + " 279\n", " \n", " \n", " \n", @@ -8183,7 +7916,7 @@ " \n", " \n", " \n", - " 279292 \n", @@ -8212,16 +7945,11 @@ " 1\n", " \n", " \n", - " \n", - " \n", - " \n", + " NULL\n", " \n", - " \n", - " 294 \n", - " \n", + " \n", + " \n", + " 276\n", " \n", " \n", " \n", @@ -8230,7 +7958,7 @@ " \n", " \n", " \n", - " 276294 \n", @@ -8259,16 +7987,11 @@ " 2\n", " \n", " \n", - " \n", - " \n", - " \n", + " 2014-09-12 11:15:07\n", " \n", - " \n", - " 296 \n", - " \n", + " \n", + " \n", + " 277\n", " \n", " \n", " \n", @@ -8277,7 +8000,7 @@ " \n", " \n", " \n", - " 277296 \n", @@ -8306,16 +8029,11 @@ " 3\n", " \n", " \n", - " \n", - " \n", - " \n", + " NULL\n", " \n", - " \n", - " 298 \n", - " \n", + " \n", + " \n", + " 275\n", " \n", " \n", " \n", @@ -8324,7 +8042,7 @@ " \n", " \n", " \n", - " 275298 \n", @@ -8353,16 +8071,11 @@ " 4\n", " \n", " \n", - " \n", - " \n", - " \n", + " NULL\n", " \n", - " \n", - " 300 \n", - " \n", + " \n", + " \n", + " 286\n", " \n", " \n", " \n", @@ -8371,7 +8084,7 @@ " \n", " \n", " \n", - " 286300 \n", @@ -8400,16 +8113,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " ...\n", " \n", - " \n", - " ... \n", - " \n", + " \n", + " \n", + " ...\n", " \n", " \n", " \n", @@ -8447,16 +8155,11 @@ " 696\n", " \n", " \n", - " \n", - " \n", - " \n", + " NULL\n", " \n", - " \n", - " 1988 \n", - " \n", + " \n", + " \n", + " 282\n", " \n", " \n", " \n", @@ -8465,7 +8168,7 @@ " \n", " \n", " \n", - " 2821988 \n", @@ -8494,16 +8197,11 @@ " 697\n", " \n", " \n", - " \n", - " \n", - " \n", + " 2014-09-12 11:15:07\n", " \n", - " \n", - " 1990 \n", - " \n", + " \n", + " \n", + " 281\n", " \n", " \n", " \n", @@ -8512,7 +8210,7 @@ " \n", " \n", " \n", - " 2811990 \n", @@ -8541,16 +8239,11 @@ " 698\n", " \n", " \n", - " \n", - " \n", - " \n", + " NULL\n", " \n", - " \n", - " 1992 \n", - " \n", + " \n", + " \n", + " 277\n", " \n", " \n", " \n", @@ -8559,7 +8252,7 @@ " \n", " \n", " \n", - " 2771992 \n", @@ -8588,16 +8281,11 @@ " 699\n", " \n", " \n", - " \n", - " \n", - " \n", + " NULL\n", " \n", - " \n", - " 1994 \n", - " \n", + " \n", + " \n", + " 277\n", " \n", " \n", " \n", @@ -8606,7 +8294,7 @@ " \n", " \n", " \n", - " 2771994 \n", @@ -8635,16 +8323,11 @@ " 700\n", " \n", " \n", - " \n", - " \n", - " \n", + " 2014-09-12 11:15:07\n", " \n", - " \n", - " 2051 \n", - " \n", + " \n", + " \n", + " 275\n", " \n", " \n", " \n", @@ -8653,7 +8336,7 @@ " \n", " \n", " \n", - " 2752051 \n", @@ -8682,7 +8365,7 @@ "\n", "\n", "

\n", - " 701 rows x 6 columns
\n", + " 701 rows x 7 columns
\n", " memory usage: 0.38 MB
\n", " name: Store
\n", " type: getml.DataFrame
\n", @@ -8690,36 +8373,38 @@ "

\n" ], "text/plain": [ - "name BusinessEntityID SalesPersonID Name Demographics \n", - "role unused_float unused_float unused_string unused_string \n", - " 0 292 279 Next-Door Bike Store NULL\n", + " nan\n", " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -9202,11 +8887,11 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -9361,11 +9046,11 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -9520,11 +9205,11 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -9679,11 +9364,11 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -10785,7 +10470,7 @@ "\n", "

\n", " 504 rows x 25 columns
\n", - " memory usage: 0.17 MB
\n", + " memory usage: 0.16 MB
\n", " name: Product
\n", " type: getml.DataFrame
\n", " url: http://localhost:1709/#/getdataframe/adventure_works/Product/\n", @@ -10794,11 +10479,11 @@ "text/plain": [ "name ProductID MakeFlag ProductSubcategoryID ProductModelID ... SellStartDate SellEndDate \n", "role join_key categorical categorical categorical ... unused_string unused_string\n", - " 0 1 0 NULL NULL ... 2008-04-30 00:00:00 NULL \n", - " 1 2 0 NULL NULL ... 2008-04-30 00:00:00 NULL \n", - " 2 3 1 NULL NULL ... 2008-04-30 00:00:00 NULL \n", - " 3 4 0 NULL NULL ... 2008-04-30 00:00:00 NULL \n", - " 4 316 1 NULL NULL ... 2008-04-30 00:00:00 NULL \n", + " 0 1 0 nan nan ... 2008-04-30 00:00:00 NULL \n", + " 1 2 0 nan nan ... 2008-04-30 00:00:00 NULL \n", + " 2 3 1 nan nan ... 2008-04-30 00:00:00 NULL \n", + " 3 4 0 nan nan ... 2008-04-30 00:00:00 NULL \n", + " 4 316 1 nan nan ... 2008-04-30 00:00:00 NULL \n", " ... ... ... ... ... ... \n", " 499 995 1 5 96 ... 2013-05-30 00:00:00 NULL \n", " 500 996 1 5 97 ... 2013-05-30 00:00:00 NULL \n", @@ -10822,7 +10507,7 @@ "\n", "\n", "504 rows x 25 columns\n", - "memory usage: 0.17 MB\n", + "memory usage: 0.16 MB\n", "name: Product\n", "type: getml.DataFrame\n", "url: http://localhost:1709/#/getdataframe/adventure_works/Product/" @@ -11989,7 +11674,7 @@ "\n", "

\n", " 121317 rows x 11 columns
\n", - " memory usage: 14.32 MB
\n", + " memory usage: 14.08 MB
\n", " name: SalesOrderDetail
\n", " type: getml.DataFrame
\n", " url: http://localhost:1709/#/getdataframe/adventure_works/SalesOrderDetail/\n", @@ -12028,7 +11713,7 @@ "\n", "\n", "121317 rows x 11 columns\n", - "memory usage: 14.32 MB\n", + "memory usage: 14.08 MB\n", "name: SalesOrderDetail\n", "type: getml.DataFrame\n", "url: http://localhost:1709/#/getdataframe/adventure_works/SalesOrderDetail/" @@ -14264,15 +13949,15 @@ " \n", " \n", " \n", - " SalesPersonIDCat\n", + " ShipMethodID\n", " \n", " \n", " \n", - " TerritoryIDCat\n", + " SalesPersonIDCat\n", " \n", " \n", " \n", - " ShipMethodID\n", + " TerritoryIDCat\n", " \n", " \n", " \n", @@ -14312,6 +13997,10 @@ " \n", " \n", " \n", + " churn\n", + " \n", + " \n", + " \n", " SalesOrderNumber\n", " \n", " \n", @@ -14384,15 +14073,15 @@ " \n", " \n", " \n", - " categorical \n", + " categorical \n", " \n", " \n", " \n", - " categorical \n", + " categorical \n", " \n", " \n", " \n", - " categorical \n", + " categorical \n", " \n", " \n", " \n", @@ -14432,6 +14121,10 @@ " \n", " \n", " \n", + " unused_float\n", + " \n", + " \n", + " \n", " unused_string \n", " \n", " \n", @@ -14504,15 +14197,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14552,6 +14245,10 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14624,11 +14321,11 @@ " \n", " \n", " \n", - " 279\n", + " 5\n", " \n", " \n", " \n", - " 5\n", + " 279\n", " \n", " \n", " \n", @@ -14753,6 +14450,19 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 0 \n", + " \n", + " \n", + " \n", + " \n", " SO43659\n", " \n", " \n", @@ -14822,11 +14532,11 @@ " \n", " \n", " \n", - " 279\n", + " 5\n", " \n", " \n", " \n", - " 5\n", + " 279\n", " \n", " \n", " \n", @@ -14951,6 +14661,19 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 0 \n", + " \n", + " \n", + " \n", + " \n", " SO43660\n", " \n", " \n", @@ -15020,15 +14743,15 @@ " \n", " \n", " \n", - " 282\n", + " 5\n", " \n", " \n", " \n", - " 6\n", + " 282\n", " \n", " \n", " \n", - " 5\n", + " 6\n", " \n", " \n", " \n", @@ -15149,6 +14872,19 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 0 \n", + " \n", + " \n", + " \n", + " \n", " SO43661\n", " \n", " \n", @@ -15218,15 +14954,15 @@ " \n", " \n", " \n", - " 282\n", + " 5\n", " \n", " \n", " \n", - " 6\n", + " 282\n", " \n", " \n", " \n", - " 5\n", + " 6\n", " \n", " \n", " \n", @@ -15347,6 +15083,19 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 0 \n", + " \n", + " \n", + " \n", + " \n", " SO43662\n", " \n", " \n", @@ -15416,15 +15165,15 @@ " \n", " \n", " \n", - " 276\n", + " 5\n", " \n", " \n", " \n", - " 4\n", + " 276\n", " \n", " \n", " \n", - " 5\n", + " 4\n", " \n", " \n", " \n", @@ -15545,6 +15294,19 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 0 \n", + " \n", + " \n", + " \n", + " \n", " SO43663\n", " \n", " \n", @@ -15743,6 +15505,19 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " ... \n", + " \n", + " \n", + " \n", + " \n", " ...\n", " \n", " \n", @@ -15796,7 +15571,7 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -15812,11 +15587,11 @@ " \n", " \n", " \n", - " NULL\n", + " 1\n", " \n", " \n", " \n", - " 1\n", + " nan\n", " \n", " \n", " \n", @@ -15941,6 +15716,19 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " nan \n", + " \n", + " \n", + " \n", + " \n", " SO75119\n", " \n", " \n", @@ -15994,7 +15782,7 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -16010,15 +15798,15 @@ " \n", " \n", " \n", - " NULL\n", + " 1\n", " \n", " \n", " \n", - " 6\n", + " nan\n", " \n", " \n", " \n", - " 1\n", + " 6\n", " \n", " \n", " \n", @@ -16139,6 +15927,19 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " nan \n", + " \n", + " \n", + " \n", + " \n", " SO75120\n", " \n", " \n", @@ -16192,7 +15993,7 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -16208,15 +16009,15 @@ " \n", " \n", " \n", - " NULL\n", + " 1\n", " \n", " \n", " \n", - " 6\n", + " nan\n", " \n", " \n", " \n", - " 1\n", + " 6\n", " \n", " \n", " \n", @@ -16337,6 +16138,19 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " nan \n", + " \n", + " \n", + " \n", + " \n", " SO75121\n", " \n", " \n", @@ -16390,7 +16204,7 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -16406,15 +16220,15 @@ " \n", " \n", " \n", - " NULL\n", + " 1\n", " \n", " \n", " \n", - " 6\n", + " nan\n", " \n", " \n", " \n", - " 1\n", + " 6\n", " \n", " \n", " \n", @@ -16535,6 +16349,19 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " nan \n", + " \n", + " \n", + " \n", + " \n", " SO75122\n", " \n", " \n", @@ -16588,7 +16415,7 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -16604,15 +16431,15 @@ " \n", " \n", " \n", - " NULL\n", + " 1\n", " \n", " \n", " \n", - " 6\n", + " nan\n", " \n", " \n", " \n", - " 1\n", + " 6\n", " \n", " \n", " \n", @@ -16733,6 +16560,19 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " nan \n", + " \n", + " \n", + " \n", + " \n", " SO75123\n", " \n", " \n", @@ -16762,8 +16602,8 @@ "\n", "\n", "

\n", - " 31465 rows x 28 columns
\n", - " memory usage: 8.58 MB
\n", + " 31465 rows x 29 columns
\n", + " memory usage: 8.59 MB
\n", " name: SalesOrderHeader
\n", " type: getml.DataFrame
\n", " url: http://localhost:1709/#/getdataframe/adventure_works/SalesOrderHeader/\n", @@ -16812,8 +16652,8 @@ "31464 D54752FF-2B54-4BE5-95EA-3B72289C...\n", "\n", "\n", - "31465 rows x 28 columns\n", - "memory usage: 8.58 MB\n", + "31465 rows x 29 columns\n", + "memory usage: 8.59 MB\n", "name: SalesOrderHeader\n", "type: getml.DataFrame\n", "url: http://localhost:1709/#/getdataframe/adventure_works/SalesOrderHeader/" @@ -16953,15 +16793,15 @@ " \n", " \n", " \n", - " SalesPersonIDCat\n", + " ShipMethodID\n", " \n", " \n", " \n", - " TerritoryIDCat\n", + " SalesPersonIDCat\n", " \n", " \n", " \n", - " ShipMethodID\n", + " TerritoryIDCat\n", " \n", " \n", " \n", @@ -17077,15 +16917,15 @@ " \n", " \n", " \n", - " categorical \n", + " categorical \n", " \n", " \n", " \n", - " categorical \n", + " categorical \n", " \n", " \n", " \n", - " categorical \n", + " categorical \n", " \n", " \n", " \n", @@ -17201,15 +17041,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17334,11 +17174,11 @@ " \n", " \n", " \n", - " 279\n", + " 5\n", " \n", " \n", " \n", - " 5\n", + " 279\n", " \n", " \n", " \n", @@ -17545,11 +17385,11 @@ " \n", " \n", " \n", - " 279\n", + " 5\n", " \n", " \n", " \n", - " 5\n", + " 279\n", " \n", " \n", " \n", @@ -17756,15 +17596,15 @@ " \n", " \n", " \n", - " 282\n", + " 5\n", " \n", " \n", " \n", - " 6\n", + " 282\n", " \n", " \n", " \n", - " 5\n", + " 6\n", " \n", " \n", " \n", @@ -17967,15 +17807,15 @@ " \n", " \n", " \n", - " 282\n", + " 5\n", " \n", " \n", " \n", - " 6\n", + " 282\n", " \n", " \n", " \n", - " 5\n", + " 6\n", " \n", " \n", " \n", @@ -18178,15 +18018,15 @@ " \n", " \n", " \n", - " 276\n", + " 5\n", " \n", " \n", " \n", - " 4\n", + " 276\n", " \n", " \n", " \n", - " 5\n", + " 4\n", " \n", " \n", " \n", @@ -18571,7 +18411,7 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -18600,15 +18440,15 @@ " \n", " \n", " \n", - " NULL\n", + " 1\n", " \n", " \n", " \n", - " 7\n", + " nan\n", " \n", " \n", " \n", - " 1\n", + " 7\n", " \n", " \n", " \n", @@ -18782,7 +18622,7 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -18811,15 +18651,15 @@ " \n", " \n", " \n", - " NULL\n", + " 1\n", " \n", " \n", " \n", - " 10\n", + " nan\n", " \n", " \n", " \n", - " 1\n", + " 10\n", " \n", " \n", " \n", @@ -18993,7 +18833,7 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -19022,15 +18862,15 @@ " \n", " \n", " \n", - " NULL\n", + " 1\n", " \n", " \n", " \n", - " 7\n", + " nan\n", " \n", " \n", " \n", - " 1\n", + " 7\n", " \n", " \n", " \n", @@ -19204,7 +19044,7 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -19233,15 +19073,15 @@ " \n", " \n", " \n", - " NULL\n", + " 1\n", " \n", " \n", " \n", - " 8\n", + " nan\n", " \n", " \n", " \n", - " 1\n", + " 8\n", " \n", " \n", " \n", @@ -19415,7 +19255,7 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -19444,15 +19284,15 @@ " \n", " \n", " \n", - " NULL\n", + " 1\n", " \n", " \n", " \n", - " 10\n", + " nan\n", " \n", " \n", " \n", - " 1\n", + " 10\n", " \n", " \n", " \n", @@ -19603,7 +19443,7 @@ "\n", "

\n", " 19704 rows x 29 columns
\n", - " memory usage: 5.54 MB
\n", + " memory usage: 5.39 MB
\n", " name: SalesOrderHeaderRefined
\n", " type: getml.DataFrame
\n", " url: http://localhost:1709/#/getdataframe/adventure_works/SalesOrderHeaderRefined/\n", @@ -19653,7 +19493,7 @@ "\n", "\n", "19704 rows x 29 columns\n", - "memory usage: 5.54 MB\n", + "memory usage: 5.39 MB\n", "name: SalesOrderHeaderRefined\n", "type: getml.DataFrame\n", "url: http://localhost:1709/#/getdataframe/adventure_works/SalesOrderHeaderRefined/" @@ -19681,7 +19521,7 @@ " repeat_purchases[\"OrderDate_y\"] - repeat_purchases[\"OrderDate_x\"] > pd.Timedelta('180 days')\n", "]\n", "\n", - "repeat_purchases.groupby(\"SalesOrderID\", as_index=False).aggregate({\"CustomerID\": \"max\"})\n", + "repeat_purchases = repeat_purchases.groupby(\"SalesOrderID\", as_index=False).aggregate({\"CustomerID\": \"max\"})\n", "\n", "repeat_purchase_ids = {sid: True for sid in repeat_purchases[\"SalesOrderID\"]}\n", "\n", @@ -20331,9 +20171,9 @@ " columns:\n", " - RevisionNumber: categorical\n", " - OnlineOrderFlag: categorical\n", + " - ShipMethodID: categorical\n", " - SalesPersonIDCat: categorical\n", " - TerritoryIDCat: categorical\n", - " - ShipMethodID: categorical\n", " - ...\n", "\n", " joins:\n", @@ -20360,9 +20200,9 @@ " columns:\n", " - RevisionNumber: categorical\n", " - OnlineOrderFlag: categorical\n", + " - ShipMethodID: categorical\n", " - SalesPersonIDCat: categorical\n", " - TerritoryIDCat: categorical\n", - " - ShipMethodID: categorical\n", " - ...\n", "\n", "sales_order_detail:\n", @@ -20539,7 +20379,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['product', 'sales_order_detail', 'sales_order_header',\n", " 'sales_order_reason', 'special_offer', 'store'],\n", " predictors=['XGBoostClassifier'],\n", @@ -20552,7 +20392,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['product', 'sales_order_detail', 'sales_order_header',\n", " 'sales_order_reason', 'special_offer', 'store'],\n", " predictors=['XGBoostClassifier'],\n", @@ -20591,7 +20431,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['product', 'sales_order_detail', 'sales_order_header',\n", " 'sales_order_reason', 'special_offer', 'store'],\n", " predictors=['XGBoostClassifier'],\n", @@ -20604,7 +20444,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['product', 'sales_order_detail', 'sales_order_header',\n", " 'sales_order_reason', 'special_offer', 'store'],\n", " predictors=['XGBoostClassifier'],\n", @@ -20693,6 +20533,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and SALES_ORDER_REASON__STAGING_TABLE_4 over 'SalesOrderID' and 'SalesOrderID', there are no corresponding entries for 33.769352% of entries in 'SalesOrderID' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and STORE__STAGING_TABLE_5 over 'SalesPersonID' and 'SalesPersonID', there are no corresponding entries for 84.941548% of entries in 'SalesPersonID' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", @@ -20715,7 +20558,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:1m:11.471055\n", + "Time taken: 0h:0m:26.231238\n", "\n" ] }, @@ -20726,28 +20569,28 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['product', 'sales_order_detail', 'sales_order_header',\n", " 'sales_order_reason', 'special_offer', 'store'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-n1KOfl'])

url: http://localhost:1709/#/getpipeline/adventure_works/ILhAI5/0/
" + " tags=['fast_prop', 'container-3vq0tN'])
url: http://localhost:1709/#/getpipeline/adventure_works/2eA1d4/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['product', 'sales_order_detail', 'sales_order_header',\n", " 'sales_order_reason', 'special_offer', 'store'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-n1KOfl'])\n", + " tags=['fast_prop', 'container-3vq0tN'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/adventure_works/ILhAI5/0/" + "url: http://localhost:1709/#/getpipeline/adventure_works/2eA1d4/0/" ] }, "execution_count": 25, @@ -20805,6 +20648,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and SALES_ORDER_REASON__STAGING_TABLE_4 over 'SalesOrderID' and 'SalesOrderID', there are no corresponding entries for 33.769352% of entries in 'SalesOrderID' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and STORE__STAGING_TABLE_5 over 'SalesPersonID' and 'SalesPersonID', there are no corresponding entries for 84.941548% of entries in 'SalesPersonID' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", @@ -20827,7 +20673,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:1m:47.178643\n", + "Time taken: 0h:1m:4.494809\n", "\n" ] }, @@ -20838,28 +20684,28 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['product', 'sales_order_detail', 'sales_order_header',\n", " 'sales_order_reason', 'special_offer', 'store'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Seasonal', 'Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['relboost', 'container-n1KOfl'])
url: http://localhost:1709/#/getpipeline/adventure_works/8bBeRr/0/
" + " tags=['relboost', 'container-3vq0tN'])
url: http://localhost:1709/#/getpipeline/adventure_works/piQtFG/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['product', 'sales_order_detail', 'sales_order_header',\n", " 'sales_order_reason', 'special_offer', 'store'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Seasonal', 'Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['relboost', 'container-n1KOfl'])\n", + " tags=['relboost', 'container-3vq0tN'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/adventure_works/8bBeRr/0/" + "url: http://localhost:1709/#/getpipeline/adventure_works/piQtFG/0/" ] }, "execution_count": 27, @@ -20977,7 +20823,7 @@ " 0\n", " \n", " \n", - " 2022-03-19 23:17:08\n", + " 2022-07-03 21:34:50\n", " \n", " \n", " \n", @@ -20989,7 +20835,7 @@ " \n", " \n", " \n", - " 0.9151\n", + " 0.9156\n", " \n", " \n", " \n", @@ -20997,7 +20843,7 @@ " \n", " \n", " \n", - " 0.2148\n", + " 0.2151\n", " \n", " \n", " \n", @@ -21006,7 +20852,7 @@ " 1\n", " \n", " \n", - " 2022-03-19 23:19:05\n", + " 2022-07-03 21:35:59\n", " \n", " \n", " \n", @@ -21018,15 +20864,15 @@ " \n", " \n", " \n", - " 0.9129\n", + " 0.9139\n", " \n", " \n", " \n", - " 0.9712\n", + " 0.9714\n", " \n", " \n", " \n", - " 0.2236\n", + " 0.2238\n", " \n", " \n", " \n", @@ -21036,8 +20882,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-19 23:17:08 train churn 0.9151 0.9742 0.2148\n", - "1 2022-03-19 23:19:05 test churn 0.9129 0.9712 0.2236" + "0 2022-07-03 21:34:50 train churn 0.9156 0.9742 0.2151\n", + "1 2022-07-03 21:35:59 test churn 0.9139 0.9714 0.2238" ] }, "execution_count": 28, @@ -21141,7 +20987,7 @@ " 0\n", " \n", " \n", - " 2022-03-19 23:18:59\n", + " 2022-07-03 21:35:57\n", " \n", " \n", " \n", @@ -21153,15 +20999,15 @@ " \n", " \n", " \n", - " 0.9312\n", + " 0.9331\n", " \n", " \n", " \n", - " 0.9832\n", + " 0.9836\n", " \n", " \n", " \n", - " 0.1689\n", + " 0.1665\n", " \n", " \n", " \n", @@ -21170,7 +21016,7 @@ " 1\n", " \n", " \n", - " 2022-03-19 23:19:12\n", + " 2022-07-03 21:36:03\n", " \n", " \n", " \n", @@ -21182,15 +21028,15 @@ " \n", " \n", " \n", - " 0.9273\n", + " 0.9276\n", " \n", " \n", " \n", - " 0.9776\n", + " 0.9786\n", " \n", " \n", " \n", - " 0.1929\n", + " 0.1894\n", " \n", " \n", " \n", @@ -21200,8 +21046,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-19 23:18:59 train churn 0.9312 0.9832 0.1689\n", - "1 2022-03-19 23:19:12 test churn 0.9273 0.9776 0.1929" + "0 2022-07-03 21:35:57 train churn 0.9331 0.9836 0.1665\n", + "1 2022-07-03 21:36:03 test churn 0.9276 0.9786 0.1894" ] }, "execution_count": 29, @@ -22206,9 +22052,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/patrick/.local/lib/python3.9/site-packages/featuretools/entityset/entityset.py:1567: UserWarning: index index not found in dataframe, creating new integer column\n", + "/usr/local/lib/python3.9/dist-packages/featuretools/entityset/entityset.py:1567: UserWarning: index index not found in dataframe, creating new integer column\n", " warnings.warn(\"index {} not found in dataframe, creating new \"\n", - "/home/patrick/.local/lib/python3.9/site-packages/featuretools/entityset/entityset.py:362: UserWarning: Logical type Categorical for child column SalesOrderID does not match parent column SalesOrderID logical type Unknown. Changing child logical type to match parent.\n", + "/usr/local/lib/python3.9/dist-packages/featuretools/entityset/entityset.py:362: UserWarning: Logical type Categorical for child column SalesOrderID does not match parent column SalesOrderID logical type Unknown. Changing child logical type to match parent.\n", " warnings.warn(f'Logical type {child_ltype} for child column {child_column} does not match '\n" ] } @@ -22229,7 +22075,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/patrick/.local/lib/python3.9/site-packages/featuretools/entityset/entityset.py:660: UserWarning: A Woodwork-initialized DataFrame was provided, so the following parameters were ignored: index\n", + "/usr/local/lib/python3.9/dist-packages/featuretools/entityset/entityset.py:660: UserWarning: A Woodwork-initialized DataFrame was provided, so the following parameters were ignored: index\n", " warnings.warn(\"A Woodwork-initialized DataFrame was provided, so the following parameters were ignored: \" + \", \".join(extra_params))\n" ] } @@ -22269,8 +22115,8 @@ " \n", " RevisionNumber\n", " OnlineOrderFlag\n", - " TerritoryIDCat\n", " ShipMethodID\n", + " TerritoryIDCat\n", " SalesPersonID\n", " TerritoryID\n", " SubTotal\n", @@ -22367,8 +22213,8 @@ " 43661\n", " 8\n", " 0\n", - " 6\n", " 5\n", + " 6\n", " 282\n", " 6\n", " 32726.4786\n", @@ -22391,8 +22237,8 @@ " 43663\n", " 8\n", " 0\n", - " 4\n", " 5\n", + " 4\n", " 276\n", " 4\n", " 419.4589\n", @@ -22415,8 +22261,8 @@ " 43664\n", " 8\n", " 0\n", - " 1\n", " 5\n", + " 1\n", " 280\n", " 1\n", " 24432.6088\n", @@ -22463,8 +22309,8 @@ " 63358\n", " 8\n", " 1\n", - " 7\n", " 1\n", + " 7\n", " NaN\n", " 7\n", " 1173.9600\n", @@ -22487,8 +22333,8 @@ " 63359\n", " 8\n", " 1\n", - " 10\n", " 1\n", + " 10\n", " NaN\n", " 10\n", " 1179.4700\n", @@ -22511,8 +22357,8 @@ " 63360\n", " 8\n", " 1\n", - " 7\n", " 1\n", + " 7\n", " NaN\n", " 7\n", " 548.9800\n", @@ -22535,8 +22381,8 @@ " 63361\n", " 8\n", " 1\n", - " 8\n", " 1\n", + " 8\n", " NaN\n", " 8\n", " 2384.0700\n", @@ -22559,8 +22405,8 @@ " 63362\n", " 8\n", " 1\n", - " 10\n", " 1\n", + " 10\n", " NaN\n", " 10\n", " 2419.0600\n", @@ -22585,19 +22431,19 @@ "" ], "text/plain": [ - " RevisionNumber OnlineOrderFlag TerritoryIDCat ShipMethodID \\\n", + " RevisionNumber OnlineOrderFlag ShipMethodID TerritoryIDCat \\\n", "SalesOrderID \n", - "43659 8 0 5 5 \n", - "43660 8 0 5 5 \n", - "43661 8 0 6 5 \n", - "43663 8 0 4 5 \n", - "43664 8 0 1 5 \n", - "... ... ... ... ... \n", - "63358 8 1 7 1 \n", - "63359 8 1 10 1 \n", - "63360 8 1 7 1 \n", - "63361 8 1 8 1 \n", - "63362 8 1 10 1 \n", + "43659 8 0 5 5 \n", + "43660 8 0 5 5 \n", + "43661 8 0 5 6 \n", + "43663 8 0 5 4 \n", + "43664 8 0 5 1 \n", + "... ... ... ... ... \n", + "63358 8 1 1 7 \n", + "63359 8 1 1 10 \n", + "63360 8 1 1 7 \n", + "63361 8 1 1 8 \n", + "63362 8 1 1 10 \n", "\n", " SalesPersonID TerritoryID SubTotal TaxAmt Freight \\\n", "SalesOrderID \n", @@ -22812,11 +22658,11 @@ " \n", " \n", " \n", - " TerritoryIDCat\n", + " ShipMethodID\n", " \n", " \n", " \n", - " ShipMethodID\n", + " TerritoryIDCat\n", " \n", " \n", " \n", @@ -23664,11 +23510,11 @@ " \n", " \n", " \n", - " categorical \n", + " categorical \n", " \n", " \n", " \n", - " categorical \n", + " categorical \n", " \n", " \n", " \n", @@ -27933,11 +27779,11 @@ " \n", " \n", " \n", - " 6\n", + " 5\n", " \n", " \n", " \n", - " 5\n", + " 6\n", " \n", " \n", " \n", @@ -29637,11 +29483,11 @@ " \n", " \n", " \n", - " 4\n", + " 5\n", " \n", " \n", " \n", - " 5\n", + " 4\n", " \n", " \n", " \n", @@ -31341,11 +31187,11 @@ " \n", " \n", " \n", - " 1\n", + " 5\n", " \n", " \n", " \n", - " 5\n", + " 1\n", " \n", " \n", " \n", @@ -34749,11 +34595,11 @@ " \n", " \n", " \n", - " 7\n", + " 1\n", " \n", " \n", " \n", - " 1\n", + " 7\n", " \n", " \n", " \n", @@ -36453,11 +36299,11 @@ " \n", " \n", " \n", - " 10\n", + " 1\n", " \n", " \n", " \n", - " 1\n", + " 10\n", " \n", " \n", " \n", @@ -38157,11 +38003,11 @@ " \n", " \n", " \n", - " 7\n", + " 1\n", " \n", " \n", " \n", - " 1\n", + " 7\n", " \n", " \n", " \n", @@ -39861,11 +39707,11 @@ " \n", " \n", " \n", - " 8\n", + " 1\n", " \n", " \n", " \n", - " 1\n", + " 8\n", " \n", " \n", " \n", @@ -41565,11 +41411,11 @@ " \n", " \n", " \n", - " 10\n", + " 1\n", " \n", " \n", " \n", - " 1\n", + " 10\n", " \n", " \n", " \n", @@ -43256,19 +43102,19 @@ "

\n" ], "text/plain": [ - " name churn RevisionNumber OnlineOrderFlag TerritoryIDCat ... SUM(sales_order_detail.ReorderPoint)\n", - " role target categorical categorical categorical ... numerical\n", - " 0 0 8 0 5 ... 540\n", - " 1 0 8 0 5 ... 150\n", - " 2 0 8 0 6 ... 2265\n", - " 3 0 8 0 4 ... 75\n", - " 4 0 8 0 1 ... 456\n", - " ... ... ... ... ...\n", - "15820 1 8 1 7 ... 828\n", - "15821 1 8 1 10 ... 81\n", - "15822 1 8 1 7 ... 78\n", - "15823 1 8 1 8 ... 75\n", - "15824 1 8 1 10 ... 78\n", + " name churn RevisionNumber OnlineOrderFlag ShipMethodID ... SUM(sales_order_detail.ReorderPoint)\n", + " role target categorical categorical categorical ... numerical\n", + " 0 0 8 0 5 ... 540\n", + " 1 0 8 0 5 ... 150\n", + " 2 0 8 0 5 ... 2265\n", + " 3 0 8 0 5 ... 75\n", + " 4 0 8 0 5 ... 456\n", + " ... ... ... ... ...\n", + "15820 1 8 1 1 ... 828\n", + "15821 1 8 1 1 ... 81\n", + "15822 1 8 1 1 ... 78\n", + "15823 1 8 1 1 ... 75\n", + "15824 1 8 1 1 ... 78\n", "\n", " name SUM(sales_order_detail.SafetyStockLevel) SUM(sales_order_detail.StandardCost) SUM(sales_order_detail.UnitPrice)\n", " role numerical numerical numerical\n", @@ -43398,11 +43244,11 @@ " \n", " \n", " \n", - " TerritoryIDCat\n", + " ShipMethodID\n", " \n", " \n", " \n", - " ShipMethodID\n", + " TerritoryIDCat\n", " \n", " \n", " \n", @@ -44250,11 +44096,11 @@ " \n", " \n", " \n", - " categorical \n", + " categorical \n", " \n", " \n", " \n", - " categorical \n", + " categorical \n", " \n", " \n", " \n", @@ -45111,11 +44957,11 @@ " \n", " \n", " \n", - " 6\n", + " 5\n", " \n", " \n", " \n", - " 5\n", + " 6\n", " \n", " \n", " \n", @@ -46815,11 +46661,11 @@ " \n", " \n", " \n", - " 1\n", + " 5\n", " \n", " \n", " \n", - " 5\n", + " 1\n", " \n", " \n", " \n", @@ -48519,11 +48365,11 @@ " \n", " \n", " \n", - " 1\n", + " 5\n", " \n", " \n", " \n", - " 5\n", + " 1\n", " \n", " \n", " \n", @@ -51927,11 +51773,11 @@ " \n", " \n", " \n", - " 4\n", + " 5\n", " \n", " \n", " \n", - " 5\n", + " 4\n", " \n", " \n", " \n", @@ -55335,11 +55181,11 @@ " \n", " \n", " \n", - " 9\n", + " 1\n", " \n", " \n", " \n", - " 1\n", + " 9\n", " \n", " \n", " \n", @@ -57039,11 +56885,11 @@ " \n", " \n", " \n", - " 9\n", + " 1\n", " \n", " \n", " \n", - " 1\n", + " 9\n", " \n", " \n", " \n", @@ -60447,11 +60293,11 @@ " \n", " \n", " \n", - " 4\n", + " 1\n", " \n", " \n", " \n", - " 1\n", + " 4\n", " \n", " \n", " \n", @@ -63842,19 +63688,19 @@ "

\n" ], "text/plain": [ - "name churn RevisionNumber OnlineOrderFlag TerritoryIDCat ... SUM(sales_order_detail.ReorderPoint)\n", - "role target categorical categorical categorical ... numerical\n", - " 0 0 8 0 6 ... 4050\n", - " 1 0 8 0 1 ... 390\n", - " 2 0 8 0 1 ... 375\n", - " 3 0 8 0 5 ... 450\n", - " 4 0 8 0 4 ... 75\n", - " ... ... ... ... ...\n", - "3874 1 8 1 9 ... 450\n", - "3875 1 8 1 9 ... 828\n", - "3876 1 8 1 1 ... 825\n", - "3877 1 8 1 4 ... 84\n", - "3878 1 8 1 1 ... 81\n", + "name churn RevisionNumber OnlineOrderFlag ShipMethodID ... SUM(sales_order_detail.ReorderPoint)\n", + "role target categorical categorical categorical ... numerical\n", + " 0 0 8 0 5 ... 4050\n", + " 1 0 8 0 5 ... 390\n", + " 2 0 8 0 5 ... 375\n", + " 3 0 8 0 5 ... 450\n", + " 4 0 8 0 5 ... 75\n", + " ... ... ... ... ...\n", + "3874 1 8 1 1 ... 450\n", + "3875 1 8 1 1 ... 828\n", + "3876 1 8 1 1 ... 825\n", + "3877 1 8 1 1 ... 84\n", + "3878 1 8 1 1 ... 81\n", "\n", "name SUM(sales_order_detail.SafetyStockLevel) SUM(sales_order_detail.StandardCost) SUM(sales_order_detail.UnitPrice)\n", "role numerical numerical numerical\n", @@ -63926,7 +63772,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Imputation'],\n", @@ -63938,7 +63784,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Imputation'],\n", @@ -64017,7 +63863,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:13.828016\n", + "Time taken: 0h:0m:8.507248\n", "\n" ] }, @@ -64028,26 +63874,26 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Imputation'],\n", " share_selected_features=0.5,\n", - " tags=['featuretools'])
url: http://localhost:1709/#/getpipeline/adventure_works/dg4AhK/0/
" + " tags=['featuretools'])
url: http://localhost:1709/#/getpipeline/adventure_works/RlCBCt/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Imputation'],\n", " share_selected_features=0.5,\n", " tags=['featuretools'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/adventure_works/dg4AhK/0/" + "url: http://localhost:1709/#/getpipeline/adventure_works/RlCBCt/0/" ] }, "execution_count": 48, @@ -64148,7 +63994,7 @@ " 0\n", " \n", " \n", - " 2022-03-19 23:23:14\n", + " 2022-07-03 21:39:17\n", " \n", " \n", " \n", @@ -64177,7 +64023,7 @@ " 1\n", " \n", " \n", - " 2022-03-19 23:23:15\n", + " 2022-07-03 21:39:18\n", " \n", " \n", " \n", @@ -64207,8 +64053,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-19 23:23:14 featuretools_train churn 0.9132 0.9705 0.2318\n", - "1 2022-03-19 23:23:15 featuretools_test churn 0.9103 0.9664 0.2429" + "0 2022-07-03 21:39:17 featuretools_train churn 0.9132 0.9705 0.2318\n", + "1 2022-07-03 21:39:18 featuretools_test churn 0.9103 0.9664 0.2429" ] }, "execution_count": 49, @@ -64242,7 +64088,7 @@ "\n", "CREATE TABLE \"FEATURE_1_304\" AS\n", "SELECT MIN( t1.\"orderdate\" - t2.\"t4__startdate\" ) AS \"feature_1_304\",\n", - " t1.rowid AS \"rownum\"\n", + " t1.rowid AS rownum\n", "FROM \"POPULATION__STAGING_TABLE_1\" t1\n", "INNER JOIN \"SALES_ORDER_DETAIL__STAGING_TABLE_2\" t2\n", "ON t1.\"salesorderid\" = t2.\"salesorderid\"\n", @@ -64250,7 +64096,7 @@ "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"FEATURE_1_304\";\\n\\nCREATE TABLE \"FEATURE_1_304\" AS\\nSELECT MIN( t1.\"orderdate\" - t2.\"t4__startdate\" ) AS \"feature_1_304\",\\n t1.rowid AS \"rownum\"\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"SALES_ORDER_DETAIL__STAGING_TABLE_2\" t2\\nON t1.\"salesorderid\" = t2.\"salesorderid\"\\nGROUP BY t1.rowid;'" + "'DROP TABLE IF EXISTS \"FEATURE_1_304\";\\n\\nCREATE TABLE \"FEATURE_1_304\" AS\\nSELECT MIN( t1.\"orderdate\" - t2.\"t4__startdate\" ) AS \"feature_1_304\",\\n t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"SALES_ORDER_DETAIL__STAGING_TABLE_2\" t2\\nON t1.\"salesorderid\" = t2.\"salesorderid\"\\nGROUP BY t1.rowid;'" ] }, "execution_count": 50, @@ -64276,18 +64122,18 @@ "CREATE TABLE \"FEATURE_1_9\" AS\n", "SELECT AVG( \n", " CASE\n", - " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" IN ( '279', '282', '276', '280', '283', '277', '275', '278', '281', '289', '290', '287', '284', '286', '288', '285' ) ) AND ( t1.\"strftime('%m', duedate )\" IN ( '10', '11', '07', '08', '09' ) ) THEN -0.9700713956589746\n", - " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" IN ( '279', '282', '276', '280', '283', '277', '275', '278', '281', '289', '290', '287', '284', '286', '288', '285' ) ) AND ( t1.\"strftime('%m', duedate )\" NOT IN ( '10', '11', '07', '08', '09' ) OR t1.\"strftime('%m', duedate )\" IS NULL ) THEN 1.678278804705623\n", - " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" NOT IN ( '279', '282', '276', '280', '283', '277', '275', '278', '281', '289', '290', '287', '284', '286', '288', '285' ) OR t1.\"salespersonidcat\" IS NULL ) AND ( t1.\"territoryidcat\" IN ( '6' ) ) THEN 0.3673953618410275\n", - " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" NOT IN ( '279', '282', '276', '280', '283', '277', '275', '278', '281', '289', '290', '287', '284', '286', '288', '285' ) OR t1.\"salespersonidcat\" IS NULL ) AND ( t1.\"territoryidcat\" NOT IN ( '6' ) OR t1.\"territoryidcat\" IS NULL ) THEN 1.395126249580338\n", - " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" IN ( '2', '10', '9', '8' ) ) AND ( t2.\"t3__productsubcategoryid__mapping_2_target_1_avg\" > 0.552951 ) THEN 2.309774447072781\n", - " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" IN ( '2', '10', '9', '8' ) ) AND ( t2.\"t3__productsubcategoryid__mapping_2_target_1_avg\" <= 0.552951 OR t2.\"t3__productsubcategoryid__mapping_2_target_1_avg\" IS NULL ) THEN -1.211274926091625\n", - " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" NOT IN ( '2', '10', '9', '8' ) OR t1.\"territoryidcat\" IS NULL ) AND ( t2.\"t3__productmodelid\" IN ( '1', '14', '19', '21', '2', '10', '11', '4', '17', '35', '34', '36', '33', '24', '28', '37', '26', '29', '30', '32', '9', '6', '13', '8', '7', '104', '106', '59', '61', '52', '56', '42', '45', '46', '50', '51', '123', '124', '125', '78', '116', '38', '111', '118', '107', '127', '128', '79', '80', '81', '84', '66', '67', '62', '63', '64', '68', '69', '70', '53', '103', '47', '48', '102', '99', '101', '98', '95', '97' ) ) THEN -0.8764282201965673\n", - " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" NOT IN ( '2', '10', '9', '8' ) OR t1.\"territoryidcat\" IS NULL ) AND ( t2.\"t3__productmodelid\" NOT IN ( '1', '14', '19', '21', '2', '10', '11', '4', '17', '35', '34', '36', '33', '24', '28', '37', '26', '29', '30', '32', '9', '6', '13', '8', '7', '104', '106', '59', '61', '52', '56', '42', '45', '46', '50', '51', '123', '124', '125', '78', '116', '38', '111', '118', '107', '127', '128', '79', '80', '81', '84', '66', '67', '62', '63', '64', '68', '69', '70', '53', '103', '47', '48', '102', '99', '101', '98', '95', '97' ) OR t2.\"t3__productmodelid\" IS NULL ) THEN -0.2210833490057955\n", + " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" IN ( '279', '282', '276', '280', '283', '277', '275', '278', '281', '289', '290', '287', '284', '286', '288', '285' ) ) AND ( t2.\"strftime('%m', modifieddate )__mapping_2_target_1_avg\" > 0.763077 ) THEN 0.5980001018988601\n", + " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" IN ( '279', '282', '276', '280', '283', '277', '275', '278', '281', '289', '290', '287', '284', '286', '288', '285' ) ) AND ( t2.\"strftime('%m', modifieddate )__mapping_2_target_1_avg\" <= 0.763077 OR t2.\"strftime('%m', modifieddate )__mapping_2_target_1_avg\" IS NULL ) THEN -2.050123232507158\n", + " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" NOT IN ( '279', '282', '276', '280', '283', '277', '275', '278', '281', '289', '290', '287', '284', '286', '288', '285' ) OR t1.\"salespersonidcat\" IS NULL ) AND ( t1.\"territoryidcat\" IN ( '6' ) ) THEN -0.7126375400751357\n", + " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" NOT IN ( '279', '282', '276', '280', '283', '277', '275', '278', '281', '289', '290', '287', '284', '286', '288', '285' ) OR t1.\"salespersonidcat\" IS NULL ) AND ( t1.\"territoryidcat\" NOT IN ( '6' ) OR t1.\"territoryidcat\" IS NULL ) THEN 0.3150913831000781\n", + " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" IN ( '2', '10', '9', '8' ) ) AND ( t2.\"t3__productsubcategoryid__mapping_2_target_1_avg\" > 0.552951 ) THEN 1.229731415243227\n", + " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" IN ( '2', '10', '9', '8' ) ) AND ( t2.\"t3__productsubcategoryid__mapping_2_target_1_avg\" <= 0.552951 OR t2.\"t3__productsubcategoryid__mapping_2_target_1_avg\" IS NULL ) THEN -2.291310468800539\n", + " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" NOT IN ( '2', '10', '9', '8' ) OR t1.\"territoryidcat\" IS NULL ) AND ( t2.\"t3__productmodelid\" IN ( '1', '14', '19', '21', '2', '10', '11', '4', '17', '35', '34', '36', '33', '24', '28', '37', '26', '29', '30', '32', '9', '6', '13', '8', '7', '104', '106', '59', '61', '52', '56', '42', '45', '46', '50', '51', '123', '124', '125', '78', '116', '38', '111', '118', '107', '127', '128', '79', '80', '81', '84', '66', '67', '62', '63', '64', '68', '69', '70', '53', '103', '47', '48', '102', '99', '101', '98', '95', '97' ) ) THEN -1.956472141424515\n", + " WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" NOT IN ( '2', '10', '9', '8' ) OR t1.\"territoryidcat\" IS NULL ) AND ( t2.\"t3__productmodelid\" NOT IN ( '1', '14', '19', '21', '2', '10', '11', '4', '17', '35', '34', '36', '33', '24', '28', '37', '26', '29', '30', '32', '9', '6', '13', '8', '7', '104', '106', '59', '61', '52', '56', '42', '45', '46', '50', '51', '123', '124', '125', '78', '116', '38', '111', '118', '107', '127', '128', '79', '80', '81', '84', '66', '67', '62', '63', '64', '68', '69', '70', '53', '103', '47', '48', '102', '99', '101', '98', '95', '97' ) OR t2.\"t3__productmodelid\" IS NULL ) THEN -1.301121099543705\n", " ELSE NULL\n", " END\n", ") AS \"feature_1_9\",\n", - " t1.rowid AS \"rownum\"\n", + " t1.rowid AS rownum\n", "FROM \"POPULATION__STAGING_TABLE_1\" t1\n", "INNER JOIN \"SALES_ORDER_DETAIL__STAGING_TABLE_2\" t2\n", "ON t1.\"salesorderid\" = t2.\"salesorderid\"\n", @@ -64295,7 +64141,7 @@ "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"FEATURE_1_9\";\\n\\nCREATE TABLE \"FEATURE_1_9\" AS\\nSELECT AVG( \\n CASE\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" IN ( \\'279\\', \\'282\\', \\'276\\', \\'280\\', \\'283\\', \\'277\\', \\'275\\', \\'278\\', \\'281\\', \\'289\\', \\'290\\', \\'287\\', \\'284\\', \\'286\\', \\'288\\', \\'285\\' ) ) AND ( t1.\"strftime(\\'%m\\', duedate )\" IN ( \\'10\\', \\'11\\', \\'07\\', \\'08\\', \\'09\\' ) ) THEN -0.9700713956589746\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" IN ( \\'279\\', \\'282\\', \\'276\\', \\'280\\', \\'283\\', \\'277\\', \\'275\\', \\'278\\', \\'281\\', \\'289\\', \\'290\\', \\'287\\', \\'284\\', \\'286\\', \\'288\\', \\'285\\' ) ) AND ( t1.\"strftime(\\'%m\\', duedate )\" NOT IN ( \\'10\\', \\'11\\', \\'07\\', \\'08\\', \\'09\\' ) OR t1.\"strftime(\\'%m\\', duedate )\" IS NULL ) THEN 1.678278804705623\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" NOT IN ( \\'279\\', \\'282\\', \\'276\\', \\'280\\', \\'283\\', \\'277\\', \\'275\\', \\'278\\', \\'281\\', \\'289\\', \\'290\\', \\'287\\', \\'284\\', \\'286\\', \\'288\\', \\'285\\' ) OR t1.\"salespersonidcat\" IS NULL ) AND ( t1.\"territoryidcat\" IN ( \\'6\\' ) ) THEN 0.3673953618410275\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" NOT IN ( \\'279\\', \\'282\\', \\'276\\', \\'280\\', \\'283\\', \\'277\\', \\'275\\', \\'278\\', \\'281\\', \\'289\\', \\'290\\', \\'287\\', \\'284\\', \\'286\\', \\'288\\', \\'285\\' ) OR t1.\"salespersonidcat\" IS NULL ) AND ( t1.\"territoryidcat\" NOT IN ( \\'6\\' ) OR t1.\"territoryidcat\" IS NULL ) THEN 1.395126249580338\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" IN ( \\'2\\', \\'10\\', \\'9\\', \\'8\\' ) ) AND ( t2.\"t3__productsubcategoryid__mapping_2_target_1_avg\" > 0.552951 ) THEN 2.309774447072781\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" IN ( \\'2\\', \\'10\\', \\'9\\', \\'8\\' ) ) AND ( t2.\"t3__productsubcategoryid__mapping_2_target_1_avg\" <= 0.552951 OR t2.\"t3__productsubcategoryid__mapping_2_target_1_avg\" IS NULL ) THEN -1.211274926091625\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" NOT IN ( \\'2\\', \\'10\\', \\'9\\', \\'8\\' ) OR t1.\"territoryidcat\" IS NULL ) AND ( t2.\"t3__productmodelid\" IN ( \\'1\\', \\'14\\', \\'19\\', \\'21\\', \\'2\\', \\'10\\', \\'11\\', \\'4\\', \\'17\\', \\'35\\', \\'34\\', \\'36\\', \\'33\\', \\'24\\', \\'28\\', \\'37\\', \\'26\\', \\'29\\', \\'30\\', \\'32\\', \\'9\\', \\'6\\', \\'13\\', \\'8\\', \\'7\\', \\'104\\', \\'106\\', \\'59\\', \\'61\\', \\'52\\', \\'56\\', \\'42\\', \\'45\\', \\'46\\', \\'50\\', \\'51\\', \\'123\\', \\'124\\', \\'125\\', \\'78\\', \\'116\\', \\'38\\', \\'111\\', \\'118\\', \\'107\\', \\'127\\', \\'128\\', \\'79\\', \\'80\\', \\'81\\', \\'84\\', \\'66\\', \\'67\\', \\'62\\', \\'63\\', \\'64\\', \\'68\\', \\'69\\', \\'70\\', \\'53\\', \\'103\\', \\'47\\', \\'48\\', \\'102\\', \\'99\\', \\'101\\', \\'98\\', \\'95\\', \\'97\\' ) ) THEN -0.8764282201965673\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" NOT IN ( \\'2\\', \\'10\\', \\'9\\', \\'8\\' ) OR t1.\"territoryidcat\" IS NULL ) AND ( t2.\"t3__productmodelid\" NOT IN ( \\'1\\', \\'14\\', \\'19\\', \\'21\\', \\'2\\', \\'10\\', \\'11\\', \\'4\\', \\'17\\', \\'35\\', \\'34\\', \\'36\\', \\'33\\', \\'24\\', \\'28\\', \\'37\\', \\'26\\', \\'29\\', \\'30\\', \\'32\\', \\'9\\', \\'6\\', \\'13\\', \\'8\\', \\'7\\', \\'104\\', \\'106\\', \\'59\\', \\'61\\', \\'52\\', \\'56\\', \\'42\\', \\'45\\', \\'46\\', \\'50\\', \\'51\\', \\'123\\', \\'124\\', \\'125\\', \\'78\\', \\'116\\', \\'38\\', \\'111\\', \\'118\\', \\'107\\', \\'127\\', \\'128\\', \\'79\\', \\'80\\', \\'81\\', \\'84\\', \\'66\\', \\'67\\', \\'62\\', \\'63\\', \\'64\\', \\'68\\', \\'69\\', \\'70\\', \\'53\\', \\'103\\', \\'47\\', \\'48\\', \\'102\\', \\'99\\', \\'101\\', \\'98\\', \\'95\\', \\'97\\' ) OR t2.\"t3__productmodelid\" IS NULL ) THEN -0.2210833490057955\\n ELSE NULL\\n END\\n) AS \"feature_1_9\",\\n t1.rowid AS \"rownum\"\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"SALES_ORDER_DETAIL__STAGING_TABLE_2\" t2\\nON t1.\"salesorderid\" = t2.\"salesorderid\"\\nGROUP BY t1.rowid;'" + "'DROP TABLE IF EXISTS \"FEATURE_1_9\";\\n\\nCREATE TABLE \"FEATURE_1_9\" AS\\nSELECT AVG( \\n CASE\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" IN ( \\'279\\', \\'282\\', \\'276\\', \\'280\\', \\'283\\', \\'277\\', \\'275\\', \\'278\\', \\'281\\', \\'289\\', \\'290\\', \\'287\\', \\'284\\', \\'286\\', \\'288\\', \\'285\\' ) ) AND ( t2.\"strftime(\\'%m\\', modifieddate )__mapping_2_target_1_avg\" > 0.763077 ) THEN 0.5980001018988601\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" IN ( \\'279\\', \\'282\\', \\'276\\', \\'280\\', \\'283\\', \\'277\\', \\'275\\', \\'278\\', \\'281\\', \\'289\\', \\'290\\', \\'287\\', \\'284\\', \\'286\\', \\'288\\', \\'285\\' ) ) AND ( t2.\"strftime(\\'%m\\', modifieddate )__mapping_2_target_1_avg\" <= 0.763077 OR t2.\"strftime(\\'%m\\', modifieddate )__mapping_2_target_1_avg\" IS NULL ) THEN -2.050123232507158\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" NOT IN ( \\'279\\', \\'282\\', \\'276\\', \\'280\\', \\'283\\', \\'277\\', \\'275\\', \\'278\\', \\'281\\', \\'289\\', \\'290\\', \\'287\\', \\'284\\', \\'286\\', \\'288\\', \\'285\\' ) OR t1.\"salespersonidcat\" IS NULL ) AND ( t1.\"territoryidcat\" IN ( \\'6\\' ) ) THEN -0.7126375400751357\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" > 66070588.235294 ) AND ( t1.\"salespersonidcat\" NOT IN ( \\'279\\', \\'282\\', \\'276\\', \\'280\\', \\'283\\', \\'277\\', \\'275\\', \\'278\\', \\'281\\', \\'289\\', \\'290\\', \\'287\\', \\'284\\', \\'286\\', \\'288\\', \\'285\\' ) OR t1.\"salespersonidcat\" IS NULL ) AND ( t1.\"territoryidcat\" NOT IN ( \\'6\\' ) OR t1.\"territoryidcat\" IS NULL ) THEN 0.3150913831000781\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" IN ( \\'2\\', \\'10\\', \\'9\\', \\'8\\' ) ) AND ( t2.\"t3__productsubcategoryid__mapping_2_target_1_avg\" > 0.552951 ) THEN 1.229731415243227\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" IN ( \\'2\\', \\'10\\', \\'9\\', \\'8\\' ) ) AND ( t2.\"t3__productsubcategoryid__mapping_2_target_1_avg\" <= 0.552951 OR t2.\"t3__productsubcategoryid__mapping_2_target_1_avg\" IS NULL ) THEN -2.291310468800539\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" NOT IN ( \\'2\\', \\'10\\', \\'9\\', \\'8\\' ) OR t1.\"territoryidcat\" IS NULL ) AND ( t2.\"t3__productmodelid\" IN ( \\'1\\', \\'14\\', \\'19\\', \\'21\\', \\'2\\', \\'10\\', \\'11\\', \\'4\\', \\'17\\', \\'35\\', \\'34\\', \\'36\\', \\'33\\', \\'24\\', \\'28\\', \\'37\\', \\'26\\', \\'29\\', \\'30\\', \\'32\\', \\'9\\', \\'6\\', \\'13\\', \\'8\\', \\'7\\', \\'104\\', \\'106\\', \\'59\\', \\'61\\', \\'52\\', \\'56\\', \\'42\\', \\'45\\', \\'46\\', \\'50\\', \\'51\\', \\'123\\', \\'124\\', \\'125\\', \\'78\\', \\'116\\', \\'38\\', \\'111\\', \\'118\\', \\'107\\', \\'127\\', \\'128\\', \\'79\\', \\'80\\', \\'81\\', \\'84\\', \\'66\\', \\'67\\', \\'62\\', \\'63\\', \\'64\\', \\'68\\', \\'69\\', \\'70\\', \\'53\\', \\'103\\', \\'47\\', \\'48\\', \\'102\\', \\'99\\', \\'101\\', \\'98\\', \\'95\\', \\'97\\' ) ) THEN -1.956472141424515\\n WHEN ( t1.\"orderdate\" - t2.\"t4__startdate\" <= 66070588.235294 OR t1.\"orderdate\" IS NULL OR t2.\"t4__startdate\" IS NULL ) AND ( t1.\"territoryidcat\" NOT IN ( \\'2\\', \\'10\\', \\'9\\', \\'8\\' ) OR t1.\"territoryidcat\" IS NULL ) AND ( t2.\"t3__productmodelid\" NOT IN ( \\'1\\', \\'14\\', \\'19\\', \\'21\\', \\'2\\', \\'10\\', \\'11\\', \\'4\\', \\'17\\', \\'35\\', \\'34\\', \\'36\\', \\'33\\', \\'24\\', \\'28\\', \\'37\\', \\'26\\', \\'29\\', \\'30\\', \\'32\\', \\'9\\', \\'6\\', \\'13\\', \\'8\\', \\'7\\', \\'104\\', \\'106\\', \\'59\\', \\'61\\', \\'52\\', \\'56\\', \\'42\\', \\'45\\', \\'46\\', \\'50\\', \\'51\\', \\'123\\', \\'124\\', \\'125\\', \\'78\\', \\'116\\', \\'38\\', \\'111\\', \\'118\\', \\'107\\', \\'127\\', \\'128\\', \\'79\\', \\'80\\', \\'81\\', \\'84\\', \\'66\\', \\'67\\', \\'62\\', \\'63\\', \\'64\\', \\'68\\', \\'69\\', \\'70\\', \\'53\\', \\'103\\', \\'47\\', \\'48\\', \\'102\\', \\'99\\', \\'101\\', \\'98\\', \\'95\\', \\'97\\' ) OR t2.\"t3__productmodelid\" IS NULL ) THEN -1.301121099543705\\n ELSE NULL\\n END\\n) AS \"feature_1_9\",\\n t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"SALES_ORDER_DETAIL__STAGING_TABLE_2\" t2\\nON t1.\"salesorderid\" = t2.\"salesorderid\"\\nGROUP BY t1.rowid;'" ] }, "execution_count": 51, @@ -64330,7 +64176,7 @@ "outputs": [], "source": [ "# Creates a folder containing the SQL code.\n", - "pipe2.features.to_sql().save(\"adventure_works\")" + "pipe2.features.to_sql(size_threshold=None).save(\"adventure_works\", remove=True)" ] }, { @@ -64340,7 +64186,7 @@ "outputs": [], "source": [ "# Creates a folder containing the SQL code.\n", - "pipe2.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"adventure_works_spark\")" + "pipe2.features.to_sql(dialect=getml.pipeline.dialect.spark_sql, size_threshold=None).save(\"adventure_works_spark\", remove=True)" ] }, { @@ -64360,9 +64206,9 @@ "\n", "Name | Accuracy | AUC | Cross entropy\n", "-------------------- | ---------- | -------| -------------\n", - "getML: FastProp | 91.26% | 0.9711 | 0.2217\n", - "getML: Relboost | 92.73% | 0.9776 | 0.1929\n", - "featuretools | 91.00% | 0.9677 | 0.2374\n", + "getML: FastProp | 91.39% | 0.9714 | 0.2238\n", + "getML: Relboost | 92.76% | 0.9786 | 0.1894\n", + "featuretools | 91.00% | 0.9664 | 0.2429\n", "\n", "The picture we get is very consistent: Relboost outperforms FastProp and FastProp outperforms featuretools for all three measures.\n", "\n", diff --git a/air_pollution.ipynb b/air_pollution.ipynb index 7d1deec..201b1ab 100644 --- a/air_pollution.ipynb +++ b/air_pollution.ipynb @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "lines_to_next_cell": 2 }, @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -120,34 +120,23 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220323133117.log.\n", + "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220716180214.log.\n", "\n", "\n", "Loading pipelines...\n", "[========================================] 100%\n", "\n", "\n", - "Connected to project 'air_pollution'\n" + "Connected to project 'air_pollution'\n", + "http://localhost:1709/#/listprojects/air_pollution/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/air_pollution/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -173,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -209,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -346,7 +335,7 @@ "type: StringColumnView" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -368,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -672,7 +661,7 @@ " 0 population 41757 DataFrame" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -691,7 +680,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "tags": [] }, @@ -703,7 +692,7 @@ " feature_learners=['RelMT'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -715,7 +704,7 @@ " feature_learners=['RelMT'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -723,7 +712,7 @@ " tags=['getML: RelMT', 'memory: 7d', 'complex features'])" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -757,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -791,7 +780,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -822,7 +811,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:4m:23.190215\n", + "Time taken: 0h:4m:2.144618\n", "\n" ] }, @@ -833,29 +822,29 @@ " feature_learners=['RelMT'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['getML: RelMT', 'memory: 7d', 'complex features', 'container-K1O1Nc'])
url: http://localhost:1709/#/getpipeline/air_pollution/Xa4kSu/0/
" + " tags=['getML: RelMT', 'memory: 7d', 'complex features', 'container-StKSzj'])
url: http://localhost:1709/#/getpipeline/air_pollution/AikJdP/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['RelMT'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['getML: RelMT', 'memory: 7d', 'complex features', 'container-K1O1Nc'])\n", + " tags=['getML: RelMT', 'memory: 7d', 'complex features', 'container-StKSzj'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/air_pollution/Xa4kSu/0/" + "url: http://localhost:1709/#/getpipeline/air_pollution/AikJdP/0/" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -866,7 +855,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -878,6 +867,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "RelMT: Building features...\n", "[========================================] 100%\n", "\n", @@ -953,7 +945,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 13:36:01\n", + " 2022-07-16 18:06:41\n", " \n", " \n", " \n", @@ -982,7 +974,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 13:36:10\n", + " 2022-07-16 18:06:49\n", " \n", " \n", " \n", @@ -1012,11 +1004,11 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-23 13:36:01 train pm2.5 35.1664 50.9038 0.6925\n", - "1 2022-03-23 13:36:10 test pm2.5 39.6596 57.5014 0.6306" + "0 2022-07-16 18:06:41 train pm2.5 35.1664 50.9038 0.6925\n", + "1 2022-07-16 18:06:49 test pm2.5 39.6596 57.5014 0.6306" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1034,7 +1026,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1338,7 +1330,7 @@ " 0 population 41757 DataFrame" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1357,7 +1349,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1367,7 +1359,7 @@ " feature_learners=['RelMT'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -1379,7 +1371,7 @@ " feature_learners=['RelMT'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -1387,7 +1379,7 @@ " tags=['getML: RelMT', 'memory: 1d', 'complex features'])" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1414,7 +1406,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1441,7 +1433,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1472,7 +1464,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:1m:0.791243\n", + "Time taken: 0h:0m:55.473289\n", "\n" ] }, @@ -1483,29 +1475,29 @@ " feature_learners=['RelMT'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['getML: RelMT', 'memory: 1d', 'complex features', 'container-sXpcLM'])
url: http://localhost:1709/#/getpipeline/air_pollution/Wva6YJ/0/
" + " tags=['getML: RelMT', 'memory: 1d', 'complex features', 'container-2kT1kR'])
url: http://localhost:1709/#/getpipeline/air_pollution/YaEzl4/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['RelMT'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['getML: RelMT', 'memory: 1d', 'complex features', 'container-sXpcLM'])\n", + " tags=['getML: RelMT', 'memory: 1d', 'complex features', 'container-2kT1kR'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/air_pollution/Wva6YJ/0/" + "url: http://localhost:1709/#/getpipeline/air_pollution/YaEzl4/0/" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1516,7 +1508,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1528,6 +1520,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "RelMT: Building features...\n", "[========================================] 100%\n", "\n", @@ -1603,7 +1598,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 13:37:28\n", + " 2022-07-16 18:08:04\n", " \n", " \n", " \n", @@ -1632,7 +1627,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 13:37:30\n", + " 2022-07-16 18:08:05\n", " \n", " \n", " \n", @@ -1662,11 +1657,11 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-23 13:37:28 train pm2.5 38.1593 55.3541 0.6366\n", - "1 2022-03-23 13:37:30 test pm2.5 47.5451 66.9418 0.4901" + "0 2022-07-16 18:08:04 train pm2.5 38.1593 55.3541 0.6366\n", + "1 2022-07-16 18:08:05 test pm2.5 47.5451 66.9418 0.4901" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1686,7 +1681,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1990,7 +1985,7 @@ " 0 population 41757 DataFrame" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -2009,7 +2004,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -2019,7 +2014,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -2031,7 +2026,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -2039,7 +2034,7 @@ " tags=['getML: FastProp', 'memory: 7d', 'simple features'])" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2065,7 +2060,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -2092,7 +2087,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -2123,7 +2118,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:2m:47.234395\n", + "Time taken: 0h:1m:13.0781\n", "\n" ] }, @@ -2134,29 +2129,29 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['getML: FastProp', 'memory: 7d', 'simple features', 'container-AKVTYt'])
url: http://localhost:1709/#/getpipeline/air_pollution/Ra29cj/0/
" + " tags=['getML: FastProp', 'memory: 7d', 'simple features', 'container-yIeLii'])
url: http://localhost:1709/#/getpipeline/air_pollution/uP3TrY/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['getML: FastProp', 'memory: 7d', 'simple features', 'container-AKVTYt'])\n", + " tags=['getML: FastProp', 'memory: 7d', 'simple features', 'container-yIeLii'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/air_pollution/Ra29cj/0/" + "url: http://localhost:1709/#/getpipeline/air_pollution/uP3TrY/0/" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2167,7 +2162,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -2179,6 +2174,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "FastProp: Building features...\n", "[========================================] 100%\n", "\n", @@ -2254,7 +2252,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 13:40:34\n", + " 2022-07-16 18:09:37\n", " \n", " \n", " \n", @@ -2283,7 +2281,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 13:40:49\n", + " 2022-07-16 18:09:42\n", " \n", " \n", " \n", @@ -2313,11 +2311,11 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-23 13:40:34 train pm2.5 36.1998 50.8575 0.7031\n", - "1 2022-03-23 13:40:49 test pm2.5 46.2451 63.7779 0.5449" + "0 2022-07-16 18:09:37 train pm2.5 36.1998 50.8575 0.7031\n", + "1 2022-07-16 18:09:42 test pm2.5 46.2451 63.7779 0.5449" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -2337,7 +2335,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -2641,7 +2639,7 @@ " 0 population 41757 DataFrame" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2660,7 +2658,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -2670,7 +2668,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -2682,7 +2680,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -2690,7 +2688,7 @@ " tags=['getML: FastProp', 'memory: 1d', 'simple features'])" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2716,7 +2714,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -2743,7 +2741,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -2774,7 +2772,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:1m:1.842769\n", + "Time taken: 0h:0m:36.289423\n", "\n" ] }, @@ -2785,29 +2783,29 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['getML: FastProp', 'memory: 1d', 'simple features', 'container-IPHmCK'])
url: http://localhost:1709/#/getpipeline/air_pollution/t0BEM5/0/
" + " tags=['getML: FastProp', 'memory: 1d', 'simple features', 'container-5TzVmr'])
url: http://localhost:1709/#/getpipeline/air_pollution/JdWvaV/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['population'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['getML: FastProp', 'memory: 1d', 'simple features', 'container-IPHmCK'])\n", + " tags=['getML: FastProp', 'memory: 1d', 'simple features', 'container-5TzVmr'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/air_pollution/t0BEM5/0/" + "url: http://localhost:1709/#/getpipeline/air_pollution/JdWvaV/0/" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2818,7 +2816,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2830,6 +2828,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "FastProp: Building features...\n", "[========================================] 100%\n", "\n", @@ -2905,7 +2906,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 13:42:09\n", + " 2022-07-16 18:10:38\n", " \n", " \n", " \n", @@ -2934,7 +2935,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 13:42:13\n", + " 2022-07-16 18:10:39\n", " \n", " \n", " \n", @@ -2964,11 +2965,11 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-23 13:42:09 train pm2.5 38.3429 55.1886 0.6443\n", - "1 2022-03-23 13:42:13 test pm2.5 44.1997 63.4948 0.545 " + "0 2022-07-16 18:10:38 train pm2.5 38.3429 55.1886 0.6443\n", + "1 2022-07-16 18:10:39 test pm2.5 44.1997 63.4948 0.545 " ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -2988,7 +2989,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -3005,7 +3006,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -3015,7 +3016,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -3042,16 +3043,18 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Loading 'featuretools_training' from disk (project folder).\n", + "Downloading featuretools_training.csv...\n", + "[========================================] 100%\n", "\n", - "Loading 'featuretools_test' from disk (project folder).\n", + "Downloading featuretools_test.csv...\n", + "[========================================] 100%\n", "\n" ] } @@ -3068,7 +3071,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -3087,7 +3090,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -3097,7 +3100,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -3109,7 +3112,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -3117,7 +3120,7 @@ " tags=['featuretools', 'memory: 1d', 'simple features'])" ] }, - "execution_count": 31, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -3134,7 +3137,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -3161,7 +3164,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -3186,7 +3189,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:15.115218\n", + "Time taken: 0h:0m:14.593926\n", "\n" ] }, @@ -3197,29 +3200,29 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['featuretools', 'memory: 1d', 'simple features'])
url: http://localhost:1709/#/getpipeline/air_pollution/ZSDfNQ/0/
" + " tags=['featuretools', 'memory: 1d', 'simple features'])
url: http://localhost:1709/#/getpipeline/air_pollution/NJHaPZ/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", " tags=['featuretools', 'memory: 1d', 'simple features'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/air_pollution/ZSDfNQ/0/" + "url: http://localhost:1709/#/getpipeline/air_pollution/NJHaPZ/0/" ] }, - "execution_count": 33, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -3230,7 +3233,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -3242,6 +3245,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n" ] }, @@ -3314,7 +3320,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 13:42:34\n", + " 2022-07-16 18:11:06\n", " \n", " \n", " \n", @@ -3343,7 +3349,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 13:42:35\n", + " 2022-07-16 18:11:06\n", " \n", " \n", " \n", @@ -3373,11 +3379,11 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-23 13:42:34 featuretools_training pm2.5 38.0455 54.4693 0.6567\n", - "1 2022-03-23 13:42:35 featuretools_test pm2.5 45.3084 64.2717 0.5373" + "0 2022-07-16 18:11:06 featuretools_training pm2.5 38.0455 54.4693 0.6567\n", + "1 2022-07-16 18:11:06 featuretools_test pm2.5 45.3084 64.2717 0.5373" ] }, - "execution_count": 34, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -3408,7 +3414,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -3598,7 +3604,7 @@ "[33096 rows x 9 columns]" ] }, - "execution_count": 35, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -3609,7 +3615,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -3640,16 +3646,18 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Loading 'tsfresh_training' from disk (project folder).\n", + "Downloading tsfresh_training.csv...\n", + "[========================================] 100%\n", "\n", - "Loading 'tsfresh_test' from disk (project folder).\n", + "Downloading tsfresh_test.csv...\n", + "[========================================] 100%\n", "\n" ] } @@ -3673,7 +3681,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -3699,7 +3707,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -3709,7 +3717,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -3721,7 +3729,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -3729,7 +3737,7 @@ " tags=['tsfresh', 'memory: 1d', 'simple features'])" ] }, - "execution_count": 39, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -3746,7 +3754,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -3773,7 +3781,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -3798,7 +3806,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:10.410626\n", + "Time taken: 0h:0m:10.45655\n", "\n" ] }, @@ -3809,29 +3817,29 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['tsfresh', 'memory: 1d', 'simple features'])
url: http://localhost:1709/#/getpipeline/air_pollution/J5o5u2/0/
" + " tags=['tsfresh', 'memory: 1d', 'simple features'])
url: http://localhost:1709/#/getpipeline/air_pollution/nrIanU/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", " tags=['tsfresh', 'memory: 1d', 'simple features'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/air_pollution/J5o5u2/0/" + "url: http://localhost:1709/#/getpipeline/air_pollution/nrIanU/0/" ] }, - "execution_count": 41, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -3842,7 +3850,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -3854,6 +3862,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n" ] }, @@ -3926,7 +3937,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 13:42:48\n", + " 2022-07-16 18:11:25\n", " \n", " \n", " \n", @@ -3955,7 +3966,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 13:42:49\n", + " 2022-07-16 18:11:25\n", " \n", " \n", " \n", @@ -3985,11 +3996,11 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-23 13:42:48 tsfresh_training pm2.5 40.8062 57.7874 0.6106\n", - "1 2022-03-23 13:42:49 tsfresh_test pm2.5 46.698 65.9163 0.5105" + "0 2022-07-16 18:11:25 tsfresh_training pm2.5 40.8062 57.7874 0.6106\n", + "1 2022-07-16 18:11:25 tsfresh_test pm2.5 46.698 65.9163 0.5105" ] }, - "execution_count": 42, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -4007,7 +4018,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -4316,7 +4327,7 @@ "15 pm2.5 ir -0.0541 0.00060757" ] }, - "execution_count": 43, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -4334,7 +4345,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -4366,7 +4377,7 @@ "'DROP TABLE IF EXISTS \"FEATURE_1_5\";\\n\\nCREATE TABLE \"FEATURE_1_5\" AS\\nSELECT SUM( \\n CASE\\n WHEN ( t2.\"iws\" > 2.996864 ) AND ( t1.\"date\" - t2.\"date\" > 111439.618138 ) THEN COALESCE( t1.\"dewp\" - 1.514736211828887, 0.0 ) * 0.03601656954671504 + COALESCE( t1.\"temp\" - 11.89228926884439, 0.0 ) * -0.04267662247010789 + COALESCE( t1.\"is\" - 0.06612999800412481, 0.0 ) * -0.04725594429771846 + COALESCE( t1.\"ir\" - 0.2116958286208502, 0.0 ) * -0.06321612715292692 + COALESCE( t1.\"pres\" - 1016.466458208569, 0.0 ) * -0.01400320405937972 + COALESCE( t1.\"iws\" - 25.06146098064165, 0.0 ) * -0.001201539145976257 + COALESCE( t1.\"date\" - 1326377672.037789, 0.0 ) * -1.91747588977566e-07 + COALESCE( t2.\"dewp\" - 1.668379064426448, 0.0 ) * 0.001731759164268386 + COALESCE( t2.\"is\" - 0.06086063096820984, 0.0 ) * 0.06061734539826681 + COALESCE( t2.\"ir\" - 0.2111990813489665, 0.0 ) * -0.05403702019138458 + COALESCE( t2.\"temp\" - 12.06001450501632, 0.0 ) * -0.007673283821278228 + COALESCE( t2.\"pres\" - 1016.398404448205, 0.0 ) * 0.002389770661357365 + COALESCE( t2.\"iws\" - 25.00605463556588, 0.0 ) * 0.0006834212113433441 + COALESCE( t2.\"date\" - 1326341256.690439, 0.0 ) * 1.915110364308629e-07 + -3.8304206632990674e-02\\n WHEN ( t2.\"iws\" > 2.996864 ) AND ( t1.\"date\" - t2.\"date\" <= 111439.618138 OR t1.\"date\" IS NULL OR t2.\"date\" IS NULL ) THEN COALESCE( t1.\"dewp\" - 1.514736211828887, 0.0 ) * -0.09710953675504476 + COALESCE( t1.\"temp\" - 11.89228926884439, 0.0 ) * 0.1898035531135342 + COALESCE( t1.\"is\" - 0.06612999800412481, 0.0 ) * 0.2216653278054844 + COALESCE( t1.\"ir\" - 0.2116958286208502, 0.0 ) * 0.2229778643018364 + COALESCE( t1.\"pres\" - 1016.466458208569, 0.0 ) * 0.0137232972180141 + COALESCE( t1.\"iws\" - 25.06146098064165, 0.0 ) * 0.008757818778673529 + COALESCE( t1.\"date\" - 1326377672.037789, 0.0 ) * 3.828784685924325e-05 + COALESCE( t2.\"dewp\" - 1.668379064426448, 0.0 ) * 0.01390967975925731 + COALESCE( t2.\"is\" - 0.06086063096820984, 0.0 ) * -0.05439808805172083 + COALESCE( t2.\"ir\" - 0.2111990813489665, 0.0 ) * -0.1708184586046546 + COALESCE( t2.\"temp\" - 12.06001450501632, 0.0 ) * 0.04153068423706643 + COALESCE( t2.\"pres\" - 1016.398404448205, 0.0 ) * 0.06692208362422453 + COALESCE( t2.\"iws\" - 25.00605463556588, 0.0 ) * 0.0003737878687475554 + COALESCE( t2.\"date\" - 1326341256.690439, 0.0 ) * -3.82854279001483e-05 + -2.6193156234554085e+00\\n WHEN ( t2.\"iws\" <= 2.996864 OR t2.\"iws\" IS NULL ) AND ( t1.\"dewp\" > 11.000000 ) THEN COALESCE( t1.\"dewp\" - 1.514736211828887, 0.0 ) * 0.1393137945465734 + COALESCE( t1.\"temp\" - 11.89228926884439, 0.0 ) * -0.01214102681155058 + COALESCE( t1.\"is\" - 0.06612999800412481, 0.0 ) * -0.1338390047625948 + COALESCE( t1.\"ir\" - 0.2116958286208502, 0.0 ) * -0.0162258602121815 + COALESCE( t1.\"pres\" - 1016.466458208569, 0.0 ) * 0.001699285730186932 + COALESCE( t1.\"iws\" - 25.06146098064165, 0.0 ) * 0.003371800239128447 + COALESCE( t1.\"date\" - 1326377672.037789, 0.0 ) * -1.911896389633282e-07 + COALESCE( t2.\"dewp\" - 1.668379064426448, 0.0 ) * -0.06644334047600213 + COALESCE( t2.\"is\" - 0.06086063096820984, 0.0 ) * -0.1417763462234966 + COALESCE( t2.\"ir\" - 0.2111990813489665, 0.0 ) * -0.1305274026025503 + COALESCE( t2.\"temp\" - 12.06001450501632, 0.0 ) * -0.1337481445687078 + COALESCE( t2.\"pres\" - 1016.398404448205, 0.0 ) * -0.0159175033671927 + COALESCE( t2.\"iws\" - 25.00605463556588, 0.0 ) * 0.0526624167554332 + COALESCE( t2.\"date\" - 1326341256.690439, 0.0 ) * 1.855001999924372e-07 + 1.4765736620125673e+00\\n WHEN ( t2.\"iws\" <= 2.996864 OR t2.\"iws\" IS NULL ) AND ( t1.\"dewp\" <= 11.000000 OR t1.\"dewp\" IS NULL ) THEN COALESCE( t1.\"dewp\" - 1.514736211828887, 0.0 ) * 0.04638612658210784 + COALESCE( t1.\"temp\" - 11.89228926884439, 0.0 ) * -0.02616592034638174 + COALESCE( t1.\"is\" - 0.06612999800412481, 0.0 ) * -0.04279224385040904 + COALESCE( t1.\"ir\" - 0.2116958286208502, 0.0 ) * -0.02539472146735003 + COALESCE( t1.\"pres\" - 1016.466458208569, 0.0 ) * -0.0271755357448101 + COALESCE( t1.\"iws\" - 25.06146098064165, 0.0 ) * -0.002779614164530073 + COALESCE( t1.\"date\" - 1326377672.037789, 0.0 ) * -1.127852653091244e-06 + COALESCE( t2.\"dewp\" - 1.668379064426448, 0.0 ) * -0.009599325629289402 + COALESCE( t2.\"is\" - 0.06086063096820984, 0.0 ) * -0.2440324127160611 + COALESCE( t2.\"ir\" - 0.2111990813489665, 0.0 ) * -0.1198017640418239 + COALESCE( t2.\"temp\" - 12.06001450501632, 0.0 ) * -0.0723450470366292 + COALESCE( t2.\"pres\" - 1016.398404448205, 0.0 ) * -0.006849322627387147 + COALESCE( t2.\"iws\" - 25.00605463556588, 0.0 ) * -0.4129622526694541 + COALESCE( t2.\"date\" - 1326341256.690439, 0.0 ) * 1.129731320846992e-06 + -9.4331291663918275e+00\\n ELSE NULL\\n END\\n) AS \"feature_1_5\",\\n t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"POPULATION__STAGING_TABLE_2\" t2\\nON 1 = 1\\nWHERE t2.\"date\" <= t1.\"date\"\\nAND ( t2.\"date, \\'+7.000000 days\\'\" > t1.\"date\" OR t2.\"date, \\'+7.000000 days\\'\" IS NULL )\\nGROUP BY t1.rowid;'" ] }, - "execution_count": 44, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -4393,7 +4404,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": { "tags": [] }, @@ -4426,7 +4437,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -4520,7 +4531,7 @@ "5 tsfresh 1d simple 51.0 % 65.9" ] }, - "execution_count": 46, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -4612,7 +4623,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.4" } }, "nbformat": 4, diff --git a/atherosclerosis.ipynb b/atherosclerosis.ipynb index 88f057c..b46dbb9 100644 --- a/atherosclerosis.ipynb +++ b/atherosclerosis.ipynb @@ -136,7 +136,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220324215749.log.\n", + "getML engine is already running.\n", "\n", "\n", "\n", @@ -187,10 +187,8 @@ "text": [ "\n", "Loading population...\n", - "[========================================] 100%\n", "\n", - "Loading contr...\n", - "[========================================] 100%\n" + "Loading contr...\n" ] } ], @@ -1724,19 +1722,19 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -1744,75 +1742,75 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -2321,19 +2319,19 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -2341,75 +2339,75 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -2918,19 +2916,19 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -2938,75 +2936,75 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -3515,19 +3513,19 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -3535,75 +3533,75 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -4112,19 +4110,19 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -4132,75 +4130,75 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -5306,7 +5304,7 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -5314,87 +5312,87 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -5903,7 +5901,7 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -5911,87 +5909,87 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -6500,7 +6498,7 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -6508,87 +6506,87 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -7097,7 +7095,7 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -7105,87 +7103,87 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -7694,7 +7692,7 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -7702,87 +7700,87 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -7802,32 +7800,32 @@ " name REFERENCE_DATE ENTRY_DATE ICO TARGET ... DENUMR MESUMR ROKUMR PRICUMR \n", " role time_stamp time_stamp join_key target ... unused_string unused_string unused_string unused_string\n", " unit time stamp time stamp ... \n", - " 0 1977-01-01 1976-10-01 10001 0 ... \n", - " 1 1978-01-01 1976-10-01 10001 0 ... \n", - " 2 1979-01-01 1976-10-01 10001 0 ... \n", - " 3 1980-01-01 1976-10-01 10001 0 ... \n", - " 4 1981-01-01 1976-10-01 10001 0 ... \n", + " 0 1977-01-01 1976-10-01 10001 0 ... NULL NULL NULL NULL \n", + " 1 1978-01-01 1976-10-01 10001 0 ... NULL NULL NULL NULL \n", + " 2 1979-01-01 1976-10-01 10001 0 ... NULL NULL NULL NULL \n", + " 3 1980-01-01 1976-10-01 10001 0 ... NULL NULL NULL NULL \n", + " 4 1981-01-01 1976-10-01 10001 0 ... NULL NULL NULL NULL \n", " ... ... ... ... ... ... ... ... \n", - "28428 1995-01-01 1977-03-01 30065 0 ... \n", - "28429 1996-01-01 1977-03-01 30065 0 ... \n", - "28430 1997-01-01 1977-03-01 30065 0 ... \n", - "28431 1998-01-01 1977-03-01 30065 0 ... \n", - "28432 1999-01-01 1977-03-01 30065 0 ... \n", + "28428 1995-01-01 1977-03-01 30065 0 ... NULL NULL NULL NULL \n", + "28429 1996-01-01 1977-03-01 30065 0 ... NULL NULL NULL NULL \n", + "28430 1997-01-01 1977-03-01 30065 0 ... NULL NULL NULL NULL \n", + "28431 1998-01-01 1977-03-01 30065 0 ... NULL NULL NULL NULL \n", + "28432 1999-01-01 1977-03-01 30065 0 ... NULL NULL NULL NULL \n", "\n", " name DEATH_DATE \n", " role unused_string\n", " unit \n", - " 0 \n", - " 1 \n", - " 2 \n", - " 3 \n", - " 4 \n", + " 0 NULL \n", + " 1 NULL \n", + " 2 NULL \n", + " 3 NULL \n", + " 4 NULL \n", " ... \n", - "28428 \n", - "28429 \n", - "28430 \n", - "28431 \n", - "28432 \n", + "28428 NULL \n", + "28429 NULL \n", + "28430 NULL \n", + "28431 NULL \n", + "28432 NULL \n", "\n", "\n", "28433 rows x 76 columns\n", @@ -8943,23 +8941,23 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -8971,91 +8969,91 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -9095,11 +9093,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -9144,7 +9142,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -9306,23 +9304,23 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -9334,91 +9332,91 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -9458,11 +9456,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -9507,7 +9505,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -9669,23 +9667,23 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -9697,91 +9695,91 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -9821,11 +9819,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -10032,23 +10030,23 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -10060,91 +10058,91 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -10184,11 +10182,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -10233,7 +10231,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -10395,23 +10393,23 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -10423,91 +10421,91 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -10547,11 +10545,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -10959,7 +10957,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -11121,23 +11119,23 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -11149,91 +11147,91 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -11245,11 +11243,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -11273,11 +11271,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -11322,7 +11320,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -11484,23 +11482,23 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -11512,91 +11510,91 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -11608,11 +11606,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -11636,11 +11634,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -11685,7 +11683,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -11847,23 +11845,23 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -11875,91 +11873,91 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -11971,11 +11969,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -11999,11 +11997,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -12048,7 +12046,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -12210,23 +12208,23 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -12238,91 +12236,91 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -12334,11 +12332,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -12362,11 +12360,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -12411,7 +12409,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -12573,23 +12571,23 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -12601,91 +12599,91 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -12717,19 +12715,19 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -12759,22 +12757,22 @@ "10568 1987-11-01 20359 2 2 ... 174.0 0.79 70.0 \n", "10569 1987-10-01 20360 2 2 ... 213.0 1.18 104.0 \n", "10570 1987-10-01 20362 1 2 ... 189.0 0.82 73.0 \n", - "10571 1978-09-01 30037 2 1 ... 217.0 \n", + "10571 1978-09-01 30037 2 1 ... 217.0 NULL NULL \n", "\n", " name GLYKEMIE KYSMOC \n", " role unused_string unused_string\n", " unit \n", - " 0 \n", - " 1 \n", - " 2 \n", - " 3 \n", - " 4 \n", + " 0 NULL NULL \n", + " 1 NULL NULL \n", + " 2 NULL NULL \n", + " 3 NULL NULL \n", + " 4 NULL NULL \n", " ... ... \n", - "10567 \n", - "10568 \n", - "10569 \n", - "10570 \n", - "10571 \n", + "10567 NULL NULL \n", + "10568 NULL NULL \n", + "10569 NULL NULL \n", + "10570 NULL NULL \n", + "10571 NULL NULL \n", "\n", "\n", "10572 rows x 67 columns\n", @@ -13195,7 +13193,7 @@ " feature_learners=['RelMT'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['contr'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=[],\n", @@ -13207,7 +13205,7 @@ " feature_learners=['RelMT'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['contr'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=[],\n", @@ -13246,7 +13244,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['contr'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=[],\n", @@ -13258,7 +13256,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['contr'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=[],\n", @@ -13369,7 +13367,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:8m:59.20493\n", + "Time taken: 0h:8m:18.559397\n", "\n" ] }, @@ -13380,26 +13378,26 @@ " feature_learners=['RelMT'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['contr'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=[],\n", " share_selected_features=0.8,\n", - " tags=['relmt', 'container-0L4Y7m'])
url: http://localhost:1709/#/getpipeline/atherosclerosis/9gSAjM/0/
" + " tags=['relmt', 'container-DmHo4w'])
url: http://localhost:1709/#/getpipeline/atherosclerosis/UZLvIq/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['RelMT'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['contr'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=[],\n", " share_selected_features=0.8,\n", - " tags=['relmt', 'container-0L4Y7m'])\n", + " tags=['relmt', 'container-DmHo4w'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/atherosclerosis/9gSAjM/0/" + "url: http://localhost:1709/#/getpipeline/atherosclerosis/UZLvIq/0/" ] }, "execution_count": 11, @@ -13464,7 +13462,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:50.001329\n", + "Time taken: 0h:0m:41.902635\n", "\n" ] }, @@ -13475,26 +13473,26 @@ " feature_learners=['Relboost'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['contr'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=[],\n", " share_selected_features=0.8,\n", - " tags=['relboost', 'container-0L4Y7m'])
url: http://localhost:1709/#/getpipeline/atherosclerosis/jsW7KT/0/
" + " tags=['relboost', 'container-DmHo4w'])
url: http://localhost:1709/#/getpipeline/atherosclerosis/h2D0RM/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['Relboost'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['contr'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=[],\n", " share_selected_features=0.8,\n", - " tags=['relboost', 'container-0L4Y7m'])\n", + " tags=['relboost', 'container-DmHo4w'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/atherosclerosis/jsW7KT/0/" + "url: http://localhost:1709/#/getpipeline/atherosclerosis/h2D0RM/0/" ] }, "execution_count": 12, @@ -13534,6 +13532,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "RelMT: Building features...\n", "[========================================] 100%\n", "\n", @@ -13609,7 +13610,7 @@ " 0\n", " \n", " \n", - " 2022-03-24 22:06:56\n", + " 2022-07-04 20:46:53\n", " \n", " \n", " \n", @@ -13621,15 +13622,15 @@ " \n", " \n", " \n", - " 0.9891\n", + " 0.9887\n", " \n", " \n", " \n", - " 0.8921\n", + " 0.8846\n", " \n", " \n", " \n", - " 0.05011\n", + " 0.05202\n", " \n", " \n", " \n", @@ -13638,7 +13639,7 @@ " 1\n", " \n", " \n", - " 2022-03-24 22:07:48\n", + " 2022-07-04 20:47:36\n", " \n", " \n", " \n", @@ -13654,11 +13655,11 @@ " \n", " \n", " \n", - " 0.7192\n", + " 0.7125\n", " \n", " \n", " \n", - " 0.06785\n", + " 0.06909\n", " \n", " \n", " \n", @@ -13668,8 +13669,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-24 22:06:56 train TARGET 0.9891 0.8921 0.05011\n", - "1 2022-03-24 22:07:48 test TARGET 0.9867 0.7192 0.06785" + "0 2022-07-04 20:46:53 train TARGET 0.9887 0.8846 0.05202\n", + "1 2022-07-04 20:47:36 test TARGET 0.9867 0.7125 0.06909" ] }, "execution_count": 13, @@ -13695,6 +13696,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "Relboost: Building features...\n", "[========================================] 100%\n", "\n", @@ -13770,7 +13774,7 @@ " 0\n", " \n", " \n", - " 2022-03-24 22:07:46\n", + " 2022-07-04 20:47:35\n", " \n", " \n", " \n", @@ -13782,15 +13786,15 @@ " \n", " \n", " \n", - " 0.9877\n", + " 0.9878\n", " \n", " \n", " \n", - " 0.8883\n", + " 0.8881\n", " \n", " \n", " \n", - " 0.05411\n", + " 0.05407\n", " \n", " \n", " \n", @@ -13799,7 +13803,7 @@ " 1\n", " \n", " \n", - " 2022-03-24 22:07:51\n", + " 2022-07-04 20:47:39\n", " \n", " \n", " \n", @@ -13815,11 +13819,11 @@ " \n", " \n", " \n", - " 0.7111\n", + " 0.7129\n", " \n", " \n", " \n", - " 0.06741\n", + " 0.06743\n", " \n", " \n", " \n", @@ -13829,8 +13833,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-24 22:07:46 train TARGET 0.9877 0.8883 0.05411\n", - "1 2022-03-24 22:07:51 test TARGET 0.9867 0.7111 0.06741" + "0 2022-07-04 20:47:35 train TARGET 0.9878 0.8881 0.05407\n", + "1 2022-07-04 20:47:39 test TARGET 0.9867 0.7129 0.06743" ] }, "execution_count": 14, @@ -13858,7 +13862,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -13892,7 +13896,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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6QZJfTXLzTTd5YpJbJXl7kn+oqid39+vn1z00yXuWlRsnx3lnXbir259/wZlLyQMAAADYO8s8ze3sJM9Mku5+dZJrV9WVkqSqrpfkPd391u6+JMmzktxmft0XJvmiJM9eYm4AAAAALGCZxaRrJblo0/8XJbnGFte9O8k1538/KskDl5gXAAAAAAs6cuzYsaXccVX9SpLndffT5///Y5Jv7u43VdWNk5zX3XeYX/e9GYpJb0hyze5+ZFX9RJI37TRn0ic+cfGxo0dPWcpj2Gv3vf7f7er2j3/t1ywUu2jcyYwFAAAA9rUjW12xzAm435nkjE3/n57kXVtcd80k70jyDUk+t6rOSXKdJB+tqrd1919vtZH3vvdDJzXpdXLRRR/Y07i9iD399NMW3oZYsWLFit1/seuWr1ixYsWKPXix65av2IMfuy5OP/20La9bZjHpOUl+Ksnjq+qGSd7Q3R9Oku5+W1WdWlXXzTAB9+2T3Lm7f2MjeNPIpC0LSQAAAADsraUVk7r7ZVX1yqp6eZJPJLl3Vd0ryfu6+0+S3D/DBN3Hkjypu9+6rFwAAAAAODmWOTIp3X1uknM3XfSqTde9IMmXbRP7E8vLDAAAAIBFLHM1NwAAAAAOGMUkAAAAAEZTTAIAAABgNMUkAAAAAEZTTAIAAABgNMUkAAAAAEZTTAIAAABgNMUkAAAAAEZTTAIAAABgtKOrToDD6byzLtzV7c+/4Myl5AEAAADsjpFJAAAAAIymmAQAAADAaIpJAAAAAIymmAQAAADAaIpJAAAAAIymmAQAAADAaIpJAAAAAIymmAQAAADAaIpJAAAAAIymmAQAAADAaIpJAAAAAIymmAQAAADAaIpJAAAAAIymmAQAAADAaEdXnQDs1nlnXbir259/wZlLyQMAAAAOIyOTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0UYVk6rqplV1zvzvx1bVy6vqtstNDQAAAID9ZuzIpEcleWlV3TLJlyT5gSQPW1pWAAAAAOxLY4tJH+nutyS5XZJf6+4XJfnw8tICAAAAYD8aW0z61Kq6e5K7JXluVV01yWnLSwsAAACA/WhsMelBSb4lyUO6+z1JfjDJo5eWFQAAAAD70tExN+ru51fVvyT53PlFP9Xdn1heWgAAAADsR2NXc7tHkhck+Y35Rb9cVfddWlYAAAAA7EtjT3P73iRfmuQ/5///UJLvXEpGAAAAAOxbY4tJH+zu/129rbs/kuQjy0kJAAAAgP1q1JxJSf67qr4tyadU1Q2T3D3Ju5eXFgAAAAD70diRSfdJ8pVJrpDkt5J8SpLvXlZSAAAAAOxPo4pJ3f2eJI/p7i/r7hsmeeL8MgAAAAAOkbGruT0yyYM2XXTu/DIAAAAADpGxp7l9TXf/7+pt3f1NSb56OSkBAAAAsF+NnYD7WFVdvrs/liRVdeUkpywvLViO8866cFe3P/+CM5eSBwAAAKyrscWk30zy2qp6RYYi0pcnedjSsgIAAABgXxpVTOru36mqv8pQREqSH+zuty0vLQAAAAD2o7ETcH9KhkLSZyS5WpLbVNV3bh8FAAAAwEEz9jS35yb5eJK3bLrsWJInnPSMAAAAANi3xhaTLt/dN19qJgAAAADse6NOc0vyz1V1+lIzAQAAAGDfGzsy6bOS/FtV/UuG092SJDuNVqqqhye5ZZIrJrlPd79003U3TfLo+XXP6O5HVNWVkvxukmskuXKS87v7/41/OAAAAAAs09hi0s8nufi4y45tF1BVZye5UXffrKpukORXk2wuPj0xya2SvD3JP1TVk5PcOMlLu/uRVfXZSf4qiWISAAAAwD4x6jS37n5ekpcleeP85x1JfnaHsLOTPHMe/+ok156PPEpVXS/Je7r7rd19SZJnJblNdz+lux85j79Okrft8vEAAAAAsESjRiZV1Y8meUiSKyT5cJLLJ/n9HcKuleSVm/6/KMPpa2+cX3fRpuveneTam7b34iTXTHK7MfkBAAAAsDfGnuZ21yRnJHlud59dVXfNMHJoOx877v8jufTUuO2uS3d/RVXdMMlTqupL56OXTuiqV71Sjh49ZcxjOHBOP/20PY0Tu9ptiBUrVuy6xq5bvmLFihUr9uDFrlu+Yg9+7LobW0z6UHd/rKqOJkl3/3FVPTfJL20T884MBagNpyd51xbXXTPJO6rqRkne3d1v6e6XV9Xlklw9w8ilE3rvez808iEcPBdd9IE9jRN7YqefftrC2xArVqzYgx67bvmKFStWrNiDF7tu+Yo9+LHrYrti2ag5k5K8q6q+K8OKbr9fVT+SnUcmPSfJHZNkPsroDd394STp7rclObWqrltVpyS5/fz2X5Xk/vOYayQ5Lcl/jswRAAAAgCUbW0y6Z4aV1e6f5N8yFJK+abuA7n5ZkldW1cuT/FqSB1bVvarqzvOb3D/DBN3/lOQPu/utSX49yTWr6u+S/FmS79vuFDcAAAAA9tbY09x+orsfNP/7/CSpql9Pcp/tgrr73CTnbrroVZuue0GSLzvu9h9N8i0jc4I9dd5ZF+7q9udfcOZS8gAAAIBV2raYNB9FdE6Sr62qa2+66tQkN19mYgAAAADsPzuNTPqLDJNf3yjJ32y6/JIkP7GknAAAAADYp7YtJnX3h6vq75M8vrufuEc5AQAAALBP7ThnUncfq6rbVNUTu/t9e5EUHDTmWwIAAOCgGDsB9+WTvLmqOslHNy7sbvMmAQAAABwiY4tJP7XULIAtGdUEAADAfnK5MTfq7gvmf94oyQ2TfGzTZQAAAAAcEqOKSVX1U0kemeQaSa6d5HFV9eBlJgYAAADA/jP2NLdbJLlpd1+SJFV1apIXJPmZJeUFAAAAwD40amRSkiMbhaQk6e6PJ7lkm9sDAAAAcACNHZn00qp6VpLnzv+/TZJ/Wk5KAAAAAOxXY4tJ909y9yRfMf//95M8bRkJASePleAAAAA42cau5nZJkn9O8vdJXpTkld19bJmJAQAAALD/jF3N7VFJ/izJORlGKP35fIU3AAAAAA6Rsae53SrJF84n3k5VXT7Ji5P82LISAwAAAGD/Gbua239uFJLmPpHkHUvIBwAAAIB9bOzIpLdX1YuTvCDJkSRnJXlDVZ2fJN193pLyAwAAAGAfGVtMekuSN276/1lLyAUAAACAfW5UMcnIIwAAAACSkcWkqnpQkgcm+fQMp7kdSXKsuy+/xNwAAAAA2GfGnuZ2zyRfmeTtS8wFAAAAgH1ubDHptUne3N0XLzMZAAAAAPa3scWkJyb556p6WZJPbFzY3d+5lKwAAAAA2JfGFpN+IcnvxWluAAAAAIfa2GLS67v7/KVmAuwr55114a5uf/4FZy4lDwAAAPaXscWkf6yqn0jy97nsaW7PW0ZSAAAAAOxPY4tJZ2coIt38uMsVkwAAAAAOkcttd2VV/fL8zyNJTj3uZ2whCgAAAIADYqeC0BPmvx+67EQAAAAA2P+2LSZ19yvnvy/Ym3QAAAAA2M+2Pc0NAAAAADZTTAIAAABgNMUkAAAAAEZTTAIAAABgNMUkAAAAAEbbdjU3gEWcd9aFu7r9+ReceVJiAQAAWD4jkwAAAAAYTTEJAAAAgNEUkwAAAAAYTTEJAAAAgNEUkwAAAAAYTTEJAAAAgNEUkwAAAAAYTTEJAAAAgNEUkwAAAAAYTTEJAAAAgNEUkwAAAAAYTTEJAAAAgNEUkwAAAAAYTTEJAAAAgNEUkwAAAAAYTTEJAAAAgNEUkwAAAAAYTTEJAAAAgNEUkwAAAAAYTTEJAAAAgNEUkwAAAAAYTTEJAAAAgNEUkwAAAAAYTTEJAAAAgNGOrjoBgJPlvLMu3NXtz7/gzKXkAQAAcJAZmQQAAADAaIpJAAAAAIymmAQAAADAaIpJAAAAAIymmAQAAADAaIpJAAAAAIymmAQAAADAaIpJAAAAAIx2dNUJAOwH55114a5uf/4FZy4lDwAAgP3OyCQAAAAARlNMAgAAAGA0xSQAAAAARlNMAgAAAGA0E3ADTGTybgAA4DAxMgkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0RSTAAAAABhNMQkAAACA0Y6uOgGAw+y8sy7c1e3Pv+DMpeQBAAAw1lKLSVX18CS3THLFJPfp7pduuu6mSR49v+4Z3f2I+eU/neTsJKcm+bnuftoycwQAAABgvKWd5lZVZye5UXffLMk9k/zCcTd5YpJvSnKjJHeoqs+rqpsnObO7b5rkNkl+cVn5AQAAALB7y5wz6ewkz0yS7n51kmtX1ZWSpKqul+Q93f3W7r4kybMyFI/+Psnd5/HvS3L5qjKvEwAAAMA+sczT3K6V5JWb/r8oyTWSvHF+3UWbrnt3kmt39yeS/M/8snsn+fN5sQkAAACAfWCZxaSPHff/kSTHRlyXqrpjku9OcuudNnLVq14pR4+eMiHN9XX66aftaZxYsWLXJ3a/5yf2cMWuW75ixYoVK/bgxa5bvmIPfuy6W2Yx6Z1Jztj0/+lJ3rXFdddM8o4kqarbJjkvyW26+7932sh73/uhk5HrWrroog/saZxYsWLXI/b0009beBtixZ7s2HXLV6xYsWLFHrzYdctX7MGPXRfbFcuWOR/Rc5LcMUmq6oZJ3tDdH06S7n5bklOr6rpVdUqS2yd5TlV9eoaJum/X3f+1xNwAAAAAWMDSRiZ198uq6pVV9fIkn0hy76q6V5L3dfefJLl/hgm6jyV5Une/taq+J8lVk/xRVW3c1T26+y3LyhNgXZ131oW7uv35F5y5lDwAAIDDZZmnuaW7z01y7qaLXrXpuhck+bLjbv8bSX5jmTkBAAAAsLhlnuYGAAAAwAGjmAQAAADAaEs9zQ2A/cl8SwAAwKKMTAIAAABgNMUkAAAAAEZzmhsAu+IUOQAAONyMTAIAAABgNMUkAAAAAEZzmhsAe8YpcgAAsP6MTAIAAABgNMUkAAAAAEZTTAIAAABgNMUkAAAAAEZTTAIAAABgNMUkAAAAAEZTTAIAAABgNMUkAAAAAEY7uuoEAGCM8866cFe3P/+CM5eSBwAAHHZGJgEAAAAwmmISAAAAAKMpJgEAAAAwmmISAAAAAKMpJgEAAAAwmmISAAAAAKMpJgEAAAAwmmISAAAAAKMdXXUCALBs55114a5uf/4FZy4lDwAAOAiMTAIAAABgNMUkAAAAAEZTTAIAAABgNMUkAAAAAEZTTAIAAABgNMUkAAAAAEZTTAIAAABgNMUkAAAAAEY7uuoEAGA/O++sC3d1+/MvOHMpeQAAwH5hZBIAAAAAoxmZBABLYlQTAAAHkZFJAAAAAIxmZBIA7ENGNQEAsF8ZmQQAAADAaIpJAAAAAIymmAQAAADAaIpJAAAAAIymmAQAAADAaIpJAAAAAIx2dNUJAAAn13lnXbir259/wZknJRYAgMPByCQAAAAARlNMAgAAAGA0xSQAAAAARlNMAgAAAGA0xSQAAAAARlNMAgAAAGA0xSQAAAAARju66gQAgIPhvLMuHH3b8y84c2l5AACwXEYmAQAAADCaYhIAAAAAoznNDQBYqd2cHpc4RQ4AYNWMTAIAAABgNCOTAIC1ZVQTAMDeMzIJAAAAgNEUkwAAAAAYTTEJAAAAgNEUkwAAAAAYTTEJAAAAgNGs5gYAHEpWggMAWIyRSQAAAACMZmQSAMAuGdUEABxmRiYBAAAAMJpiEgAAAACjOc0NAGAPOUUOAFh3RiYBAAAAMJqRSQAAa8KoJgBgPzAyCQAAAIDRFJMAAAAAGE0xCQAAAIDRFJMAAAAAGE0xCQAAAIDRFJMAAAAAGO3oqhMAAGD5zjvrwl3d/vwLzlxKHgDA+lNMAgBgWwpRAMBmTnMDAAAAYLSljkyqqocnuWWSKya5T3e/dNN1N03y6Pl1z+juR8wvv0GSZyb5xe5+3DLzAwAAAGB3ljYyqarOTnKj7r5Zknsm+YXjbvLEJN+U5EZJ7lBVn1dVV07y2CR/s6y8AAAAAFjcMkcmnZ1hhFG6+9VVde2qulJ3f6iqrpfkPd391iSpqmcluU2S30xyuyTnLjEvAAD2iPmWAODgWeacSddKctGm/y9Kco0trnt3kmt29ye6+8NLzAkAAACACZY5Muljx/1/JMmxEdftylWveqUcPXrKIqFr7/TTT9vTOLFixYoVK/Zkxa5bvmL3X+x+z0+sWLH7K3bd8hV78GPX3TKLSe9Mcsam/09P8q4trrtmkncsspH3vvdDCyV3EFx00Qf2NE6sWLFixYo9WbHrlq/Y1cQu4xS5008/beH8xIoVu56x65av2IMfuy62K5Yt8zS35yS5Y5JU1Q2TvGHjFLbufluSU6vqulV1SpLbz28PAAAAwD62tJFJ3f2yqnplVb08ySeS3Luq7pXkfd39J0nun2GC7mNJntTdb62qL0/y6CSfk+TjVXXXJOd093uWlScAAAePib8BYHmWeZpbuvvcXHZltldtuu4FSb7suNu/LMktlpkTAAAAAItbajEJAADWjVFNALC9Zc6ZBAAAAMABo5gEAAAAwGiKSQAAAACMppgEAAAAwGiKSQAAAACMppgEAAAAwGiKSQAAAACMppgEAAAAwGiKSQAAAACMdnTVCQAAwEFx3lkX7ur2519w5lLyAIBlMjIJAAAAgNGMTAIAgH3AqCYA1oWRSQAAAACMppgEAAAAwGiKSQAAAACMppgEAAAAwGiKSQAAAACMppgEAAAAwGhHV50AAAAwzXlnXbir259/wZlLyQOAw8HIJAAAAABGU0wCAAAAYDTFJAAAAABGU0wCAAAAYDTFJAAAAABGU0wCAAAAYLSjq04AAABYnfPOunBXtz//gjOXkgcA68PIJAAAAABGMzIJAABYiFFNAIeTkUkAAAAAjKaYBAAAAMBoikkAAAAAjKaYBAAAAMBoikkAAAAAjKaYBAAAAMBoikkAAAAAjHZ01QkAAACHz3lnXbir259/wZlLyQOA3TMyCQAAAIDRFJMAAAAAGE0xCQAAAIDRFJMAAAAAGM0E3AAAwFoxeTfAahmZBAAAAMBoRiYBAACHhlFNANMpJgEAAIygEAUwcJobAAAAAKMpJgEAAAAwmtPcAAAAlswpcsBBYmQSAAAAAKMpJgEAAAAwmmISAAAAAKOZMwkAAGAfM98SsN8oJgEAABxQClHAMjjNDQAAAIDRFJMAAAAAGE0xCQAAAIDRFJMAAAAAGM0E3AAAAHySKZN3m/gbDjYjkwAAAAAYzcgkAAAA9o3djGoyoglWw8gkAAAAAEYzMgkAAIC1Z44n2DuKSQAAALAghSgOI6e5AQAAADCakUkAAACwAkY1sa6MTAIAAABgNMUkAAAAAEZzmhsAAACsGafIsUpGJgEAAAAwmpFJAAAAcIgY1cRURiYBAAAAMJqRSQAAAMAoRjWRKCYBAAAAe0Ah6uBwmhsAAAAAoykmAQAAADCaYhIAAAAAoykmAQAAADCaCbgBAACAfc3k3fuLkUkAAAAAjKaYBAAAAMBoikkAAAAAjGbOJAAAAODAMt/SyWdkEgAAAACjLXVkUlU9PMktk1wxyX26+6WbrrtpkkfPr3tGdz9ipxgAAAAAVmtpxaSqOjvJjbr7ZlV1gyS/muTmm27yxCS3SvL2JP9QVU9Oct0dYgAAAAD2hFPkTmyZp7mdneSZSdLdr05y7aq6UpJU1fWSvKe739rdlyR5VpLbbBcDAAAAwOots5h0rSQXbfr/oiTX2OK6dye55g4xAAAAAKzYkWPHji3ljqvqV5I8r7ufPv//H5N8c3e/qapunOS87r7D/LrvzVBMOn2rmKUkCQAAAMCuLHMC7ncmOWPT/6cnedcW110zyTuSfGKbGAAAAABWbJmnuT0nyR2TpKpumOQN3f3hJOnutyU5taquW1WnJLn9/PZbxgAAAACweks7zS1Jqurnktw6w4ijeyf58iTv6+4/qaqbJ/nlJMeSPKm7f+FEMd39qqUlCAAAAMCuLLWYBAAAAMDBsszT3AAAAAA4YBSTAAAAABhNMQkAAACA0RSTDqiqOjox/uonK5e9UFWnVtVnT33ch0VVee6viaq6yoq2e51VbJcTq6ojVXV6VX3aqnMBDo+p7wer6ttOVi4HSVXdYNU5MF5V3XLVOYxhv2KvmYB7n6mqqyX57iRv6+4nVdWPJblpkk7yM939nzvE3yLJLyW5Qnd/UVU9Isnfdfdzt4m5Q5JHJXlrkh9I8kcZCo2fmuS+3f2cbWJPTfKtSW6b5JoZVud7R5JnJ/mj7r5kGY+1qn65u+83//trk/xWkv9IcsY85+0e79dvPKaq+owkD09y/SSvTvIT3f1fW8Vup6p+trsftM31v5bkt7v7nxa472sm+aEkpyV5cndfsOm6x3X3/90m9usy7BPvTvIjSR6X5DOTvD/JfTbf1wliF26rKTlvZ0Q7f3qSm3f3n80LMQ9N8oUZt18t3EfH3c8XJ7lKkiMbl3X3Cxa8r+d195ZvYuaFwbsnuU2SayT5aJK3JHl6d//dDvd91yS/mOTUJH+e5P7d/f6R253y3L9KkvskuSjJ7yb5vxlW+/y3JL/c3R/YLu8T3N/zu/vsEbebcsxZuJ13yOkvuvvrtrl+lmHl0y9NcnqS1ye5cpK/TPLQ7n7nMnKeeMxZqJ0n5nuVTNynquoh3f3Tm/4/Pcnju/uuO8Uedz9j98eFc17h69j7k/x+kkdst+/tRc570c473O/SXou2uc8v7O5/3eE2U567X5/kTt19n6o6O0N7vT/D+8H/293P3mHbN0ryoCSfMb/o8knO6O7ZiMf2rd39B5v+v0KSn+7uH9om5qIMrz3JptfbuWPdfcYO25x0XK+qPz7++FBVL+rum42IfV6G96x/muSPu/vCnWK2ua9Rz4Vt4rd9Hdom7miSx3X394647Zcm+fEkswx99i9JHt7dr9khbqH3GhPfo9zjuIuOZHj+PjxJuvv3tomd8r558nvQRfarFX4+mbLdk3p8HfMcqqordPdHq+pKW9zkE939sd1s9yAwimP/eXKSFyb56qq6c5J/T/ITSb4yyROTfMMO8Q9Pcqskfzz//zFJ/l+SLYsrGQ7ut07yWRk+UJ7T3a+oqmskeWaSLYtJSf4gyZuS/EqGYsWRDAftc5J8fZLjD8ibTXmsX7Lp7/OS3KK73zT/Bu2Z2f7x/kgufUy/kuSlSR6b5KuTPCHJHbcK3OYAkgwflrZz0yRHq+ohGd7A/u0Ot9/sSUmenuQ/k/zk/KD3k/Prrr9D7HlJbpnkakn+Osmtu/ufq+ozkzw1yXZvehZuqyk5T2znpyd5yqac/yXJwzLsV09Kst0bpil9lCSpqmcluXqG4uyGY0m2LCZV1fdtcdWRDIW/7fxakv9O8rQMz+P/yfCm5Uer6vbdfe42sT+a4bn0viTfleSvquq23f3f+eQ35seb8tx/UpK/T/JFSV6U5O/m93dmkt9LcuetAqvqkiTvzPCmfyPHa1XVGzN8eLjeNtudcsxZuJ2r6nZbXHUkybW22WaS/EaSH+juV1XVlyf5lgz9duf547nFMnLOtGPOou08Nd+F9qlNPrWqfi/Dc+FuGV4bH7ZdwMT9cUrOq3ode1mG/v39qnpbhi+f/mbkG+gpOa+knVf4WrSVX83wer6dKc/dhye5/fzvn0hyq+7+96o6I8OH8G2LSRn680FJfj5D0e5uSf52h5gNX19VX9TdD62qr87wWJ+0XUB3nz7yvrey0DGnqu6S4XF+aVW9e37xkQxfxL5izIa7+5ZVddUMx8Mfq6rrJfnL7n7wdnGLPhcmvg5t3Me9k/xkhi82Ppbh8f7pmNgMhcmHJPmn+TZvkuH59+U7xC36XmPKe5TzkvxXhv19o42vmORzd8g1mfa+efJ70AX3q1V9Ppmy3YWPrxNeT34nw3uw12R4X7/5ffKxJFeoqld392138TjWnmLS/nP57j6/qo4keV1332V++UvnL147+Wh3/1dVHUuS7n53VV28Q8yHuvstSd5SVe/q7lfMY99VVTu9QbxOd9/9uMs6yQVV9eIdYqc81s1D6j7Q3W+a5/yfG499pGt196Pnf/9rVX3LDrf/7yRvP0EuRzJ8q7Wd93T3d81HGtxvXpF/YYY3Lu/o7qduE3u0ux+fJFX19Axv5M/v7vOy84f+j3b3O5K8o6re293/nCTd/faq+vgOsZvttq2m5PzfWbydr9zdvzX/+zrd/a3zv19Ww0ic7Uzpow1X7+6vHHG7zR6YodB3om/6T90h9gs2fZvynKr663kbP7Wqdvq25xPd/d75379RVe/KUFC6XS77HDuRKc/9K2+MAJm/8P7o/PK/nH+rtp2vz/Am/rHd/Yz5ffxDd+/0wS6ZdsyZ0s6/k2E/ev8Jrtvpw9Ap3f2q+d+vSPJL3X1xkj+uqh9eYs5Tnr+LtvOUfKfsU0mS7n7I/Bjx2gxvFm+23bfIc1P2x8k5z+3l69ix7n5hkq+tqhtnGIH2S1X14QzHyK0+sE7NeVXt/N/Z49eiqnrkFlcdSfJ5O2wzmfbcTYYP0Unywe7+9+R/30v+z4jYD3f3BVX14e5+SZKXVNVzsnMRKt39bVX1Q/Pn+keS3LW7XzdimxujXh6T5Asy9M9rkvxg7zCKKwsec7r76UmeXlU/3N2PGpPjFvfz3qr6qwyFittl+AC8bTEpiz8XprwObbhPks/PUCy5ZZK7ZPgieoz/7Mue6fDsqnrAiLhF32tMeY9ygwxfJnxJkgd291uq6us2FUnG2u1x7mS8B11kv1rV55Mp253yXn+h51B3f8v89ycVFavq23oYif3EHbZ94Cgm7T+nVNVnd/ebq+p/hwbWcNrMmP56Q1X9ZJKrV9X/SXKnDG+Mt/OujRfEjQ/BVfVZSe6fy46uOJH3zZ+0f9bdH53HXnG+3Q/tEDvlsd6gqp6a4UD1OVX1zd39lKp6WIZvILZz9U3f0Hy4qm7Y3S+fb/fKO8T+cIYh2w89/oqqev4OsRsFvtcl+f6q+pQMo4K+IsnXZBgltJWPzj+E/Ul3X1JV357kd6rqCRmGlm7nvVX10xmGvb61qn4jwxuBG2f4JmE7U9pqSs5T2vnfquq8DN94Paeq7pjhxenrMgxx3s6UPtpwQVX9f73D0O3j3CnDG+H7bTyPNtRw6uq2qurWGUYMfH2Gbws3voXcchj33N/WMJLq7t39oe5+ZlV9JMnzM5ymt50pz/3LV9UXZPiW8PSquml3/8P8situF9jdz62qv03ykPkbswdm58LXhknH1wntfPckD0jynd19mVxH7M+vrqo/zPDt5q0z7Mupqt/K8E3csnKe8vxduJ0n5LvwPlVVP5/L7kOvy/Ch9NyqyqbiwyeZuD8unHNW9zq2+dTdf8owyiA1jHTdaXTDwjmvsJ1X8Vp0doYvF05UCNlpdHoy7bn7C0n+vqqeneRNVfXHSf4hQ+HgD7aNHHywqu6W4b3Gz2YYlXjd7QLqsiNzP5LhfefVMxQsv7a7f3XEdh+b4TTtl8/v86ZJHp+hLbc14ZiTJD1v3z/IMBrixkl+vLufsn1YUlU/nmEU2CUZRvc8aEzxbMJzYeN16N593CleI/blDR/t7o/UcHrbke7+46r66wyny+/kdVX1mCQbp0HdPMkbN44J3f3nW8Qt+l5j4fco3f2RDKN6KsmvVNUFGVeITaYdmye/B11wv1rV55Mp2134vf7E15ON03nPzXDGRzI/nTfJk7r7nmPv56BQTNp/zs0wf9Hdej7vT1Wdk+THMnwDuJP7JvnmJM/LMIT0TzMMQ9/OvZJ843GXnZFheOiD5zlc4fgPuXP3yDCc+VFV9akZXog/kOQvkvyfHbY75bHe7bj//23++1/n97ldzi/bFP8fST59/vdDM7xx3FJ3P6aqvr2qrtzdHzzu6r/aIefLFG66+8MZ3jD+9Q5xSfIdGYafPyfDSLJLktyzqr41Se0Q++0Z+viV3f1H8wP9rTPMvXLPZDltleQ7k5y/SM4T2/k7MjzmxyS5ToYPC+/McOrjd+0QO6WPNpyT5IdrmFvkE/PLtp27obtfXVW3T3KikWI/lGzbR9+TYZ//giSvSrIxd8GNM7TDlnoYifE1Gd7Ab1z23Kr6hyTftMN2j3/ufzzDN55jnvsPSfKHGeYwuWWSx8zfaL0zyVan/G3O+6NJHjb/MPjYDB88xjh3nvPdFzjmHN/O951fPqadL6iqtya5Qja19dxv7LDd78vw5vcLMnyLtvGG+5d7PmJpF/vG6Jwz7ZizaDtPyfdE+9QNMhy3dtqnXn3c/7spBE/ZH6c8D1b1Ovb7W9zn2zMfwbOk15Mp7fzgLNjOK3otOifJb2eYl+oy26yqe+2wzY3tLvTc7e4/rKpnZphn5rMyvA+8KMl39zDCebv+TYZTQK6R4TXzfhlGduw0Affxo2JeucXl27lko5A0fxz/UMPIyJ0s/Po597AM76fuNP//phmmltixmJTh9PJz5s+dJElV3aq7/2anwE3PhVlGPhc2vQ5dPp/8OvTzI/JNkn+sYTTRBRm+OHtzdi6QbDgtycW59DTKDXfL8EF+q2LS5vcap2X4nDHmvcbxcR/N+M8nSZLu7iR3mL9vftPm65Z0nJv0HnTTPn+X7n7bmJiTsN0p7xWmbnfR9/oLPYc2mXI678Fz7NgxP2vyM5vNrjPiNveazWb3OO7nW2az2VdM3PbzFoi5ygIxVz+J7TU659lsdssJ21ko59lsdupsNvvs2Wx2dOLj/Kw9bqtVP94dnwfH3f7IlP1qt7Gz2ezI1P6Y2kebYq6yV9udzWaXn81mn7NI/06MPXKSngO7bquTcbxapF+n3MfEY92exk49Zsxms1vt8vbXm81mN5v/fZ/ZbPabs9nsi3d5H5efzWY3nbAvn34S9oer7GU7n+D+xh4zpvbvtRd5vFPaah6z8PNgynbncdeauN2rLrN/Z7PZN89ms1OPu+xbR97v1Waz2Q3mf99uNpv92Gw2+8yRsU+fzWbnzmazG81msxvPZrMHz2azp098nGPec18w//3E2Wx28/nffzvy/q83m81+fjabPWH+86TZbPbmBXO9xy5u+9ATXPbIkbFX2ejf2Wx2i9lsds5sNjtt6j61wOMdfWw/0Xuy3b6P3OJ+F3lPtvB2xx4zZrPZ38xms1NOYlvvOueJ77lPxvF1kZyvPTZ2o+83nv/zv59zstp83X6MTNpnapvVlTIM5dtp4sVbJPmqDBNMHktyVpIXZxh2+bru/sEFUxs7vHOzZ2SbfGuLVeTmoxzu29usIjfSCXOuT16lIUl+vKrGrNKwcM61zQp0VXXf3n4Fuu32iydm5/1iJ1u11e2TPDr76/Fu+zzYoo+OzL+Z2innhR/vJp9VVednmNz1kgynJz2sd7ny0Qmc9OfglO0e17+3yqX9e40R/XuifeOdI2MX7t8dLO14VdMmWB9j7LHuSJKHjjzW7XnsCfap3874Y8bCx/VNfj/JD1bVV2X41vNRGSb2vPk2251ynFvVvrxwziONPWaM7t8T2Rgpk2nHuZ3aauHnwZTtzrf9SSuFZVhcZceVwrbx9J22O8J2r0WPz3B66Pf0pSs03TvjTpF7SpKfrWGFtZ/PMMfP72VYVGYn35FhJNSPZ3jd/af5Zds6Ce+531lVf5Nh9Mnf1TC9xE6neW/43QzPgQcm+akM8w99z4ictzrWJdn2+HpOhhE5N6+qzYvYnJrkyzIs7LCTF2c4Ne2PM5zWNHoFxrrsyntHM7TZm7p723nAFj22b+7bGk7bvH9funLjmL7dyVbHubtkWEF50X1qK2OPcx/OcArYP2d+2maS9CfPH3VScp743mjK+4zjn7v3G9u/W8S+Y0zs3K5P5z3IFJP2nymrKyXDcqxf3JeeH/wpSX63u7+uql44Ia8Tnks68UPSlFXkxtjq/NcpqzRMyXnKCnRT94udbNdW6/Z4p/TRlMe74bczvKH+kXmut8qwgsfXj4jdzjKegwtvN5ft34clOXsX/Ttl31i4f1d4vJoywfoYyzjWrSL2+H1qN/vFlHw3fLy7X1bDHEqP7O5nbLPPnCjnddmXp+Q8xthjxujtTnm8E9tq4f1q0e3WxJXCVviakCQXZijq/GZVvSDDPj7WFbr7b2qY9/Pnuvv3ausVyC6ju99fVX+a4fSrI/Mcz8w2q6jOTX1v9W1JvjjJv3b3sar6l4w8hSrD8eaJVfUdPUw0/NR50WOn599C++T8ePbyJI/LUCTfcElGzL83v4+q4RTROyZ5VlV9IMnTu/vXR8Re5rTFqvq8jDgdKYs/B4/v27/eo/fN52bBfeokPXdPdMriTvMBLZxzpr/nXvR1e0r/Tt03vj/Dc+AHMswv/CtJvnZE3IGkmLT/TFldKUk+J8P5yxvn8V4+yfWr6ioZf17zbkz5kDRlFbkppqzSMCXnKSvQTd0vFrWOj3dKzidjnzy156tDzD25qnb8tnGCZRcqtjKlf6fETumjVR2v7pQJE6xPMOVYt4rYKfvFyVh953JV9dAMS8U/rKq+PDtP0LyO+/LJWg11t6Zsd8rjnRI7Zb9aaLs9faWwVb0mJEm6+59rGN33IxlGCF1pZOjRqvq2DPMu3biqPjsj37fWsJDEZyR5Wy79MHgsOxeTpr63unaGD5NfWsNy4y/NUCh934jYI1V1mwyLpHxvhtENYz5EL7xPzou390tyze5+0fy9yY0zvD7ttCDLxn28al40+8cMc9Y8PMmOxaQT3M/rq+rzR9x00ce7qvfNU7Z7Mp67P9DHjWisqhdl+xGNq3rPPeX4OiXnqfvGE5L8xvw+HlbDyo8/memj3daSYtL+M2V1pST5uSQvn39bcCzJVTMc5G+VYZWORW1Vqb1TFv+QNGUVuTFOmHNPW6VhSs5TVqCbul/sZKvHv46Pd0rOJ2Of/GhVfVOSv8nw2L82m4YaT7CM5+CU7U7p3ymxU/roTlnB8aqnTbA+xkk/1q0oduH9YuJxfcO3Jrlrkjt394fmH3S+f1k5Z3X78pScx1jGMeNOWfzxLhw7cb+aknOy+EphU7e7k51Oc0t3X5zhlLU/zbCC2Bjfl+HUtPt293/XMIHv2JFNp3f3V4y87WZT31ttjET+4ex+JPK3ZVgB8f4ZPojeLkMxYVsn4Vj3e7n0dN7vzIjTeTfMi33fmOHD//Mz7Jv3HrPRqnpaLvtB/eq59IvvLU14vKt63zxlu3fKgs/d+uQRjRv5XS7Jy7cMnJ7zlPdGU/blKTlP3TdO6WE04cbjeFZV7biIxEF1uVUnwGV190MyFIQus7pShnmQfjIZPnRscxevy/DNSDL071uS3KO7n97dJ1yFZaTXbpHvqzOszLDth6Qt7vNe+eQDzcYqct+xQ+wYJ8x5Qw/ukORdOcEqDVuE3SuL53y3DC/Yj8vwpmnjG7N/zXzVkK1iT8J+sZOt2upeWb/HOyXnKbEbvjPJbTIUk/4qw9LE37lDzBjLeA4uvN1M6N+JsffKgn20yuPV/A3LJy013ZeuQDTltN5lHOtWETtlv1g436ra+BD6xUk6w7xnt8uw8s9Oy92v4748uZ13cNKPGVMe78k4Ri6yX52E7T4sybNy2ZXCdpz3coWvCclxBYnu/tckn7bdndUwAikZvnT59STvqKrrZ/hgN3ZFqudW1f838rab85v63urU7n5Gd1/U3e/u7icn+ZSRm//O7n5Zd7+5u+/V3d+YXYxsmHBs/nh3vyzDCMxHdvcf59KVZ3dywyS/nOSLuvu+3f2XSb5wZOzG6XUbx4AHZCigjbLbx7uq981TtjvxOPf07r5xkod09xndffr852rZoaC7wvfcG9ta5Pg6pZ2n7htvrqqfr6pzququVfXYJG/e7jEeaKueAdzP7n92WEnjH2az2Vmz2ezFs9nsJvOVIr5hwrZ+d5n5LiN2HXPWVusfO5vNrjD/faX5z5U3/X2lCdtbSR/ZN/Y89vn6aDXbnM1m585//84Jfp5w2Np40djDuD8uM3bKSmF73b+z2ewus9nsRbPZ7P2z2ewlm35eMZvNXr3D/f7C/PfzZ7PZ8+a/N362zXM2m100m83ePf998Ww2e+/874tms9m7l91Ws9nsubPZ7Jtms9nVZ7PZ6bPZ7P/MZrPn7nB/58xms6fNZrN3zWazp276ecZsNnvTHuT8gtls9tDZbPZv8/cnXz6bzf5+5P1edTabff9sNjtv/vOI2Wz2xpGxvzHbtMrYbDa7/tjtLmN/XjRuvx/npvTRqnJet9jZsBLpt89ms1+e/3z77LhVLA/Tj9Pc1tN2QwA/3N0XVNVHuvslSV5SVc/JMLnZCc2/AdrKFy2a5CZTJro7Yew65ryqWG21Z7G/k2Guh9fkskO5NyYDvd5Wd7iqPrJv7KvYrSZY10dL3mZ3/1xVHUnyhu5++IT739V21zHW/rinse+sxVcKW2i7i/Zvdz+9qv4sw3QKmycAviTDin1b6u4Hzp9/f9jdv7ndbU8Qe/rOt5psuz76ziTnJ3lILl1FbtuRyH3ZibD/OcPqy1+Y5JsyH6F3EmyX88bpvOf0+NN5Nzw1yQszvNf57Qyn8Y9dJfplSZ5dwwpe351hpOJ9R8bu5KS+nqz5cW5KH03Z7qGJ7e6PZ1j9dcoZPweGYtJ62m5ysEWWK3xxklfmxMMqv2CxFC9jykR3W8WuY86ritVWexDb3d8y//PufemSyEmSqtpp6Pqq+si+sX9it6KP9mCbPazEdJWq+toMp4pvXlJ56gf4dWvj7WLtj3sXu7FSWM/3z39N8tMTtjVmuwv3b3d/rKoun+StG6f0VtUXZVhh7at2iD1WVbeoqgu6+3U7Zn+cqrpuhqLOmRmKOi9N8rDuPtFExru13XHj7VX1gAzzrGx8cXTKTnfYw0TYn5Hh9OYrZDgd6FFJfioj5i6aknOSP0nyh5lPuN3df7SL+71cd/9kVd2iux9ZVY9J8uQkf7ZTYHf/eg3L1b84wymuN+nuk7XYzsl+PVnn49zCfTRxu2IPKcWkg+dbklwjw2oA98swSd5O33R8Z5Kv6+5PmkSvqp5/0jM8OdYx51XRVntg/u1eJfnpqnpQLv1W42iSX0zyeduEr6qP7Bv7x1bfgumjvXPHJOccd9m2owoPIfvj3vnUDEWYOwzz0+byGUaVfO4Stzm1f1+W5M8XHH3yFUleW1X/k6GYeyTJse4+Y0TsxkTYP5LdT4S9sKr69fk2NkZfbRSUbjIi/GPd/bKq+vkMcxc9o7ZeGv5kumOGSbR/e75fPS3J07v7/SNir1BVN8mw0Mhtk7w+ybYrstUnT7z99gxLyT+pqtLdd1/gMSzbOh/ndt1HMIVi0nrabujdBzJMGpokPzHmzrr7aVX1rqq6cnd/8LirT8YQvmWclrB2Oa8qVlvtWeynJLlRhokHN785uiTD0rlbWlUf2Tf2VexWk3nqo73b5v9ZYFThydju2sTaH/c09mlZzukqW253av8uOvpkfprbJz3/duHU7n7Gpv+fXMOy9yfDdn104ySf28Pqdbt1uap6aIYJ1h9WVV+e5IoL3M+JbPc54e0ZCm+Pr6obJfnVJD9fVc9Ocm53b3da4vdmeI9zboaJuK+a5Jd2yOVxu8h7USf19WTNj3OL9NHJ2K7YQ0oxaT1tu3LPIrr7BVtc/oQkqarHd/ei5zZPyXfL2HXMeVWx2mr5sd39qiSvqqqnz1fk+F9V9eCd7nRVfWTfWG1sVf1uDyv5bDlnhT5a7jaPG1W4+bk6ZlThwttd11j7457FLut0lW23u0j/Th19Mj/N7Wer6jYLFmY+WlXflGEV1SMZCm8n6xSq7froFUk+I8lFC9zvlLmLdrJlzlX1uRnmZzonw2p5P5Nh1cCvyVDA/Jot4o4kuVN3P2J+0S3GJNLdF8zjb5LkjB6WUf+ZDF++/cyY+xjhpL+erONxbtE+mrpdsYfcqmcA9zP+52SsIDBh23u64sE6rmC1jrHa6uTHzmaz281ms5fOZrM3zH/eNpvNXrhufWTfOHmx81Vrtvp5sT5aef988Ww2e9hsNnvnbDZ7wqaf35rNZvdcp3ZaZeym+zjw++NexM5msxfOhlV5/2I2m912Npt9/mw2e9XEvllK/86GVYS3+vmmkff77Plr5p9uXuVsZOxnzmaz357NZq+cDSvI/cZsNrv2sttqNpv97Ww2+8BsNnv5bFi97p9ms9lLprbxknO+aDab/dhsNvuM2Wx2n9ls9puz2ewG8+vO2yH20bPZ7Gtns9lVZrtcrXbePteb78vPmc1mN5rNZi9a9uM9mXHH3ce+PM5N6aNV5XxQYg/rj5FJ+8werCBwUk3Jd1WPdVU5a6uDHbvJw5LcJcNQ6LtmOOXtPSNjd82+sf9js/zJPE9o3dpqVfluGlV4LMPIgMttuvobkzxxGdtdx9gp1vHxrridFzpdZRX9u2n0ydEkt01ytflVl88wj9GYSZ4fOSGFzz9+fpuq+oEkj90u6CS01XdkOJV9z5yEnP8tyV9kWEFuY+LvX01y8+4+f4fYKfPKfai731BVP5zkl7v7pVU15hTIPX89mWIfHJt33UfreIzcB+3MnGLS/rOSDx0TTMl3VY91VTlrq4Mdu+HD3f3m+dD+dyd5XFX9ZYbVU5bBvrH/Y1c1mee6tdWqn7t3z/Ch9AM73vLkbHcdY6dYx8e757FVdYXu/miSN8x/kuQb5r/HrDC0yveRT03yviS3TPLsJGcn+cmRsZ99gssul2E1uJ08tKo+v7t/u6o+L8Pk268ZETe1rU7Ppadu/XSGOZR+OsmbR8QuamrOC0/83d2fnyRVdbUMk6Pv5ouyj1TVbyW5WZL7zSeHvtwOMclqXk+mWOmxecE+Wqtj5IpjOY5i0v6zbisITMl3HVewWsfYKdbx8a66nd9SVd+V5NVV9eQMK2mMWY1mUfaNfR67B5N5bmXd2mrVz90Ls/t5VtatjafGTrGOj3cVsb+TYdLt1+STi0fHsvMcXqt8H3nV7j6nqv62u79v/oH2cRn3ZcoXb/r7aJIbJnlrkt8dEfv1SX6xqv40wwiMH+zuvx0RN7WtHpfkm+eFkS9L8uAMo8huNiJ2UVNzXnji76q6Z5Lzk7w/yZGqulKSH+vuJ48Iv3uGVfYe2t0fr6pPJPm2EXGreD2ZYqXH5gX7aN2OkauM5ThjKsLsoe5+WpInVtWVT3D1Mj907GTLFQ+yYL578Fj3Vc7a6mDHbvIdSf40yf2TPDfJf2U4VWaqk95Hq9ruYYudx7/gBIWky0zmudN9bONA9NGq8q2qp1XVUzOcmtNV9YyqeurGz7K2u46xIx2I/XGFsTetqo0RSUeO+zllu21OzXmk7VY5ukJVzZJcXFVfmORDGSa331F3/8imnwd091lJPumYuVlV3a6qbpfkNhlO3fpYkk5ypfnlO21zalt9qLvfkOHUol/u7pfm5E38fUInIedvzdCu53T3hzIsGz924u8HJjmzu7+4u2+QYRLtc0fGnpbkOkm+u6rOy1Bw+46dglbxejLSvjrObbLrPlrDY+R+aGc2rHrSJj+7/5nNZo9f0v2eNpvNHjSbzX5x/v/Zs9nsKvO/T11FvjvFrmPO2upgx85ms8+azWaPms1m/282mz1zNpv97NiJQFfVR/aNfRG77WSe+mh529xm4uCzZrPZWYtub13beEys/XF5sbPZ7Mqz2exTZ7PZT81ms7vOZrPTZ7PZGbPZ7Jt3miB51f07m81uOZvNzpnf/k2zYbLnh43M6UrH/XzWbDb7xx1ifmebnycsu61mw+TovzWbzf5lNpudOhsml77gZGx3mf074X6ffdz/R2az2Z+PjH3FbDb7mdls9v2bf1b5eA/icW5KH60q54MWe9h+nOa2nkZ9y7OAJyb56yR3mP9/Roahybfr7hOdVzrWlHx3il3HnFcVq632JvZpGdr6KRm+ubrx/LIxw95X1Uf2jdXH7kQfLWmbPZ88eInWrY3HxNoflxS7MYKxqr66u39s01VPqaq/mrCtbbd7nIX6t7uft+nfz9llTpvnODqW5L+T/MoOMWNH1EyxXVttnLr145tO3fr2PchpJ8t6LfqfqnpFkhfO//+qDKf2PzJJuvtHt4l9b3c/eEl5Lev1ZB2Pc1P6aMp2xR5STnNjs0/r7l/NfIhud/9RkiutNqUdrWPOq6Kt9sYHu/vx3f3S7v6nzW0+wqr6yL6x/+kj9hP74/J9pKoeWVV3qapzapjgecfT3E6SXfVvVV1UVe+e/1x03P/vHrPB7v7c7v7cDHMlfVl337C7f3uHsNckefUWP+8bs92J7pTh9K1bV9U9knxmhsnHD6rnZlhR8KXzn8dkOK3/Ndl5wvO/rqrvr6ovqarrb/wsM9mTYB2Pc1P6CHbNyCQuo6qul/mEj1X19VmDguM65rwq2mpPvKKqHphLV6C5eZJ/3njT1N2v3S54VX1k39j/9BH7if1x6e6aYYLiW2QY5dpJ7rxXG99N/3b36Sdhe1+bYYn6j2WYODhJvqe7X7TNdj93HnvjDPPCXG1+1eWTXDw1pxGmTBq+jp6UYc6lMzO070uTPKW7LxkRe9skl2TYrzccyz4vvq3hcW5KH8GuKSax2f9N8mtJvrSq/iPDyjbfs9KMdraOOa+KttobN8nwAn6H4y7/lez8xmlVfWTfWL3tJrVN9BH7i/1xybr7A0mmTMw/xUL9W1U3SPKoJKd1982q6n5J/q67Xz5im+cnuXl3/8f8vq6b4ZSirx4R+5gkD0ryyCQ/kORuSf52RNwk3f0jx19WVb++7O2u0G8neU+GU6iOZHg/c3aS794pcD6heqrqqkku7u73LzHPk2Udj3ML9xEsQjFpPe30oWPXqupIkq/u7tuc7PvOtHy3jF3HnFcVq632Lra7b77Ina6qj+wbexdbVadlmOPjGt39gKo6O8kruvu/M6xGtFWcPlrtNqdatzbeNtb+uG9ip1hW/z42wzFuY66jv0rymxk3Z+DHNwpJSdLdb6mqsaOLPtzdF1TVR7r7JUleUlXPSfLsXeS+le3a6vhTnq6WYUTIqi1rv/qs7t48J9RTq2rUnHOLjDzbhZP+erLGx7mF+2jidsUeUopJ+9SiHzoW1d3HqursqnpBd79ut/FT8l00dh1zXlWstlp+bFX9SXffuaouynxI9NyRJMe6+4zttrmqPrJv7F1sFp/U9lD10Qr7Z2Hr1sZTYg/b/rjK2ClW1L+f6O7XzgsFmf89tiD0+qp6fJK/yfC6+bVJ/n1k7Aer6m5J3lpVPzuPu+7YpCf00as3/X0swzxNjxu73SlWtF9dvqo+s7vfPs/huklOHRk7ZeTZnr+erOtxLhP6aB2Pket4bD5o9vt5n4fZE5O8P8MpM8mlHzqy3YeOib4yyWur6n21aQLFkbFT8p0Su445a6sDGNvdd57/Pr27z9j0c/pOhaRNVtVH9o29iZ0ymedh6qNV5TvFurXx1NjDtD+uMnaKVfTve6rqO5JcuapuWlU/l+RdI/N9Q5K/T/I1GVagelGSq4yM/ZYkr8hwittHknxJdreq2q7aqqreWFVvyFD02vi5XJKrJjlvF9udYhX71YMzTKT96qp6TZK/yDBX1RifNPIsu5vXahWvJ+t4nJvSR+t4jFzHY/OBopi0f+35CgLd/XndfbS7P33+AXg3H4Kn5Ltw7DrmvKpYbbU3sVV1t6r6k03//1VVffOY2FX1kX1jz2IXnszzkPXRyvpngnVr40mxh2x/XGXsFKvo39ckuVaSdyT50STvTfKmkfleKcmtk/xIhkmDH5Lkj0bm+4Hu/vfufm93/0R3/2CPm6dpw27b6gYZClZ/OM/3Jkm+IsMH+SfsYrtT7Nl+tal49jtJrpjkszKsXPcpSX5/5N28vqoeX1V3nb9P+vWMH3mWrOD1ZJ2Ocyepj9bxGLmOx+YDxWlu+9iiHzombO9EL4CX6+57jYxfON9FY9cx51XFaqs9i/2hDKuWbLhDholAnzJimyvpI/vGnsUuPJnnYeujVeU7xbq18ZTYw7Y/rjJ2ir3q36o6J8n/ybB66QVJNr7V/4okX5ah4LKt7n5IVd01yWszFKVu1t3/NSbfk2E3bdXdH5zf7qu7+8c2XfWUqvqrpSa6yR7uVzfIMPrqwRlGgF2QSyd3no28jzckeXOGkWeXZBh5dsfdJLHXrydrdpw7GX20lsfIdTw2HySKSfvXKlYQePqmv48m+fIkVx4ZOyXfKbHrmLO2OtixFyf50Kb/j+SycyhtZ1V9ZN9YcmxNn8zzMPXRqvKdYt3aeGrsYdofVxk7xZ71b3c/o6penmG+oF/ZdNUlSf5luw1V1c/nsq+Rr0vyBUnOrap094+OzHmKRdvqI1X1yCQvzvAYbpTklGUleZw9269OUvFsY+TZd2VYbe/Hs7tTAlfxerI2x7mT1EfreIxcx2PzgaKYtA+dhA8dC+nu41e9eGZV/eZOcVPynfpY1y1nbXWwY+cel+SVVfXvGd5UfkFGvmFaRR+taruHLbYnTuZ5WPpoxc/dhaxbG0+NTQ7P/rjK2ClW0b/d/aYkt19gc68+7v/XLHAfC5vYVndN8m1JbpHhi6NOcueTl92JrWq/yoTi2ZSRZ6t4PUnW7zg3t1AfreMxch2PzQfRkWPHxn5hzl6qqj9I8pOLfOiYsM3bHXfR1ZI8oLtvOCJ24Xwnxq5jztrqAMfO46+cS4cVv27jG6MRcavqI/vG3sS+PslnJ/lg5ufoZ8RKf/PYQ9NHq3zuLmrd2vgkxB6a/XGVsVOsqn/X0ar6aIoVfU44LUPx7Pq5tHj2e939vm1ijh95doMkn5PkWUlGjzxb0evJOh7ndt1HJ2m7hyqWSxmZtH9trCCw6w8dE9xt098bS5zee2TslHynxK5jztrqAMZW1cO6+yer6mk57rS2Gobq333EdlfVR/aNPYjt7s8bcf9bOUx9tKp8p1i3Np4ae5j2x1XGTrGq/l1Hq+qjKfY85+7+QJLH7zLsZI08W8Xrydod5xbso8nbPYSxzBmZxP+qqu/q7t867rL7d/cvrSilHa1jzquirZarqr60u19ZVWed4Opj3f2CEfexkj6yb+yNmjCZpz5iP7E/Hmz6FzwPYAwjk/apKR86FtjWrZPcJsndq2q26apTM1Tlf2nEfUz5kLTr2HXMeVWx2mpvYrv7lfM/f6C773rc/b0oyc222d5K+si+sbexWWAyz8PYRyvsn4WtWxsvGnsY98dVxk6xqv5dR6vqoynWMecp9vL1ZJ2Pc1Os4zFyHdv5oFFM2r+mrCCwW/+YYRnXr89lh59ekmTHiebmpuS7SOw65ryqWG21B7FVdZckD8qwKsS7N111uQzLtG5nVX1k39jD2F5sMs/D2EeryneKdWvjRWMP4/64ytgpVtW/62hVfTTFOuY8xV6+nqzzcW6KdTxGrmM7HyzHjh3zsyY/s9nsN/dgG586m82uO//5gtls9pxV5Lub2HXMWVsdzNjZbHZkNps9dNFtrLKP7BvLj53NZrc77ufbZ7PZy/XR/tzm1J91a+Pdxh7m/XGVsVN+VtW/6/izqj46bDmv4vEetuPcKtr4MMYe1h8jk/apOvEKAl++5G3+eJJ7Jbl6krcn+cwkvzIyduF8J8auY87a6oDG9rD8+62TPGLMdk6w3VX1kX1jD2IzYTLPw9RHK+yfha1bG5+E2EOzP64ydopV9e86WlUfTbGOOU+xoteTtTvOTbGOx8h1bOeDRjFp/1rFShq36+7Pq6rnd/fZVfWVSe40MnZKvlNi1zFnbXWwY/+jql6Y5KW5dHWIscvfrqqP7Bt7E/uiE03mmZ1Pg0wOVx+tKt8p1q2Np8Yepv1xlbFTrKp/19E6rl63jjlPsYrXk3U8zk2xjsfIdWznA0Uxaf+a8qFjUceq6gpJTqmqK3X3P1bVz42MnZLvlNh1zFlbHezYPz/BZWOPtavqI/vGEmNP0qS2h6mPVpXvFOvWxlNjD9P+uMrYKVbVv+toVX00xTrmPMUqXk/W8Tg3xToeI9exnQ+UI8eOHVt1Dmyy+UNHkj/adNWpSe7W3ddZ4rYfMP/zE0l+MMm7kry/u48fBrg5ZuF8T8ZjXaectdXBjj3ufv6/DMNlk+QKSX6+u88cEbenfbSq7R7C2NMyDJ1+XJKf33TVJUle1t2v3Sp2030c+D7aD8/d3Vq3Np4au+k+Dvz+uMrYKVbVv+tole+5F7WOOU+xiteTTfexNse5KdbxGLmO7XxQGZm0/6xsJY3u/sWNv6vqWUmumuTCHcKm5Dv5sa5ZztrqYMcmSarq15JUkusneVmSM5P8zJjYFfTRqrZ7qGK7+wNJ/jbJDarqU5N8xvyqKyR5zPw+t3VI+milz90FrVsbT41Ncmj2x1XGTrGq/l1H67h63TrmPMUqXk+SrN1xbop1PEauYzsfSEYm7WMn+tDR3Tt+6JiwvRskeVSS07r7ZvOhfi/o7pePjF8430Vj1zHnVcVqqz1r57/r7q+pS8+x/7wk53b394yIXUkf2Tf2bN844WSe3f2QEbGHqo9Wle8U69bGU2IP2/64ytgpVtW/62hVfTTFOuY8xV6/nqzrcW6KdTxGrmM7HySXW3UCnNj8Q8crk7wqyV9kmMx32edwPjbJA3PppMF/Ob9sR1PynfhY1y5nbXWwY5Mcraozklyuqs7o7tcn+ZKRsSvpo1Vt97DFZj6ZZ5KXd/f1k9w241+HD00frbB/FrZubTw1Nodof1xl7BSr6t91tKo+mmIdc55iFa8nWcPj3BTreIxcx3Y+aBST9q8pHzoW9YnNc3fM/754ZOyUfKfErmPO2upgx/5ykm/IsHzsW6vqrRleqMZYVR/ZN/Ym9pMm80xy05Gxh6mPVpXvFOvWxlNjD9P+uMrYKVbVv+toVX00xTrmPMUqXk/W8Tg3xToeI9exnQ8UDbZ/TfnQsaj3VNV3JLlyVd20hhUL3j0ydkq+U2LXMWdtdbBjr5jkJ5Ocl+RtST49yfNHxq6qj+wbexP7tCTfN//9yqp6YZIPjow9TH20qnynWLc2nhp7mPbHVcZOsar+XUer6qMp1jHnKVbxerKOx7kp1vEYuY7tfKAoJu1fUz507EpVPXH+5weTXCvJO5L8aJL3JrnnyLuZku+uY9cx51XFaqs9j31Aki/t7hvMv/H4nAztvaVV9ZF9Y29ju/sX5z+PzbCSyA8muf12MYe0j1aV7xTr1sYLxR7S/XGVsVOsqn/X0ar6aIp1zHmKPXs9WfPj3BTreIxcx3Y+UEzAvQaq6nMzX0Gguy9Zwv3/Y4ZJx66X5HXHXX2su2+yy/tbON+xseuY86pitdXexlbVs7v7Gzb9fyTJs3v7pWRX0kf2jT3fN3Y9medh76NV5TvFurXxbmIP+/64ytgpVtW/62hVfTTFOuY8xbJfTw7KcW6KdTxGrmM7HwRHV50AJ3aCDx13nF+1jJU0bpbk2kl+MckPLXIHU/JdMHYdc15VrLbaw9gk/1NVr0jywvn/X5XkLVX1yCTp7hONUlpVH9k39nbfeGyS788wn1YyTOb5mxn6YSuHro9W2D8LW7c2nhB76PbHVcZOsar+XUer6qMp1jHnKfb49WRtj3NTrOMxch3b+aBRTNq/FvnQsZDuvjjJW5PcdcLdTMl317HrmPOqYrXVnsc+N8mfb/r/pTsFrKqP7Bt7HvuJ7n5tVSUZJvOsqm0n8zykfbSqfKdYtzZeKPaQ7o+rjJ1iVf27jlbVR1OsY85T7NnryZof56ZYx2PkOrbzgWLOpP1r3VbSmJLvqh7rqnLWVgc4truf0N1P3Opn5PYXYd/Y/7GrmtR23drKMXL/x06xjo/3sLXzYbOObbWOOU+xiteTKQ7bMeOwxTJnZNL+dZkPHUnulP29ksaUfFf1WFeVs7Y62LGrYt/Yp7FV9cTuvmc+eTLPF2dvJrVdm7Zacb5TrFsbT42dYh0f72Fr58NmHdtqHXOeYhWvJ1MctmPGYYtlzsikfabWbCWNKfmu6rGuKmdtdbBjV8W+sf9jh/B6RZK7JLlzks9Mcp35/8/fIXZh69ZW6/jcXbc2nho7xTo+3sPWzofNOrbVOuY8xSpeT1aR79TYKdbxGLmO7XxQWc1tn6k1W0ljSr6reqyryllbHezYVbFvrEXsKdlmMs/ufvNWsVOsW1ut43N33dp4auwU6/h4D1s7Hzbr2FbrmPMUq3g9meKwHTMOWyyfzGlu+8/Nsl4raUzJd1WPdVU5a6uDHbsq9o19Hturm9R23dpqHZ+769bGU2OnWMfHe9ja+bBZx7Zax5ynWMXryRSH7Zhx2GI5jpFJAAAAAIxmziQAAAAARlNMAgAAAGA0cyYBAGyjqv4oSSW5fXe/bRdxX5XkP7r7DUtLDgBgBYxMAgDY3l2TfOVuCklz35FhxZhRqurILu8fAGAlTMANALCFqvqtJPdO8oIkT0jy3Uk+nuQDSb6ru99dVd+d5LuSfCTJR5PcPcnZSX4nyZuTPCDJeUke0d1/XVWfk+SF3X2dqvrdedz/l+T/JDk9yaOSHMkwgvyHu/slVfWDSe6R5H+SfCzJt3f3u5bfAgAAn8zIJACALXT3d83/vEeSBya5VXefneS5SX58ft2nJLljd5+V5E1Jvq27/yTJhUl+qLuft8NmTuvur5mPfHpihiLVLZPcN8lvz2/z8Ayn2d0iyU8nuc5JeHgAAAsxZxIAwM5unORaSZ5bVUly+SRvn1/3kSR/XFUXJ/mcJO/Y5X3/fZJU1VWSfGGS35lvI0k+papOTfLk+bb/OMkzuvs1Cz8SAICJFJMAAHb28SQv6e7bb75wfsraTyf54u5+Z1X90hbxm+cVOP7910c33eaj89FHx/veqvr8JLdL8v+q6sHd/dTdPQQAgJPDaW4AADt7RZKbVNU1kqSqzqmquyT5jCQXzQtJV09y6yRXmMdckuTU+d/vTXLG/O8bn2gD3f2+JG+qqq+fb+Pzqur8qrpqVT0syeu7+zEZ5mL6qpP/EAEAxjEyCQBgZ+9Icr8kz6qqD2cYTXTPJP+RpKvqxUlen+ShSR5XVc9O8ldJHl9VD0jyuCS/UFW3mN9uqy/07pHkMVX14AxFqR/p7vdW1ZWSvLiqPji/3b2X8SABAMawmhsAAAAAoznNDQAAAIDRFJMAAAAAGE0xCQAAAIDRFJMAAAAAGE0xCQAAAIDRFJMAAAAAGE0xCQAAAIDRFJMAAAAAGO3/B9SObsuA2oPtAAAAAElFTkSuQmCC\n", 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" ] @@ -13945,10 +13949,10 @@ "CREATE TABLE \"FEATURE_1_2\" AS\n", "SELECT SUM( \n", " CASE\n", - " WHEN ( t2.\"poccig\" IN ( '0.0', '2.0', '1.0', '12.0', '10.0', '35.0', '25.0', '20.0', '15.0', '30.0', '5.0', '3.0', '28.0', '4.0', '22.0', '17.0', '8.0', '6.0', '40.0', '18.0', '14.0', '7.0', '13.0', '23.0', '16.0', '21.0', '50.0', '11.0', '27.0', '9.0', '60.0', '70.0', '80.0' ) ) AND ( t1.\"htrisk\" IN ( '6' ) ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * -3.397095496386469 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * -8.217082116534575 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 0.8989800432775159 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * -1.237316756163966 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * -0.9928111708027221 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * -2.788498399735765 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * -0.4818583551937765 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * 6.971943651250299 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * -3.694050975182785 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * -5.856998651527096 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * 2.110663631459102 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * 0.119131970047223 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * -19.83581231536901 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * -0.6738932939324694 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * 1.356032686173819e-07 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 43.42683265738321 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * -188.4516196796772 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * 149.8310937108893 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * -7.925434240864883 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * 7.372069762323473 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * -6.522002678957503 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * 3.705927741723315e-07 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * -1.783586052963881e-06 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * -0.03696877820155445 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.8975936237922416 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -2.613310598875336 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * 33.09171658345281 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * 2.06224169478161 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * -5.888017807774677e-10 + -1.0081362925125225e+02\n", - " WHEN ( t2.\"poccig\" IN ( '0.0', '2.0', '1.0', '12.0', '10.0', '35.0', '25.0', '20.0', '15.0', '30.0', '5.0', '3.0', '28.0', '4.0', '22.0', '17.0', '8.0', '6.0', '40.0', '18.0', '14.0', '7.0', '13.0', '23.0', '16.0', '21.0', '50.0', '11.0', '27.0', '9.0', '60.0', '70.0', '80.0' ) ) AND ( t1.\"htrisk\" NOT IN ( '6' ) OR t1.\"htrisk\" IS NULL ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * 0.002411294099492637 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * -0.0626104782770142 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 0.002566103012395416 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * -0.005532871003705906 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * -0.001054498518485803 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * 0.004007094220024539 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * 0.001871053399197597 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * 0.0002121004831901501 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * -0.002691696833181459 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * 0.0001431658988299645 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * -0.0001289298054566767 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * -3.355295825128488e-05 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * 0.01324732393277822 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * 0.002395697464216007 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * -0.0003315238122537618 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 0.02567624314633523 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * -0.005160409052324677 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * -0.004594191940926427 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * 0.02593337789132812 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * -0.01167465366012087 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * 0.002144534133525993 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * 3.425815197311889e-09 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * -2.057864437834938e-09 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * 0.0044411821410372 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.002401235266795935 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -0.0211518305113043 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * 0.03021101637486201 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * 0.02532570959453475 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * -2.69202639260626e-09 + -2.7877747341427978e-01\n", - " WHEN ( t2.\"poccig\" NOT IN ( '0.0', '2.0', '1.0', '12.0', '10.0', '35.0', '25.0', '20.0', '15.0', '30.0', '5.0', '3.0', '28.0', '4.0', '22.0', '17.0', '8.0', '6.0', '40.0', '18.0', '14.0', '7.0', '13.0', '23.0', '16.0', '21.0', '50.0', '11.0', '27.0', '9.0', '60.0', '70.0', '80.0' ) OR t2.\"poccig\" IS NULL ) AND ( t1.\"reference_date\" > 852038400.000000 ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * 0.7005540342173785 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * -0.3721815968225617 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 0.001514347782697983 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * 0.7143978295176737 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * 0.6208372807968181 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * -0.2854287364907576 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * 0.5421938420599178 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * -0.7689222625697328 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * -0.4929019826671734 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * 0.223780579913266 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * -0.2303339841644577 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * -0.08730665947172637 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * 3.578307299045961 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * 3.424298336831009 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * 1.469247538772654 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 2.007088802312646 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * 1.508201373333506 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * 1.694371256928238 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * 15.34300789713041 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * 10.98526961717351 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * 1.131391278158518 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * -1.552221861580311e-08 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * -2.200646489251252e-07 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * 0.2536479591053882 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.04144600919003553 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -10.71915452291016 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * -1.584720081647418 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * -1.712372865814812 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * -6.542237117486055e-08 + 6.6081461026926416e+00\n", - " WHEN ( t2.\"poccig\" NOT IN ( '0.0', '2.0', '1.0', '12.0', '10.0', '35.0', '25.0', '20.0', '15.0', '30.0', '5.0', '3.0', '28.0', '4.0', '22.0', '17.0', '8.0', '6.0', '40.0', '18.0', '14.0', '7.0', '13.0', '23.0', '16.0', '21.0', '50.0', '11.0', '27.0', '9.0', '60.0', '70.0', '80.0' ) OR t2.\"poccig\" IS NULL ) AND ( t1.\"reference_date\" <= 852038400.000000 OR t1.\"reference_date\" IS NULL ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * 6.963983784467057 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * 12.7201171250798 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 1.296534129086624 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * -3.178987445661821 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * 0.4173045416261214 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * 1.92693099294999 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * 0.6497416198716232 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * -2.934698113722341 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * 2.43950913153937 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * 1.47634891241367 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * -0.04727950911958443 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * 0.2284859457718159 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * -4.284780550478363 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * 5.640112799208898 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * 2.782893387299822 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 18.72747293890264 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * -11.08758851568889 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * -14.05642864637471 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * 15.29700767610475 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * -8.372507661585622 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * -3.785073697931573 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * -6.216702473867759e-07 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * 5.610046107243454e-07 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * 3.030242084663193 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.7262712487263796 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -3.932089120575836 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * -29.67762684885863 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * 1.104743672199196 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * 2.142548400344535e-07 + -7.5267732055428329e+00\n", + " WHEN ( t2.\"zmkour\" IN ( '7' ) ) AND ( t1.\"reference_date\" > 852038400.000000 ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * 0.6992075915192288 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * -0.3722162052975991 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 0.002072665325539906 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * 0.7135789936249097 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * 0.6204509957556447 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * -0.2852251406284317 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * 0.5416045970884422 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * -0.7691376230609942 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * -0.4921979178739925 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * 0.2235547808836321 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * -0.2305456390285043 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * -0.08732404144815611 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * 3.578257813757872 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * 3.420605423460954 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * 1.468999926745384 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 2.006077827378471 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * 1.509295328270136 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * 1.689453613870151 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * 15.33082510657582 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * 10.97404738286759 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * 1.130351262906143 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * -1.551420482850177e-08 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * -2.196562174142811e-07 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * 0.2535134048176608 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.04134495275193414 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -10.72183117055155 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * -1.580592374193315 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * -1.709624186115308 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * -6.539468045584373e-08 + 6.5967578763439345e+00\n", + " WHEN ( t2.\"zmkour\" IN ( '7' ) ) AND ( t1.\"reference_date\" <= 852038400.000000 OR t1.\"reference_date\" IS NULL ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * 7.009110879137228 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * 13.11693059229238 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 1.269893142479568 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * -3.162664037823355 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * 0.3787421899628546 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * 1.971210134455975 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * 0.6635424419434129 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * -2.939270936123821 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * 2.383223854209837 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * 1.498266105418248 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * -0.0517718309107393 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * 0.2276783275568421 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * -4.23254041935678 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * 5.719150573247517 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * 2.70196864001066 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 18.79720042999637 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * -10.93785280575887 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * -13.52764654128384 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * 15.62103938182056 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * -8.797933019568511 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * -3.757872625493459 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * -6.356934996127425e-07 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * 5.640187513549882e-07 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * 3.023356785615304 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.7240192100118329 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -4.024059582855235 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * -29.58919968380793 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * 1.778411316422035 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * 2.163236433930246e-07 + -7.9015375976371462e+00\n", + " WHEN ( t2.\"zmkour\" NOT IN ( '7' ) OR t2.\"zmkour\" IS NULL ) AND ( t1.\"htrisk\" IN ( '6' ) ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * -3.397114537185319 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * -8.217148782839798 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 0.8989975149085871 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * -1.237315019051493 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * -0.9928276153180301 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * -2.788494436812396 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * -0.4818397334334769 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * 6.971936650721264 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * -3.694055970730235 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * -5.857003884016523 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * 2.110663998001987 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * 0.1191325417763164 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * -19.83587241194438 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * -0.6739075204128018 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * -2.401197249091638e-07 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 43.42691366138832 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * -188.452548812023 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * 149.8317616555433 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * -7.92533116285952 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * 7.371945970513856 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * -6.522018830745494 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * 3.705953810711698e-07 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * -1.783593808548376e-06 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * -0.03696650252475624 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.8978599033836492 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -2.613193795898457 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * 33.10188686443065 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * 2.062126624888299 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * -5.886932876564901e-10 + -1.0081350693584685e+02\n", + " WHEN ( t2.\"zmkour\" NOT IN ( '7' ) OR t2.\"zmkour\" IS NULL ) AND ( t1.\"htrisk\" NOT IN ( '6' ) OR t1.\"htrisk\" IS NULL ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * 0.002419801161207402 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * -0.06277853150672247 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 0.002571038648386481 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * -0.005578860882900542 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * -0.001057898151656378 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * 0.004014557721064409 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * 0.001873173639385572 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * 0.0002124825706128939 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * -0.002640732624754488 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * 0.0001474535382830381 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * -0.0001256948379644612 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * -3.474245058868025e-05 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * 0.0132708577852937 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * 0.002454283595539609 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * -0.0003505117806351746 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 0.02562835710639392 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * -0.005171308330700418 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * -0.004672335054095883 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * 0.02581134186668081 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * -0.01154259733921239 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * 0.002194525846818011 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * 3.411798694161753e-09 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * -2.075596685480708e-09 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * 0.004478272836447239 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.002438061446690539 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -0.02099226608599071 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * 0.03134631902567388 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * 0.02487247493931399 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * -2.655718509874403e-09 + -2.7439158467228797e-01\n", " ELSE NULL\n", " END\n", ") AS \"feature_1_2\",\n", @@ -13961,7 +13965,7 @@ "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"FEATURE_1_2\";\\n\\nCREATE TABLE \"FEATURE_1_2\" AS\\nSELECT SUM( \\n CASE\\n WHEN ( t2.\"poccig\" IN ( \\'0.0\\', \\'2.0\\', \\'1.0\\', \\'12.0\\', \\'10.0\\', \\'35.0\\', \\'25.0\\', \\'20.0\\', \\'15.0\\', \\'30.0\\', \\'5.0\\', \\'3.0\\', \\'28.0\\', \\'4.0\\', \\'22.0\\', \\'17.0\\', \\'8.0\\', \\'6.0\\', \\'40.0\\', \\'18.0\\', \\'14.0\\', \\'7.0\\', \\'13.0\\', \\'23.0\\', \\'16.0\\', \\'21.0\\', \\'50.0\\', \\'11.0\\', \\'27.0\\', \\'9.0\\', \\'60.0\\', \\'70.0\\', \\'80.0\\' ) ) AND ( t1.\"htrisk\" IN ( \\'6\\' ) ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * -3.397095496386469 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * -8.217082116534575 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 0.8989800432775159 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * -1.237316756163966 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * -0.9928111708027221 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * -2.788498399735765 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * -0.4818583551937765 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * 6.971943651250299 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * -3.694050975182785 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * -5.856998651527096 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * 2.110663631459102 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * 0.119131970047223 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * -19.83581231536901 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * -0.6738932939324694 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * 1.356032686173819e-07 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 43.42683265738321 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * -188.4516196796772 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * 149.8310937108893 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * -7.925434240864883 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * 7.372069762323473 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * -6.522002678957503 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * 3.705927741723315e-07 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * -1.783586052963881e-06 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * -0.03696877820155445 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.8975936237922416 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -2.613310598875336 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * 33.09171658345281 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * 2.06224169478161 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * -5.888017807774677e-10 + -1.0081362925125225e+02\\n WHEN ( t2.\"poccig\" IN ( \\'0.0\\', \\'2.0\\', \\'1.0\\', \\'12.0\\', \\'10.0\\', \\'35.0\\', \\'25.0\\', \\'20.0\\', \\'15.0\\', \\'30.0\\', \\'5.0\\', \\'3.0\\', \\'28.0\\', \\'4.0\\', \\'22.0\\', \\'17.0\\', \\'8.0\\', \\'6.0\\', \\'40.0\\', \\'18.0\\', \\'14.0\\', \\'7.0\\', \\'13.0\\', \\'23.0\\', \\'16.0\\', \\'21.0\\', \\'50.0\\', \\'11.0\\', \\'27.0\\', \\'9.0\\', \\'60.0\\', \\'70.0\\', \\'80.0\\' ) ) AND ( t1.\"htrisk\" NOT IN ( \\'6\\' ) OR t1.\"htrisk\" IS NULL ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * 0.002411294099492637 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * -0.0626104782770142 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 0.002566103012395416 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * -0.005532871003705906 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * -0.001054498518485803 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * 0.004007094220024539 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * 0.001871053399197597 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * 0.0002121004831901501 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * -0.002691696833181459 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * 0.0001431658988299645 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * -0.0001289298054566767 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * -3.355295825128488e-05 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * 0.01324732393277822 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * 0.002395697464216007 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * -0.0003315238122537618 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 0.02567624314633523 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * -0.005160409052324677 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * -0.004594191940926427 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * 0.02593337789132812 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * -0.01167465366012087 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * 0.002144534133525993 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * 3.425815197311889e-09 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * -2.057864437834938e-09 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * 0.0044411821410372 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.002401235266795935 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -0.0211518305113043 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * 0.03021101637486201 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * 0.02532570959453475 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * -2.69202639260626e-09 + -2.7877747341427978e-01\\n WHEN ( t2.\"poccig\" NOT IN ( \\'0.0\\', \\'2.0\\', \\'1.0\\', \\'12.0\\', \\'10.0\\', \\'35.0\\', \\'25.0\\', \\'20.0\\', \\'15.0\\', \\'30.0\\', \\'5.0\\', \\'3.0\\', \\'28.0\\', \\'4.0\\', \\'22.0\\', \\'17.0\\', \\'8.0\\', \\'6.0\\', \\'40.0\\', \\'18.0\\', \\'14.0\\', \\'7.0\\', \\'13.0\\', \\'23.0\\', \\'16.0\\', \\'21.0\\', \\'50.0\\', \\'11.0\\', \\'27.0\\', \\'9.0\\', \\'60.0\\', \\'70.0\\', \\'80.0\\' ) OR t2.\"poccig\" IS NULL ) AND ( t1.\"reference_date\" > 852038400.000000 ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * 0.7005540342173785 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * -0.3721815968225617 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 0.001514347782697983 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * 0.7143978295176737 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * 0.6208372807968181 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * -0.2854287364907576 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * 0.5421938420599178 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * -0.7689222625697328 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * -0.4929019826671734 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * 0.223780579913266 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * -0.2303339841644577 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * -0.08730665947172637 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * 3.578307299045961 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * 3.424298336831009 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * 1.469247538772654 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 2.007088802312646 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * 1.508201373333506 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * 1.694371256928238 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * 15.34300789713041 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * 10.98526961717351 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * 1.131391278158518 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * -1.552221861580311e-08 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * -2.200646489251252e-07 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * 0.2536479591053882 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.04144600919003553 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -10.71915452291016 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * -1.584720081647418 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * -1.712372865814812 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * -6.542237117486055e-08 + 6.6081461026926416e+00\\n WHEN ( t2.\"poccig\" NOT IN ( \\'0.0\\', \\'2.0\\', \\'1.0\\', \\'12.0\\', \\'10.0\\', \\'35.0\\', \\'25.0\\', \\'20.0\\', \\'15.0\\', \\'30.0\\', \\'5.0\\', \\'3.0\\', \\'28.0\\', \\'4.0\\', \\'22.0\\', \\'17.0\\', \\'8.0\\', \\'6.0\\', \\'40.0\\', \\'18.0\\', \\'14.0\\', \\'7.0\\', \\'13.0\\', \\'23.0\\', \\'16.0\\', \\'21.0\\', \\'50.0\\', \\'11.0\\', \\'27.0\\', \\'9.0\\', \\'60.0\\', \\'70.0\\', \\'80.0\\' ) OR t2.\"poccig\" IS NULL ) AND ( t1.\"reference_date\" <= 852038400.000000 OR t1.\"reference_date\" IS NULL ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * 6.963983784467057 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * 12.7201171250798 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 1.296534129086624 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * -3.178987445661821 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * 0.4173045416261214 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * 1.92693099294999 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * 0.6497416198716232 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * -2.934698113722341 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * 2.43950913153937 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * 1.47634891241367 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * -0.04727950911958443 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * 0.2284859457718159 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * -4.284780550478363 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * 5.640112799208898 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * 2.782893387299822 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 18.72747293890264 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * -11.08758851568889 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * -14.05642864637471 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * 15.29700767610475 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * -8.372507661585622 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * -3.785073697931573 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * -6.216702473867759e-07 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * 5.610046107243454e-07 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * 3.030242084663193 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.7262712487263796 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -3.932089120575836 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * -29.67762684885863 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * 1.104743672199196 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * 2.142548400344535e-07 + -7.5267732055428329e+00\\n ELSE NULL\\n END\\n) AS \"feature_1_2\",\\n t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"CONTR__STAGING_TABLE_2\" t2\\nON t1.\"ico\" = t2.\"ico\"\\nWHERE t2.\"control_date\" <= t1.\"reference_date\"\\nGROUP BY t1.rowid;'" + "'DROP TABLE IF EXISTS \"FEATURE_1_2\";\\n\\nCREATE TABLE \"FEATURE_1_2\" AS\\nSELECT SUM( \\n CASE\\n WHEN ( t2.\"zmkour\" IN ( \\'7\\' ) ) AND ( t1.\"reference_date\" > 852038400.000000 ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * 0.6992075915192288 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * -0.3722162052975991 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 0.002072665325539906 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * 0.7135789936249097 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * 0.6204509957556447 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * -0.2852251406284317 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * 0.5416045970884422 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * -0.7691376230609942 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * -0.4921979178739925 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * 0.2235547808836321 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * -0.2305456390285043 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * -0.08732404144815611 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * 3.578257813757872 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * 3.420605423460954 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * 1.468999926745384 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 2.006077827378471 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * 1.509295328270136 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * 1.689453613870151 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * 15.33082510657582 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * 10.97404738286759 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * 1.130351262906143 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * -1.551420482850177e-08 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * -2.196562174142811e-07 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * 0.2535134048176608 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.04134495275193414 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -10.72183117055155 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * -1.580592374193315 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * -1.709624186115308 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * -6.539468045584373e-08 + 6.5967578763439345e+00\\n WHEN ( t2.\"zmkour\" IN ( \\'7\\' ) ) AND ( t1.\"reference_date\" <= 852038400.000000 OR t1.\"reference_date\" IS NULL ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * 7.009110879137228 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * 13.11693059229238 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 1.269893142479568 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * -3.162664037823355 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * 0.3787421899628546 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * 1.971210134455975 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * 0.6635424419434129 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * -2.939270936123821 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * 2.383223854209837 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * 1.498266105418248 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * -0.0517718309107393 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * 0.2276783275568421 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * -4.23254041935678 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * 5.719150573247517 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * 2.70196864001066 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 18.79720042999637 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * -10.93785280575887 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * -13.52764654128384 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * 15.62103938182056 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * -8.797933019568511 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * -3.757872625493459 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * -6.356934996127425e-07 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * 5.640187513549882e-07 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * 3.023356785615304 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.7240192100118329 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -4.024059582855235 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * -29.58919968380793 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * 1.778411316422035 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * 2.163236433930246e-07 + -7.9015375976371462e+00\\n WHEN ( t2.\"zmkour\" NOT IN ( \\'7\\' ) OR t2.\"zmkour\" IS NULL ) AND ( t1.\"htrisk\" IN ( \\'6\\' ) ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * -3.397114537185319 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * -8.217148782839798 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 0.8989975149085871 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * -1.237315019051493 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * -0.9928276153180301 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * -2.788494436812396 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * -0.4818397334334769 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * 6.971936650721264 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * -3.694055970730235 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * -5.857003884016523 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * 2.110663998001987 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * 0.1191325417763164 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * -19.83587241194438 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * -0.6739075204128018 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * -2.401197249091638e-07 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 43.42691366138832 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * -188.452548812023 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * 149.8317616555433 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * -7.92533116285952 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * 7.371945970513856 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * -6.522018830745494 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * 3.705953810711698e-07 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * -1.783593808548376e-06 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * -0.03696650252475624 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.8978599033836492 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -2.613193795898457 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * 33.10188686443065 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * 2.062126624888299 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * -5.886932876564901e-10 + -1.0081350693584685e+02\\n WHEN ( t2.\"zmkour\" NOT IN ( \\'7\\' ) OR t2.\"zmkour\" IS NULL ) AND ( t1.\"htrisk\" NOT IN ( \\'6\\' ) OR t1.\"htrisk\" IS NULL ) THEN COALESCE( t1.\"age\" - 58.55541532813217, 0.0 ) * 0.002419801161207402 + COALESCE( t1.\"participation\" - -1887.414410279945, 0.0 ) * -0.06277853150672247 + COALESCE( t1.\"vyska\" - 174.626548875631, 0.0 ) * 0.002571038648386481 + COALESCE( t1.\"vaha\" - 80.15500229463056, 0.0 ) * -0.005578860882900542 + COALESCE( t1.\"syst1\" - 132.0390087195962, 0.0 ) * -0.001057898151656378 + COALESCE( t1.\"diast1\" - 83.58329508949059, 0.0 ) * 0.004014557721064409 + COALESCE( t1.\"syst2\" - 129.4263423588802, 0.0 ) * 0.001873173639385572 + COALESCE( t1.\"diast2\" - 83.2121385956861, 0.0 ) * 0.0002124825706128939 + COALESCE( t1.\"tric\" - 9.410853602569986, 0.0 ) * -0.002640732624754488 + COALESCE( t1.\"subsc\" - 18.22670949977054, 0.0 ) * 0.0001474535382830381 + COALESCE( t1.\"chlst\" - 232.7238412115649, 0.0 ) * -0.0001256948379644612 + COALESCE( t1.\"trigl\" - 134.5320100963745, 0.0 ) * -3.474245058868025e-05 + COALESCE( t1.\"koureni\" - 3.382973841211565, 0.0 ) * 0.0132708577852937 + COALESCE( t1.\"dobakour\" - 6.960188159706287, 0.0 ) * 0.002454283595539609 + COALESCE( t1.\"byvkurak\" - 1.689421753097751, 0.0 ) * -0.0003505117806351746 + COALESCE( t1.\"pivomn\" - 1.800826067003213, 0.0 ) * 0.02562835710639392 + COALESCE( t1.\"vinomn\" - 4.298990362551629, 0.0 ) * -0.005171308330700418 + COALESCE( t1.\"lihmn\" - 6.948141349242772, 0.0 ) * -0.004672335054095883 + COALESCE( t1.\"kava\" - 1.919343735658559, 0.0 ) * 0.02581134186668081 + COALESCE( t1.\"caj\" - 4.731642955484167, 0.0 ) * -0.01154259733921239 + COALESCE( t1.\"cukr\" - 4.495869664983938, 0.0 ) * 0.002194525846818011 + COALESCE( t1.\"reference_date\" - 614493572.4644332, 0.0 ) * 3.411798694161753e-09 + COALESCE( t1.\"entry_date\" - 230484053.9697109, 0.0 ) * -2.075596685480708e-09 + COALESCE( t2.\"hmot\" - 81.27387640449439, 0.0 ) * 0.004478272836447239 + COALESCE( t2.\"hdlmg\" - 35.46147672552167, 0.0 ) * -0.002438061446690539 + COALESCE( t2.\"chlst\" - 5.892755818619529, 0.0 ) * -0.02099226608599071 + COALESCE( t2.\"hdl\" - 0.9149819422150931, 0.0 ) * 0.03134631902567388 + COALESCE( t2.\"ldl\" - 2.463498194221503, 0.0 ) * 0.02487247493931399 + COALESCE( t2.\"control_date\" - 533301113.6436597, 0.0 ) * -2.655718509874403e-09 + -2.7439158467228797e-01\\n ELSE NULL\\n END\\n) AS \"feature_1_2\",\\n t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"CONTR__STAGING_TABLE_2\" t2\\nON t1.\"ico\" = t2.\"ico\"\\nWHERE t2.\"control_date\" <= t1.\"reference_date\"\\nGROUP BY t1.rowid;'" ] }, "execution_count": 17, @@ -13982,27 +13986,11 @@ "data": { "text/markdown": [ "```sql\n", - "DROP TABLE IF EXISTS \"FEATURE_1_3\";\n", "\n", - "CREATE TABLE \"FEATURE_1_3\" AS\n", - "SELECT AVG( \n", - " CASE\n", - " WHEN ( t1.\"reference_date\" - t2.\"control_date\" > 615123663.157895 ) THEN 9.667613221179364\n", - " WHEN ( t1.\"reference_date\" - t2.\"control_date\" <= 615123663.157895 OR t1.\"reference_date\" IS NULL OR t2.\"control_date\" IS NULL ) AND ( t2.\"zmkour\" IN ( '4', '1', '2', '8' ) ) THEN 0.0681595669898879\n", - " WHEN ( t1.\"reference_date\" - t2.\"control_date\" <= 615123663.157895 OR t1.\"reference_date\" IS NULL OR t2.\"control_date\" IS NULL ) AND ( t2.\"zmkour\" NOT IN ( '4', '1', '2', '8' ) OR t2.\"zmkour\" IS NULL ) THEN 2.081264699633921\n", - " ELSE NULL\n", - " END\n", - ") AS \"feature_1_3\",\n", - " t1.rowid AS rownum\n", - "FROM \"POPULATION__STAGING_TABLE_1\" t1\n", - "INNER JOIN \"CONTR__STAGING_TABLE_2\" t2\n", - "ON t1.\"ico\" = t2.\"ico\"\n", - "WHERE t2.\"control_date\" <= t1.\"reference_date\"\n", - "GROUP BY t1.rowid;\n", "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"FEATURE_1_3\";\\n\\nCREATE TABLE \"FEATURE_1_3\" AS\\nSELECT AVG( \\n CASE\\n WHEN ( t1.\"reference_date\" - t2.\"control_date\" > 615123663.157895 ) THEN 9.667613221179364\\n WHEN ( t1.\"reference_date\" - t2.\"control_date\" <= 615123663.157895 OR t1.\"reference_date\" IS NULL OR t2.\"control_date\" IS NULL ) AND ( t2.\"zmkour\" IN ( \\'4\\', \\'1\\', \\'2\\', \\'8\\' ) ) THEN 0.0681595669898879\\n WHEN ( t1.\"reference_date\" - t2.\"control_date\" <= 615123663.157895 OR t1.\"reference_date\" IS NULL OR t2.\"control_date\" IS NULL ) AND ( t2.\"zmkour\" NOT IN ( \\'4\\', \\'1\\', \\'2\\', \\'8\\' ) OR t2.\"zmkour\" IS NULL ) THEN 2.081264699633921\\n ELSE NULL\\n END\\n) AS \"feature_1_3\",\\n t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"CONTR__STAGING_TABLE_2\" t2\\nON t1.\"ico\" = t2.\"ico\"\\nWHERE t2.\"control_date\" <= t1.\"reference_date\"\\nGROUP BY t1.rowid;'" + "''" ] }, "execution_count": 18, @@ -14031,7 +14019,7 @@ "source": [ "# Creates a folder named atherosclerosis_pipeline containing\n", "# the SQL code.\n", - "pipe2.features.to_sql().save(\"atherosclerosis_pipeline\")" + "pipe2.features.to_sql(size_threshold=None).save(\"atherosclerosis_pipeline\", remove=True)" ] }, { @@ -14040,7 +14028,7 @@ "metadata": {}, "outputs": [], "source": [ - "pipe2.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"atherosclerosis_pipeline_spark\")" + "pipe2.features.to_sql(dialect=getml.pipeline.dialect.spark_sql, size_threshold=None).save(\"atherosclerosis_pipeline_spark\", remove=True)" ] }, { diff --git a/baseball.ipynb b/baseball.ipynb index cac42a1..291b00d 100644 --- a/baseball.ipynb +++ b/baseball.ipynb @@ -104,27 +104,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220321223057.log.\n", + "getML engine is already running.\n", "\n", "\n", - "Loading pipelines...\n", - "[========================================] 100%\n", - "\n", "\n", - "Connected to project 'baseball'\n" + "Connected to project 'baseball'\n", + "http://localhost:1709/#/listprojects/baseball/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/baseball/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -38461,19 +38447,19 @@ " \n", " \n", " \n", - " SH\n", + " yearID\n", " \n", " \n", " \n", - " SF\n", + " SH\n", " \n", " \n", " \n", - " GIDP\n", + " SF\n", " \n", " \n", " \n", - " yearID\n", + " GIDP\n", " \n", " \n", " \n", @@ -39069,7 +39055,7 @@ " \n", " \n", " \n", - " nan2004 \n", @@ -39108,7 +39094,7 @@ " \n", " \n", " \n", - " 2004nan \n", @@ -39441,7 +39427,7 @@ " \n", " \n", " \n", - " nan2006 \n", @@ -39480,7 +39466,7 @@ " \n", " \n", " \n", - " 2006nan \n", @@ -39813,7 +39799,7 @@ " \n", " \n", " \n", - " nan2007 \n", @@ -39852,7 +39838,7 @@ " \n", " \n", " \n", - " 2007nan \n", @@ -40185,7 +40171,7 @@ " \n", " \n", " \n", - " nan2008 \n", @@ -40224,7 +40210,7 @@ " \n", " \n", " \n", - " 2008nan \n", @@ -40557,7 +40543,7 @@ " \n", " \n", " \n", - " nan2009 \n", @@ -40596,7 +40582,7 @@ " \n", " \n", " \n", - " 2009nan \n", @@ -41301,7 +41287,7 @@ " \n", " \n", " \n", - " nan1955 \n", @@ -41340,7 +41326,7 @@ " \n", " \n", " \n", - " 1955nan \n", @@ -41673,7 +41659,7 @@ " \n", " \n", " \n", - " nan1956 \n", @@ -41712,7 +41698,7 @@ " \n", " \n", " \n", - " 1956nan \n", @@ -42045,7 +42031,7 @@ " \n", " \n", " \n", - " nan1957 \n", @@ -42084,7 +42070,7 @@ " \n", " \n", " \n", - " 1957nan \n", @@ -42417,7 +42403,7 @@ " \n", " \n", " \n", - " nan1958 \n", @@ -42456,7 +42442,7 @@ " \n", " \n", " \n", - " 1958nan \n", @@ -42789,7 +42775,7 @@ " \n", " \n", " \n", - " nan1959 \n", @@ -42828,7 +42814,7 @@ " \n", " \n", " \n", - " 1959nan \n", @@ -42849,35 +42835,35 @@ "

\n" ], "text/plain": [ - " name year playerID teamID lgID ... R SH SF\n", + " name year playerID teamID lgID ... R yearID SH\n", " role time_stamp join_key join_key categorical ... numerical unused_float unused_float\n", " unit time stamp, comparison only ... \n", - " 0 2004-01-01 aardsda01 SFN NL ... 8 nan nan \n", - " 1 2006-01-01 aardsda01 CHN NL ... 25 nan nan \n", - " 2 2007-01-01 aardsda01 CHA AL ... 24 nan nan \n", - " 3 2008-01-01 aardsda01 BOS AL ... 32 nan nan \n", - " 4 2009-01-01 aardsda01 SEA AL ... 23 nan nan \n", + " 0 2004-01-01 aardsda01 SFN NL ... 8 2004 nan \n", + " 1 2006-01-01 aardsda01 CHN NL ... 25 2006 nan \n", + " 2 2007-01-01 aardsda01 CHA AL ... 24 2007 nan \n", + " 3 2008-01-01 aardsda01 BOS AL ... 32 2008 nan \n", + " 4 2009-01-01 aardsda01 SEA AL ... 23 2009 nan \n", " ... ... ... ... ... ... ...\n", - "39356 1955-01-01 zuverge01 BAL AL ... 28 nan nan \n", - "39357 1956-01-01 zuverge01 BAL AL ... 52 nan nan \n", - "39358 1957-01-01 zuverge01 BAL AL ... 37 nan nan \n", - "39359 1958-01-01 zuverge01 BAL AL ... 29 nan nan \n", - "39360 1959-01-01 zuverge01 BAL AL ... 7 nan nan \n", + "39356 1955-01-01 zuverge01 BAL AL ... 28 1955 nan \n", + "39357 1956-01-01 zuverge01 BAL AL ... 52 1956 nan \n", + "39358 1957-01-01 zuverge01 BAL AL ... 37 1957 nan \n", + "39359 1958-01-01 zuverge01 BAL AL ... 29 1958 nan \n", + "39360 1959-01-01 zuverge01 BAL AL ... 7 1959 nan \n", "\n", - " name GIDP yearID\n", + " name SF GIDP\n", " role unused_float unused_float\n", " unit \n", - " 0 nan 2004\n", - " 1 nan 2006\n", - " 2 nan 2007\n", - " 3 nan 2008\n", - " 4 nan 2009\n", + " 0 nan nan \n", + " 1 nan nan \n", + " 2 nan nan \n", + " 3 nan nan \n", + " 4 nan nan \n", " ... ...\n", - "39356 nan 1955\n", - "39357 nan 1956\n", - "39358 nan 1957\n", - "39359 nan 1958\n", - "39360 nan 1959\n", + "39356 nan nan \n", + "39357 nan nan \n", + "39358 nan nan \n", + "39359 nan nan \n", + "39360 nan nan \n", "\n", "\n", "39361 rows x 31 columns\n", @@ -49048,7 +49034,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['allstarfull', 'awardsplayers', 'awardsshareplayers', 'batting',\n", " 'battingpost', 'fielding', 'fieldingpost', 'pitching', 'pitchingpost'],\n", " predictors=['XGBoostRegressor'],\n", @@ -49061,7 +49047,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['allstarfull', 'awardsplayers', 'awardsshareplayers', 'batting',\n", " 'battingpost', 'fielding', 'fieldingpost', 'pitching', 'pitchingpost'],\n", " predictors=['XGBoostRegressor'],\n", @@ -49100,7 +49086,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['allstarfull', 'awardsplayers', 'awardsshareplayers', 'batting',\n", " 'battingpost', 'fielding', 'fieldingpost', 'pitching', 'pitchingpost'],\n", " predictors=['XGBoostRegressor'],\n", @@ -49113,7 +49099,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['allstarfull', 'awardsplayers', 'awardsshareplayers', 'batting',\n", " 'battingpost', 'fielding', 'fieldingpost', 'pitching', 'pitchingpost'],\n", " predictors=['XGBoostRegressor'],\n", @@ -49207,6 +49193,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining SALARIES__STAGING_TABLE_1 and ALLSTARFULL__STAGING_TABLE_2 over 'playerID' and 'playerID', there are no corresponding entries for 64.710317% of entries in 'playerID' in 'SALARIES__STAGING_TABLE_1'. You might want to double-check your join keys.\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining SALARIES__STAGING_TABLE_1 and AWARDSPLAYERS__STAGING_TABLE_3 over 'playerID' and 'playerID', there are no corresponding entries for 75.376911% of entries in 'playerID' in 'SALARIES__STAGING_TABLE_1'. You might want to double-check your join keys.\n", @@ -49236,7 +49225,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:2m:42.325598\n", + "Time taken: 0h:1m:19.394197\n", "\n" ] }, @@ -49247,28 +49236,28 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['allstarfull', 'awardsplayers', 'awardsshareplayers', 'batting',\n", " 'battingpost', 'fielding', 'fieldingpost', 'pitching', 'pitchingpost'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-LPegHo'])
url: http://localhost:1709/#/getpipeline/baseball/BytunZ/0/
" + " tags=['fast_prop', 'container-jIFfio'])
url: http://localhost:1709/#/getpipeline/baseball/hZI4d8/0/
" ], "text/plain": [ "Pipeline(data_model='salaries',\n", " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['allstarfull', 'awardsplayers', 'awardsshareplayers', 'batting',\n", " 'battingpost', 'fielding', 'fieldingpost', 'pitching', 'pitchingpost'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-LPegHo'])\n", + " tags=['fast_prop', 'container-jIFfio'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/baseball/BytunZ/0/" + "url: http://localhost:1709/#/getpipeline/baseball/hZI4d8/0/" ] }, "execution_count": 31, @@ -49333,6 +49322,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining SALARIES__STAGING_TABLE_1 and ALLSTARFULL__STAGING_TABLE_2 over 'playerID' and 'playerID', there are no corresponding entries for 64.710317% of entries in 'playerID' in 'SALARIES__STAGING_TABLE_1'. You might want to double-check your join keys.\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining SALARIES__STAGING_TABLE_1 and AWARDSPLAYERS__STAGING_TABLE_3 over 'playerID' and 'playerID', there are no corresponding entries for 75.376911% of entries in 'playerID' in 'SALARIES__STAGING_TABLE_1'. You might want to double-check your join keys.\n", @@ -49362,7 +49354,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:2m:28.115219\n", + "Time taken: 0h:1m:56.864662\n", "\n" ] }, @@ -49373,28 +49365,28 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['allstarfull', 'awardsplayers', 'awardsshareplayers', 'batting',\n", " 'battingpost', 'fielding', 'fieldingpost', 'pitching', 'pitchingpost'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['relboost', 'container-LPegHo'])
url: http://localhost:1709/#/getpipeline/baseball/KxaP0T/0/
" + " tags=['relboost', 'container-jIFfio'])
url: http://localhost:1709/#/getpipeline/baseball/P0fptB/0/
" ], "text/plain": [ "Pipeline(data_model='salaries',\n", " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['allstarfull', 'awardsplayers', 'awardsshareplayers', 'batting',\n", " 'battingpost', 'fielding', 'fieldingpost', 'pitching', 'pitchingpost'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['relboost', 'container-LPegHo'])\n", + " tags=['relboost', 'container-jIFfio'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/baseball/KxaP0T/0/" + "url: http://localhost:1709/#/getpipeline/baseball/P0fptB/0/" ] }, "execution_count": 33, @@ -49512,7 +49504,7 @@ " 0\n", " \n", " \n", - " 2022-03-21 22:33:59\n", + " 2022-07-04 20:52:24\n", " \n", " \n", " \n", @@ -49524,15 +49516,15 @@ " \n", " \n", " \n", - " 693195.922\n", + " 692250.5237\n", " \n", " \n", " \n", - " 1251354.7312\n", + " 1243487.0958\n", " \n", " \n", " \n", - " 0.8222\n", + " 0.8247\n", " \n", " \n", " \n", @@ -49541,7 +49533,7 @@ " 1\n", " \n", " \n", - " 2022-03-21 22:36:38\n", + " 2022-07-04 20:54:28\n", " \n", " \n", " \n", @@ -49553,15 +49545,15 @@ " \n", " \n", " \n", - " 765292.554\n", + " 763597.0323\n", " \n", " \n", " \n", - " 1402960.9303\n", + " 1395836.3007\n", " \n", " \n", " \n", - " 0.788\n", + " 0.7902\n", " \n", " \n", " \n", @@ -49571,8 +49563,8 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-21 22:33:59 train salary 693195.922 1251354.7312 0.8222\n", - "1 2022-03-21 22:36:38 test salary 765292.554 1402960.9303 0.788 " + "0 2022-07-04 20:52:24 train salary 692250.5237 1243487.0958 0.8247\n", + "1 2022-07-04 20:54:28 test salary 763597.0323 1395836.3007 0.7902" ] }, "execution_count": 34, @@ -49678,7 +49670,7 @@ " 0\n", " \n", " \n", - " 2022-03-21 22:36:28\n", + " 2022-07-04 20:54:22\n", " \n", " \n", " \n", @@ -49690,15 +49682,15 @@ " \n", " \n", " \n", - " 461650.473\n", + " 455013.562\n", " \n", " \n", " \n", - " 798183.2356\n", + " 791428.479\n", " \n", " \n", " \n", - " 0.9276\n", + " 0.9287\n", " \n", " \n", " \n", @@ -49707,7 +49699,7 @@ " 1\n", " \n", " \n", - " 2022-03-21 22:36:42\n", + " 2022-07-04 20:54:33\n", " \n", " \n", " \n", @@ -49719,15 +49711,15 @@ " \n", " \n", " \n", - " 668857.0542\n", + " 669614.4093\n", " \n", " \n", " \n", - " 1229058.3564\n", + " 1232280.2312\n", " \n", " \n", " \n", - " 0.8371\n", + " 0.8363\n", " \n", " \n", " \n", @@ -49737,8 +49729,8 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-21 22:36:28 train salary 461650.473 798183.2356 0.9276\n", - "1 2022-03-21 22:36:42 test salary 668857.0542 1229058.3564 0.8371" + "0 2022-07-04 20:54:22 train salary 455013.562 791428.479 0.9287\n", + "1 2022-07-04 20:54:33 test salary 669614.4093 1232280.2312 0.8363" ] }, "execution_count": 35, @@ -258416,7 +258408,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Imputation'],\n", @@ -258428,7 +258420,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Imputation'],\n", @@ -258509,7 +258501,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:56.683629\n", + "Time taken: 0h:0m:48.45522\n", "\n" ] }, @@ -258520,26 +258512,26 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Imputation'],\n", " share_selected_features=0.5,\n", - " tags=['featuretools'])
url: http://localhost:1709/#/getpipeline/baseball/xR9Dtg/0/
" + " tags=['featuretools'])
url: http://localhost:1709/#/getpipeline/baseball/rQXinV/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Imputation'],\n", " share_selected_features=0.5,\n", " tags=['featuretools'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/baseball/xR9Dtg/0/" + "url: http://localhost:1709/#/getpipeline/baseball/rQXinV/0/" ] }, "execution_count": 61, @@ -258640,7 +258632,7 @@ " 0\n", " \n", " \n", - " 2022-03-21 22:44:15\n", + " 2022-07-04 21:02:01\n", " \n", " \n", " \n", @@ -258669,7 +258661,7 @@ " 1\n", " \n", " \n", - " 2022-03-21 22:44:20\n", + " 2022-07-04 21:02:03\n", " \n", " \n", " \n", @@ -258699,8 +258691,8 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-21 22:44:15 featuretools_train salary 704893.7458 1288741.874 0.8128\n", - "1 2022-03-21 22:44:20 featuretools_test salary 776053.9972 1445682.6312 0.775 " + "0 2022-07-04 21:02:01 featuretools_train salary 704893.7458 1288741.874 0.8128\n", + "1 2022-07-04 21:02:03 featuretools_test salary 776053.9972 1445682.6312 0.775 " ] }, "execution_count": 62, @@ -258764,263 +258756,15 @@ "data": { "text/markdown": [ "```sql\n", + "-- The size of the SQL code for FEATURE_1_1 is 139595 characters, which is greater than the size_threshold of 50000!\n", + "-- To display very long features like this anyway, increase the size_threshold or set the size_threshold to None.\n", "DROP TABLE IF EXISTS \"FEATURE_1_1\";\n", "\n", - "CREATE TABLE \"FEATURE_1_1\" AS\n", - "SELECT SUM( \n", - " CASE\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" > 3524469.221923 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2817578.941387 ) AND ( t2.\"sf\" > 4.000000 ) AND ( t1.\"teamidcat__mapping_target_1_avg\" > 3278373.379265 ) THEN 17831446.69388786\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" > 3524469.221923 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2817578.941387 ) AND ( t2.\"sf\" > 4.000000 ) AND ( t1.\"teamidcat__mapping_target_1_avg\" <= 3278373.379265 OR t1.\"teamidcat__mapping_target_1_avg\" IS NULL ) THEN 13033019.84917508\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" > 3524469.221923 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2817578.941387 ) AND ( t2.\"sf\" <= 4.000000 OR t2.\"sf\" IS NULL ) THEN 10225458.61579112\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" > 3524469.221923 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2817578.941387 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) THEN 4368014.022868423\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" <= 3524469.221923 OR t2.\"3b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"so\" > 133.000000 ) AND ( t2.\"g\" > 158.000000 ) THEN 8308478.870471586\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" <= 3524469.221923 OR t2.\"3b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"so\" > 133.000000 ) AND ( t2.\"g\" <= 158.000000 OR t2.\"g\" IS NULL ) THEN 13381597.54436921\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" <= 3524469.221923 OR t2.\"3b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"so\" <= 133.000000 OR t2.\"so\" IS NULL ) AND ( t2.\"r\" > 125.000000 ) AND ( t1.\"yearid\" > 2007.000000 ) THEN 4780941.209306366\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" <= 3524469.221923 OR t2.\"3b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"so\" <= 133.000000 OR t2.\"so\" IS NULL ) AND ( t2.\"r\" > 125.000000 ) AND ( t1.\"yearid\" <= 2007.000000 OR t1.\"yearid\" IS NULL ) THEN 797450.006041609\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" <= 3524469.221923 OR t2.\"3b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"so\" <= 133.000000 OR t2.\"so\" IS NULL ) AND ( t2.\"r\" <= 125.000000 OR t2.\"r\" IS NULL ) AND ( t2.\"ibb\" > 5.000000 ) THEN -1405503.972683714\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" <= 3524469.221923 OR t2.\"3b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"so\" <= 133.000000 OR t2.\"so\" IS NULL ) AND ( t2.\"r\" <= 125.000000 OR t2.\"r\" IS NULL ) AND ( t2.\"ibb\" <= 5.000000 OR t2.\"ibb\" IS NULL ) THEN -2977321.709443686\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'PHI', 'CHA', 'CLE', 'PIT', 'TOR', 'FLO', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'WAS' ) ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 10547907.624393 ) AND ( t1.\"year\" - t2.\"year\" > 315576000.000000 ) THEN -12335562.02567152\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'PHI', 'CHA', 'CLE', 'PIT', 'TOR', 'FLO', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'WAS' ) ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 10547907.624393 ) AND ( t1.\"year\" - t2.\"year\" <= 315576000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN -6638308.479956674\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'PHI', 'CHA', 'CLE', 'PIT', 'TOR', 'FLO', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'WAS' ) ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 10547907.624393 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 7667817.821429 ) THEN 5183399.795475774\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'PHI', 'CHA', 'CLE', 'PIT', 'TOR', 'FLO', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'WAS' ) ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 10547907.624393 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 7667817.821429 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 159.000000 ) THEN -1420930.177117448\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'PHI', 'CHA', 'CLE', 'PIT', 'TOR', 'FLO', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'WAS' ) ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 10547907.624393 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 7667817.821429 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 159.000000 OR t2.\"g\" IS NULL ) THEN -7764.261980338949\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'PHI', 'CHA', 'CLE', 'PIT', 'TOR', 'FLO', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 7787763.421053 ) AND ( t2.\"rbi\" > 114.000000 ) AND ( t2.\"gidp\" > 17.000000 ) THEN -1458760.064980729\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'PHI', 'CHA', 'CLE', 'PIT', 'TOR', 'FLO', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 7787763.421053 ) AND ( t2.\"rbi\" > 114.000000 ) AND ( t2.\"gidp\" <= 17.000000 OR t2.\"gidp\" IS NULL ) THEN 2140816.005140664\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'PHI', 'CHA', 'CLE', 'PIT', 'TOR', 'FLO', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 7787763.421053 ) AND ( t2.\"rbi\" <= 114.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"rbi\" > 109.000000 ) THEN 7908513.647891598\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'PHI', 'CHA', 'CLE', 'PIT', 'TOR', 'FLO', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 7787763.421053 ) AND ( t2.\"rbi\" <= 114.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"rbi\" <= 109.000000 OR t2.\"rbi\" IS NULL ) THEN 5355025.682490992\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'PHI', 'CHA', 'CLE', 'PIT', 'TOR', 'FLO', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 7787763.421053 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"3b\" > 0.000000 ) AND ( t2.\"gidp__mapping_4_target_1_avg\" > 5161728.874583 ) THEN 1734029.449810291\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'PHI', 'CHA', 'CLE', 'PIT', 'TOR', 'FLO', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 7787763.421053 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"3b\" > 0.000000 ) AND ( t2.\"gidp__mapping_4_target_1_avg\" <= 5161728.874583 OR t2.\"gidp__mapping_4_target_1_avg\" IS NULL ) THEN 16736.0820059774\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'PHI', 'CHA', 'CLE', 'PIT', 'TOR', 'FLO', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 7787763.421053 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"3b\" <= 0.000000 OR t2.\"3b\" IS NULL ) AND ( t2.\"so\" > 101.000000 ) THEN 592324.4282805945\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'PHI', 'CHA', 'CLE', 'PIT', 'TOR', 'FLO', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 7787763.421053 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"3b\" <= 0.000000 OR t2.\"3b\" IS NULL ) AND ( t2.\"so\" <= 101.000000 OR t2.\"so\" IS NULL ) THEN 6297964.528270307\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" > 4.000000 ) AND ( t2.\"hr\" > 52.000000 ) AND ( t1.\"yearid\" > 2009.000000 ) THEN -2469058.761296282\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" > 4.000000 ) AND ( t2.\"hr\" > 52.000000 ) AND ( t1.\"yearid\" <= 2009.000000 OR t1.\"yearid\" IS NULL ) THEN -342969.1731427578\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" > 4.000000 ) AND ( t2.\"hr\" <= 52.000000 OR t2.\"hr\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'CHA', 'NYN', 'SLN', 'CLE', 'PIT', 'SDN', 'TOR', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'LAA', 'OAK', 'TBA', 'LAN', 'SEA', 'MIL', 'WAS' ) ) THEN 2286106.374797928\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" > 4.000000 ) AND ( t2.\"hr\" <= 52.000000 OR t2.\"hr\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'CHA', 'NYN', 'SLN', 'CLE', 'PIT', 'SDN', 'TOR', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'LAA', 'OAK', 'TBA', 'LAN', 'SEA', 'MIL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) THEN 2943768.288048326\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" <= 4.000000 OR t2.\"gidp\" IS NULL ) AND ( t1.\"teamidcat__mapping_target_1_avg\" > 2348903.676268 ) AND ( t1.\"year\" - t2.\"year\" > 110419200.000000 ) THEN 11337799.38868177\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" <= 4.000000 OR t2.\"gidp\" IS NULL ) AND ( t1.\"teamidcat__mapping_target_1_avg\" > 2348903.676268 ) AND ( t1.\"year\" - t2.\"year\" <= 110419200.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN 11938856.72068626\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" <= 4.000000 OR t2.\"gidp\" IS NULL ) AND ( t1.\"teamidcat__mapping_target_1_avg\" <= 2348903.676268 OR t1.\"teamidcat__mapping_target_1_avg\" IS NULL ) AND ( t2.\"hr\" > 40.000000 ) THEN 5717192.316234716\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" <= 4.000000 OR t2.\"gidp\" IS NULL ) AND ( t1.\"teamidcat__mapping_target_1_avg\" <= 2348903.676268 OR t1.\"teamidcat__mapping_target_1_avg\" IS NULL ) AND ( t2.\"hr\" <= 40.000000 OR t2.\"hr\" IS NULL ) THEN 412127.935812093\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" > 5.000000 ) AND ( t1.\"year\" - t2.\"year\" > 78840000.000000 ) AND ( t2.\"lgid\" IN ( 'NL' ) ) THEN 12637045.6269819\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" > 5.000000 ) AND ( t1.\"year\" - t2.\"year\" > 78840000.000000 ) AND ( t2.\"lgid\" NOT IN ( 'NL' ) OR t2.\"lgid\" IS NULL ) THEN 9943870.374674743\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" > 5.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 78840000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"cs__mapping_4_target_1_avg\" > 2599268.209696 ) THEN 5853149.807168381\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" > 5.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 78840000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"cs__mapping_4_target_1_avg\" <= 2599268.209696 OR t2.\"cs__mapping_4_target_1_avg\" IS NULL ) THEN 10297123.203706\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" <= 5.000000 OR t2.\"hbp\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'HOU', 'LAA' ) ) AND ( t2.\"lgid\" IN ( 'NL' ) ) THEN 2301092.665443173\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" <= 5.000000 OR t2.\"hbp\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'HOU', 'LAA' ) ) AND ( t2.\"lgid\" NOT IN ( 'NL' ) OR t2.\"lgid\" IS NULL ) THEN 1137106.222470851\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" <= 5.000000 OR t2.\"hbp\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'HOU', 'LAA' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sh\" > 1.000000 ) THEN 6594427.200743338\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" <= 5.000000 OR t2.\"hbp\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'HOU', 'LAA' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sh\" <= 1.000000 OR t2.\"sh\" IS NULL ) THEN 4269536.548606931\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'PHI', 'CHA', 'CLE', 'SFN', 'MON', 'HOU', 'CHN', 'ANA', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'COL', 'MIL' ) ) AND ( t1.\"teamidcat\" IN ( 'MON', 'SEA' ) ) AND ( t2.\"sf\" > 10.000000 ) THEN 4953529.841573869\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'PHI', 'CHA', 'CLE', 'SFN', 'MON', 'HOU', 'CHN', 'ANA', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'COL', 'MIL' ) ) AND ( t1.\"teamidcat\" IN ( 'MON', 'SEA' ) ) AND ( t2.\"sf\" <= 10.000000 OR t2.\"sf\" IS NULL ) THEN 759470.5739710848\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'PHI', 'CHA', 'CLE', 'SFN', 'MON', 'HOU', 'CHN', 'ANA', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'COL', 'MIL' ) ) AND ( t1.\"teamidcat\" NOT IN ( 'MON', 'SEA' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"yearid\" > 2000.000000 ) THEN 2124673.314738019\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'PHI', 'CHA', 'CLE', 'SFN', 'MON', 'HOU', 'CHN', 'ANA', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'COL', 'MIL' ) ) AND ( t1.\"teamidcat\" NOT IN ( 'MON', 'SEA' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"yearid\" <= 2000.000000 OR t1.\"yearid\" IS NULL ) THEN 1628674.49788051\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'PHI', 'CHA', 'CLE', 'SFN', 'MON', 'HOU', 'CHN', 'ANA', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 7594790.985034 ) AND ( t2.\"so__mapping_4_target_1_avg\" > 5623273.796491 ) THEN 9887623.989501275\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'PHI', 'CHA', 'CLE', 'SFN', 'MON', 'HOU', 'CHN', 'ANA', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 7594790.985034 ) AND ( t2.\"so__mapping_4_target_1_avg\" <= 5623273.796491 OR t2.\"so__mapping_4_target_1_avg\" IS NULL ) THEN 4015018.961425884\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'PHI', 'CHA', 'CLE', 'SFN', 'MON', 'HOU', 'CHN', 'ANA', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 7594790.985034 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" > 19.000000 ) THEN -2903895.573442627\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'PHI', 'CHA', 'CLE', 'SFN', 'MON', 'HOU', 'CHN', 'ANA', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 7594790.985034 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" <= 19.000000 OR t2.\"hbp\" IS NULL ) THEN 2655399.300956024\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'DET', 'PIT', 'TOR', 'MON', 'HOU', 'ANA', 'TEX', 'KCA', 'NYA', 'MIL' ) ) AND ( t1.\"teamidcat\" IN ( 'TOR', 'MON', 'TEX', 'NYA', 'MIL' ) ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 6837325.738196 ) THEN 74519.11647139768\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'DET', 'PIT', 'TOR', 'MON', 'HOU', 'ANA', 'TEX', 'KCA', 'NYA', 'MIL' ) ) AND ( t1.\"teamidcat\" IN ( 'TOR', 'MON', 'TEX', 'NYA', 'MIL' ) ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 6837325.738196 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) THEN -2656056.44245984\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'DET', 'PIT', 'TOR', 'MON', 'HOU', 'ANA', 'TEX', 'KCA', 'NYA', 'MIL' ) ) AND ( t1.\"teamidcat\" NOT IN ( 'TOR', 'MON', 'TEX', 'NYA', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"h\" > 156.000000 ) THEN 807713.0075840863\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'DET', 'PIT', 'TOR', 'MON', 'HOU', 'ANA', 'TEX', 'KCA', 'NYA', 'MIL' ) ) AND ( t1.\"teamidcat\" NOT IN ( 'TOR', 'MON', 'TEX', 'NYA', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"h\" <= 156.000000 OR t2.\"h\" IS NULL ) THEN -322806.8891217232\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'DET', 'PIT', 'TOR', 'MON', 'HOU', 'ANA', 'TEX', 'KCA', 'NYA', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so__mapping_4_target_1_avg\" > 2767704.524823 ) AND ( t2.\"so\" > 86.000000 ) THEN 1375092.34227541\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'DET', 'PIT', 'TOR', 'MON', 'HOU', 'ANA', 'TEX', 'KCA', 'NYA', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so__mapping_4_target_1_avg\" > 2767704.524823 ) AND ( t2.\"so\" <= 86.000000 OR t2.\"so\" IS NULL ) THEN 3548323.987237754\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'DET', 'PIT', 'TOR', 'MON', 'HOU', 'ANA', 'TEX', 'KCA', 'NYA', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so__mapping_4_target_1_avg\" <= 2767704.524823 OR t2.\"so__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 81.000000 ) THEN -2861693.372766722\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'DET', 'PIT', 'TOR', 'MON', 'HOU', 'ANA', 'TEX', 'KCA', 'NYA', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so__mapping_4_target_1_avg\" <= 2767704.524823 OR t2.\"so__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 81.000000 OR t2.\"r\" IS NULL ) THEN 2427780.671129029\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) ) AND ( t1.\"teamidcat\" IN ( 'DET', 'FLO' ) ) AND ( t2.\"g\" > 149.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 980878.920716 ) THEN -2066963.654041064\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) ) AND ( t1.\"teamidcat\" IN ( 'DET', 'FLO' ) ) AND ( t2.\"g\" > 149.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 980878.920716 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) THEN -2561457.314695126\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) ) AND ( t1.\"teamidcat\" IN ( 'DET', 'FLO' ) ) AND ( t2.\"g\" <= 149.000000 OR t2.\"g\" IS NULL ) THEN -56939.49897956364\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) ) AND ( t1.\"teamidcat\" NOT IN ( 'DET', 'FLO' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"2b\" > 40.000000 ) AND ( t2.\"ibb\" > 11.000000 ) THEN -756334.6424572867\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) ) AND ( t1.\"teamidcat\" NOT IN ( 'DET', 'FLO' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"2b\" > 40.000000 ) AND ( t2.\"ibb\" <= 11.000000 OR t2.\"ibb\" IS NULL ) THEN -2482246.014018427\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) ) AND ( t1.\"teamidcat\" NOT IN ( 'DET', 'FLO' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"2b\" <= 40.000000 OR t2.\"2b\" IS NULL ) AND ( t2.\"gidp__mapping_4_target_1_avg\" > 2924844.781863 ) THEN -264734.4104444506\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) ) AND ( t1.\"teamidcat\" NOT IN ( 'DET', 'FLO' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"2b\" <= 40.000000 OR t2.\"2b\" IS NULL ) AND ( t2.\"gidp__mapping_4_target_1_avg\" <= 2924844.781863 OR t2.\"gidp__mapping_4_target_1_avg\" IS NULL ) THEN -1275854.008238457\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" > 8.000000 ) AND ( t2.\"r__mapping_4_target_1_avg\" > 6862400.153388 ) AND ( t2.\"ab\" > 598.000000 ) THEN 2930711.219524218\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" > 8.000000 ) AND ( t2.\"r__mapping_4_target_1_avg\" > 6862400.153388 ) AND ( t2.\"ab\" <= 598.000000 OR t2.\"ab\" IS NULL ) THEN 888491.7479723304\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" > 8.000000 ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 6862400.153388 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"3b\" > 6.000000 ) THEN -1968940.528744845\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" > 8.000000 ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 6862400.153388 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"3b\" <= 6.000000 OR t2.\"3b\" IS NULL ) THEN -434760.8464144517\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" <= 8.000000 OR t2.\"sb\" IS NULL ) AND ( t1.\"yearid\" > 1993.000000 ) AND ( t2.\"sb\" > 0.000000 ) THEN 787722.9288528051\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" <= 8.000000 OR t2.\"sb\" IS NULL ) AND ( t1.\"yearid\" > 1993.000000 ) AND ( t2.\"sb\" <= 0.000000 OR t2.\"sb\" IS NULL ) THEN 1741508.752292541\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" <= 8.000000 OR t2.\"sb\" IS NULL ) AND ( t1.\"yearid\" <= 1993.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"3b\" > 3.000000 ) THEN -887089.0926061296\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'CHN', 'ANA', 'BOS', 'KCA', 'LAN', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" <= 8.000000 OR t2.\"sb\" IS NULL ) AND ( t1.\"yearid\" <= 1993.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"3b\" <= 3.000000 OR t2.\"3b\" IS NULL ) THEN 725467.1947242735\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'CLE', 'SDN', 'CAL', 'ANA', 'KCA', 'OAK', 'CIN' ) ) AND ( t2.\"rbi\" > 118.000000 ) AND ( t2.\"rbi\" > 127.000000 ) THEN 1729058.542727086\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'CLE', 'SDN', 'CAL', 'ANA', 'KCA', 'OAK', 'CIN' ) ) AND ( t2.\"rbi\" > 118.000000 ) AND ( t2.\"rbi\" <= 127.000000 OR t2.\"rbi\" IS NULL ) THEN 4287607.932803141\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'CLE', 'SDN', 'CAL', 'ANA', 'KCA', 'OAK', 'CIN' ) ) AND ( t2.\"rbi\" <= 118.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"2b\" > 36.000000 ) THEN -22215.63506613447\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'CLE', 'SDN', 'CAL', 'ANA', 'KCA', 'OAK', 'CIN' ) ) AND ( t2.\"rbi\" <= 118.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"2b\" <= 36.000000 OR t2.\"2b\" IS NULL ) THEN 1242531.21738428\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'CLE', 'SDN', 'CAL', 'ANA', 'KCA', 'OAK', 'CIN' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"cs__mapping_4_target_1_avg\" > 3165616.760381 ) AND ( t2.\"h\" > 143.000000 ) THEN 2286294.225443862\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'CLE', 'SDN', 'CAL', 'ANA', 'KCA', 'OAK', 'CIN' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"cs__mapping_4_target_1_avg\" > 3165616.760381 ) AND ( t2.\"h\" <= 143.000000 OR t2.\"h\" IS NULL ) THEN 435769.5655653203\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'CLE', 'SDN', 'CAL', 'ANA', 'KCA', 'OAK', 'CIN' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"cs__mapping_4_target_1_avg\" <= 3165616.760381 OR t2.\"cs__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 155.000000 ) THEN 634917.4267129193\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'CLE', 'SDN', 'CAL', 'ANA', 'KCA', 'OAK', 'CIN' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"cs__mapping_4_target_1_avg\" <= 3165616.760381 OR t2.\"cs__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 155.000000 OR t2.\"g\" IS NULL ) THEN 1532029.268131789\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'PHI', 'CHA', 'DET', 'CLE', 'TOR', 'CAL', 'SFN', 'CHN', 'CIN', 'COL' ) ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1050962.991468 ) AND ( t1.\"teamidcat\" IN ( 'PHI', 'CAL', 'CHN', 'CIN', 'COL' ) ) THEN 81608.02082083141\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'PHI', 'CHA', 'DET', 'CLE', 'TOR', 'CAL', 'SFN', 'CHN', 'CIN', 'COL' ) ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1050962.991468 ) AND ( t1.\"teamidcat\" NOT IN ( 'PHI', 'CAL', 'CHN', 'CIN', 'COL' ) OR t1.\"teamidcat\" IS NULL ) THEN 839482.9562567974\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'PHI', 'CHA', 'DET', 'CLE', 'TOR', 'CAL', 'SFN', 'CHN', 'CIN', 'COL' ) ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1050962.991468 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'DET', 'TOR', 'SFN' ) ) THEN 1105652.300210021\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'PHI', 'CHA', 'DET', 'CLE', 'TOR', 'CAL', 'SFN', 'CHN', 'CIN', 'COL' ) ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1050962.991468 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'DET', 'TOR', 'SFN' ) OR t1.\"teamidcat\" IS NULL ) THEN 2079241.528639275\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'PHI', 'CHA', 'DET', 'CLE', 'TOR', 'CAL', 'SFN', 'CHN', 'CIN', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 6366219.620404 ) AND ( t1.\"teamidcat\" IN ( 'TEX' ) ) THEN 1291619.970540897\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'PHI', 'CHA', 'DET', 'CLE', 'TOR', 'CAL', 'SFN', 'CHN', 'CIN', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 6366219.620404 ) AND ( t1.\"teamidcat\" NOT IN ( 'TEX' ) OR t1.\"teamidcat\" IS NULL ) THEN -798230.9275025943\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'PHI', 'CHA', 'DET', 'CLE', 'TOR', 'CAL', 'SFN', 'CHN', 'CIN', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 6366219.620404 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BOS', 'TEX', 'KCA', 'OAK', 'LAN' ) ) THEN 1321777.302176246\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'PHI', 'CHA', 'DET', 'CLE', 'TOR', 'CAL', 'SFN', 'CHN', 'CIN', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 6366219.620404 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BOS', 'TEX', 'KCA', 'OAK', 'LAN' ) OR t1.\"teamidcat\" IS NULL ) THEN 1690212.481067271\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'CHA', 'DET', 'SLN', 'CLE', 'SDN', 'CAL', 'SFN', 'CHN', 'TEX', 'OAK', 'CIN', 'SEA' ) ) AND ( t1.\"yearid\" > 1990.000000 ) AND ( t1.\"year\" - t2.\"year\" > 126213120.000000 ) AND ( t2.\"g\" > 144.000000 ) THEN -438208.7514224367\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'CHA', 'DET', 'SLN', 'CLE', 'SDN', 'CAL', 'SFN', 'CHN', 'TEX', 'OAK', 'CIN', 'SEA' ) ) AND ( t1.\"yearid\" > 1990.000000 ) AND ( t1.\"year\" - t2.\"year\" > 126213120.000000 ) AND ( t2.\"g\" <= 144.000000 OR t2.\"g\" IS NULL ) THEN 554577.7550109718\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'CHA', 'DET', 'SLN', 'CLE', 'SDN', 'CAL', 'SFN', 'CHN', 'TEX', 'OAK', 'CIN', 'SEA' ) ) AND ( t1.\"yearid\" > 1990.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 126213120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"hbp__mapping_4_target_1_avg\" > 3457023.509385 ) THEN 1221852.947222068\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'CHA', 'DET', 'SLN', 'CLE', 'SDN', 'CAL', 'SFN', 'CHN', 'TEX', 'OAK', 'CIN', 'SEA' ) ) AND ( t1.\"yearid\" > 1990.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 126213120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"hbp__mapping_4_target_1_avg\" <= 3457023.509385 OR t2.\"hbp__mapping_4_target_1_avg\" IS NULL ) THEN 1742833.827132344\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'CHA', 'DET', 'SLN', 'CLE', 'SDN', 'CAL', 'SFN', 'CHN', 'TEX', 'OAK', 'CIN', 'SEA' ) ) AND ( t1.\"yearid\" <= 1990.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hbp\" > 2.000000 ) AND ( t2.\"hr\" > 45.000000 ) THEN 851936.8234773523\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'CHA', 'DET', 'SLN', 'CLE', 'SDN', 'CAL', 'SFN', 'CHN', 'TEX', 'OAK', 'CIN', 'SEA' ) ) AND ( t1.\"yearid\" <= 1990.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hbp\" > 2.000000 ) AND ( t2.\"hr\" <= 45.000000 OR t2.\"hr\" IS NULL ) THEN -803448.4141810529\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'CHA', 'DET', 'SLN', 'CLE', 'SDN', 'CAL', 'SFN', 'CHN', 'TEX', 'OAK', 'CIN', 'SEA' ) ) AND ( t1.\"yearid\" <= 1990.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hbp\" <= 2.000000 OR t2.\"hbp\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'DET', 'SLN', 'CLE', 'CAL', 'CHN', 'SEA' ) ) THEN -393165.5748894861\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'CHA', 'DET', 'SLN', 'CLE', 'SDN', 'CAL', 'SFN', 'CHN', 'TEX', 'OAK', 'CIN', 'SEA' ) ) AND ( t1.\"yearid\" <= 1990.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hbp\" <= 2.000000 OR t2.\"hbp\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'DET', 'SLN', 'CLE', 'CAL', 'CHN', 'SEA' ) OR t1.\"teamidcat\" IS NULL ) THEN 165084.4405416189\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" NOT IN ( 'ML4', 'CHA', 'DET', 'SLN', 'CLE', 'SDN', 'CAL', 'SFN', 'CHN', 'TEX', 'OAK', 'CIN', 'SEA' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"hbp\" > 16.000000 ) THEN -2076590.359304502\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" NOT IN ( 'ML4', 'CHA', 'DET', 'SLN', 'CLE', 'SDN', 'CAL', 'SFN', 'CHN', 'TEX', 'OAK', 'CIN', 'SEA' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"hbp\" <= 16.000000 OR t2.\"hbp\" IS NULL ) AND ( t2.\"2b\" > 44.000000 ) AND ( t2.\"r\" > 106.000000 ) THEN -84839.33213770755\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" NOT IN ( 'ML4', 'CHA', 'DET', 'SLN', 'CLE', 'SDN', 'CAL', 'SFN', 'CHN', 'TEX', 'OAK', 'CIN', 'SEA' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"hbp\" <= 16.000000 OR t2.\"hbp\" IS NULL ) AND ( t2.\"2b\" > 44.000000 ) AND ( t2.\"r\" <= 106.000000 OR t2.\"r\" IS NULL ) THEN -1435082.757727482\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" NOT IN ( 'ML4', 'CHA', 'DET', 'SLN', 'CLE', 'SDN', 'CAL', 'SFN', 'CHN', 'TEX', 'OAK', 'CIN', 'SEA' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"hbp\" <= 16.000000 OR t2.\"hbp\" IS NULL ) AND ( t2.\"2b\" <= 44.000000 OR t2.\"2b\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 7917315.804014 ) THEN 705114.0847788993\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" NOT IN ( 'ML4', 'CHA', 'DET', 'SLN', 'CLE', 'SDN', 'CAL', 'SFN', 'CHN', 'TEX', 'OAK', 'CIN', 'SEA' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"hbp\" <= 16.000000 OR t2.\"hbp\" IS NULL ) AND ( t2.\"2b\" <= 44.000000 OR t2.\"2b\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 7917315.804014 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) THEN -33736.08383041415\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" > 9.000000 ) AND ( t2.\"sb\" > 7.000000 ) AND ( t1.\"year\" - t2.\"year\" > 115689600.000000 ) AND ( t1.\"teamidcat__mapping_target_1_avg\" > 1695158.861504 ) THEN -1273856.73182487\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" > 9.000000 ) AND ( t2.\"sb\" > 7.000000 ) AND ( t1.\"year\" - t2.\"year\" > 115689600.000000 ) AND ( t1.\"teamidcat__mapping_target_1_avg\" <= 1695158.861504 OR t1.\"teamidcat__mapping_target_1_avg\" IS NULL ) THEN -1441184.077447976\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" > 9.000000 ) AND ( t2.\"sb\" > 7.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 115689600.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"lgid\" IN ( 'NL' ) ) THEN -891894.7535893385\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" > 9.000000 ) AND ( t2.\"sb\" > 7.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 115689600.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"lgid\" NOT IN ( 'NL' ) OR t2.\"lgid\" IS NULL ) THEN -978669.8744009694\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" > 9.000000 ) AND ( t2.\"sb\" <= 7.000000 OR t2.\"sb\" IS NULL ) AND ( t2.\"ab\" > 601.000000 ) THEN -1929719.659923676\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" > 9.000000 ) AND ( t2.\"sb\" <= 7.000000 OR t2.\"sb\" IS NULL ) AND ( t2.\"ab\" <= 601.000000 OR t2.\"ab\" IS NULL ) THEN -1784951.611518287\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" > 5747507.856062 ) AND ( t2.\"rbi\" > 104.000000 ) AND ( t2.\"ab\" > 613.000000 ) THEN 299571.4887037456\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" > 5747507.856062 ) AND ( t2.\"rbi\" > 104.000000 ) AND ( t2.\"ab\" <= 613.000000 OR t2.\"ab\" IS NULL ) THEN -108783.1213653798\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" > 5747507.856062 ) AND ( t2.\"rbi\" <= 104.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"ab\" > 605.000000 ) THEN -576979.1608678327\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" > 5747507.856062 ) AND ( t2.\"rbi\" <= 104.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"ab\" <= 605.000000 OR t2.\"ab\" IS NULL ) THEN -780620.9167845835\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" <= 5747507.856062 OR t2.\"2b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" > 60.000000 ) AND ( t2.\"so\" > 83.000000 ) THEN -1195250.620596347\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" <= 5747507.856062 OR t2.\"2b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" > 60.000000 ) AND ( t2.\"so\" <= 83.000000 OR t2.\"so\" IS NULL ) THEN -1669797.597007224\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" <= 5747507.856062 OR t2.\"2b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" <= 60.000000 OR t2.\"bb\" IS NULL ) AND ( t1.\"yearid\" > 1987.000000 ) THEN -1029048.205249076\n", - " WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" <= 5747507.856062 OR t2.\"2b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" <= 60.000000 OR t2.\"bb\" IS NULL ) AND ( t1.\"yearid\" <= 1987.000000 OR t1.\"yearid\" IS NULL ) THEN -497075.1454558557\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 4484061.659837 ) AND ( t2.\"ab\" > 658.000000 ) THEN 9015795.32266753\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 4484061.659837 ) AND ( t2.\"ab\" <= 658.000000 OR t2.\"ab\" IS NULL ) AND ( t2.\"bb\" > 117.000000 ) THEN -2940802.256897952\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 4484061.659837 ) AND ( t2.\"ab\" <= 658.000000 OR t2.\"ab\" IS NULL ) AND ( t2.\"bb\" <= 117.000000 OR t2.\"bb\" IS NULL ) THEN 539510.027445049\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 4484061.659837 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'PHI', 'DET', 'NYN', 'CLE', 'PIT', 'SDN', 'TOR', 'SFN', 'MON', 'FLO', 'HOU', 'ANA', 'TEX', 'KCA', 'CIN', 'NYA', 'LAN', 'SEA', 'ARI', 'COL', 'MIL', 'WAS' ) ) AND ( t2.\"cs\" > 6.000000 ) THEN -464477.9698077337\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 4484061.659837 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'PHI', 'DET', 'NYN', 'CLE', 'PIT', 'SDN', 'TOR', 'SFN', 'MON', 'FLO', 'HOU', 'ANA', 'TEX', 'KCA', 'CIN', 'NYA', 'LAN', 'SEA', 'ARI', 'COL', 'MIL', 'WAS' ) ) AND ( t2.\"cs\" <= 6.000000 OR t2.\"cs\" IS NULL ) THEN -1246812.213610361\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 4484061.659837 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'PHI', 'DET', 'NYN', 'CLE', 'PIT', 'SDN', 'TOR', 'SFN', 'MON', 'FLO', 'HOU', 'ANA', 'TEX', 'KCA', 'CIN', 'NYA', 'LAN', 'SEA', 'ARI', 'COL', 'MIL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so\" > 116.000000 ) THEN 696630.2115335638\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 4484061.659837 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'PHI', 'DET', 'NYN', 'CLE', 'PIT', 'SDN', 'TOR', 'SFN', 'MON', 'FLO', 'HOU', 'ANA', 'TEX', 'KCA', 'CIN', 'NYA', 'LAN', 'SEA', 'ARI', 'COL', 'MIL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so\" <= 116.000000 OR t2.\"so\" IS NULL ) THEN -334497.1976938579\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'PHI', 'MIN', 'DET', 'NYN', 'CLE', 'SDN', 'TOR', 'FLO', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'LAN', 'SEA', 'ARI', 'COL', 'WAS' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" > 4024078.581067 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 2389827.865089 ) THEN 207152.8141326556\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'PHI', 'MIN', 'DET', 'NYN', 'CLE', 'SDN', 'TOR', 'FLO', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'LAN', 'SEA', 'ARI', 'COL', 'WAS' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" > 4024078.581067 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 2389827.865089 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) THEN 2313501.101834453\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'PHI', 'MIN', 'DET', 'NYN', 'CLE', 'SDN', 'TOR', 'FLO', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'LAN', 'SEA', 'ARI', 'COL', 'WAS' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 4024078.581067 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'SDN', 'FLO', 'BOS', 'TEX', 'KCA', 'SEA', 'ARI' ) ) THEN -1178443.556344871\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'PHI', 'MIN', 'DET', 'NYN', 'CLE', 'SDN', 'TOR', 'FLO', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'LAN', 'SEA', 'ARI', 'COL', 'WAS' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 4024078.581067 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'SDN', 'FLO', 'BOS', 'TEX', 'KCA', 'SEA', 'ARI' ) OR t1.\"teamidcat\" IS NULL ) THEN -364152.5416652811\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'PHI', 'MIN', 'DET', 'NYN', 'CLE', 'SDN', 'TOR', 'FLO', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'LAN', 'SEA', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 283996800.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 3354247.290435 ) THEN 3780702.470172695\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'PHI', 'MIN', 'DET', 'NYN', 'CLE', 'SDN', 'TOR', 'FLO', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'LAN', 'SEA', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 283996800.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 3354247.290435 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) THEN 762443.5603790393\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'PHI', 'MIN', 'DET', 'NYN', 'CLE', 'SDN', 'TOR', 'FLO', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'LAN', 'SEA', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 283996800.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"h\" > 4.000000 ) THEN 1562467.724549611\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'PHI', 'MIN', 'DET', 'NYN', 'CLE', 'SDN', 'TOR', 'FLO', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'LAN', 'SEA', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 283996800.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"h\" <= 4.000000 OR t2.\"h\" IS NULL ) THEN 5862052.941262188\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 3372308.829642 ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'CHA', 'DET', 'SLN', 'CLE', 'PIT', 'SDN', 'TOR', 'FLO', 'HOU', 'CHN', 'BOS', 'TEX', 'CIN', 'TBA', 'LAN', 'COL', 'MIL', 'WAS' ) ) AND ( t1.\"year\" - t2.\"year\" > 245096640.000000 ) THEN -432045.6323970378\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 3372308.829642 ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'CHA', 'DET', 'SLN', 'CLE', 'PIT', 'SDN', 'TOR', 'FLO', 'HOU', 'CHN', 'BOS', 'TEX', 'CIN', 'TBA', 'LAN', 'COL', 'MIL', 'WAS' ) ) AND ( t1.\"year\" - t2.\"year\" <= 245096640.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN 944821.2334703418\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 3372308.829642 ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'CHA', 'DET', 'SLN', 'CLE', 'PIT', 'SDN', 'TOR', 'FLO', 'HOU', 'CHN', 'BOS', 'TEX', 'CIN', 'TBA', 'LAN', 'COL', 'MIL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 9258746.205729 ) THEN 5808551.10047887\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 3372308.829642 ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'CHA', 'DET', 'SLN', 'CLE', 'PIT', 'SDN', 'TOR', 'FLO', 'HOU', 'CHN', 'BOS', 'TEX', 'CIN', 'TBA', 'LAN', 'COL', 'MIL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 9258746.205729 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) THEN 1257324.355683805\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 3372308.829642 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'DET', 'SLN', 'SDN', 'SFN', 'FLO', 'OAK', 'TBA', 'NYA', 'LAN', 'ARI', 'COL', 'WAS' ) ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 2297583.020898 ) THEN 1401751.569226111\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 3372308.829642 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'DET', 'SLN', 'SDN', 'SFN', 'FLO', 'OAK', 'TBA', 'NYA', 'LAN', 'ARI', 'COL', 'WAS' ) ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 2297583.020898 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) THEN -88745.88956094126\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 3372308.829642 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'DET', 'SLN', 'SDN', 'SFN', 'FLO', 'OAK', 'TBA', 'NYA', 'LAN', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 2421385.281385 ) THEN 2420104.908118949\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 3372308.829642 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'DET', 'SLN', 'SDN', 'SFN', 'FLO', 'OAK', 'TBA', 'NYA', 'LAN', 'ARI', 'COL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 2421385.281385 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) THEN 920857.3464225928\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'PHI', 'MIN', 'DET', 'SLN', 'PIT', 'SDN', 'TOR', 'SFN', 'FLO', 'CHN', 'ANA', 'KCA', 'CIN', 'TBA', 'COL', 'MIL', 'WAS' ) ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 2306899.036874 ) AND ( t2.\"r__mapping_4_target_1_avg\" > 5901332.613177 ) THEN 1345985.226921735\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'PHI', 'MIN', 'DET', 'SLN', 'PIT', 'SDN', 'TOR', 'SFN', 'FLO', 'CHN', 'ANA', 'KCA', 'CIN', 'TBA', 'COL', 'MIL', 'WAS' ) ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 2306899.036874 ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 5901332.613177 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) THEN 652533.1484379417\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'PHI', 'MIN', 'DET', 'SLN', 'PIT', 'SDN', 'TOR', 'SFN', 'FLO', 'CHN', 'ANA', 'KCA', 'CIN', 'TBA', 'COL', 'MIL', 'WAS' ) ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 2306899.036874 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'MIN', 'DET', 'PIT', 'SDN', 'TOR', 'SFN', 'FLO', 'CHN', 'ANA', 'KCA', 'CIN', 'COL', 'MIL', 'WAS' ) ) THEN -53265.81894274038\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'PHI', 'MIN', 'DET', 'SLN', 'PIT', 'SDN', 'TOR', 'SFN', 'FLO', 'CHN', 'ANA', 'KCA', 'CIN', 'TBA', 'COL', 'MIL', 'WAS' ) ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 2306899.036874 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'MIN', 'DET', 'PIT', 'SDN', 'TOR', 'SFN', 'FLO', 'CHN', 'ANA', 'KCA', 'CIN', 'COL', 'MIL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) THEN 1182642.778025358\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'PHI', 'MIN', 'DET', 'SLN', 'PIT', 'SDN', 'TOR', 'SFN', 'FLO', 'CHN', 'ANA', 'KCA', 'CIN', 'TBA', 'COL', 'MIL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r\" > 10.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 2380634.856708 ) THEN 957799.7936397812\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'PHI', 'MIN', 'DET', 'SLN', 'PIT', 'SDN', 'TOR', 'SFN', 'FLO', 'CHN', 'ANA', 'KCA', 'CIN', 'TBA', 'COL', 'MIL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r\" > 10.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 2380634.856708 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) THEN -285286.1183103392\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'PHI', 'MIN', 'DET', 'SLN', 'PIT', 'SDN', 'TOR', 'SFN', 'FLO', 'CHN', 'ANA', 'KCA', 'CIN', 'TBA', 'COL', 'MIL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r\" <= 10.000000 OR t2.\"r\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 2680589.378820 ) THEN 4177453.288317214\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'PHI', 'MIN', 'DET', 'SLN', 'PIT', 'SDN', 'TOR', 'SFN', 'FLO', 'CHN', 'ANA', 'KCA', 'CIN', 'TBA', 'COL', 'MIL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r\" <= 10.000000 OR t2.\"r\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 2680589.378820 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) THEN 1126348.606888345\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" > 277490078.350515 ) AND ( t1.\"teamidcat\" IN ( 'PHI', 'CHA', 'FLO', 'HOU', 'CHN', 'ANA', 'BOS', 'KCA', 'CIN', 'TBA' ) ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 1950532.235769 ) THEN -186366.1893148199\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" > 277490078.350515 ) AND ( t1.\"teamidcat\" IN ( 'PHI', 'CHA', 'FLO', 'HOU', 'CHN', 'ANA', 'BOS', 'KCA', 'CIN', 'TBA' ) ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 1950532.235769 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) THEN -1515655.815755094\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" > 277490078.350515 ) AND ( t1.\"teamidcat\" NOT IN ( 'PHI', 'CHA', 'FLO', 'HOU', 'CHN', 'ANA', 'BOS', 'KCA', 'CIN', 'TBA' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"g_batting__mapping_4_target_1_avg\" > 3354271.113067 ) THEN 1243978.023249262\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" > 277490078.350515 ) AND ( t1.\"teamidcat\" NOT IN ( 'PHI', 'CHA', 'FLO', 'HOU', 'CHN', 'ANA', 'BOS', 'KCA', 'CIN', 'TBA' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"g_batting__mapping_4_target_1_avg\" <= 3354271.113067 OR t2.\"g_batting__mapping_4_target_1_avg\" IS NULL ) THEN 43279.24666501496\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" <= 277490078.350515 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2007.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'PHI', 'SLN', 'CLE', 'PIT', 'SDN', 'TOR', 'SFN', 'FLO', 'HOU', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'NYA', 'LAN', 'ARI', 'MIL', 'WAS' ) ) THEN 493393.1459213803\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" <= 277490078.350515 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2007.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'PHI', 'SLN', 'CLE', 'PIT', 'SDN', 'TOR', 'SFN', 'FLO', 'HOU', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'NYA', 'LAN', 'ARI', 'MIL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) THEN 897475.1254280825\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" <= 277490078.350515 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2007.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'CHA', 'MIN', 'SLN', 'CLE', 'PIT', 'SDN', 'MON', 'FLO', 'HOU', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'ARI', 'COL', 'MIL', 'WAS' ) ) THEN 323287.5884791795\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" <= 277490078.350515 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2007.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'CHA', 'MIN', 'SLN', 'CLE', 'PIT', 'SDN', 'MON', 'FLO', 'HOU', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'ARI', 'COL', 'MIL', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) THEN 492909.1888216178\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" > 1.000000 ) AND ( t1.\"teamidcat\" IN ( 'PHI', 'CHA', 'MIN', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'FLO', 'HOU', 'CHN', 'ANA', 'LAA', 'TEX', 'KCA', 'TBA', 'NYA', 'SEA' ) ) AND ( t2.\"stint\" > 1.000000 ) THEN -1735851.766498642\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" > 1.000000 ) AND ( t1.\"teamidcat\" IN ( 'PHI', 'CHA', 'MIN', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'FLO', 'HOU', 'CHN', 'ANA', 'LAA', 'TEX', 'KCA', 'TBA', 'NYA', 'SEA' ) ) AND ( t2.\"stint\" <= 1.000000 OR t2.\"stint\" IS NULL ) THEN -29815.28077956744\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" > 1.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'PHI', 'CHA', 'MIN', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'FLO', 'HOU', 'CHN', 'ANA', 'LAA', 'TEX', 'KCA', 'TBA', 'NYA', 'SEA' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so__mapping_4_target_1_avg\" > 3572171.452907 ) THEN 3788305.749866242\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" > 1.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'PHI', 'CHA', 'MIN', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'FLO', 'HOU', 'CHN', 'ANA', 'LAA', 'TEX', 'KCA', 'TBA', 'NYA', 'SEA' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so__mapping_4_target_1_avg\" <= 3572171.452907 OR t2.\"so__mapping_4_target_1_avg\" IS NULL ) THEN 708890.2750639512\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" <= 1.000000 OR t2.\"sh\" IS NULL ) AND ( t2.\"2b\" > 28.000000 ) AND ( t1.\"year\" - t2.\"year\" > 141998400.000000 ) THEN 7195605.973000124\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" <= 1.000000 OR t2.\"sh\" IS NULL ) AND ( t2.\"2b\" > 28.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 141998400.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN 2288343.30097137\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" <= 1.000000 OR t2.\"sh\" IS NULL ) AND ( t2.\"2b\" <= 28.000000 OR t2.\"2b\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1937568.581933 ) THEN 405193.9535521567\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" <= 1.000000 OR t2.\"sh\" IS NULL ) AND ( t2.\"2b\" <= 28.000000 OR t2.\"2b\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1937568.581933 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) THEN -96082.27285004737\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 3265075.202703 ) AND ( t2.\"sb__mapping_4_target_1_avg\" > 4039401.373069 ) AND ( t1.\"year\" - t2.\"year\" > 94737600.000000 ) THEN 3890433.011964486\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 3265075.202703 ) AND ( t2.\"sb__mapping_4_target_1_avg\" > 4039401.373069 ) AND ( t1.\"year\" - t2.\"year\" <= 94737600.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN 7165341.223999773\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 3265075.202703 ) AND ( t2.\"sb__mapping_4_target_1_avg\" <= 4039401.373069 OR t2.\"sb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'PHI', 'CHA', 'MIN', 'NYN', 'SLN', 'SDN', 'SFN', 'MON', 'FLO', 'HOU', 'CHN', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'LAN', 'SEA', 'ARI', 'WAS' ) ) THEN 60143.30416753495\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 3265075.202703 ) AND ( t2.\"sb__mapping_4_target_1_avg\" <= 4039401.373069 OR t2.\"sb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'PHI', 'CHA', 'MIN', 'NYN', 'SLN', 'SDN', 'SFN', 'MON', 'FLO', 'HOU', 'CHN', 'LAA', 'TEX', 'OAK', 'TBA', 'NYA', 'LAN', 'SEA', 'ARI', 'WAS' ) OR t1.\"teamidcat\" IS NULL ) THEN 785465.8790671246\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 3265075.202703 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'MIN', 'NYN', 'PIT', 'LAA', 'TEX', 'CIN', 'SEA', 'MIL' ) ) AND ( t2.\"bb__mapping_4_target_1_avg\" > 4008061.580488 ) THEN -3073536.829298617\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 3265075.202703 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'MIN', 'NYN', 'PIT', 'LAA', 'TEX', 'CIN', 'SEA', 'MIL' ) ) AND ( t2.\"bb__mapping_4_target_1_avg\" <= 4008061.580488 OR t2.\"bb__mapping_4_target_1_avg\" IS NULL ) THEN -168259.6478932631\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 3265075.202703 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'MIN', 'NYN', 'PIT', 'LAA', 'TEX', 'CIN', 'SEA', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 4377701.761505 ) THEN 2813497.7520666\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 3265075.202703 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'MIN', 'NYN', 'PIT', 'LAA', 'TEX', 'CIN', 'SEA', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 4377701.761505 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) THEN -34059.23370697356\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 93048406.779661 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'SDN', 'TOR', 'SFN', 'HOU', 'CHN', 'BOS', 'OAK', 'NYA', 'LAN', 'SEA', 'ARI' ) ) AND ( t2.\"so__mapping_4_target_1_avg\" > 2862333.087314 ) THEN 586424.2968394028\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 93048406.779661 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'SDN', 'TOR', 'SFN', 'HOU', 'CHN', 'BOS', 'OAK', 'NYA', 'LAN', 'SEA', 'ARI' ) ) AND ( t2.\"so__mapping_4_target_1_avg\" <= 2862333.087314 OR t2.\"so__mapping_4_target_1_avg\" IS NULL ) THEN 15803.25934379456\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 93048406.779661 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'SDN', 'TOR', 'SFN', 'HOU', 'CHN', 'BOS', 'OAK', 'NYA', 'LAN', 'SEA', 'ARI' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"gidp\" > 0.000000 ) THEN 141567.8693129552\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 93048406.779661 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'DET', 'CLE', 'PIT', 'SDN', 'TOR', 'SFN', 'HOU', 'CHN', 'BOS', 'OAK', 'NYA', 'LAN', 'SEA', 'ARI' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"gidp\" <= 0.000000 OR t2.\"gidp\" IS NULL ) THEN 297961.6136731131\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 93048406.779661 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"gidp__mapping_4_target_1_avg\" > 2365745.163558 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'NYN', 'SLN', 'CLE', 'SDN', 'SFN', 'MON', 'ANA', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'MIL' ) ) THEN -166153.6106408061\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 93048406.779661 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"gidp__mapping_4_target_1_avg\" > 2365745.163558 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'PHI', 'CHA', 'MIN', 'NYN', 'SLN', 'CLE', 'SDN', 'SFN', 'MON', 'ANA', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'NYA', 'SEA', 'ARI', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) THEN 394602.9759363749\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 93048406.779661 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"gidp__mapping_4_target_1_avg\" <= 2365745.163558 OR t2.\"gidp__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'PHI', 'DET', 'NYN', 'CLE', 'PIT', 'SFN', 'HOU', 'CHN', 'ANA', 'TEX', 'LAN' ) ) THEN -503163.5372068749\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 93048406.779661 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"gidp__mapping_4_target_1_avg\" <= 2365745.163558 OR t2.\"gidp__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'PHI', 'DET', 'NYN', 'CLE', 'PIT', 'SFN', 'HOU', 'CHN', 'ANA', 'TEX', 'LAN' ) OR t1.\"teamidcat\" IS NULL ) THEN -148632.3815167727\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'CHA', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'CAL', 'SFN', 'MON', 'FLO', 'HOU', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'LAN', 'COL', 'MIL' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3844897.418860 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'SLN', 'CLE', 'PIT', 'SFN', 'HOU', 'TEX', 'KCA', 'LAN', 'COL', 'MIL' ) ) THEN 26317.34223697035\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'CHA', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'CAL', 'SFN', 'MON', 'FLO', 'HOU', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'LAN', 'COL', 'MIL' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3844897.418860 ) AND ( t1.\"teamidcat\" NOT IN ( 'ML4', 'SLN', 'CLE', 'PIT', 'SFN', 'HOU', 'TEX', 'KCA', 'LAN', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) THEN 259333.6616293485\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'CHA', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'CAL', 'SFN', 'MON', 'FLO', 'HOU', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'LAN', 'COL', 'MIL' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3844897.418860 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"2b\" > 1.000000 ) THEN -198499.170444293\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'CHA', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'CAL', 'SFN', 'MON', 'FLO', 'HOU', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'LAN', 'COL', 'MIL' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3844897.418860 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"2b\" <= 1.000000 OR t2.\"2b\" IS NULL ) THEN 33637.81807537523\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" NOT IN ( 'ML4', 'CHA', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'CAL', 'SFN', 'MON', 'FLO', 'HOU', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'LAN', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sf\" > 0.000000 ) AND ( t2.\"hr\" > 1.000000 ) THEN 100834.4321334281\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" NOT IN ( 'ML4', 'CHA', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'CAL', 'SFN', 'MON', 'FLO', 'HOU', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'LAN', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sf\" > 0.000000 ) AND ( t2.\"hr\" <= 1.000000 OR t2.\"hr\" IS NULL ) THEN -528931.3488220501\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" NOT IN ( 'ML4', 'CHA', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'CAL', 'SFN', 'MON', 'FLO', 'HOU', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'LAN', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sf\" <= 0.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 2400631.103154 ) THEN 993383.2764401076\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" NOT IN ( 'ML4', 'CHA', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'CAL', 'SFN', 'MON', 'FLO', 'HOU', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'LAN', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sf\" <= 0.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 2400631.103154 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) THEN 184605.7795152585\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3755593.787617 ) AND ( t1.\"yearid\" > 1995.000000 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'BAL', 'PHI', 'CHA', 'MIN', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'SDN', 'CAL', 'SFN', 'MON', 'CHN', 'ANA', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'NYA', 'LAN', 'SEA', 'ARI', 'MIL' ) ) THEN 571944.0133884383\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3755593.787617 ) AND ( t1.\"yearid\" > 1995.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ML4', 'BAL', 'PHI', 'CHA', 'MIN', 'DET', 'NYN', 'SLN', 'CLE', 'PIT', 'SDN', 'CAL', 'SFN', 'MON', 'CHN', 'ANA', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'NYA', 'LAN', 'SEA', 'ARI', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) THEN 1076254.475643927\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3755593.787617 ) AND ( t1.\"yearid\" <= 1995.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'BAL', 'PHI', 'CHA', 'MIN', 'DET', 'SLN', 'CLE', 'TOR', 'FLO', 'HOU', 'OAK', 'LAN' ) ) THEN 288696.1657275385\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3755593.787617 ) AND ( t1.\"yearid\" <= 1995.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'BAL', 'PHI', 'CHA', 'MIN', 'DET', 'SLN', 'CLE', 'TOR', 'FLO', 'HOU', 'OAK', 'LAN' ) OR t1.\"teamidcat\" IS NULL ) THEN 489818.7057362146\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3755593.787617 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 2495401.212121 ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'PHI', 'CHA', 'MIN', 'DET', 'NYN', 'PIT', 'SDN', 'TOR', 'SFN', 'MON', 'FLO', 'HOU', 'ANA', 'KCA', 'OAK', 'TBA', 'COL' ) ) THEN 266597.649319868\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3755593.787617 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 2495401.212121 ) AND ( t1.\"teamidcat\" NOT IN ( 'ML4', 'PHI', 'CHA', 'MIN', 'DET', 'NYN', 'PIT', 'SDN', 'TOR', 'SFN', 'MON', 'FLO', 'HOU', 'ANA', 'KCA', 'OAK', 'TBA', 'COL' ) OR t1.\"teamidcat\" IS NULL ) THEN 470476.0631748594\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3755593.787617 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 2495401.212121 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h\" > 14.000000 ) THEN 69770.31528237762\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3755593.787617 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 2495401.212121 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h\" <= 14.000000 OR t2.\"h\" IS NULL ) THEN 298744.622680815\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'MIN', 'DET', 'PIT', 'TOR', 'SFN', 'HOU', 'CHN', 'KCA', 'NYA', 'LAN' ) ) AND ( t2.\"hr\" > 20.000000 ) AND ( t2.\"2b\" > 24.000000 ) THEN 428290.7475129453\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'MIN', 'DET', 'PIT', 'TOR', 'SFN', 'HOU', 'CHN', 'KCA', 'NYA', 'LAN' ) ) AND ( t2.\"hr\" > 20.000000 ) AND ( t2.\"2b\" <= 24.000000 OR t2.\"2b\" IS NULL ) THEN 102915.6160999416\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'MIN', 'DET', 'PIT', 'TOR', 'SFN', 'HOU', 'CHN', 'KCA', 'NYA', 'LAN' ) ) AND ( t2.\"hr\" <= 20.000000 OR t2.\"hr\" IS NULL ) AND ( t2.\"g\" > 118.000000 ) THEN 30737.78353356822\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'MIN', 'DET', 'PIT', 'TOR', 'SFN', 'HOU', 'CHN', 'KCA', 'NYA', 'LAN' ) ) AND ( t2.\"hr\" <= 20.000000 OR t2.\"hr\" IS NULL ) AND ( t2.\"g\" <= 118.000000 OR t2.\"g\" IS NULL ) THEN 1059157.360125734\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'MIN', 'DET', 'PIT', 'TOR', 'SFN', 'HOU', 'CHN', 'KCA', 'NYA', 'LAN' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 3652422.087697 ) AND ( t2.\"rbi\" > 58.000000 ) THEN 353205.0511713083\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'MIN', 'DET', 'PIT', 'TOR', 'SFN', 'HOU', 'CHN', 'KCA', 'NYA', 'LAN' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 3652422.087697 ) AND ( t2.\"rbi\" <= 58.000000 OR t2.\"rbi\" IS NULL ) THEN -216934.803958123\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'MIN', 'DET', 'PIT', 'TOR', 'SFN', 'HOU', 'CHN', 'KCA', 'NYA', 'LAN' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 3652422.087697 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 504900000.000000 ) THEN 1597455.261739174\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'MIN', 'DET', 'PIT', 'TOR', 'SFN', 'HOU', 'CHN', 'KCA', 'NYA', 'LAN' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 3652422.087697 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 504900000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN 79378.25632302785\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" > 1.000000 ) AND ( t2.\"r\" > 65.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'ML4', 'BAL', 'PHI', 'MIN', 'DET', 'NYN', 'CLE', 'PIT', 'SDN', 'TOR', 'CAL', 'SFN', 'MON', 'HOU', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'NYA', 'LAN', 'SEA' ) ) THEN 90779.34670529928\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" > 1.000000 ) AND ( t2.\"r\" > 65.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'ML4', 'BAL', 'PHI', 'MIN', 'DET', 'NYN', 'CLE', 'PIT', 'SDN', 'TOR', 'CAL', 'SFN', 'MON', 'HOU', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'NYA', 'LAN', 'SEA' ) OR t1.\"teamidcat\" IS NULL ) THEN 260366.5124278691\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" > 1.000000 ) AND ( t2.\"r\" <= 65.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"yearid\" > 1989.000000 ) THEN 65311.4575797993\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" > 1.000000 ) AND ( t2.\"r\" <= 65.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"yearid\" <= 1989.000000 OR t1.\"yearid\" IS NULL ) THEN 2524.615618461127\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" <= 1.000000 OR t2.\"bb\" IS NULL ) AND ( t2.\"g_old\" > 33.000000 ) AND ( t2.\"sh__mapping_4_target_1_avg\" > 3233879.322917 ) THEN 1596488.852203071\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" <= 1.000000 OR t2.\"bb\" IS NULL ) AND ( t2.\"g_old\" > 33.000000 ) AND ( t2.\"sh__mapping_4_target_1_avg\" <= 3233879.322917 OR t2.\"sh__mapping_4_target_1_avg\" IS NULL ) THEN 198650.7766067838\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" <= 1.000000 OR t2.\"bb\" IS NULL ) AND ( t2.\"g_old\" <= 33.000000 OR t2.\"g_old\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'ML4', 'BAL', 'PHI', 'CHA', 'MIN', 'DET', 'SLN', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'CHN', 'TEX', 'KCA', 'OAK', 'CIN', 'NYA', 'LAN', 'SEA' ) ) THEN 9038.071363796065\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" <= 1.000000 OR t2.\"bb\" IS NULL ) AND ( t2.\"g_old\" <= 33.000000 OR t2.\"g_old\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'ML4', 'BAL', 'PHI', 'CHA', 'MIN', 'DET', 'SLN', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'CHN', 'TEX', 'KCA', 'OAK', 'CIN', 'NYA', 'LAN', 'SEA' ) OR t1.\"teamidcat\" IS NULL ) THEN 106460.9590260297\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'NYN', 'CLE', 'CAL', 'HOU', 'CHN', 'ANA', 'TEX', 'OAK', 'CIN', 'TBA', 'COL' ) ) AND ( t2.\"r__mapping_4_target_1_avg\" > 3803688.108904 ) THEN -1578549.508293867\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'NYN', 'CLE', 'CAL', 'HOU', 'CHN', 'ANA', 'TEX', 'OAK', 'CIN', 'TBA', 'COL' ) ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 3803688.108904 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 465472800.000000 ) THEN -1858762.370473573\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'NYN', 'CLE', 'CAL', 'HOU', 'CHN', 'ANA', 'TEX', 'OAK', 'CIN', 'TBA', 'COL' ) ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 3803688.108904 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 465472800.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN 99626.25097574403\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'NYN', 'CLE', 'CAL', 'HOU', 'CHN', 'ANA', 'TEX', 'OAK', 'CIN', 'TBA', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" > 8.000000 ) AND ( t2.\"3b\" > 1.000000 ) THEN 839300.3144662131\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'NYN', 'CLE', 'CAL', 'HOU', 'CHN', 'ANA', 'TEX', 'OAK', 'CIN', 'TBA', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" > 8.000000 ) AND ( t2.\"3b\" <= 1.000000 OR t2.\"3b\" IS NULL ) THEN 2357809.448525155\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'NYN', 'CLE', 'CAL', 'HOU', 'CHN', 'ANA', 'TEX', 'OAK', 'CIN', 'TBA', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" <= 8.000000 OR t2.\"sb\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'PHI', 'MIN', 'DET', 'SDN', 'TOR', 'FLO', 'NYA', 'LAN', 'SEA' ) ) THEN 220864.1936158802\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'NYN', 'CLE', 'CAL', 'HOU', 'CHN', 'ANA', 'TEX', 'OAK', 'CIN', 'TBA', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" <= 8.000000 OR t2.\"sb\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'PHI', 'MIN', 'DET', 'SDN', 'TOR', 'FLO', 'NYA', 'LAN', 'SEA' ) OR t1.\"teamidcat\" IS NULL ) THEN 695726.1020682579\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" > 5.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'ML4', 'BAL', 'MIN', 'NYN', 'CLE', 'SDN', 'TOR', 'CAL', 'MON', 'FLO', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'NYA', 'ARI', 'MIL' ) ) AND ( t1.\"year\" - t2.\"year\" > 90704571.428571 ) THEN 3455.802687050847\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" > 5.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'ML4', 'BAL', 'MIN', 'NYN', 'CLE', 'SDN', 'TOR', 'CAL', 'MON', 'FLO', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'NYA', 'ARI', 'MIL' ) ) AND ( t1.\"year\" - t2.\"year\" <= 90704571.428571 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN -131057.8385539503\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" > 5.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'ML4', 'BAL', 'MIN', 'NYN', 'CLE', 'SDN', 'TOR', 'CAL', 'MON', 'FLO', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'NYA', 'ARI', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"bb__mapping_4_target_1_avg\" > 5665635.637450 ) THEN 2196129.991609469\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" > 5.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'ML4', 'BAL', 'MIN', 'NYN', 'CLE', 'SDN', 'TOR', 'CAL', 'MON', 'FLO', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'TBA', 'NYA', 'ARI', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"bb__mapping_4_target_1_avg\" <= 5665635.637450 OR t2.\"bb__mapping_4_target_1_avg\" IS NULL ) THEN 22562.22441162112\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" <= 5.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2174612.509929 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'BAL', 'PHI', 'MIN', 'NYN', 'SLN', 'CLE', 'PIT', 'CAL', 'MON', 'FLO', 'BOS', 'TEX', 'OAK', 'CIN', 'LAN', 'SEA', 'ARI', 'COL', 'MIL' ) ) THEN 217655.0111794165\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" <= 5.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2174612.509929 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'BAL', 'PHI', 'MIN', 'NYN', 'SLN', 'CLE', 'PIT', 'CAL', 'MON', 'FLO', 'BOS', 'TEX', 'OAK', 'CIN', 'LAN', 'SEA', 'ARI', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) THEN 971804.2321065853\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" <= 5.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2174612.509929 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'ML4', 'BAL', 'PHI', 'CHA', 'DET', 'SLN', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'HOU', 'ANA', 'BOS', 'KCA', 'OAK', 'TBA', 'NYA', 'LAN', 'ARI', 'MIL' ) ) THEN 9093.799790580963\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" <= 5.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2174612.509929 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'ML4', 'BAL', 'PHI', 'CHA', 'DET', 'SLN', 'CLE', 'PIT', 'TOR', 'CAL', 'SFN', 'MON', 'FLO', 'HOU', 'ANA', 'BOS', 'KCA', 'OAK', 'TBA', 'NYA', 'LAN', 'ARI', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) THEN 101905.3529629898\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'ML4', 'PHI', 'CHA', 'MIN', 'DET', 'CLE', 'SDN', 'CAL', 'MON', 'ANA', 'TBA', 'NYA', 'MIL' ) ) AND ( t1.\"year\" - t2.\"year\" > 206458971.428571 ) AND ( t1.\"yearid\" > 1990.000000 ) THEN -220633.6421108029\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'ML4', 'PHI', 'CHA', 'MIN', 'DET', 'CLE', 'SDN', 'CAL', 'MON', 'ANA', 'TBA', 'NYA', 'MIL' ) ) AND ( t1.\"year\" - t2.\"year\" > 206458971.428571 ) AND ( t1.\"yearid\" <= 1990.000000 OR t1.\"yearid\" IS NULL ) THEN -46202.41086603263\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'ML4', 'PHI', 'CHA', 'MIN', 'DET', 'CLE', 'SDN', 'CAL', 'MON', 'ANA', 'TBA', 'NYA', 'MIL' ) ) AND ( t1.\"year\" - t2.\"year\" <= 206458971.428571 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 2003930.720137 ) THEN -86891.55848225483\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" IN ( 'ATL', 'ML4', 'PHI', 'CHA', 'MIN', 'DET', 'CLE', 'SDN', 'CAL', 'MON', 'ANA', 'TBA', 'NYA', 'MIL' ) ) AND ( t1.\"year\" - t2.\"year\" <= 206458971.428571 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 2003930.720137 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) THEN 184975.8157626599\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'ML4', 'PHI', 'CHA', 'MIN', 'DET', 'CLE', 'SDN', 'CAL', 'MON', 'ANA', 'TBA', 'NYA', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 529141730.232558 ) AND ( t1.\"teamidcat\" IN ( 'TOR', 'CHN' ) ) THEN -1709147.241353896\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'ML4', 'PHI', 'CHA', 'MIN', 'DET', 'CLE', 'SDN', 'CAL', 'MON', 'ANA', 'TBA', 'NYA', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 529141730.232558 ) AND ( t1.\"teamidcat\" NOT IN ( 'TOR', 'CHN' ) OR t1.\"teamidcat\" IS NULL ) THEN -351895.5154386133\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'ML4', 'PHI', 'CHA', 'MIN', 'DET', 'CLE', 'SDN', 'CAL', 'MON', 'ANA', 'TBA', 'NYA', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 529141730.232558 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1037323.855048 ) THEN 100271.6762462905\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" NOT IN ( 'ATL', 'ML4', 'PHI', 'CHA', 'MIN', 'DET', 'CLE', 'SDN', 'CAL', 'MON', 'ANA', 'TBA', 'NYA', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 529141730.232558 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1037323.855048 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) THEN -42.6917691065336\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'MIN', 'DET', 'PIT', 'SDN', 'TOR', 'CAL', 'MON', 'HOU', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'ARI', 'COL', 'MIL' ) ) AND ( t1.\"teamidcat\" IN ( 'HOU', 'CHN', 'KCA', 'CIN', 'TBA', 'COL' ) ) AND ( t2.\"sh\" > 3.000000 ) THEN 145993.2364913361\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'MIN', 'DET', 'PIT', 'SDN', 'TOR', 'CAL', 'MON', 'HOU', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'ARI', 'COL', 'MIL' ) ) AND ( t1.\"teamidcat\" IN ( 'HOU', 'CHN', 'KCA', 'CIN', 'TBA', 'COL' ) ) AND ( t2.\"sh\" <= 3.000000 OR t2.\"sh\" IS NULL ) THEN -270973.3848916991\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'MIN', 'DET', 'PIT', 'SDN', 'TOR', 'CAL', 'MON', 'HOU', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'ARI', 'COL', 'MIL' ) ) AND ( t1.\"teamidcat\" NOT IN ( 'HOU', 'CHN', 'KCA', 'CIN', 'TBA', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so\" > 10.000000 ) THEN -377878.6932995659\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" IN ( 'CHA', 'MIN', 'DET', 'PIT', 'SDN', 'TOR', 'CAL', 'MON', 'HOU', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'ARI', 'COL', 'MIL' ) ) AND ( t1.\"teamidcat\" NOT IN ( 'HOU', 'CHN', 'KCA', 'CIN', 'TBA', 'COL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so\" <= 10.000000 OR t2.\"so\" IS NULL ) THEN -189235.067402297\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'MIN', 'DET', 'PIT', 'SDN', 'TOR', 'CAL', 'MON', 'HOU', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'ARI', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1877456.091549 ) AND ( t1.\"yearid\" > 1997.000000 ) THEN 159947.9031707442\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'MIN', 'DET', 'PIT', 'SDN', 'TOR', 'CAL', 'MON', 'HOU', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'ARI', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1877456.091549 ) AND ( t1.\"yearid\" <= 1997.000000 OR t1.\"yearid\" IS NULL ) THEN -68648.56264199023\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'MIN', 'DET', 'PIT', 'SDN', 'TOR', 'CAL', 'MON', 'HOU', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'ARI', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1877456.091549 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"stint__mapping_4_target_1_avg\" > 2260456.862436 ) THEN -205745.180846416\n", - " WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( 'CHA', 'MIN', 'DET', 'PIT', 'SDN', 'TOR', 'CAL', 'MON', 'HOU', 'CHN', 'BOS', 'TEX', 'KCA', 'OAK', 'CIN', 'TBA', 'SEA', 'ARI', 'COL', 'MIL' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1877456.091549 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"stint__mapping_4_target_1_avg\" <= 2260456.862436 OR t2.\"stint__mapping_4_target_1_avg\" IS NULL ) THEN -29901.2042456888\n", - " ELSE NULL\n", - " END\n", - ") AS \"feature_1_1\",\n", - " t1.rowid AS rownum\n", - "FROM \"SALARIES__STAGING_TABLE_1\" t1\n", - "INNER JOIN \"BATTING__STAGING_TABLE_5\" t2\n", - "ON t1.\"playerid\" = t2.\"playerid\"\n", - "WHERE t2.\"year, '+1.000000 days'\" <= t1.\"year\"\n", - "GROUP BY t1.rowid;\n", + "CREATE TABLE \"FEATURE_1_1\";\n", "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"FEATURE_1_1\";\\n\\nCREATE TABLE \"FEATURE_1_1\" AS\\nSELECT SUM( \\n CASE\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" > 3524469.221923 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2817578.941387 ) AND ( t2.\"sf\" > 4.000000 ) AND ( t1.\"teamidcat__mapping_target_1_avg\" > 3278373.379265 ) THEN 17831446.69388786\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" > 3524469.221923 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2817578.941387 ) AND ( t2.\"sf\" > 4.000000 ) AND ( t1.\"teamidcat__mapping_target_1_avg\" <= 3278373.379265 OR t1.\"teamidcat__mapping_target_1_avg\" IS NULL ) THEN 13033019.84917508\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" > 3524469.221923 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2817578.941387 ) AND ( t2.\"sf\" <= 4.000000 OR t2.\"sf\" IS NULL ) THEN 10225458.61579112\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" > 3524469.221923 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2817578.941387 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) THEN 4368014.022868423\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" <= 3524469.221923 OR t2.\"3b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"so\" > 133.000000 ) AND ( t2.\"g\" > 158.000000 ) THEN 8308478.870471586\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" <= 3524469.221923 OR t2.\"3b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"so\" > 133.000000 ) AND ( t2.\"g\" <= 158.000000 OR t2.\"g\" IS NULL ) THEN 13381597.54436921\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" <= 3524469.221923 OR t2.\"3b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"so\" <= 133.000000 OR t2.\"so\" IS NULL ) AND ( t2.\"r\" > 125.000000 ) AND ( t1.\"yearid\" > 2007.000000 ) THEN 4780941.209306366\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" <= 3524469.221923 OR t2.\"3b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"so\" <= 133.000000 OR t2.\"so\" IS NULL ) AND ( t2.\"r\" > 125.000000 ) AND ( t1.\"yearid\" <= 2007.000000 OR t1.\"yearid\" IS NULL ) THEN 797450.006041609\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" <= 3524469.221923 OR t2.\"3b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"so\" <= 133.000000 OR t2.\"so\" IS NULL ) AND ( t2.\"r\" <= 125.000000 OR t2.\"r\" IS NULL ) AND ( t2.\"ibb\" > 5.000000 ) THEN -1405503.972683714\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" > 204.000000 ) AND ( t2.\"3b__mapping_4_target_1_avg\" <= 3524469.221923 OR t2.\"3b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"so\" <= 133.000000 OR t2.\"so\" IS NULL ) AND ( t2.\"r\" <= 125.000000 OR t2.\"r\" IS NULL ) AND ( t2.\"ibb\" <= 5.000000 OR t2.\"ibb\" IS NULL ) THEN -2977321.709443686\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'FLO\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 10547907.624393 ) AND ( t1.\"year\" - t2.\"year\" > 315576000.000000 ) THEN -12335562.02567152\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'FLO\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 10547907.624393 ) AND ( t1.\"year\" - t2.\"year\" <= 315576000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN -6638308.479956674\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'FLO\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 10547907.624393 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 7667817.821429 ) THEN 5183399.795475774\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'FLO\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 10547907.624393 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 7667817.821429 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 159.000000 ) THEN -1420930.177117448\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'FLO\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 10547907.624393 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 7667817.821429 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 159.000000 OR t2.\"g\" IS NULL ) THEN -7764.261980338949\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'FLO\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 7787763.421053 ) AND ( t2.\"rbi\" > 114.000000 ) AND ( t2.\"gidp\" > 17.000000 ) THEN -1458760.064980729\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'FLO\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 7787763.421053 ) AND ( t2.\"rbi\" > 114.000000 ) AND ( t2.\"gidp\" <= 17.000000 OR t2.\"gidp\" IS NULL ) THEN 2140816.005140664\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'FLO\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 7787763.421053 ) AND ( t2.\"rbi\" <= 114.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"rbi\" > 109.000000 ) THEN 7908513.647891598\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'FLO\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 7787763.421053 ) AND ( t2.\"rbi\" <= 114.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"rbi\" <= 109.000000 OR t2.\"rbi\" IS NULL ) THEN 5355025.682490992\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'FLO\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 7787763.421053 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"3b\" > 0.000000 ) AND ( t2.\"gidp__mapping_4_target_1_avg\" > 5161728.874583 ) THEN 1734029.449810291\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'FLO\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 7787763.421053 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"3b\" > 0.000000 ) AND ( t2.\"gidp__mapping_4_target_1_avg\" <= 5161728.874583 OR t2.\"gidp__mapping_4_target_1_avg\" IS NULL ) THEN 16736.0820059774\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'FLO\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 7787763.421053 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"3b\" <= 0.000000 OR t2.\"3b\" IS NULL ) AND ( t2.\"so\" > 101.000000 ) THEN 592324.4282805945\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" > 220896000.000000 ) AND ( t2.\"h\" <= 204.000000 OR t2.\"h\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'FLO\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 7787763.421053 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"3b\" <= 0.000000 OR t2.\"3b\" IS NULL ) AND ( t2.\"so\" <= 101.000000 OR t2.\"so\" IS NULL ) THEN 6297964.528270307\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" > 4.000000 ) AND ( t2.\"hr\" > 52.000000 ) AND ( t1.\"yearid\" > 2009.000000 ) THEN -2469058.761296282\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" > 4.000000 ) AND ( t2.\"hr\" > 52.000000 ) AND ( t1.\"yearid\" <= 2009.000000 OR t1.\"yearid\" IS NULL ) THEN -342969.1731427578\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" > 4.000000 ) AND ( t2.\"hr\" <= 52.000000 OR t2.\"hr\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'CHA\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'LAA\\', \\'OAK\\', \\'TBA\\', \\'LAN\\', \\'SEA\\', \\'MIL\\', \\'WAS\\' ) ) THEN 2286106.374797928\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" > 4.000000 ) AND ( t2.\"hr\" <= 52.000000 OR t2.\"hr\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'CHA\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'LAA\\', \\'OAK\\', \\'TBA\\', \\'LAN\\', \\'SEA\\', \\'MIL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 2943768.288048326\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" <= 4.000000 OR t2.\"gidp\" IS NULL ) AND ( t1.\"teamidcat__mapping_target_1_avg\" > 2348903.676268 ) AND ( t1.\"year\" - t2.\"year\" > 110419200.000000 ) THEN 11337799.38868177\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" <= 4.000000 OR t2.\"gidp\" IS NULL ) AND ( t1.\"teamidcat__mapping_target_1_avg\" > 2348903.676268 ) AND ( t1.\"year\" - t2.\"year\" <= 110419200.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN 11938856.72068626\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" <= 4.000000 OR t2.\"gidp\" IS NULL ) AND ( t1.\"teamidcat__mapping_target_1_avg\" <= 2348903.676268 OR t1.\"teamidcat__mapping_target_1_avg\" IS NULL ) AND ( t2.\"hr\" > 40.000000 ) THEN 5717192.316234716\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2445850.359960 ) AND ( t2.\"gidp\" <= 4.000000 OR t2.\"gidp\" IS NULL ) AND ( t1.\"teamidcat__mapping_target_1_avg\" <= 2348903.676268 OR t1.\"teamidcat__mapping_target_1_avg\" IS NULL ) AND ( t2.\"hr\" <= 40.000000 OR t2.\"hr\" IS NULL ) THEN 412127.935812093\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" > 5.000000 ) AND ( t1.\"year\" - t2.\"year\" > 78840000.000000 ) AND ( t2.\"lgid\" IN ( \\'NL\\' ) ) THEN 12637045.6269819\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" > 5.000000 ) AND ( t1.\"year\" - t2.\"year\" > 78840000.000000 ) AND ( t2.\"lgid\" NOT IN ( \\'NL\\' ) OR t2.\"lgid\" IS NULL ) THEN 9943870.374674743\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" > 5.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 78840000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"cs__mapping_4_target_1_avg\" > 2599268.209696 ) THEN 5853149.807168381\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" > 5.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 78840000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"cs__mapping_4_target_1_avg\" <= 2599268.209696 OR t2.\"cs__mapping_4_target_1_avg\" IS NULL ) THEN 10297123.203706\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" <= 5.000000 OR t2.\"hbp\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'HOU\\', \\'LAA\\' ) ) AND ( t2.\"lgid\" IN ( \\'NL\\' ) ) THEN 2301092.665443173\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" <= 5.000000 OR t2.\"hbp\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'HOU\\', \\'LAA\\' ) ) AND ( t2.\"lgid\" NOT IN ( \\'NL\\' ) OR t2.\"lgid\" IS NULL ) THEN 1137106.222470851\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" <= 5.000000 OR t2.\"hbp\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'HOU\\', \\'LAA\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sh\" > 1.000000 ) THEN 6594427.200743338\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 2532065.426830 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2445850.359960 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" <= 5.000000 OR t2.\"hbp\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'HOU\\', \\'LAA\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sh\" <= 1.000000 OR t2.\"sh\" IS NULL ) THEN 4269536.548606931\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'SFN\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'COL\\', \\'MIL\\' ) ) AND ( t1.\"teamidcat\" IN ( \\'MON\\', \\'SEA\\' ) ) AND ( t2.\"sf\" > 10.000000 ) THEN 4953529.841573869\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'SFN\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'COL\\', \\'MIL\\' ) ) AND ( t1.\"teamidcat\" IN ( \\'MON\\', \\'SEA\\' ) ) AND ( t2.\"sf\" <= 10.000000 OR t2.\"sf\" IS NULL ) THEN 759470.5739710848\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'SFN\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'COL\\', \\'MIL\\' ) ) AND ( t1.\"teamidcat\" NOT IN ( \\'MON\\', \\'SEA\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"yearid\" > 2000.000000 ) THEN 2124673.314738019\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'SFN\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'COL\\', \\'MIL\\' ) ) AND ( t1.\"teamidcat\" NOT IN ( \\'MON\\', \\'SEA\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"yearid\" <= 2000.000000 OR t1.\"yearid\" IS NULL ) THEN 1628674.49788051\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'SFN\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 7594790.985034 ) AND ( t2.\"so__mapping_4_target_1_avg\" > 5623273.796491 ) THEN 9887623.989501275\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'SFN\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 7594790.985034 ) AND ( t2.\"so__mapping_4_target_1_avg\" <= 5623273.796491 OR t2.\"so__mapping_4_target_1_avg\" IS NULL ) THEN 4015018.961425884\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'SFN\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 7594790.985034 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" > 19.000000 ) THEN -2903895.573442627\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 88.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'CLE\\', \\'SFN\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 7594790.985034 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"hbp\" <= 19.000000 OR t2.\"hbp\" IS NULL ) THEN 2655399.300956024\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'MON\\', \\'HOU\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'NYA\\', \\'MIL\\' ) ) AND ( t1.\"teamidcat\" IN ( \\'TOR\\', \\'MON\\', \\'TEX\\', \\'NYA\\', \\'MIL\\' ) ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 6837325.738196 ) THEN 74519.11647139768\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'MON\\', \\'HOU\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'NYA\\', \\'MIL\\' ) ) AND ( t1.\"teamidcat\" IN ( \\'TOR\\', \\'MON\\', \\'TEX\\', \\'NYA\\', \\'MIL\\' ) ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 6837325.738196 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) THEN -2656056.44245984\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'MON\\', \\'HOU\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'NYA\\', \\'MIL\\' ) ) AND ( t1.\"teamidcat\" NOT IN ( \\'TOR\\', \\'MON\\', \\'TEX\\', \\'NYA\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"h\" > 156.000000 ) THEN 807713.0075840863\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'MON\\', \\'HOU\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'NYA\\', \\'MIL\\' ) ) AND ( t1.\"teamidcat\" NOT IN ( \\'TOR\\', \\'MON\\', \\'TEX\\', \\'NYA\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"h\" <= 156.000000 OR t2.\"h\" IS NULL ) THEN -322806.8891217232\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'MON\\', \\'HOU\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'NYA\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so__mapping_4_target_1_avg\" > 2767704.524823 ) AND ( t2.\"so\" > 86.000000 ) THEN 1375092.34227541\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'MON\\', \\'HOU\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'NYA\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so__mapping_4_target_1_avg\" > 2767704.524823 ) AND ( t2.\"so\" <= 86.000000 OR t2.\"so\" IS NULL ) THEN 3548323.987237754\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'MON\\', \\'HOU\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'NYA\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so__mapping_4_target_1_avg\" <= 2767704.524823 OR t2.\"so__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"r\" > 81.000000 ) THEN -2861693.372766722\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1422344.232425 ) AND ( t1.\"year\" - t2.\"year\" <= 220896000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 2532065.426830 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 88.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'MON\\', \\'HOU\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'NYA\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so__mapping_4_target_1_avg\" <= 2767704.524823 OR t2.\"so__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"r\" <= 81.000000 OR t2.\"r\" IS NULL ) THEN 2427780.671129029\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) ) AND ( t1.\"teamidcat\" IN ( \\'DET\\', \\'FLO\\' ) ) AND ( t2.\"g\" > 149.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 980878.920716 ) THEN -2066963.654041064\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) ) AND ( t1.\"teamidcat\" IN ( \\'DET\\', \\'FLO\\' ) ) AND ( t2.\"g\" > 149.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 980878.920716 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) THEN -2561457.314695126\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) ) AND ( t1.\"teamidcat\" IN ( \\'DET\\', \\'FLO\\' ) ) AND ( t2.\"g\" <= 149.000000 OR t2.\"g\" IS NULL ) THEN -56939.49897956364\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) ) AND ( t1.\"teamidcat\" NOT IN ( \\'DET\\', \\'FLO\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"2b\" > 40.000000 ) AND ( t2.\"ibb\" > 11.000000 ) THEN -756334.6424572867\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) ) AND ( t1.\"teamidcat\" NOT IN ( \\'DET\\', \\'FLO\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"2b\" > 40.000000 ) AND ( t2.\"ibb\" <= 11.000000 OR t2.\"ibb\" IS NULL ) THEN -2482246.014018427\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) ) AND ( t1.\"teamidcat\" NOT IN ( \\'DET\\', \\'FLO\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"2b\" <= 40.000000 OR t2.\"2b\" IS NULL ) AND ( t2.\"gidp__mapping_4_target_1_avg\" > 2924844.781863 ) THEN -264734.4104444506\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) ) AND ( t1.\"teamidcat\" NOT IN ( \\'DET\\', \\'FLO\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"2b\" <= 40.000000 OR t2.\"2b\" IS NULL ) AND ( t2.\"gidp__mapping_4_target_1_avg\" <= 2924844.781863 OR t2.\"gidp__mapping_4_target_1_avg\" IS NULL ) THEN -1275854.008238457\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" > 8.000000 ) AND ( t2.\"r__mapping_4_target_1_avg\" > 6862400.153388 ) AND ( t2.\"ab\" > 598.000000 ) THEN 2930711.219524218\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" > 8.000000 ) AND ( t2.\"r__mapping_4_target_1_avg\" > 6862400.153388 ) AND ( t2.\"ab\" <= 598.000000 OR t2.\"ab\" IS NULL ) THEN 888491.7479723304\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" > 8.000000 ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 6862400.153388 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"3b\" > 6.000000 ) THEN -1968940.528744845\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" > 8.000000 ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 6862400.153388 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"3b\" <= 6.000000 OR t2.\"3b\" IS NULL ) THEN -434760.8464144517\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" <= 8.000000 OR t2.\"sb\" IS NULL ) AND ( t1.\"yearid\" > 1993.000000 ) AND ( t2.\"sb\" > 0.000000 ) THEN 787722.9288528051\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" <= 8.000000 OR t2.\"sb\" IS NULL ) AND ( t1.\"yearid\" > 1993.000000 ) AND ( t2.\"sb\" <= 0.000000 OR t2.\"sb\" IS NULL ) THEN 1741508.752292541\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" <= 8.000000 OR t2.\"sb\" IS NULL ) AND ( t1.\"yearid\" <= 1993.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"3b\" > 3.000000 ) THEN -887089.0926061296\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" > 188028000.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'LAN\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" <= 8.000000 OR t2.\"sb\" IS NULL ) AND ( t1.\"yearid\" <= 1993.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"3b\" <= 3.000000 OR t2.\"3b\" IS NULL ) THEN 725467.1947242735\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\' ) ) AND ( t2.\"rbi\" > 118.000000 ) AND ( t2.\"rbi\" > 127.000000 ) THEN 1729058.542727086\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\' ) ) AND ( t2.\"rbi\" > 118.000000 ) AND ( t2.\"rbi\" <= 127.000000 OR t2.\"rbi\" IS NULL ) THEN 4287607.932803141\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\' ) ) AND ( t2.\"rbi\" <= 118.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"2b\" > 36.000000 ) THEN -22215.63506613447\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\' ) ) AND ( t2.\"rbi\" <= 118.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"2b\" <= 36.000000 OR t2.\"2b\" IS NULL ) THEN 1242531.21738428\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"cs__mapping_4_target_1_avg\" > 3165616.760381 ) AND ( t2.\"h\" > 143.000000 ) THEN 2286294.225443862\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"cs__mapping_4_target_1_avg\" > 3165616.760381 ) AND ( t2.\"h\" <= 143.000000 OR t2.\"h\" IS NULL ) THEN 435769.5655653203\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"cs__mapping_4_target_1_avg\" <= 3165616.760381 OR t2.\"cs__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 155.000000 ) THEN 634917.4267129193\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 1994.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'CIN\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"cs__mapping_4_target_1_avg\" <= 3165616.760381 OR t2.\"cs__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 155.000000 OR t2.\"g\" IS NULL ) THEN 1532029.268131789\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'PHI\\', \\'CHA\\', \\'DET\\', \\'CLE\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'CIN\\', \\'COL\\' ) ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1050962.991468 ) AND ( t1.\"teamidcat\" IN ( \\'PHI\\', \\'CAL\\', \\'CHN\\', \\'CIN\\', \\'COL\\' ) ) THEN 81608.02082083141\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'PHI\\', \\'CHA\\', \\'DET\\', \\'CLE\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'CIN\\', \\'COL\\' ) ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1050962.991468 ) AND ( t1.\"teamidcat\" NOT IN ( \\'PHI\\', \\'CAL\\', \\'CHN\\', \\'CIN\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 839482.9562567974\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'PHI\\', \\'CHA\\', \\'DET\\', \\'CLE\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'CIN\\', \\'COL\\' ) ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1050962.991468 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'DET\\', \\'TOR\\', \\'SFN\\' ) ) THEN 1105652.300210021\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'PHI\\', \\'CHA\\', \\'DET\\', \\'CLE\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'CIN\\', \\'COL\\' ) ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1050962.991468 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'DET\\', \\'TOR\\', \\'SFN\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 2079241.528639275\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'PHI\\', \\'CHA\\', \\'DET\\', \\'CLE\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'CIN\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 6366219.620404 ) AND ( t1.\"teamidcat\" IN ( \\'TEX\\' ) ) THEN 1291619.970540897\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'PHI\\', \\'CHA\\', \\'DET\\', \\'CLE\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'CIN\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 6366219.620404 ) AND ( t1.\"teamidcat\" NOT IN ( \\'TEX\\' ) OR t1.\"teamidcat\" IS NULL ) THEN -798230.9275025943\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'PHI\\', \\'CHA\\', \\'DET\\', \\'CLE\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'CIN\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 6366219.620404 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'LAN\\' ) ) THEN 1321777.302176246\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 1991.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 188028000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 1994.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'PHI\\', \\'CHA\\', \\'DET\\', \\'CLE\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'CIN\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 6366219.620404 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'LAN\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 1690212.481067271\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'SEA\\' ) ) AND ( t1.\"yearid\" > 1990.000000 ) AND ( t1.\"year\" - t2.\"year\" > 126213120.000000 ) AND ( t2.\"g\" > 144.000000 ) THEN -438208.7514224367\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'SEA\\' ) ) AND ( t1.\"yearid\" > 1990.000000 ) AND ( t1.\"year\" - t2.\"year\" > 126213120.000000 ) AND ( t2.\"g\" <= 144.000000 OR t2.\"g\" IS NULL ) THEN 554577.7550109718\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'SEA\\' ) ) AND ( t1.\"yearid\" > 1990.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 126213120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"hbp__mapping_4_target_1_avg\" > 3457023.509385 ) THEN 1221852.947222068\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'SEA\\' ) ) AND ( t1.\"yearid\" > 1990.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 126213120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"hbp__mapping_4_target_1_avg\" <= 3457023.509385 OR t2.\"hbp__mapping_4_target_1_avg\" IS NULL ) THEN 1742833.827132344\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'SEA\\' ) ) AND ( t1.\"yearid\" <= 1990.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hbp\" > 2.000000 ) AND ( t2.\"hr\" > 45.000000 ) THEN 851936.8234773523\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'SEA\\' ) ) AND ( t1.\"yearid\" <= 1990.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hbp\" > 2.000000 ) AND ( t2.\"hr\" <= 45.000000 OR t2.\"hr\" IS NULL ) THEN -803448.4141810529\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'SEA\\' ) ) AND ( t1.\"yearid\" <= 1990.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hbp\" <= 2.000000 OR t2.\"hbp\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'DET\\', \\'SLN\\', \\'CLE\\', \\'CAL\\', \\'CHN\\', \\'SEA\\' ) ) THEN -393165.5748894861\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'SEA\\' ) ) AND ( t1.\"yearid\" <= 1990.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hbp\" <= 2.000000 OR t2.\"hbp\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'DET\\', \\'SLN\\', \\'CLE\\', \\'CAL\\', \\'CHN\\', \\'SEA\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 165084.4405416189\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'SEA\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"hbp\" > 16.000000 ) THEN -2076590.359304502\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'SEA\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"hbp\" <= 16.000000 OR t2.\"hbp\" IS NULL ) AND ( t2.\"2b\" > 44.000000 ) AND ( t2.\"r\" > 106.000000 ) THEN -84839.33213770755\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'SEA\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"hbp\" <= 16.000000 OR t2.\"hbp\" IS NULL ) AND ( t2.\"2b\" > 44.000000 ) AND ( t2.\"r\" <= 106.000000 OR t2.\"r\" IS NULL ) THEN -1435082.757727482\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'SEA\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"hbp\" <= 16.000000 OR t2.\"hbp\" IS NULL ) AND ( t2.\"2b\" <= 44.000000 OR t2.\"2b\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 7917315.804014 ) THEN 705114.0847788993\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 4380933.785714 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'CHN\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'SEA\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"hbp\" <= 16.000000 OR t2.\"hbp\" IS NULL ) AND ( t2.\"2b\" <= 44.000000 OR t2.\"2b\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 7917315.804014 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) THEN -33736.08383041415\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" > 9.000000 ) AND ( t2.\"sb\" > 7.000000 ) AND ( t1.\"year\" - t2.\"year\" > 115689600.000000 ) AND ( t1.\"teamidcat__mapping_target_1_avg\" > 1695158.861504 ) THEN -1273856.73182487\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" > 9.000000 ) AND ( t2.\"sb\" > 7.000000 ) AND ( t1.\"year\" - t2.\"year\" > 115689600.000000 ) AND ( t1.\"teamidcat__mapping_target_1_avg\" <= 1695158.861504 OR t1.\"teamidcat__mapping_target_1_avg\" IS NULL ) THEN -1441184.077447976\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" > 9.000000 ) AND ( t2.\"sb\" > 7.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 115689600.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"lgid\" IN ( \\'NL\\' ) ) THEN -891894.7535893385\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" > 9.000000 ) AND ( t2.\"sb\" > 7.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 115689600.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"lgid\" NOT IN ( \\'NL\\' ) OR t2.\"lgid\" IS NULL ) THEN -978669.8744009694\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" > 9.000000 ) AND ( t2.\"sb\" <= 7.000000 OR t2.\"sb\" IS NULL ) AND ( t2.\"ab\" > 601.000000 ) THEN -1929719.659923676\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" > 9.000000 ) AND ( t2.\"sb\" <= 7.000000 OR t2.\"sb\" IS NULL ) AND ( t2.\"ab\" <= 601.000000 OR t2.\"ab\" IS NULL ) THEN -1784951.611518287\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" > 5747507.856062 ) AND ( t2.\"rbi\" > 104.000000 ) AND ( t2.\"ab\" > 613.000000 ) THEN 299571.4887037456\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" > 5747507.856062 ) AND ( t2.\"rbi\" > 104.000000 ) AND ( t2.\"ab\" <= 613.000000 OR t2.\"ab\" IS NULL ) THEN -108783.1213653798\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" > 5747507.856062 ) AND ( t2.\"rbi\" <= 104.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"ab\" > 605.000000 ) THEN -576979.1608678327\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" > 5747507.856062 ) AND ( t2.\"rbi\" <= 104.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"ab\" <= 605.000000 OR t2.\"ab\" IS NULL ) THEN -780620.9167845835\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" <= 5747507.856062 OR t2.\"2b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" > 60.000000 ) AND ( t2.\"so\" > 83.000000 ) THEN -1195250.620596347\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" <= 5747507.856062 OR t2.\"2b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" > 60.000000 ) AND ( t2.\"so\" <= 83.000000 OR t2.\"so\" IS NULL ) THEN -1669797.597007224\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" <= 5747507.856062 OR t2.\"2b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" <= 60.000000 OR t2.\"bb\" IS NULL ) AND ( t1.\"yearid\" > 1987.000000 ) THEN -1029048.205249076\\n WHEN ( t2.\"rbi\" > 98.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1422344.232425 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 1991.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 4380933.785714 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sf\" <= 9.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"2b__mapping_4_target_1_avg\" <= 5747507.856062 OR t2.\"2b__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" <= 60.000000 OR t2.\"bb\" IS NULL ) AND ( t1.\"yearid\" <= 1987.000000 OR t1.\"yearid\" IS NULL ) THEN -497075.1454558557\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 4484061.659837 ) AND ( t2.\"ab\" > 658.000000 ) THEN 9015795.32266753\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 4484061.659837 ) AND ( t2.\"ab\" <= 658.000000 OR t2.\"ab\" IS NULL ) AND ( t2.\"bb\" > 117.000000 ) THEN -2940802.256897952\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 4484061.659837 ) AND ( t2.\"ab\" <= 658.000000 OR t2.\"ab\" IS NULL ) AND ( t2.\"bb\" <= 117.000000 OR t2.\"bb\" IS NULL ) THEN 539510.027445049\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 4484061.659837 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'PHI\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'CIN\\', \\'NYA\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) ) AND ( t2.\"cs\" > 6.000000 ) THEN -464477.9698077337\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 4484061.659837 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'PHI\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'CIN\\', \\'NYA\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) ) AND ( t2.\"cs\" <= 6.000000 OR t2.\"cs\" IS NULL ) THEN -1246812.213610361\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 4484061.659837 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'PHI\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'CIN\\', \\'NYA\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so\" > 116.000000 ) THEN 696630.2115335638\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" > 6.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 4484061.659837 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'PHI\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'CIN\\', \\'NYA\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so\" <= 116.000000 OR t2.\"so\" IS NULL ) THEN -334497.1976938579\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" > 4024078.581067 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 2389827.865089 ) THEN 207152.8141326556\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" > 4024078.581067 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 2389827.865089 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) THEN 2313501.101834453\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 4024078.581067 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'SDN\\', \\'FLO\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'SEA\\', \\'ARI\\' ) ) THEN -1178443.556344871\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 4024078.581067 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'SDN\\', \\'FLO\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'SEA\\', \\'ARI\\' ) OR t1.\"teamidcat\" IS NULL ) THEN -364152.5416652811\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 283996800.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 3354247.290435 ) THEN 3780702.470172695\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 283996800.000000 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 3354247.290435 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) THEN 762443.5603790393\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 283996800.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"h\" > 4.000000 ) THEN 1562467.724549611\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" > 279385990.243902 ) AND ( t2.\"r\" <= 6.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 283996800.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"h\" <= 4.000000 OR t2.\"h\" IS NULL ) THEN 5862052.941262188\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 3372308.829642 ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'CIN\\', \\'TBA\\', \\'LAN\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) ) AND ( t1.\"year\" - t2.\"year\" > 245096640.000000 ) THEN -432045.6323970378\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 3372308.829642 ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'CIN\\', \\'TBA\\', \\'LAN\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) ) AND ( t1.\"year\" - t2.\"year\" <= 245096640.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN 944821.2334703418\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 3372308.829642 ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'CIN\\', \\'TBA\\', \\'LAN\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 9258746.205729 ) THEN 5808551.10047887\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 3372308.829642 ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'CIN\\', \\'TBA\\', \\'LAN\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 9258746.205729 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) THEN 1257324.355683805\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 3372308.829642 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'DET\\', \\'SLN\\', \\'SDN\\', \\'SFN\\', \\'FLO\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'LAN\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 2297583.020898 ) THEN 1401751.569226111\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 3372308.829642 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'DET\\', \\'SLN\\', \\'SDN\\', \\'SFN\\', \\'FLO\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'LAN\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 2297583.020898 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) THEN -88745.88956094126\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 3372308.829642 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'DET\\', \\'SLN\\', \\'SDN\\', \\'SFN\\', \\'FLO\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'LAN\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 2421385.281385 ) THEN 2420104.908118949\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2006.000000 ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 3372308.829642 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'DET\\', \\'SLN\\', \\'SDN\\', \\'SFN\\', \\'FLO\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'LAN\\', \\'ARI\\', \\'COL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 2421385.281385 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) THEN 920857.3464225928\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'SLN\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'CIN\\', \\'TBA\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 2306899.036874 ) AND ( t2.\"r__mapping_4_target_1_avg\" > 5901332.613177 ) THEN 1345985.226921735\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'SLN\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'CIN\\', \\'TBA\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 2306899.036874 ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 5901332.613177 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) THEN 652533.1484379417\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'SLN\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'CIN\\', \\'TBA\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 2306899.036874 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'CIN\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) ) THEN -53265.81894274038\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'SLN\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'CIN\\', \\'TBA\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 2306899.036874 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'CIN\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 1182642.778025358\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'SLN\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'CIN\\', \\'TBA\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r\" > 10.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 2380634.856708 ) THEN 957799.7936397812\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'SLN\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'CIN\\', \\'TBA\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r\" > 10.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 2380634.856708 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) THEN -285286.1183103392\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'SLN\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'CIN\\', \\'TBA\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r\" <= 10.000000 OR t2.\"r\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 2680589.378820 ) THEN 4177453.288317214\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3415161.667035 ) AND ( t1.\"year\" - t2.\"year\" <= 279385990.243902 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2006.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'SLN\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'FLO\\', \\'CHN\\', \\'ANA\\', \\'KCA\\', \\'CIN\\', \\'TBA\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"r\" <= 10.000000 OR t2.\"r\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 2680589.378820 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) THEN 1126348.606888345\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" > 277490078.350515 ) AND ( t1.\"teamidcat\" IN ( \\'PHI\\', \\'CHA\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'CIN\\', \\'TBA\\' ) ) AND ( t2.\"hr__mapping_4_target_1_avg\" > 1950532.235769 ) THEN -186366.1893148199\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" > 277490078.350515 ) AND ( t1.\"teamidcat\" IN ( \\'PHI\\', \\'CHA\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'CIN\\', \\'TBA\\' ) ) AND ( t2.\"hr__mapping_4_target_1_avg\" <= 1950532.235769 OR t2.\"hr__mapping_4_target_1_avg\" IS NULL ) THEN -1515655.815755094\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" > 277490078.350515 ) AND ( t1.\"teamidcat\" NOT IN ( \\'PHI\\', \\'CHA\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'CIN\\', \\'TBA\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"g_batting__mapping_4_target_1_avg\" > 3354271.113067 ) THEN 1243978.023249262\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" > 277490078.350515 ) AND ( t1.\"teamidcat\" NOT IN ( \\'PHI\\', \\'CHA\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'CIN\\', \\'TBA\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"g_batting__mapping_4_target_1_avg\" <= 3354271.113067 OR t2.\"g_batting__mapping_4_target_1_avg\" IS NULL ) THEN 43279.24666501496\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" <= 277490078.350515 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2007.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'LAN\\', \\'ARI\\', \\'MIL\\', \\'WAS\\' ) ) THEN 493393.1459213803\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" <= 277490078.350515 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" > 2007.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'LAN\\', \\'ARI\\', \\'MIL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 897475.1254280825\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" <= 277490078.350515 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2007.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'CHA\\', \\'MIN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'ARI\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) ) THEN 323287.5884791795\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1948155.254896 ) AND ( t1.\"year\" - t2.\"year\" <= 277490078.350515 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid\" <= 2007.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'CHA\\', \\'MIN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'ARI\\', \\'COL\\', \\'MIL\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 492909.1888216178\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" > 1.000000 ) AND ( t1.\"teamidcat\" IN ( \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'LAA\\', \\'TEX\\', \\'KCA\\', \\'TBA\\', \\'NYA\\', \\'SEA\\' ) ) AND ( t2.\"stint\" > 1.000000 ) THEN -1735851.766498642\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" > 1.000000 ) AND ( t1.\"teamidcat\" IN ( \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'LAA\\', \\'TEX\\', \\'KCA\\', \\'TBA\\', \\'NYA\\', \\'SEA\\' ) ) AND ( t2.\"stint\" <= 1.000000 OR t2.\"stint\" IS NULL ) THEN -29815.28077956744\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" > 1.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'LAA\\', \\'TEX\\', \\'KCA\\', \\'TBA\\', \\'NYA\\', \\'SEA\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so__mapping_4_target_1_avg\" > 3572171.452907 ) THEN 3788305.749866242\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" > 1.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'LAA\\', \\'TEX\\', \\'KCA\\', \\'TBA\\', \\'NYA\\', \\'SEA\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so__mapping_4_target_1_avg\" <= 3572171.452907 OR t2.\"so__mapping_4_target_1_avg\" IS NULL ) THEN 708890.2750639512\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" <= 1.000000 OR t2.\"sh\" IS NULL ) AND ( t2.\"2b\" > 28.000000 ) AND ( t1.\"year\" - t2.\"year\" > 141998400.000000 ) THEN 7195605.973000124\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" <= 1.000000 OR t2.\"sh\" IS NULL ) AND ( t2.\"2b\" > 28.000000 ) AND ( t1.\"year\" - t2.\"year\" <= 141998400.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN 2288343.30097137\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" <= 1.000000 OR t2.\"sh\" IS NULL ) AND ( t2.\"2b\" <= 28.000000 OR t2.\"2b\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1937568.581933 ) THEN 405193.9535521567\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" > 2346729.043478 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1948155.254896 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"sh\" <= 1.000000 OR t2.\"sh\" IS NULL ) AND ( t2.\"2b\" <= 28.000000 OR t2.\"2b\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1937568.581933 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) THEN -96082.27285004737\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 3265075.202703 ) AND ( t2.\"sb__mapping_4_target_1_avg\" > 4039401.373069 ) AND ( t1.\"year\" - t2.\"year\" > 94737600.000000 ) THEN 3890433.011964486\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 3265075.202703 ) AND ( t2.\"sb__mapping_4_target_1_avg\" > 4039401.373069 ) AND ( t1.\"year\" - t2.\"year\" <= 94737600.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN 7165341.223999773\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 3265075.202703 ) AND ( t2.\"sb__mapping_4_target_1_avg\" <= 4039401.373069 OR t2.\"sb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'NYN\\', \\'SLN\\', \\'SDN\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'WAS\\' ) ) THEN 60143.30416753495\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 3265075.202703 ) AND ( t2.\"sb__mapping_4_target_1_avg\" <= 4039401.373069 OR t2.\"sb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'NYN\\', \\'SLN\\', \\'SDN\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'CHN\\', \\'LAA\\', \\'TEX\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'WAS\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 785465.8790671246\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 3265075.202703 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'MIN\\', \\'NYN\\', \\'PIT\\', \\'LAA\\', \\'TEX\\', \\'CIN\\', \\'SEA\\', \\'MIL\\' ) ) AND ( t2.\"bb__mapping_4_target_1_avg\" > 4008061.580488 ) THEN -3073536.829298617\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 3265075.202703 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'MIN\\', \\'NYN\\', \\'PIT\\', \\'LAA\\', \\'TEX\\', \\'CIN\\', \\'SEA\\', \\'MIL\\' ) ) AND ( t2.\"bb__mapping_4_target_1_avg\" <= 4008061.580488 OR t2.\"bb__mapping_4_target_1_avg\" IS NULL ) THEN -168259.6478932631\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 3265075.202703 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'MIN\\', \\'NYN\\', \\'PIT\\', \\'LAA\\', \\'TEX\\', \\'CIN\\', \\'SEA\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 4377701.761505 ) THEN 2813497.7520666\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" > 2003.000000 ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 3265075.202703 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'MIN\\', \\'NYN\\', \\'PIT\\', \\'LAA\\', \\'TEX\\', \\'CIN\\', \\'SEA\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 4377701.761505 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) THEN -34059.23370697356\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 93048406.779661 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'OAK\\', \\'NYA\\', \\'LAN\\', \\'SEA\\', \\'ARI\\' ) ) AND ( t2.\"so__mapping_4_target_1_avg\" > 2862333.087314 ) THEN 586424.2968394028\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 93048406.779661 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'OAK\\', \\'NYA\\', \\'LAN\\', \\'SEA\\', \\'ARI\\' ) ) AND ( t2.\"so__mapping_4_target_1_avg\" <= 2862333.087314 OR t2.\"so__mapping_4_target_1_avg\" IS NULL ) THEN 15803.25934379456\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 93048406.779661 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'OAK\\', \\'NYA\\', \\'LAN\\', \\'SEA\\', \\'ARI\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"gidp\" > 0.000000 ) THEN 141567.8693129552\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 93048406.779661 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'OAK\\', \\'NYA\\', \\'LAN\\', \\'SEA\\', \\'ARI\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"gidp\" <= 0.000000 OR t2.\"gidp\" IS NULL ) THEN 297961.6136731131\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 93048406.779661 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"gidp__mapping_4_target_1_avg\" > 2365745.163558 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'SFN\\', \\'MON\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\' ) ) THEN -166153.6106408061\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 93048406.779661 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"gidp__mapping_4_target_1_avg\" > 2365745.163558 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'SDN\\', \\'SFN\\', \\'MON\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'NYA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 394602.9759363749\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 93048406.779661 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"gidp__mapping_4_target_1_avg\" <= 2365745.163558 OR t2.\"gidp__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'PIT\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'TEX\\', \\'LAN\\' ) ) THEN -503163.5372068749\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" > 1999.000000 ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3415161.667035 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h__mapping_4_target_1_avg\" <= 2346729.043478 OR t2.\"h__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"yearid\" <= 2003.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 93048406.779661 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"gidp__mapping_4_target_1_avg\" <= 2365745.163558 OR t2.\"gidp__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'PIT\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'TEX\\', \\'LAN\\' ) OR t1.\"teamidcat\" IS NULL ) THEN -148632.3815167727\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'LAN\\', \\'COL\\', \\'MIL\\' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3844897.418860 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SFN\\', \\'HOU\\', \\'TEX\\', \\'KCA\\', \\'LAN\\', \\'COL\\', \\'MIL\\' ) ) THEN 26317.34223697035\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'LAN\\', \\'COL\\', \\'MIL\\' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3844897.418860 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ML4\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SFN\\', \\'HOU\\', \\'TEX\\', \\'KCA\\', \\'LAN\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 259333.6616293485\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'LAN\\', \\'COL\\', \\'MIL\\' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3844897.418860 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"2b\" > 1.000000 ) THEN -198499.170444293\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'LAN\\', \\'COL\\', \\'MIL\\' ) ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3844897.418860 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"2b\" <= 1.000000 OR t2.\"2b\" IS NULL ) THEN 33637.81807537523\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'LAN\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sf\" > 0.000000 ) AND ( t2.\"hr\" > 1.000000 ) THEN 100834.4321334281\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'LAN\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sf\" > 0.000000 ) AND ( t2.\"hr\" <= 1.000000 OR t2.\"hr\" IS NULL ) THEN -528931.3488220501\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'LAN\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sf\" <= 0.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 2400631.103154 ) THEN 993383.2764401076\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" > 246894776.470588 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ML4\\', \\'CHA\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'LAN\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sf\" <= 0.000000 OR t2.\"sf\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 2400631.103154 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) THEN 184605.7795152585\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3755593.787617 ) AND ( t1.\"yearid\" > 1995.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'CHN\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'NYA\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'MIL\\' ) ) THEN 571944.0133884383\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3755593.787617 ) AND ( t1.\"yearid\" > 1995.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ML4\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'CHN\\', \\'ANA\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'NYA\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 1076254.475643927\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3755593.787617 ) AND ( t1.\"yearid\" <= 1995.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'TOR\\', \\'FLO\\', \\'HOU\\', \\'OAK\\', \\'LAN\\' ) ) THEN 288696.1657275385\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 3755593.787617 ) AND ( t1.\"yearid\" <= 1995.000000 OR t1.\"yearid\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'TOR\\', \\'FLO\\', \\'HOU\\', \\'OAK\\', \\'LAN\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 489818.7057362146\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3755593.787617 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 2495401.212121 ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'COL\\' ) ) THEN 266597.649319868\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3755593.787617 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" > 2495401.212121 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ML4\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'ANA\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 470476.0631748594\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3755593.787617 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 2495401.212121 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h\" > 14.000000 ) THEN 69770.31528237762\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" > 852266.372683 ) AND ( t1.\"year\" - t2.\"year\" <= 246894776.470588 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 3755593.787617 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 2495401.212121 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"h\" <= 14.000000 OR t2.\"h\" IS NULL ) THEN 298744.622680815\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'KCA\\', \\'NYA\\', \\'LAN\\' ) ) AND ( t2.\"hr\" > 20.000000 ) AND ( t2.\"2b\" > 24.000000 ) THEN 428290.7475129453\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'KCA\\', \\'NYA\\', \\'LAN\\' ) ) AND ( t2.\"hr\" > 20.000000 ) AND ( t2.\"2b\" <= 24.000000 OR t2.\"2b\" IS NULL ) THEN 102915.6160999416\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'KCA\\', \\'NYA\\', \\'LAN\\' ) ) AND ( t2.\"hr\" <= 20.000000 OR t2.\"hr\" IS NULL ) AND ( t2.\"g\" > 118.000000 ) THEN 30737.78353356822\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'KCA\\', \\'NYA\\', \\'LAN\\' ) ) AND ( t2.\"hr\" <= 20.000000 OR t2.\"hr\" IS NULL ) AND ( t2.\"g\" <= 118.000000 OR t2.\"g\" IS NULL ) THEN 1059157.360125734\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'KCA\\', \\'NYA\\', \\'LAN\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 3652422.087697 ) AND ( t2.\"rbi\" > 58.000000 ) THEN 353205.0511713083\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'KCA\\', \\'NYA\\', \\'LAN\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ibb__mapping_4_target_1_avg\" > 3652422.087697 ) AND ( t2.\"rbi\" <= 58.000000 OR t2.\"rbi\" IS NULL ) THEN -216934.803958123\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'KCA\\', \\'NYA\\', \\'LAN\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 3652422.087697 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 504900000.000000 ) THEN 1597455.261739174\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3528828.289696 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'TOR\\', \\'SFN\\', \\'HOU\\', \\'CHN\\', \\'KCA\\', \\'NYA\\', \\'LAN\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"ibb__mapping_4_target_1_avg\" <= 3652422.087697 OR t2.\"ibb__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 504900000.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN 79378.25632302785\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" > 1.000000 ) AND ( t2.\"r\" > 65.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'ML4\\', \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'NYA\\', \\'LAN\\', \\'SEA\\' ) ) THEN 90779.34670529928\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" > 1.000000 ) AND ( t2.\"r\" > 65.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'ML4\\', \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'DET\\', \\'NYN\\', \\'CLE\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'NYA\\', \\'LAN\\', \\'SEA\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 260366.5124278691\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" > 1.000000 ) AND ( t2.\"r\" <= 65.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"yearid\" > 1989.000000 ) THEN 65311.4575797993\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" > 1.000000 ) AND ( t2.\"r\" <= 65.000000 OR t2.\"r\" IS NULL ) AND ( t1.\"yearid\" <= 1989.000000 OR t1.\"yearid\" IS NULL ) THEN 2524.615618461127\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" <= 1.000000 OR t2.\"bb\" IS NULL ) AND ( t2.\"g_old\" > 33.000000 ) AND ( t2.\"sh__mapping_4_target_1_avg\" > 3233879.322917 ) THEN 1596488.852203071\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" <= 1.000000 OR t2.\"bb\" IS NULL ) AND ( t2.\"g_old\" > 33.000000 ) AND ( t2.\"sh__mapping_4_target_1_avg\" <= 3233879.322917 OR t2.\"sh__mapping_4_target_1_avg\" IS NULL ) THEN 198650.7766067838\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" <= 1.000000 OR t2.\"bb\" IS NULL ) AND ( t2.\"g_old\" <= 33.000000 OR t2.\"g_old\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'ML4\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'CHN\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'NYA\\', \\'LAN\\', \\'SEA\\' ) ) THEN 9038.071363796065\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 2358691.683334 ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 852266.372683 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3528828.289696 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"bb\" <= 1.000000 OR t2.\"bb\" IS NULL ) AND ( t2.\"g_old\" <= 33.000000 OR t2.\"g_old\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'ML4\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'CHN\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'NYA\\', \\'LAN\\', \\'SEA\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 106460.9590260297\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'NYN\\', \\'CLE\\', \\'CAL\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'COL\\' ) ) AND ( t2.\"r__mapping_4_target_1_avg\" > 3803688.108904 ) THEN -1578549.508293867\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'NYN\\', \\'CLE\\', \\'CAL\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'COL\\' ) ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 3803688.108904 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 465472800.000000 ) THEN -1858762.370473573\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'NYN\\', \\'CLE\\', \\'CAL\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'COL\\' ) ) AND ( t2.\"r__mapping_4_target_1_avg\" <= 3803688.108904 OR t2.\"r__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 465472800.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN 99626.25097574403\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'NYN\\', \\'CLE\\', \\'CAL\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" > 8.000000 ) AND ( t2.\"3b\" > 1.000000 ) THEN 839300.3144662131\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'NYN\\', \\'CLE\\', \\'CAL\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" > 8.000000 ) AND ( t2.\"3b\" <= 1.000000 OR t2.\"3b\" IS NULL ) THEN 2357809.448525155\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'NYN\\', \\'CLE\\', \\'CAL\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" <= 8.000000 OR t2.\"sb\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'PHI\\', \\'MIN\\', \\'DET\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'NYA\\', \\'LAN\\', \\'SEA\\' ) ) THEN 220864.1936158802\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" > 3014921.257216 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'NYN\\', \\'CLE\\', \\'CAL\\', \\'HOU\\', \\'CHN\\', \\'ANA\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"sb\" <= 8.000000 OR t2.\"sb\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'PHI\\', \\'MIN\\', \\'DET\\', \\'SDN\\', \\'TOR\\', \\'FLO\\', \\'NYA\\', \\'LAN\\', \\'SEA\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 695726.1020682579\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" > 5.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'ML4\\', \\'BAL\\', \\'MIN\\', \\'NYN\\', \\'CLE\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'ARI\\', \\'MIL\\' ) ) AND ( t1.\"year\" - t2.\"year\" > 90704571.428571 ) THEN 3455.802687050847\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" > 5.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'ML4\\', \\'BAL\\', \\'MIN\\', \\'NYN\\', \\'CLE\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'ARI\\', \\'MIL\\' ) ) AND ( t1.\"year\" - t2.\"year\" <= 90704571.428571 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) THEN -131057.8385539503\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" > 5.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'ML4\\', \\'BAL\\', \\'MIN\\', \\'NYN\\', \\'CLE\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'ARI\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"bb__mapping_4_target_1_avg\" > 5665635.637450 ) THEN 2196129.991609469\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" > 5.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'ML4\\', \\'BAL\\', \\'MIN\\', \\'NYN\\', \\'CLE\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'MON\\', \\'FLO\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'ARI\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"bb__mapping_4_target_1_avg\" <= 5665635.637450 OR t2.\"bb__mapping_4_target_1_avg\" IS NULL ) THEN 22562.22441162112\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" <= 5.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2174612.509929 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'CAL\\', \\'MON\\', \\'FLO\\', \\'BOS\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\' ) ) THEN 217655.0111794165\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" <= 5.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"g_old__mapping_4_target_1_avg\" > 2174612.509929 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'BAL\\', \\'PHI\\', \\'MIN\\', \\'NYN\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'CAL\\', \\'MON\\', \\'FLO\\', \\'BOS\\', \\'TEX\\', \\'OAK\\', \\'CIN\\', \\'LAN\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 971804.2321065853\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" <= 5.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2174612.509929 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'ML4\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'LAN\\', \\'ARI\\', \\'MIL\\' ) ) THEN 9093.799790580963\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" > 17.000000 ) AND ( t2.\"rbi__mapping_4_target_1_avg\" <= 3014921.257216 OR t2.\"rbi__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"rbi\" <= 5.000000 OR t2.\"rbi\" IS NULL ) AND ( t2.\"g_old__mapping_4_target_1_avg\" <= 2174612.509929 OR t2.\"g_old__mapping_4_target_1_avg\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'ML4\\', \\'BAL\\', \\'PHI\\', \\'CHA\\', \\'DET\\', \\'SLN\\', \\'CLE\\', \\'PIT\\', \\'TOR\\', \\'CAL\\', \\'SFN\\', \\'MON\\', \\'FLO\\', \\'HOU\\', \\'ANA\\', \\'BOS\\', \\'KCA\\', \\'OAK\\', \\'TBA\\', \\'NYA\\', \\'LAN\\', \\'ARI\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) THEN 101905.3529629898\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'ML4\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'MON\\', \\'ANA\\', \\'TBA\\', \\'NYA\\', \\'MIL\\' ) ) AND ( t1.\"year\" - t2.\"year\" > 206458971.428571 ) AND ( t1.\"yearid\" > 1990.000000 ) THEN -220633.6421108029\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'ML4\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'MON\\', \\'ANA\\', \\'TBA\\', \\'NYA\\', \\'MIL\\' ) ) AND ( t1.\"year\" - t2.\"year\" > 206458971.428571 ) AND ( t1.\"yearid\" <= 1990.000000 OR t1.\"yearid\" IS NULL ) THEN -46202.41086603263\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'ML4\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'MON\\', \\'ANA\\', \\'TBA\\', \\'NYA\\', \\'MIL\\' ) ) AND ( t1.\"year\" - t2.\"year\" <= 206458971.428571 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" > 2003930.720137 ) THEN -86891.55848225483\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" IN ( \\'ATL\\', \\'ML4\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'MON\\', \\'ANA\\', \\'TBA\\', \\'NYA\\', \\'MIL\\' ) ) AND ( t1.\"year\" - t2.\"year\" <= 206458971.428571 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t2.\"ab__mapping_4_target_1_avg\" <= 2003930.720137 OR t2.\"ab__mapping_4_target_1_avg\" IS NULL ) THEN 184975.8157626599\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'ML4\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'MON\\', \\'ANA\\', \\'TBA\\', \\'NYA\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 529141730.232558 ) AND ( t1.\"teamidcat\" IN ( \\'TOR\\', \\'CHN\\' ) ) THEN -1709147.241353896\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'ML4\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'MON\\', \\'ANA\\', \\'TBA\\', \\'NYA\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 529141730.232558 ) AND ( t1.\"teamidcat\" NOT IN ( \\'TOR\\', \\'CHN\\' ) OR t1.\"teamidcat\" IS NULL ) THEN -351895.5154386133\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'ML4\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'MON\\', \\'ANA\\', \\'TBA\\', \\'NYA\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 529141730.232558 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" > 1037323.855048 ) THEN 100271.6762462905\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" > 154617120.000000 ) AND ( t1.\"teamidcat\" NOT IN ( \\'ATL\\', \\'ML4\\', \\'PHI\\', \\'CHA\\', \\'MIN\\', \\'DET\\', \\'CLE\\', \\'SDN\\', \\'CAL\\', \\'MON\\', \\'ANA\\', \\'TBA\\', \\'NYA\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 529141730.232558 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"yearid__mapping_target_1_avg\" <= 1037323.855048 OR t1.\"yearid__mapping_target_1_avg\" IS NULL ) THEN -42.6917691065336\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\' ) ) AND ( t1.\"teamidcat\" IN ( \\'HOU\\', \\'CHN\\', \\'KCA\\', \\'CIN\\', \\'TBA\\', \\'COL\\' ) ) AND ( t2.\"sh\" > 3.000000 ) THEN 145993.2364913361\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\' ) ) AND ( t1.\"teamidcat\" IN ( \\'HOU\\', \\'CHN\\', \\'KCA\\', \\'CIN\\', \\'TBA\\', \\'COL\\' ) ) AND ( t2.\"sh\" <= 3.000000 OR t2.\"sh\" IS NULL ) THEN -270973.3848916991\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\' ) ) AND ( t1.\"teamidcat\" NOT IN ( \\'HOU\\', \\'CHN\\', \\'KCA\\', \\'CIN\\', \\'TBA\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so\" > 10.000000 ) THEN -377878.6932995659\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\' ) ) AND ( t1.\"teamidcat\" NOT IN ( \\'HOU\\', \\'CHN\\', \\'KCA\\', \\'CIN\\', \\'TBA\\', \\'COL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"so\" <= 10.000000 OR t2.\"so\" IS NULL ) THEN -189235.067402297\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1877456.091549 ) AND ( t1.\"yearid\" > 1997.000000 ) THEN 159947.9031707442\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" > 1877456.091549 ) AND ( t1.\"yearid\" <= 1997.000000 OR t1.\"yearid\" IS NULL ) THEN -68648.56264199023\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1877456.091549 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"stint__mapping_4_target_1_avg\" > 2260456.862436 ) THEN -205745.180846416\\n WHEN ( t2.\"rbi\" <= 98.000000 OR t2.\"rbi\" IS NULL ) AND ( t1.\"yearid\" <= 1999.000000 OR t1.\"yearid\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 2358691.683334 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"g\" <= 17.000000 OR t2.\"g\" IS NULL ) AND ( t1.\"year\" - t2.\"year\" <= 154617120.000000 OR t1.\"year\" IS NULL OR t2.\"year\" IS NULL ) AND ( t1.\"teamidcat\" NOT IN ( \\'CHA\\', \\'MIN\\', \\'DET\\', \\'PIT\\', \\'SDN\\', \\'TOR\\', \\'CAL\\', \\'MON\\', \\'HOU\\', \\'CHN\\', \\'BOS\\', \\'TEX\\', \\'KCA\\', \\'OAK\\', \\'CIN\\', \\'TBA\\', \\'SEA\\', \\'ARI\\', \\'COL\\', \\'MIL\\' ) OR t1.\"teamidcat\" IS NULL ) AND ( t2.\"g__mapping_4_target_1_avg\" <= 1877456.091549 OR t2.\"g__mapping_4_target_1_avg\" IS NULL ) AND ( t2.\"stint__mapping_4_target_1_avg\" <= 2260456.862436 OR t2.\"stint__mapping_4_target_1_avg\" IS NULL ) THEN -29901.2042456888\\n ELSE NULL\\n END\\n) AS \"feature_1_1\",\\n t1.rowid AS rownum\\nFROM \"SALARIES__STAGING_TABLE_1\" t1\\nINNER JOIN \"BATTING__STAGING_TABLE_5\" t2\\nON t1.\"playerid\" = t2.\"playerid\"\\nWHERE t2.\"year, \\'+1.000000 days\\'\" <= t1.\"year\"\\nGROUP BY t1.rowid;'" + "'-- The size of the SQL code for FEATURE_1_1 is 139595 characters, which is greater than the size_threshold of 50000!\\n-- To display very long features like this anyway, increase the size_threshold or set the size_threshold to None.\\nDROP TABLE IF EXISTS \"FEATURE_1_1\";\\n\\nCREATE TABLE \"FEATURE_1_1\";'" ] }, "execution_count": 64, @@ -259056,7 +258800,7 @@ "source": [ "# Creates a folder named baseball_pipeline containing\n", "# the SQL code.\n", - "pipe2.features.to_sql().save(\"baseball_pipeline\")" + "pipe2.features.to_sql(size_threshold=None).save(\"baseball_pipeline\", remove=True)" ] }, { @@ -259067,7 +258811,7 @@ "source": [ "# Creates a folder named baseball_pipeline_spark containing\n", "# the SQL code.\n", - "pipe2.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"baseball_pipeline_spark\")" + "pipe2.features.to_sql(dialect=getml.pipeline.dialect.spark_sql, size_threshold=None).save(\"baseball_pipeline_spark\", remove=True)" ] }, { diff --git a/consumer_expenditures.ipynb b/consumer_expenditures.ipynb index f30395d..00d9c42 100644 --- a/consumer_expenditures.ipynb +++ b/consumer_expenditures.ipynb @@ -143,28 +143,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220322113925.log.\n", + "getML engine is already running.\n", "\n", "\n", + "Loading pipelines...\n", + "[========================================] 100%\n", + "\n", "\n", - "Connected to project 'consumer_expenditures'\n" + "Connected to project 'consumer_expenditures'\n", + "http://localhost:1709/#/listprojects/consumer_expenditures/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/consumer_expenditures/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "getml.engine.launch(in_memory=False)\n", + "getml.engine.launch(in_memory=True)\n", "getml.engine.set_project(\"consumer_expenditures\")" ] }, @@ -7660,7 +7652,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", @@ -7672,7 +7664,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", @@ -7711,7 +7703,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Substring', 'Substring', 'Substring', 'Substring', 'Substring',\n", @@ -7724,7 +7716,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Substring', 'Substring', 'Substring', 'Substring', 'Substring',\n", @@ -7764,7 +7756,7 @@ " feature_learners=['FastProp', 'Relboost'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", @@ -7776,7 +7768,7 @@ " feature_learners=['FastProp', 'Relboost'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", @@ -7871,6 +7863,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "OK.\n", "\n", @@ -7895,7 +7890,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:38m:6.572944\n", + "Time taken: 0h:17m:34.36448\n", "\n" ] }, @@ -7906,26 +7901,26 @@ " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.4,\n", - " tags=['FastProp', 'container-DHBPvM'])
url: http://localhost:1709/#/getpipeline/consumer_expenditures/NN5v4z/0/
" + " tags=['FastProp', 'container-0mVU59'])
url: http://localhost:1709/#/getpipeline/consumer_expenditures/pJbuSN/0/
" ], "text/plain": [ "Pipeline(data_model='POPULATION',\n", " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.4,\n", - " tags=['FastProp', 'container-DHBPvM'])\n", + " tags=['FastProp', 'container-0mVU59'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/consumer_expenditures/NN5v4z/0/" + "url: http://localhost:1709/#/getpipeline/consumer_expenditures/pJbuSN/0/" ] }, "execution_count": 20, @@ -7982,6 +7977,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "OK.\n", "\n", @@ -8006,7 +8004,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 1h:3m:14.575413\n", + "Time taken: 0h:47m:6.528231\n", "\n" ] }, @@ -8017,28 +8015,28 @@ " feature_learners=['Relboost'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Substring', 'Substring', 'Substring', 'Substring', 'Substring',\n", " 'Mapping'],\n", " share_selected_features=0.9,\n", - " tags=['Relboost', 'container-DHBPvM'])
url: http://localhost:1709/#/getpipeline/consumer_expenditures/C8e86R/0/
" + " tags=['Relboost', 'container-0mVU59'])
url: http://localhost:1709/#/getpipeline/consumer_expenditures/Q60z4i/0/
" ], "text/plain": [ "Pipeline(data_model='POPULATION',\n", " feature_learners=['Relboost'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Substring', 'Substring', 'Substring', 'Substring', 'Substring',\n", " 'Mapping'],\n", " share_selected_features=0.9,\n", - " tags=['Relboost', 'container-DHBPvM'])\n", + " tags=['Relboost', 'container-0mVU59'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/consumer_expenditures/C8e86R/0/" + "url: http://localhost:1709/#/getpipeline/consumer_expenditures/Q60z4i/0/" ] }, "execution_count": 22, @@ -8095,6 +8093,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "OK.\n", "\n", @@ -8125,7 +8126,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:57m:22.483178\n", + "Time taken: 0h:36m:14.399778\n", "\n" ] }, @@ -8136,26 +8137,26 @@ " feature_learners=['FastProp', 'Relboost'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.2,\n", - " tags=['FastProp', 'Relboost', 'container-DHBPvM'])
url: http://localhost:1709/#/getpipeline/consumer_expenditures/Yq82NU/0/
" + " tags=['FastProp', 'Relboost', 'container-0mVU59'])
url: http://localhost:1709/#/getpipeline/consumer_expenditures/nmWDcL/0/
" ], "text/plain": [ "Pipeline(data_model='POPULATION',\n", " feature_learners=['FastProp', 'Relboost'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.2,\n", - " tags=['FastProp', 'Relboost', 'container-DHBPvM'])\n", + " tags=['FastProp', 'Relboost', 'container-0mVU59'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/consumer_expenditures/Yq82NU/0/" + "url: http://localhost:1709/#/getpipeline/consumer_expenditures/nmWDcL/0/" ] }, "execution_count": 24, @@ -8275,7 +8276,7 @@ " 0\n", " \n", " \n", - " 2022-03-22 12:22:09\n", + " 2022-07-04 15:10:13\n", " \n", " \n", " \n", @@ -8287,15 +8288,15 @@ " \n", " \n", " \n", - " 0.9824\n", + " 0.9825\n", " \n", " \n", " \n", - " 0.9342\n", + " 0.9354\n", " \n", " \n", " \n", - " 0.06075\n", + " 0.06033\n", " \n", " \n", " \n", @@ -8304,7 +8305,7 @@ " 1\n", " \n", " \n", - " 2022-03-22 14:32:29\n", + " 2022-07-04 16:36:06\n", " \n", " \n", " \n", @@ -8316,15 +8317,15 @@ " \n", " \n", " \n", - " 0.9804\n", + " 0.9805\n", " \n", " \n", " \n", - " 0.8614\n", + " 0.8654\n", " \n", " \n", " \n", - " 0.0777\n", + " 0.07696\n", " \n", " \n", " \n", @@ -8334,8 +8335,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-22 12:22:09 train GIFT 0.9824 0.9342 0.06075\n", - "1 2022-03-22 14:32:29 test GIFT 0.9804 0.8614 0.0777 " + "0 2022-07-04 15:10:13 train GIFT 0.9825 0.9354 0.06033\n", + "1 2022-07-04 16:36:06 test GIFT 0.9805 0.8654 0.07696" ] }, "execution_count": 25, @@ -8439,7 +8440,7 @@ " 0\n", " \n", " \n", - " 2022-03-22 13:32:28\n", + " 2022-07-04 15:59:30\n", " \n", " \n", " \n", @@ -8451,15 +8452,15 @@ " \n", " \n", " \n", - " 0.9821\n", + " 0.9823\n", " \n", " \n", " \n", - " 0.918\n", + " 0.92\n", " \n", " \n", " \n", - " 0.06437\n", + " 0.06389\n", " \n", " \n", " \n", @@ -8468,7 +8469,7 @@ " 1\n", " \n", " \n", - " 2022-03-22 14:37:54\n", + " 2022-07-04 16:40:10\n", " \n", " \n", " \n", @@ -8480,15 +8481,15 @@ " \n", " \n", " \n", - " 0.9804\n", + " 0.9806\n", " \n", " \n", " \n", - " 0.8651\n", + " 0.8649\n", " \n", " \n", " \n", - " 0.07685\n", + " 0.07688\n", " \n", " \n", " \n", @@ -8498,8 +8499,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-22 13:32:28 train GIFT 0.9821 0.918 0.06437\n", - "1 2022-03-22 14:37:54 test GIFT 0.9804 0.8651 0.07685" + "0 2022-07-04 15:59:30 train GIFT 0.9823 0.92 0.06389\n", + "1 2022-07-04 16:40:10 test GIFT 0.9806 0.8649 0.07688" ] }, "execution_count": 26, @@ -8606,7 +8607,7 @@ " 0\n", " \n", " \n", - " 2022-03-22 14:31:21\n", + " 2022-07-04 16:35:50\n", " \n", " \n", " \n", @@ -8618,7 +8619,7 @@ " \n", " \n", " \n", - " 0.9824\n", + " 0.9825\n", " \n", " \n", " \n", @@ -8626,7 +8627,7 @@ " \n", " \n", " \n", - " 0.06205\n", + " 0.06159\n", " \n", " \n", " \n", @@ -8635,7 +8636,7 @@ " 1\n", " \n", " \n", - " 2022-03-22 14:39:00\n", + " 2022-07-04 16:40:47\n", " \n", " \n", " \n", @@ -8647,15 +8648,15 @@ " \n", " \n", " \n", - " 0.9807\n", + " 0.9806\n", " \n", " \n", " \n", - " 0.8689\n", + " 0.8677\n", " \n", " \n", " \n", - " 0.07637\n", + " 0.07641\n", " \n", " \n", " \n", @@ -8665,8 +8666,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-22 14:31:21 train GIFT 0.9824 0.9293 0.06205\n", - "1 2022-03-22 14:39:00 test GIFT 0.9807 0.8689 0.07637" + "0 2022-07-04 16:35:50 train GIFT 0.9825 0.9293 0.06159\n", + "1 2022-07-04 16:40:47 test GIFT 0.9806 0.8677 0.07641" ] }, "execution_count": 27, @@ -8701,7 +8702,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -8737,7 +8738,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -8787,7 +8788,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -8821,7 +8822,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -8857,7 +8858,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -8866,9 +8867,9 @@ "```sql\n", "DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\n", "\n", - "CREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(key TEXT NOT NULL PRIMARY KEY, value REAL);\n", + "CREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(\"key\" TEXT, \"value\" REAL);\n", "\n", - "INSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (key, value)\n", + "INSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (\"key\", \"value\")\n", "VALUES('410901', 0.5265553869499241),\n", " ('410140', 0.5248618784530387),\n", " ('004190', 0.5073846153846154),\n", @@ -9321,10 +9322,10 @@ "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\\n\\nCREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(key TEXT NOT NULL PRIMARY KEY, value REAL);\\n\\nINSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (key, 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0),\\n (\\'200511\\', 0);\\n\\nALTER TABLE \"POPULATION__STAGING_TABLE_1\" ADD COLUMN \"product_code__mapping_target_1_avg\";\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\" SET \"product_code__mapping_target_1_avg\" = 0.0;\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\"\\nSET \"product_code__mapping_target_1_avg\" = t2.\"value\"\\nFROM \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" AS t2\\nWHERE \"POPULATION__STAGING_TABLE_1\".\"product_code\" = t2.\"key\";\\n\\nDROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";'" + "'DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\\n\\nCREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(\"key\" TEXT, \"value\" REAL);\\n\\nINSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (\"key\", \"value\")\\nVALUES(\\'410901\\', 0.5265553869499241),\\n (\\'410140\\', 0.5248618784530387),\\n (\\'004190\\', 0.5073846153846154),\\n (\\'410120\\', 0.5013123359580053),\\n (\\'410110\\', 0.4444444444444444),\\n (\\'004100\\', 0.3336306868867083),\\n 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(\\'110210\\', 0.005692403229145104),\\n (\\'030110\\', 0.005622410731899783),\\n (\\'260210\\', 0.0055542698449433),\\n (\\'080110\\', 0.005548549810844893),\\n (\\'120110\\', 0.005436931593515224),\\n (\\'040310\\', 0.005404077622205846),\\n (\\'250210\\', 0.005342831700801425),\\n (\\'010310\\', 0.005331627212625293),\\n (\\'440120\\', 0.005319148936170213),\\n (\\'100110\\', 0.005308219178082192),\\n (\\'470112\\', 0.005277044854881266),\\n (\\'110110\\', 0.005152378864284149),\\n (\\'160110\\', 0.005109489051094891),\\n (\\'270410\\', 0.00496031746031746),\\n (\\'060110\\', 0.004922542348342262),\\n (\\'520516\\', 0.004901960784313725),\\n (\\'270310\\', 0.004885574697865775),\\n (\\'120410\\', 0.004865350089766607),\\n (\\'220120\\', 0.004815409309791332),\\n (\\'040210\\', 0.004786324786324786),\\n (\\'070230\\', 0.004725554343874954),\\n (\\'130110\\', 0.004694835680751174),\\n (\\'140110\\', 0.004555336991406978),\\n (\\'340530\\', 0.004530011325028313),\\n (\\'060210\\', 0.00400114318376679),\\n (\\'230900\\', 0.003992015968063872),\\n (\\'520410\\', 0.003937007874015748),\\n (\\'140340\\', 0.003897369275738876),\\n (\\'490313\\', 0.003875968992248062),\\n (\\'009000\\', 0.002952029520295203),\\n (\\'350110\\', 0.002881844380403458),\\n (\\'140330\\', 0.002380952380952381),\\n (\\'130122\\', 0.002169197396963124),\\n (\\'150212\\', 0.001451378809869376),\\n (\\'130121\\', 0.001373626373626374),\\n (\\'190323\\', 0.0009389671361502347),\\n (\\'190311\\', 0.0008796003096193089),\\n (\\'200532\\', 0.0005934718100890207),\\n (\\'190312\\', 0.0005761198329252485),\\n (\\'190314\\', 0.0004549590536851683),\\n (\\'190324\\', 0.0004541326067211626),\\n (\\'200522\\', 0.0004464285714285714),\\n (\\'190212\\', 0.0004089793692629283),\\n (\\'190114\\', 0.0003787878787878788),\\n (\\'190112\\', 0.0003610760064993681),\\n (\\'190322\\', 0.0002765869174388052),\\n (\\'190211\\', 0.0002144925463840132),\\n (\\'190111\\', 0.0002058036633052068),\\n (\\'200512\\', 0.0001853911753800519),\\n (\\'190321\\', 7.427213309566251e-05),\\n (\\'440140\\', 0),\\n (\\'200112\\', 0),\\n (\\'620925\\', 0),\\n (\\'250110\\', 0),\\n (\\'200531\\', 0),\\n (\\'310242\\', 0),\\n (\\'600130\\', 0),\\n (\\'580901\\', 0),\\n (\\'200521\\', 0),\\n (\\'490316\\', 0),\\n (\\'200523\\', 0),\\n (\\'190113\\', 0),\\n (\\'310241\\', 0),\\n (\\'550340\\', 0),\\n (\\'450350\\', 0),\\n (\\'190214\\', 0),\\n (\\'300410\\', 0),\\n (\\'530903\\', 0),\\n (\\'200513\\', 0),\\n (\\'140410\\', 0),\\n (\\'002200\\', 0),\\n (\\'630900\\', 0),\\n (\\'680210\\', 0),\\n (\\'290210\\', 0),\\n (\\'140310\\', 0),\\n (\\'200533\\', 0),\\n (\\'440110\\', 0),\\n (\\'190313\\', 0),\\n (\\'190213\\', 0),\\n (\\'270311\\', 0),\\n (\\'270900\\', 0),\\n (\\'200511\\', 0);\\n\\nALTER TABLE \"POPULATION__STAGING_TABLE_1\" ADD COLUMN \"product_code__mapping_target_1_avg\";\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\" SET \"product_code__mapping_target_1_avg\" = 0.0;\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\"\\nSET \"product_code__mapping_target_1_avg\" = t2.\"value\"\\nFROM \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" AS t2\\nWHERE \"POPULATION__STAGING_TABLE_1\".\"product_code\" = t2.\"key\";\\n\\nDROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";'" ] }, - "execution_count": 32, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -9335,7 +9336,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -9344,9 +9345,9 @@ "```sql\n", "DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\n", "\n", - "CREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(key TEXT NOT NULL PRIMARY KEY, value REAL);\n", + "CREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(\"key\" TEXT, \"value\" REAL);\n", "\n", - "INSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (key, value)\n", + "INSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (\"key\", \"value\")\n", "VALUES('410901', 0.5265553869499241),\n", " ('410140', 0.5248618784530387),\n", " ('004190', 0.5073846153846154),\n", @@ -9799,10 +9800,10 @@ "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\\n\\nCREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(key TEXT NOT NULL PRIMARY KEY, value REAL);\\n\\nINSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (key, value)\\nVALUES(\\'410901\\', 0.5265553869499241),\\n (\\'410140\\', 0.5248618784530387),\\n (\\'004190\\', 0.5073846153846154),\\n (\\'410120\\', 0.5013123359580053),\\n (\\'410110\\', 0.4444444444444444),\\n (\\'004100\\', 0.3336306868867083),\\n (\\'390110\\', 0.3132530120481928),\\n (\\'390120\\', 0.3067484662576687),\\n (\\'410130\\', 0.2967448902346707),\\n (\\'370110\\', 0.2948717948717949),\\n (\\'370212\\', 0.2944444444444445),\\n (\\'370220\\', 0.2920353982300885),\\n (\\'680140\\', 0.288135593220339),\\n (\\'390322\\', 0.2795918367346939),\\n (\\'390321\\', 0.2764227642276423),\\n (\\'370901\\', 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0.0004549590536851683),\\n (\\'190324\\', 0.0004541326067211626),\\n (\\'200522\\', 0.0004464285714285714),\\n (\\'190212\\', 0.0004089793692629283),\\n (\\'190114\\', 0.0003787878787878788),\\n (\\'190112\\', 0.0003610760064993681),\\n (\\'190322\\', 0.0002765869174388052),\\n (\\'190211\\', 0.0002144925463840132),\\n (\\'190111\\', 0.0002058036633052068),\\n (\\'200512\\', 0.0001853911753800519),\\n (\\'190321\\', 7.427213309566251e-05),\\n (\\'440140\\', 0),\\n (\\'200112\\', 0),\\n (\\'620925\\', 0),\\n (\\'250110\\', 0),\\n (\\'200531\\', 0),\\n (\\'310242\\', 0),\\n (\\'600130\\', 0),\\n (\\'580901\\', 0),\\n (\\'200521\\', 0),\\n (\\'490316\\', 0),\\n (\\'200523\\', 0),\\n (\\'190113\\', 0),\\n (\\'310241\\', 0),\\n (\\'550340\\', 0),\\n (\\'450350\\', 0),\\n (\\'190214\\', 0),\\n (\\'300410\\', 0),\\n (\\'530903\\', 0),\\n (\\'200513\\', 0),\\n (\\'140410\\', 0),\\n (\\'002200\\', 0),\\n (\\'630900\\', 0),\\n (\\'680210\\', 0),\\n (\\'290210\\', 0),\\n (\\'140310\\', 0),\\n (\\'200533\\', 0),\\n (\\'440110\\', 0),\\n (\\'190313\\', 0),\\n (\\'190213\\', 0),\\n (\\'270311\\', 0),\\n (\\'270900\\', 0),\\n (\\'200511\\', 0);\\n\\nALTER TABLE \"POPULATION__STAGING_TABLE_1\" ADD COLUMN \"product_code__mapping_target_1_avg\";\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\" SET \"product_code__mapping_target_1_avg\" = 0.0;\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\"\\nSET \"product_code__mapping_target_1_avg\" = t2.\"value\"\\nFROM \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" AS t2\\nWHERE \"POPULATION__STAGING_TABLE_1\".\"product_code\" = t2.\"key\";\\n\\nDROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";'" ] }, - "execution_count": 33, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -9813,7 +9814,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -9822,9 +9823,9 @@ "```sql\n", "DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\n", "\n", - "CREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(key TEXT NOT NULL PRIMARY KEY, value REAL);\n", + "CREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(\"key\" TEXT, \"value\" REAL);\n", "\n", - "INSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (key, value)\n", + "INSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (\"key\", \"value\")\n", "VALUES('410901', 0.5265553869499241),\n", " ('410140', 0.5248618784530387),\n", " ('004190', 0.5073846153846154),\n", @@ -10277,10 +10278,10 @@ "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\\n\\nCREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(key TEXT NOT NULL PRIMARY KEY, value REAL);\\n\\nINSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (key, value)\\nVALUES(\\'410901\\', 0.5265553869499241),\\n (\\'410140\\', 0.5248618784530387),\\n (\\'004190\\', 0.5073846153846154),\\n (\\'410120\\', 0.5013123359580053),\\n (\\'410110\\', 0.4444444444444444),\\n (\\'004100\\', 0.3336306868867083),\\n (\\'390110\\', 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\"product_code__mapping_target_1_avg\" = t2.\"value\"\\nFROM \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" AS t2\\nWHERE \"POPULATION__STAGING_TABLE_1\".\"product_code\" = t2.\"key\";\\n\\nDROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";'" + "'DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\\n\\nCREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(\"key\" TEXT, \"value\" REAL);\\n\\nINSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (\"key\", \"value\")\\nVALUES(\\'410901\\', 0.5265553869499241),\\n (\\'410140\\', 0.5248618784530387),\\n (\\'004190\\', 0.5073846153846154),\\n (\\'410120\\', 0.5013123359580053),\\n (\\'410110\\', 0.4444444444444444),\\n (\\'004100\\', 0.3336306868867083),\\n (\\'390110\\', 0.3132530120481928),\\n (\\'390120\\', 0.3067484662576687),\\n (\\'410130\\', 0.2967448902346707),\\n (\\'370110\\', 0.2948717948717949),\\n (\\'370212\\', 0.2944444444444445),\\n (\\'370220\\', 0.2920353982300885),\\n (\\'680140\\', 0.288135593220339),\\n 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(\\'200523\\', 0),\\n (\\'190113\\', 0),\\n (\\'310241\\', 0),\\n (\\'550340\\', 0),\\n (\\'450350\\', 0),\\n (\\'190214\\', 0),\\n (\\'300410\\', 0),\\n (\\'530903\\', 0),\\n (\\'200513\\', 0),\\n (\\'140410\\', 0),\\n (\\'002200\\', 0),\\n (\\'630900\\', 0),\\n (\\'680210\\', 0),\\n (\\'290210\\', 0),\\n (\\'140310\\', 0),\\n (\\'200533\\', 0),\\n (\\'440110\\', 0),\\n (\\'190313\\', 0),\\n (\\'190213\\', 0),\\n (\\'270311\\', 0),\\n (\\'270900\\', 0),\\n (\\'200511\\', 0);\\n\\nALTER TABLE \"POPULATION__STAGING_TABLE_1\" ADD COLUMN \"product_code__mapping_target_1_avg\";\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\" SET \"product_code__mapping_target_1_avg\" = 0.0;\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\"\\nSET \"product_code__mapping_target_1_avg\" = t2.\"value\"\\nFROM \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" AS t2\\nWHERE \"POPULATION__STAGING_TABLE_1\".\"product_code\" = t2.\"key\";\\n\\nDROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";'" ] }, - "execution_count": 34, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -10307,21 +10308,21 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "# Creates a folder named containing the SQL code.\n", - "pipe3.features.to_sql().save(\"consumer_expenditures_pipeline\")" + "pipe3.features.to_sql(size_threshold=None).save(\"consumer_expenditures_pipeline\", remove=True)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ - "pipe3.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"consumer_expenditures_spark\")" + "pipe3.features.to_sql(dialect=getml.pipeline.dialect.spark_sql, size_threshold=None).save(\"consumer_expenditures_spark\", remove=True)" ] }, { diff --git a/cora.ipynb b/cora.ipynb index c68fbae..acaf83e 100644 --- a/cora.ipynb +++ b/cora.ipynb @@ -98,27 +98,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220323125129.log.\n", + "getML engine is already running.\n", "\n", "\n", - "Loading pipelines...\n", - "[========================================] 100%\n", - "\n", "\n", - "Connected to project 'cora'\n" + "Connected to project 'cora'\n", + "http://localhost:1709/#/listprojects/cora/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/cora/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -3797,7 +3783,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['cites', 'content', 'paper'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", @@ -3809,7 +3795,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['cites', 'content', 'paper'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", @@ -3846,7 +3832,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['cites', 'content', 'paper'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=[],\n", @@ -3858,7 +3844,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['cites', 'content', 'paper'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=[],\n", @@ -3943,6 +3929,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "INFO [MIGHT TAKE LONG]: The number of unique entries in column 'word_cited_id' in CONTENT__STAGING_TABLE_4 is 1432. This might take a long time to fit. You should consider setting its role to unused_string or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and CITES__STAGING_TABLE_2 over 'paper_id' and 'cited_paper_id', there are no corresponding entries for 41.759406% of entries in 'paper_id' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", @@ -3990,7 +3979,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:58.58263\n", + "Time taken: 0h:0m:26.423322\n", "\n" ] }, @@ -4001,26 +3990,26 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['cites', 'content', 'paper'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-qKr9Z3'])
url: http://localhost:1709/#/getpipeline/cora/KllcU4/0/
" + " tags=['fast_prop', 'container-1q4Tl5'])
url: http://localhost:1709/#/getpipeline/cora/HZGCdm/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['cites', 'content', 'paper'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-qKr9Z3'])\n", + " tags=['fast_prop', 'container-1q4Tl5'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/cora/KllcU4/0/" + "url: http://localhost:1709/#/getpipeline/cora/HZGCdm/0/" ] }, "execution_count": 19, @@ -4409,7 +4398,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:2m:16.161633\n", + "Time taken: 0h:0m:49.575816\n", "\n" ] }, @@ -4420,26 +4409,26 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['cites', 'content', 'paper'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['relboost', 'container-qKr9Z3'])
url: http://localhost:1709/#/getpipeline/cora/Pu439J/0/
" + " tags=['relboost', 'container-1q4Tl5'])
url: http://localhost:1709/#/getpipeline/cora/PZ5okn/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['cites', 'content', 'paper'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['relboost', 'container-qKr9Z3'])\n", + " tags=['relboost', 'container-1q4Tl5'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/cora/Pu439J/0/" + "url: http://localhost:1709/#/getpipeline/cora/PZ5okn/0/" ] }, "execution_count": 21, @@ -4565,7 +4554,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 12:52:32\n", + " 2022-07-04 17:01:52\n", " \n", " \n", " \n", @@ -4594,7 +4583,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 12:52:32\n", + " 2022-07-04 17:01:52\n", " \n", " \n", " \n", @@ -4623,7 +4612,7 @@ " 2\n", " \n", " \n", - " 2022-03-23 12:52:32\n", + " 2022-07-04 17:01:52\n", " \n", " \n", " \n", @@ -4652,7 +4641,7 @@ " 3\n", " \n", " \n", - " 2022-03-23 12:52:32\n", + " 2022-07-04 17:01:52\n", " \n", " \n", " \n", @@ -4681,7 +4670,7 @@ " 4\n", " \n", " \n", - " 2022-03-23 12:52:32\n", + " 2022-07-04 17:01:52\n", " \n", " \n", " \n", @@ -4739,7 +4728,7 @@ " 9\n", " \n", " \n", - " 2022-03-23 12:54:50\n", + " 2022-07-04 17:02:43\n", " \n", " \n", " \n", @@ -4768,7 +4757,7 @@ " 10\n", " \n", " \n", - " 2022-03-23 12:54:50\n", + " 2022-07-04 17:02:43\n", " \n", " \n", " \n", @@ -4797,7 +4786,7 @@ " 11\n", " \n", " \n", - " 2022-03-23 12:54:50\n", + " 2022-07-04 17:02:43\n", " \n", " \n", " \n", @@ -4826,7 +4815,7 @@ " 12\n", " \n", " \n", - " 2022-03-23 12:54:50\n", + " 2022-07-04 17:02:43\n", " \n", " \n", " \n", @@ -4855,7 +4844,7 @@ " 13\n", " \n", " \n", - " 2022-03-23 12:54:50\n", + " 2022-07-04 17:02:43\n", " \n", " \n", " \n", @@ -4885,17 +4874,17 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - " 0 2022-03-23 12:52:32 train class_label=Case_Based 0.9978802331743508 0.9999 0.02323 \n", - " 1 2022-03-23 12:52:32 train class_label=Genetic_Algorithms 1 1. 0.004915\n", - " 2 2022-03-23 12:52:32 train class_label=Neural_Networks 0.9846316905140434 0.9983 0.065852\n", - " 3 2022-03-23 12:52:32 train class_label=Probabilistic_Method... 0.9957604663487016 0.9998 0.02765 \n", - " 4 2022-03-23 12:52:32 train class_label=Reinforcement_Learni... 0.9994700582935877 1. 0.009078\n", + " 0 2022-07-04 17:01:52 train class_label=Case_Based 0.9978802331743508 0.9999 0.02323 \n", + " 1 2022-07-04 17:01:52 train class_label=Genetic_Algorithms 1 1. 0.004915\n", + " 2 2022-07-04 17:01:52 train class_label=Neural_Networks 0.9846316905140434 0.9983 0.065852\n", + " 3 2022-07-04 17:01:52 train class_label=Probabilistic_Method... 0.9957604663487016 0.9998 0.02765 \n", + " 4 2022-07-04 17:01:52 train class_label=Reinforcement_Learni... 0.9994700582935877 1. 0.009078\n", " ... ... ... ... ... ...\n", - " 9 2022-03-23 12:54:50 test class_label=Neural_Networks 0.951278928136419 0.9787 0.163552\n", - "10 2022-03-23 12:54:50 test class_label=Probabilistic_Method... 0.9732034104750305 0.9872 0.083174\n", - "11 2022-03-23 12:54:50 test class_label=Reinforcement_Learni... 0.9805115712545676 0.9736 0.074599\n", - "12 2022-03-23 12:54:50 test class_label=Rule_Learning 0.9841656516443362 0.9937 0.052146\n", - "13 2022-03-23 12:54:50 test class_label=Theory 0.9573690621193667 0.977 0.128597" + " 9 2022-07-04 17:02:43 test class_label=Neural_Networks 0.951278928136419 0.9787 0.163552\n", + "10 2022-07-04 17:02:43 test class_label=Probabilistic_Method... 0.9732034104750305 0.9872 0.083174\n", + "11 2022-07-04 17:02:43 test class_label=Reinforcement_Learni... 0.9805115712545676 0.9736 0.074599\n", + "12 2022-07-04 17:02:43 test class_label=Rule_Learning 0.9841656516443362 0.9937 0.052146\n", + "13 2022-07-04 17:02:43 test class_label=Theory 0.9573690621193667 0.977 0.128597" ] }, "execution_count": 22, @@ -4921,6 +4910,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "Relboost: Building subfeatures...\n", "[========================================] 100%\n", "\n", @@ -5098,7 +5090,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 12:54:49\n", + " 2022-07-04 17:02:42\n", " \n", " \n", " \n", @@ -5127,7 +5119,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 12:54:49\n", + " 2022-07-04 17:02:42\n", " \n", " \n", " \n", @@ -5156,7 +5148,7 @@ " 2\n", " \n", " \n", - " 2022-03-23 12:54:49\n", + " 2022-07-04 17:02:42\n", " \n", " \n", " \n", @@ -5185,7 +5177,7 @@ " 3\n", " \n", " \n", - " 2022-03-23 12:54:49\n", + " 2022-07-04 17:02:42\n", " \n", " \n", " \n", @@ -5214,7 +5206,7 @@ " 4\n", " \n", " \n", - " 2022-03-23 12:54:49\n", + " 2022-07-04 17:02:42\n", " \n", " \n", " \n", @@ -5272,7 +5264,7 @@ " 9\n", " \n", " \n", - " 2022-03-23 12:54:54\n", + " 2022-07-04 17:02:47\n", " \n", " \n", " \n", @@ -5301,7 +5293,7 @@ " 10\n", " \n", " \n", - " 2022-03-23 12:54:54\n", + " 2022-07-04 17:02:47\n", " \n", " \n", " \n", @@ -5330,7 +5322,7 @@ " 11\n", " \n", " \n", - " 2022-03-23 12:54:54\n", + " 2022-07-04 17:02:47\n", " \n", " \n", " \n", @@ -5359,7 +5351,7 @@ " 12\n", " \n", " \n", - " 2022-03-23 12:54:54\n", + " 2022-07-04 17:02:47\n", " \n", " \n", " \n", @@ -5388,7 +5380,7 @@ " 13\n", " \n", " \n", - " 2022-03-23 12:54:54\n", + " 2022-07-04 17:02:47\n", " \n", " \n", " \n", @@ -5418,17 +5410,17 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - " 0 2022-03-23 12:54:49 train class_label=Case_Based 1 1. 0.008368\n", - " 1 2022-03-23 12:54:49 train class_label=Genetic_Algorithms 1 1. 0.004185\n", - " 2 2022-03-23 12:54:49 train class_label=Neural_Networks 0.9925808161102279 0.9995 0.03748 \n", - " 3 2022-03-23 12:54:49 train class_label=Probabilistic_Method... 0.9978802331743508 1. 0.014195\n", - " 4 2022-03-23 12:54:49 train class_label=Reinforcement_Learni... 1 1. 0.004341\n", + " 0 2022-07-04 17:02:42 train class_label=Case_Based 1 1. 0.008368\n", + " 1 2022-07-04 17:02:42 train class_label=Genetic_Algorithms 1 1. 0.004185\n", + " 2 2022-07-04 17:02:42 train class_label=Neural_Networks 0.9925808161102279 0.9995 0.03748 \n", + " 3 2022-07-04 17:02:42 train class_label=Probabilistic_Method... 0.9978802331743508 1. 0.014195\n", + " 4 2022-07-04 17:02:42 train class_label=Reinforcement_Learni... 1 1. 0.004341\n", " ... ... ... ... ... ...\n", - " 9 2022-03-23 12:54:54 test class_label=Neural_Networks 0.9390986601705238 0.9802 0.182987\n", - "10 2022-03-23 12:54:54 test class_label=Probabilistic_Method... 0.9732034104750305 0.9874 0.090399\n", - "11 2022-03-23 12:54:54 test class_label=Reinforcement_Learni... 0.9817295980511571 0.9779 0.077956\n", - "12 2022-03-23 12:54:54 test class_label=Rule_Learning 0.9817295980511571 0.9918 0.06703 \n", - "13 2022-03-23 12:54:54 test class_label=Theory 0.9500609013398295 0.966 0.164147" + " 9 2022-07-04 17:02:47 test class_label=Neural_Networks 0.9390986601705238 0.9802 0.182987\n", + "10 2022-07-04 17:02:47 test class_label=Probabilistic_Method... 0.9732034104750305 0.9874 0.090399\n", + "11 2022-07-04 17:02:47 test class_label=Reinforcement_Learni... 0.9817295980511571 0.9779 0.077956\n", + "12 2022-07-04 17:02:47 test class_label=Rule_Learning 0.9817295980511571 0.9918 0.06703 \n", + "13 2022-07-04 17:02:47 test class_label=Theory 0.9500609013398295 0.966 0.164147" ] }, "execution_count": 23, @@ -5521,7 +5513,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 12:54:50\n", + " 2022-07-04 17:02:43\n", " \n", " \n", " \n", @@ -5550,7 +5542,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 12:54:50\n", + " 2022-07-04 17:02:43\n", " \n", " \n", " \n", @@ -5579,7 +5571,7 @@ " 2\n", " \n", " \n", - " 2022-03-23 12:54:50\n", + " 2022-07-04 17:02:43\n", " \n", " \n", " \n", @@ -5608,7 +5600,7 @@ " 3\n", " \n", " \n", - " 2022-03-23 12:54:50\n", + " 2022-07-04 17:02:43\n", " \n", " \n", " \n", @@ -5637,7 +5629,7 @@ " 4\n", " \n", " \n", - " 2022-03-23 12:54:50\n", + " 2022-07-04 17:02:43\n", " \n", " \n", " \n", @@ -5666,7 +5658,7 @@ " 5\n", " \n", " \n", - " 2022-03-23 12:54:50\n", + " 2022-07-04 17:02:43\n", " \n", " \n", " \n", @@ -5695,7 +5687,7 @@ " 6\n", " \n", " \n", - " 2022-03-23 12:54:50\n", + " 2022-07-04 17:02:43\n", " \n", " \n", " \n", @@ -5725,13 +5717,13 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-23 12:54:50 test class_label=Case_Based 0.9708 0.9861 0.08689\n", - "1 2022-03-23 12:54:50 test class_label=Genetic_Algorithms 0.9854 0.998 0.04898\n", - "2 2022-03-23 12:54:50 test class_label=Neural_Networks 0.9513 0.9787 0.16355\n", - "3 2022-03-23 12:54:50 test class_label=Probabilistic_Method... 0.9732 0.9872 0.08317\n", - "4 2022-03-23 12:54:50 test class_label=Reinforcement_Learni... 0.9805 0.9736 0.0746 \n", - "5 2022-03-23 12:54:50 test class_label=Rule_Learning 0.9842 0.9937 0.05215\n", - "6 2022-03-23 12:54:50 test class_label=Theory 0.9574 0.977 0.1286 " + "0 2022-07-04 17:02:43 test class_label=Case_Based 0.9708 0.9861 0.08689\n", + "1 2022-07-04 17:02:43 test class_label=Genetic_Algorithms 0.9854 0.998 0.04898\n", + "2 2022-07-04 17:02:43 test class_label=Neural_Networks 0.9513 0.9787 0.16355\n", + "3 2022-07-04 17:02:43 test class_label=Probabilistic_Method... 0.9732 0.9872 0.08317\n", + "4 2022-07-04 17:02:43 test class_label=Reinforcement_Learni... 0.9805 0.9736 0.0746 \n", + "5 2022-07-04 17:02:43 test class_label=Rule_Learning 0.9842 0.9937 0.05215\n", + "6 2022-07-04 17:02:43 test class_label=Theory 0.9574 0.977 0.1286 " ] }, "execution_count": 24, @@ -5817,7 +5809,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 12:54:54\n", + " 2022-07-04 17:02:47\n", " \n", " \n", " \n", @@ -5846,7 +5838,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 12:54:54\n", + " 2022-07-04 17:02:47\n", " \n", " \n", " \n", @@ -5875,7 +5867,7 @@ " 2\n", " \n", " \n", - " 2022-03-23 12:54:54\n", + " 2022-07-04 17:02:47\n", " \n", " \n", " \n", @@ -5904,7 +5896,7 @@ " 3\n", " \n", " \n", - " 2022-03-23 12:54:54\n", + " 2022-07-04 17:02:47\n", " \n", " \n", " \n", @@ -5933,7 +5925,7 @@ " 4\n", " \n", " \n", - " 2022-03-23 12:54:54\n", + " 2022-07-04 17:02:47\n", " \n", " \n", " \n", @@ -5962,7 +5954,7 @@ " 5\n", " \n", " \n", - " 2022-03-23 12:54:54\n", + " 2022-07-04 17:02:47\n", " \n", " \n", " \n", @@ -5991,7 +5983,7 @@ " 6\n", " \n", " \n", - " 2022-03-23 12:54:54\n", + " 2022-07-04 17:02:47\n", " \n", " \n", " \n", @@ -6021,13 +6013,13 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-23 12:54:54 test class_label=Case_Based 0.9756 0.9801 0.10383\n", - "1 2022-03-23 12:54:54 test class_label=Genetic_Algorithms 0.9915 0.9992 0.03394\n", - "2 2022-03-23 12:54:54 test class_label=Neural_Networks 0.9391 0.9802 0.18299\n", - "3 2022-03-23 12:54:54 test class_label=Probabilistic_Method... 0.9732 0.9874 0.0904 \n", - "4 2022-03-23 12:54:54 test class_label=Reinforcement_Learni... 0.9817 0.9779 0.07796\n", - "5 2022-03-23 12:54:54 test class_label=Rule_Learning 0.9817 0.9918 0.06703\n", - "6 2022-03-23 12:54:54 test class_label=Theory 0.9501 0.966 0.16415" + "0 2022-07-04 17:02:47 test class_label=Case_Based 0.9756 0.9801 0.10383\n", + "1 2022-07-04 17:02:47 test class_label=Genetic_Algorithms 0.9915 0.9992 0.03394\n", + "2 2022-07-04 17:02:47 test class_label=Neural_Networks 0.9391 0.9802 0.18299\n", + "3 2022-07-04 17:02:47 test class_label=Probabilistic_Method... 0.9732 0.9874 0.0904 \n", + "4 2022-07-04 17:02:47 test class_label=Reinforcement_Learni... 0.9817 0.9779 0.07796\n", + "5 2022-07-04 17:02:47 test class_label=Rule_Learning 0.9817 0.9918 0.06703\n", + "6 2022-07-04 17:02:47 test class_label=Theory 0.9501 0.966 0.16415" ] }, "execution_count": 25, @@ -6132,6 +6124,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "Relboost: Building subfeatures...\n", "[========================================] 100%\n", "\n", @@ -6589,7 +6584,7 @@ "outputs": [], "source": [ "# Creates a folder containing the SQL code.\n", - "pipe1.features.to_sql().save(\"cora_pipeline\")" + "pipe1.features.to_sql(size_threshold=None).save(\"cora_pipeline\", remove=True)" ] }, { @@ -6598,7 +6593,7 @@ "metadata": {}, "outputs": [], "source": [ - "pipe1.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"cora_spark\")" + "pipe1.features.to_sql(dialect=getml.pipeline.dialect.spark_sql, size_threshold=None).save(\"cora_spark\", remove=True)" ] }, { diff --git a/dodgers.ipynb b/dodgers.ipynb index eb9bb04..d7b581b 100644 --- a/dodgers.ipynb +++ b/dodgers.ipynb @@ -176,24 +176,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220323174302.log.\n", + "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220716231628.log.\n", "\n", "\n", "\n", - "Connected to project 'dodgers'\n" + "Connected to project 'dodgers'\n", + "http://localhost:1709/#/listprojects/dodgers/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/dodgers/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -1508,6 +1497,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "OK.\n", "\n", @@ -1535,7 +1527,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:5m:20.765755\n", + "Time taken: 0h:1m:59.534678\n", "\n" ] }, @@ -1546,26 +1538,26 @@ " feature_learners=['FastProp', 'Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['data_full'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Seasonal', 'Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['memory: 2h', 'horizon: 1h', 'fast_prop', 'relboost', 'container-rBnEUw'])
url: http://localhost:1709/#/getpipeline/dodgers/C9QGi3/0/
" + " tags=['memory: 2h', 'horizon: 1h', 'fast_prop', 'relboost', 'container-e6dM03'])
url: http://localhost:1709/#/getpipeline/dodgers/nSi84W/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['FastProp', 'Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['data_full'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Seasonal', 'Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['memory: 2h', 'horizon: 1h', 'fast_prop', 'relboost', 'container-rBnEUw'])\n", + " tags=['memory: 2h', 'horizon: 1h', 'fast_prop', 'relboost', 'container-e6dM03'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/dodgers/C9QGi3/0/" + "url: http://localhost:1709/#/getpipeline/dodgers/nSi84W/0/" ] }, "execution_count": 17, @@ -1688,7 +1680,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 17:49:48\n", + " 2022-07-16 23:19:24\n", " \n", " \n", " \n", @@ -1717,7 +1709,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 17:49:58\n", + " 2022-07-16 23:19:29\n", " \n", " \n", " \n", @@ -1747,8 +1739,8 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-23 17:49:48 train y 4.4735 6.1568 0.7806\n", - "1 2022-03-23 17:49:58 test y 4.6544 6.4017 0.7615" + "0 2022-07-16 23:19:24 train y 4.4735 6.1568 0.7806\n", + "1 2022-07-16 23:19:29 test y 4.6544 6.4017 0.7615" ] }, "execution_count": 18, @@ -2237,7 +2229,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 29, @@ -2246,7 +2238,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -2484,13 +2476,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_121447/754041395.py:3: SettingWithCopyWarning: \n", + "/tmp/ipykernel_5970/754041395.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " data_full_tsfresh[\"y_lagged\"] = y_lagged\n", - "/tmp/ipykernel_121447/754041395.py:5: SettingWithCopyWarning: \n", + "/tmp/ipykernel_5970/754041395.py:5: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -5333,7 +5325,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:2.073184\n", + "Time taken: 0h:0m:2.147736\n", "\n" ] }, @@ -5344,26 +5336,26 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['tsfresh'])
url: http://localhost:1709/#/getpipeline/dodgers/EP6HFH/0/
" + " tags=['tsfresh'])
url: http://localhost:1709/#/getpipeline/dodgers/6lHvWs/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", " tags=['tsfresh'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/dodgers/EP6HFH/0/" + "url: http://localhost:1709/#/getpipeline/dodgers/6lHvWs/0/" ] }, "execution_count": 41, @@ -5389,6 +5381,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n" ] }, @@ -5461,7 +5456,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 17:50:11\n", + " 2022-07-16 23:19:38\n", " \n", " \n", " \n", @@ -5490,7 +5485,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 17:50:11\n", + " 2022-07-16 23:19:38\n", " \n", " \n", " \n", @@ -5520,8 +5515,8 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-23 17:50:11 tsfresh_train y 6.8096 8.7569 0.5565\n", - "1 2022-03-23 17:50:11 tsfresh_test y 7.1877 9.3024 0.4943" + "0 2022-07-16 23:19:38 tsfresh_train y 6.8096 8.7569 0.5565\n", + "1 2022-07-16 23:19:38 tsfresh_test y 7.1877 9.3024 0.4943" ] }, "execution_count": 42, @@ -5547,6 +5542,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n" ] } @@ -5570,7 +5568,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 44, @@ -5579,7 +5577,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -5742,7 +5740,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:5.405146\n", + "Time taken: 0h:0m:5.431029\n", "\n" ] }, @@ -5753,26 +5751,26 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['prophet + tsfresh'])
url: http://localhost:1709/#/getpipeline/dodgers/0bn5RY/0/
" + " tags=['prophet + tsfresh'])
url: http://localhost:1709/#/getpipeline/dodgers/QdxjrE/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", " tags=['prophet + tsfresh'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/dodgers/0bn5RY/0/" + "url: http://localhost:1709/#/getpipeline/dodgers/QdxjrE/0/" ] }, "execution_count": 49, @@ -5798,6 +5796,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n" ] }, @@ -5870,7 +5871,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 17:50:19\n", + " 2022-07-16 23:19:46\n", " \n", " \n", " \n", @@ -5899,7 +5900,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 17:50:19\n", + " 2022-07-16 23:19:46\n", " \n", " \n", " \n", @@ -5929,8 +5930,8 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-23 17:50:19 combined_train y 4.6572 6.4011 0.7633\n", - "1 2022-03-23 17:50:19 combined_test y 6.1772 8.4081 0.6661" + "0 2022-07-16 23:19:46 combined_train y 4.6572 6.4011 0.7633\n", + "1 2022-07-16 23:19:46 combined_test y 6.1772 8.4081 0.6661" ] }, "execution_count": 50, @@ -5963,6 +5964,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n" ] } @@ -5979,7 +5983,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 52, @@ -5988,7 +5992,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -6189,7 +6193,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.4" }, "toc": { "base_numbering": 1, diff --git a/formula1.ipynb b/formula1.ipynb index 7824c16..dbd228c 100644 --- a/formula1.ipynb +++ b/formula1.ipynb @@ -104,27 +104,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220321205745.log.\n", + "getML engine is already running.\n", "\n", "\n", - "Loading pipelines...\n", - "[========================================] 100%\n", - "\n", "\n", - "Connected to project 'formula1'\n" + "Connected to project 'formula1'\n", + "http://localhost:1709/#/listprojects/formula1/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/formula1/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -16573,7 +16559,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['driver_standings', 'drivers', 'lap_times', 'pit_stops', 'qualifying'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", @@ -16585,7 +16571,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['driver_standings', 'drivers', 'lap_times', 'pit_stops', 'qualifying'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", @@ -16671,6 +16657,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and LAP_TIMES__STAGING_TABLE_3 over 'driverId' and 'driverId', there are no corresponding entries for 68.551028% of entries in 'driverId' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and PIT_STOPS__STAGING_TABLE_4 over 'driverId' and 'driverId', there are no corresponding entries for 82.527910% of entries in 'driverId' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", @@ -16693,7 +16682,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:9m:19.802415\n", + "Time taken: 0h:3m:42.575093\n", "\n" ] }, @@ -16704,26 +16693,26 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['driver_standings', 'drivers', 'lap_times', 'pit_stops', 'qualifying'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-VSpWyY'])
url: http://localhost:1709/#/getpipeline/formula1/WzzMiM/0/
" + " tags=['fast_prop', 'container-pbX9SU'])
url: http://localhost:1709/#/getpipeline/formula1/QE2zyI/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['driver_standings', 'drivers', 'lap_times', 'pit_stops', 'qualifying'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-VSpWyY'])\n", + " tags=['fast_prop', 'container-pbX9SU'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/formula1/WzzMiM/0/" + "url: http://localhost:1709/#/getpipeline/formula1/QE2zyI/0/" ] }, "execution_count": 25, @@ -16843,7 +16832,7 @@ " 0\n", " \n", " \n", - " 2022-03-21 21:07:26\n", + " 2022-07-04 17:14:33\n", " \n", " \n", " \n", @@ -16872,7 +16861,7 @@ " 1\n", " \n", " \n", - " 2022-03-21 21:07:48\n", + " 2022-07-04 17:14:43\n", " \n", " \n", " \n", @@ -16902,8 +16891,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-21 21:07:26 train win 0.9731 0.9568 0.07554\n", - "1 2022-03-21 21:07:48 test win 0.9725 0.9238 0.08497" + "0 2022-07-04 17:14:33 train win 0.9731 0.9568 0.07554\n", + "1 2022-07-04 17:14:43 test win 0.9725 0.9238 0.08497" ] }, "execution_count": 26, @@ -38024,7 +38013,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Imputation'],\n", @@ -38036,7 +38025,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Imputation'],\n", @@ -38116,7 +38105,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:7.204432\n", + "Time taken: 0h:0m:6.759273\n", "\n" ] }, @@ -38127,26 +38116,26 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Imputation'],\n", " share_selected_features=0.5,\n", - " tags=['featuretools'])
url: http://localhost:1709/#/getpipeline/formula1/EacHbP/0/
" + " tags=['featuretools'])
url: http://localhost:1709/#/getpipeline/formula1/mLyxiz/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Imputation'],\n", " share_selected_features=0.5,\n", " tags=['featuretools'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/formula1/EacHbP/0/" + "url: http://localhost:1709/#/getpipeline/formula1/mLyxiz/0/" ] }, "execution_count": 46, @@ -38247,7 +38236,7 @@ " 0\n", " \n", " \n", - " 2022-03-21 21:10:00\n", + " 2022-07-04 17:16:52\n", " \n", " \n", " \n", @@ -38276,7 +38265,7 @@ " 1\n", " \n", " \n", - " 2022-03-21 21:10:00\n", + " 2022-07-04 17:16:53\n", " \n", " \n", " \n", @@ -38306,8 +38295,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-21 21:10:00 featuretools_train win 0.9715 0.9389 0.0838 \n", - "1 2022-03-21 21:10:00 featuretools_test win 0.9722 0.9198 0.08691" + "0 2022-07-04 17:16:52 featuretools_train win 0.9715 0.9389 0.0838 \n", + "1 2022-07-04 17:16:53 featuretools_test win 0.9722 0.9198 0.08691" ] }, "execution_count": 47, @@ -38391,9 +38380,9 @@ "```sql\n", "DROP TABLE IF EXISTS \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\";\n", "\n", - "CREATE TABLE \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\"(key TEXT NOT NULL PRIMARY KEY, value REAL);\n", + "CREATE TABLE \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\"(\"key\" TEXT, \"value\" REAL);\n", "\n", - "INSERT INTO \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\" (key, value)\n", + "INSERT INTO \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\" (\"key\", \"value\")\n", "VALUES('fangio', 0.3653846153846154),\n", " ('ascari', 0.3055555555555556),\n", " ('michael_schumacher', 0.3032786885245902),\n", @@ -38654,7 +38643,7 @@ "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\";\\n\\nCREATE TABLE \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\"(key TEXT NOT NULL PRIMARY KEY, value REAL);\\n\\nINSERT INTO \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\" (key, value)\\nVALUES(\\'fangio\\', 0.3653846153846154),\\n (\\'ascari\\', 0.3055555555555556),\\n (\\'michael_schumacher\\', 0.3032786885245902),\\n (\\'hamilton\\', 0.2981366459627329),\\n (\\'stewart\\', 0.2948717948717949),\\n (\\'prost\\', 0.264367816091954),\\n (\\'vettel\\', 0.2467532467532468),\\n (\\'clark\\', 0.2394366197183098),\\n (\\'senna\\', 0.2323943661971831),\\n (\\'damon_hill\\', 0.2159090909090909),\\n (\\'moss\\', 0.1866666666666667),\\n (\\'mansell\\', 0.1569767441860465),\\n (\\'lauda\\', 0.1390728476821192),\\n (\\'alonso\\', 0.1282051282051282),\\n (\\'hakkinen\\', 0.1278195488721804),\\n (\\'rosberg\\', 0.10625),\\n (\\'hunt\\', 0.1058823529411765),\\n (\\'jack_brabham\\', 0.1052631578947368),\\n (\\'piquet\\', 0.09714285714285714),\\n (\\'farina\\', 0.09523809523809523),\\n (\\'jones\\', 0.09259259259259259),\\n (\\'brooks\\', 0.09090909090909091),\\n (\\'scheckter\\', 0.09),\\n (\\'montoya\\', 0.08536585365853659),\\n (\\'emerson_fittipaldi\\', 0.08403361344537816),\\n (\\'rindt\\', 0.08333333333333333),\\n (\\'hulme\\', 0.08247422680412371),\\n (\\'peterson\\', 0.08247422680412371),\\n (\\'reutemann\\', 0.07936507936507936),\\n (\\'mario_andretti\\', 0.07142857142857142),\\n (\\'raikkonen\\', 0.0684931506849315),\\n (\\'villeneuve\\', 0.06153846153846154),\\n (\\'surtees\\', 0.0576923076923077),\\n (\\'hill\\', 0.05194805194805195),\\n (\\'gilles_villeneuve\\', 0.05),\\n (\\'berger\\', 0.05),\\n (\\'keke_rosberg\\', 0.04901960784313725),\\n (\\'hawthorn\\', 0.04878048780487805),\\n (\\'ickx\\', 0.04878048780487805),\\n (\\'gurney\\', 0.04819277108433735),\\n (\\'collins\\', 0.04761904761904762),\\n (\\'arnoux\\', 0.04697986577181208),\\n (\\'massa\\', 0.04587155963302753),\\n (\\'button\\', 0.04511278195488722),\\n (\\'pironi\\', 0.04347826086956522),\\n (\\'coulthard\\', 0.0427807486631016),\\n (\\'revson\\', 0.0425531914893617),\\n (\\'ricciardo\\', 0.0425531914893617),\\n (\\'webber\\', 0.04216867469879518),\\n (\\'ralf_schumacher\\', 0.03846153846153846),\\n (\\'barrichello\\', 0.036),\\n (\\'phil_hill\\', 0.03448275862068965),\\n (\\'rodriguez\\', 0.03333333333333333),\\n (\\'scarfiotti\\', 0.03333333333333333),\\n (\\'laffite\\', 0.03311258278145696),\\n (\\'regazzoni\\', 0.03225806451612903),\\n (\\'jabouille\\', 0.03076923076923077),\\n (\\'baghetti\\', 0.02941176470588235),\\n (\\'flaherty\\', 0.02941176470588235),\\n (\\'watson\\', 0.02898550724637681),\\n (\\'mclaren\\', 0.02803738317757009),\\n (\\'gethin\\', 0.02777777777777778),\\n (\\'musso\\', 0.02702702702702703),\\n (\\'cevert\\', 0.02631578947368421),\\n (\\'ruttman\\', 0.025),\\n (\\'irvine\\', 0.025),\\n (\\'max_verstappen\\', 0.02380952380952381),\\n (\\'hanks\\', 0.02325581395348837),\\n (\\'alboreto\\', 0.02298850574712644),\\n (\\'boutsen\\', 0.02290076335877863),\\n (\\'herbert\\', 0.02222222222222222),\\n (\\'parsons\\', 0.02083333333333333),\\n (\\'bryan\\', 0.02040816326530612),\\n (\\'trintignant\\', 0.02),\\n (\\'kubica\\', 0.0196078431372549),\\n (\\'patrese\\', 0.01951219512195122),\\n (\\'rathmann\\', 0.01923076923076923),\\n (\\'bandini\\', 0.01851851851851852),\\n (\\'tambay\\', 0.01834862385321101),\\n (\\'ginther\\', 0.01639344262295082),\\n (\\'fisichella\\', 0.01621621621621622),\\n (\\'ward\\', 0.01612903225806452),\\n (\\'frentzen\\', 0.01574803149606299),\\n (\\'brambilla\\', 0.01492537313432836),\\n (\\'bottas\\', 0.01470588235294118),\\n (\\'pace\\', 0.0136986301369863),\\n (\\'maldonado\\', 0.01298701298701299),\\n (\\'beltoise\\', 0.01282051282051282),\\n (\\'siffert\\', 0.01265822784810127),\\n (\\'depailler\\', 0.0108695652173913),\\n (\\'angelis\\', 0.0108695652173913),\\n (\\'bonnier\\', 0.009009009009009009),\\n (\\'mass\\', 0.008771929824561403),\\n (\\'panis\\', 0.007692307692307693),\\n (\\'darter\\', 0),\\n (\\'pretorius\\', 0),\\n (\\'adamich\\', 0),\\n (\\'trevor_taylor\\', 0),\\n (\\'love\\', 0),\\n (\\'anderson\\', 0),\\n (\\'bianchi\\', 0),\\n (\\'courage\\', 0),\\n (\\'tingle\\', 0),\\n (\\'attwood\\', 0),\\n (\\'spence\\', 0),\\n (\\'resta\\', 0),\\n (\\'chiron\\', 0),\\n (\\'reece\\', 0),\\n (\\'linden\\', 0),\\n (\\'agabashian\\', 0),\\n (\\'manzon\\', 0),\\n (\\'rosier\\', 0),\\n (\\'villoresi\\', 0),\\n (\\'graffenried\\', 0),\\n (\\'hulkenberg\\', 0),\\n (\\'petrov\\', 0),\\n (\\'bruno_senna\\', 0),\\n (\\'daywalt\\', 0),\\n (\\'perez\\', 0),\\n (\\'vergne\\', 0),\\n (\\'pic\\', 0),\\n (\\'chilton\\', 0),\\n (\\'gutierrez\\', 0),\\n (\\'jules_bianchi\\', 0),\\n (\\'kevin_magnussen\\', 0),\\n (\\'kvyat\\', 0),\\n (\\'ericsson\\', 0),\\n (\\'nasr\\', 0),\\n (\\'sainz\\', 0),\\n (\\'schell\\', 0),\\n (\\'maggs\\', 0),\\n (\\'gregory\\', 0),\\n (\\'andre_pilette\\', 0),\\n (\\'beaufort\\', 0),\\n (\\'burgess\\', 0),\\n (\\'salvadori\\', 0),\\n (\\'trips\\', 0),\\n (\\'herrmann\\', 0),\\n (\\'scarlatti\\', 0),\\n (\\'menditeguy\\', 0),\\n (\\'gonzalez\\', 0),\\n (\\'ireland\\', 0),\\n (\\'thomson\\', 0),\\n (\\'johnson\\', 0),\\n (\\'ertl\\', 0),\\n (\\'hartley\\', 0),\\n (\\'stevenson\\', 0),\\n (\\'freeland\\', 0),\\n (\\'bettenhausen\\', 0),\\n (\\'boyd\\', 0),\\n (\\'gould\\', 0),\\n (\\'behra\\', 0),\\n (\\'paul_russo\\', 0),\\n (\\'martini\\', 0),\\n (\\'larini\\', 0),\\n (\\'katayama\\', 0),\\n (\\'morbidelli\\', 0),\\n (\\'lamy\\', 0),\\n (\\'brundle\\', 0),\\n (\\'montermini\\', 0),\\n (\\'blundell\\', 0),\\n (\\'suzuki\\', 0),\\n (\\'moreno\\', 0),\\n (\\'wendlinger\\', 0),\\n (\\'gachot\\', 0),\\n (\\'magnussen\\', 0),\\n (\\'tarquini\\', 0),\\n (\\'comas\\', 0),\\n (\\'bernard\\', 0),\\n (\\'fittipaldi\\', 0),\\n (\\'lehto\\', 0),\\n (\\'cesaris\\', 0),\\n (\\'alliot\\', 0),\\n (\\'dalmas\\', 0),\\n (\\'warwick\\', 0),\\n (\\'capelli\\', 0),\\n (\\'gugelmin\\', 0),\\n (\\'rosa\\', 0),\\n (\\'heidfeld\\', 0),\\n (\\'kovalainen\\', 0),\\n (\\'glock\\', 0),\\n (\\'sato\\', 0),\\n (\\'trulli\\', 0),\\n (\\'sutil\\', 0),\\n (\\'liuzzi\\', 0),\\n (\\'wurz\\', 0),\\n (\\'speed\\', 0),\\n (\\'albers\\', 0),\\n (\\'klien\\', 0),\\n (\\'grouillard\\', 0),\\n (\\'karthikeyan\\', 0),\\n (\\'zonta\\', 0),\\n (\\'gene\\', 0),\\n (\\'verstappen\\', 0),\\n (\\'alesi\\', 0),\\n (\\'salo\\', 0),\\n (\\'diniz\\', 0),\\n (\\'buemi\\', 0),\\n (\\'badoer\\', 0),\\n (\\'zanardi\\', 0),\\n (\\'hoffmann\\', 0),\\n (\\'henton\\', 0),\\n (\\'daly\\', 0),\\n (\\'villota\\', 0),\\n (\\'keegan\\', 0),\\n (\\'rebaque\\', 0),\\n (\\'merzario\\', 0),\\n (\\'stuck\\', 0),\\n (\\'lunger\\', 0),\\n (\\'stommelen\\', 0),\\n (\\'ian_scheckter\\', 0),\\n (\\'pryce\\', 0),\\n (\\'jarier\\', 0),\\n (\\'oliver\\', 0),\\n (\\'amon\\', 0),\\n (\\'pescarolo\\', 0),\\n (\\'wilson_fittipaldi\\', 0),\\n (\\'keizan\\', 0),\\n (\\'charlton\\', 0),\\n (\\'hailwood\\', 0),\\n (\\'ganley\\', 0),\\n (\\'redman\\', 0),\\n (\\'schenken\\', 0),\\n (\\'palmer\\', 0),\\n (\\'modena\\', 0),\\n (\\'caffi\\', 0),\\n (\\'lammers\\', 0),\\n (\\'satoru_nakajima\\', 0),\\n (\\'johansson\\', 0),\\n (\\'nannini\\', 0),\\n (\\'schneider\\', 0),\\n (\\'giacomelli\\', 0),\\n (\\'alguersuari\\', 0),\\n (\\'grosjean\\', 0),\\n (\\'kobayashi\\', 0),\\n (\\'wisell\\', 0),\\n (\\'danner\\', 0),\\n (\\'cheever\\', 0),\\n (\\'ghinzani\\', 0),\\n (\\'streiff\\', 0),\\n (\\'fabi\\', 0),\\n (\\'surer\\', 0),\\n (\\'manfred_winkelhock\\', 0),\\n (\\'baldi\\', 0),\\n (\\'serra\\', 0),\\n (\\'salazar\\', 0);\\n\\nALTER TABLE \"POPULATION__STAGING_TABLE_1\" ADD COLUMN \"t4__driverref__mapping_target_1_avg\";\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\" SET \"t4__driverref__mapping_target_1_avg\" = 0.0;\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\"\\nSET \"t4__driverref__mapping_target_1_avg\" = t2.\"value\"\\nFROM \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\" AS t2\\nWHERE \"POPULATION__STAGING_TABLE_1\".\"t4__driverref\" = t2.\"key\";\\n\\nDROP TABLE IF EXISTS \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\";'" + "'DROP TABLE IF EXISTS \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\";\\n\\nCREATE TABLE \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\"(\"key\" TEXT, \"value\" REAL);\\n\\nINSERT INTO \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\" (\"key\", \"value\")\\nVALUES(\\'fangio\\', 0.3653846153846154),\\n (\\'ascari\\', 0.3055555555555556),\\n (\\'michael_schumacher\\', 0.3032786885245902),\\n (\\'hamilton\\', 0.2981366459627329),\\n (\\'stewart\\', 0.2948717948717949),\\n (\\'prost\\', 0.264367816091954),\\n (\\'vettel\\', 0.2467532467532468),\\n (\\'clark\\', 0.2394366197183098),\\n (\\'senna\\', 0.2323943661971831),\\n (\\'damon_hill\\', 0.2159090909090909),\\n (\\'moss\\', 0.1866666666666667),\\n (\\'mansell\\', 0.1569767441860465),\\n (\\'lauda\\', 0.1390728476821192),\\n (\\'alonso\\', 0.1282051282051282),\\n (\\'hakkinen\\', 0.1278195488721804),\\n (\\'rosberg\\', 0.10625),\\n (\\'hunt\\', 0.1058823529411765),\\n (\\'jack_brabham\\', 0.1052631578947368),\\n (\\'piquet\\', 0.09714285714285714),\\n (\\'farina\\', 0.09523809523809523),\\n (\\'jones\\', 0.09259259259259259),\\n (\\'brooks\\', 0.09090909090909091),\\n (\\'scheckter\\', 0.09),\\n (\\'montoya\\', 0.08536585365853659),\\n (\\'emerson_fittipaldi\\', 0.08403361344537816),\\n (\\'rindt\\', 0.08333333333333333),\\n (\\'hulme\\', 0.08247422680412371),\\n (\\'peterson\\', 0.08247422680412371),\\n (\\'reutemann\\', 0.07936507936507936),\\n (\\'mario_andretti\\', 0.07142857142857142),\\n (\\'raikkonen\\', 0.0684931506849315),\\n (\\'villeneuve\\', 0.06153846153846154),\\n (\\'surtees\\', 0.0576923076923077),\\n (\\'hill\\', 0.05194805194805195),\\n (\\'gilles_villeneuve\\', 0.05),\\n (\\'berger\\', 0.05),\\n (\\'keke_rosberg\\', 0.04901960784313725),\\n (\\'hawthorn\\', 0.04878048780487805),\\n (\\'ickx\\', 0.04878048780487805),\\n (\\'gurney\\', 0.04819277108433735),\\n (\\'collins\\', 0.04761904761904762),\\n (\\'arnoux\\', 0.04697986577181208),\\n (\\'massa\\', 0.04587155963302753),\\n (\\'button\\', 0.04511278195488722),\\n (\\'pironi\\', 0.04347826086956522),\\n (\\'coulthard\\', 0.0427807486631016),\\n (\\'revson\\', 0.0425531914893617),\\n (\\'ricciardo\\', 0.0425531914893617),\\n (\\'webber\\', 0.04216867469879518),\\n (\\'ralf_schumacher\\', 0.03846153846153846),\\n (\\'barrichello\\', 0.036),\\n (\\'phil_hill\\', 0.03448275862068965),\\n (\\'rodriguez\\', 0.03333333333333333),\\n (\\'scarfiotti\\', 0.03333333333333333),\\n (\\'laffite\\', 0.03311258278145696),\\n (\\'regazzoni\\', 0.03225806451612903),\\n (\\'jabouille\\', 0.03076923076923077),\\n (\\'baghetti\\', 0.02941176470588235),\\n (\\'flaherty\\', 0.02941176470588235),\\n (\\'watson\\', 0.02898550724637681),\\n (\\'mclaren\\', 0.02803738317757009),\\n (\\'gethin\\', 0.02777777777777778),\\n (\\'musso\\', 0.02702702702702703),\\n (\\'cevert\\', 0.02631578947368421),\\n (\\'ruttman\\', 0.025),\\n (\\'irvine\\', 0.025),\\n (\\'max_verstappen\\', 0.02380952380952381),\\n (\\'hanks\\', 0.02325581395348837),\\n (\\'alboreto\\', 0.02298850574712644),\\n (\\'boutsen\\', 0.02290076335877863),\\n (\\'herbert\\', 0.02222222222222222),\\n (\\'parsons\\', 0.02083333333333333),\\n (\\'bryan\\', 0.02040816326530612),\\n (\\'trintignant\\', 0.02),\\n (\\'kubica\\', 0.0196078431372549),\\n (\\'patrese\\', 0.01951219512195122),\\n (\\'rathmann\\', 0.01923076923076923),\\n (\\'bandini\\', 0.01851851851851852),\\n (\\'tambay\\', 0.01834862385321101),\\n (\\'ginther\\', 0.01639344262295082),\\n (\\'fisichella\\', 0.01621621621621622),\\n (\\'ward\\', 0.01612903225806452),\\n (\\'frentzen\\', 0.01574803149606299),\\n (\\'brambilla\\', 0.01492537313432836),\\n (\\'bottas\\', 0.01470588235294118),\\n (\\'pace\\', 0.0136986301369863),\\n (\\'maldonado\\', 0.01298701298701299),\\n (\\'beltoise\\', 0.01282051282051282),\\n (\\'siffert\\', 0.01265822784810127),\\n (\\'depailler\\', 0.0108695652173913),\\n (\\'angelis\\', 0.0108695652173913),\\n (\\'bonnier\\', 0.009009009009009009),\\n (\\'mass\\', 0.008771929824561403),\\n (\\'panis\\', 0.007692307692307693),\\n (\\'darter\\', 0),\\n (\\'pretorius\\', 0),\\n (\\'adamich\\', 0),\\n (\\'trevor_taylor\\', 0),\\n (\\'love\\', 0),\\n (\\'anderson\\', 0),\\n (\\'bianchi\\', 0),\\n (\\'courage\\', 0),\\n (\\'tingle\\', 0),\\n (\\'attwood\\', 0),\\n (\\'spence\\', 0),\\n (\\'resta\\', 0),\\n (\\'chiron\\', 0),\\n (\\'reece\\', 0),\\n (\\'linden\\', 0),\\n (\\'agabashian\\', 0),\\n (\\'manzon\\', 0),\\n (\\'rosier\\', 0),\\n (\\'villoresi\\', 0),\\n (\\'graffenried\\', 0),\\n (\\'hulkenberg\\', 0),\\n (\\'petrov\\', 0),\\n (\\'bruno_senna\\', 0),\\n (\\'daywalt\\', 0),\\n (\\'perez\\', 0),\\n (\\'vergne\\', 0),\\n (\\'pic\\', 0),\\n (\\'chilton\\', 0),\\n (\\'gutierrez\\', 0),\\n (\\'jules_bianchi\\', 0),\\n (\\'kevin_magnussen\\', 0),\\n (\\'kvyat\\', 0),\\n (\\'ericsson\\', 0),\\n (\\'nasr\\', 0),\\n (\\'sainz\\', 0),\\n (\\'schell\\', 0),\\n (\\'maggs\\', 0),\\n (\\'gregory\\', 0),\\n (\\'andre_pilette\\', 0),\\n (\\'beaufort\\', 0),\\n (\\'burgess\\', 0),\\n (\\'salvadori\\', 0),\\n (\\'trips\\', 0),\\n (\\'herrmann\\', 0),\\n (\\'scarlatti\\', 0),\\n (\\'menditeguy\\', 0),\\n (\\'gonzalez\\', 0),\\n (\\'ireland\\', 0),\\n (\\'thomson\\', 0),\\n (\\'johnson\\', 0),\\n (\\'ertl\\', 0),\\n (\\'hartley\\', 0),\\n (\\'stevenson\\', 0),\\n (\\'freeland\\', 0),\\n (\\'bettenhausen\\', 0),\\n (\\'boyd\\', 0),\\n (\\'gould\\', 0),\\n (\\'behra\\', 0),\\n (\\'paul_russo\\', 0),\\n (\\'martini\\', 0),\\n (\\'larini\\', 0),\\n (\\'katayama\\', 0),\\n (\\'morbidelli\\', 0),\\n (\\'lamy\\', 0),\\n (\\'brundle\\', 0),\\n (\\'montermini\\', 0),\\n (\\'blundell\\', 0),\\n (\\'suzuki\\', 0),\\n (\\'moreno\\', 0),\\n (\\'wendlinger\\', 0),\\n (\\'gachot\\', 0),\\n (\\'magnussen\\', 0),\\n (\\'tarquini\\', 0),\\n (\\'comas\\', 0),\\n (\\'bernard\\', 0),\\n (\\'fittipaldi\\', 0),\\n (\\'lehto\\', 0),\\n (\\'cesaris\\', 0),\\n (\\'alliot\\', 0),\\n (\\'dalmas\\', 0),\\n (\\'warwick\\', 0),\\n (\\'capelli\\', 0),\\n (\\'gugelmin\\', 0),\\n (\\'rosa\\', 0),\\n (\\'heidfeld\\', 0),\\n (\\'kovalainen\\', 0),\\n (\\'glock\\', 0),\\n (\\'sato\\', 0),\\n (\\'trulli\\', 0),\\n (\\'sutil\\', 0),\\n (\\'liuzzi\\', 0),\\n (\\'wurz\\', 0),\\n (\\'speed\\', 0),\\n (\\'albers\\', 0),\\n (\\'klien\\', 0),\\n (\\'grouillard\\', 0),\\n (\\'karthikeyan\\', 0),\\n (\\'zonta\\', 0),\\n (\\'gene\\', 0),\\n (\\'verstappen\\', 0),\\n (\\'alesi\\', 0),\\n (\\'salo\\', 0),\\n (\\'diniz\\', 0),\\n (\\'buemi\\', 0),\\n (\\'badoer\\', 0),\\n (\\'zanardi\\', 0),\\n (\\'hoffmann\\', 0),\\n (\\'henton\\', 0),\\n (\\'daly\\', 0),\\n (\\'villota\\', 0),\\n (\\'keegan\\', 0),\\n (\\'rebaque\\', 0),\\n (\\'merzario\\', 0),\\n (\\'stuck\\', 0),\\n (\\'lunger\\', 0),\\n (\\'stommelen\\', 0),\\n (\\'ian_scheckter\\', 0),\\n (\\'pryce\\', 0),\\n (\\'jarier\\', 0),\\n (\\'oliver\\', 0),\\n (\\'amon\\', 0),\\n (\\'pescarolo\\', 0),\\n (\\'wilson_fittipaldi\\', 0),\\n (\\'keizan\\', 0),\\n (\\'charlton\\', 0),\\n (\\'hailwood\\', 0),\\n (\\'ganley\\', 0),\\n (\\'redman\\', 0),\\n (\\'schenken\\', 0),\\n (\\'palmer\\', 0),\\n (\\'modena\\', 0),\\n (\\'caffi\\', 0),\\n (\\'lammers\\', 0),\\n (\\'satoru_nakajima\\', 0),\\n (\\'johansson\\', 0),\\n (\\'nannini\\', 0),\\n (\\'schneider\\', 0),\\n (\\'giacomelli\\', 0),\\n (\\'alguersuari\\', 0),\\n (\\'grosjean\\', 0),\\n (\\'kobayashi\\', 0),\\n (\\'wisell\\', 0),\\n (\\'danner\\', 0),\\n (\\'cheever\\', 0),\\n (\\'ghinzani\\', 0),\\n (\\'streiff\\', 0),\\n (\\'fabi\\', 0),\\n (\\'surer\\', 0),\\n (\\'manfred_winkelhock\\', 0),\\n (\\'baldi\\', 0),\\n (\\'serra\\', 0),\\n (\\'salazar\\', 0);\\n\\nALTER TABLE \"POPULATION__STAGING_TABLE_1\" ADD COLUMN \"t4__driverref__mapping_target_1_avg\";\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\" SET \"t4__driverref__mapping_target_1_avg\" = 0.0;\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\"\\nSET \"t4__driverref__mapping_target_1_avg\" = t2.\"value\"\\nFROM \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\" AS t2\\nWHERE \"POPULATION__STAGING_TABLE_1\".\"t4__driverref\" = t2.\"key\";\\n\\nDROP TABLE IF EXISTS \"T4__DRIVERREF__MAPPING_TARGET_1_AVG\";'" ] }, "execution_count": 49, @@ -38813,7 +38802,7 @@ "source": [ "# Creates a folder named formula1_pipeline containing\n", "# the SQL code.\n", - "pipe1.features.to_sql().save(\"formula1_pipeline\")" + "pipe1.features.to_sql(size_threshold=None).save(\"formula1_pipeline\", remove=True)" ] }, { @@ -38822,7 +38811,7 @@ "metadata": {}, "outputs": [], "source": [ - "pipe1.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"formula1_spark\")" + "pipe1.features.to_sql(dialect=getml.pipeline.dialect.spark_sql, size_threshold=None).save(\"formula1_spark\", remove=True)" ] }, { diff --git a/imdb.ipynb b/imdb.ipynb index 487976d..2a819a1 100644 --- a/imdb.ipynb +++ b/imdb.ipynb @@ -104,27 +104,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220322172429.log.\n", + "getML engine is already running.\n", "\n", "\n", - "Loading pipelines...\n", - "[========================================] 100%\n", - "\n", "\n", - "Connected to project 'imdb'\n" + "Connected to project 'imdb'\n", + "http://localhost:1709/#/listprojects/imdb/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/imdb/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -1200,7 +1186,7 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -1229,7 +1215,7 @@ "3431962 845462 208838 Magga \n", "3431963 845463 870 Gunna \n", "3431964 845464 378123 Gudrun \n", - "3431965 845465 378123 \n", + "3431965 845465 378123 NULL \n", "\n", "\n", "3431966 rows x 3 columns\n", @@ -3136,7 +3122,7 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -3165,7 +3151,7 @@ "3431962 845462 208838 Magga \n", "3431963 845463 870 Gunna \n", "3431964 845464 378123 Gudrun \n", - "3431965 845465 378123 \n", + "3431965 845465 378123 NULL \n", "\n", "\n", "3431966 rows x 3 columns\n", @@ -4790,6 +4776,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining ROLES__STAGING_TABLE_3 and MOVIES_GENRES__STAGING_TABLE_2 over 'id' and 'movie_id', there are no corresponding entries for 26.899421% of entries in 'id' in 'ROLES__STAGING_TABLE_3'. You might want to double-check your join keys.\n", "\n", @@ -4820,7 +4809,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:10m:59.716192\n", + "Time taken: 0h:7m:55.474803\n", "\n" ] }, @@ -4831,26 +4820,26 @@ " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['movies', 'movies_genres', 'roles'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['TextFieldSplitter', 'Mapping'],\n", " share_selected_features=0.1,\n", - " tags=['fast_prop', 'container-99dJnF'])
url: http://localhost:1709/#/getpipeline/imdb/PpQOSV/0/
" + " tags=['fast_prop', 'container-pC2TpH'])
url: http://localhost:1709/#/getpipeline/imdb/Pixjb0/0/
" ], "text/plain": [ "Pipeline(data_model='actors',\n", " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['movies', 'movies_genres', 'roles'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['TextFieldSplitter', 'Mapping'],\n", " share_selected_features=0.1,\n", - " tags=['fast_prop', 'container-99dJnF'])\n", + " tags=['fast_prop', 'container-pC2TpH'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/imdb/PpQOSV/0/" + "url: http://localhost:1709/#/getpipeline/imdb/Pixjb0/0/" ] }, "execution_count": 21, @@ -4899,7 +4888,7 @@ "[========================================] 100%\n", "\n", "FastProp: Building features...\n", - "[========================================] 100%==] 122%\n", + "[========================================] 100%==] 120%\n", "\n", "\n" ] @@ -4973,7 +4962,7 @@ " 0\n", " \n", " \n", - " 2022-03-22 17:36:28\n", + " 2022-07-04 17:38:51\n", " \n", " \n", " \n", @@ -4985,15 +4974,15 @@ " \n", " \n", " \n", - " 0.8413\n", + " 0.8418\n", " \n", " \n", " \n", - " 0.9136\n", + " 0.9138\n", " \n", " \n", " \n", - " 0.3223\n", + " 0.3213\n", " \n", " \n", " \n", @@ -5002,7 +4991,7 @@ " 1\n", " \n", " \n", - " 2022-03-22 17:36:49\n", + " 2022-07-04 17:39:08\n", " \n", " \n", " \n", @@ -5014,15 +5003,15 @@ " \n", " \n", " \n", - " 0.8417\n", + " 0.842\n", " \n", " \n", " \n", - " 0.9134\n", + " 0.9138\n", " \n", " \n", " \n", - " 0.3236\n", + " 0.3227\n", " \n", " \n", " \n", @@ -5032,8 +5021,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-22 17:36:28 train target 0.8413 0.9136 0.3223\n", - "1 2022-03-22 17:36:49 test target 0.8417 0.9134 0.3236" + "0 2022-07-04 17:38:51 train target 0.8418 0.9138 0.3213\n", + "1 2022-07-04 17:39:08 test target 0.842 0.9138 0.3227" ] }, "execution_count": 22, @@ -5063,21 +5052,21 @@ "data": { "text/markdown": [ "```sql\n", - "DROP TABLE IF EXISTS \"FEATURE_1_142\";\n", + "DROP TABLE IF EXISTS \"FEATURE_1_114\";\n", "\n", - "CREATE TABLE \"FEATURE_1_142\" AS\n", - "SELECT MAX( COALESCE( f_1_1_16.\"feature_1_1_16\", 0.0 ) ) AS \"feature_1_142\",\n", + "CREATE TABLE \"FEATURE_1_114\" AS\n", + "SELECT AVG( COALESCE( f_1_1_13.\"feature_1_1_13\", 0.0 ) ) AS \"feature_1_114\",\n", " t1.rowid AS rownum\n", "FROM \"ACTORS__STAGING_TABLE_1\" t1\n", "INNER JOIN \"ROLES__STAGING_TABLE_3\" t2\n", "ON t1.\"id\" = t2.\"actor_id\"\n", - "LEFT JOIN \"FEATURE_1_1_16\" f_1_1_16\n", - "ON t2.rowid = f_1_1_16.rownum\n", + "LEFT JOIN \"FEATURE_1_1_13\" f_1_1_13\n", + "ON t2.rowid = f_1_1_13.rownum\n", "GROUP BY t1.rowid;\n", "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"FEATURE_1_142\";\\n\\nCREATE TABLE \"FEATURE_1_142\" AS\\nSELECT MAX( COALESCE( f_1_1_16.\"feature_1_1_16\", 0.0 ) ) AS \"feature_1_142\",\\n t1.rowid AS rownum\\nFROM \"ACTORS__STAGING_TABLE_1\" t1\\nINNER JOIN \"ROLES__STAGING_TABLE_3\" t2\\nON t1.\"id\" = t2.\"actor_id\"\\nLEFT JOIN \"FEATURE_1_1_16\" f_1_1_16\\nON t2.rowid = f_1_1_16.rownum\\nGROUP BY t1.rowid;'" + "'DROP TABLE IF EXISTS \"FEATURE_1_114\";\\n\\nCREATE TABLE \"FEATURE_1_114\" AS\\nSELECT AVG( COALESCE( f_1_1_13.\"feature_1_1_13\", 0.0 ) ) AS \"feature_1_114\",\\n t1.rowid AS rownum\\nFROM \"ACTORS__STAGING_TABLE_1\" t1\\nINNER JOIN \"ROLES__STAGING_TABLE_3\" t2\\nON t1.\"id\" = t2.\"actor_id\"\\nLEFT JOIN \"FEATURE_1_1_13\" f_1_1_13\\nON t2.rowid = f_1_1_13.rownum\\nGROUP BY t1.rowid;'" ] }, "execution_count": 23, @@ -5111,7 +5100,7 @@ "metadata": {}, "outputs": [], "source": [ - "pipe.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"imdb_spark\")" + "pipe.features.to_sql(dialect=getml.pipeline.dialect.spark_sql, size_threshold=None).save(\"imdb_spark\", remove=True)" ] }, { diff --git a/interstate94.ipynb b/interstate94.ipynb index f9b45b9..d4cbdf8 100644 --- a/interstate94.ipynb +++ b/interstate94.ipynb @@ -109,29 +109,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "getML API version: 1.2.0\n", + "getML API version: 1.3.0\n", "\n", - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220324095333.log.\n", + "getML engine is already running.\n", "\n", "\n", - "Loading pipelines...\n", - "[========================================] 100%\n", - "\n", "\n", - "Connected to project 'interstate94'\n" + "Connected to project 'interstate94'\n", + "http://localhost:1709/#/listprojects/interstate94/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/interstate94/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -186,8 +172,7 @@ "output_type": "stream", "text": [ "\n", - "Loading traffic...\n", - "[========================================] 100%\n" + "Loading traffic...\n" ] } ], @@ -985,7 +970,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 5, @@ -2002,7 +1987,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:5m:2.947328\n", + "Time taken: 0h:4m:20.188674\n", "\n" ] }, @@ -2013,26 +1998,26 @@ " feature_learners=['RelMT'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['traffic'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['memory: 7d', 'horizon: 1h', 'relmt', 'container-3HBAyM'])
url: http://localhost:1709/#/getpipeline/interstate94/FG5uXx/0/
" + " tags=['memory: 7d', 'horizon: 1h', 'relmt', 'container-VKCWUO'])
url: http://localhost:1709/#/getpipeline/interstate94/7NzZbQ/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['RelMT'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['traffic'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['memory: 7d', 'horizon: 1h', 'relmt', 'container-3HBAyM'])\n", + " tags=['memory: 7d', 'horizon: 1h', 'relmt', 'container-VKCWUO'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/interstate94/FG5uXx/0/" + "url: http://localhost:1709/#/getpipeline/interstate94/7NzZbQ/0/" ] }, "execution_count": 11, @@ -2067,6 +2052,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "RelMT: Building features...\n", "[========================================] 100%\n", "\n", @@ -2142,7 +2130,7 @@ " 0\n", " \n", " \n", - " 2022-03-24 09:58:47\n", + " 2022-07-04 20:01:54\n", " \n", " \n", " \n", @@ -2171,7 +2159,7 @@ " 1\n", " \n", " \n", - " 2022-03-24 09:59:00\n", + " 2022-07-04 20:02:07\n", " \n", " \n", " \n", @@ -2201,8 +2189,8 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-24 09:58:47 train traffic_volume 201.298 295.6177 0.9774\n", - "1 2022-03-24 09:59:00 test traffic_volume 183.2422 271.2663 0.9814" + "0 2022-07-04 20:01:54 train traffic_volume 201.298 295.6177 0.9774\n", + "1 2022-07-04 20:02:07 test traffic_volume 183.2422 271.2663 0.9814" ] }, "execution_count": 12, @@ -2782,6 +2770,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "RelMT: Building features...\n", "[========================================] 100%\n", "\n", @@ -2803,7 +2794,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 19, @@ -2912,7 +2903,7 @@ "source": [ "# Creates a folder named interstate94_pipeline containing\n", "# the SQL code.\n", - "pipe.features.to_sql().save(\"interstate94_pipeline\")" + "pipe.features.to_sql(size_threshold=None).save(\"interstate94_pipeline\", remove=True)" ] }, { diff --git a/loans.ipynb b/loans.ipynb index ae31e7b..3d1b0dc 100644 --- a/loans.ipynb +++ b/loans.ipynb @@ -73,24 +73,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220324104157.log.\n", + "getML engine is already running.\n", "\n", "\n", "\n", - "Connected to project 'loans'\n" + "Connected to project 'loans'\n", + "http://localhost:1709/#/listprojects/loans/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/loans/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -2038,7 +2027,7 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -2470,7 +2459,7 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -2542,7 +2531,7 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -2614,7 +2603,7 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -2686,7 +2675,7 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -2758,7 +2747,7 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -2793,17 +2782,17 @@ " name trans_id account \n", " role unused_float unused_string\n", " unit \n", - " 0 1 \n", + " 0 1 NULL \n", " 1 5 41403269.0 \n", " 2 6 41403269.0 \n", " 3 7 41403269.0 \n", " 4 8 41403269.0 \n", " ... ... \n", - "1056315 3682983 \n", - "1056316 3682984 \n", - "1056317 3682985 \n", - "1056318 3682986 \n", - "1056319 3682987 \n", + "1056315 3682983 NULL \n", + "1056316 3682984 NULL \n", + "1056317 3682985 NULL \n", + "1056318 3682986 NULL \n", + "1056319 3682987 NULL \n", "\n", "\n", "1056320 rows x 10 columns\n", @@ -3541,7 +3530,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -3774,11 +3763,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -3799,7 +3788,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -4032,11 +4021,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -4057,7 +4046,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -4290,11 +4279,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -4315,7 +4304,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -4548,11 +4537,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -4573,7 +4562,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -4806,11 +4795,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -5089,7 +5078,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -5322,11 +5311,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -5347,7 +5336,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -5580,11 +5569,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -5863,7 +5852,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -6096,11 +6085,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -6121,7 +6110,7 @@ " \n", " \n", " \n", - " NULL\n", + " \n", " \n", " \n", " \n", @@ -6354,11 +6343,11 @@ " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", - " \n", + " NULL\n", " \n", " \n", " \n", @@ -6381,31 +6370,31 @@ "text/plain": [ "name account_id type_disp type_card gender ... birth_date district_id card_id \n", "role join_key categorical categorical categorical ... unused_float unused_float unused_string\n", - " 0 1 OWNER NULL F ... 1970 18 \n", - " 1 2 OWNER NULL M ... 1945 1 \n", - " 2 2 DISPONENT NULL F ... 1940 1 \n", - " 3 3 OWNER NULL M ... 1956 5 \n", - " 4 3 DISPONENT NULL F ... 1960 5 \n", + " 0 1 OWNER F ... 1970 18 NULL \n", + " 1 2 OWNER M ... 1945 1 NULL \n", + " 2 2 DISPONENT F ... 1940 1 NULL \n", + " 3 3 OWNER M ... 1956 5 NULL \n", + " 4 3 DISPONENT F ... 1960 5 NULL \n", " ... ... ... ... ... ... ... \n", - "5364 11349 OWNER NULL F ... 1945 1 \n", - "5365 11349 DISPONENT NULL M ... 1943 1 \n", + "5364 11349 OWNER F ... 1945 1 NULL \n", + "5365 11349 DISPONENT M ... 1943 1 NULL \n", "5366 11359 OWNER classic M ... 1968 61 1247.0 \n", - "5367 11362 OWNER NULL F ... 1962 67 \n", - "5368 11382 OWNER NULL F ... 1953 74 \n", + "5367 11362 OWNER F ... 1962 67 NULL \n", + "5368 11382 OWNER F ... 1953 74 NULL \n", "\n", "name issued A2 \n", "role unused_string unused_string \n", - " 0 Pisek \n", - " 1 Hl.m. Praha \n", - " 2 Hl.m. Praha \n", - " 3 Kolin \n", - " 4 Kolin \n", + " 0 NULL Pisek \n", + " 1 NULL Hl.m. Praha \n", + " 2 NULL Hl.m. Praha \n", + " 3 NULL Kolin \n", + " 4 NULL Kolin \n", " ... ... \n", - "5364 Hl.m. Praha \n", - "5365 Hl.m. Praha \n", + "5364 NULL Hl.m. Praha \n", + "5365 NULL Hl.m. Praha \n", "5366 1995-06-13 Trebic \n", - "5367 Bruntal \n", - "5368 Ostrava - mesto\n", + "5367 NULL Bruntal \n", + "5368 NULL Ostrava - mesto\n", "\n", "\n", "5369 rows x 25 columns\n", @@ -6563,7 +6552,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:2.111608\n", + "Time taken: 0h:0m:1.296924\n", "\n" ] }, @@ -6574,26 +6563,26 @@ " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['meta', 'order', 'trans'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['container-o7N3L9'])
url: http://localhost:1709/#/getpipeline/loans/paFZ9f/0/
" + " tags=['container-sw51g1'])
url: http://localhost:1709/#/getpipeline/loans/ZOzTKy/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['meta', 'order', 'trans'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['container-o7N3L9'])\n", + " tags=['container-sw51g1'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/loans/paFZ9f/0/" + "url: http://localhost:1709/#/getpipeline/loans/ZOzTKy/0/" ] }, "execution_count": 11, @@ -6704,7 +6693,7 @@ " 0\n", " \n", " \n", - " 2022-03-24 10:42:21\n", + " 2022-07-04 20:07:13\n", " \n", " \n", " \n", @@ -6716,15 +6705,15 @@ " \n", " \n", " \n", - " 0.9978\n", + " 0.9956\n", " \n", " \n", " \n", - " 1.\n", + " 0.9997\n", " \n", " \n", " \n", - " 0.06577\n", + " 0.0654\n", " \n", " \n", " \n", @@ -6733,7 +6722,7 @@ " 1\n", " \n", " \n", - " 2022-03-24 10:42:22\n", + " 2022-07-04 20:07:13\n", " \n", " \n", " \n", @@ -6745,15 +6734,15 @@ " \n", " \n", " \n", - " 0.9686\n", + " 0.9641\n", " \n", " \n", " \n", - " 0.9564\n", + " 0.9368\n", " \n", " \n", " \n", - " 0.13536\n", + " 0.14632\n", " \n", " \n", " \n", @@ -6763,8 +6752,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-24 10:42:21 train default 0.9978 1. 0.06577\n", - "1 2022-03-24 10:42:22 test default 0.9686 0.9564 0.13536" + "0 2022-07-04 20:07:13 train default 0.9956 0.9997 0.0654 \n", + "1 2022-07-04 20:07:13 test default 0.9641 0.9368 0.14632" ] }, "execution_count": 12, @@ -6808,10 +6797,10 @@ "data": { "text/markdown": [ "```sql\n", - "DROP TABLE IF EXISTS \"FEATURE_1_26\";\n", + "DROP TABLE IF EXISTS \"FEATURE_1_24\";\n", "\n", - "CREATE TABLE \"FEATURE_1_26\" AS\n", - "SELECT Q1( t2.\"balance\" ) AS \"feature_1_26\",\n", + "CREATE TABLE \"FEATURE_1_24\" AS\n", + "SELECT Q1( t2.\"balance\" ) AS \"feature_1_24\",\n", " t1.rowid AS rownum\n", "FROM \"POPULATION__STAGING_TABLE_1\" t1\n", "INNER JOIN \"TRANS__STAGING_TABLE_4\" t2\n", @@ -6821,7 +6810,7 @@ "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"FEATURE_1_26\";\\n\\nCREATE TABLE \"FEATURE_1_26\" AS\\nSELECT Q1( t2.\"balance\" ) AS \"feature_1_26\",\\n t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"TRANS__STAGING_TABLE_4\" t2\\nON t1.\"account_id\" = t2.\"account_id\"\\nWHERE t2.\"date\" <= t1.\"date_loan\"\\nGROUP BY t1.rowid;'" + "'DROP TABLE IF EXISTS \"FEATURE_1_24\";\\n\\nCREATE TABLE \"FEATURE_1_24\" AS\\nSELECT Q1( t2.\"balance\" ) AS \"feature_1_24\",\\n t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"TRANS__STAGING_TABLE_4\" t2\\nON t1.\"account_id\" = t2.\"account_id\"\\nWHERE t2.\"date\" <= t1.\"date_loan\"\\nGROUP BY t1.rowid;'" ] }, "execution_count": 13, @@ -6857,7 +6846,7 @@ "outputs": [ { "data": { - "image/png": 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A3Q7udQEAMGfn3/aNO/r5Cw6fOWoeAAAMzZ5JAAAAAHTTTAIAAACgm2YSAAAAAN00kwAAAADoppkEAAAAQDfNJAAAAAC6aSYBAAAA0E0zCQAAAIBuB/e6AABgXs6/7Rt39PMXHD5zkDoAANgb9kwCAAAAoJtmEgAAAADdHOYGAOwph80BAMyLPZMAAAAA6KaZBAAAAEA3zSQAAAAAujlnEgDAKTivEwDAZ9kzCQAAAIBumkkAAAAAdNNMAgAAAKCbZhIAAAAA3TSTAAAAAOimmQQAAABAN80kAAAAALppJgEAAADQ7eBeFwAAwPScf9s37ujnLzh85iB1AADTY88kAAAAALppJgEAAADQTTMJAAAAgG6aSQAAAAB000wCAAAAoJuruQEAMLqdXh0ucYU4AJgKeyYBAAAA0E0zCQAAAIBumkkAAAAAdNNMAgAAAKCbZhIAAAAA3TSTAAAAAOh2cK8LAACA/ej8275xxxkXHD5z0Mxj8wBgO+yZBAAAAEA3eyYBAMCKsqcTANuhmQQAAAxijEP7AJgeh7kBAAAA0E0zCQAAAIBumkkAAAAAdNNMAgAAAKCbZhIAAAAA3VzNDQAAWBmuOAewc/ZMAgAAAKCbZhIAAAAA3TSTAAAAAOimmQQAAABAN80kAAAAALq5mhsAAMCE7PSKc642B4xNMwkAAGAf22lzKtGgAj6Xw9wAAAAA6KaZBAAAAEA3zSQAAAAAumkmAQAAANBNMwkAAACAbppJAAAAAHQ7uNcFAAAAsNrOv+0bd/TzFxw+c5A6gD72TAIAAACgm2YSAAAAAN0c5gYAAMC+stPD5hKHzsHJ2DMJAAAAgG6aSQAAAAB000wCAAAAoJtmEgAAAADdNJMAAAAA6KaZBAAAAEA3zSQAAAAAumkmAQAAANBNMwkAAACAbppJAAAAAHTTTAIAAACg28G9LgAAAADYufNv+8Yd/fwFh88cpA72P3smAQAAANBNMwkAAACAbppJAAAAAHRzziQAAADg8zgHEydizyQAAAAAumkmAQAAANBNMwkAAACAbppJAAAAAHRzAm4AAABglpwkfG/YMwkAAACAbppJAAAAAHTTTAIAAACgm2YSAAAAAN00kwAAAADoppkEAAAAQDfNJAAAAAC6aSYBAAAA0E0zCQAAAIBumkkAAAAAdNNMAgAAAKCbZhIAAAAA3Q5u5cmttQNJDhz9vqquGLwiAAAAACarq5nUWntUkkclObS860CSjSSnbfUFW2uPS3K7JFdN8uCqeu2mx26Z5AnLx36vqh6/1XwAAAAAxtO7Z9K5Sb66qt69kxdrrZ2d5Buq6lattZsleWqSb9n0lGcn+dYk703y6tba86vq7Tt5TQAAAACG03vOpLfttJG0dHaSi5Kkqt6S5AattaslSWvtRkk+WlXvXh4+98dJ7jDAawIAAAAwkAMbGxunfFJr7fFJviLJy5JcevT+qnrWVl6stfbMJC+pqhcuv391kntX1Ttaa7dK8qiq+s7lYw9OcoOqeuzJMi+77PKNgwe3fLTdJD3kpq/c0c8/7a23GTVvrMwh7bS+ZHfexyHNYcxzWBanljdG5tTzxsic2/o8hjHmZermMOapLdvHy5yDqb2Pc3wP52AO8zKHGodmzFtnmzOP31erOC8nceBED/Qe5vZlST6Z5Bab7ttIsqVmUpLPHPP90XMvneqxE7r44k9ssYT968iRSyadt53MtbVDo9RxMnv9Pq7imHc7b4zMVazRmPsMvU7PYRthzOObwvq3ivM8dN4YY57DvEx9fZnDsjiHeT6VvR7zWJkns9fbnMSysxt2+/0Y09raoRM+1tVMqqr7JUlr7dpJrqiqi7dZy/uTnLG5tiQfPMFj10vyvm2+DgAAAAAj6DpnUmvtNq21dyR5a5Jqrf19a+0btvF6L05yt2Xm1yX5p6r6ZJJU1XuSnN5a+7LW2mlJ7rJ8PgAAAAAT0XuY288muWtVvTlJlo2kJ+Zzr8R2SlX1utbam1prr09yWZIHttbOTfKxqvr9JD+UxQm6N5L85kAn/QYAAABgIL3NpEuPNpKSpKpe21q7YjsvWFWPTPLITXdtzn1Fkv+8nVwAAAAAxtfbTLq8tfbdSV66/P6OSS4fpyQAAAAApqrrnElJvi/JeUnetfw6N8mDR6oJAAAAgInqvZrbP2SxNxIAAAAAK+ykzaTW2i9X1UNba6/M4qTYn6OqtnQCbgAAAADm7VR7Jj1r+e+jxy4EAAAAgOk7aTOpqt60vPmAqjp382OttRclOTxSXQAAAAC76oLDZ5708bW1Qzly5JLdKWbCTnWY2/dmcfLtm7XWXrHpodOTXG/MwgAAAACYnlPtmfRbrbWXJ/mtJI/d9NAVSf7fiHUBAAAAMEGnvJpbVb03yVmb72utnZ7kOUn+6zhlAQAAADBFp2wmJUlr7T5JfinJFy/vuiLJn49VFAAAAADT1NVMSvLQJF+T5PlJ7pbkvkk+MlZRAAAAAEzTlTqfd0lVvT/JlarqY1X15CwaSgAAAACskN49kz7TWrtnkve31h6f5E1JbjReWQAAAABMUe+eSfdJUkl+OMkNlt//wFhFAQAAADBNJ90zqbV2tNn00eVXkpw3akUAAADAvnPB4TNP+vja2qEcOXLJ7hTDjpzqMLfLkmwsbx9Y/ruxvL2R5LSR6gIAAABggk7aTKqq3sPgAAAAAFgBXSfgbq1dJ8ljkly3qv5La+3uSV5dVR8YtToAAAAAJqV3z6MLszgB9w033ffs4csBAAAAYMp6m0mHquqpSS5Nkqr6/SRXGa0qAAAAACapt5l05dba6VmejLu1dkaSq41WFQAAAACT1HXOpCRPTfK3Sa7fWvvDJDdP8tDRqgIAAABgkrqaSVX1/Nba4STfmMXeSQ+uqvePWhkAAAAAk3PKZlJr7UCS362q70py0fglAQAAADBVp2wmVdVGa+0fWmsPTPJXST696bF/GrM4AAAAAKal95xJ33Oc+zaS3GjAWgAAAACYuN5m0q2r6r2jVgIAAADA5F2p83nPG7UKAAAAAGahd8+kt7bWnpvPP2fSs0apCgAAACbkgsNnnvTxtbVDOXLkkj3Lg93U20y6apLLktx8030bSTSTAAAAAFZIVzOpqh6QJK21aye5oqouHrUqAAAAACapq5nUWrtNkuckuVqSA621jya5T1W9dsziAAAAAJiW3hNw/2ySu1bVdavqjCT3SfLE8coCAAAAYIp6m0mXVtWbj36z3CPpinFKAgAAAGCqek/AfXlr7Z5JXrL8/o5JLh+nJAAAAACmqreZ9H1JfjXJM7K4itsbkvz3sYoCAAAAYJp6D3O7S5JPV9W1quqLlz/3HeOVBQAAAMAU9TaT/kuSe2z6/vZJ7j18OQAAAABM2VZOwL35HEkbYxQDAAAAwLT1njPpT1prr07yqiwaUGcnecFoVQEAAAAwSV17JlXVTyf5oSTvSfKuJA+pqp8ZsS4AAAAAJqh3z6RU1WuSvGbEWgAAAJiBCw6fedLH19YO5ciRS3anGGDX9Z4zCQAAAAA0kwAAAADop5kEAAAAQLfucyYBAADw+Zw/CFg19kwCAAAAoJtmEgAAAADdNJMAAAAA6KaZBAAAAEA3zSQAAAAAumkmAQAAANBNMwkAAACAbgf3ugAAAIATueDwmSd9fG3tUI4cuWR3igEgiT2TAAAAANgCzSQAAAAAumkmAQAAANBNMwkAAACAbppJAAAAAHTTTAIAAACgm2YSAAAAAN00kwAAAADodnCvCwAAgCm44PCZJ318be1Qjhy5ZHeKAYAJs2cSAAAAAN00kwAAAADo5jA3AABmyWFpALA37JkEAAAAQDfNJAAAAAC6aSYBAAAA0E0zCQAAAIBumkkAAAAAdNNMAgAAAKCbZhIAAAAA3TSTAAAAAOh2cK8LAABYNRccPvOkj6+tHcqRI5cMlredTACAE7FnEgAAAADdNJMAAAAA6KaZBAAAAEA3zSQAAAAAumkmAQAAANBNMwkAAACAbppJAAAAAHQ7uNcFAAAM6YLDZ57yOWtrh3LkyCXjFwMAsA/ZMwkAAACAbppJAAAAAHTTTAIAAACgm2YSAAAAAN00kwAAAADo5mpuALCPnerKZq5qBgDAVtkzCQAAAIBumkkAAAAAdNNMAgAAAKCbZhIAAAAA3TSTAAAAAOimmQQAAABAN80kAAAAALppJgEAAADQ7eBeFwAAu+WCw2ee9PG1tUM5cuSS3SkGAABmyp5JAAAAAHTTTAIAAACgm2YSAAAAAN2cMwmAyXKOIwAAmB57JgEAAADQTTMJAAAAgG67ephba+20JE9NcrMkB5J8b1W945jnfHeSH11++7KqetRu1ggAAADAie32nkn3S3JFVd0qyU8n+anND7bWrprkF5J8a5JbJDmrtXazXa4RAAAAgBPY7WbS2UkuWt5+aZKzNj9YVZ9KcmZV/WtVbST5aJJr7GqFAAAAAJzQbl/N7fpJjiRJVV3aWjuttXZaVV1+9AlV9bEkaa19dZIvTfLaXa4RgG041ZXXEldfAwCA/WC0ZlJr7bwk5x1z99d2/uxNkjw/yX2r6jMne+61rnW1HDx42vaK3GfW1g5NOm+7mWPUsZuvZ8zTyxsjc441Pu2ttxk0f7t17Pe8MTKnnjdG5irWaMzTzJx63hiZq1ijMU8zcxVrNOZpZu7232tTNFozqaouTHLh5vtaaxcmOWN5+8pJLt28V9Ly/hsm+cMk96uqN5zqdS6++BOD1Tx3Q/9v/xh7D2w1cy/2Ytjr93EVx7zbeWNk7scax1gWh86cet4YmVPPGyNzFWs05mlmTj1vjMxVrNGYp5m5ijUa8zQzV2lP+5M1zXb7nEkvTnLO8vZdkvzZcZ7zrCTfX1V/u1tFAQAAANBnt8+Z9AdJ7tpae22STyS5d5K01h6V5HCSjyS5dZLHttYeu/yZJ1bVH+5ynQAAAAAcx642k5aHtN3/OPf/7KZvr7Z7FQEAAACwFbt9mBsAAAAAM6aZBAAAAEA3zSQAAAAAumkmAQAAANBNMwkAAACAbrt6NTcAtu+Cw2ee9PG1tUM5cuSS3SkGAABYWZpJACPQ+AEAAPYrh7kBAAAA0M2eSQCxJxEAAEAveyYBAAAA0E0zCQAAAIBumkkAAAAAdNNMAgAAAKCbZhIAAAAA3VzNDZglV18DAADYG/ZMAgAAAKCbPZOA0dmLCAAAYP+wZxIAAAAA3TSTAAAAAOimmQQAAABAN80kAAAAALppJgEAAADQTTMJAAAAgG6aSQAAAAB000wCAAAAoJtmEgAAAADdNJMAAAAA6KaZBAAAAEC3g3tdALAzFxw+85TPWVs7lCNHLhksc6t5AAAA7B/2TAIAAACgm2YSAAAAAN00kwAAAADoppkEAAAAQDcn4IZTcDJqAAAA+Cx7JgEAAADQTTMJAAAAgG4Oc2NfcUgaAAAAjMueSQAAAAB000wCAAAAoJtmEgAAAADdNJMAAAAA6KaZBAAAAEA3zSQAAAAAumkmAQAAANBNMwkAAACAbppJAAAAAHTTTAIAAACgm2YSAAAAAN00kwAAAADoppkEAAAAQDfNJAAAAAC6aSYBAAAA0E0zCQAAAIBumkkAAAAAdNNMAgAAAKCbZhIAAAAA3TSTAAAAAOimmQQAAABAN80kAAAAALppJgEAAADQTTMJAAAAgG6aSQAAAAB000wCAAAAoJtmEgAAAADdNJMAAAAA6KaZBAAAAEA3zSQAAAAAumkmAQAAANBNMwkAAACAbppJAAAAAHTTTAIAAACgm2YSAAAAAN00kwAAAADoppkEAAAAQDfNJAAAAAC6aSYBAAAA0E0zCQAAAIBumkkAAAAAdNNMAgAAAKCbZhIAAAAA3TSTAAAAAOimmQQAAABAN80kAAAAALppJgEAAADQTTMJAAAAgG6aSQAAAAB000wCAAAAoJtmEgAAAADdNJMAAAAA6KaZBAAAAEA3zSQAAAAAumkmAQAAANBNMwkAAACAbppJAAAAAHTTTAIAAACgm2YSAAAAAN00kwAAAADoppkEAAAAQDfNJAAAAAC6aSYBAAAA0E0zCQAAAIBumkkAAAAAdNNMAgAAAKCbZhIAAAAA3TSTAAAAAOh2cDdfrLV2WpKnJrlZkgNJvreq3nGC5z4/yaer6tzdqxAAAACAk9ntPZPul+SKqrpVkp9O8lPHe1Jr7fZJbrybhQEAAABwarvdTDo7yUXL2y9NctaxT2itXSXJTyR5/O6VBQAAAECP3W4mXT/JkSSpqkuTnLY89G2zH0vylCQf3+XaAAAAADiFAxsbG6MEt9bOS3LeMXd/bZJbV9Xrls95b5Ivq6rLl9/fJMnPVdU9WmtnJTn3VOdMuuyyyzcOHjy2HzVPD7npK3f08097621GzRsrEwAAAJicAyd6YLQTcFfVhUku3Hxfa+3CJGcsb185yaVHG0lL35Hkxq21v05yjSRrrbVHVNXPn+h1Lr74E4PXPldHjlwy6bztZK6tHRq0jqHzxshcxRqNeZqZq1ijMU8zcxVrNOZpZk49b4zMVazRmKeZuYo1GvM0M8eocarW1g6d8LFdvZpbkhcnOWf5712S/NnmB6vqSUmelCSb9kw6YSMJAAAAgN21282kP0hy19baa5N8Ism9k6S19qgkh6vq1btcDwAAAABbsKvNpOUhbfc/zv0/e5z7Xp7k5eNXBQAAAECv3b6aGwAAAAAzttuHuTFzFxw+86SPr9LJyAAAAGAV2TMJAAAAgG6aSQAAAAB000wCAAAAoJtmEgAAAADdNJMAAAAA6KaZBAAAAEA3zSQAAAAAumkmAQAAANBNMwkAAACAbppJAAAAAHTTTAIAAACgm2YSAAAAAN00kwAAAADoppkEAAAAQDfNJAAAAAC6HdzrAhjPBYfPPOnja2uHcuTIJbtTDAAAALAv2DMJAAAAgG6aSQAAAAB000wCAAAAoJtmEgAAAADdNJMAAAAA6KaZBAAAAEA3zSQAAAAAumkmAQAAANBNMwkAAACAbppJAAAAAHTTTAIAAACgm2YSAAAAAN00kwAAAADoppkEAAAAQDfNJAAAAAC6aSYBAAAA0O3gXhfAZ11w+MyTPr62dihHjlyyO8UAAAAAHIc9kwAAAADoppkEAAAAQDfNJAAAAAC6aSYBAAAA0E0zCQAAAIBumkkAAAAAdNNMAgAAAKCbZhIAAAAA3TSTAAAAAOimmQQAAABAN80kAAAAALppJgEAAADQTTMJAAAAgG6aSQAAAAB000wCAAAAoJtmEgAAAADdNJMAAAAA6KaZBAAAAEA3zSQAAAAAumkmAQAAANBNMwkAAACAbppJAAAAAHTTTAIAAACgm2YSAAAAAN0ObGxs7HUNAAAAAMyEPZMAAAAA6KaZBAAAAEA3zSQAAAAAumkmAQAAANBNMwkAAACAbppJAAAAAHTTTAIAAACgm2YSAJ+ntXZ6a+0/tNYO7nUtPVpr19zrGhhPa+06A+UcaK2ttdauMUTeXLXWbrjXNcCqaa3dbq9rABjSgY2Njb2ugeNorT09ya9V1d8OlHfNJA9OciTJbyT5wSRfn+QfkvxyVV2yjcwvSvItVfVHy/xHJ/nKJJXkZ6rqw1vMu3aSByV5T1X9ZmvtJ5Lccgd5g76Hy8wrJblXkjskuW6STyd5V5IXVtUr9zqv4/V+tqoetcWfuV6SH05yKMnzq+rwpseeXFU/uJd5y587Pcn3Jvn2JNdLspHkfUlelOS3q+qKPc4bfJ5HWF9+uaoeurz9bUkuTPKBJGckeUhVvXQbNd6pql68vP3FSR6X5KZJ3pLkJ6vqI1vNPMlr/UVVbemD+tDbsGXmoMvOKV5ry+vzCXJeVlVn7+Dnh15f7pTknKp6cGvt7Cx+Z308yRcm+cGqetE2alxP8stJvjbJWpK3J7l6kj9N8uiqev8W80bfdg8wL9+d5JeSnJ7kT5L8UFV9fPnYdtaXQbc5J3iNnY556O3ix5M8N8njt7qMnCTzP1fVG5a3r5LkfyT5qiy2i0+uqk8P8BrPq6p7b/NnB99uD/0ZdOgaW2uPSvKbVfWerfzcKTLvd8xdB7L4HfO4JKmq52wx75oZ9j2cxe+/1tpZx8urqldvNWuZN/TfV0N/Rr5mhv977WpJzk1yjSx+R/3DpsceXVWP32rmSV7rJVV1xy3+zOB/r53i9X6oqp40QM6Ofl/tF7P4H+cVdcskB1trP57FxuPlO8z7zSR/leQ/JfnLJK9M8ltJzkzynCR330bmC5P8n+XtpyT5/5I8Nsktlq+3pY1JkucneVWSW7fW7p7kH5P85DLv2Um+Y4t5Q7+HSfL0JP+S5AVJbp/kX7P48PKI1tpdquqRe5x39JfGidxyq3lZzOULk3w4yU8tN54/tXzsphPISxbL8juzWA4/lMWHtusluUeSOyU59kPdbucNPs8Zfn35mk23z09yVlW9c7lHyEVJttxMSvKjSV68vP2UJK9N8qtJbp3kWUnutpWw1tr3n+ChA0m+ZBv1Db0NSwZedoZen1trVyR5fxbNjwPLu6/fWntHko2qutFWMzP8+vK4JHdZ3v7JJN9aVf/YWjsjiz9KttxMSvKMJP+jqt7cWvv6JPdO8ogsfvc9P8lZW8wbdJ0eaV4ekcV6/bEk5yX5s9bat1fVv2x6ja0YdJsz0piH3i6+bpn53Nbae5L8dpL/W1Wf2UZtRz0hydFG3i8nuXSZe+skv5bkPlsJO/p+Lb89+j5ebwfv46Db7aWhP4MOXeMDslhm3pHkiVX1ji3+/PGcn+QjWWyvjs7LVZP8x23mDf0ezuH335OzaNZflORlm/Ie2Vr7h6r60W3UOMbfV0N/Rh7677XnJ/n7ZY2/11p7QlX9xvKx2yXZUjOptXbnEzx0IMn1t1HfGH+vncxdkzxpKz8w0u+rfUEzabo+WlXnLf839aHLru2rsviw+r6q+p0t5l29qn46SVprb6mqRyzv/9PW2l9ss8arV9WFy9s3rKrvXd5+3fJ/RLfqylV1QWvtQJK3VdV3Le9/bWvtu072gycw9HuYJDfZ1IV+cWvtz6vq/CS/01rbTkd96Lxk8QfOe4+5byOLjd91t5F3sKqeliSttRdm8aH6gmWd2/mDZOi8ZLH83euY+yrJ4dbaayaQN8Y8D72+bN5N9ZKqemeSVNWHW2tD7MJ6/ap6wvL237fWtvO/5g9P8udZ/EI/1unbyBt6G3Y0Z8hl518y7Pp8pySPSvKrVfV7SdJae3VVbafRfNTQY04Wf4Qlyb9V1T8mSVV9qLX2r9vMO62q3ry8/YYkT6qqy5P8bmvtR7aRN/Q6Pca8XFZVFy9vP6O19sEsGkp3zueu772G3uaMMebBt4tV9aok39Za+8Ys9np6Umvtk1l8jjjRH1Uns/n33E2r6luWt1/SWnvZNvJ+Nos/MB9dVa9NBnkfjxpiu52M8xl0yBrfV1V3aYvD0H51ubfTn+Sznxf/ZhuZN0vymCwaug+vqne11u64qdGwVUO/h3P4/feNVfVNx7n/2Tv47DT03wZDf6YdY135oqP/wdFae2qSi1prp1XVr22zxl/P4j37+HEeW9tG3uB/r7XWPnSChw5ksYfWVo3x+2pf0Eyaro0kqaq3JfmB1toXJLlVkm9KcpskW12xrtxau0kWHf211totq+rVy/uuus0a/6G1dn4WnfIXt9bulsXKf8csdkPdqtNaa/+hqv65tfbvu4W21r4621tWh34Pj9Zz+yz+t/JOST6zvO/OSbZ1+MrQeUl+JMkZVfXo47zWdj6ofnr5Ifz3q+qK1tp9k/x6a+1ZWezWu9d5SfKx5YefP6rlIQKttasmOSfJJyaQN8Y8D72+3Ky19jtZ/KL98tbaf6mq/9Nae2wW/8O4HdfZ9D9Yn2ytfV1VvX5Z49W3kXdOkl9J8tA65lCQttgVfquG3oYlwy87g67PVfXS1trLk/z48o+uh2d7jYXNhh7zE5P8VWvtRUne2Vr73SSvzuJ/UH9rmzW+pbX2vCz2YLh9FvOc1tqFWfyP/JYNuU6PNC8vb639cZJ7VdUnquqi1tqnsvgf/mtuI2/Qbc5IYx56u/jvf2jV4hCMv13mfUm29z/wSXK11tp/WmZ/uLX2Fcs9766bbfwOrKr/3Vr7vSQ/31q7JMlPZGfv49Db7WT4z6BD13j08+JfJPmL1tqXZnFo1Xcsa/7OrQZW1aeS/ERrrSV5SmvtcLb/H2bJ8O/hHH7/fbK19g1Hm6RHtda+Kcnl26xx6L8Nhv5MO8bfawdaa19fVa+rqn9bzvUfLLdj2/mPuHsleViS/1ZVn7Ot2ebfGWP8vfasJO+sqqcf+8CEPjvtC5pJ0/U5xypX1Sez+B/5P99m3o8neV4Wx+DeLsmvLH/pvj/JiQ4dOZUHJLlvFn/c3TCLjdz7szgc5rxt5D0yyS8muWctz8/SWrtHFh+MHrSNvKHfwyT578sab5LkzUm+b3n/N2bxXuw07yE7zEtV/Upr7b6ttatX1b8d8/CfbSPyAVkcdvLiJJ+oxTHv92+tfW+SNoG8ZLHr9C8k+cXW2qEsdkO9JMlLkvzXHeZ9YRaHIXx8B3lDLzfJ8OvLPY/5/ugx9X+/fJ201q5ybBPnFF63KfcDSb5oefvRWTRJtqSq3tJau0sW83GsH95GjUNvw5KBl50R1ucs35/HtsX/Av5qkp2e3PrY9e8z2dmYn9dauyiLP+a+NIvDJo4keVBVvS/Z1rL4/Vn8QXOTLP5n8U+W9//y0T2Wtpg5xrb76LzcJAPMS1X9eGvtNkk+tem+l7bWXp3ke5Itj/mRWczzvQba5gw+5hFqfO7x7qyq92a5x+A2lsVPJnnqpu+/KovD8Z6f5H9to8ZU1ZEkD2it3TbJH2R7zcKjBt1uLw39GXToGj+nyVNV787ivIEXHv/p/aqqknznssnwzs2PbXHZGfo93I3ff5/Jzj6LPSjJM1trN07y0Szm6ZpZ7F36gG3WOPTfBkN/pv2xDP/32g8k+eXW2t2r6uPLhtIds9jTZsuHXVbV4dbau5NcJZt+vyw9Yxv1jfH32o8ledQJPju9+Xg/cCoj/L7aHzY2NnzN7Gt9ff2GU84bMnN9ff06I72H15zwmG83xpjHmOv19fUvndI8r6+vHxhjzOvr6wfW19fXRpqTa059njfl/sWUxz1kjUPVt76+fuX19fUvX19fPzjgGAfdRqyvr99g+e9gy836+vq3jjG/Q8/zkJlD/77aNC/XnOr7OOSyuNzODvI75QT5o7yPYyyLO6zn9PX19ZsPnDn4Z7ERf6dueTt2vM8OuzRX2152hv5csswb5TP3Mv+aO/jZ09fX12+wvr5+nfX19dNHqG2svzWuNZX3cLe+BvwMNtqyOFB9o/6+mtOXPZMmarnL5JNynKuwZLFL6lavwnLsVV0eWp+9IsCW88bIbK19Zxb/0/vuLK5u8ttJrrT8342H1PLKHQP5va3Wt6zxhFfHyfbGfNyrfbTWtnW1j5FqPFnes7eRN/g8b65xeWjMD42wLB5Y7nWxL5fFTjvZRf9Y2xp3h6Fq3O68bL4q3rfms1fFu25r7SG1xaviDb2NOMF2++ghDdv9XXC8k6o+ZifbsQ5DLotbztyN31eb5mWsdSXZ2piHXhZ3czubTGSb0z57hbjHVdUHdvri7ZgrzlXVpUm2c46fo3nHvZrictne7tUUj5f5seVcbzlz6M/Iy1oGvWpfp51uc7a9viwPE7zbUHPSYcvr33LvmSdlcaj9jyZ5cpIbtMXhnA+uTVdO20Lm4Mv3Sbwww25ztvu5ZNCrUraBL4ay3OP8CRn274KhxzzoNmw/0Uyarkdm2KuwHHtVlz/fYd4YmY/J4lwWX5rFh4N7VNUb2uI8Ahfls1fu6DL0xm5p6KvjDH21jzFqHDpv0Hk+QY2WxXFs6fjwkcZ9Kt01jlTf5qviPTbJ2bWzq+INvY0Y43fBGNuxUxnjXAVbyZzDNqLHVsY89DwP/rtg6tucpaNXiPvNNswV4oa+4twYV1McOnPoz8hjXLWvx55tc5JckIHneYT17/wsmifXzuKQp9tX1d+1xbl+fieL8+ps1aDL4giNlTG2YUMv30NfDOX8DP93wRy2i/uCZtJ0DX0VlqHzxsj8RFW9K8m7WmsfrKo3JElVfbC1tp2Vf+iNXTL8mIe+2scYNU59nudQ4xyWxTGMMe4hjVHf0FfFG3obMcZyM8Z2bOrmsI0Y2tDzPMbvgjm8j0NfIW6MK84NfTXFoTOH3o6N8R4ObYz1Zeh5Hnr9+/RyD833tdYurqq/W9b43tba8c6d2GvIcQ895lE+lwy8fJ+TYS+GMsayPZft4uxpJk3X0FdhGTpvjMwPttZ+pKp+sapukSRtcUWNH8pi18etOifDbuySgcdcw1/tY/AaR8gbep7nUOM5mfiy2Gmry+Y5GX7cp7KVGs/J8PUNelW8EbYRgy83I23HTmVPD3PLPLYRPbrHPMI8j/G74JxMe5vzOc+vYa4QN3TeGFdTHDpz6O3YGFft29Lrdhh6fRljns/JsOvfxa21n05yRpJ3t9aekcVeKt+YY07avAVDj/ucDDvmofOSgZfvGv5iKGP8LpjDdnFfuNJeF8DxVdWPJ/m5HHMVliTfnOSnksWKuld5I2Wem8/faJyRxdUvHrDVvKp6Sxa7JJ50Y7eF+kZ5H5cZVVXfmeSDOc7VPvayxqnP8xxqnNOyeApv3cqTxxh3h+4aR6rvnkmeksW5Hb4/ySuW9/99llf52sttxJjLzZDbsQ5bWhZHyDw3E99GdNry+zjgPJ+b4X8XTHqbs3TCK8TV8hLoW6xx0Lyqel6Ss5L8XZLK4vxLR6+meOE26hs8c4Tt2NBz0msvtzljzPPQ6999s7hq4p9V1e2TvDKLw6E+kuT+26xx6GVx0DGPtA0bfPleNnGvOM79r1/e3Mqhaedm4N8FmcF2cd/Y6zOA+9r+19BXEJni1XHmlqfGaebNocYpj3l9ff03hq5tLjVOeV7mkjf1eZ7LsjPw1RRXbsxDZ85huziXvDnUaP2bZuYq1jiTMb9syvXNZV7m8OUwt3kbejf/vT5sYD/kjZG5ijUa8y5nttZuepKH/9MAtZzI1Gu0LO5y5hjzvE+Wna1eNWzlxjx05hy2i/skb4xM69/08sbIXMUa5zDmoc/dOYcxj314/yRpJs3b0CvqXl8dZz/kjZG5ijUa8+5nvibJm3L8XatvMkw5xzX1Gvd6XvZD3lYzx5jn/bDsbDVvFcc8dOYctov7IW+MzL3Os/7tTuYq1jiHMQ9tDmOe+ns4Cs0kAJLkvyW5Y1U98NgHWmsv24N6jmcONbJzY8zzKi47qzjmoXkP2S7LDqtsJffSWUVOwD1vc9g9b+o1ruKYx8icet4YmVPP21JmVb0gybNba1c/zsPHPZHhQKZeo2VxlzPHmOd9suxsKW8Vxzx05hy2i/skb4xM69/08sbIXMUa5zDmoS+SMYcxr2QDTTNp3oZeUff66jj7IW+MzFWs0Zj3ILOqXlFV/3ac+5+VJK21pw1V2CZTr3HP52Uf5G05c4x53gfLznauvLZyYx46cw7bxX2QN0bmnudZ/3YlcxVrnOyYW2u/kSRV9QND5G0y2TGPmDcPe30GcF9b/xr6KhBzuDrO1PPUOM28OdQ4hzFvyl2Zq9nMYV6mnjeHeR4jcw7zsopjnsOyOPX3cQ7zMvW8Y7KtfxPJm0ONUxnz+vr6TU/y9Zq9rm8/zMucvpwzaaKGvgrEHK6OM/W8MTJXsUZj3nneWJlDm3qNc5iXqeeNlTl1c5iXoc1hzN7H6eWNkTn1vDHMYcxqnF7eSJmDnlx+DmOewzZir2gmTdfQV4GYw9Vxpp43RuYq1mjMO88bK3NoU69xDvMy9byxMqduDvMytDmM2fs4vbwxMqeeN4Y5jFmN08sbI3Pok8vPYcxz2EbsCc2k6Rp6RZ3D1XGmnjdG5irWaMw7zxsrc2hTr3EO8zL1vLEyp24O8zK0OYzZ+zi9vDEyp543hjmMWY3Tyxs8s6pe0Fr7YGvt6sc5J9h2Ti4/+TGPkLdvOAH3RA19FYg5XB1n6nljZK5ijca887yxMjvsq6vZzGFepp43VmaHfXWVrzlcNWwOY57Dsjj193EO8zL1vC2w/u1i3hiZU88bMXOwk8vPYcx7uI2Yvr0+aZOv7X+tr68/bcp5c6hxFcc8hxqNee8y19fXD62vrz9qfX39l5bfn72+vn7N5e3TV63GqczLnPOmNM9zXna2m7eKYx46cw7bxTnnzaFG6980M1exxpmMedCLZMxkzIPXOIcveybNW5t43hiZU88bI3MVazTmvct8dpKPJ7n58vszkjwvSarqeMeK79TUa5zKvMw5b7uZY8zznJed7eat4piHzpzDdnHOeWNkTiXP+jdu5irWOIcxD20OY576ezgKzSQANrtGVT01yWeSpKp+O8nV9rakzzOHGtm5MeZ5FZedVRzz0LyHbJdlB9i3NJMA+ByttRsl2VjevlMm+LtiDjWyc2PM8youO6s45qF5D9kuyw6wX7maGwCb/WCSpyf52tbaB5K8Mcl/39OKPt8camTnxpjnVVx2VnHMQ/Mesl2WHVbRGBfJYII0k+Zt6BV1T6+Os0/yxshcxRqNeQ8yW2sHkty6qu4wQi0nMvUa93xe9kHeljPHmOd9sOxsOW8Vxzx05hy2i/sgb4zMPc+z/u1K5irWOIkxt9YOJfmBJNetqoe11s5O8oaq+pckQy/zkxjzLufNgmbSxA29oo6x4k+9xlUc8xxqNObpjbmqNlprZ7fWXlFVb9tOPXOscerzMoe8oTPHmOc5LDtD563imIfOnMN2cQ55c6jR+rca8zyHGucw5ixOLv/nSb5z+f3Rk8vfeTsnl5/DmHe5gTYLjtmdvqGvAjGHq+NMPU+N08ybQ41zGPMtkry1tfax1tqR5deHtlnbXGqcw7xMPW+MzDGWxakvO2PMyyqOeQ7L4tTfxznMy9TzEuufGqeRN0bm0CeXn8OYx6hx1jSTpm/oFXUOV8eZep4ap5k3hxonP+aqunFVHayqL6qqteXXGfu8xsnPywzyBs8cY1mcwbIz+Lys4piHzpzDdnEGeXOo0fq3GvM8hxrnMOahTy4/hzG7OuMxHOY2AwOvqLO4Os7U89Q4zbw51Dj1MbfWnnWcu69UVeduN3OZO+kapz4vc8gbOnOkeZ78sjNC3sqNeejMOWwX55A3hxqtf6sxz3OocQZjHvzk8jMY8yg1zplm0vQNvaLO4eo4U89T4zTz5lDjHMb8wk23Dyb5+iRX30FeMv0a5zAvU88bI3OMZXHqy84Y87KKY57Dsjj193EO8zL1vMT6p8Zp5A2a2cY5ufykxzxijfO2sbHha6Jf6+vrB9bX18+bat4calzFMc+hRmOebuYJXueZ+7XGOczL1PPmMM9jZM5hXlZxzHNYFqf+Ps5hXqaed4rXsv6pcdZjXmb+1vr6+vqE65v8vOyHr5XeLWvqqmojydmttfUp5o2ROfW8MTJXsUZjHsYYma21Ox/zdd8s/id1X9Y4h3mZet5YmUMvi0NnzmFektUb8xyWxam/j3OYl6nnHWX9m1beGJlTzxsrMwOeXH4OYx5rGzF3DnObvqMr6r9lebKvJBs7OHnf0HlzqHEVxzyHGo1553ljZN5z0+2NJB9L8sAd1JdMv8Y5zMvU88bIHGNZnPqyM8a8rOKY57AsTv19nMO8TD0vsf6pcRp5g2dW1Y13UMvxTH7MI9U4awc2Njb2ugYAJqK1dl5VXXjMfT9UVU/ao5I+zxxqZOfGmOdVXHZWccxD8x6yXZYd9qs20oUJmBd7Jk3c0CvqGCv+1GtcxTGPkTn1vDEyp543ZGZr7fZJ7pDkXsfswnt6Fv+z+qT9WuOU52UueUNmjjHPc1l2hsxbxTEPnTmH7eJc8sbInHKe9W+4zFWscQ5jzsAnl5/DmDXQPp9m0vQNfRWIOVwdZ+p5Y2SuYo3GPK0x/3WSS5PcKcn/23T/FUmeue3qFqZe45TnZS55Q2aOMc9zWXaGzFvFMQ+dOYft4lzyxsiccp71b7jMVaxx8mOuqhcdc9dFrbUpLdtjZI5R47zt9RnAfW39a+ir2Uzp6jhzzVPjNPPmUOMUx7y+vv6F6+vrX7b8usn6+vqLV63GKc7L3PKmOs9zW3aGyFvFMQ+dOYft4tzy5lCj9W+amatY49TGvL6+fudjvu67vr7++qnUN+d5mdOXPZMmrrV252PuunZ2cAWRofPGyJx63hiZq1ijMe88b4zM1tpjkpyb5DpJ3pvkS5I8Zbt5y8xJ1ziTeZl03hiZIy2Lk152RpqXVRzzHJbFSb+PM5mXSectM61/atzzvJEyBz25/BzGPEaNc6eZNH1DXwViDlfHmXreGJmrWKMxT3PMd66qG7fWXlZVZ7fWbpHknJ0UmOnXOId5mXreGJljLItTX3bGmJdVHPMclsWpv49zmJep5yXWPzVOI2+MzL883snlk7xhm3lzGPMYNc6aZtL0Db2iDp03RubU88bIXMUajXnneWNkbrTWrpLktNba1arqr1trP7eD+uZQ4xzmZep5Y2SOsSxOfdkZY15WccxzWBan/j7OYV6mnpdY/4bIXMUaJzvmNt6FCSY75hHzZu/AxsbGXtfAcWxeUZP89qaHTk9yz6q64V7mzaHGVRzzHGo05mmOeVPuw5Y3L0vyP5N8MMnHq+rYXXtnX+Mc5mXqeWNlLnMHWxaHzpzDvGzKXpkxz2FZnPr7OId5mXreMdnWPzXuWd5INR7K4tCuJyf5hU0PXZHkdVX11r2sb4zMMbcRc2fPpOka+ioQc7g6ztTz1DjNvDnUOIcxJ0mq6peO3m6t/XGSayV54zbjpl7jHOZl6nljZQ69LA6dOYd5SbJyY57Dsjj193EO8zL1vH9n/VPjHucNnllVlyR5eZKbtda+MMkXLx+6SpJfWb7OntU3UuaYV/acNXsmzcDxVtSq2uqKOlreHGpcxTHPoUZjnt6YW2s3S/KLSQ5V1a2Wu+++oqpev59rnPq8zCFv6MyR5nnyy84IeSs35qEz57BdnEPeHGq0/q3GPM+hxqmPuZ3g5PJV9eNTqG+szDFqnLMr7XUBnNxyRX1TkjcneUmS12YHx2UOnTeHGldxzHOo0ZinOeYkv5rk4Uk+s/z+T5f37dsa5zAvU88bKXPwZXHozDnMS1ZwzHNYFqf+Ps5hXqaet2T9U+Oe542UeeequnGS11fVTZN8e3bQW5jDmEfaRsyaZtL0DbqijpA3hxpXccxzqNGYpznmyzYf7768ffk+r3EO8zL1vDEyx1gWp77sjDEvqzjmOSyLU38f5zAvU89LrH9qnEbeGJmfd3L5JLecUH1jZI5R46yt9OBnYugVdei8OdS4imOeQ43GPM0xf7S19oAkV2+t3bItrjrzoX1e4xzmZep5Y2SOsSxOfdkZY15WccxzWBan/j7OYV6mnpdY/9Q4jbwxMl+Q5PuX/76ptfaqJP82ofrGyByjxllzAu7pO3ZF/WCSj08obw41ruKY51CjMU9ozK21Z1fV/bP4IHD9JO9L8ogkr0ly/31e42TnZUZ5g2WOMc8zWnYGy1vFMQ+dOYft4ozy5lCj9W815nkONU5+zDX8RTImP+aRapw1J+Cekdbaf8xyRa2qK6aWN0bm1PPGyFzFGo1578fcWvvrLE4keKMkbzvm4Y2quvkq1Di1eZlj3k4zx5jnOS47O81bxTEPnTmH7eIc88bInFqe9W+czFWscapjbiNdmGCo+sbOHKPGObJn0sQdZ0W92/Khba2oQ+fNocZVHPMcajTmyY35VklukOSXkvzwdus5nqnXOPF5mUXewJljzPMslp2B81ZxzENnzmG7OIu8OdRo/VuNeZ5DjXMYcxYnkv+BJE9Zfv+nSZ6ZxbI/hfpmMS9zp5k0fYOuqCPkzaHGVRzzHGo05gmNuaouT/LuJN+9g1pOZOo1TnZeZpQ3WOYY8zyjZWewvFUc89CZc9guzihvDjVa/1ZjnudQ4xzGfFlVvbW1lmRxcvnW2k5OLj+HMY9R46w5Aff0DX0ViDlcHWfqeWNkrmKNxjzNMY9h6jXOYV6mnjdW5tTNYV6GNocxex+nlzdG5tTzxjCHMatxenljZA59cvk5jHkO24hdZc+k6fucFTXJOdnZijp03hxqXMUxz6FGY57mmMcw9RrnMC9Tzxsrc+rmMC9Dm8OYvY/Ty5tDjZab1ZjnOdQ42TG38U4uP9kxj1zjrNkzaaJaa89e3jx2Rb0421hRh86bQ42rOOY51GjM0xzzGKZe4xzmZep5Y2VO3RzmZWhzGLP3cXp5c6jRcrMa8zyHGucw5kVke0OS70py9yRfkuSGy+9fNoH6ZjEv+4WruU1UG/gqEEPnzaHGVRzzHGo05mmOeQxTr3EO8zL1vLEyp24O8zK0OYzZ+zi9vDnUaLlZjXmeQ40zGfNpOcnJ5avqn/eyvjEy57CN2CsOc5uuW2XYq0AMnTdG5tTzxshcxRqNeRhjZA5t6jXOYV6mnjdW5tTNYV6GNocxex+nlzdG5tTzxjCHMatxenmDZ9bwJ5ef/JhHyNs37JkEAAAAQDfnTAIAAACgm2YSAAAAAN2cMwkAoFNr7beTtCR3qar3bOHnvjnJB6rqn0YrDgBgl9gzCQCg33cnucVWGklLD8jiSjBdWmsHtpgPALBrnIAbAKBDa+3CJA9M8ookz0ryoCSXJrkkyXlV9aHW2oOSnJfkU0k+neReSc5O8utJ/jnJw5Kcn+TxVfXnrbUvT/Kqqrpha+03lj/3VUn+a5K1JL+Y5EAWe5P/SFX9ze6MFgDgxOyZBADQoarOW968X5KHJ/nWqjo7yUuTPGb52BckuVtV3TbJO5Pcp6p+P8kbk/xwVf3FKV7mUFXdZrnn07OzaFLdLslDkvzakOMBANgu50wCANiab0xy/SQvba0lyZWTvHf52KeS/G5r7fIkX57kfVvM/qskaa1dM8lXJvn15WskyRe01k6vqkt3UjwAwE5pJgEAbM2lSf6mqu6y+c7lIWs/neSrq+r9rbUnneDnN59j4NjPYp/e9JxPV9VZO64WAGBgDnMDANiaNyS5eWvtuknSWrtHa+27knxxkiPLRtJ1ktw+yVWWP3NFktOXty9Ocsby9jce7wWq6mNJ3tlau9PyNW7cWrtglNEAAGyRPZMAALbmfUkemuSPW2ufzGJvovsn+UCSaq29Jsnbkzw6yZNbay9K8mdJntZae1iSJyd5YmvtrOXzTvSfe/dL8iuttR/Loin1o+MNCQCgn6u5AQAAANDNYW4AAAAAdNNMAgAAAKCbZhIAAAAA3TSTAAAAAOimmQQAAABAN80kAAAAALppJgEAAADQTTMJAAAAgG7/P1H9R03dJNb/AAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -6904,7 +6893,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -6953,7 +6942,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -6993,10 +6982,10 @@ "data": { "text/markdown": [ "```sql\n", - "DROP TABLE IF EXISTS \"FEATURE_1_26\";\n", + "DROP TABLE IF EXISTS \"FEATURE_1_24\";\n", "\n", - "CREATE TABLE \"FEATURE_1_26\" AS\n", - "SELECT Q1( t2.\"balance\" ) AS \"feature_1_26\",\n", + "CREATE TABLE \"FEATURE_1_24\" AS\n", + "SELECT Q1( t2.\"balance\" ) AS \"feature_1_24\",\n", " t1.rowid AS rownum\n", "FROM \"POPULATION__STAGING_TABLE_1\" t1\n", "INNER JOIN \"TRANS__STAGING_TABLE_4\" t2\n", @@ -7006,7 +6995,7 @@ "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"FEATURE_1_26\";\\n\\nCREATE TABLE \"FEATURE_1_26\" AS\\nSELECT Q1( t2.\"balance\" ) AS \"feature_1_26\",\\n t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"TRANS__STAGING_TABLE_4\" t2\\nON t1.\"account_id\" = t2.\"account_id\"\\nWHERE t2.\"date\" <= t1.\"date_loan\"\\nGROUP BY t1.rowid;'" + "'DROP TABLE IF EXISTS \"FEATURE_1_24\";\\n\\nCREATE TABLE \"FEATURE_1_24\" AS\\nSELECT Q1( t2.\"balance\" ) AS \"feature_1_24\",\\n t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"TRANS__STAGING_TABLE_4\" t2\\nON t1.\"account_id\" = t2.\"account_id\"\\nWHERE t2.\"date\" <= t1.\"date_loan\"\\nGROUP BY t1.rowid;'" ] }, "execution_count": 17, @@ -7035,7 +7024,7 @@ "source": [ "# Creates a folder named loans_pipeline containing\n", "# the SQL code.\n", - "pipe.features.to_sql().save(\"loans_pipeline\")" + "pipe.features.to_sql(size_threshold=None).save(\"loans_pipeline\", remove=True)" ] }, { @@ -7046,7 +7035,7 @@ "source": [ "# Creates a folder named baseball_pipeline_spark containing\n", "# the SQL code.\n", - "pipe.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"loans_pipeline_spark\")" + "pipe.features.to_sql(dialect=getml.pipeline.dialect.spark_sql, size_threshold=None).save(\"loans_pipeline_spark\", remove=True)" ] }, { diff --git a/movie_lens.ipynb b/movie_lens.ipynb index dbd4373..840c8cf 100644 --- a/movie_lens.ipynb +++ b/movie_lens.ipynb @@ -85,27 +85,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220320122747.log.\n", + "getML engine is already running.\n", "\n", "\n", - "Loading pipelines...\n", - "[========================================] 100%\n", - "\n", "\n", - "Connected to project 'MovieLens'\n" + "Connected to project 'MovieLens'\n", + "http://localhost:1709/#/listprojects/MovieLens/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/MovieLens/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -4431,7 +4417,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n", " 'u2base'],\n", " predictors=['XGBoostClassifier'],\n", @@ -4444,7 +4430,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n", " 'u2base'],\n", " predictors=['XGBoostClassifier'],\n", @@ -4482,7 +4468,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n", " 'u2base'],\n", " predictors=['XGBoostClassifier'],\n", @@ -4495,7 +4481,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n", " 'u2base'],\n", " predictors=['XGBoostClassifier'],\n", @@ -4581,6 +4567,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining U2BASE__STAGING_TABLE_4 and MOVIES2DIRECTORS__STAGING_TABLE_3 over 'movieid' and 'movieid', there are no corresponding entries for 0.159513% of entries in 'movieid' in 'U2BASE__STAGING_TABLE_4'. You might want to double-check your join keys.\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining U2BASE__STAGING_TABLE_4 and MOVIES2ACTORS__STAGING_TABLE_2 over 'movieid' and 'movieid', there are no corresponding entries for 0.340408% of entries in 'movieid' in 'U2BASE__STAGING_TABLE_4'. You might want to double-check your join keys.\n", @@ -4606,7 +4595,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:9m:52.645106\n", + "Time taken: 0h:2m:1.560341\n", "\n" ] }, @@ -4617,28 +4606,28 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n", " 'u2base'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-ixJT2v'])
url: http://localhost:1709/#/getpipeline/MovieLens/46EfC1/0/
" + " tags=['fast_prop', 'container-Yr9GZp'])
url: http://localhost:1709/#/getpipeline/MovieLens/d1q45I/0/
" ], "text/plain": [ "Pipeline(data_model='users',\n", " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n", " 'u2base'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-ixJT2v'])\n", + " tags=['fast_prop', 'container-Yr9GZp'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/MovieLens/46EfC1/0/" + "url: http://localhost:1709/#/getpipeline/MovieLens/d1q45I/0/" ] }, "execution_count": 20, @@ -4696,6 +4685,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining U2BASE__STAGING_TABLE_4 and MOVIES2DIRECTORS__STAGING_TABLE_3 over 'movieid' and 'movieid', there are no corresponding entries for 0.159513% of entries in 'movieid' in 'U2BASE__STAGING_TABLE_4'. You might want to double-check your join keys.\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining U2BASE__STAGING_TABLE_4 and MOVIES2ACTORS__STAGING_TABLE_2 over 'movieid' and 'movieid', there are no corresponding entries for 0.340408% of entries in 'movieid' in 'U2BASE__STAGING_TABLE_4'. You might want to double-check your join keys.\n", @@ -4724,7 +4716,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 1h:16m:23.545015\n", + "Time taken: 0h:30m:47.046291\n", "\n" ] }, @@ -4735,28 +4727,28 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n", " 'u2base'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['relboost', 'container-ixJT2v'])
url: http://localhost:1709/#/getpipeline/MovieLens/oQi9pX/0/
" + " tags=['relboost', 'container-Yr9GZp'])
url: http://localhost:1709/#/getpipeline/MovieLens/rbOA2f/0/
" ], "text/plain": [ "Pipeline(data_model='users',\n", " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n", " 'u2base'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['relboost', 'container-ixJT2v'])\n", + " tags=['relboost', 'container-Yr9GZp'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/MovieLens/oQi9pX/0/" + "url: http://localhost:1709/#/getpipeline/MovieLens/rbOA2f/0/" ] }, "execution_count": 22, @@ -4879,7 +4871,7 @@ " 0\n", " \n", " \n", - " 2022-03-20 12:38:25\n", + " 2022-07-04 21:08:39\n", " \n", " \n", " \n", @@ -4908,7 +4900,7 @@ " 1\n", " \n", " \n", - " 2022-03-20 13:58:37\n", + " 2022-07-04 21:40:08\n", " \n", " \n", " \n", @@ -4938,8 +4930,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-20 12:38:25 train target 0.9114 0.9658 0.2847\n", - "1 2022-03-20 13:58:37 test target 0.7776 0.7896 0.4757" + "0 2022-07-04 21:08:39 train target 0.9114 0.9658 0.2847\n", + "1 2022-07-04 21:40:08 test target 0.7776 0.7896 0.4757" ] }, "execution_count": 23, @@ -5046,7 +5038,7 @@ " 0\n", " \n", " \n", - " 2022-03-20 13:54:53\n", + " 2022-07-04 21:39:27\n", " \n", " \n", " \n", @@ -5075,7 +5067,7 @@ " 1\n", " \n", " \n", - " 2022-03-20 14:08:11\n", + " 2022-07-04 21:43:51\n", " \n", " \n", " \n", @@ -5105,8 +5097,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-20 13:54:53 train target 0.9691 0.9948 0.1577\n", - "1 2022-03-20 14:08:11 test target 0.816 0.8368 0.4398" + "0 2022-07-04 21:39:27 train target 0.9691 0.9948 0.1577\n", + "1 2022-07-04 21:43:51 test target 0.816 0.8368 0.4398" ] }, "execution_count": 24, @@ -5150,7 +5142,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -5229,17 +5221,17 @@ "\n", "CREATE TABLE \"FEATURE_1_138\" AS\n", "SELECT MEDIAN( COALESCE( f_1_1_69.\"feature_1_1_69\", 0.0 ) ) AS \"feature_1_138\",\n", - " t1.rowid AS \"rownum\"\n", + " t1.rowid AS rownum\n", "FROM \"USERS__STAGING_TABLE_1\" t1\n", "INNER JOIN \"U2BASE__STAGING_TABLE_4\" t2\n", "ON t1.\"userid\" = t2.\"userid\"\n", "LEFT JOIN \"FEATURE_1_1_69\" f_1_1_69\n", - "ON t2.rowid = f_1_1_69.\"rownum\"\n", + "ON t2.rowid = f_1_1_69.rownum\n", "GROUP BY t1.rowid;\n", "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"FEATURE_1_138\";\\n\\nCREATE TABLE \"FEATURE_1_138\" AS\\nSELECT MEDIAN( COALESCE( f_1_1_69.\"feature_1_1_69\", 0.0 ) ) AS \"feature_1_138\",\\n t1.rowid AS \"rownum\"\\nFROM \"USERS__STAGING_TABLE_1\" t1\\nINNER JOIN \"U2BASE__STAGING_TABLE_4\" t2\\nON t1.\"userid\" = t2.\"userid\"\\nLEFT JOIN \"FEATURE_1_1_69\" f_1_1_69\\nON t2.rowid = f_1_1_69.\"rownum\"\\nGROUP BY t1.rowid;'" + "'DROP TABLE IF EXISTS \"FEATURE_1_138\";\\n\\nCREATE TABLE \"FEATURE_1_138\" AS\\nSELECT MEDIAN( COALESCE( f_1_1_69.\"feature_1_1_69\", 0.0 ) ) AS \"feature_1_138\",\\n t1.rowid AS rownum\\nFROM \"USERS__STAGING_TABLE_1\" t1\\nINNER JOIN \"U2BASE__STAGING_TABLE_4\" t2\\nON t1.\"userid\" = t2.\"userid\"\\nLEFT JOIN \"FEATURE_1_1_69\" f_1_1_69\\nON t2.rowid = f_1_1_69.rownum\\nGROUP BY t1.rowid;'" ] }, "execution_count": 27, @@ -5276,7 +5268,7 @@ " ELSE NULL\n", " END\n", ") AS \"feature_1_1\",\n", - " t1.rowid AS \"rownum\"\n", + " t1.rowid AS rownum\n", "FROM \"USERS__STAGING_TABLE_1\" t1\n", "INNER JOIN \"U2BASE__STAGING_TABLE_4\" t2\n", "ON t1.\"userid\" = t2.\"userid\"\n", @@ -5286,7 +5278,7 @@ "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"FEATURE_1_1\";\\n\\nCREATE TABLE \"FEATURE_1_1\" AS\\nSELECT AVG( \\n CASE\\n WHEN ( p_1_1.\"feature_1_1_69\" > 0.242159 ) AND ( p_1_1.\"feature_1_1_21\" > 0.232813 ) AND ( t2.\"t3__year__mapping_1_target_1_avg\" > 0.282119 ) THEN 20.46317569156853\\n WHEN ( p_1_1.\"feature_1_1_69\" > 0.242159 ) AND ( p_1_1.\"feature_1_1_21\" > 0.232813 ) AND ( t2.\"t3__year__mapping_1_target_1_avg\" <= 0.282119 OR t2.\"t3__year__mapping_1_target_1_avg\" IS NULL ) THEN 7.321538279840953\\n WHEN ( p_1_1.\"feature_1_1_69\" > 0.242159 ) AND ( p_1_1.\"feature_1_1_21\" <= 0.232813 OR p_1_1.\"feature_1_1_21\" IS NULL ) AND ( p_1_1.\"feature_1_1_69\" > 0.243429 ) THEN 5.046599618766721\\n WHEN ( p_1_1.\"feature_1_1_69\" > 0.242159 ) AND ( p_1_1.\"feature_1_1_21\" <= 0.232813 OR p_1_1.\"feature_1_1_21\" IS NULL ) AND ( p_1_1.\"feature_1_1_69\" <= 0.243429 OR p_1_1.\"feature_1_1_69\" IS NULL ) THEN -8.250725468943104\\n WHEN ( p_1_1.\"feature_1_1_69\" <= 0.242159 OR p_1_1.\"feature_1_1_69\" IS NULL ) AND ( p_1_1.\"feature_1_1_21\" > 0.273123 ) AND ( p_1_1.\"feature_1_1_76\" > 0.008673 ) THEN -3.885674068832839\\n WHEN ( p_1_1.\"feature_1_1_69\" <= 0.242159 OR p_1_1.\"feature_1_1_69\" IS NULL ) AND ( p_1_1.\"feature_1_1_21\" > 0.273123 ) AND ( p_1_1.\"feature_1_1_76\" <= 0.008673 OR p_1_1.\"feature_1_1_76\" IS NULL ) THEN -12.86974979841147\\n WHEN ( p_1_1.\"feature_1_1_69\" <= 0.242159 OR p_1_1.\"feature_1_1_69\" IS NULL ) AND ( p_1_1.\"feature_1_1_21\" <= 0.273123 OR p_1_1.\"feature_1_1_21\" IS NULL ) AND ( p_1_1.\"feature_1_1_85\" > 0.003477 ) THEN 26.50336909269918\\n WHEN ( p_1_1.\"feature_1_1_69\" <= 0.242159 OR p_1_1.\"feature_1_1_69\" IS NULL ) AND ( p_1_1.\"feature_1_1_21\" <= 0.273123 OR p_1_1.\"feature_1_1_21\" IS NULL ) AND ( p_1_1.\"feature_1_1_85\" <= 0.003477 OR p_1_1.\"feature_1_1_85\" IS NULL ) THEN -2.663699179011978\\n ELSE NULL\\n END\\n) AS \"feature_1_1\",\\n t1.rowid AS \"rownum\"\\nFROM \"USERS__STAGING_TABLE_1\" t1\\nINNER JOIN \"U2BASE__STAGING_TABLE_4\" t2\\nON t1.\"userid\" = t2.\"userid\"\\nLEFT JOIN \"FEATURES_1_1_PROPOSITIONALIZATION\" p_1_1\\nON t2.rowid = p_1_1.\"rownum\"\\nGROUP BY t1.rowid;'" + "'DROP TABLE IF EXISTS \"FEATURE_1_1\";\\n\\nCREATE TABLE \"FEATURE_1_1\" AS\\nSELECT AVG( \\n CASE\\n WHEN ( p_1_1.\"feature_1_1_69\" > 0.242159 ) AND ( p_1_1.\"feature_1_1_21\" > 0.232813 ) AND ( t2.\"t3__year__mapping_1_target_1_avg\" > 0.282119 ) THEN 20.46317569156853\\n WHEN ( p_1_1.\"feature_1_1_69\" > 0.242159 ) AND ( p_1_1.\"feature_1_1_21\" > 0.232813 ) AND ( t2.\"t3__year__mapping_1_target_1_avg\" <= 0.282119 OR t2.\"t3__year__mapping_1_target_1_avg\" IS NULL ) THEN 7.321538279840953\\n WHEN ( p_1_1.\"feature_1_1_69\" > 0.242159 ) AND ( p_1_1.\"feature_1_1_21\" <= 0.232813 OR p_1_1.\"feature_1_1_21\" IS NULL ) AND ( p_1_1.\"feature_1_1_69\" > 0.243429 ) THEN 5.046599618766721\\n WHEN ( p_1_1.\"feature_1_1_69\" > 0.242159 ) AND ( p_1_1.\"feature_1_1_21\" <= 0.232813 OR p_1_1.\"feature_1_1_21\" IS NULL ) AND ( p_1_1.\"feature_1_1_69\" <= 0.243429 OR p_1_1.\"feature_1_1_69\" IS NULL ) THEN -8.250725468943104\\n WHEN ( p_1_1.\"feature_1_1_69\" <= 0.242159 OR p_1_1.\"feature_1_1_69\" IS NULL ) AND ( p_1_1.\"feature_1_1_21\" > 0.273123 ) AND ( p_1_1.\"feature_1_1_76\" > 0.008673 ) THEN -3.885674068832839\\n WHEN ( p_1_1.\"feature_1_1_69\" <= 0.242159 OR p_1_1.\"feature_1_1_69\" IS NULL ) AND ( p_1_1.\"feature_1_1_21\" > 0.273123 ) AND ( p_1_1.\"feature_1_1_76\" <= 0.008673 OR p_1_1.\"feature_1_1_76\" IS NULL ) THEN -12.86974979841147\\n WHEN ( p_1_1.\"feature_1_1_69\" <= 0.242159 OR p_1_1.\"feature_1_1_69\" IS NULL ) AND ( p_1_1.\"feature_1_1_21\" <= 0.273123 OR p_1_1.\"feature_1_1_21\" IS NULL ) AND ( p_1_1.\"feature_1_1_85\" > 0.003477 ) THEN 26.50336909269918\\n WHEN ( p_1_1.\"feature_1_1_69\" <= 0.242159 OR p_1_1.\"feature_1_1_69\" IS NULL ) AND ( p_1_1.\"feature_1_1_21\" <= 0.273123 OR p_1_1.\"feature_1_1_21\" IS NULL ) AND ( p_1_1.\"feature_1_1_85\" <= 0.003477 OR p_1_1.\"feature_1_1_85\" IS NULL ) THEN -2.663699179011978\\n ELSE NULL\\n END\\n) AS \"feature_1_1\",\\n t1.rowid AS rownum\\nFROM \"USERS__STAGING_TABLE_1\" t1\\nINNER JOIN \"U2BASE__STAGING_TABLE_4\" t2\\nON t1.\"userid\" = t2.\"userid\"\\nLEFT JOIN \"FEATURES_1_1_PROPOSITIONALIZATION\" p_1_1\\nON t2.rowid = p_1_1.\"rownum\"\\nGROUP BY t1.rowid;'" ] }, "execution_count": 28, @@ -5322,7 +5314,7 @@ "source": [ "# Creates a folder named movie_lens_pipeline containing\n", "# the SQL code.\n", - "pipe2.features.to_sql().save(\"movie_lens_pipeline\")" + "pipe2.features.to_sql(size_threshold=None).save(\"movie_lens_pipeline\", remove=True)" ] }, { @@ -5331,7 +5323,7 @@ "metadata": {}, "outputs": [], "source": [ - "pipe2.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"movie_lens_spark\")" + "pipe2.features.to_sql(dialect=getml.pipeline.dialect.spark_sql, size_threshold=None).save(\"movie_lens_spark\", remove=True)" ] }, { diff --git a/occupancy.ipynb b/occupancy.ipynb index ab9b382..c0dcb7e 100644 --- a/occupancy.ipynb +++ b/occupancy.ipynb @@ -147,15 +147,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220324214757.log.\n", + "getML engine is already running.\n", "\n", "\n", - "Loading pipelines...\n", - "[========================================] 100%\n", - "\n", - "Loading hyperopts...\n", - "[========================================] 100%\n", - "\n", "\n", "Connected to project 'occupancy'\n", "http://localhost:1709/#/listprojects/occupancy/\n" @@ -1620,7 +1614,7 @@ " \n", " \n", " \n", - " date\n", + " date\n", " \n", " \n", " \n", @@ -1656,7 +1650,7 @@ " \n", " \n", " \n", - " time_stamp\n", + " time_stamp\n", " \n", " \n", " \n", @@ -1692,7 +1686,7 @@ " \n", " \n", " \n", - " time stamp, comparison only\n", + " time stamp\n", " \n", " \n", " \n", @@ -2693,20 +2687,20 @@ "

\n" ], "text/plain": [ - " name date Occupancy Temperature Humidity Light CO2 HumidityRatio\n", - " role time_stamp target numerical numerical numerical numerical numerical\n", - " unit time stamp, comparison only \n", - " 0 2015-02-04 17:51:00 1 23.18 27.272 426 721.25 0.004793\n", - " 1 2015-02-04 17:51:59 1 23.15 27.2675 429.5 714 0.004783\n", - " 2 2015-02-04 17:53:00 1 23.15 27.245 426 713.5 0.004779\n", - " 3 2015-02-04 17:54:00 1 23.15 27.2 426 708.25 0.004772\n", - " 4 2015-02-04 17:55:00 1 23.1 27.2 426 704.5 0.004757\n", - " ... ... ... ... ... ... ... \n", - "20555 2015-02-18 09:15:00 1 20.815 27.7175 429.75 1505.25 0.004213\n", - "20556 2015-02-18 09:16:00 1 20.865 27.745 423.5 1514.5 0.00423 \n", - "20557 2015-02-18 09:16:59 1 20.89 27.745 423.5 1521.5 0.004237\n", - "20558 2015-02-18 09:17:59 1 20.89 28.0225 418.75 1632 0.004279\n", - "20559 2015-02-18 09:19:00 1 21 28.1 409 1864 0.004321\n", + " name date Occupancy Temperature Humidity Light CO2 HumidityRatio\n", + " role time_stamp target numerical numerical numerical numerical numerical\n", + " unit time stamp \n", + " 0 2015-02-04 17:51:00 1 23.18 27.272 426 721.25 0.004793\n", + " 1 2015-02-04 17:51:59 1 23.15 27.2675 429.5 714 0.004783\n", + " 2 2015-02-04 17:53:00 1 23.15 27.245 426 713.5 0.004779\n", + " 3 2015-02-04 17:54:00 1 23.15 27.2 426 708.25 0.004772\n", + " 4 2015-02-04 17:55:00 1 23.1 27.2 426 704.5 0.004757\n", + " ... ... ... ... ... ... ... \n", + "20555 2015-02-18 09:15:00 1 20.815 27.7175 429.75 1505.25 0.004213\n", + "20556 2015-02-18 09:16:00 1 20.865 27.745 423.5 1514.5 0.00423 \n", + "20557 2015-02-18 09:16:59 1 20.89 27.745 423.5 1521.5 0.004237\n", + "20558 2015-02-18 09:17:59 1 20.89 28.0225 418.75 1632 0.004279\n", + "20559 2015-02-18 09:19:00 1 21 28.1 409 1864 0.004321\n", "\n", "\n", "20560 rows x 7 columns\n", @@ -3128,7 +3122,7 @@ " feature_learners=['Multirel'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['population'],\n", " predictors=['LogisticRegression'],\n", " preprocessors=[],\n", @@ -3140,7 +3134,7 @@ " feature_learners=['Multirel'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['population'],\n", " predictors=['LogisticRegression'],\n", " preprocessors=[],\n", @@ -3236,7 +3230,7 @@ "[========================================] 100%\n", "\n", "\n", - "Time taken: 0h:4m:40.16873\n", + "Time taken: 0h:5m:27.147802\n", "\n", "Building final pipeline...\n", "\n", @@ -3259,24 +3253,21 @@ "Multirel: Building features...\n", "[========================================] 100%\n", "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Multirel: Building features...\n", - "[========================================] 100%\n", - "\n", "LogisticRegression: Training as predictor...\n", "[========================================] 100%\n", "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:23.30512\n", + "Time taken: 0h:0m:15.813622\n", "\n", "\n", "\n", "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "Multirel: Building features...\n", "[========================================] 100%\n", "\n", @@ -3323,6 +3314,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "Multirel: Building features...\n", "[========================================] 100%\n", "\n", @@ -3398,7 +3392,7 @@ " 0\n", " \n", " \n", - " 2022-03-24 21:53:07\n", + " 2022-07-16 23:44:39\n", " \n", " \n", " \n", @@ -3410,15 +3404,15 @@ " \n", " \n", " \n", - " 0.9887\n", + " 0.9889\n", " \n", " \n", " \n", - " 0.9946\n", + " 0.9948\n", " \n", " \n", " \n", - " 0.5783\n", + " 0.5519\n", " \n", " \n", " \n", @@ -3427,7 +3421,7 @@ " 1\n", " \n", " \n", - " 2022-03-24 21:53:08\n", + " 2022-07-16 23:44:40\n", " \n", " \n", " \n", @@ -3443,11 +3437,11 @@ " \n", " \n", " \n", - " 0.9921\n", + " 0.9912\n", " \n", " \n", " \n", - " 0.5556\n", + " 0.5258\n", " \n", " \n", " \n", @@ -3456,7 +3450,7 @@ " 2\n", " \n", " \n", - " 2022-03-24 21:53:14\n", + " 2022-07-16 23:44:45\n", " \n", " \n", " \n", @@ -3468,7 +3462,7 @@ " \n", " \n", " \n", - " 0.9933\n", + " 0.9934\n", " \n", " \n", " \n", @@ -3476,7 +3470,7 @@ " \n", " \n", " \n", - " 0.5786\n", + " 0.5519\n", " \n", " \n", " \n", @@ -3486,9 +3480,9 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-24 21:53:07 train Occupancy 0.9887 0.9946 0.5783\n", - "1 2022-03-24 21:53:08 validation Occupancy 0.9786 0.9921 0.5556\n", - "2 2022-03-24 21:53:14 test Occupancy 0.9933 0.9976 0.5786" + "0 2022-07-16 23:44:39 train Occupancy 0.9889 0.9948 0.5519\n", + "1 2022-07-16 23:44:40 validation Occupancy 0.9786 0.9912 0.5258\n", + "2 2022-07-16 23:44:45 test Occupancy 0.9934 0.9976 0.5519" ] }, "execution_count": 11, @@ -3520,7 +3514,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -3555,7 +3549,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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SzozQ7n3wyVqjtuf2TOcRj9qfYyeOr2o727Up4UJ3If38tbAwv24H3y4LYQN8VjqdTuZ2+2EGYNKmpjrJVLblL5gX0g/4W2lwCmg/2DozKm7gxIGRcmtZae6u0bbqvMETNrl+jRutWVPabRs+a93z12nPxs9vtQ+estnXO+T0ybWXCuiP2O6NzB4YlX10KYv3nMqJo0uf1TTd/tqUvaBpYH3KjYKppm1w1PBKcJqhAHZwnbaptddwW2t69co07FbQm9bacK0guLP6+UCb0AgAAFjX4JRuzl/dbrcJnNrT/k8eXcqJgfbpzOTT9xzLiWNDwVSzNuXRT5/MyWPLF9wotLXCpuGp7f3+0J5Kf2YduM3apgem2/em16+egt9qWzm2ftvwVP/p2U5275/O7v3T2TM/vbK9a++UgIyzIjQCAAC4wHU6nczu6mR211T2PXTjXwNHGZXXX5ty7RDqzDqVJ48uray7mGTVwvCDa7+trOM2vCZbs4bbmeND67OtOn7mWP/+y83nrqwsVj+8rttyMriu3Ko14FbWcWx/smt/BN5wW/u8c/87G4fOVLJ733R2DwRJe/af2d49P7Q/FDrN7hY67TRCIwAAAM7K1FSnmYo2nflLZiddzrY1/AEkqz5cYL22pTRruJ1pWwmjllYHUqvaBo6dPtXN8QeWen8We6/H+vvNn3s/cSInj53dpxlPTWcgTJpZFS6tFTKthFJN2+yuqTG904yL0AgAAADGoNNppo1Nb8/ROUunV4dLg8HSscXTq0KmM+291yOHjuX0ybObozgz11kzZJp/2Gwu6v95+Fwueths9l8yk5lZIdOkCY0AAABgB5qe6WTfQ2c2nbK4nlMnlnPiM2eCpLXCpZX9wXDqyFLu+5sTm07f23dgJhctnAmU5vvbC3Mr7bv2mTI3TkIjAAAA4KzN7prK7K6p7L/47KcodrvdnDrRzdH7T2fxnlM5cs+pLB7qvR45dCpH7jmZI4dO5Z6PH8/dHzm27n3m9kzlooXZM6OVFmYHgqa5zC/MZv+BmW072mu7ExoBAAAAW6rT6WRudydzu+fy0EfMrXtet9vNscWlM4FSP1Rq9hf74dL/ObHuPaamk/0XnwmT5ofDpYW5zD9sNnO7TYcbJjQCAAAAtqVOp5O9F81k70UzecTn7Vn3vFMnlrN4by9MWlwnXLr7I8fyyT8/uu49VtZXWjgzaunAZbvyRdc8dMcu4i00AgAAAM5rs7umcvFlu3LxZbvWPWd5uZujnz69EiithEufGhzFdDKf+tjxVdd1l7u54rmXjPtL2JaERgAAAMAFb2qqk/0Xz2b/xbO57AvWP+/E0aUs3nMqf3X7Ym69+W/y6b89uXVFbjNCIwAAAIDGrr3T2fU50+ku9/YX7z092YImaGdOygMAAADYwP5LeuNsHrjv1IQrmRyhEQAAAMCQ3funMzPXyeK9QiMAAAAAGp1Obw2kB+4zPQ0AAACAAfOXzOSB+05lebk76VImQmgEAAAAsIb5S2azvJQcO7I06VImQmgEAAAAsIb9F88myY5d10hoBAAAALCG/ieoCY0AAAAAWDF/SW+k0U5dDFtoBAAAALCGfmhkpBEAAAAAK/Zf3Jue9oDQCAAAAIC+MyONTE8DAAAAoLHvoTPpTCUP3GekEQAAAACNqelO9j10xppGAAAAAKy2/+JZn54GAAAAwGrzl8zk5LHlnDi6NOlStpzQCAAAAGAd+y/uLYa9E0cbCY0AAAAA1nHmE9R23rpGQiMAAACAdcxfMpNEaAQAAADAgP2XmJ4GAAAAwJD9FxtpBAAAAMCQ/ppGDwiNAAAAAOjrf3ra4r2mpwEAAADQmNs9lV37pvLAfUYaAQAAADBgz/x0jn9madJlbDmhEQAAAMCGOpMuYCKERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFpmxnnzUspNSa5M0k3yslrr7QPHvibJDyc5keQ3aq2vGWctAAAAAIxubCONSilXJ7m81npVkhcluXng2FSS1yR5bpJnJPmqUsqjx1ULAAAAAGdnnNPTrk1yS5LUWj+c5EAp5aLm2MOSfLrWeqjWupzkHUmePcZaAAAAADgL45yedmmSOwb2DzVtR5rt+VLK5UnuSnJNkndtdLMDB/ZmZmZ6LIVOwsLC/KRLgPOCvgKj0VdgNPoKjEZfgdWmpzvpLndW9Y2d0E/GuqbRkE5/o9baLaX88yS/kuT+JB8bPL6Ww4ePjre6LbSwMJ9DhxYnXQZse/oKjEZfgdHoKzAafQXalpa66Xa7K33jQuonG4Vf4wyNDqY3sqjvsiR393dqre9O8vQkKaW8Mr0RRwAAAABsA+Nc0+jtSa5LklLKFUkO1lpXYrhSyu+XUh5eStmX5KuS/MEYawEAAADgLIxtpFGt9bZSyh2llNuSLCe5oZRyfZL7a61vSvKf0guWukleWWu9Z1y1AAAAAHB2xrqmUa315UNNdw4c++0kvz3O5wMAAABwbsY5PQ0AAACA85TQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAGAz3UkXsPWERgAAAAAbmJpOlpcnXcXWExoBAAAAbGB6dipLp3ZeaiQ0AgAAANjAzGwnp0/uvPlpQiMAAACADUzPdrJ0WmgEAAAAwIDp2U6WTnXT7e6s4EhoBAAAALCBmdlefLJ0SmgEAAAAQGN6tpMkO26KmtAIAAAAYAMzTWi00xbDFhoBAAAAbMBIIwAAAABaVkKjU8sTrmRrCY0AAAAANtBfCNv0NAAAAABWTM/szOlpM+O8eSnlpiRXJukmeVmt9faBYzck+aYkS0neX2v9rnHWAgAAAHAupuea0MhIowdHKeXqJJfXWq9K8qIkNw8cuyjJ9yd5eq31aUmeWEq5cly1AAAAAJyrlelpp61p9GC5NsktSVJr/XCSA01YlCQnmz/7SykzSfYmuW+MtQAAAACckzMLYe+skUbjnJ52aZI7BvYPNW1Haq3HSyk/muSjSY4l+Y1a60c2utmBA3szMzM9tmK32sLC/KRLgPOCvgKj0VdgNPoKjEZfgdUe8tBPJ0n27dm90j92Qj8Z65pGQzr9jWbE0Q8m+YIkR5K8s5TyxbXWO9e7+PDho+OvcIssLMzn0KHFSZcB256+AqPRV2A0+gqMRl+BtuMnTyZJ7rv3aA4dmr2g+slG4dc4p6cdTG9kUd9lSe5utp+Q5KO11ntqrSeTvCfJk8dYCwAAAMA5WZmedtKaRg+Wtye5LklKKVckOVhr7cdwdyV5QillT7P/JUn+coy1AAAAAJyTlYWwrWn04Ki13lZKuaOUcluS5SQ3lFKuT3J/rfVNpZT/kOQPSymnk9xWa33PuGoBAAAAOFedZshNd1lo9KCptb58qOnOgWO/mOQXx/l8AAAAgM9Wp7P5OReicU5PAwAAAOA8JTQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABaZsZ581LKTUmuTNJN8rJa6+1N+6OS/PrAqY9P8vJa6+vHWQ8AAAAAoxlbaFRKuTrJ5bXWq0opT0jyK0muSpJa698keWZz3kySdyV587hqAQAAAODsjHN62rVJbkmSWuuHkxwopVy0xnnXJ/mtWusDY6wFAAAAgLMwzulplya5Y2D/UNN2ZOi8Fyd5zmY3O3Bgb2Zmph+86iZsYWF+0iXAeUFfgdHoKzAafQVGo6/AavPzR5vXPSv9Yyf0k7GuaTSkM9xQSrkqyV/UWoeDpJbDh4+OpahJWFiYz6FDi5MuA7Y9fQVGo6/AaPQVGI2+Am2Li8dWXg8dWryg+slG4dc4p6cdTG9kUd9lSe4eOud5Sf5gjDUAAAAAcA7GGRq9Pcl1SVJKuSLJwVrrcAz3pUnuHGMNAAAAAJyDsYVGtdbbktxRSrktyc1JbiilXF9Kef7AaY9M8qlx1QAAAADAuRnrmka11pcPNd05dPzvj/P5AAAAAJybcU5PAwAAAOA8JTQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAIARdLuTrmBrCY0AAAAANjA924tPlk7vrNRIaAQAAACwgdndvfjk1PHlCVeytYRGAAAAABuY3dWERieERgAAAAA0Znd3kiSnjpueBgAAAEBjzkgjAAAAAIb11zQ6eUxoBAAAAEBjZSFsI40AAAAA6FtZCNunpwEAAADQZ6QRAAAAAC0zc/1PTxMaAQAAANDodDqZ3T1lpBEAAAAAq83u6hhpBAAAAMBqs7unclJoBAAAAMCgud1TRhoBAAAAsNqs0AgAAACAYb2FsLtZXu5OupQtIzQCAAAA2MTc7l6EcnoHfYKa0AgAAABgE7NNaLSTFsMWGgEAAABsoj/SaCetayQ0AgAAANjE7A4MjWZGOamUcmmSb0hycZJOv73W+m/HVBcAAADAtmF62vrekuSLkywnWRr4AwAAAHDB24nT00YaaZTkgVrr/zvWSgAAAAC2KSON1ve+UsoXjrUSAAAAgG1qbk8z0ujYzgmNRh1p9BVJvqeUcijJ6fTWNerWWj9nbJUBAAAAbBMzc73Q6PTJ7oQr2TqjhkZfPdYqAAAAALaxzuanXHBGDY0+meQFSb40STfJ+2qtbxhbVQAAAABM1KhrGt2c3mijmuQvk3x9KeXVY6sKAAAAgIkadaTRF9Varx7Yf00p5T3jKAgAAACAyRt1pNFcKWXl3FLKdEYPnAAAAAA4z4wa/Lwlye2llHc3+9ck+Y3xlAQAAADApI000qjW+hNJbkjy8SR3JXlJrfWnx1gXAAAAABO0YWhUSnlS8/qsJHuT/K8kH0qyv2kDAAAA4AK02fS0b07ywSSvWONYN8k7H/SKAAAAAJi4DUOjWuv3NK/XDLaXUqZqrcvjLAwAAACAyRlpIexSyvXpTU/7xSTvTvKYUspP1Vp/YYy1AQAAADAhIy2EneQlSf5zkuent6bR45J8w7iKAgAAAGCyRg2NjtVaTyR5bpL/1kxN646vLAAAAAAmadTQKKWUn0/y1CTvLqVclWT32KoCAAAAYKJGWtMoyQvTm452c611qZTy2CT/crOLSik3JbkyvVFJL6u13j5w7DFJ3pBkLskHaq2b3g8AAACArbHhSKNSypOazSck+bMkjyqlPCvJ3yW5ZJNrr05yea31qiQvSnLz0Ck3Jrmx1vqUJEullM85h/oBAAAAGIPNRhp9c5IPJnnFGse6Sd65wbXXJrklSWqtHy6lHCilXFRrPVJKmUry9CTf2By/4WwLBwAAAGB8NgyNaq3f07xeU0p5SK31/iQppVxaa/3bTe59aZI7BvYPNW1HkiwkWUxyUynliiTvqbX+wEY3O3Bgb2Zmpjd55PljYWF+0iXAeUFfgdHoKzAafQVGo69A2/z8Z3qvF/WWeN4J/WSkNY1KKS9N8pwkX9s0vb6U8tu11tecxbM6Q9uPSvLqJHcleUsp5StrrW9Z7+LDh4+exaO2t4WF+Rw6tDjpMmDb01dgNPoKjEZfgdHoK7C2xcXjvdcjvdcLpZ9sFH6N+ulp35zkuoH95yR5wSbXHExvZFHfZUnubrbvSfLxWutf11qXkrwjyd8bsRYAAAAAxmzU0Gi61np6YL+b1SOH1vL2NEFTMwXtYK11MUmae320lHJ5c+6Tk9SRqwYAAABgrEaanpbkzaWU25K8J72g6dokv7XRBbXW20opdzTXLSe5oZRyfZL7a61vSvJdSV7XLIr9v5L87rl9CQAAAABj1gydWV7uTraOLTRSaFRr/YlSyruSfFl6o4xeWmt93wjXvXyo6c6BY3+V5GmjlwoAAAAwGXN7eh/Oder48oQr2TqjTk9LkockOVFrfVWSQ6WUzaanAQAAAFwQ5vb0IpQTR4VGq5RSfjrJi5J8a9P0giQ3j6soAAAAgO1k195ehHLymNBo2NW11n+S5EiS1Fp/PMkVY6sKAAAAYBvZtbc3Pe3E0aUJV7J1Rg2NjjWv3SQppUxn9EW0AQAAAM5r/elpJ01Pa7mtlPJfklxWSvmeJO9O8q6xVQUAAACwjcytTE8z0miVWusPJXlLknckeXSSV9Va/804CwMAAADYLlamp+2gNY1GmmJWSnl5rfWnkrxxzPUAAAAAbDszc51MTfv0tLV8USnl88daCQAAAMA21el0MrdnKid30ELYoy5m/Q+S/Hkp5b4kJ/uNtdbPGUtVAAAAANvM7O7pnDy+c0YajRoavTDJM5M8N71PUPudJO8ZU00AAAAA206nM+kKttaoodErk9yb5JYknSRPT/KPk3ztWKoCAAAAYKJGDY0O1FqfN7D/2lKKkUYAAAAAF6hRF8L+WCnl0v5OKeURSf5yPCUBAAAAMGmjjjT63CR/XUr53+kFTV+Y3sLY/zNJaq3PGFN9AAAAAEzAqKHRD4+1CgAAAAC2lZFCo1rru8ddCAAAAADbx6hrGgEAAACwgwiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAGFV30gVsHaERAAAAwAhmd0/l1InlSZexZYRGAAAAACPYvW86xx9YmnQZW0ZoBAAAADCC3func/pkd8eMNhIaAQAAAIxg975ejHJs8fSEK9kaQiMAAACAEezeP50kOfaA0AgAAACAxu75JjQ6sjPWNRIaAQAAAIxg175eaHTU9DQAAAAA+vb0p6cdERoBAAAA0Jie7cUoS6e7E65kawiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGiZGefNSyk3JbkySTfJy2qttw8cuyvJJ5IsNU0vrLX+zTjrAQAAAGA0YwuNSilXJ7m81npVKeUJSX4lyVVDp/3jWusD46oBAAAAgHMzzulp1ya5JUlqrR9OcqCUctEYnwcAAADAg2Sc09MuTXLHwP6hpu3IQNtrSymPTfLeJD9Qa+2ud7MDB/ZmZmZ6HHVOxMLC/KRLgPOCvgKj0VdgNPoKjEZfgbXNz5+ZLLUT+slY1zQa0hna/7dJ3prkvvRGJP3TJG9c7+LDh4+OrbCttrAwn0OHFiddBmx7+gqMRl+B0egrMBp9Bda3uHhiZftC6ScbhV/jDI0OpjeyqO+yJHf3d2qt/7W/XUq5NcnfzwahEQAAAABbZ5xrGr09yXVJUkq5IsnBWutis/+QUsrbSilzzblXJ/nQGGsBAAAA4CyMbaRRrfW2UsodpZTbkiwnuaGUcn2S+2utb2pGF72vlHIsyQdjlBEAAADAtjHWNY1qrS8farpz4Nirk7x6nM8HAAAA4NyMc3oaAAAAAOcpoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaJkZ581LKTcluTJJN8nLaq23r3HOK5NcVWt95jhrAQAAAGB0YxtpVEq5Osnltdarkrwoyc1rnPPEJM8YVw0AAAAAnJtxTk+7NsktSVJr/XCSA6WUi4bOuTHJD42xBgAAAADOwTinp12a5I6B/UNN25EkKaVcn+TdSe4a5WYHDuzNzMz0g1vhBC0szE+6BDgv6CswGn0FRqOvwGj0FVjb/PwDK9s7oZ+MdU2jIZ3+Rinl4iTfmuTZSR41ysWHDx8dU1lbb2FhPocOLU66DNj29BUYjb4Co9FXYDT6CqxvcfHEyvaF0k82Cr/GOT3tYHoji/ouS3J3s/2sJAtJ3pPkTUmuaBbNBgAAAGAbGGdo9PYk1yVJKeWKJAdrrYtJUmt9Y631ibXWK5M8P8kHaq3fPcZaAAAAADgLYwuNaq23JbmjlHJbep+cdkMp5fpSyvPH9UwAAAAAHhxjXdOo1vryoaY71zjnriTPHGcdAAAAAJydcU5PAwAAAOA8JTQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABaZsZ581LKTUmuTNJN8rJa6+0Dx74tyYuSLCW5M8kNtdbuOOsBAAAAYDRjG2lUSrk6yeW11qvSC4duHji2N8k/S/L0WutTk3xhkqvGVQsAAAAAZ2ec09OuTXJLktRaP5zkQCnlomb/aK312lrrqSZAekiSvx1jLQAAAACchXFOT7s0yR0D+4eatiP9hlLKy5O8LMnP1lo/utHNDhzYm5mZ6XHUORELC/OTLgHOC/oKjEZfgdHoKzAafQXWNj//wMr2TugnY13TaEhnuKHW+lOllFcnubWU8t5a6x+td/Hhw0fHWtxWWliYz6FDi5MuA7Y9fQVGo6/AaPQVGI2+AutbXDyxsn2h9JONwq9xTk87mN7Ior7LktydJKWUi0spz0iSWuuxJL+f5KljrAUAAACAszDO0OjtSa5LklLKFUkO1lr7MdxskteVUvY3+09JUsdYCwAAAABnYWzT02qtt5VS7iil3JZkOckNpZTrk9xfa31TKeXHkvxhKeV0kjuTvHlctQAAAABwdsa6plGt9eVDTXcOHHtdkteN8/kAAAAAnJtxTk8DAAAA4DwlNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAIAWoREAAAAALUIjAAAAgBFMNSnKyeNLky1kiwiNAAAAAEZwWdmbJPmrO45MuJKtITQCAAAAGMHDH787u/dP5eMfWpx0KVtCaAQAAAAwgqmpTmZ3TWV5Z8xOExoBAAAAjGpuz3QW7zuZpdPdSZcydkIjAAAAgBE97or9Oba4lL/5i6OTLmXshEYAAAAAI7r08/YkST79tycnXMn4CY0AAAAARtTZQUnKDvpSAQAAABiV0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC1CIwAAAABahEYAAAAAtAiNAAAAAEb0yMv35GGP2Z1HPH73pEsZu5lJFwAAAABwvnj0E/blx9/2pTl0aHHSpYydkUYAAAAAtIx1pFEp5aYkVybpJnlZrfX2gWPXJHllkqUkNcmLa63L46wHAAAAgNGMbaRRKeXqJJfXWq9K8qIkNw+d8ktJrqu1PjXJfJKvGFctAAAAAJydcU5PuzbJLUlSa/1wkgOllIsGjj+51vrJZvtQkkvGWAsAAAAAZ2Gc09MuTXLHwP6hpu1IktRajyRJKeWRSZ6T5BUb3ezAgb2ZmZkeT6UTsLAwP+kS4Lygr8Bo9BUYjb4Co9FXYHM7oZ9s5aendYYbSikPT/K7SV5aa713o4sPHz46rrq23MLC/I5YZR0+W/oKjEZfgdHoKzAafQU2dyH1k43Cr3GGRgfTG1nUd1mSu/s7zVS130/yQ7XWt4+xDgAAAADO0jjXNHp7kuuSpJRyRZKDtdbBGO7GJDfVWt86xhoAAAAAOAdjG2lUa72tlHJHKeW2JMtJbiilXJ/k/iRvS/ItSS4vpby4ueT1tdZfGlc9AAAAAIxurGsa1VpfPtR058D2rnE+GwAAAIBzN87paQAAAACcp4RGAAAAALQIjQAAAABoERoBAAAA0CI0AgAAAKBFaAQAAABAi9AIAAAAgBahEQAAAAAtQiMAAAAAWoRGAAAAALQIjQAAAABoERoBAAAA0CI0AgAAAKCl0+12J10DAAAAANuMkUYAAAAAtAiNAAAAAGgRGgEAAADQIjQCAAAAoEVoBAAAAECL0AgAAACAFqERAAAAAC0zky7gQlZKuSnJlUm6SV5Wa7194Nizk/z7JEtJbq21/vhkqoTJ26SvXJPklen1lZrkxbXW5YkUChO2UV8ZOOeVSa6qtT5zi8uDbWOT7yuPSfKGJHNJPlBr/ZeTqRImb5O+ckOSb0rvZ7D311q/ayJFwjZQSvmiJL+T5KZa62uGjl3Qv9sbaTQmpZSrk1xea70qyYuS3Dx0ys1J/mmSpyZ5TinliVtcImwLI/SVX0pyXa31qUnmk3zFFpcI28IIfSXN95JnbHVtsJ2M0FduTHJjrfUpSZZKKZ+z1TXCdrBRXymlXJTk+5M8vdb6tCRPLKVcOZlKYbJKKfuS/FySd6xzygX9u73QaHyuTXJLktRaP5zkQPM/35RSHp/kvlrrJ5oRE7c258NOtG5faTy51vrJZvtQkku2tjzYNjbrK0nvl+Ef2uK6YLvZ6GewqSRPT/Lm5vgNtdb/M6E6YdI2+r5ysvmzv5Qyk2RvkvsmUSRsAyeSPDfJweEDO+F3e6HR+Fya3i+4fYeatrWOfSrJI7eoLthuNuorqbUeSZJSyiOTPCe9/xHDTrRhXymlXJ/k3Unu2tKqYPvZqK8sJFlMclMp5b3NdE7YqdbtK7XW40l+NMlHk3w8yZ/UWj+y5RXCNlBrPV1rPbbO4Qv+d3uh0dbpnOMx2Gla/aGU8vAkv5vkpbXWe7e+JNiWVvpKKeXiJN+a3kgjYLXO0Pajkrw6ydVJnlRK+cqJVAXbz+D3lYuS/GCSL0jyuCRfVkr54kkVBueRC+53e6HR+BzMwL8AJ7ksyd3rHHtU1hjqBjvERn2l/0PL7yf54Vrr27e4NthONuorz0pvBMV7krwpyRXN4qawE23UV+5J8vFa61/XWpfSW5/i721xfbBdbNRXnpDko7XWe2qtJ9P7/vLkLa4PzgcX/O/2QqPxeXuS65KklHJFkoO11sUkqbXeleSiUspjmznCz2vOh51o3b7SuDG9Tyl46ySKg21ko+8rb6y1PrHWemWS56f3iVDfPblSYaI26iunk3y0lHJ5c+6T0/tkTtiJNvoZ7K4kTyil7Gn2vyTJX255hbDN7YTf7TvdbnfSNVywSik/ld6n2CwnuSHJk5LcX2t9UynlGUl+ujn1t2qtPzOhMmHi1usrSd6W5HCSPx44/fW11l/a8iJhG9jo+8rAOY9N8rpa6zMnUSNsB5v8DPb5SV6X3j+e/q8k394sXgo7ziZ95SXpTX0+neS2Wuu/nlylMDmllCen9w/Zj01yKsnfpPeBCh/bCb/bC40AAAAAaDE9DQAAAIAWoREAAAAALUIjAAAAAFqERgAAAAC0CI0AAAAAaBEaAQDntVLKu0opzy6lPLOU8t51zrm2lPLbpZROKeUFpZRt+TNQKeUfllJ+rtl+Yinlimb75aWUr9yiGi4rpTxrk3PeWEp5zlbUAwBMzrb8gQkA4MFSStmf5LVJvq3W2k3yo9mmPwPVWv//Wut3NrvPT3JF0/5Ttda3bFEZ1yTZMDRK8pIk/7F5bwGAC9TMpAsAAHaGUsozk7wiyfEkv53k15L8fJLPTzKf5A211hubUUA3J/mS5tIba63/vZTy/CT/url+Jsk311rvGuHR35bkrbXWe0spP9o87x3N/e5K8p+TTDc1/USt9WlNva9L8t5a6y+XUr4+yXcm6SQ5lOTFtdZ7h76+00l+PL3QZX+S62utHyqlfFmSG5OcStJN8h211j8vpbwsyTclOdr8+aYkfz/JTyT5/uZ595dSjiZ5TpL3JvnyJG+qtb6+eeYvJ7kjyW+kF4wtJHlI8569fqi+H0nyuCSfm+R7k+xJ8tNJTiTZm+SlSQ4n+ckknVLKfUles9bfUfNe/l6SFyf52RH+DgCA89C2/Fc2AOCC9SXphT3/OcnLkhystV6T5MuS/LNSyj9I8sIkj6i1XpnkK5JcX0qZTvLQJN/QnH9rku8Y8ZlfkeStSVJr/XdN27W11vvSC3durbX+q/UuLqU8JskPJXl2Eyi9K8kPrnHqdJIP1VqfmeQXkvxY0/5fk3x3U/er0gth0hx/Xq316vSCl8v6N6q1/nFT838YCn9+Pcl1TV2zSb4yyW+mFzS9tdb6rCTPSPJjpZSFNWp8XJJraq13JHlYkm9vrnl1kh+stX4syeuS/Fqt9VVZ/+8oSf5Heu8tAHCBMtIIANhKtQlrkt6InEeXUq5u9nenN6Lly9ILZlJr/XR6wUhKKX+X5FebkUiXJvnjEZ/5mCSfWOdYJ8kfbXL9VUkemeRtpZQk2ZXkY+uc+7bm9Y+SfH8p5aHpBWC3N+3vSm9UUNIb4fTWUsobk/z3WutHmtFYG7k1yc+XUvYluTrJn9Ra7yulXJPkS0sp/7w571R6AdGhoevf10zRS5K/TfIzpZTd6Y1OOrzG89b7O/qzJB9P8thN6gUAzmNCIwBgK50c2D6R5MdqrW8cPKEJQKaG2mbTG1FzRa31L0sp35Ez09cerJq6Q+1zA3X+aa31eSPcq193p7nf8D377am1fk8p5XOTPDfJLaWU701ybKOb11pPllJuTS9I+8r0pvj1a3xprfX9m9Q3+P7/WpKX1FrfWUp5XpLvW+P8Nf+OAICdwfQ0AGBS3pvk65OklDJVSnlVKeXiJLelmfZUSrmolPInSQ4kWU5yVzMy5mvSG/Ezik+kN9qor5tkdo3zjiR5VPMJa3vTG/GUJLcneUop5dKmpq8rpXzNOs/qLyD9tCR/Vmu9P8ndzbpGSfLsJO8rpRxo1hj6RK31F9KbsvaUoXstr1Pnryf5J80zfq9pG3wv95RS/mMpZbN/HHxEkv/dTP37upx5Pwefu97fUdJbG+muTZ4BAJzHhEYAwKT8fJIHSil/nOR9ST7dTF37b0k+Vkq5Lb11c15Va/1UktenF+D8ZpL/kORZpZSvG+E5b01vAenB/feXUj5v6Lw705t29YEkv5peeJVa68H01vb5vVLK/0zyoqbetTyplPK29Bbf7q+f9C3pTQN7V3rrMN1Qaz2c3sLSt5dS/iDJ85L8p6F7vTPJvyulvHSo/X8muTLJO2qtJ5q2H0lyeSnlvc3xD9ZaT69TY99PN8/43fTWMXpMKeW7krwnybeWUn486/8dJb0A7K2bPAMAOI91ut3hUdMAABeO5mPhP5jkyuFPPHuQn9NNMjtCWHPeK6VckuRPkjyp1ro46XoAgPEw0ggAuKDVWh9I8i+T/KdSSmfS9VwgfjG9NZQERgBwATPSCAAAAIAWI40AAAAAaBEaAQAAANAiNAIAAACgRWgEAAAAQIvQCAAAAICW/wurWfUgUjtSqQAAAABJRU5ErkJggg==\n", 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" ] @@ -3590,7 +3584,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -3707,7 +3701,7 @@ " \n", " \n", " \n", - " 0.009952\n", + " 0.009914\n", " \n", " \n", " \n", @@ -3728,7 +3722,7 @@ " \n", " \n", " \n", - " 0.009949\n", + " 0.00991\n", " \n", " \n", " \n", @@ -3749,7 +3743,7 @@ " \n", " \n", " \n", - " 0.009636\n", + " 0.009596\n", " \n", " \n", " \n", @@ -3770,7 +3764,7 @@ " \n", " \n", " \n", - " 0.009951\n", + " 0.009912\n", " \n", " \n", " \n", @@ -3787,11 +3781,11 @@ " \n", " \n", " \n", - " 0.9755\n", + " 0.9795\n", " \n", " \n", " \n", - " 0.009644\n", + " 0.009932\n", " \n", " \n", " \n", @@ -3833,7 +3827,7 @@ " \n", " \n", " \n", - " 0.005516\n", + " 0.005487\n", " \n", " \n", " \n", @@ -3854,7 +3848,7 @@ " \n", " \n", " \n", - " 0.00126\n", + " 0.001252\n", " \n", " \n", " \n", @@ -3875,7 +3869,7 @@ " \n", " \n", " \n", - " 0.00937\n", + " 0.00933\n", " \n", " \n", " \n", @@ -3896,7 +3890,7 @@ " \n", " \n", " \n", - " 0.007245\n", + " 0.007221\n", " \n", " \n", " \n", @@ -3917,7 +3911,7 @@ " \n", " \n", " \n", - " 0.00297\n", + " 0.002952\n", " \n", " \n", " \n", @@ -3927,17 +3921,17 @@ ], "text/plain": [ " target name correlation importance\n", - " 0 Occupancy feature_1_1 0.979 0.009952\n", - " 1 Occupancy feature_1_2 0.9785 0.009949\n", - " 2 Occupancy feature_1_3 0.9726 0.009636\n", - " 3 Occupancy feature_1_4 -0.9776 0.009951\n", - " 4 Occupancy feature_1_5 0.9755 0.009644\n", + " 0 Occupancy feature_1_1 0.979 0.009914\n", + " 1 Occupancy feature_1_2 0.9785 0.00991 \n", + " 2 Occupancy feature_1_3 0.9726 0.009596\n", + " 3 Occupancy feature_1_4 -0.9776 0.009912\n", + " 4 Occupancy feature_1_5 0.9795 0.009932\n", " ... ... ... ...\n", - "100 Occupancy temperature 0.5217 0.005516\n", - "101 Occupancy humidity -0.0878 0.00126 \n", - "102 Occupancy light 0.9145 0.00937 \n", - "103 Occupancy co2 0.2618 0.007245\n", - "104 Occupancy humidityratio 0.19 0.00297 " + "100 Occupancy temperature 0.5217 0.005487\n", + "101 Occupancy humidity -0.0878 0.001252\n", + "102 Occupancy light 0.9145 0.00933 \n", + "103 Occupancy co2 0.2618 0.007221\n", + "104 Occupancy humidityratio 0.19 0.002952" ] }, "execution_count": 15, @@ -3956,7 +3950,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -4248,7 +4242,7 @@ " \n", " \n", " \n", - " 0.102797\n", + " 0.078184\n", " \n", " \n", " \n", @@ -4273,7 +4267,7 @@ " \n", " \n", " \n", - " 0.001841\n", + " 0.001754\n", " \n", " \n", " \n", @@ -4298,7 +4292,7 @@ " \n", " \n", " \n", - " 0.013586\n", + " 0.013018\n", " \n", " \n", " \n", @@ -4323,7 +4317,7 @@ " \n", " \n", " \n", - " 0.140016\n", + " 0.131578\n", " \n", " \n", " \n", @@ -4348,7 +4342,7 @@ " \n", " \n", " \n", - " 0.038488\n", + " 0.032793\n", " \n", " \n", " \n", @@ -4398,7 +4392,7 @@ " \n", " \n", " \n", - " 0.00126\n", + " 0.001252\n", " \n", " \n", " \n", @@ -4423,7 +4417,7 @@ " \n", " \n", " \n", - " 0.00297\n", + " 0.002952\n", " \n", " \n", " \n", @@ -4448,7 +4442,7 @@ " \n", " \n", " \n", - " 0.678229\n", + " 0.71703\n", " \n", " \n", " \n", @@ -4473,7 +4467,7 @@ " \n", " \n", " \n", - " 0.006178\n", + " 0.006146\n", " \n", " \n", " \n", @@ -4498,7 +4492,7 @@ " \n", " \n", " \n", - " 0.003636\n", + " 0.003505\n", " \n", " \n", " \n", @@ -4512,17 +4506,17 @@ ], "text/plain": [ " name marker table importance target \n", - " 0 CO2 [PERIPHERAL] population 0.102797 Occupancy\n", - " 1 Humidity [PERIPHERAL] population 0.001841 Occupancy\n", - " 2 HumidityRatio [PERIPHERAL] population 0.013586 Occupancy\n", - " 3 Light [PERIPHERAL] population 0.140016 Occupancy\n", - " 4 Temperature [PERIPHERAL] population 0.038488 Occupancy\n", + " 0 CO2 [PERIPHERAL] population 0.078184 Occupancy\n", + " 1 Humidity [PERIPHERAL] population 0.001754 Occupancy\n", + " 2 HumidityRatio [PERIPHERAL] population 0.013018 Occupancy\n", + " 3 Light [PERIPHERAL] population 0.131578 Occupancy\n", + " 4 Temperature [PERIPHERAL] population 0.032793 Occupancy\n", " ... ... ... ... ... \n", - " 7 Humidity [POPULATION] population 0.00126 Occupancy\n", - " 8 HumidityRatio [POPULATION] population 0.00297 Occupancy\n", - " 9 Light [POPULATION] population 0.678229 Occupancy\n", - "10 Temperature [POPULATION] population 0.006178 Occupancy\n", - "11 date [POPULATION] population 0.003636 Occupancy" + " 7 Humidity [POPULATION] population 0.001252 Occupancy\n", + " 8 HumidityRatio [POPULATION] population 0.002952 Occupancy\n", + " 9 Light [POPULATION] population 0.71703 Occupancy\n", + "10 Temperature [POPULATION] population 0.006146 Occupancy\n", + "11 date [POPULATION] population 0.003505 Occupancy" ] }, "execution_count": 21, @@ -4626,7 +4620,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.4" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/online_retail.ipynb b/online_retail.ipynb index fba0218..1643bdf 100644 --- a/online_retail.ipynb +++ b/online_retail.ipynb @@ -109,27 +109,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220323121536.log.\n", + "getML engine is already running.\n", "\n", "\n", - "Loading pipelines...\n", - "[========================================] 100%\n", - "\n", "\n", - "Connected to project 'online_retail'\n" + "Connected to project 'online_retail'\n", + "http://localhost:1709/#/listprojects/online_retail/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/online_retail/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -2289,7 +2275,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['full_data'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Substring', 'Substring', 'Substring', 'Mapping', 'TextFieldSplitter'],\n", @@ -2301,7 +2287,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['full_data'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Substring', 'Substring', 'Substring', 'Mapping', 'TextFieldSplitter'],\n", @@ -2387,6 +2373,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "OK.\n", "\n", @@ -2414,7 +2403,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:6m:50.075449\n", + "Time taken: 0h:3m:41.590005\n", "\n" ] }, @@ -2425,26 +2414,26 @@ " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['full_data'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Substring', 'Substring', 'Substring', 'Mapping', 'TextFieldSplitter'],\n", " share_selected_features=0.2,\n", - " tags=['fast_prop', 'container-DNrDmX'])
url: http://localhost:1709/#/getpipeline/online_retail/F0kxVy/0/
" + " tags=['fast_prop', 'container-AMUp3p'])
url: http://localhost:1709/#/getpipeline/online_retail/dG8sn3/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostClassifier'],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='CrossEntropyLoss',\n", " peripheral=['full_data'],\n", " predictors=['XGBoostClassifier'],\n", " preprocessors=['Substring', 'Substring', 'Substring', 'Mapping', 'TextFieldSplitter'],\n", " share_selected_features=0.2,\n", - " tags=['fast_prop', 'container-DNrDmX'])\n", + " tags=['fast_prop', 'container-AMUp3p'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/online_retail/F0kxVy/0/" + "url: http://localhost:1709/#/getpipeline/online_retail/dG8sn3/0/" ] }, "execution_count": 20, @@ -2564,7 +2553,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 12:23:29\n", + " 2022-07-17 00:09:35\n", " \n", " \n", " \n", @@ -2593,7 +2582,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 12:23:37\n", + " 2022-07-17 00:09:39\n", " \n", " \n", " \n", @@ -2623,8 +2612,8 @@ ], "text/plain": [ " date time set used target accuracy auc cross entropy\n", - "0 2022-03-23 12:23:29 train cancelled 0.9825 0.8446 0.0736 \n", - "1 2022-03-23 12:23:37 test cancelled 0.9825 0.8119 0.07529" + "0 2022-07-17 00:09:35 train cancelled 0.9825 0.8446 0.0736 \n", + "1 2022-07-17 00:09:39 test cancelled 0.9825 0.8119 0.07529" ] }, "execution_count": 21, @@ -2862,7 +2851,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.4" }, "toc": { "base_numbering": 1, diff --git a/propositionalization/air_pollution_prop.ipynb b/propositionalization/air_pollution_prop.ipynb deleted file mode 100644 index 1679c7b..0000000 --- a/propositionalization/air_pollution_prop.ipynb +++ /dev/null @@ -1,4186 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [ - "hide_input" - ] - }, - "source": [ - "# Propositionalization: Predicting air pollution in Beijing\n", - "\n", - "*NOTE: Due to featuretools's and tsfresh's memory requirements, this notebook will not run on [try.getml.com](https://try.getml.com/).*\n", - "\n", - "In this notebook we will compare getML to featuretools and tsfresh, both of which open-source libraries for feature engineering. We find that advanced algorithms featured in getML yield significantly better predictions on this dataset. We then discuss why that is.\n", - "\n", - "Summary:\n", - "\n", - "- Prediction type: __Regression model__\n", - "- Domain: __Air pollution__\n", - "- Prediction target: __pm 2.5 concentration__ \n", - "- Source data: __Multivariate time series__\n", - "- Population size: __41757__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Background\n", - "\n", - "A common approach to feature engineering is to generate attribute-value representations from relational data by applying a fixed set of aggregations to columns of interest and perform a feature selection on the (possibly large) set of generated features afterwards. In academia, this approach is called _propositionalization._\n", - "\n", - "getML's [FastProp](https://docs.getml.com/latest/user_guide/feature_engineering/feature_engineering.html#fastprop) is an implementation of this propositionalization approach that has been optimized for speed and memory efficiency. In this notebook, we want to demonstrate how – well – fast FastProp is. To this end, we will benchmark FastProp against the popular feature engineering libraries [featuretools](https://www.featuretools.com/) and [tsfresh](https://tsfresh.readthedocs.io/en/latest/). Both of these libraries use propositionalization approaches for feature engineering.\n", - "\n", - "As our example dataset, we use a publicly available dataset on air pollution in Beijing, China (https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data). For further details about the data set refer to [the full notebook](../air_pollution.ipynb)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## A web frontend for getML\n", - "\n", - "The getML monitor is a frontend built to support your work with getML. The getML monitor displays information such as the imported data frames, trained pipelines and allows easy data and feature exploration. You can launch the getML monitor [here](http://localhost:1709)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Table of contents\n", - "\n", - "1. [Loading data](#1.-Loading-data)\n", - "2. [Predictive modeling](#2.-Predictive-modeling)\n", - "3. [Comparison](#3.-Comparison)\n", - "4. [Conclusion](#4.-Conclusion)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import datetime\n", - "import os\n", - "import sys\n", - "import time\n", - "from urllib import request\n", - "\n", - "import getml\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from scipy.stats import pearsonr\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "sys.path.append(os.path.join(sys.path[0], \"..\"))\n", - "\n", - "from utils import Benchmark, FTTimeSeriesBuilder, TSFreshBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Loading data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1 Download from source\n", - "\n", - "We begin by downloading the data from the UCI Machine Learning repository." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "FEATURETOOLS_FILES = [\"featuretools_training.csv\", \"featuretools_test.csv\"]\n", - "\n", - "for fname in FEATURETOOLS_FILES:\n", - " if not os.path.exists(fname):\n", - " fname, res = request.urlretrieve(\n", - " \"https://static.getml.com/datasets/air_pollution/featuretools/\" + fname,\n", - " fname,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "TSFRESH_FILES = [\"tsfresh_training.csv\", \"tsfresh_test.csv\"]\n", - "\n", - "for fname in TSFRESH_FILES:\n", - " if not os.path.exists(fname):\n", - " fname, res = request.urlretrieve(\n", - " \"https://static.getml.com/datasets/air_pollution/tsfresh/\" + fname, fname\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "fname = \"PRSA_data_2010.1.1-2014.12.31.csv\"\n", - "\n", - "if not os.path.exists(fname):\n", - " fname, res = request.urlretrieve(\n", - " \"https://archive.ics.uci.edu/ml/machine-learning-databases/00381/\" + fname,\n", - " fname,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.2 Prepare data for tsfresh and getML\n", - "\n", - "Our our goal is to predict the pm2.5 concentration from factors such as weather or time of day. However, there are some **missing entries** for pm2.5, so we get rid of them." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "data_full_pandas = pd.read_csv(fname)\n", - "\n", - "data_full_pandas = data_full_pandas[\n", - " data_full_pandas[\"pm2.5\"] == data_full_pandas[\"pm2.5\"]\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "tsfresh requires a date column, so we build one." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def add_leading_zero(val):\n", - " if len(str(val)) == 1:\n", - " return \"0\" + str(val)\n", - " return str(val)\n", - "\n", - "\n", - "data_full_pandas[\"month\"] = [add_leading_zero(val) for val in data_full_pandas[\"month\"]]\n", - "\n", - "data_full_pandas[\"day\"] = [add_leading_zero(val) for val in data_full_pandas[\"day\"]]\n", - "\n", - "data_full_pandas[\"hour\"] = [add_leading_zero(val) for val in data_full_pandas[\"hour\"]]\n", - "\n", - "\n", - "def make_date(year, month, day, hour):\n", - " return year + \"-\" + month + \"-\" + day + \" \" + hour + \":00:00\"\n", - "\n", - "\n", - "data_full_pandas[\"date\"] = [\n", - " make_date(str(year), month, day, hour)\n", - " for year, month, day, hour in zip(\n", - " data_full_pandas[\"year\"],\n", - " data_full_pandas[\"month\"],\n", - " data_full_pandas[\"day\"],\n", - " data_full_pandas[\"hour\"],\n", - " )\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "tsfresh also requires the time series to have ids. Since there is only a single time series, that series has the same id." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "data_full_pandas[\"id\"] = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset now contains many columns that we do not need or that tsfresh cannot process. For instance, *cbwd* might actually contain useful information, but it is a categorical variable, which is difficult to handle for tsfresh, so we remove it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We also want to split our data into a training and testing set." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "data_train_pandas = data_full_pandas[data_full_pandas[\"year\"] < 2014]\n", - "data_test_pandas = data_full_pandas[data_full_pandas[\"year\"] == 2014]\n", - "data_full_pandas = data_full_pandas" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def remove_unwanted_columns(df):\n", - " del df[\"cbwd\"]\n", - " del df[\"year\"]\n", - " del df[\"month\"]\n", - " del df[\"day\"]\n", - " del df[\"hour\"]\n", - " del df[\"No\"]\n", - " return df\n", - "\n", - "\n", - "data_full_pandas = remove_unwanted_columns(data_full_pandas)\n", - "data_train_pandas = remove_unwanted_columns(data_train_pandas)\n", - "data_test_pandas = remove_unwanted_columns(data_test_pandas)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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We begin by setting a project." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220330000521.log.\n", - "\n", - "\n", - "\n", - "Connected to project 'air_pollution'\n", - "http://localhost:1709/#/listprojects/air_pollution/\n" - ] - } - ], - "source": [ - "getml.engine.launch()\n", - "getml.engine.set_project(\"air_pollution\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "df_full = getml.data.DataFrame.from_pandas(data_full_pandas, name=\"full\")\n", - "df_full[\"date\"] = df_full[\"date\"].as_ts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We need to **assign roles** to the columns, such as defining the target column." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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name date pm2.5 DEWP TEMP PRES Iws Is Ir id
role time_stamptargetnumericalnumericalnumericalnumericalnumericalnumericalunused_float
unittime stamp, comparison only
02010-01-02\n", - " 129 \n", - " \n", - " -16 \n", - " \n", - " -4 \n", - " \n", - " 1020 \n", - " \n", - " 1.79\n", - " \n", - " 0 \n", - " \n", - " 0 \n", - " \n", - " 1 \n", - "
12010-01-02 01:00:00\n", - " 148 \n", - " \n", - " -15 \n", - " \n", - " -4 \n", - " \n", - " 1020 \n", - " \n", - " 2.68\n", - " \n", - " 0 \n", - " \n", - " 0 \n", - " \n", - " 1 \n", - "
22010-01-02 02:00:00\n", - " 159 \n", - " \n", - " -11 \n", - " \n", - " -5 \n", - " \n", - " 1021 \n", - " \n", - " 3.57\n", - " \n", - " 0 \n", - " \n", - " 0 \n", - " \n", - " 1 \n", - "
32010-01-02 03:00:00\n", - " 181 \n", - " \n", - " -7 \n", - " \n", - " -5 \n", - " \n", - " 1022 \n", - " \n", - " 5.36\n", - " \n", - " 1 \n", - " \n", - " 0 \n", - " \n", - " 1 \n", - "
42010-01-02 04:00:00\n", - " 138 \n", - " \n", - " -7 \n", - " \n", - " -5 \n", - " \n", - " 1022 \n", - " \n", - " 6.25\n", - " \n", - " 2 \n", - " \n", - " 0 \n", - " \n", - " 1 \n", - "
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417522014-12-31 19:00:00\n", - " 8 \n", - " \n", - " -23 \n", - " \n", - " -2 \n", - " \n", - " 1034 \n", - " \n", - " 231.97\n", - " \n", - " 0 \n", - " \n", - " 0 \n", - " \n", - " 1 \n", - "
417532014-12-31 20:00:00\n", - " 10 \n", - " \n", - " -22 \n", - " \n", - " -3 \n", - " \n", - " 1034 \n", - " \n", - " 237.78\n", - " \n", - " 0 \n", - " \n", - " 0 \n", - " \n", - " 1 \n", - "
417542014-12-31 21:00:00\n", - " 10 \n", - " \n", - " -22 \n", - " \n", - " -3 \n", - " \n", - " 1034 \n", - " \n", - " 242.7\n", - " \n", - " 0 \n", - " \n", - " 0 \n", - " \n", - " 1 \n", - "
417552014-12-31 22:00:00\n", - " 8 \n", - " \n", - " -22 \n", - " \n", - " -4 \n", - " \n", - " 1034 \n", - " \n", - " 246.72\n", - " \n", - " 0 \n", - " \n", - " 0 \n", - " \n", - " 1 \n", - "
417562014-12-31 23:00:00\n", - " 12 \n", - " \n", - " -21 \n", - " \n", - " -3 \n", - " \n", - " 1034 \n", - " \n", - " 249.85\n", - " \n", - " 0 \n", - " \n", - " 0 \n", - " \n", - " 1 \n", - "
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\n", - " 41757 rows x 9 columns
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0train
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data model

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diagram


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fullpopulationdate <= dateMemory: 1.0 days
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staging

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data framesstaging table
0populationPOPULATION__STAGING_TABLE_1
1fullFULL__STAGING_TABLE_2
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population

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subsetname rowstype
0testfull8661View
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name rowstype
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" - ], - "text/plain": [ - "data model\n", - "\n", - " population:\n", - " columns:\n", - " - DEWP: numerical\n", - " - TEMP: numerical\n", - " - PRES: numerical\n", - " - Iws: numerical\n", - " - Is: numerical\n", - " - ...\n", - "\n", - " joins:\n", - " - right: 'full'\n", - " time_stamps: (population.date, full.date)\n", - " relationship: 'many-to-many'\n", - " memory: 86400.0\n", - " lagged_targets: False\n", - "\n", - " full:\n", - " columns:\n", - " - DEWP: numerical\n", - " - TEMP: numerical\n", - " - PRES: numerical\n", - " - Iws: numerical\n", - " - Is: numerical\n", - " - ...\n", - "\n", - "\n", - "container\n", - "\n", - " population\n", - " subset name rows type\n", - " 0 test full 8661 View\n", - " 1 train full 33096 View\n", - "\n", - " peripheral\n", - " name rows type \n", - " 0 full 41757 DataFrame" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "time_series = getml.data.TimeSeries(\n", - " population=df_full,\n", - " alias=\"population\",\n", - " split=split,\n", - " time_stamps=\"date\",\n", - " memory=getml.data.time.days(1),\n", - ")\n", - "\n", - "time_series" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "         feature_learners=['FastProp'],\n",
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-       "         loss_function=None,\n",
-       "         peripheral=['full'],\n",
-       "         predictors=[],\n",
-       "         preprocessors=[],\n",
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" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=['FastProp'],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=['full'],\n", - " predictors=[],\n", - " preprocessors=[],\n", - " share_selected_features=0.5,\n", - " tags=['memory: 1d', 'simple features'])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fast_prop = getml.feature_learning.FastProp(\n", - " loss_function=getml.feature_learning.loss_functions.SquareLoss,\n", - " num_threads=1,\n", - " aggregation=getml.feature_learning.FastProp.agg_sets.All,\n", - ")\n", - "\n", - "pipe_fp_fl = getml.pipeline.Pipeline(\n", - " tags=[\"memory: 1d\", \"simple features\"],\n", - " data_model=time_series.data_model,\n", - " feature_learners=[fast_prop],\n", - ")\n", - "\n", - "pipe_fp_fl" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n" - ] - } - ], - "source": [ - "pipe_fp_fl.check(time_series.train)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "benchmark = Benchmark()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Trying 289 features...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:11.80147\n", - "\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Building features...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - } - ], - "source": [ - "with benchmark(\"fastprop\"):\n", - " pipe_fp_fl.fit(time_series.train)\n", - " fastprop_train = pipe_fp_fl.transform(time_series.train, df_name=\"fastprop_train\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Building features...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - } - ], - "source": [ - "fastprop_test = pipe_fp_fl.transform(time_series.test, df_name=\"fastprop_test\")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "predictor = getml.predictors.XGBoostRegressor()\n", - "\n", - "pipe_fp_pr = getml.pipeline.Pipeline(\n", - " tags=[\"prediction\", \"fastprop\"], predictors=[predictor]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "XGBoost: Training as predictor...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:27.053707\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=[],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=[],\n", - " predictors=['XGBoostRegressor'],\n", - " preprocessors=[],\n", - " share_selected_features=0.5,\n", - " tags=['prediction', 'fastprop'])\n", - "\n", - "url: http://localhost:1709/#/getpipeline/air_pollution/UiZEwj/0/" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_fp_pr.fit(fastprop_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
date time set used target mae rmsersquared
02022-03-30 00:06:33fastprop_trainpm2.538.342955.18860.6443
12022-03-30 00:06:34fastprop_testpm2.544.199763.49480.545
" - ], - "text/plain": [ - " date time set used target mae rmse rsquared\n", - "0 2022-03-30 00:06:33 fastprop_train pm2.5 38.3429 55.1886 0.6443\n", - "1 2022-03-30 00:06:34 fastprop_test pm2.5 44.1997 63.4948 0.545 " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_fp_pr.score(fastprop_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Using featuretools\n", - "\n", - "To make things a bit easier, we have written a high-level wrapper around featuretools which we placed in a separate module (`utils`)." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "ft_builder = FTTimeSeriesBuilder(\n", - " num_features=200,\n", - " horizon=pd.Timedelta(days=0),\n", - " memory=pd.Timedelta(days=1),\n", - " column_id=\"id\",\n", - " time_stamp=\"date\",\n", - " target=\"pm2.5\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "featuretools: Trying features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.9/dist-packages/featuretools/synthesis/dfs.py:309: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n", - " agg_primitives: ['all', 'any', 'count', 'num_true', 'percent_true']\n", - "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data.\n", - " warnings.warn(warning_msg, UnusedPrimitiveWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selecting the best out of 114 features...\n", - "Time taken: 0h:12m:2.53883\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.9/dist-packages/featuretools/synthesis/dfs.py:309: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n", - " agg_primitives: ['all', 'any', 'count', 'num_true', 'percent_true']\n", - "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data.\n", - " warnings.warn(warning_msg, UnusedPrimitiveWarning)\n" - ] - } - ], - "source": [ - "with benchmark(\"featuretools\"):\n", - " featuretools_training = ft_builder.fit(data_train_pandas)\n", - "\n", - "featuretools_test = ft_builder.transform(data_test_pandas)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "df_featuretools_training = getml.data.DataFrame.from_pandas(\n", - " featuretools_training, name=\"featuretools_training\"\n", - ")\n", - "df_featuretools_test = getml.data.DataFrame.from_pandas(\n", - " featuretools_test, name=\"featuretools_test\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "def set_roles_featuretools(df):\n", - " df[\"date\"] = df[\"date\"].as_ts()\n", - " df.set_role([\"pm2.5\"], getml.data.roles.target)\n", - " df.set_role([\"date\"], getml.data.roles.time_stamp)\n", - " df.set_role(df.roles.unused, getml.data.roles.numerical)\n", - " df.set_role([\"id\"], getml.data.roles.unused_float)\n", - " return df\n", - "\n", - "\n", - "df_featuretools_training = set_roles_featuretools(df_featuretools_training)\n", - "df_featuretools_test = set_roles_featuretools(df_featuretools_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=[],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=[],\n", - " predictors=['XGBoostRegressor'],\n", - " preprocessors=[],\n", - " share_selected_features=0.5,\n", - " tags=['featuretools', 'memory: 1d'])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predictor = getml.predictors.XGBoostRegressor()\n", - "\n", - "pipe_ft_pr = getml.pipeline.Pipeline(\n", - " tags=[\"featuretools\", \"memory: 1d\"], predictors=[predictor]\n", - ")\n", - "\n", - "pipe_ft_pr" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n" - ] - } - ], - "source": [ - "pipe_ft_pr.check(df_featuretools_training)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "XGBoost: Training as predictor...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:9.751226\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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url: http://localhost:1709/#/getpipeline/air_pollution/zzHrWS/0/
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date time set used target mae rmsersquared
02022-03-30 00:22:01featuretools_trainingpm2.538.74955.47340.6417
12022-03-30 00:22:02featuretools_testpm2.546.605764.02930.5489
" - ], - "text/plain": [ - " date time set used target mae rmse rsquared\n", - "0 2022-03-30 00:22:01 featuretools_training pm2.5 38.749 55.4734 0.6417\n", - "1 2022-03-30 00:22:02 featuretools_test pm2.5 46.6057 64.0293 0.5489" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_ft_pr.score(df_featuretools_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3 Using tsfresh\n", - "\n", - "tsfresh is a rather low-level library. To make things a bit easier, we have written a high-level wrapper which we placed in a separate module (`utils`).\n", - "\n", - "To limit the memory consumption, we undertake the following steps:\n", - "\n", - "- We limit ourselves to a memory of 1 day from any point in time. This is necessary, because tsfresh duplicates records for every time stamp. That means that looking back 7 days instead of one day, the memory consumption would be seven times as high.\n", - "- We extract only tsfresh's **MinimalFCParameters** and **IndexBasedFCParameters** (the latter is a superset of **TimeBasedFCParameters**).\n", - "\n", - "In order to make sure that tsfresh's features can be compared to getML's features, we also do the following:\n", - "\n", - "- We apply tsfresh's built-in feature selection algorithm.\n", - "- Of the remaining features, we only keep the 40 features most correlated with the target (in terms of the absolute value of the correlation).\n", - "- We add the original columns as additional features.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pm2.5DEWPTEMPPRESIwsIsIrdateid
24129.0-16-4.01020.01.79002010-01-02 00:00:001
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27181.0-7-5.01022.05.36102010-01-02 03:00:001
28138.0-7-5.01022.06.25202010-01-02 04:00:001
..............................
3505922.0-197.01013.0114.87002013-12-31 19:00:001
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33096 rows × 9 columns

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" - ], - "text/plain": [ - " pm2.5 DEWP TEMP PRES Iws Is Ir date id\n", - "24 129.0 -16 -4.0 1020.0 1.79 0 0 2010-01-02 00:00:00 1\n", - "25 148.0 -15 -4.0 1020.0 2.68 0 0 2010-01-02 01:00:00 1\n", - "26 159.0 -11 -5.0 1021.0 3.57 0 0 2010-01-02 02:00:00 1\n", - "27 181.0 -7 -5.0 1022.0 5.36 1 0 2010-01-02 03:00:00 1\n", - "28 138.0 -7 -5.0 1022.0 6.25 2 0 2010-01-02 04:00:00 1\n", - "... ... ... ... ... ... .. .. ... ..\n", - "35059 22.0 -19 7.0 1013.0 114.87 0 0 2013-12-31 19:00:00 1\n", - "35060 18.0 -21 7.0 1014.0 119.79 0 0 2013-12-31 20:00:00 1\n", - "35061 23.0 -21 7.0 1014.0 125.60 0 0 2013-12-31 21:00:00 1\n", - "35062 20.0 -21 6.0 1014.0 130.52 0 0 2013-12-31 22:00:00 1\n", - "35063 23.0 -20 7.0 1014.0 137.67 0 0 2013-12-31 23:00:00 1\n", - "\n", - "[33096 rows x 9 columns]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_train_pandas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One of the issues about tsfresh is that is actually requires more memory than allowed by MyBinder. We therefore have to remove the parts that relate to this." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Rolling: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:24<00:00, 2.50it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:41<00:00, 1.45it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:40<00:00, 1.48it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selecting the best out of 78 features...\n", - "Time taken: 0h:1m:59.605555\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Rolling: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:05<00:00, 11.22it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:10<00:00, 5.67it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:10<00:00, 5.84it/s]\n" - ] - } - ], - "source": [ - "tsfresh_builder = TSFreshBuilder(\n", - " num_features=200, memory=24, column_id=\"id\", time_stamp=\"date\", target=\"pm2.5\"\n", - ")\n", - "\n", - "with benchmark(\"tsfresh\"):\n", - " tsfresh_training = tsfresh_builder.fit(data_train_pandas)\n", - "\n", - "tsfresh_test = tsfresh_builder.transform(data_test_pandas)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "tsfresh does not contain built-in machine learning algorithms. In order to ensure a fair comparison, we use the exact same machine learning algorithm we have also used for getML: An XGBoost regressor with all hyperparameters set to their default value.\n", - "\n", - "In order to do so, we load the tsfresh features into the getML engine." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "df_tsfresh_training = getml.data.DataFrame.from_pandas(\n", - " tsfresh_training, name=\"tsfresh_training\"\n", - ")\n", - "df_tsfresh_test = getml.data.DataFrame.from_pandas(tsfresh_test, name=\"tsfresh_test\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As usual, we need to set roles:" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "def set_roles_tsfresh(df):\n", - " df[\"date\"] = df[\"date\"].as_ts()\n", - " df.set_role([\"pm2.5\"], getml.data.roles.target)\n", - " df.set_role([\"date\"], getml.data.roles.time_stamp)\n", - " df.set_role(df.roles.unused, getml.data.roles.numerical)\n", - " df.set_role([\"id\"], getml.data.roles.unused_float)\n", - " return df\n", - "\n", - "\n", - "df_tsfresh_training = set_roles_tsfresh(df_tsfresh_training)\n", - "df_tsfresh_test = set_roles_tsfresh(df_tsfresh_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, our pipeline is very simple. It only consists of a single XGBoostRegressor." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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url: http://localhost:1709/#/getpipeline/air_pollution/69bBHO/0/
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date time set used target mae rmsersquared
02022-03-30 00:24:45tsfresh_trainingpm2.540.891757.95170.6099
12022-03-30 00:24:46tsfresh_testpm2.547.110666.60.5015
" - ], - "text/plain": [ - " date time set used target mae rmse rsquared\n", - "0 2022-03-30 00:24:45 tsfresh_training pm2.5 40.8917 57.9517 0.6099\n", - "1 2022-03-30 00:24:46 tsfresh_test pm2.5 47.1106 66.6 0.5015" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_tsf_pr.score(df_tsfresh_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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runtimenum_featuresfeatures_per_secondnormalized_runtimenormalized_runtime_per_featuremaermsersquared
getML: FastProp0 days 00:00:19.51436828914.8095491.0000001.00000044.19967163.4947690.545049
featuretools0 days 00:12:02.5410751140.15777637.02610793.86410846.60566564.0293170.548865
tsfresh0 days 00:01:59.606251720.6019756.12913824.60159447.11059466.5999820.501524
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" - ], - "text/plain": [ - " runtime num_features features_per_second \\\n", - "getML: FastProp 0 days 00:00:19.514368 289 14.809549 \n", - "featuretools 0 days 00:12:02.541075 114 0.157776 \n", - "tsfresh 0 days 00:01:59.606251 72 0.601975 \n", - "\n", - " normalized_runtime normalized_runtime_per_feature \\\n", - "getML: FastProp 1.000000 1.000000 \n", - "featuretools 37.026107 93.864108 \n", - "tsfresh 6.129138 24.601594 \n", - "\n", - " mae rmse rsquared \n", - "getML: FastProp 44.199671 63.494769 0.545049 \n", - "featuretools 46.605665 64.029317 0.548865 \n", - "tsfresh 47.110594 66.599982 0.501524 " - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "num_features = dict(\n", - " fastprop=289,\n", - " featuretools=114,\n", - " tsfresh=72,\n", - ")\n", - "\n", - "runtime_per_feature = [\n", - " benchmark.runtimes[\"fastprop\"] / num_features[\"fastprop\"],\n", - " benchmark.runtimes[\"featuretools\"] / num_features[\"featuretools\"],\n", - " benchmark.runtimes[\"tsfresh\"] / num_features[\"tsfresh\"],\n", - "]\n", - "\n", - "features_per_second = [1.0 / r.total_seconds() for r in runtime_per_feature]\n", - "\n", - "normalized_runtime_per_feature = [\n", - " r / runtime_per_feature[0] for r in runtime_per_feature\n", - "]\n", - "\n", - "comparison = pd.DataFrame(\n", - " dict(\n", - " runtime=[\n", - " benchmark.runtimes[\"fastprop\"],\n", - " benchmark.runtimes[\"featuretools\"],\n", - " benchmark.runtimes[\"tsfresh\"],\n", - " ],\n", - " num_features=num_features.values(),\n", - " features_per_second=features_per_second,\n", - " normalized_runtime=[\n", - " 1,\n", - " benchmark.runtimes[\"featuretools\"] / benchmark.runtimes[\"fastprop\"],\n", - " benchmark.runtimes[\"tsfresh\"] / benchmark.runtimes[\"fastprop\"],\n", - " ],\n", - " normalized_runtime_per_feature=normalized_runtime_per_feature,\n", - " mae=[pipe_fp_pr.mae, pipe_ft_pr.mae, pipe_tsf_pr.mae],\n", - " rmse=[pipe_fp_pr.rmse, pipe_ft_pr.rmse, pipe_tsf_pr.rmse],\n", - " rsquared=[pipe_fp_pr.rsquared, pipe_ft_pr.rsquared, pipe_tsf_pr.rsquared],\n", - " )\n", - ")\n", - "\n", - "comparison.index = [\"getML: FastProp\", \"featuretools\", \"tsfresh\"]\n", - "\n", - "comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# export for further use\n", - "comparison.to_csv(\"comparisons/air_pollution.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Conclusion\n", - "\n", - "We have compared getML's feature learning algorithms to tsfresh's brute-force feature engineering approaches on a data set related to air pollution in China. We found that getML significantly outperforms featuretools and tsfresh. These results are consistent with the view that feature learning can yield significant improvements over simple propositionalization approaches.\n", - "\n", - "However, there are other datasets on which simple propositionalization performs well. Our suggestion is therefore to think of algorithms like `FastProp` and `RelMT` as tools in a toolbox. If a simple tool like `FastProp` gets the job done, then use that. But when you need more advanced approaches, like `RelMT`, you should have them at your disposal as well.\n", - "\n", - "You are encouraged to reproduce these results." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Why is FastProp so fast?\n", - "\n", - "First, FastProp hugely benefits from getML's custom-built C++-native in-memory database engine. The engine is highly optimized for working with relational data structures and makes use of information about the relational structure of the data to efficiently store and carry out computations on such data. This matters in particular for time series where we [relate the current observation to a certain number of observations from the past](https://docs.getml.com/latest/user_guide/data_model/data_model.html#time-series): Other libraries have to deal explicitly with this inherent structure of (multivariate) time series; and such explicit transformations are costly, in terms of consumption of both, memory and computational resources. All operations on data stored in getML's engine benefit from implementations in modern C++. Further, we are taking advantage of functional design patterns where all column-based operations are evaluated lazily. So, for example, aggregations are carried out only on rows that matter (taking into account even complex conditions that might span multiple tables in the relational model). Duplicate operations are reduced to a bare minimum by keeping track of the relational data model. In addition to the mere advantage in performance, FastProp, by building on an abstract data model, also has an edge in memory consumption based on the abstract database design, the reliance on efficient storage patterns (utilizing pointers and indices) for concrete data, and by taking advantage of functional design patterns and lazy computations. This allows working with data sets of substantial size even without falling back to distributed computing models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next Steps\n", - "\n", - "If you are interested in further real-world applications of getML, visit the [notebook section on getml.com](https://notebooks.getml.com/). If you want to gain a deeper understanding about our notebooks' contents or download the code behind the notebooks, have a look at the [getml-demo repository](https://github.com/getml/getml-demo/). Here, you can also find [futher benchmarks of getML](https://github.com/getml/getml-demo/#benchmarks).\n", - "\n", - "Want to try out without much hassle? Just head to [try.getml.com](https://try.getml.com) to launch an instance of getML directly in your browser.\n", - "\n", - "Further, here is some additional material from our [documentation](https://docs.getml.com/latest/) if you want to learn more about getML:\n", - "* [Annotating data within getML's data frames](https://docs.getml.com/latest/user_guide/annotating_data/annotating_data.html),\n", - "* [Defining your relational structure through getML's abstract data model](https://docs.getml.com/latest/user_guide/data_model/data_model.html), or\n", - "* [An introduction to feature learning](https://docs.getml.com/latest/user_guide/feature_engineering/feature_engineering.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Get in contact\n", - "\n", - "If you have any questions, just write us an [email](https://getml.com/contact/lets-talk/). Prefer a private demo of getML for your team? Just [contact us](https://getml.com/contact/lets-talk/) to arrange an introduction to getML." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/propositionalization/comparisons/air_pollution.csv b/propositionalization/comparisons/air_pollution.csv deleted file mode 100644 index 0231403..0000000 --- a/propositionalization/comparisons/air_pollution.csv +++ /dev/null @@ -1,4 +0,0 @@ -,runtime,num_features,features_per_second,normalized_runtime,normalized_runtime_per_feature,mae,rmse,rsquared -getML: FastProp,0 days 00:00:19.514368,289,14.809549197322433,1.0,1.0,44.19967145096795,63.49476875160757,0.5450488898720095 -featuretools,0 days 00:12:02.541075,114,0.15777648751672432,37.02610686648935,93.86410757656537,46.6056648945646,64.02931737674268,0.5488645070483237 -tsfresh,0 days 00:01:59.606251,72,0.6019752010296184,6.129137822962035,24.601593507493632,47.11059400765295,66.59998183642728,0.5015240507138585 diff --git a/propositionalization/comparisons/auc-rsquared_fps.png b/propositionalization/comparisons/auc-rsquared_fps.png deleted file mode 100644 index f0787dd..0000000 Binary files a/propositionalization/comparisons/auc-rsquared_fps.png and /dev/null differ diff --git a/propositionalization/comparisons/comparisons.py b/propositionalization/comparisons/comparisons.py deleted file mode 100644 index 74b53bc..0000000 --- a/propositionalization/comparisons/comparisons.py +++ /dev/null @@ -1,137 +0,0 @@ -import pathlib - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd - -files = pathlib.Path(".").glob("*.csv") - -dfs = {} - -for file in files: - df = pd.read_csv(file, index_col=0) - name = file.stem - df.index = [[name] * df.shape[0], df.index] - dfs[name] = df - -comparisons = pd.concat(dfs.values()) - -colors = { - "getML: FastProp": (0.25, 0.17, 0.51), - "featuretools": (0.96, 0.60, 0.05), - "tsfresh": (0.32, 0.71, 0.24), -} - -# -------------------------------------------------------------------------- - -ax = ( - comparisons.speedup_per_feature.unstack() - .iloc[:, [1, 0, 2]] - .plot.bar(color=colors.values()) -) - -ax.set_ylabel("Normalized runtime/feature \n (getML=1)") -ax.set_title("Runtime per feature on different data sets (lower is better)") - -plt.tight_layout() -plt.savefig("nrpf.png") - -# -------------------------------------------------------------------------- - -fig, axes = plt.subplots(nrows=2) - -ax2 = ( - comparisons.features_per_second.unstack() - .iloc[:, [1, 0, 2]] - .plot.bar(color=colors.values(), ax=axes[0]) -) - -# for container in ax2.containers: -# ax2.bar_label(container, label_type="edge") -ax2.set_ylabel("Features created/second") -ax2.set_xticklabels([]) -ax2.set_title("Features created per second (higher is better)") - -sc_data = comparisons.copy()[["features_per_second", "rsquared"]] -sc_data.rename(columns={"rsquared": "auc/rsquared"}, inplace=True) -sc_data["auc/rsquared"]["occupancy"] = comparisons["auc"]["occupancy"].values - -ax4 = ( - sc_data["auc/rsquared"] - .unstack() - .iloc[:, [1, 0, 2]] - .plot.bar(color=colors.values(), ax=axes[1], legend=None) -) - -ax4.set_ylabel("AUC/Rsquared") -ax4.set_title("Performance (higher is better)") - -fig.tight_layout(pad=1) - -plt.savefig("fps_performance.png") - -# -------------------------------------------------------------------------- - -ax5 = ( - sc_data["auc/rsquared"].unstack().iloc[:, [1, 0, 2]].plot.bar(color=colors.values()) -) - -ax5.set_ylabel("AUC/Rsquared") -ax5.set_title("Performance (higher is better)") - -fig.tight_layout() - -plt.savefig("performance.png") - -# -------------------------------------------------------------------------- - -col = [colors[tool] for tool in comparisons.index.get_level_values(1)] - -ax3 = sc_data.plot.scatter(x="features_per_second", y="auc/rsquared", c=col) - -# for i, dat in enumerate(sc_data.index.get_level_values(0)): -# point = ax3.get_children()[0].get_offsets().data[i] -# offset = (0.001, 0.01) -# ax3.annotate(dat, (point[0] + offset[0], point[1] + offset[1])) - -ax3.grid(True) - -ax3.set_ylabel("AUC/Rsquared") -ax3.set_xlabel("Features/second") -ax3.set_title("Performance vs. speed") - -plt.tight_layout() -plt.savefig("auc-rsquared_fps.png") - -# -------------------------------------------------------------------------- - -plt.style.use("seaborn") - -fig, axes = plt.subplots(nrows=2) - -ax = ( - comparisons.speedup_per_feature.unstack() - .iloc[:, [1, 0, 2]] - .plot.bar(color=colors.values(), ax=axes[0]) -) - -ax.set_ylabel("Normalized runtime/feature \n (getML=1)") -ax.set_title("Runtime per feature on different data sets (lower is better)") -ax.set_xticklabels([]) - -ax4 = ( - sc_data["auc/rsquared"] - .unstack() - .iloc[:, [1, 0, 2]] - .plot.bar(color=colors.values(), ax=axes[1], legend=None) -) - -ax4.set_ylabel("AUC/Rsquared") -ax4.set_title("Predictive performance (higher is better)") - -fig.tight_layout(pad=1) - - -plt.savefig("nrpf_performance.png") - -# -------------------------------------------------------------------------- diff --git a/propositionalization/comparisons/dodgers.csv b/propositionalization/comparisons/dodgers.csv deleted file mode 100644 index b06037e..0000000 --- a/propositionalization/comparisons/dodgers.csv +++ /dev/null @@ -1,4 +0,0 @@ -,runtime,num_features,features_per_second,normalized_runtime,normalized_runtime_per_feature,rsquared,rmse,mae -getML: FastProp,0 days 00:00:49.212908,526,10.688214106304978,1.0,1.0,0.674739861410577,7.824273388224941,5.615137721839041 -featuretools,0 days 00:05:58.182900,59,0.1647203041000366,7.278230743852812,64.88704695332457,0.6500414383216022,8.67110464844564,6.28455552872203 -tsfresh,0 days 00:01:26.650363,12,0.13848758264938932,1.760724300218146,77.17814046450978,0.5778110797835911,8.913407825293008,6.788610043264059 diff --git a/propositionalization/comparisons/fps.png b/propositionalization/comparisons/fps.png deleted file mode 100644 index 8613533..0000000 Binary files a/propositionalization/comparisons/fps.png and /dev/null differ diff --git a/propositionalization/comparisons/fps_performance.png b/propositionalization/comparisons/fps_performance.png deleted file mode 100644 index 633d27a..0000000 Binary files a/propositionalization/comparisons/fps_performance.png and /dev/null differ diff --git a/propositionalization/comparisons/interstate94.csv b/propositionalization/comparisons/interstate94.csv deleted file mode 100644 index 1a442d7..0000000 --- a/propositionalization/comparisons/interstate94.csv +++ /dev/null @@ -1,3 +0,0 @@ -,runtime,num_features,features_per_second,normalized_runtime,normalized_runtime_per_feature,rsquared,rmse,mae -getML: FastProp,0 days 00:00:18.836219,461,24.474412002251647,1.0,1.0,0.9826778318692238,261.9388731680907,180.4867341518402 -featuretools,0 days 00:03:00.333020,59,0.3271724578372854,9.57373770181797,74.80584448958614,0.9723894228427081,330.5634172020007,209.56964022954304 diff --git a/propositionalization/comparisons/nrpf.png b/propositionalization/comparisons/nrpf.png deleted file mode 100644 index 6ccbc5a..0000000 Binary files a/propositionalization/comparisons/nrpf.png and /dev/null differ diff --git a/propositionalization/comparisons/nrpf_performance.png b/propositionalization/comparisons/nrpf_performance.png deleted file mode 100644 index a8fde93..0000000 Binary files a/propositionalization/comparisons/nrpf_performance.png and /dev/null differ diff --git a/propositionalization/comparisons/occupancy.csv b/propositionalization/comparisons/occupancy.csv deleted file mode 100644 index 9f30dfe..0000000 --- a/propositionalization/comparisons/occupancy.csv +++ /dev/null @@ -1,4 +0,0 @@ -,runtime,num_features,features_per_second,normalized_runtime,normalized_runtime_per_feature,accuracy,auc,cross_entropy -getML: FastProp,0 days 00:00:04.814605,289,60.024009603841534,1.0,1.0,0.9889462048636699,0.9982429566688791,0.04624509649060969 -featuretools,0 days 00:03:11.436908,103,0.5380362001516186,39.7617058927991,111.56128451380552,0.9880864652419553,0.9971036393912504,0.05063737013006627 -tsfresh,0 days 00:00:34.374999,60,1.7454535299179461,7.139733996869941,34.388775510204084,0.9877180054040776,0.9978609436533588,0.04935860166671465 diff --git a/propositionalization/comparisons/performance.png b/propositionalization/comparisons/performance.png deleted file mode 100644 index 5ec8bff..0000000 Binary files a/propositionalization/comparisons/performance.png and /dev/null differ diff --git a/propositionalization/comparisons/robot.csv b/propositionalization/comparisons/robot.csv deleted file mode 100644 index 044d80c..0000000 --- a/propositionalization/comparisons/robot.csv +++ /dev/null @@ -1,4 +0,0 @@ -,runtime,num_features,features_per_second,normalized_runtime,normalized_runtime_per_feature,rsquared,rmse,mae -getML: FastProp,0 days 00:00:00.667496,134,200.7628990162618,1.0,1.0,0.9950588372738192,0.7236222637718679,0.5515208330459932 -featuretools,0 days 00:05:36.014673,158,0.47021759319124923,503.3957851432818,426.95743826540854,0.9948004257315357,0.7452864312565746,0.5657953266811849 -tsfresh,0 days 00:00:51.376962,120,2.335679133743323,76.96969270227837,85.95482834772135,0.9938361598092297,0.7906022324089085,0.5986000367767544 diff --git a/propositionalization/dodgers_prop.ipynb b/propositionalization/dodgers_prop.ipynb deleted file mode 100644 index 2e0e424..0000000 --- a/propositionalization/dodgers_prop.ipynb +++ /dev/null @@ -1,2727 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Propositionalization: Traffic near Dodgers' stadium\n", - "\n", - "In this notebook, we compare getML's FastProp against well-known feature engineering libraries featuretools and tsfresh.\n", - "\n", - "Summary:\n", - "\n", - "- Prediction type: __Regression model__\n", - "- Domain: __Transportation__\n", - "- Prediction target: __traffic volume__ \n", - "- Source data: __Univariate time series__\n", - "- Population size: __47497__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Background\n", - "\n", - "A common approach to feature engineering is to generate attribute-value representations from relational data by applying a fixed set of aggregations to columns of interest and perform a feature selection on the (possibly large) set of generated features afterwards. In academia, this approach is called _propositionalization._\n", - "\n", - "getML's [FastProp](https://docs.getml.com/latest/user_guide/feature_engineering/feature_engineering.html#fastprop) is an implementation of this propositionalization approach that has been optimized for speed and memory efficiency. In this notebook, we want to demonstrate how – well – fast FastProp is. To this end, we will benchmark FastProp against the popular feature engineering libraries [featuretools](https://www.featuretools.com/) and [tsfresh](https://tsfresh.readthedocs.io/en/latest/). Both of these libraries use propositionalization approaches for feature engineering.\n", - "\n", - "In this notebook, we use traffic data that was collected for the Glendale on ramp for the 101 North freeway in Los Angeles. For further details about the data set refer to [the full notebook](../dodgers.ipynb)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### A web frontend for getML\n", - "\n", - "The getML monitor is a frontend built to support your work with getML. The getML monitor displays information such as the imported data frames, trained pipelines and allows easy data and feature exploration. You can launch the getML monitor [here](http://localhost:1709)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Table of contents\n", - "\n", - "1. [Loading data](#1.-Loading-data)\n", - "2. [Predictive modeling](#2.-Predictive-modeling)\n", - "3. [Comparison](#3.-Comparison)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's get started with the analysis and set-up your session:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import datetime\n", - "import gc\n", - "import os\n", - "import sys\n", - "import time\n", - "from urllib import request\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import scipy\n", - "from IPython.display import Image\n", - "from scipy.stats import pearsonr\n", - "\n", - "plt.style.use(\"seaborn\")\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "sys.path.append(os.path.join(sys.path[0], \"..\"))\n", - "\n", - "from utils import Benchmark, FTTimeSeriesBuilder, TSFreshBuilder" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "getML engine is already running.\n", - "\n", - "\n", - "\n", - "Connected to project 'dodgers'\n", - "http://localhost:1709/#/listprojects/dodgers/\n" - ] - } - ], - "source": [ - "import getml\n", - "\n", - "getml.engine.launch()\n", - "getml.engine.set_project(\"dodgers\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Loading data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1 Download from source\n", - "\n", - "We begin by downloading the data from the UC Irvine Machine Learning repository:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "fname = \"Dodgers.data\"\n", - "\n", - "if not os.path.exists(fname):\n", - " fname, res = request.urlretrieve(\n", - " \"https://archive.ics.uci.edu/ml/machine-learning-databases/event-detection/\"\n", - " + fname,\n", - " fname,\n", - " )\n", - "\n", - "data_full_pandas = pd.read_csv(fname, header=None)\n", - "data_full_pandas.columns = [\"ds\", \"y\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "data_full_pandas[\"ds\"] = [\n", - " datetime.datetime.strptime(dt, \"%m/%d/%Y %H:%M\") for dt in data_full_pandas[\"ds\"]\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "data model\n", - "\n", - " population:\n", - " columns:\n", - " - y: target\n", - " - ds: time_stamp\n", - "\n", - " joins:\n", - " - right: 'data_full'\n", - " time_stamps: (population.ds, data_full.ds)\n", - " relationship: 'many-to-many'\n", - " memory: 7200.0\n", - " horizon: 3600.0\n", - " lagged_targets: True\n", - "\n", - " data_full:\n", - " columns:\n", - " - y: target\n", - " - ds: time_stamp\n", - "\n", - "\n", - "container\n", - "\n", - " population\n", - " subset name rows type\n", - " 0 test data_full 12384 View\n", - " 1 train data_full 38016 View\n", - "\n", - " peripheral\n", - " name rows type \n", - " 0 data_full 50400 DataFrame" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 1. The horizon is 1 hour (we predict the traffic volume in one hour).\n", - "# 2. The memory is 2 hours, so we allow the algorithm to\n", - "# use information from up to 2 hours ago.\n", - "# 3. We allow lagged targets. Thus, the algorithm can\n", - "# identify autoregressive processes.\n", - "\n", - "time_series = getml.data.TimeSeries(\n", - " population=data_full,\n", - " alias=\"population\",\n", - " split=split,\n", - " time_stamps=\"ds\",\n", - " horizon=getml.data.time.hours(1),\n", - " memory=getml.data.time.hours(2),\n", - " lagged_targets=True,\n", - ")\n", - "\n", - "time_series" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Predictive modeling\n", - "\n", - "We loaded the data, defined the roles, units and the abstract data model. Next, we create a getML pipeline for relational learning." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Propositionalization with getML's FastProp" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "seasonal = getml.preprocessors.Seasonal()\n", - "\n", - "fast_prop = getml.feature_learning.FastProp(\n", - " loss_function=getml.feature_learning.loss_functions.SquareLoss,\n", - " num_threads=1,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Build the pipeline__" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(data_model='population',\n",
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" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=['FastProp'],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=['data_full'],\n", - " predictors=[],\n", - " preprocessors=['Seasonal'],\n", - " share_selected_features=0.5,\n", - " tags=['feature learning', 'fastprop'])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_fp_fl = getml.pipeline.Pipeline(\n", - " preprocessors=[seasonal],\n", - " feature_learners=[fast_prop],\n", - " data_model=time_series.data_model,\n", - " tags=[\"feature learning\", \"fastprop\"],\n", - ")\n", - "\n", - "pipe_fp_fl" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Preprocessing...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n" - ] - } - ], - "source": [ - "pipe_fp_fl.check(time_series.train)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "benchmark = Benchmark()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Preprocessing...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Trying 526 features...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:34.680276\n", - "\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Preprocessing...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Building features...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - } - ], - "source": [ - "with benchmark(\"fastprop\"):\n", - " pipe_fp_fl.fit(time_series.train)\n", - " fastprop_train = pipe_fp_fl.transform(time_series.train, df_name=\"fastprop_train\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Preprocessing...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Building features...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - } - ], - "source": [ - "fastprop_test = pipe_fp_fl.transform(time_series.test, df_name=\"fastprop_test\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "predictor = getml.predictors.XGBoostRegressor()\n", - "\n", - "pipe_fp_pr = getml.pipeline.Pipeline(\n", - " tags=[\"prediction\", \"fastprop\"], predictors=[predictor]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "XGBoost: Training as predictor...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:17.37808\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=[],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=[],\n", - " predictors=['XGBoostRegressor'],\n", - " preprocessors=[],\n", - " share_selected_features=0.5,\n", - " tags=['prediction', 'fastprop'])\n", - "\n", - "url: http://localhost:1709/#/getpipeline/dodgers/TbKJxm/0/" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_fp_pr.fit(fastprop_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
date time set used target mae rmsersquared
02022-03-30 00:28:52fastprop_trainy5.41887.53470.699
12022-03-30 00:28:52fastprop_testy5.61517.82430.6747
" - ], - "text/plain": [ - " date time set used target mae rmse rsquared\n", - "0 2022-03-30 00:28:52 fastprop_train y 5.4188 7.5347 0.699 \n", - "1 2022-03-30 00:28:52 fastprop_test y 5.6151 7.8243 0.6747" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_fp_pr.score(fastprop_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Propositionalization with featuretools" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "data_train = time_series.train.population.to_df(\"data_train\")\n", - "data_test = time_series.test.population.to_df(\"data_test\")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "dfs_pandas = {}\n", - "\n", - "for df in getml.project.data_frames:\n", - " dfs_pandas[df.name] = df.to_pandas()\n", - " dfs_pandas[df.name][\"id\"] = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "ft_builder = FTTimeSeriesBuilder(\n", - " num_features=200,\n", - " horizon=pd.Timedelta(hours=1),\n", - " memory=pd.Timedelta(hours=2),\n", - " column_id=\"id\",\n", - " time_stamp=\"ds\",\n", - " target=\"y\",\n", - " allow_lagged_targets=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "featuretools: Trying features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.9/dist-packages/featuretools/synthesis/dfs.py:309: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n", - " agg_primitives: ['all', 'any', 'count', 'num_true', 'percent_true']\n", - "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data.\n", - " warnings.warn(warning_msg, UnusedPrimitiveWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selecting the best out of 59 features...\n", - "Time taken: 0h:5m:58.182095\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.9/dist-packages/featuretools/synthesis/dfs.py:309: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n", - " agg_primitives: ['all', 'any', 'count', 'num_true', 'percent_true']\n", - "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data.\n", - " warnings.warn(warning_msg, UnusedPrimitiveWarning)\n" - ] - } - ], - "source": [ - "with benchmark(\"featuretools\"):\n", - " featuretools_train = ft_builder.fit(dfs_pandas[\"data_train\"])\n", - "\n", - "featuretools_test = ft_builder.transform(dfs_pandas[\"data_test\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "df_featuretools_train = getml.data.DataFrame.from_pandas(\n", - " featuretools_train, name=\"featuretools_train\", roles=data_train.roles\n", - ")\n", - "\n", - "df_featuretools_test = getml.data.DataFrame.from_pandas(\n", - " featuretools_test, name=\"featuretools_test\", roles=data_train.roles\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "df_featuretools_train.set_role(\n", - " df_featuretools_train.roles.unused, getml.data.roles.numerical\n", - ")\n", - "\n", - "df_featuretools_test.set_role(\n", - " df_featuretools_test.roles.unused, getml.data.roles.numerical\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "         feature_selectors=[],\n",
-       "         include_categorical=False,\n",
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-       "         peripheral=[],\n",
-       "         predictors=['XGBoostRegressor'],\n",
-       "         preprocessors=[],\n",
-       "         share_selected_features=0.5,\n",
-       "         tags=['prediction', 'featuretools'])
" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=[],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=[],\n", - " predictors=['XGBoostRegressor'],\n", - " preprocessors=[],\n", - " share_selected_features=0.5,\n", - " tags=['prediction', 'featuretools'])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predictor = getml.predictors.XGBoostRegressor()\n", - "\n", - "pipe_ft_pr = getml.pipeline.Pipeline(\n", - " tags=[\"prediction\", \"featuretools\"], predictors=[predictor]\n", - ")\n", - "\n", - "pipe_ft_pr" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'id' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n" - ] - } - ], - "source": [ - "pipe_ft_pr.check(df_featuretools_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'id' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "XGBoost: Training as predictor...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:3.437791\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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date time set used target mae rmsersquared
02022-03-30 00:36:53featuretools_trainy5.477.6030.6934
12022-03-30 00:36:53featuretools_testy6.28468.67110.65
" - ], - "text/plain": [ - " date time set used target mae rmse rsquared\n", - "0 2022-03-30 00:36:53 featuretools_train y 5.47 7.603 0.6934\n", - "1 2022-03-30 00:36:53 featuretools_test y 6.2846 8.6711 0.65 " - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_ft_pr.score(df_featuretools_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3 Propositionalization with tsfresh" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "tsfresh_builder = TSFreshBuilder(\n", - " num_features=200,\n", - " horizon=20,\n", - " memory=60,\n", - " column_id=\"id\",\n", - " time_stamp=\"ds\",\n", - " target=\"y\",\n", - " allow_lagged_targets=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Rolling: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:24<00:00, 2.42it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:22<00:00, 2.64it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:23<00:00, 2.51it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selecting the best out of 13 features...\n", - "Time taken: 0h:1m:26.650208\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Rolling: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:07<00:00, 7.84it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:07<00:00, 7.84it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:08<00:00, 7.30it/s]\n" - ] - } - ], - "source": [ - "with benchmark(\"tsfresh\"):\n", - " tsfresh_train = tsfresh_builder.fit(dfs_pandas[\"data_train\"])\n", - "\n", - "tsfresh_test = tsfresh_builder.transform(dfs_pandas[\"data_test\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "df_tsfresh_train = getml.data.DataFrame.from_pandas(\n", - " tsfresh_train, name=\"tsfresh_train\", roles=data_train.roles\n", - ")\n", - "\n", - "df_tsfresh_test = getml.data.DataFrame.from_pandas(\n", - " tsfresh_test, name=\"tsfresh_test\", roles=data_train.roles\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "df_tsfresh_train.set_role(df_tsfresh_train.roles.unused, getml.data.roles.numerical)\n", - "\n", - "df_tsfresh_test.set_role(df_tsfresh_test.roles.unused, getml.data.roles.numerical)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=[],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=[],\n", - " predictors=['XGBoostRegressor'],\n", - " preprocessors=[],\n", - " share_selected_features=0.5,\n", - " tags=['predicition', 'tsfresh'])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_tsf_pr = getml.pipeline.Pipeline(\n", - " tags=[\"predicition\", \"tsfresh\"], predictors=[predictor]\n", - ")\n", - "\n", - "pipe_tsf_pr" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'id' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "XGBoost: Training as predictor...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:3.127005\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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date time set used target mae rmsersquared
02022-03-30 00:38:51tsfresh_trainy6.31468.23480.6418
12022-03-30 00:38:52tsfresh_testy6.78868.91340.5778
" - ], - "text/plain": [ - " date time set used target mae rmse rsquared\n", - "0 2022-03-30 00:38:51 tsfresh_train y 6.3146 8.2348 0.6418\n", - "1 2022-03-30 00:38:52 tsfresh_test y 6.7886 8.9134 0.5778" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_tsf_pr.score(df_tsfresh_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "num_features = dict(\n", - " fastprop=526,\n", - " featuretools=59,\n", - " tsfresh=12,\n", - ")\n", - "\n", - "runtime_per_feature = [\n", - " benchmark.runtimes[\"fastprop\"] / num_features[\"fastprop\"],\n", - " benchmark.runtimes[\"featuretools\"] / num_features[\"featuretools\"],\n", - " benchmark.runtimes[\"tsfresh\"] / num_features[\"tsfresh\"],\n", - "]\n", - "\n", - "features_per_second = [1.0 / r.total_seconds() for r in runtime_per_feature]\n", - "\n", - "normalized_runtime_per_feature = [\n", - " r / runtime_per_feature[0] for r in runtime_per_feature\n", - "]\n", - "\n", - "comparison = pd.DataFrame(\n", - " dict(\n", - " runtime=[\n", - " benchmark.runtimes[\"fastprop\"],\n", - " benchmark.runtimes[\"featuretools\"],\n", - " benchmark.runtimes[\"tsfresh\"],\n", - " ],\n", - " num_features=num_features.values(),\n", - " features_per_second=features_per_second,\n", - " normalized_runtime=[\n", - " 1,\n", - " benchmark.runtimes[\"featuretools\"] / benchmark.runtimes[\"fastprop\"],\n", - " benchmark.runtimes[\"tsfresh\"] / benchmark.runtimes[\"fastprop\"],\n", - " ],\n", - " normalized_runtime_per_feature=normalized_runtime_per_feature,\n", - " rsquared=[pipe_fp_pr.rsquared, pipe_ft_pr.rsquared, pipe_tsf_pr.rsquared],\n", - " rmse=[pipe_fp_pr.rmse, pipe_ft_pr.rmse, pipe_tsf_pr.rmse],\n", - " mae=[pipe_fp_pr.mae, pipe_ft_pr.mae, pipe_tsf_pr.mae],\n", - " )\n", - ")\n", - "\n", - "comparison.index = [\"getML: FastProp\", \"featuretools\", \"tsfresh\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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runtimenum_featuresfeatures_per_secondnormalized_runtimenormalized_runtime_per_featurersquaredrmsemae
getML: FastProp0 days 00:00:49.21290852610.6882141.0000001.0000000.6747407.8242735.615138
featuretools0 days 00:05:58.182900590.1647207.27823164.8870470.6500418.6711056.284556
tsfresh0 days 00:01:26.650363120.1384881.76072477.1781400.5778118.9134086.788610
\n", - "
" - ], - "text/plain": [ - " runtime num_features features_per_second \\\n", - "getML: FastProp 0 days 00:00:49.212908 526 10.688214 \n", - "featuretools 0 days 00:05:58.182900 59 0.164720 \n", - "tsfresh 0 days 00:01:26.650363 12 0.138488 \n", - "\n", - " normalized_runtime normalized_runtime_per_feature rsquared \\\n", - "getML: FastProp 1.000000 1.000000 0.674740 \n", - "featuretools 7.278231 64.887047 0.650041 \n", - "tsfresh 1.760724 77.178140 0.577811 \n", - "\n", - " rmse mae \n", - "getML: FastProp 7.824273 5.615138 \n", - "featuretools 8.671105 6.284556 \n", - "tsfresh 8.913408 6.788610 " - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# export for further use\n", - "comparison.to_csv(\"comparisons/dodgers.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Why is FastProp so fast?\n", - "\n", - "First, FastProp hugely benefits from getML's custom-built C++-native in-memory database engine. The engine is highly optimized for working with relational data structures and makes use of information about the relational structure of the data to efficiently store and carry out computations on such data. This matters in particular for time series where we [relate the current observation to a certain number of observations from the past](https://docs.getml.com/latest/user_guide/data_model/data_model.html#time-series): Other libraries have to deal explicitly with this inherent structure of (multivariate) time series; and such explicit transformations are costly, in terms of consumption of both, memory and computational resources. All operations on data stored in getML's engine benefit from implementations in modern C++. Further, we are taking advantage of functional design patterns where all column-based operations are evaluated lazily. So, for example, aggregations are carried out only on rows that matter (taking into account even complex conditions that might span multiple tables in the relational model). Duplicate operations are reduced to a bare minimum by keeping track of the relational data model. In addition to the mere advantage in performance, FastProp, by building on an abstract data model, also has an edge in memory consumption based on the abstract database design, the reliance on efficient storage patterns (utilizing pointers and indices) for concrete data, and by taking advantage of functional design patterns and lazy computations. This allows working with data sets of substantial size even without falling back to distributed computing models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next Steps\n", - "\n", - "If you are interested in further real-world applications of getML, visit the [notebook section on getml.com](https://notebooks.getml.com/). If you want to gain a deeper understanding about our notebooks' contents or download the code behind the notebooks, have a look at the [getml-demo repository](https://github.com/getml/getml-demo/). Here, you can also find [futher benchmarks of getML](https://github.com/getml/getml-demo/#benchmarks).\n", - "\n", - "Want to try out without much hassle? Just head to [try.getml.com](https://try.getml.com) to launch an instance of getML directly in your browser.\n", - "\n", - "Further, here is some additional material from our [documentation](https://docs.getml.com/latest/) if you want to learn more about getML:\n", - "* [Annotating data within getML's data frames](https://docs.getml.com/latest/user_guide/annotating_data/annotating_data.html),\n", - "* [Defining your relational structure through getML's abstract data model](https://docs.getml.com/latest/user_guide/data_model/data_model.html), or\n", - "* [An introduction to feature learning](https://docs.getml.com/latest/user_guide/feature_engineering/feature_engineering.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Get in contact\n", - "\n", - "If you have any questions, just write us an [email](https://getml.com/contact/lets-talk/). Prefer a private demo of getML for your team? Just [contact us](https://getml.com/contact/lets-talk/) to arrange an introduction to getML." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - } - ], - "metadata": { - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:percent,md" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": false, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/propositionalization/interstate94_prop.ipynb b/propositionalization/interstate94_prop.ipynb deleted file mode 100644 index 0d0fd64..0000000 --- a/propositionalization/interstate94_prop.ipynb +++ /dev/null @@ -1,2344 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Propositionalization: Interstate 94\n", - "\n", - "In this notebbok, we compare getML's FastProp against well-known feature engineering libraries featuretools and tsfresh.\n", - "\n", - "Summary:\n", - "\n", - "- Prediction type: __Regression model__\n", - "- Domain: __Transportation__\n", - "- Prediction target: __Hourly traffic volume__ \n", - "- Source data: __Multivariate time series, 5 components__\n", - "- Population size: __24096__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Background\n", - "\n", - "A common approach to feature engineering is to generate attribute-value representations from relational data by applying a fixed set of aggregations to columns of interest and perform a feature selection on the (possibly large) set of generated features afterwards. In academia, this approach is called _propositionalization._\n", - "\n", - "getML's [FastProp](https://docs.getml.com/latest/user_guide/feature_engineering/feature_engineering.html#fastprop) is an implementation of this propositionalization approach that has been optimized for speed and memory efficiency. In this notebook, we want to demonstrate how – well – fast FastProp is. To this end, we will benchmark FastProp against the popular feature engineering libraries [featuretools](https://www.featuretools.com/) and [tsfresh](https://tsfresh.readthedocs.io/en/latest/). Both of these libraries use propositionalization approaches for feature engineering.\n", - "\n", - "In this notebook, we predict the hourly traffic volume on I-94 westbound from Minneapolis-St Paul. The analysis is built on top of a dataset provided by the [MN Department of Transportation](https://www.dot.state.mn.us), with some data preparation done by [John Hogue](https://github.com/dreyco676/Anomaly_Detection_A_to_Z/). For further details about the data set refer to [the full notebook](../interstate94.ipynb)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Table of contents\n", - "\n", - "1. [Loading data](#1.-Loading-data)\n", - "2. [Predictive modeling](#2.-Predictive-modeling)\n", - "3. [Comparison](#3.-Comparison)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's get started with the analysis and set-up your session:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "getML API version: 1.2.0\n", - "\n", - "getML engine is already running.\n", - "\n", - "\n", - "\n", - "Connected to project 'interstate94'\n", - "http://localhost:1709/#/listprojects/interstate94/\n" - ] - } - ], - "source": [ - "import datetime\n", - "import os\n", - "import sys\n", - "import time\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from IPython.display import Image\n", - "\n", - "plt.style.use(\"seaborn\")\n", - "%matplotlib inline\n", - "\n", - "import getml\n", - "\n", - "print(f\"getML API version: {getml.__version__}\\n\")\n", - "\n", - "getml.engine.launch()\n", - "getml.engine.set_project(\"interstate94\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "sys.path.append(os.path.join(sys.path[0], \"..\"))\n", - "\n", - "from utils import Benchmark, FTTimeSeriesBuilder, TSFreshBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Loading data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1 Download from source\n", - "\n", - "We begin by downloading the data from the UC Irvine Machine Learning repository:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Loading traffic...\n", - "[========================================] 100%\n" - ] - } - ], - "source": [ - "traffic = getml.datasets.load_interstate94(roles=True, units=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "traffic.set_role(traffic.roles.categorical, getml.data.roles.unused_string)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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" \n", - " \n", - " \n", - " \n", - "
name dstraffic_volumeholiday day month weekday hour year
role time_stamp targetunused_stringunused_stringunused_stringunused_stringunused_stringunused_string
unittime stamp, comparison only day month weekday hour year
02016-01-01\n", - " 1513 \n", - " New Years Day11402016
12016-01-01 01:00:00\n", - " 1550 \n", - " New Years Day11412016
22016-01-01 02:00:00\n", - " 993 \n", - " New Years Day11422016
32016-01-01 03:00:00\n", - " 719 \n", - " New Years Day11432016
42016-01-01 04:00:00\n", - " 533 \n", - " New Years Day11442016
...\n", - " ... \n", - " ..................
240912018-09-30 19:00:00\n", - " 3543 \n", - " No holiday3096192018
240922018-09-30 20:00:00\n", - " 2781 \n", - " No holiday3096202018
240932018-09-30 21:00:00\n", - " 2159 \n", - " No holiday3096212018
240942018-09-30 22:00:00\n", - " 1450 \n", - " No holiday3096222018
240952018-09-30 23:00:00\n", - " 954 \n", - " No holiday3096232018
\n", - "\n", - "

\n", - " 24096 rows x 8 columns
\n", - " memory usage: 2.16 MB
\n", - " name: traffic
\n", - " type: getml.DataFrame
\n", - " url: http://localhost:1709/#/getdataframe/interstate94/traffic/\n", - "

\n" - ], - "text/plain": [ - " name ds traffic_volume holiday day month weekday \n", - " role time_stamp target unused_string unused_string unused_string unused_string\n", - " unit time stamp, comparison only day month weekday \n", - " 0 2016-01-01 1513 New Years Day 1 1 4 \n", - " 1 2016-01-01 01:00:00 1550 New Years Day 1 1 4 \n", - " 2 2016-01-01 02:00:00 993 New Years Day 1 1 4 \n", - " 3 2016-01-01 03:00:00 719 New Years Day 1 1 4 \n", - " 4 2016-01-01 04:00:00 533 New Years Day 1 1 4 \n", - " ... ... ... ... ... ... \n", - "24091 2018-09-30 19:00:00 3543 No holiday 30 9 6 \n", - "24092 2018-09-30 20:00:00 2781 No holiday 30 9 6 \n", - "24093 2018-09-30 21:00:00 2159 No holiday 30 9 6 \n", - "24094 2018-09-30 22:00:00 1450 No holiday 30 9 6 \n", - "24095 2018-09-30 23:00:00 954 No holiday 30 9 6 \n", - "\n", - " name hour year \n", - " role unused_string unused_string\n", - " unit hour year \n", - " 0 0 2016 \n", - " 1 1 2016 \n", - " 2 2 2016 \n", - " 3 3 2016 \n", - " 4 4 2016 \n", - " ... ... \n", - "24091 19 2018 \n", - "24092 20 2018 \n", - "24093 21 2018 \n", - "24094 22 2018 \n", - "24095 23 2018 \n", - "\n", - "\n", - "24096 rows x 8 columns\n", - "memory usage: 2.16 MB\n", - "name: traffic\n", - "type: getml.DataFrame\n", - "url: http://localhost:1709/#/getdataframe/interstate94/traffic/" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "traffic" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.2 Define relational model\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "split = getml.data.split.time(traffic, \"ds\", test=getml.data.time.datetime(2018, 3, 15))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

data model

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traffictrafficds <= dsMemory: 1.0 daysHorizon: 1.0 hoursLagged targets allowed
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data framesstaging table
0trafficTRAFFIC__STAGING_TABLE_1
1trafficTRAFFIC__STAGING_TABLE_2
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container

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population

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subsetname rowstype
0testtraffic4800View
1traintraffic19296View
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peripheral

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name rowstype
0traffic24096DataFrame
\n", - "
" - ], - "text/plain": [ - "data model\n", - "\n", - " traffic:\n", - " columns:\n", - " - traffic_volume: target\n", - " - ds: time_stamp\n", - " - holiday: unused_string\n", - " - day: unused_string\n", - " - month: unused_string\n", - " - ...\n", - "\n", - " joins:\n", - " - right: 'traffic'\n", - " time_stamps: (traffic.ds, traffic.ds)\n", - " relationship: 'many-to-many'\n", - " memory: 86400.0\n", - " horizon: 3600.0\n", - " lagged_targets: True\n", - "\n", - " traffic:\n", - " columns:\n", - " - traffic_volume: target\n", - " - ds: time_stamp\n", - " - holiday: unused_string\n", - " - day: unused_string\n", - " - month: unused_string\n", - " - ...\n", - "\n", - "\n", - "container\n", - "\n", - " population\n", - " subset name rows type\n", - " 0 test traffic 4800 View\n", - " 1 train traffic 19296 View\n", - "\n", - " peripheral\n", - " name rows type \n", - " 0 traffic 24096 DataFrame" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "time_series = getml.data.TimeSeries(\n", - " population=traffic,\n", - " split=split,\n", - " alias=\"traffic\",\n", - " time_stamps=\"ds\",\n", - " horizon=getml.data.time.hours(1),\n", - " memory=getml.data.time.hours(24),\n", - " lagged_targets=True,\n", - ")\n", - "\n", - "time_series" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Predictive modeling\n", - "\n", - "We loaded the data, defined the roles, units and the abstract data model. Next, we create a getML pipeline for relational learning." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Propositionalization with getML's FastProp" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "seasonal = getml.preprocessors.Seasonal()\n", - "\n", - "fast_prop = getml.feature_learning.FastProp(\n", - " loss_function=getml.feature_learning.loss_functions.SquareLoss,\n", - " num_threads=1,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Build the pipeline__" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(data_model='traffic',\n",
-       "         feature_learners=['FastProp'],\n",
-       "         feature_selectors=[],\n",
-       "         include_categorical=False,\n",
-       "         loss_function=None,\n",
-       "         peripheral=['traffic'],\n",
-       "         predictors=[],\n",
-       "         preprocessors=['Seasonal'],\n",
-       "         share_selected_features=0.5,\n",
-       "         tags=['feature learning', 'fastprop'])
" - ], - "text/plain": [ - "Pipeline(data_model='traffic',\n", - " feature_learners=['FastProp'],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=['traffic'],\n", - " predictors=[],\n", - " preprocessors=['Seasonal'],\n", - " share_selected_features=0.5,\n", - " tags=['feature learning', 'fastprop'])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_fp_fl = getml.pipeline.Pipeline(\n", - " preprocessors=[seasonal],\n", - " feature_learners=[fast_prop],\n", - " data_model=time_series.data_model,\n", - " tags=[\"feature learning\", \"fastprop\"],\n", - ")\n", - "\n", - "pipe_fp_fl" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Preprocessing...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n" - ] - } - ], - "source": [ - "pipe_fp_fl.check(time_series.train)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "benchmark = Benchmark()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Preprocessing...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Trying 365 features...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:12.490392\n", - "\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Preprocessing...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Building features...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - } - ], - "source": [ - "with benchmark(\"fastprop\"):\n", - " pipe_fp_fl.fit(time_series.train)\n", - " fastprop_train = pipe_fp_fl.transform(time_series.train, df_name=\"fastprop_train\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Preprocessing...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Building features...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - } - ], - "source": [ - "fastprop_test = pipe_fp_fl.transform(time_series.test, df_name=\"fastprop_test\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "predictor = getml.predictors.XGBoostRegressor()\n", - "\n", - "pipe_fp_pr = getml.pipeline.Pipeline(\n", - " tags=[\"prediction\", \"fastprop\"], predictors=[predictor]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "XGBoost: Training as predictor...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:9.742785\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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-       "         preprocessors=[],\n",
-       "         share_selected_features=0.5,\n",
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url: http://localhost:1709/#/getpipeline/interstate94/NdqZ5G/0/
" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=[],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=[],\n", - " predictors=['XGBoostRegressor'],\n", - " preprocessors=[],\n", - " share_selected_features=0.5,\n", - " tags=['prediction', 'fastprop'])\n", - "\n", - "url: http://localhost:1709/#/getpipeline/interstate94/NdqZ5G/0/" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_fp_pr.fit(fastprop_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
date time set used target mae rmsersquared
02022-03-30 00:48:07fastprop_traintraffic_volume198.9482292.24930.9779
12022-03-30 00:48:07fastprop_testtraffic_volume180.4867261.93890.9827
" - ], - "text/plain": [ - " date time set used target mae rmse rsquared\n", - "0 2022-03-30 00:48:07 fastprop_train traffic_volume 198.9482 292.2493 0.9779\n", - "1 2022-03-30 00:48:07 fastprop_test traffic_volume 180.4867 261.9389 0.9827" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_fp_pr.score(fastprop_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Propositionalization with featuretools" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "traffic_train = time_series.train.population\n", - "traffic_test = time_series.test.population" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "dfs_pandas = {}\n", - "\n", - "for df in [traffic_train, traffic_test, traffic]:\n", - " dfs_pandas[df.name] = df.drop(df.roles.unused).to_pandas()\n", - " dfs_pandas[df.name][\"join_key\"] = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "ft_builder = FTTimeSeriesBuilder(\n", - " num_features=200,\n", - " horizon=pd.Timedelta(hours=1),\n", - " memory=pd.Timedelta(hours=24),\n", - " column_id=\"join_key\",\n", - " time_stamp=\"ds\",\n", - " target=\"traffic_volume\",\n", - " allow_lagged_targets=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "featuretools: Trying features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.9/dist-packages/featuretools/synthesis/dfs.py:309: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n", - " agg_primitives: ['all', 'any', 'count', 'num_true', 'percent_true']\n", - "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data.\n", - " warnings.warn(warning_msg, UnusedPrimitiveWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selecting the best out of 59 features...\n", - "Time taken: 0h:3m:0.332521\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.9/dist-packages/featuretools/synthesis/dfs.py:309: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n", - " agg_primitives: ['all', 'any', 'count', 'num_true', 'percent_true']\n", - "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data.\n", - " warnings.warn(warning_msg, UnusedPrimitiveWarning)\n" - ] - } - ], - "source": [ - "with benchmark(\"featuretools\"):\n", - " featuretools_train = ft_builder.fit(dfs_pandas[\"train\"])\n", - "\n", - "featuretools_test = ft_builder.transform(dfs_pandas[\"test\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "roles = {\n", - " getml.data.roles.join_key: [\"join_key\"],\n", - " getml.data.roles.target: [\"traffic_volume\"],\n", - " getml.data.roles.time_stamp: [\"ds\"],\n", - "}\n", - "\n", - "df_featuretools_train = getml.data.DataFrame.from_pandas(\n", - " featuretools_train, name=\"featuretools_train\", roles=roles\n", - ")\n", - "\n", - "df_featuretools_test = getml.data.DataFrame.from_pandas(\n", - " featuretools_test, name=\"featuretools_test\", roles=roles\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "df_featuretools_train.set_role(\n", - " df_featuretools_train.roles.unused, getml.data.roles.numerical\n", - ")\n", - "\n", - "df_featuretools_test.set_role(\n", - " df_featuretools_test.roles.unused, getml.data.roles.numerical\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "         peripheral=[],\n",
-       "         predictors=['XGBoostRegressor'],\n",
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date time set used target mae rmsersquared
02022-03-30 00:51:58featuretools_traintraffic_volume217.4832317.15630.974
12022-03-30 00:51:58featuretools_testtraffic_volume209.5696330.56340.9724
" - ], - "text/plain": [ - " date time set used target mae rmse rsquared\n", - "0 2022-03-30 00:51:58 featuretools_train traffic_volume 217.4832 317.1563 0.974 \n", - "1 2022-03-30 00:51:58 featuretools_test traffic_volume 209.5696 330.5634 0.9724" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_ft_pr.score(df_featuretools_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3 Propositionalization with tsfresh\n", - "\n", - "tsfresh failed to run through due to an apparent bug in the tsfresh library and is therefore excluded from this analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "num_features = dict(\n", - " fastprop=461,\n", - " featuretools=59,\n", - ")\n", - "\n", - "runtime_per_feature = [\n", - " benchmark.runtimes[\"fastprop\"] / num_features[\"fastprop\"],\n", - " benchmark.runtimes[\"featuretools\"] / num_features[\"featuretools\"],\n", - "]\n", - "\n", - "features_per_second = [1.0 / r.total_seconds() for r in runtime_per_feature]\n", - "\n", - "normalized_runtime_per_feature = [\n", - " r / runtime_per_feature[0] for r in runtime_per_feature\n", - "]\n", - "\n", - "comparison = pd.DataFrame(\n", - " dict(\n", - " runtime=[benchmark.runtimes[\"fastprop\"], benchmark.runtimes[\"featuretools\"]],\n", - " num_features=num_features.values(),\n", - " features_per_second=features_per_second,\n", - " normalized_runtime=[\n", - " 1,\n", - " benchmark.runtimes[\"featuretools\"] / benchmark.runtimes[\"fastprop\"],\n", - " ],\n", - " normalized_runtime_per_feature=normalized_runtime_per_feature,\n", - " rsquared=[pipe_fp_pr.rsquared, pipe_ft_pr.rsquared],\n", - " rmse=[pipe_fp_pr.rmse, pipe_ft_pr.rmse],\n", - " mae=[pipe_fp_pr.mae, pipe_ft_pr.mae],\n", - " )\n", - ")\n", - "\n", - "comparison.index = [\"getML: FastProp\", \"featuretools\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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runtimenum_featuresfeatures_per_secondnormalized_runtimenormalized_runtime_per_featurersquaredrmsemae
getML: FastProp0 days 00:00:18.83621946124.4744121.0000001.0000000.982678261.938873180.486734
featuretools0 days 00:03:00.333020590.3271729.57373874.8058440.972389330.563417209.569640
\n", - "
" - ], - "text/plain": [ - " runtime num_features features_per_second \\\n", - "getML: FastProp 0 days 00:00:18.836219 461 24.474412 \n", - "featuretools 0 days 00:03:00.333020 59 0.327172 \n", - "\n", - " normalized_runtime normalized_runtime_per_feature rsquared \\\n", - "getML: FastProp 1.000000 1.000000 0.982678 \n", - "featuretools 9.573738 74.805844 0.972389 \n", - "\n", - " rmse mae \n", - "getML: FastProp 261.938873 180.486734 \n", - "featuretools 330.563417 209.569640 " - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "comparison.to_csv(\"comparisons/interstate94.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Why is FastProp so fast?\n", - "\n", - "First, FastProp hugely benefits from getML's custom-built C++-native in-memory database engine. The engine is highly optimized for working with relational data structures and makes use of information about the relational structure of the data to efficiently store and carry out computations on such data. This matters in particular for time series where we [relate the current observation to a certain number of observations from the past](https://docs.getml.com/latest/user_guide/data_model/data_model.html#time-series): Other libraries have to deal explicitly with this inherent structure of (multivariate) time series; and such explicit transformations are costly, in terms of consumption of both, memory and computational resources. All operations on data stored in getML's engine benefit from implementations in modern C++. Further, we are taking advantage of functional design patterns where all column-based operations are evaluated lazily. So, for example, aggregations are carried out only on rows that matter (taking into account even complex conditions that might span multiple tables in the relational model). Duplicate operations are reduced to a bare minimum by keeping track of the relational data model. In addition to the mere advantage in performance, FastProp, by building on an abstract data model, also has an edge in memory consumption based on the abstract database design, the reliance on efficient storage patterns (utilizing pointers and indices) for concrete data, and by taking advantage of functional design patterns and lazy computations. This allows working with data sets of substantial size even without falling back to distributed computing models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next Steps\n", - "\n", - "If you are interested in further real-world applications of getML, visit the [notebook section on getml.com](https://notebooks.getml.com/). If you want to gain a deeper understanding about our notebooks' contents or download the code behind the notebooks, have a look at the [getml-demo repository](https://github.com/getml/getml-demo/). Here, you can also find [futher benchmarks of getML](https://github.com/getml/getml-demo/#benchmarks).\n", - "\n", - "Want to try out without much hassle? Just head to [try.getml.com](https://try.getml.com) to launch an instance of getML directly in your browser.\n", - "\n", - "Further, here is some additional material from our [documentation](https://docs.getml.com/latest/) if you want to learn more about getML:\n", - "* [Annotating data within getML's data frames](https://docs.getml.com/latest/user_guide/annotating_data/annotating_data.html),\n", - "* [Defining your relational structure through getML's abstract data model](https://docs.getml.com/latest/user_guide/data_model/data_model.html), or\n", - "* [An introduction to feature learning](https://docs.getml.com/latest/user_guide/feature_engineering/feature_engineering.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Get in contact\n", - "\n", - "If you have any questions, just write us an [email](https://getml.com/contact/lets-talk/). Prefer a private demo of getML for your team? Just [contact us](https://getml.com/contact/lets-talk/) to arrange an introduction to getML." - ] - } - ], - "metadata": { - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:percent,md" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": false, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/propositionalization/occupancy_prop.ipynb b/propositionalization/occupancy_prop.ipynb deleted file mode 100644 index bc93396..0000000 --- a/propositionalization/occupancy_prop.ipynb +++ /dev/null @@ -1,3204 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - }, - "tags": [ - "hide_input" - ] - }, - "source": [ - "# Propositionalization: Occupancy detection\n", - "\n", - "In this notebbok, we compare getML's FastProp against well-known feature engineering libraries featuretools and tsfresh.\n", - "\n", - "Summary:\n", - "\n", - "- Prediction type: __Binary classification__\n", - "- Domain: __Energy__\n", - "- Prediction target: __Room occupancy__\n", - "- Source data: __1 table, 32k rows__\n", - "- Population size: __32k__" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Background\n", - "\n", - "A common approach to feature engineering is to generate attribute-value representations from relational data by applying a fixed set of aggregations to columns of interest and perform a feature selection on the (possibly large) set of generated features afterwards. In academia, this approach is called _propositionalization._\n", - "\n", - "getML's [FastProp](https://docs.getml.com/latest/user_guide/feature_engineering/feature_engineering.html#fastprop) is an implementation of this propositionalization approach that has been optimized for speed and memory efficiency. In this notebook, we want to demonstrate how – well – fast FastProp is. To this end, we will benchmark FastProp against the popular feature engineering libraries [featuretools](https://www.featuretools.com/) and [tsfresh](https://tsfresh.readthedocs.io/en/latest/). Both of these libraries use propositionalization approaches for feature engineering.\n", - "\n", - "Our use case here is a public domain data set for predicting room occupancy from sensor data. For further details about the data set refer to [the full notebook](../occupancy.ipynb)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "### A web frontend for getML\n", - "\n", - "The getML monitor is a frontend built to support your work with getML. The getML monitor displays information such as the imported data frames, trained pipelines and allows easy data and feature exploration. You can launch the getML monitor [here](http://localhost:1709)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Where is this running?\n", - "\n", - "Your getML live session is running inside a docker container on [mybinder.org](https://mybinder.org/), a service built by the Jupyter community and funded by Google Cloud, OVH, GESIS Notebooks and the Turing Institute. As it is a free service, this session will shut down after 10 minutes of inactivity." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Table of contents\n", - "\n", - "1. [Loading data](#1.-Loading-data)\n", - "2. [Predictive modeling](#2.-Predictive-modeling)\n", - "3. [Comparison](#3.-Comparison)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's get started with the analysis and set-up your session:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "getML API version: 1.2.0\n", - "\n", - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220330010336.log.\n", - "\n", - "\n", - "\n", - "Connected to project 'occupancy'\n", - "http://localhost:1709/#/listprojects/occupancy/\n" - ] - } - ], - "source": [ - "import datetime\n", - "import os\n", - "import sys\n", - "import time\n", - "from urllib import request\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "plt.style.use(\"seaborn\")\n", - "%matplotlib inline\n", - "\n", - "import getml\n", - "\n", - "print(f\"getML API version: {getml.__version__}\\n\")\n", - "\n", - "getml.engine.launch()\n", - "getml.engine.set_project(\"occupancy\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "sys.path.append(os.path.join(sys.path[0], \"..\"))\n", - "\n", - "from utils import Benchmark, FTTimeSeriesBuilder, TSFreshBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Loading data\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data set can be downloaded directly from GitHub. It is conveniently separated into a train, a validation and a testing set. This allows us to directly benchmark our results against the results of the original paper later." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Loading population_train...\n", - "[========================================] 100%\n", - "\n", - "Loading population_test...\n", - "[========================================] 100%\n", - "\n", - "Loading population_validation...\n", - "[========================================] 100%\n" - ] - } - ], - "source": [ - "data_test, data_train, data_validate = getml.datasets.load_occupancy(roles=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "data_all, split = getml.data.split.concat(\n", - " \"data_all\",\n", - " train=data_train,\n", - " validation=data_validate,\n", - " test=data_test,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - 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name dateOccupancyTemperature Humidity Light CO2HumidityRatio
role time_stamp target numericalnumericalnumericalnumerical numerical
unit time stamp
02015-02-11 14:48:00\n", - " 1 \n", - " \n", - " 21.76\n", - " \n", - " 31.1333\n", - " \n", - " 437.3333\n", - " \n", - " 1029.6667\n", - " \n", - " 0.005021\n", - "
12015-02-11 14:49:00\n", - " 1 \n", - " \n", - " 21.79\n", - " \n", - " 31 \n", - " \n", - " 437.3333\n", - " \n", - " 1000 \n", - " \n", - " 0.005009\n", - "
22015-02-11 14:50:00\n", - " 1 \n", - " \n", - " 21.7675\n", - " \n", - " 31.1225\n", - " \n", - " 434 \n", - " \n", - " 1003.75\n", - " \n", - " 0.005022\n", - "
32015-02-11 14:51:00\n", - " 1 \n", - " \n", - " 21.7675\n", - " \n", - " 31.1225\n", - " \n", - " 439 \n", - " \n", - " 1009.5\n", - " \n", - " 0.005022\n", - "
42015-02-11 14:51:59\n", - " 1 \n", - " \n", - " 21.79\n", - " \n", - " 31.1333\n", - " \n", - " 437.3333\n", - " \n", - " 1005.6667\n", - " \n", - " 0.00503\n", - "
...\n", - " ... \n", - " \n", - " ... \n", - " \n", - " ... \n", - " \n", - " ... \n", - " \n", - " ... \n", - " \n", - " ... \n", - "
97472015-02-18 09:15:00\n", - " 1 \n", - " \n", - " 20.815\n", - " \n", - " 27.7175\n", - " \n", - " 429.75\n", - " \n", - " 1505.25\n", - " \n", - " 0.004213\n", - "
97482015-02-18 09:16:00\n", - " 1 \n", - " \n", - " 20.865\n", - " \n", - " 27.745\n", - " \n", - " 423.5\n", - " \n", - " 1514.5\n", - " \n", - " 0.00423\n", - "
97492015-02-18 09:16:59\n", - " 1 \n", - " \n", - " 20.89\n", - " \n", - " 27.745\n", - " \n", - " 423.5\n", - " \n", - " 1521.5\n", - " \n", - " 0.004237\n", - "
97502015-02-18 09:17:59\n", - " 1 \n", - " \n", - " 20.89\n", - " \n", - " 28.0225\n", - " \n", - " 418.75\n", - " \n", - " 1632 \n", - " \n", - " 0.004279\n", - "
97512015-02-18 09:19:00\n", - " 1 \n", - " \n", - " 21 \n", - " \n", - " 28.1\n", - " \n", - " 409 \n", - " \n", - " 1864 \n", - " \n", - " 0.004321\n", - "
\n", - "\n", - "

\n", - " 9752 rows x 7 columns
\n", - " memory usage: 0.55 MB
\n", - " name: population_test
\n", - " type: getml.DataFrame
\n", - " url: http://localhost:1709/#/getdataframe/occupancy/population_test/\n", - "

\n" - ], - "text/plain": [ - "name date Occupancy Temperature Humidity Light CO2 HumidityRatio\n", - "role time_stamp target numerical numerical numerical numerical numerical\n", - "unit time stamp \n", - " 0 2015-02-11 14:48:00 1 21.76 31.1333 437.3333 1029.6667 0.005021\n", - " 1 2015-02-11 14:49:00 1 21.79 31 437.3333 1000 0.005009\n", - " 2 2015-02-11 14:50:00 1 21.7675 31.1225 434 1003.75 0.005022\n", - " 3 2015-02-11 14:51:00 1 21.7675 31.1225 439 1009.5 0.005022\n", - " 4 2015-02-11 14:51:59 1 21.79 31.1333 437.3333 1005.6667 0.00503 \n", - " ... ... ... ... ... ... ... \n", - "9747 2015-02-18 09:15:00 1 20.815 27.7175 429.75 1505.25 0.004213\n", - "9748 2015-02-18 09:16:00 1 20.865 27.745 423.5 1514.5 0.00423 \n", - "9749 2015-02-18 09:16:59 1 20.89 27.745 423.5 1521.5 0.004237\n", - "9750 2015-02-18 09:17:59 1 20.89 28.0225 418.75 1632 0.004279\n", - "9751 2015-02-18 09:19:00 1 21 28.1 409 1864 0.004321\n", - "\n", - "\n", - "9752 rows x 7 columns\n", - "memory usage: 0.55 MB\n", - "name: population_test\n", - "type: getml.DataFrame\n", - "url: http://localhost:1709/#/getdataframe/occupancy/population_test/" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_train" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Predictive modeling\n", - "\n", - "We loaded the data, defined the roles, units and the abstract data model. Next, we create a getML pipeline for relational learning." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.1 Propositionalization with getML's FastProp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use all possible aggregations. Because tsfresh and featuretools are single-threaded, we limit our FastProp algorithm to one thread as well, to ensure a fair comparison." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

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" - ], - "text/plain": [ - "data model\n", - "\n", - " population:\n", - " columns:\n", - " - Temperature: numerical\n", - " - Humidity: numerical\n", - " - Light: numerical\n", - " - CO2: numerical\n", - " - HumidityRatio: numerical\n", - " - ...\n", - "\n", - " joins:\n", - " - right: 'data_all'\n", - " time_stamps: (population.date, data_all.date)\n", - " relationship: 'many-to-many'\n", - " memory: 900.0\n", - " horizon: 0.0\n", - " lagged_targets: False\n", - "\n", - " data_all:\n", - " columns:\n", - " - Temperature: numerical\n", - " - Humidity: numerical\n", - " - Light: numerical\n", - " - CO2: numerical\n", - " - HumidityRatio: numerical\n", - " - ...\n", - "\n", - "\n", - "container\n", - "\n", - " population\n", - " subset name rows type\n", - " 0 test data_all 8142 View\n", - " 1 train data_all 9753 View\n", - " 2 validation data_all 2665 View\n", - "\n", - " peripheral\n", - " name rows type \n", - " 0 data_all 20560 DataFrame" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Our forecast horizon is 0.\n", - "# We do not predict the future, instead we infer\n", - "# the present state from current and past sensor data.\n", - "horizon = 0.0\n", - "\n", - "# We do not allow the time series features\n", - "# to use target values from the past.\n", - "# (Otherwise, we would need the horizon to\n", - "# be greater than 0.0).\n", - "allow_lagged_targets = False\n", - "\n", - "# We want our time series features to only use\n", - "# data from the last 15 minutes\n", - "memory = getml.data.time.minutes(15)\n", - "\n", - "time_series = getml.data.TimeSeries(\n", - " population=data_all,\n", - " split=split,\n", - " time_stamps=\"date\",\n", - " horizon=horizon,\n", - " memory=memory,\n", - " lagged_targets=allow_lagged_targets,\n", - ")\n", - "\n", - "time_series" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "feature_learner = getml.feature_learning.FastProp(\n", - " loss_function=getml.feature_learning.loss_functions.CrossEntropyLoss,\n", - " aggregation=getml.feature_learning.FastProp.agg_sets.All,\n", - " num_threads=1,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we create the pipeline. In contrast to our usual approach, we create _two pipelines_ in\n", - "this notebook. One for feature learning (suffix `_fl`) and one for predicition (suffix `_pr`).\n", - "This allows for a fair comparison of runtimes." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "pipe_fp_fl = getml.pipeline.Pipeline(\n", - " feature_learners=[feature_learner],\n", - " data_model=time_series.data_model,\n", - " tags=[\"feature learning\", \"fastprop\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n" - ] - } - ], - "source": [ - "pipe_fp_fl.check(time_series.train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The wrappers around featuretools and tsfresh fit on the training set and then return the training features. We therefore measure the time it takes getML's FastProp algorithm to fit on the training set and create the training features." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "benchmark = Benchmark()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Trying 289 features...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:2.91909\n", - "\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Building features...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - } - ], - "source": [ - "with benchmark(\"fastprop\"):\n", - " pipe_fp_fl.fit(time_series.train)\n", - " fastprop_train = pipe_fp_fl.transform(time_series.train, df_name=\"fastprop_train\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Building features...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - } - ], - "source": [ - "fastprop_test = pipe_fp_fl.transform(time_series.test, df_name=\"fastprop_test\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we create a dedicated prediction pipeline and provide the fast prop features\n", - "(contrained in `fastprop_train` and `fastprop_test`.)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "predictor = getml.predictors.XGBoostClassifier()\n", - "\n", - "pipe_fp_pr = getml.pipeline.Pipeline(\n", - " tags=[\"prediction\", \"fastprop\"], predictors=[predictor]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n", - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "XGBoost: Training as predictor...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:8.948003\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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url: http://localhost:1709/#/getpipeline/occupancy/S0K2yk/0/
" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=[],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=[],\n", - " predictors=['XGBoostClassifier'],\n", - " preprocessors=[],\n", - " share_selected_features=0.5,\n", - " tags=['prediction', 'fastprop'])\n", - "\n", - "url: http://localhost:1709/#/getpipeline/occupancy/S0K2yk/0/" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_fp_pr.check(fastprop_train)\n", - "\n", - "pipe_fp_pr.fit(fastprop_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
date time set used target accuracy auccross entropy
02022-03-30 01:04:00fastprop_trainOccupancy0.99971.0.004466
12022-03-30 01:04:01fastprop_testOccupancy0.98890.99820.046245
" - ], - "text/plain": [ - " date time set used target accuracy auc cross entropy\n", - "0 2022-03-30 01:04:00 fastprop_train Occupancy 0.9997 1. 0.004466\n", - "1 2022-03-30 01:04:01 fastprop_test Occupancy 0.9889 0.9982 0.046245" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_fp_pr.score(fastprop_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Propositionalization with featuretools" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "data_train = time_series.train.population.to_df(\"train\")\n", - "data_test = time_series.test.population.to_df(\"test\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "dfs_pandas = {}\n", - "\n", - "for df in getml.project.data_frames:\n", - " dfs_pandas[df.name] = df.to_pandas()\n", - " dfs_pandas[df.name][\"id\"] = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "ft_builder = FTTimeSeriesBuilder(\n", - " num_features=200,\n", - " horizon=pd.Timedelta(minutes=0),\n", - " memory=pd.Timedelta(minutes=15),\n", - " column_id=\"id\",\n", - " time_stamp=\"date\",\n", - " target=\"Occupancy\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `FTTimeSeriesBuilder` provides a `fit` method that is designed to be equivilant to\n", - "to the `fit` method of the predictorless getML pipeline above." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "featuretools: Trying features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.9/dist-packages/featuretools/synthesis/dfs.py:309: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n", - " agg_primitives: ['all', 'any', 'count', 'num_true', 'percent_true']\n", - "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data.\n", - " warnings.warn(warning_msg, UnusedPrimitiveWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selecting the best out of 103 features...\n", - "Time taken: 0h:3m:11.436529\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.9/dist-packages/featuretools/synthesis/dfs.py:309: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n", - " agg_primitives: ['all', 'any', 'count', 'num_true', 'percent_true']\n", - "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data.\n", - " warnings.warn(warning_msg, UnusedPrimitiveWarning)\n" - ] - } - ], - "source": [ - "with benchmark(\"featuretools\"):\n", - " featuretools_train = ft_builder.fit(dfs_pandas[\"train\"])\n", - "\n", - "featuretools_test = ft_builder.transform(dfs_pandas[\"test\"])\n", - "\n", - "df_featuretools_train = getml.data.DataFrame.from_pandas(\n", - " featuretools_train, name=\"featuretools_train\", roles=data_train.roles\n", - ")\n", - "df_featuretools_test = getml.data.DataFrame.from_pandas(\n", - " featuretools_test, name=\"featuretools_test\", roles=data_train.roles\n", - ")\n", - "\n", - "df_featuretools_train.set_role(\n", - " df_featuretools_train.roles.unused, getml.data.roles.numerical\n", - ")\n", - "\n", - "df_featuretools_test.set_role(\n", - " df_featuretools_test.roles.unused, getml.data.roles.numerical\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=[],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=[],\n", - " predictors=['XGBoostClassifier'],\n", - " preprocessors=[],\n", - " share_selected_features=0.5,\n", - " tags=['prediction', 'featuretools'])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predictor = getml.predictors.XGBoostClassifier()\n", - "\n", - "pipe_ft_pr = getml.pipeline.Pipeline(\n", - " tags=[\"prediction\", \"featuretools\"], predictors=[predictor]\n", - ")\n", - "\n", - "pipe_ft_pr" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'id' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n" - ] - } - ], - "source": [ - "pipe_ft_pr.check(df_featuretools_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'id' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "XGBoost: Training as predictor...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:3.44978\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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-       "         tags=['prediction', 'featuretools'])

url: http://localhost:1709/#/getpipeline/occupancy/JKgueG/0/
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date time set used target accuracy auccross entropy
02022-03-30 01:10:00featuretools_trainOccupancy0.99931.0.00537
12022-03-30 01:10:00featuretools_testOccupancy0.98810.99710.050637
" - ], - "text/plain": [ - " date time set used target accuracy auc cross entropy\n", - "0 2022-03-30 01:10:00 featuretools_train Occupancy 0.9993 1. 0.00537 \n", - "1 2022-03-30 01:10:00 featuretools_test Occupancy 0.9881 0.9971 0.050637" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_ft_pr.score(df_featuretools_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3 Propositionalization with tsfresh" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/patrick/.local/lib/python3.9/site-packages/tsfresh/utilities/dataframe_functions.py:520: UserWarning: Your time stamps are not uniformly sampled, which makes rolling nonsensical in some domains.\n", - " warnings.warn(\n", - "Rolling: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:05<00:00, 10.27it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:12<00:00, 4.95it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:11<00:00, 5.29it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selecting the best out of 65 features...\n", - "Time taken: 0h:0m:34.374818\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/patrick/.local/lib/python3.9/site-packages/tsfresh/utilities/dataframe_functions.py:520: UserWarning: Your time stamps are not uniformly sampled, which makes rolling nonsensical in some domains.\n", - " warnings.warn(\n", - "Rolling: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:04<00:00, 12.34it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:10<00:00, 5.96it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:09<00:00, 6.16it/s]\n" - ] - } - ], - "source": [ - "tsfresh_builder = TSFreshBuilder(\n", - " num_features=200, memory=15, column_id=\"id\", time_stamp=\"date\", target=\"Occupancy\"\n", - ")\n", - "\n", - "with benchmark(\"tsfresh\"):\n", - " tsfresh_train = tsfresh_builder.fit(dfs_pandas[\"train\"])\n", - "\n", - "tsfresh_test = tsfresh_builder.transform(dfs_pandas[\"test\"])\n", - "\n", - "df_tsfresh_train = getml.data.DataFrame.from_pandas(\n", - " tsfresh_train, name=\"tsfresh_train\", roles=data_train.roles\n", - ")\n", - "df_tsfresh_test = getml.data.DataFrame.from_pandas(\n", - " tsfresh_test, name=\"tsfresh_test\", roles=data_train.roles\n", - ")\n", - "\n", - "df_tsfresh_train.set_role(df_tsfresh_train.roles.unused, getml.data.roles.numerical)\n", - "\n", - "df_tsfresh_test.set_role(df_tsfresh_test.roles.unused, getml.data.roles.numerical)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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date time set used target accuracy auccross entropy
02022-03-30 01:11:09tsfresh_trainOccupancy0.99851.0.006898
12022-03-30 01:11:09tsfresh_testOccupancy0.98770.99790.049359
" - ], - "text/plain": [ - " date time set used target accuracy auc cross entropy\n", - "0 2022-03-30 01:11:09 tsfresh_train Occupancy 0.9985 1. 0.006898\n", - "1 2022-03-30 01:11:09 tsfresh_test Occupancy 0.9877 0.9979 0.049359" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_tsf_pr.score(df_tsfresh_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "lines_to_next_cell": 2 - }, - "source": [ - "## 3. Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "num_features = dict(\n", - " fastprop=289,\n", - " featuretools=103,\n", - " tsfresh=60,\n", - ")\n", - "\n", - "runtime_per_feature = [\n", - " benchmark.runtimes[\"fastprop\"] / num_features[\"fastprop\"],\n", - " benchmark.runtimes[\"featuretools\"] / num_features[\"featuretools\"],\n", - " benchmark.runtimes[\"tsfresh\"] / num_features[\"tsfresh\"],\n", - "]\n", - "\n", - "features_per_second = [1.0 / r.total_seconds() for r in runtime_per_feature]\n", - "\n", - "normalized_runtime_per_feature = [\n", - " r / runtime_per_feature[0] for r in runtime_per_feature\n", - "]\n", - "\n", - "comparison = pd.DataFrame(\n", - " dict(\n", - " runtime=[\n", - " benchmark.runtimes[\"fastprop\"],\n", - " benchmark.runtimes[\"featuretools\"],\n", - " benchmark.runtimes[\"tsfresh\"],\n", - " ],\n", - " num_features=num_features.values(),\n", - " features_per_second=features_per_second,\n", - " normalized_runtime=[\n", - " 1,\n", - " benchmark.runtimes[\"featuretools\"] / benchmark.runtimes[\"fastprop\"],\n", - " benchmark.runtimes[\"tsfresh\"] / benchmark.runtimes[\"fastprop\"],\n", - " ],\n", - " normalized_runtime_per_feature=normalized_runtime_per_feature,\n", - " accuracy=[pipe_fp_pr.accuracy, pipe_ft_pr.accuracy, pipe_tsf_pr.accuracy],\n", - " auc=[pipe_fp_pr.auc, pipe_ft_pr.auc, pipe_tsf_pr.auc],\n", - " cross_entropy=[\n", - " pipe_fp_pr.cross_entropy,\n", - " pipe_ft_pr.cross_entropy,\n", - " pipe_tsf_pr.cross_entropy,\n", - " ],\n", - " )\n", - ")\n", - "\n", - "comparison.index = [\"getML: FastProp\", \"featuretools\", \"tsfresh\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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runtimenum_featuresfeatures_per_secondnormalized_runtimenormalized_runtime_per_featureaccuracyauccross_entropy
getML: FastProp0 days 00:00:04.81460528960.0240101.0000001.0000000.9889460.9982430.046245
featuretools0 days 00:03:11.4369081030.53803639.761706111.5612850.9880860.9971040.050637
tsfresh0 days 00:00:34.374999601.7454547.13973434.3887760.9877180.9978610.049359
\n", - "
" - ], - "text/plain": [ - " runtime num_features features_per_second \\\n", - "getML: FastProp 0 days 00:00:04.814605 289 60.024010 \n", - "featuretools 0 days 00:03:11.436908 103 0.538036 \n", - "tsfresh 0 days 00:00:34.374999 60 1.745454 \n", - "\n", - " normalized_runtime normalized_runtime_per_feature accuracy \\\n", - "getML: FastProp 1.000000 1.000000 0.988946 \n", - "featuretools 39.761706 111.561285 0.988086 \n", - "tsfresh 7.139734 34.388776 0.987718 \n", - "\n", - " auc cross_entropy \n", - "getML: FastProp 0.998243 0.046245 \n", - "featuretools 0.997104 0.050637 \n", - "tsfresh 0.997861 0.049359 " - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# export for further use\n", - "comparison.to_csv(\"comparisons/occupancy.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Why is FastProp so fast?\n", - "\n", - "First, FastProp hugely benefits from getML's custom-built C++-native in-memory database engine. The engine is highly optimized for working with relational data structures and makes use of information about the relational structure of the data to efficiently store and carry out computations on such data. This matters in particular for time series where we [relate the current observation to a certain number of observations from the past](https://docs.getml.com/latest/user_guide/data_model/data_model.html#time-series): Other libraries have to deal explicitly with this inherent structure of (multivariate) time series; and such explicit transformations are costly, in terms of consumption of both, memory and computational resources. All operations on data stored in getML's engine benefit from implementations in modern C++. Further, we are taking advantage of functional design patterns where all column-based operations are evaluated lazily. So, for example, aggregations are carried out only on rows that matter (taking into account even complex conditions that might span multiple tables in the relational model). Duplicate operations are reduced to a bare minimum by keeping track of the relational data model. In addition to the mere advantage in performance, FastProp, by building on an abstract data model, also has an edge in memory consumption based on the abstract database design, the reliance on efficient storage patterns (utilizing pointers and indices) for concrete data, and by taking advantage of functional design patterns and lazy computations. This allows working with data sets of substantial size even without falling back to distributed computing models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next Steps\n", - "\n", - "If you are interested in further real-world applications of getML, visit the [notebook section on getml.com](https://notebooks.getml.com/). If you want to gain a deeper understanding about our notebooks' contents or download the code behind the notebooks, have a look at the [getml-demo repository](https://github.com/getml/getml-demo/). Here, you can also find [futher benchmarks of getML](https://github.com/getml/getml-demo/#benchmarks).\n", - "\n", - "Want to try out without much hassle? Just head to [try.getml.com](https://try.getml.com) to launch an instance of getML directly in your browser.\n", - "\n", - "Further, here is some additional material from our [documentation](https://docs.getml.com/latest/) if you want to learn more about getML:\n", - "* [Annotating data within getML's data frames](https://docs.getml.com/latest/user_guide/annotating_data/annotating_data.html),\n", - "* [Defining your relational structure through getML's abstract data model](https://docs.getml.com/latest/user_guide/data_model/data_model.html), or\n", - "* [An introduction to feature learning](https://docs.getml.com/latest/user_guide/feature_engineering/feature_engineering.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Get in contact\n", - "\n", - "If you have any questions, just write us an [email](https://getml.com/contact/lets-talk/). Prefer a private demo of getML for your team? Just [contact us](https://getml.com/contact/lets-talk/) to arrange an introduction to getML." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:percent" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - }, - "toc-autonumbering": false, - "toc-showcode": false, - "toc-showmarkdowntxt": false, - "toc-showtags": false, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/propositionalization/robot_prop.ipynb b/propositionalization/robot_prop.ipynb deleted file mode 100644 index f770e7b..0000000 --- a/propositionalization/robot_prop.ipynb +++ /dev/null @@ -1,32211 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Propositionalization: Robot sensor data\n", - "\n", - "In this notebook, we compare getML's FastProp against well-known feature engineering libraries featuretools and tsfresh.\n", - "\n", - "\n", - "Summary:\n", - "\n", - "- Prediction type: __Regression__\n", - "- Domain: __Robotics__\n", - "- Prediction target: __The force vector on the robot's arm__ \n", - "- Population size: __15001__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Background\n", - "\n", - "A common approach to feature engineering is to generate attribute-value representations from relational data by applying a fixed set of aggregations to columns of interest and perform a feature selection on the (possibly large) set of generated features afterwards. In academia, this approach is called _propositionalization._\n", - "\n", - "getML's [FastProp](https://docs.getml.com/latest/user_guide/feature_engineering/feature_engineering.html#fastprop) is an implementation of this propositionalization approach that has been optimized for speed and memory efficiency. In this notebook, we want to demonstrate how – well – fast FastProp is. To this end, we will benchmark FastProp against the popular feature engineering libraries [featuretools](https://www.featuretools.com/) and [tsfresh](https://tsfresh.readthedocs.io/en/latest/). Both of these libraries use propositionalization approaches for feature engineering.\n", - "\n", - "The data set has been generously provided by Erik Berger who originally collected it for his dissertation:\n", - "\n", - "> Berger, E. (2018). *Behavior-Specific Proprioception Models for Robotic Force Estimation: A Machine Learning Approach.* Freiberg, Germany: Technische Universitaet Bergakademie Freiberg." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## A web frontend for getML\n", - "\n", - "The getML monitor is a frontend built to support your work with getML. The getML monitor displays information such as the imported data frames, trained pipelines and allows easy data and feature exploration. You can launch the getML monitor [here](http://localhost:1709)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Table of contents\n", - "\n", - "1. [Loading data](#1.-Loading-data)\n", - "2. [Predictive modeling](#2.-Predictive-modeling)\n", - "3. [Comparison](#3.-Comparison)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We begin by importing the libraries and setting the project." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "getML engine is already running.\n", - "\n", - "\n", - "\n", - "Connected to project 'robot'\n", - "http://localhost:1709/#/listprojects/robot/\n" - ] - } - ], - "source": [ - "import datetime\n", - "import os\n", - "import sys\n", - "import time\n", - "from urllib import request\n", - "\n", - "import getml\n", - "import getml.data as data\n", - "import getml.data.roles as roles\n", - "import getml.database as database\n", - "import getml.engine as engine\n", - "import getml.feature_learning.aggregations as agg\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from IPython.display import Image\n", - "\n", - "plt.style.use(\"seaborn\")\n", - "%matplotlib inline\n", - "\n", - "getml.engine.launch()\n", - "getml.engine.set_project(\"robot\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "sys.path.append(os.path.join(sys.path[0], \"..\"))\n", - "\n", - "from utils import Benchmark, FTTimeSeriesBuilder, TSFreshBuilder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Loading data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1 Download from source\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading robot-demo.csv...\n", - "[========================================] 100%\n" - ] - } - ], - "source": [ - "data_all = getml.data.DataFrame.from_csv(\n", - " \"https://static.getml.com/datasets/robotarm/robot-demo.csv\", \"data_all\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", - " 15001 rows x 96 columns
\n", - " memory usage: 11.52 MB
\n", - " name: data_all
\n", - " type: getml.DataFrame
\n", - " url: http://localhost:1709/#/getdataframe/robot/data_all/\n", - "

\n" - ], - "text/plain": [ - " name 3 4 5 6 ... 105 106 f_x\n", - " role unused_float unused_float unused_float unused_float ... unused_float unused_float unused_float\n", - " 0 3.4098 -0.3274 0.9604 -3.7436 ... 47.955 47.971 -11.03 \n", - " 1 3.4098 -0.3274 0.9604 -3.7436 ... 47.955 47.971 -10.848\n", - " 2 3.4098 -0.3274 0.9604 -3.7436 ... 47.955 47.971 -10.666\n", - " 3 3.4098 -0.3274 0.9604 -3.7436 ... 47.955 47.971 -10.507\n", - " 4 3.4098 -0.3274 0.9604 -3.7436 ... 47.955 47.971 -10.413\n", - " ... ... ... ... ... ... ... \n", - "14996 3.0837 -0.8836 1.4501 -2.2102 ... 47.94 47.94 10.84 \n", - "14997 3.0835 -0.884 1.4505 -2.2091 ... 47.94 47.94 10.857\n", - "14998 3.0833 -0.8844 1.4508 -2.208 ... 47.94 47.94 10.89 \n", - "14999 3.0831 -0.8847 1.4511 -2.2071 ... 47.94 47.94 11.29 \n", - "15000 3.0829 -0.885 1.4514 -2.2062 ... 47.94 47.955 11.69 \n", - "\n", - " name f_y f_z\n", - " role unused_float unused_float\n", - " 0 6.9 -7.33 \n", - " 1 6.7218 -7.4427\n", - " 2 6.5436 -7.5555\n", - " 3 6.4533 -7.65 \n", - " 4 6.6267 -7.69 \n", - " ... ... \n", - "14996 -1.41 16.14 \n", - "14997 -1.52 15.943 \n", - "14998 -1.74 15.55 \n", - "14999 -1.4601 15.743 \n", - "15000 -1.1801 15.937 \n", - "\n", - "\n", - "15001 rows x 96 columns\n", - "memory usage: 11.52 MB\n", - "name: data_all\n", - "type: getml.DataFrame\n", - "url: http://localhost:1709/#/getdataframe/robot/data_all/" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_all" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.2 Prepare data\n", - "\n", - "The force vector consists of three component (*f_x*, *f_y* and *f_z*), meaning that we have three targets. For this comparison, we only predict the first component (*f_x*). \n", - "\n", - "Also, we want to speed things up a little, so we only use 10 columns. A previous analysis has revealed that the predictive power is mainly extracted from these 10 columns:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "only_use = [\"30\", \"34\", \"37\", \"38\", \"4\", \"59\", \"61\", \"7\", \"77\", \"78\"]\n", - "\n", - "data_all.set_role([\"f_x\"], getml.data.roles.target)\n", - "data_all.set_role(only_use, getml.data.roles.numerical)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is what the data set looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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0train
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\n" - ], - "text/plain": [ - " \n", - " 0 train\n", - " 1 train\n", - " 2 train\n", - " 3 train\n", - " 4 train\n", - " ... \n", - "\n", - "\n", - "15001 rows\n", - "type: StringColumnView" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "split = getml.data.split.time(data_all, \"rowid\", test=10500)\n", - "split" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

data model

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diagram


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data_allpopulationrowid <= rowidMemory: 30 time steps
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staging

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data framesstaging table
0populationPOPULATION__STAGING_TABLE_1
1data_allDATA_ALL__STAGING_TABLE_2
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container

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population

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subsetname rowstype
0testdata_all4501View
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peripheral

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name rowstype
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" - ], - "text/plain": [ - "data model\n", - "\n", - " population:\n", - " columns:\n", - " - 30: numerical\n", - " - 34: numerical\n", - " - 37: numerical\n", - " - 38: numerical\n", - " - 4: numerical\n", - " - ...\n", - "\n", - " joins:\n", - " - right: 'data_all'\n", - " time_stamps: (population.rowid, data_all.rowid)\n", - " relationship: 'many-to-many'\n", - " memory: 30\n", - " lagged_targets: False\n", - "\n", - " data_all:\n", - " columns:\n", - " - 30: numerical\n", - " - 34: numerical\n", - " - 37: numerical\n", - " - 38: numerical\n", - " - 4: numerical\n", - " - ...\n", - "\n", - "\n", - "container\n", - "\n", - " population\n", - " subset name rows type\n", - " 0 test data_all 4501 View\n", - " 1 train data_all 10500 View\n", - "\n", - " peripheral\n", - " name rows type\n", - " 0 data_all 15001 View" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "time_series = getml.data.TimeSeries(\n", - " population=data_all,\n", - " split=split,\n", - " time_stamps=\"rowid\",\n", - " lagged_targets=False,\n", - " memory=30,\n", - ")\n", - "\n", - "time_series" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Predictive modeling\n", - "\n", - "### 2.1 Propositionalization with getML's FastProp" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "fast_prop = getml.feature_learning.FastProp(\n", - " loss_function=getml.feature_learning.loss_functions.SquareLoss,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "pipe_fp_fl = getml.pipeline.Pipeline(\n", - " data_model=time_series.data_model,\n", - " feature_learners=[fast_prop],\n", - " tags=[\"feature learning\", \"fastprop\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n" - ] - } - ], - "source": [ - "pipe_fp_fl.check(time_series.train)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "benchmark = Benchmark()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Trying 134 features...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:0.043236\n", - "\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Building features...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - } - ], - "source": [ - "with benchmark(\"fastprop\"):\n", - " pipe_fp_fl.fit(time_series.train)\n", - " fastprop_train = pipe_fp_fl.transform(time_series.train, df_name=\"fastprop_train\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "FastProp: Building features...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - } - ], - "source": [ - "fastprop_test = pipe_fp_fl.transform(time_series.test, df_name=\"fastprop_test\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "predictor = getml.predictors.XGBoostRegressor()\n", - "\n", - "pipe_fp_pr = getml.pipeline.Pipeline(\n", - " tags=[\"prediction\", \"fastprop\"], predictors=[predictor]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'feature_1_123' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'feature_1_125' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'feature_1_128' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'feature_1_129' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'feature_1_132' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n" - ] - } - ], - "source": [ - "pipe_fp_pr.check(fastprop_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'feature_1_123' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'feature_1_125' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'feature_1_128' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'feature_1_129' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", - "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 'feature_1_132' in POPULATION__STAGING_TABLE_1 are equal to each other. You should consider setting its role to unused_float or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "XGBoost: Training as predictor...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:9.341887\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
Pipeline(data_model='population',\n",
-       "         feature_learners=[],\n",
-       "         feature_selectors=[],\n",
-       "         include_categorical=False,\n",
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-       "         peripheral=[],\n",
-       "         predictors=['XGBoostRegressor'],\n",
-       "         preprocessors=[],\n",
-       "         share_selected_features=0.5,\n",
-       "         tags=['prediction', 'fastprop'])

url: http://localhost:1709/#/getpipeline/robot/1MiGNS/0/
" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=[],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=[],\n", - " predictors=['XGBoostRegressor'],\n", - " preprocessors=[],\n", - " share_selected_features=0.5,\n", - " tags=['prediction', 'fastprop'])\n", - "\n", - "url: http://localhost:1709/#/getpipeline/robot/1MiGNS/0/" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_fp_pr.fit(fastprop_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
date time set used target mae rmsersquared
02022-03-30 01:14:22fastprop_trainf_x0.43830.57640.9963
12022-03-30 01:14:22fastprop_testf_x0.55150.72360.9951
" - ], - "text/plain": [ - " date time set used target mae rmse rsquared\n", - "0 2022-03-30 01:14:22 fastprop_train f_x 0.4383 0.5764 0.9963\n", - "1 2022-03-30 01:14:22 fastprop_test f_x 0.5515 0.7236 0.9951" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_fp_pr.score(fastprop_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Propositionalization with featuretools" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "data_train = time_series.train.population.to_df(\"data_train\")\n", - "data_test = time_series.test.population.to_df(\"data_test\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "dfs_pandas = {}\n", - "\n", - "for df in [data_train, data_test, data_all]:\n", - " dfs_pandas[df.name] = df.to_pandas()\n", - " delete_columns = [\n", - " col for col in dfs_pandas[df.name].columns if col not in only_use + [\"f_x\"]\n", - " ]\n", - " for col in delete_columns:\n", - " del dfs_pandas[df.name][col]\n", - " dfs_pandas[df.name][\"id\"] = 1\n", - " dfs_pandas[df.name][\"ds\"] = pd.to_datetime(\n", - " np.arange(0, dfs_pandas[df.name].shape[0]), unit=\"s\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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..........................................
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"metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "featuretools_train" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "roles = {\n", - " getml.data.roles.target: [\"f_x\"],\n", - " getml.data.roles.join_key: [\"id\"],\n", - " getml.data.roles.time_stamp: [\"ds\"],\n", - "}\n", - "\n", - "df_featuretools_train = getml.data.DataFrame.from_pandas(\n", - " featuretools_train, name=\"featuretools_train\", roles=roles\n", - ")\n", - "\n", - "df_featuretools_test = getml.data.DataFrame.from_pandas(\n", - " featuretools_test, name=\"featuretools_test\", roles=roles\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "df_featuretools_train.set_role(\n", - " df_featuretools_train.roles.unused, getml.data.roles.numerical\n", - ")\n", - "\n", - "df_featuretools_test.set_role(\n", - " df_featuretools_test.roles.unused, getml.data.roles.numerical\n", - 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Pipeline(data_model='population',\n",
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-       "         tags=['prediction', 'featuretools'])

url: http://localhost:1709/#/getpipeline/robot/aJco9c/0/
" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=[],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=[],\n", - " predictors=['XGBoostRegressor'],\n", - " preprocessors=[],\n", - " share_selected_features=0.5,\n", - " tags=['prediction', 'featuretools'])\n", - "\n", - "url: http://localhost:1709/#/getpipeline/robot/aJco9c/0/" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_ft_pr.fit(df_featuretools_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
date time set used target mae rmsersquared
02022-03-30 01:22:36featuretools_trainf_x0.43120.57140.9963
12022-03-30 01:22:36featuretools_testf_x0.56580.74530.9948
" - ], - "text/plain": [ - " date time set used target mae rmse rsquared\n", - "0 2022-03-30 01:22:36 featuretools_train f_x 0.4312 0.5714 0.9963\n", - "1 2022-03-30 01:22:36 featuretools_test f_x 0.5658 0.7453 0.9948" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_ft_pr.score(df_featuretools_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3 Propositionalization with tsfresh" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "tsfresh_builder = TSFreshBuilder(\n", - " num_features=200,\n", - " memory=15,\n", - " column_id=\"id\",\n", - " time_stamp=\"ds\",\n", - " target=\"f_x\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Rolling: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:06<00:00, 9.44it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:19<00:00, 3.03it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:18<00:00, 3.24it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selecting the best out of 130 features...\n", - "Time taken: 0h:0m:51.376822\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Rolling: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:02<00:00, 22.10it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:08<00:00, 7.04it/s]\n", - "Feature Extraction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:07<00:00, 7.68it/s]\n" - ] - } - ], - "source": [ - "with benchmark(\"tsfresh\"):\n", - " tsfresh_train = tsfresh_builder.fit(dfs_pandas[\"data_train\"])\n", - "\n", - "tsfresh_test = tsfresh_builder.transform(dfs_pandas[\"data_test\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "roles = {\n", - " getml.data.roles.target: [\"f_x\"],\n", - " getml.data.roles.join_key: [\"id\"],\n", - " getml.data.roles.time_stamp: [\"ds\"],\n", - "}\n", - "\n", - "df_tsfresh_train = getml.data.DataFrame.from_pandas(\n", - " tsfresh_train, name=\"tsfresh_train\", roles=roles\n", - ")\n", - "\n", - "df_tsfresh_test = getml.data.DataFrame.from_pandas(\n", - " tsfresh_test, name=\"tsfresh_test\", roles=roles\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "df_tsfresh_train.set_role(df_tsfresh_train.roles.unused, getml.data.roles.numerical)\n", - "\n", - "df_tsfresh_test.set_role(df_tsfresh_test.roles.unused, getml.data.roles.numerical)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(data_model='population',\n",
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" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=[],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=[],\n", - " predictors=['XGBoostRegressor'],\n", - " preprocessors=[],\n", - " share_selected_features=0.5,\n", - " tags=['predicition', 'tsfresh'])" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_tsf_pr = getml.pipeline.Pipeline(\n", - " tags=[\"predicition\", \"tsfresh\"], predictors=[predictor]\n", - ")\n", - "\n", - "pipe_tsf_pr" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "Checking...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n" - ] - } - ], - "source": [ - "pipe_tsf_pr.check(df_tsfresh_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking data model...\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n", - "OK.\n", - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "XGBoost: Training as predictor...\n", - "[========================================] 100%\n", - "\n", - "\n", - "Trained pipeline.\n", - "Time taken: 0h:0m:9.285027\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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url: http://localhost:1709/#/getpipeline/robot/EHdkUY/0/
" - ], - "text/plain": [ - "Pipeline(data_model='population',\n", - " feature_learners=[],\n", - " feature_selectors=[],\n", - " include_categorical=False,\n", - " loss_function=None,\n", - " peripheral=[],\n", - " predictors=['XGBoostRegressor'],\n", - " preprocessors=[],\n", - " share_selected_features=0.5,\n", - " tags=['predicition', 'tsfresh'])\n", - "\n", - "url: http://localhost:1709/#/getpipeline/robot/EHdkUY/0/" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_tsf_pr.fit(df_tsfresh_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "Staging...\n", - "[========================================] 100%\n", - "\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
date time set used target mae rmsersquared
02022-03-30 01:24:04tsfresh_trainf_x0.49160.66360.9951
12022-03-30 01:24:04tsfresh_testf_x0.59860.79060.9938
" - ], - "text/plain": [ - " date time set used target mae rmse rsquared\n", - "0 2022-03-30 01:24:04 tsfresh_train f_x 0.4916 0.6636 0.9951\n", - "1 2022-03-30 01:24:04 tsfresh_test f_x 0.5986 0.7906 0.9938" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe_tsf_pr.score(df_tsfresh_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "num_features = dict(\n", - " fastprop=134,\n", - " featuretools=158,\n", - " tsfresh=120,\n", - ")\n", - "\n", - "runtime_per_feature = [\n", - " benchmark.runtimes[\"fastprop\"] / num_features[\"fastprop\"],\n", - " benchmark.runtimes[\"featuretools\"] / num_features[\"featuretools\"],\n", - " benchmark.runtimes[\"tsfresh\"] / num_features[\"tsfresh\"],\n", - "]\n", - "\n", - "features_per_second = [1.0 / r.total_seconds() for r in runtime_per_feature]\n", - "\n", - "normalized_runtime_per_feature = [\n", - " r / runtime_per_feature[0] for r in runtime_per_feature\n", - "]\n", - "\n", - "comparison = pd.DataFrame(\n", - " dict(\n", - " runtime=[\n", - " benchmark.runtimes[\"fastprop\"],\n", - " benchmark.runtimes[\"featuretools\"],\n", - " benchmark.runtimes[\"tsfresh\"],\n", - " ],\n", - " num_features=num_features.values(),\n", - " features_per_second=features_per_second,\n", - " normalized_runtime=[\n", - " 1,\n", - " benchmark.runtimes[\"featuretools\"] / benchmark.runtimes[\"fastprop\"],\n", - " benchmark.runtimes[\"tsfresh\"] / benchmark.runtimes[\"fastprop\"],\n", - " ],\n", - " normalized_runtime_per_feature=normalized_runtime_per_feature,\n", - " rsquared=[pipe_fp_pr.rsquared, pipe_ft_pr.rsquared, pipe_tsf_pr.rsquared],\n", - " rmse=[pipe_fp_pr.rmse, pipe_ft_pr.rmse, pipe_tsf_pr.rmse],\n", - " mae=[pipe_fp_pr.mae, pipe_ft_pr.mae, pipe_tsf_pr.mae],\n", - " )\n", - ")\n", - "\n", - "comparison.index = [\"getML: FastProp\", \"featuretools\", \"tsfresh\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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runtimenum_featuresfeatures_per_secondnormalized_runtimenormalized_runtime_per_featurersquaredrmsemae
getML: FastProp0 days 00:00:00.667496134200.7628991.0000001.0000000.9950590.7236220.551521
featuretools0 days 00:05:36.0146731580.470218503.395785426.9574380.9948000.7452860.565795
tsfresh0 days 00:00:51.3769621202.33567976.96969385.9548280.9938360.7906020.598600
\n", - "
" - ], - "text/plain": [ - " runtime num_features features_per_second \\\n", - "getML: FastProp 0 days 00:00:00.667496 134 200.762899 \n", - "featuretools 0 days 00:05:36.014673 158 0.470218 \n", - "tsfresh 0 days 00:00:51.376962 120 2.335679 \n", - "\n", - " normalized_runtime normalized_runtime_per_feature rsquared \\\n", - "getML: FastProp 1.000000 1.000000 0.995059 \n", - "featuretools 503.395785 426.957438 0.994800 \n", - "tsfresh 76.969693 85.954828 0.993836 \n", - "\n", - " rmse mae \n", - "getML: FastProp 0.723622 0.551521 \n", - "featuretools 0.745286 0.565795 \n", - "tsfresh 0.790602 0.598600 " - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "comparison.to_csv(\"comparisons/robot.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Why is FastProp so fast?\n", - "\n", - "First, FastProp hugely benefits from getML's custom-built C++-native in-memory database engine. The engine is highly optimized for working with relational data structures and makes use of information about the relational structure of the data to efficiently store and carry out computations on such data. This matters in particular for time series where we [relate the current observation to a certain number of observations from the past](https://docs.getml.com/latest/user_guide/data_model/data_model.html#time-series): Other libraries have to deal explicitly with this inherent structure of (multivariate) time series; and such explicit transformations are costly, in terms of consumption of both, memory and computational resources. All operations on data stored in getML's engine benefit from implementations in modern C++. Further, we are taking advantage of functional design patterns where all column-based operations are evaluated lazily. So, for example, aggregations are carried out only on rows that matter (taking into account even complex conditions that might span multiple tables in the relational model). Duplicate operations are reduced to a bare minimum by keeping track of the relational data model. In addition to the mere advantage in performance, FastProp, by building on an abstract data model, also has an edge in memory consumption based on the abstract database design, the reliance on efficient storage patterns (utilizing pointers and indices) for concrete data, and by taking advantage of functional design patterns and lazy computations. This allows working with data sets of substantial size even without falling back to distributed computing models." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next Steps\n", - "\n", - "If you are interested in further real-world applications of getML, visit the [notebook section on getml.com](https://notebooks.getml.com/). If you want to gain a deeper understanding about our notebooks' contents or download the code behind the notebooks, have a look at the [getml-demo repository](https://github.com/getml/getml-demo/). Here, you can also find [futher benchmarks of getML](https://github.com/getml/getml-demo/#benchmarks).\n", - "\n", - "Want to try out without much hassle? Just head to [try.getml.com](https://try.getml.com) to launch an instance of getML directly in your browser.\n", - "\n", - "Further, here is some additional material from our [documentation](https://docs.getml.com/latest/) if you want to learn more about getML:\n", - "* [Annotating data within getML's data frames](https://docs.getml.com/latest/user_guide/annotating_data/annotating_data.html),\n", - "* [Defining your relational structure through getML's abstract data model](https://docs.getml.com/latest/user_guide/data_model/data_model.html), or\n", - "* [An introduction to feature learning](https://docs.getml.com/latest/user_guide/feature_engineering/feature_engineering.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Get in contact\n", - "\n", - "If you have any questions, just write us an [email](https://getml.com/contact/lets-talk/). Prefer a private demo of getML for your team? Just [contact us](https://getml.com/contact/lets-talk/) to arrange an introduction to getML." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/robot.ipynb b/robot.ipynb index 96d871b..da17ae8 100644 --- a/robot.ipynb +++ b/robot.ipynb @@ -125,24 +125,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220324212608.log.\n", + "getML engine is already running.\n", "\n", "\n", + "Loading pipelines...\n", + "[========================================] 100%\n", + "\n", "\n", - "Connected to project 'robot'\n" + "Connected to project 'robot'\n", + "http://localhost:1709/#/listprojects/robot/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/robot/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -30164,7 +30156,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:4m:2.339313\n", + "Time taken: 0h:3m:13.055531\n", "\n" ] }, @@ -30175,26 +30167,26 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['data_all'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['container-y0rs18'])
url: http://localhost:1709/#/getpipeline/robot/kDSQEy/0/
" + " tags=['container-QG45Mx'])
url: http://localhost:1709/#/getpipeline/robot/xDktkt/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['data_all'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['container-y0rs18'])\n", + " tags=['container-QG45Mx'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/robot/kDSQEy/0/" + "url: http://localhost:1709/#/getpipeline/robot/xDktkt/0/" ] }, "execution_count": 10, @@ -30227,6 +30219,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "Relboost: Building features...\n", "[========================================] 100%\n", "\n", @@ -30308,7 +30303,7 @@ " 0\n", " \n", " \n", - " 2022-03-24 21:30:20\n", + " 2022-07-16 23:54:02\n", " \n", " \n", " \n", @@ -30337,7 +30332,7 @@ " 1\n", " \n", " \n", - " 2022-03-24 21:30:20\n", + " 2022-07-16 23:54:02\n", " \n", " \n", " \n", @@ -30366,7 +30361,7 @@ " 2\n", " \n", " \n", - " 2022-03-24 21:30:20\n", + " 2022-07-16 23:54:02\n", " \n", " \n", " \n", @@ -30395,7 +30390,7 @@ " 3\n", " \n", " \n", - " 2022-03-24 21:30:37\n", + " 2022-07-16 23:54:17\n", " \n", " \n", " \n", @@ -30424,7 +30419,7 @@ " 4\n", " \n", " \n", - " 2022-03-24 21:30:37\n", + " 2022-07-16 23:54:17\n", " \n", " \n", " \n", @@ -30453,7 +30448,7 @@ " 5\n", " \n", " \n", - " 2022-03-24 21:30:37\n", + " 2022-07-16 23:54:17\n", " \n", " \n", " \n", @@ -30483,12 +30478,12 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-24 21:30:20 train f_x 0.4449 0.5852 0.9962\n", - "1 2022-03-24 21:30:20 train f_y 0.5159 0.6813 0.9893\n", - "2 2022-03-24 21:30:20 train f_z 0.275 0.3576 0.9988\n", - "3 2022-03-24 21:30:37 test f_x 0.5681 0.7374 0.9949\n", - "4 2022-03-24 21:30:37 test f_y 0.5654 0.7516 0.9871\n", - "5 2022-03-24 21:30:37 test f_z 0.2957 0.3845 0.9986" + "0 2022-07-16 23:54:02 train f_x 0.4449 0.5852 0.9962\n", + "1 2022-07-16 23:54:02 train f_y 0.5159 0.6813 0.9893\n", + "2 2022-07-16 23:54:02 train f_z 0.275 0.3576 0.9988\n", + "3 2022-07-16 23:54:17 test f_x 0.5681 0.7374 0.9949\n", + "4 2022-07-16 23:54:17 test f_y 0.5654 0.7516 0.9871\n", + "5 2022-07-16 23:54:17 test f_z 0.2957 0.3845 0.9986" ] }, "execution_count": 11, @@ -30520,7 +30515,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -30588,7 +30583,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -30631,7 +30626,7 @@ "outputs": [ { "data": { - "image/png": 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b5GGttc8mSWvtBWNj4E5Vdb4M8w6df3xuJblna+1/97Gs947f7z6+zj8m+bsMp9f8alV9McPcPjcZX/cBSR6X5Jer6myttaN9Q/1rGY5q+duq+v3xtX90fP3HtNb+e+F5185wifIrZbiK1GUzjKNTkvzJ+Ly3ZmgOPXE81epNGd5Uf1eSu2U4SuQVR5kxSf4wQ8PqIVX1TRmaDMcmuWuGK6fdYh9z+ezlvUm+t6oemOSD204J24+HZtjefnu8StqrM0xaf5cx5+P3+sdV9V1JfivJi1prf5UkrbVWVb+b5EFVdafW2pNPJ8NzM8w7dfexMXJSku9Icu8MR/I8cXzdM2usHq05t6MlrbW3VtVjMkw0/pqq+pMM4/YmSW6W5Gmttffu/gp7+nrHDgBnAY40AqALrbWPZrgU+nOT/FKGiZGvk+S3k9x6vFx6xjdW18xwmepfSPL0JPdJ8vok19zHvB63z3D5719M8sokv5nkgUleMD5+mOY2elCGy7r/cpI/SHKxJCdkeJO/6KcyrIfvzXB0yO9lmOj3J1trj9vPglprr81wVMhlM7zZv8x4qfMbZ7ii1AkZJqP+VJLrZli/r0tyvQyNw6PSWntXhnHymiT3z3BJ+8rQjLnPwvP+K8n3J/nLDM2bp47POSnJD2xN2Dxe6eu4DE2S64/fn5hhQuxHJbn2eKWvo835pfH/+AcZanFihvH4niTXa629YNd/vD+/muTkDE2FH9r7qTvm+0SSa2Robt0gQ31+PcN8Q8e31l66278dT0s7MUOz7a7bHn54hkbbI8dTBffKsJlh/quHZDia8M8ybMfPyVD3Tyw8/eseq2fAbNvRTlpr986wP9tM8pgkj03ybRnW+dczN9jXNXYAOGs4srm5ueoMAMCMxiMHHpDhTf9Jq00D68l2BMBh4EgjAAAAACY0jQAAAACY0DQCAAAAYMKcRgAAAABMONIIAAAAgIljVh1gv04++bOH/pCoC13ovPnkJz+/6hhyyCGHHHLIIYcccsghxy56ySKHHHKsX45VOfbY8x/Z7TFHGq2RY445+6ojJJFjOzmWybFMjmVyLJNjmRzL5FgmxzI5lvWSI+knixzL5Fgmx7JecvRI0wgAAACACU0jAAAAACY0jQAAAACY0DQCAAAAYELTCAAAAIAJTSMAAAAAJjSNAAAAAJjQNAIAAABgQtMIAAAAgAlNIwAAAAAmNI0AAAAAmNA0AgAAAGBC0wgAAACACU0jAAAAACY0jQAAAACY0DQCAAAAYELTCAAAAIAJTSMAAAAAJjSNAAAAAJjQNAIAAABgQtMIAAAAgAlNIwAAAAAmNI0AAAAAmNA0AgAAAGDimFUHOIxOuO5bZlvWg1/1PbMtCwAAADjrcKQRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADBxzEG+eFU9JMn1kpw7yZ1ba2/a4TkPS3LN1tpxB5kFAAAAgP07sCONqur4JFdrrV0ryW2SPHqH51wxyXUOKgMAAAAAZ8xBnp52fJIXJklr7R1JLlVV5932nEcm+Y0DzAAAAADAGXCQTaNLJjl54fbJSS6+daOqbpvklUnef4AZAAAAADgDjmxubh7IC1fVE5O8orX2vPH2G5LcsrX2vqq6cJLnJLlhkksnOfH05jQ65ZSvbh5zzNkPJOvcfumKr5ltWX/0rmvPtiwAAABg7RzZ7YGDnAj7I0kutnD72CQfHX++XoYjkV6b5FxJvq2q/qC1du/dXuyTn/z8QeU8Szv55M+e6a957LHnP5DXlUMOOeSQQw455JBDjnXOkfSTRQ455Fi/HKty7LHn3/Wxgzw97cVJbpokVXXVJO9prX0hSVprz22tXam1do0kP5nkX/ZqGAEAAAAwrwM70qi19uaqemtV/UuSU5LcYZzH6NOttRcc1HLZvxOu+5bZlvXgV33PbMsCAAAAvn4HeXpaWmv3TXLfhbvevsNz3pfkuIPMAQAAAMDROcjT0wAAAABYU5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMHHMQb54VT0kyfWSnDvJnVtrb1p47E5Jbp9kM8nbktyltbZ5kHkAAAAA2J8DO9Koqo5PcrXW2rWS3CbJoxceO2+SWya5dmvtmkm+I8k1DyoLAAAAAEfnIE9POz7JC5OktfaOJJcam0VprX2+tXa91tpXxvvOn+R/DjALAAAAAEfhIJtGl0xy8sLtk5NcfPEJVXW/JO9N8uzW2nsOMAsAAAAAR+HI5ubBTCNUVU9M8orW2vPG229IcsvW2vu2Pe+8Sf42yQNaa6/e7fVOOeWrm8ccc/YDyTq3X7ria2Zb1h+969rd5wAAAABW5shuDxzkRNgfSXKxhdvHJvloklTVhZN8V2vtpNba56vq75JcI8muTaNPfvLzBxj1rOvkkz+76ghJDibHsceev4v/nxxyyCGHHHLIIYcccmzpJYsccsixfjlW5dhjz7/rYwd5etqLk9w0Sarqqkne01r7wsJyn1JV5xtvXz1JO8AsAAAAAByFAzvSqLX25qp6a1X9S5JTktyhqm6b5NOttRdU1YOSvLKqTkny1iT/76CyAAAAAHB0DvL0tLTW7pvkvgt3vX3hsacnefpBLh8AAACAM+YgT08DAAAAYE1pGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMDEvptGVXXxqrrm+PMxBxcJAAAAgFXbV9Ooqm6T5DVJnjze9diq+qUDSwUAAADASu33SKM7J/nuJB8fb/9qktsfSCIAAAAAVm6/TaPPtda+sHWjtfbFJF88mEgAAAAArNp+5yb6VFX9XJLzVNVVk/x0ko8dXCwAAAAAVuloTk+7RpJzJfnTJOdJcseDCgUAAADAau2radRa+0SSx7XWrtJau2qSp433AQAAAHAWtN+rpz0iyf0W7rrveB8AAAAAZ0H7PT3t2q21r10trbX2M0l+8GAiAQAAALBq+20abVbVObduVNX5kpz9YCIBAAAAsGr7vXranyR5V1X9a4Zm0fcmecCBpQIAAABgpfbVNGqtPbWqXp6hWZQk92itffDgYgEAAACwSvudCPs8GRpGF05ykSQ/UlW33/tfAQAAALCu9nt62kuTfCXJBxbu20zyZ2d6IgAAAABWbr9No3O21q5zoEkAAAAA6MZ+r572tqo69kCTAAAAANCN/R5pdJkk766qf8twmlqSxNFHAAAAAGdN+20a/X6Sr267b/NMzgIAAABAJ/Z1elpr7RVJ3pzkvePXh5M8/ABzAQAAALBC+2oaVdWvJ/lgkpbkLUn+NclbDy4WAAAAAKu034mwb57kYkne0Fq7cJLbZmggAQAAAHAWtN+m0edba1/OOAdSa+25SW50YKkAAAAAWKn9ToT90ar6xQxXUHtGkrclufTBxQIAAABglfZ7pNFtkrw8yb2SvDtDw+hnDigTAAAAACu23yONHthau9/484OTpKr+OMmdDyQVAAAAACu1Z9Ooqn4yyc2S/HBVXWrhoXMkuc5BBgMAAABgdU7vSKOXJPlYkqsl+YeF+09N8sADygQAAADAiu3ZNGqtfaGqXpfkj1prT5spEwAAAAArdroTYbfWNpP8SFVdYIY8AAAAAHRgvxNhnzPJ+6uqJfnS1p2tNfMaAQAAAJwF7bdp9DsHmgIAAACArpzu6WlJ0lp71fjj1ZJcNcmXF+4DAAAA4CxmX02jqvqdJI9IcvEkl0ryhKq6/0EGAwAAAGB19nt62nFJrtlaOzVJquocSV6d5GEHlAsAAACAFdrXkUZJjmw1jJKktfaVJKfu8XwAAAAA1th+jzR6U1W9KMlLx9s/kuSfDyYSAAAAAKu236bRvZL8dJKrj7efkeSvDiIQAAAAAKu336unnZrkbUlel+Qfk7y1tbZ5kMEAAAAAWJ39Xj3tkUn+JsnNMhxx9HfjFdUAAAAAOAva7+lpP5Tk8uME2KmqcyZ5Y5LfPKhgAAAAAKzOfq+e9vGthtHolCQfPoA8AAAAAHRgv0cafaiq3pjk1UmOJLlukvdU1YOTpLV2wgHlAwAAAGAF9ts0+kCS9y7cftEBZAEAAACgE/tqGjmSCAAAAOBw2VfTqKrul+RXklwgw+lpR5JsttbOeYDZAAAAAFiR/Z6edpsk10jyoQPMAgAAAEAn9ts0eleS97fWvnqQYQAAAADow36bRk9L8raqenOSU7bubK3d/kBSAQAAALBS+20aPTrJ0+P0NAAAAIBDYb9No/9qrT34QJMAAAAA0I39No3eUFUPTPK6LJ+e9oqDCAUAAADAau23aXR8hmbRdbbdr2kEAAAAcBZ0tr0erKrHjj8eSXKObV/7bTgBAAAAsGZOr/HzZ+P33zroIAAAAAD0Y8+mUWvtreP3V80TBwAAAIAe7Hl6GgAAAACHk6YRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE8cc5ItX1UOSXC/JuZPcubX2poXHrpvkYUk2k/xnktu11k49yDwAAAAA7M+BHWlUVccnuVpr7VpJbpPk0due8uQktxgfP0+SGx9UFgAAAACOzkGennZ8khcmSWvtHUkuVVXnXXj86q21D40/fzzJNx5gFgAAAACOwpHNzc0DeeGq+pMkL2mtPW+8/fokt2qtvXfb8y6Z5FVJrtla+9/dXu+UU766ecwxZz+QrHP7pSu+ZrZl/dG7rt19DgAAAGBljuz2wEHOafTlHUIsdaiq6mJJXpTkHns1jJLkk5/8/Jmb7pA4+eTPrjpCkoPJceyx5+/i/yeHHHLIIYcccsghhxxbeskihxxyrF+OVTn22PPv+thBNo0+kuRiizmSfHTrRlV9Y5KXJPnt1tpLDjAHAAAAAEfpIOc0enGSmyZJVV01yXtaa19YePxRSR7XWvvbA8wAAAAAwBlwYEcatdbeXFVvrap/SXJKkjtU1W2TfDrJS5P8QpLvGO9Lkme21p58UHkAAAAA2L+DPD0trbX7Jrnvwl1vX/j5XAe5bAAAAADOuIM8PQ0AAACANaVpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABPHHOSLV9VDklwvybmT3Lm19qaFx86d5I+TXKm1drWDzAEAAADA0TmwI42q6vgkV2utXSvJbZI8ettTfj/JWw5q+QAAAACccQd5etrxSV6YJK21dyS5VFWdd+Hx30jyggNcPgAAAABn0EE2jS6Z5OSF2ycnufjWjdbaZw9w2QAAAAB8HQ5yTqMvb7t9JMnmGX2xC13ovDnmmLN/fYkOoWOPPf+qIyQ5uBxn9f/f0ZJjmRzL5FgmxzI5lsmxTI5lciyTY6qXLHIsk2OZHMt6ydGbg2wafSTJxRZuH5vko2f0xT75yc9/3YEOo5NP7uOArr1ynHDdt8yW48Gv+p4z/TWPPfb8XdRZDjnkkEMOOeSQQ47V6yWLHHLIsX45VmWvhtlBnp724iQ3TZKqumqS97TWvnCAywMAAADgTHJgTaPW2puTvLWq/iXJk5L8SlXdtqp+Mkmq6q+S/OXwY51UVbc6qCwAAAAAHJ2DPD0trbX7Jrnvwl1vX3jsFge5bAAAAADOuIM8PQ0AAACANaVpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMaBoBAAAAMKFpBAAAAMCEphEAAAAAE5pGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMHLPqANCLE677ltmW9eBXfc9sywIAAIAzwpFGAAAAAExoGgEAAAAwoWkEAAAAwISmEQAAAAATmkYAAAAATGgaAQAAADChaQQAAADAhKYRAAAAABOaRgAAAABMHLPqAMCyE677ltmW9eBXfc9sywIAAGC9ONIIAAAAgAlNIwAAAAAmNI0AAAAAmNA0AgAAAGDCRNjAjkzIDQAAcLg50ggAAACACU0jAAAAACY0jQAAAACY0DQCAAAAYELTCAAAAIAJTSMAAAAAJjSNAAAAAJjQNAIAAABg4piDfPGqekiS6yU5d5I7t9betPDYNZM8anzs+a21hx5kFgAAAAD278CaRlV1fJKrtdauVVVXTvKHSa6z8JSnJfmhJB9K8vqqelZr7b8OKg+wnk647ltmW9aDX/U9sy0LAACgdwd5pNHxSV6YJK21d1TVparqvK21z1fVtyb5RGvtv5Okql6U5EeS/NEB5gE4w3ppXs2ZI9k9ixwAAHDWd2Rzc/NAXriq/iTJS1przxtvvz7JrVpr762qayW5X2vtx8bH7pzkUq21BxxIGAAAAACOykFOhP3lbbePJNncx2MAAAAArNhBNo0+kuRiC7ePTfLRXR67RJIPH2AWAAAAAI7CQTaNXpzkpklSVVdN8p7W2heSpLX2wSTnqKpvrqqzJ/nR8fkAAAAAdODA5jRKkqr6vSTXT3JKkjsk+d4kn26tvaCqrpPksRlOS/vz1tqjDywIAAAAAEflQJtGAAAAAKyngzw9DQAAAIA1pWkEAAAAwISm0ZqoquutOgOcnqqyTwGAmVTVMavOkKwuR1Udqaojq1g2+1NVF1x1BuDrY06jDlXVL2y760iS30rykCRprT199lCjqnpla+34mZf5pCRPaa3985zL3SHHZ5I8I8lDW2sfWWGOCya5c5KTk5yY5O4ZJpl/d5LHttY+O1OOGyZ5TJKPJfm1JE9I8k1JPpPkzq21V82U42xJfjrJjyS5eJIvJflAkue11l4zR4bTU1UPb63db6ZlXTXJ72ZYLw9I8qgkV0vyH0nu1Vp7x0w5LpHkV5OcP8mzFsdDVT2htXb3mXLcqLX24vHnC2fYj14xyTuSPLC19r8z5bhAkuu01v5m3IZ/K8nlk7QkD2utfXymHL3sT8+R5GZJTm6tvaKqfizDOH13kme21k6dKYf9x3R518i0Hi9orb1/xgwXTHLvJDcYc2wm+XCSv03yuNba52bKcY4ktx5zXGJbjmfPOE6vneTRGfanz0zyu621U8bHXtFam+WDxY5yfEuSByc5Lqd9AP6VJC9P8qDW2ofnyLGQ5xJJPtta+1xVXS7D32Sttfb2GTNcNsn9Mvzuf2SSB+a0vw0f0Fr70Ew5vnnbXUeSPD/JTyQ50lr7wEw5uvidu0u2+7TWHjnzMv89yVOSPL619sU5l70tx7ckuUFr7cnjWHl0Tlsv922t/edMOa7SWvvX8edzJfnlJFfK8LfhE1prX5ojxzrp4tMJJk5I8r8Z/ijZ+vTk3EkuN2eIqjo1yUcy/NG4leOSVfXeJJuttW+dKco1kxxTVb+RoSly0kzL3e7NSZ6V5BlV9cEkz07yD621L8+c48+TvC7JFZL8Y5LXJPmLJN+T5OlJfnKmHCckuV6SiyT5+yTXb629raq+KclzklxrphxPSvKpJH+V4WqN/5dhp//rVfWjrbX7zhGiqs67x8PXnCPD6DEZ/nC8SJJXZ2gw3jLJVTI09o6bKcefJ3leko8nedDYcH7Q+NgVZ8qQDA3NF48/PzHJm5I8PskPJvmzJDedKcfzkvzlQo5/y9DUu0aGWt1wphy97E+flmFbvWBV/VKSryZ5RZIfSPLDSW47Uw77j+UcD02ykeSVSb4ryfuTnDPJ86rq6a21x80U5dlJXpjk5hneBB/J0LS52fjYj86U4y+SvC/DNrs9x42SbP+Q76D8/risjye5V5IXVdWPj39/zHmUTS85/iLJw5LcYaFpdY4kP5ahmXXcXEGq6oQkP5fkK1X14CT3T/LaJPepqv/XWvvdmaI8Zfy6cJJ/znCF6idl+N3/Zxkan3N49/i1tb0kybdn+Pt0M8PfjXPo4nduVf3ZtruOJLlxVV0xSVprt58jR5KPZnhPd1JVvSRD0/vfZlr2omdm+GAzGf4mfU6Gv9GumWH8XnemHI/KaWPxsRmazs/O8LfhUzJs0yzQNOrTlZP8doY/2H6ltfaBqrrhwhuuudwow5vPx7fWnp8kVfX61tqcb4CT5BOttV+sqo0k9xw/KX9thj/sP9xae85MOTZba69N8sNV9X1J7pjkMVX1hTHHjWfKcb6tP0Kq6h2ttV8f739ZVb1ipgxJ8qXx07wPV9UnW2tvS5LW2oeq6isz5viOhaPfXlxVf99aOyHJc6pqzqMpPpVk+yd5mxn+QLj4jDlOaa29Lkmq6mGttb8b739jVc15aOkxrbU/GnM8L0Oz9cHjulnVqQSXbK09avz536vqVjMu+3yttT8df750a+3W489vrqqbz5ijl/3ppVprx42nlfx7a622Hqiqk2bKkNh/bHft1tp1k68dlfbi1tqvVtWjk/xTkrmaRt/QWvvDbfe9P8kfzLzdXrq19tPb7mtJXlVVb5wxx5cX3uD9ZlXdLUPD5qYZxslhy5HW2t9uu/2VJM+vqvvPmSPJTTIcKXHhDPvRK7TWPllVZ8/wwd5cTaMjrbVnJklV3Wlh+/mPqrrzTBmS4eim389w1NdjWmunju8dZj1LIf38zj1Phg/9H5rksxn26d+f4YOTOX21tfbnVfWXGT5cfmxVXTrDUegfbq3ddaYcxyxsu9/QWvvz8ecXVdWvzJQhWf479IqtteuMP7+kql45Y461oWnUofGwwd+sqkryxKp6VVbwJqu19tLxj/ffGP9I+5XM/EfBaHPM8x9J7lZV58lwFMvVk1w7Q5d6Dl9bB+OpHf+cJOORNZecKUOSnLOqviPDp53HVtU1W2uvH+8794w5PllVv5vkYkn+u6qenOHTgu/L8CnkbKrq+hmOBLtRki+P9904ySynDozuk+RirbXf2iHfnL+AvlJVd8qwXk4djyjZWi9zHm77par6qQyntZxaVT+f5Knjp27nnzHHRcexkCRfqKqrttb+paq+M8n5Zszx7vET6adnaE7cNEOz5oYZTneZSy/703NV1TckOTbJRarqW1tr76mqC2X4I3s29h9LjqmqjXF8/ECSc433XyHz1uNDVfVrGY42Onm872IZ3uzMeXr4p8c3mH+zdbpCVZ07w6k2n58xx7ur6glJ7tNa+2Jr7YlV9cUM+5ALHMIc76iqJ2bn8fHOGXMkw5vxU5N8vKqe3Vr75Hj/nNtLkpytqrZOK/3GqvrZJC/J8Lt/tvkmx1Pgb1RVt0vy0qr6zazmvUMXv3Nbaz87/o75jQynPT2vqj7dZprCYcGRMc8pGY6s/avx9/9VM+97mNeMfwc+Pcmrq+peGc6YuGGSd82Y47xVdYUMdfl4VX17a+0/q+rimfdv1LWhadSx1lpL8mPjm633rSjDl5I8YGxIPD7JRVcQY6kB0Vr7QobTof5+5hzP2OnO8TzxWc4VH/1GhsM7T85waOXjxjfAH0ky1ycFSfLzGU4heWtr7dnjOL1+kv9KcpsZc9wpw/n735Hk7Ul+abz/+8aMs2itPa6qfr6qzrfDfBsvnytHhnXyKxnGx/dnOD3rd5L8Z+ZdL7fLMH/Qi5N8fvyj+jZVdesktee/PHO9Ocktxp//J6e9sfmtDG/U53K7DOPxcUkunaHB+5EkL03yizPm6GV/+qgMR2x8IsnPJPnr8Ui4C2WYW2Au2/cfdxnvP6z7j1/NcJTVpZK8N0N9kuSeSe4xY47bJvn1JH+S4Q3NlzJsLy9O8rMz5viFDEdNPHJscn4lw7x9L5k5xx0zzK10ytYdrbWnjA3FOxzCHHfNsG5unWF8fDmnjY+/njFHMhyZ8OzW2s+01u6ZJFV1tQynRD13xhx3yzCP0ckZPgT43QxnLfxnTvu7aDattadW1QuTPDxDQ29uvfzOTWvt5eMBAPcfP4Sf9YOR0eT3yPj7/x/nDNFau09VHZfhFN/LZPhg4juTvCzDKadz+UKSxaNZr5RhW3lWhr+Z2cZE2Gumqs61qsm5xtMILt1a++9VLH9blouuchK77TpYLxdtrZ18uk+eyYrrcY4kl0ryoa25DlZlVeO0hnPlvzHJmxZrUMMcLS+aO8/C8rvYbnvIMW63F1l1jkVVdenW2gdXtOwjGY46+nibaXLh07PKeozLX/k4XZWqukZr7Q2rzrHdKn/fVtW5xyPRU1VXynD0V2szTrY8LrvL3y8LOY5rM8/VVlWXa629d+H2pZNcaM5109M2Mx6t8bnW2v9V1cUyTLvxn22mSbBPT1VdsLX2qRUt+9uT3KS19tiZl3uktba5cPtqGSd+bq29ecYc3YxTjo6m0Zqpqme11mb5dKuGq9k8Msl/Z/jk99kZDuM7f5JfauMViWbI8aMZPpVezHG2JN8wZ469HMb1speZ6/HYhU/3fijDBHb/k+GTrV9qrb10phw7rZfZx2lVPSzDJKCfyPDp2s+3cb6pmvfqNjdK8hOttTtX1fEZrvT3mQz1uPv2+SgOMEcX+49ettvxlMHHJDlHkr/LcEW9z4yPzTk+PpPh8PSHttb+Z45l7pLj5kn+IKuvx42T3LSD7WXrKqEPWfF6+WiGiWsf31p73gpz9LLd/laGeTduVVX3zHAUxWsyXHHw71prD5kpRxe/X/bSS44kqaqrt9Zmmfuqo23mvhnG56kZjuS4c4ajOK+Y5M/afJPp76qXMTLz+Pja/7mGuYN+JsMFD66ZYe66h8+Uo5dx2sXvunXi9LQ1M9cb8dFvZzjd6DIZ/pi+WWvtX8dPEF6Y065IdNBO6CTHrg7petnVzPX4roWfH5DkuNba+6rqohnqMUvTKP2sl+PaOFn9eNriM6vqNq21f8m8c6M9JKdd4eiBSX5oPF/8YhmuDDnLm+D0s//oZXzcN8M28+kMh+i/vKpuMH7qOuf4eHOGK9v8ea32apS/nj7q8eD0sb1sXSV01evlXRmubHifqrpfhivr/U2GT8U/NWOOXrbbH2+tff/48y2SXLO19oU6bbLlWZpG6eT3S1XtNvfakQxHT/TiuCRzTZjeyzZz0wxHwZ0nw3QF1Vr7zDhWX5OZJtOvqt2maziS5JvmyLAPx2W+8bG4ff5kkuu21r44rpdXZziNcA69jNNeftetDU2jDlXV72ePSePaaVfLOmifHw8l/UBVfbS19q/j8j9aVXNuVF3ksF6WdVSPxQyfba29b1z+x2veq4V1sV6SHKmq87TWvtBae3tV3SzDfDF3zfyTUf7v+P1zrbX/TJLW2seq6v9mzNDLeuklxynttIlanzx+6vfy8UiXOcdHL1ej7KUeSR/bSy/rZXM84uuEqvqdJD+VYb6pq4+nlhw7U45etttTq+rbWmv/leGop3NkmJPjGzLvhNy9/H45f4YGxPb5WI4k+bYZc+yptfZ7My6ul20mrbXNqvpqhqONvjze99Xx9M65/EqGufp2mjj/HDPm2NXc42Ph5w9lqMEXx/vPOWeOTsZpL7/r1oamUZ/eseoAo49W1X1aa49srV0jSarqMknuleGPlsOWw3pZ1ks9rjx+6ngkyWWr6pattb+sqgck+diMOXpZL4/McGWZ72mtfba19u6qukGG0/a+/3T+7Znp0UleV1V/m+R9VfXcJK/PMHn7X8yYo5f10kuOk6rqRUl+urX2+dbaC2u4+tErk1xwxhy9XI2yl3r0sr30sl4Wc3wpw8Ufnjnj8rf0st3eMcmTxqMCvpDkLVX1zgxzC/3ajDl6+f3ys0melOSxbdvE8VX16RlzLC73thkmi79AhvF7JMMb02+dKUIv28wrq+r1GY40+qMMV8h6fYardL1sxhw/keGopnu2bXNs1jAR86w6GB/XrqqPjcs9d4Zt6MlJnp95z1DoZZz28rtubWga9amXCY1vm+THt913sQxXcrv/IcxhvSzrpR632Hb73eP3f8/wB+5cbpsO1ktr7blV9TeLfyS1YTLfG9RwFcS5cjyzhiun3CDDqR3vyzBm7tham/MS87dNB+ullxyttd+oqmtn+IRx676Xjn/U/8xcOdLJ1Sh7qUdH20sX6yXDVdx6cNv0sd2+Pcn1q+oiGeYSOleS/2kzTyzc0e+XTyW55S4P/9BcOba5X4ZGxaouFtPFNtNa+80aJmr/VGvtQ1X1zAxzbz29zTjhcmvtHTXMafiVHR5eRa1WOj5aa7sdXfWr4xGMc+linKaf33Vrw0TYHaqqp+7x8GZr7fazhdlFrfDqWKvKYb1MlqUe65djtkkXTydHL/WQQw451i+H/dhyjl7qcahzVNULW2s3nXu5+3HY180OOWbfdo2PtcrRxb69N4406lBr7XY73V/DpcQfP3Oc3ZyY4dDGVTsxM+WwXpapx1E5MX3kOC7zTbq4lxPTRz1OjByLTowci06MHItOTB85jov92KLj0kc9jsvhzvGx8QjF1yc5ZevONt/8jns5Lod73Wx3Yubfdo2P03dc+shxYvrYt3fFkUYdq6rbZ7iiyrEZJpI7W5K/bq3deqXBDjnrZZl6AACHWVVtXWZ+SWvtaSuIQ2eMD9adI436dpck355hgrLrZZhh/jJzLbw6uTpWLzkWWC/L1KOjHFtWPeliL/WQQw451i/HFvuxZauuhxy7a609dZxg+XsyNAfe1Fp73dw5eqnJqnP0tu0aH33l6G18rANNo759qbX2xao6JsmRcQLCv0/yBzMtv5erY/WSY4v1skw9Br3k2LLqSTl7qYccy+RYJseyXnJssR9btup6yLGLqvqDJN+W5DUZ3gD/dlW9ubX2WzNH6aUmq87R1bZrfHSXo6vxsQ40jfr2hqq6d5JXJXlVVb0/yflmXH4vV8fqJccW62WZegx6ybGltdb+fYXL76UeciyTY5kcy3rJscV+bNmq67FFjqnvba1dZ+vGeCTDa1aQo5earDpHb9uu8bFs1Tl6Gx/d0zTqWGvt16rqnK21L4+HNF44ycuT2WaY334p80WbSf7ugJffW44k1st26tFdji2rnnSxl3rIIYcc65dji/3YslXXQ47dnbOqztta+/x4+3xZzfusXmqy6hy9bbvGR185ehsf3dM06lxr7cvj95O2PfSwDPPHHOSyu7g6Vi85Flkvy9SjnxwLXpfktStYbpJ+6iGHHHKsX44F9mPLVlqPBXJMPTrJ26rq3zNcEOQ7ktx/BTl6qYltd5nxscz4WDOaRuvryFwL2u3qWHMtv7ccp+PQrZfTcejq0UuOjiZd7KIecsghx/rlsB9b1ks95Ngxy3Oq6m+TbGQ4UuE/Fo4qmTNHFzXpJUdH267x0WGOXsbHOjjbqgNwhu064/sB2Lo61uuSfGOS2yR504zL7y3HXg7jetnLYaxHFznGSRd/Jck5kpw7w6SLD507RzqphxxyyLF+OezHlvVSDzmWMjxg/P5XSZ6a4eiR30hyYlU9Z84sY46V16SnHFnxtmt89J0jnezb14GmEfvxpdbaFzMcmXaktfbcJDc5xDl6oR7LeqlHLzm+t7X2462132+tPSLJjZMct4IcvdRDDjnkWL8c9mPLeqmHHKf56/H7E5I8cdvXE2bOkvRRk55yrHrb/evxu/HRZ45Vj4+14fS09TXbaT9Z/dWxesuxl8O4XvZyGOvRS45eJl3spR5yyCHH+uWwH1vWSz3kGLXW3jr++MuttZsvPlZV/5jkWnPmSQc16SzHSrdd46P7HL3s27unabRmqur+rbWHJXnmXMtsq786Vlc5dnKY18tODnM9esmRTiZd7KUecsghx/rliP3Ydl3UQ47TVNVPJblfku+uqo/ltA/LzpbkX+bMMlp5TXrKsept1/joO8eqx8c6ObK5OeeUIxyNqrpxhsm5Ljzedc4k72ut/eDqUi2rqle01g706li95bBeJstSj05zVNX5suJJF/dyWNeLHHLIcVTLsx9bXl4X9ZBjkuM+rbVHbrvvyq21d6wgSy816SLHbmb+W9n46DTHbnr5ndsLRxr17QFJfirJM5LcPMlPJ/nEShNNzXn60V7mzGG9LFOP/TvwHFX1gNbag2qYdHFz22Nprf30QWc4CodmveyTHMvkWHZoctiPLeulHnLs6SlVdbckFxlvnzPJrZNcbo6F91KTXnLs05z7VOOjoxz71Mvv3C5oGvXtC621948b0ceSPKGqXpYZT/nZh14OVZszh/WyTD32b44cfz1+32mCxV7qsKWXPHIsk2OZHMvsx5YdpnrIsbvnJHltklsleUqSH05yjxmX/9fj91XXpJcc+zFnHuOjrxz70VueldI06tsHquoXk7yjqp6V5L+SXGzFmbBetlOPjnQ46SLAUbEfW9ZLPeTY09nGIyiOa609oqoel+RZSf5mjoX3UpNecnTI+OgoB0dP06hvt8swT8zTM3SmL5zkx1aaaKqXQ/fmzGG9LFOP/ZvjNIbeJl3cy6FZL/skxzI5lh2aHPZjy3qphxx7OldVfX+SL1XVDTJ8gPbtcy28l5r0kmOf5tynGh8d5dinXn7n9mFzc9NXp18bGxvP3eG+f1x1rjHH/cfvdzxsOawX9ViHHBsbG/fZ4b4rr3qdHPb1IoccchzVMu3HOqyHHDtmufLGxsb1NjY2vntjY+OkjY2Nt25sbNzhsNaklxy7ZFvFtmt8dJijl/GxDl+untahxS5skk9l6HRuZujC/mtr7foz5+ni6lirzmG9TJavHn3nuFCGI7+WJl1src0y6eJCjl7qIYcccqxfDvux5Ry91EOO0zKcd5eHjiTZnPuKUD3UpLMcq/5b2fjoO0cX+/Z14PS0DrXWnpfkebtdnnEFkXq5OtZKc1gvy9Sj+xyrnnRxSy/1kEMOOdYvh/3Ysl7qIcdp3pnhA7PFU1m2bm8m+daZ8/RQk55yrHrbNT76zrHq8bE+Vn2ok6/dvzY2Ni60sbFxt42NjRPGr4dubGy8dwU5Thq/v3rhvpcd4hzWi3qsQ45/GL+/cvx+7o2NjRcc4nrIIYcc65fDfqzPesjR6VcvNekox0nj95Vuu718dbReeslhfOzzy5FGfeulC9vL1bF6yWG9LFOPPnOsdNLFBb3UQw455Fi/HPZjy3qphxzbVNUrM71E99laa8fNHKWXmvSSo4tt1/joNkcX42MdaBr1baWXZ1zQy9WxeslhvSxTjz5z3CXDL777JnlskgslecwKcvRSDznkkGP9ctiPLeulHnJM3X3h52OSfG+Sb15Bjl5q0kuOXrZd46PPHL2Mj+5pGvWtly7ss1trNx9/PjFJquofk1zrkOawXpapR0c5FiZdfM/4lSQ3zmnnz8/NepFDDjmOiv3Ysl7qIcfuWmvv3HbXW6vqqXMtv5ea9JJjQRf7VOOjrxwLuhgf60DTqG8r7cLWwtWxqupj2XZ1rMOWY4H1skw9OsqRTiZd7KUecsghx/rliP3Ydl3UQ47dVdVdt9110SRXmjFCLzXpIkdH2+5WHuOjoxy9jY91cGRzcyUNefZQVXsdrni21tr75sqSJLXL1bFaa+84TDmsl8ny1aPjHL3opR5yyCHH+uXohXpweqrqAQs3N5N8OskLWmsfWFEk0s+2a3z0qZfxsQ4cadSn52XYoZwzyeWT/GeGzue3JfmXJNecOc9TqupuSS4y3j5nklsnudwhy2G9LFOPjnN0NOliF/WQQw451i+H/diyXuohx45+J8kNk1SSU5O0JP89d4heatJLjnSy7cb46DJH+hkf3dM06lBr7fuSpKqekeTGrbUPj7cvk+QhK4jUy9WxVprDelmmHt3n6GXSxV7qIYcccqxfDvuxZb3UQ46pZyf5SpI3jVlun2GS3Z+ZOUcvNeklRy/brvHRZ45exkf/Njc3fXX6tbGx8cYd7nvdCnL8w/j9leP3c29sbLzgEOewXtSj+xy7ZHvqYa2HHHLIsX45dsn2VPVYbT3k2HG5/7jDfbP/LdRTTXrI0cu2a3z0maOX8bEOX4406tsbq+oNSf45wyF8V03y1hXkOFf1cXWsXnJYL8vUo8McHUy6uKWLesghhxzrl8N+bFkv9ZBjR2+qqqu01v41SarqKknePHeIXmrSS450su3G+OgyR/oZH93TNOpYa+0eVXWFJFfMMKv7n7TW3r6CKCu9OlZvOayXZerRbY5jF37emnTx5rs89yD1Ug855JBj/XLYjy3rpR5yTN0iyS9X1ecy/C103iT/W1W3TLLZWrvYTDl6qUkvOXrZdo2PPnP0Mj66p2nUudbavyX5t1Usu067OtZnxq8k+YXx+9kOW45F1ssy9egnx4KVTrrYSz3kkEOO9cuxwH5sWReT6cqxo0u31k5d0bIX9VIT2+4y46OjHB2Oj+5pGrGXXq6O1UuOXqjHsl7q0UuOLauedLGXesghhxzrl2OL/diyVddDjt19Z1U9Lsl3ZBgz70xyj9bav8+co5earDpHb9uu8dFXjt7GR/c0jdhVL1fH6iVHL9RjWS/16CXHgku01q61eEdVvW6uhfdSDznkkGP9ciywH1u20nrIsafHJ7lXa+1fxhzXTPJHSY6fOUcvNbHtLjM+OsrR4fjonqZRh6rqn07nKUeSnNpau/oceZJsbG1MSdJa+++q2php2d3ksF6WqUf3ObqYdDH91EMOOeRYvxz2Y8t6qYccU6duNQSSpLX2+qo6soIcvdSklxy9bLvGR585ehkf3dM06tNXk9xyj8ePJHnWTFmSfq6Oteoc1ssy9eg7Ry+TLvZSDznkkGP9ctiPLeulHnJM/W9V3TfJP4xZfjjJ/864/C291KSXHL1su8ZHnzl6GR/dO7K5ubnqDGxTVVdd7EZve+xqrbU37fWcA8q0eHWstqKrY600h/UyWbZ6dJyjqs7WyaSLXdRDDjnkWL8c9mOTDF3UQ46pqvrGJPdM8r0Z3nz+c5IntNY+s+c/PPNzdFGTXnIk3Wy7xkeHOZI+xsc60DRaM1X1itba9Vadg2XWyzL1WL2q+u4kPUy6CHCG2I8t66UecuyY5exJbpThalCbSd6V5KWttVnfaPVSk15y9ML46DMH++eScutnFee/cvqsl2XqsXqPT3Lv1tqlWmvflOQBGSZdBFgX9mPLeqmHHFPPSfJzGf7+OXuGq0H95Qpy9FKTXnL0wvjoMwf7ZE6j9ePQsD5ZL8vUY/V6mXQR4IyyH1vWSz3kmOrlqlS91KSXHL0wPvrMwT5pGnWoqrYm49ruSJJDd3WsjnJYL8s51KPDHAtWOuliL/WQQw451i/HAvuxZb1MpivHVC9Xg+qlJrbdZcZHRzk6HB/d0zTq0833eGzOIzh6uTpWLzmsl2Xq0WeOLbfLMOnib+W0SRdvN+Pye6mHHHLIsX45ttiPLVt1PeTY3dbVoP4vw/Qfq7oaVC81WXWO3rZd46OvHL2Nj+5pGnWotfb+7fdV1eWT3CrDTucKM0W5205ZxjxbV8e622HJYb0sU49uc2z5XJJ/TfL5nDbp4mdnXH4v9ZBDDjnWL8cW+7Flq66HHLu7dCdXg+qlJqvO0du2a3z0laO38dE9V0/rWFV9S4Yu6K0yzC7/u0me3lr7wEqDpZ+rY60ih/UyWaZ6dJijqp6X5CtJ3pThE5Pvy/Bp1s/MmWMnh3m9yCGHHEe1TPux5WV2UQ85+tVLTXrJsZNe9qmr0Mt66SXHTg7z+NiLI406VFX3yPAm/FIZZtu/XZKntNYeutJgy3qZrGy2HNbLMvU4KqvI0cukizs5zOtlJ3Isk2PZYc5hP7asl3rI0a9eatJLjp30sk9dhV7WSy85dnKYx8euzrbqAOzoQUm+McmvJfntcXb53g4J6yXPnDmsl2XqsX+ryPGmcaLFJCuddHEnh3m97ESOZXIsO8w57MeW9VIPOfrVS016ybGTXvapq9DLeuklx04O8/jYldPTOlRV50pykwyn+1w7yUuT/ECS72itzbbCTu/qWK21CxyyHNbLcg716DDHQp4PJ7lEkqVJF8eHD3zSxV7qIYcccqxfjoU89mPLeVZaDzl2zPCx03nK1lWYLn7QWcY8K69JDzl62XaNjz5z9DI+1onT0zrUWvtSkucneX5VnT/JT2XYsD5QVc9qrf36TFF6uTpWFzmsl2XqMdFLji2rnnSxl3rIsUyOZXIs6yXHFvuxZauuxxY5TvPO1trxez2hql45V5j0UZNk9Tl62XaNj52tOkcv42NtONJojVTVJZLcorX2+BVm+NrVsVprc10dq9scYxbrZTmLenSWoxe91EMOOeRYvxy9UA8WVdUlW2sfOZ3nXKq19uG5MrGzVWy7xsf6sG/fmyONOlRV50tyrwxXonpTkj8cu7GbSa6eZNY347tcHesGc2boIYf1Mlm+enScoxe91EMOOeRYvxy9UA/28Myq2szy5LmbSc6dYbLfb9UQWJ0Otl3jo2MdjI+14UijDlXVXyV5R5I3JrlZkk8n+WCSuyf5vdban86UY/vVsf4yw9WxrrLnPzzr5rBelnOoR4c5etFLPeSQQ471y9EL9eBoVdXZktwmyb2TPL219sgVRzqUet12jY8+9Do+euZIoz5dorV2iySpqpcm+Z8kT0vyva21z8yY40FJPpTh6lj/r7X2pbFbPrdeclgvy9SjwxwdTbrYRT3kkEOO9cthP7asl3rIsbequkmSByR5ZZLrtNY+NeOyu6hJLznSyba7yPjoJ0c6HB+90zTq0ylbP7TWNqvqXTNOKrzoEjnt6lhPGBsD31hVR9qMV8fqKIf1skw9+szRy6SLvdRDDjnkWL8c9mPLeqmHHDsv6/uTPDzJ+5LcrLX2wbmWvaCXmvSSo5dt1/joM0c342NdnG3VAdjR9sG6ksHbWvtSa+35rbWbJ/n2JK9I8p4MV8d6xGHLEetlO/XoMEeGX4Cn59YHHaKXesghhxzrlyP2Y9t1UY/IMVFVz03yx0keleSBSc5WVd+89TVHhlEvNekiRy/brvEx0UWOXsbHOjGnUYeq6itJPjnePJLkAkk+Nf682Vq72IqiJenj6liryGG9TJanHh3mGD+h2XPSxTly7Oawrhc55JDjqJZnP7a8vC7qIceOWZ66x8ObrbXbz5Sji5r0kmM3K9h2jY8Oc+yml9+5PXJ6Wodaa+dYdYakn6tj9ZLDelmmHn3m2H7Yby1PuviHc2QYl9tFPeSQQ471y2E/tqyXesixY5bbLeRY2aktvdSklxwdbbvGR4c5ehkf68TpaR2qqltuu/0tCz//1oxRTszQWPzLJN+V5Peq6p5JXpvkpMOWw3pZph7d5viaGiZdfEOSy2eYdHHOq3ScmD7qIYcccqxfjq+xH1u24nrIsXOGO1fVO5P8T1WdXFVvrarbne4/PLg8K69JBzlOTCfbrvHRZY4T08n4WBeONOrTnTIM4i1PTXK98efrJXnoTDl6uTpWLzmsl2Xq0WeOXiZd7KUecsghx/rlsB/bppN6yDHNca8kP5jkhq21/x7vu2ySR1bVeVtrT5wxSy816SFHF9uu8dFtji7GxzrRNOrTkT1ub3/sIPVydaxeclgvy9Sjwxw1TLr4bUl+K8nbM066uJDtAzNF6aIecsghx/rlsB9b1ks95NjRrZL8QGttcay8r6puleQ1SWZpCvRSk15ypJNtN8ZHlznSz/hYG5pGfdrrqlRzngvbxdWxdlhujzmsF/XoJcdnk7wlyc3Hr0WbSWaZdDH91EOOvZcrx9635yLHMvuxZb3UQ46pryw2BLa01r5cwwVD5tJLTXrJ0cu2a3z0maOX8bE2XD2tQ1X1xiS3zWlHazx1vH22JH/WWrv6TDm6uDpWRzmsl+Uc6tFhjm2ZVjbpYi/1kEMOOdYvx7ZMh34/ti3Tyuohx47L/8ckP7P9FJuq+rYkT22tXWcFmQ79uull2zU++szRy/hYJ5pGHaqqk7JHx7Ntm3meeVgvy9SjX1V15yT3SHLRDE28Dyd5TGvtqSsNBrBP9mPLeqmHHJMcP5zkcUkek+StSc6e5PuS3C3JHVprr5kxSy816SJHD4yPfnNwdFw9rUOtteNaa8fv9jVXjurk6li95LBelqlHtznuleT6GSZdvHhr7dgkN01yk6q624w5eqmHHHLIsX457hX7scXl3it91EOObVprf5/kRkkul+R+Se6f5OJjtjkbAvdKBzXpKEcX267x0W2OLsbHOjGnUYeq6hF7PT7jRF29XB2rixzWyzL16DZHF5Mupp96yCGHHOuXw35sWS/1kGMHrbX3Z2gGrFIvNeklRy/brvHRZ45uxse60DTq0zsXfr5vkt9bUY5ero7VSw7rZZl69Jmjl0kXe6mHHHLIsX457MeW9VIPObapqn/Ozqfqb82N8v0zRemlJr3k6GLbNT66zdHF+FgnmkYdaq09bevnqrrt4u2Z7TWz/JyTYXWRw3pZph7d5khVXXqXSRdPnTFGL/WQQw451i+H/dg2ndRDjqntV4BamV5q0kmOXrZd46PPHL2Mj7WhadS/VQ7c81XVFXJax3Xr9tmSnO8Q5lhkvSxTj35yPCDJy6rqMdlh0sUZc/RSDznkkGP9ctiPLeulHnJM/Vpr7e4zL3MnvdSklxy9bLvGR585ehkfa0PTiL18IckfLtz+/MLtzx/CHL1Qj2W91KOLHK21v6+qGyW5S4bJF49J8vYMkw6+Z64c6aQecsghx/rlsB9b1ks95NjRFWde3o56qUkvOdLJthvjo8sc6Wd8rI0jm5uOwOpNVZ2c4ciNI0kukORT40Nb579ebEXRDjXrZZl6AACHWVX9R4bLqe+otfaHuz3GWZ/xwVmFI406NF5+cOV6uTpWLzmsl8ly1KPPHF1MuthRPeSQQ471y2E/tpyjl3rIMXWOJBfNiifP7aUmHeXoYtuN8dFrjl7Gx9rQNOpQVR3JcEnCb0/yz621v1tRlF6ujtVFDutlmXp0m6OXSRd7qYcccsixfjnsx5b1Ug85pt7XWnvwqkOkn5r0kqOXbdf4WNZLjl7Gx/rY3Nz01dnXxsbGH29sbDxlY2PjzhsbG3+9sbFxvw4yvXLVGVadw3pRj3XIsbGx8YRV//97qocccsixfjnsx/qshxw7ZvnzXe6/3MbGxv0PW016ybEt0ytXuGzjo8Mc2zK9ctUZ1uHLkUZ9unJr7VpJUlVPSfIPSR6+2kjdXH5wlTmsl2XqsbtV5uhi0sVtrJdlciyTY5kc9mPb9VIPObZprf3c1s9VdckkP5PklkkunORpM0bppSa95Fi0sm3X+JjoJceiXn7ndk3TqE9f2vqhtXZKVX11lWH4GutlmXr06dJVddfdHjTpIrAG7MeW9VIPObapqgtnOOVm63T95yW5YGttY64Mo15q0kuOLhgf3ebgKGka9el8VXXFbbevkHEStdbau+YIsf3qWFX1sfGhWa+O1UuOWC/bqUeHOdLPpItd1EMOOeRYvxyxH9uui3rIsaP/SfIfSe6T5GWttVOr6l9XkKOXmnSRo6Nt1/joMEdH42NtHNncdERWb6rqpOx+qNxma+16M8ZhZL0sU48+VdUrW2vHrzoHwBllP7asl3rIMVVVP5vkZ5N8X5K/SfKXSR7VWrvKzDm6qEkvOXphfPSZg6PnSKMOtdaOW3WGpJ+rY/WSw3pZph595kjyoZ3urKrLJblla+1hc4TopR5yyCHH+uWI/dh2XdRDjqnW2rOSPKuqLpTkFklOSHL5qvr9JE+d66jr9FOTLnL0su0aH33m6GV8rBNHGnVor3M9k/nO96yqP87QWPynJDdK8obW2uwTHXeUw3pZzqEeHebYlmky6WJr7XdmWnYX9ZBDDjnWL8e2TId+P7Yt08rqIce+M31ThqNLbtlau9oKlt9FTWy7OzM+Vp+j5/HRK0ca9enYVQcY9XJ1rF5yWC/L1KPDHB1NuthFPeSQQ471y2E/tqyXesixP621DyV5ZFWdZ65l9lKTXnKkk213J8bH6nOk4/HRK02jDrXWHrTbY1V1zhmj9HJ1rC5yWC/L1KPbHL1MuthLPeSQQ471y2E/tqyXeshxdI5P8pCZltVLTXrJ0cu2uxfjw/hYG5pGHaqqyyd5QoYO7JuS/FJr7eSqunGSRyW5wkxRurg6Vi85rJdl6tFtjttkOOz5qUn+pqr+cqblbtdLPeSQQ471y2E/tqyXesjRr15q0kuOXrbdXvSyXnrJYXwcJXMadaiGq1I9MMkbctohfF9Kcu4kv9Ja+7cZc6z86lid5XhgrJfFHA+MenSVY0udNunirZJcPUODb7ZJF3uphxxyyLF+ObbYjy1bdT3k2DHDFfd4+KmttavPlSXpoyY95Ohl2zU++szRy/hYJ5pGHaqqk9rClamqqiW5dzOz+0pZL8vUY33UiiddBPh62Y8t66Uehz1HVb1yr8fbCi8vftjXTQ+Mj/XJwd6cntanU7fd/vAq3ohXP1fH6iJHrJft1KPDHLssexWTLnZRDznkkGP9cuyy7EO7H9tl2bPXQ44dl7uyN/2n5zCvm162XeOjzxy9jI91omnUp6XzKpOcd0XnWfZydaxeclgvy9RjWS859jLnpIu91EOOZXIsk2NZLzn2chj3Y3uZsx57OZQ5quoR2+7aTPLpJC9qrb1trhyn4zCumy62XePjqBy68bFONI369IUkix3Ozy/c3kwyy3mWvVwdq5ccsV62U48Oc/Sil3rIIYcc65ejF+rBPrxzh/sukuRPq+rxrbVnzB2IrrZd46NDHY2PtaFp1KHFeWJWqTq5OlYvOayXZerRbY69Jl083xwZxhy91EMOOeRYvxz2Y8s5eqmHHNu01p620/1V9cdJXpFklqZALzXpKEcX267x0W2OLsbHOtE06lBVPby1dr+F2zdtrb1w/Pm5rbWbzxTlSVm+OtbTqmrr6lg3mylDNzmsl2Xq0W2OJ+7x2OdnS9FPPeSQQ471y2E/tqyXesixT621z41jZS691KSXHL1suzsyPnZkfPRsc3PTV2dfGxsbr9jt9sbGxitnzHHSttttY2PjxiuoRy85rBf16D5HL1+91EMOOeRYvxy9fKmHrzP6tbGx8QMbGxt/v+och/Wr923X+Fh5/U/adrur8dHjlyON+nRkj9ubM+bo4upYHeWwXpapR4c5Opp0sYt6yCGHHOuXw35sWS/1kGPHLP+c6d88F0zyySS3njFHFzXpJUf62XaNjw5zpJPxsU40jfq0fecy5xvwRb1cHauXHNbLMvXoM0cvky72Ug855JBj/XLYjy3rpR5yTO10Kv5nW2ufmDFD0k9NesnRy7ZrfPSZo5fxsTaObG6u6n0eu6mq/0jymIW77jXePpLkHq21minHSdm9EbDZWpvl6lgd5bBelnOoR4c5dlNV50vyitba1Wda3knpoB5yyCHH+uXYzWHdj+1m7nrIseMyjyS5VcYJdVtrfzvXsvfjsK6bXrZd46PPHL2Mj3WiadShqnrAXo+3PS4TyMGxXpapx/qpqle31q6z6hwAZ5T92LJe6nFYc4xXwTomyT8luVGSN7TWHj7X8vfjsK6bHhgf65eDXax6UiVfu39tbGwcWfHyH77t9k0Xfn7uYcthvajHOuTYI9+sky72Ug855JBj/XLske9Q7sd6qYccOy7zHxd+PmZjY+NVq67DqmvSQ45etl3jo88cvYyPdfoyp1GHqupaSZ6S5Buq6kNJfr619h8riPL9227fM8kLx58vcthyWC/L1KPPHL1MuphO6iGHHHKsXw77sWW91EOOHX3tsumttVOq6qszLz9JPzXpJUc62XZjfHSZI/2Mj7WhadSn309yw9ba+6rq6uPtm64gRy9Xx+olh/WyTD36zNHLpIu91EMOOeRYvxz2Y8t6qYccU+erqituu32YJ1zuJUcv267x0WeOXsbH2tA06tOXW2vvS5LW2hur6gIrytHL1bF6yWG9LFOPvZe7qhwfSB+TLvZSDzn2Xq4ce9+eixzL7MeW9VIPOaY+n+SJ227/4fjzZpK5JtTtpSa95Ohl2zU++szRy/hYG5pGfTr1dG7P5dJVddcdbh9J8k2HMIf1skw9+szxpJw26eIdq+o7VzTpYi/1kEMOOdYvh/3Ysl7qIcc2rbXjV7HcHfRSk15ydLHtGh/d5uhifKwTTaM+XbmqnjP+fGTb7bTWfnqmHH+R5Nhdbj9zpgw95bBelqlHnzmu3Fq7VpJU1VOS/EOSVfxC7qUecsghx/rlsB9b1ks95NhBVd0gyS8nuWKGIxbekeSxrbVXzBijl5r0kqOXbdf46DNHN+NjXWga9ekW224/YRUhti6ZXlVHWmsrO2yvlxyxXrZTjw5zpJNJF3uphxxyyLF+OWI/tl0X9ZBjqqpuleSuSe6f5C1JzpbkKkkeUlUXaa391UxReqlJFzl62XaNjz5z9DI+1smRzU116k1VPbm1dqev9zlnQo6vXR0rycqujtVRDutlOYd69JnjjUlut3DXU5PcNjNPuthRPeSQQ471y2E/tpyjl3rIMc3y+iQ/0lr77Lb7L5Dkxa21H5gpRxc16ShHL9uu8dFnji7GxzpxpFGffqKqLrjH40eSXCfJgb4ZTz9Xx+olh/WyTD36zNHLpIu91EMOOeRYvxz2Y8t6qYccU1/d3hBIktbap2c+iqKXmvSSo5dt1/joM0cv42NtaBr1aftpPzuZ41SgXq6O1UsO62WZenSYo6NJF7uohxxyyLF+OezHlvVSDzl2dK6qukBr7dOLd1bVsUnOMVeIXmrSS450su3G+FjSS470Mz7WhqZRh1prr1p1hlEvV8fqIof1skw9JnrJ0cuki73UQ469lyvH3rfnIsc29mPLOqmHHFOPTvLSqjohyVuTnD3J9yV5YJIHzxmkl5p0kqOXbdf46DNHL+NjbZjTiF1V1ceSnDTePJLkugu3Z7s6Vi85eqEey3qpR0c5dp10Mcnj5pp0saN6yCGHHOuXw35sOUcv9ZBj5zzXSHLPJJfP8IH825I8sbX2uhkzdFGTjnJ0se2OWYyP/nJ0Mz7WhSON2EsXV8dKPzl6oR7LeqlHLzl+OdNJF0+qqh9N8uIkc/0x3Us95FgmxzI5lvWSw35sWS/1kGMHrbU3VNUb22qvwtRLTXrJ0cu2a3z0maOb8bE2Njc3ffna8WtjY+PJZ8Zzzio5evlSjz7r0VGO1+7x2GusFznkkGMNctiP9VkPOabL+8GNjY1/39jY+ODGxsYbNzY2vmPO5fdWk45y9LLtGh995uhifKzTlyON2EsvV8fqJUcv1GNZL/XoJUcXky6mn3rIIYcc65fDfmxZL/WQY+oRWb4K0yOzmqsw9VKTXnL0su0aH33m6GV8rA1NI/bSy9WxesnRC/VY1ks9esnRy6SLvdRDjmVyLJNjWS857MeW9VIPOaZ6uQpTLzXpJUcv267x0WeOXsbH2jARNsBZTA+TLgJ8PezHlvVSDzkmOV7RWrvebrdnztJLTbrI0QPjo98cHB1NI4CzoKo6suJJFwG+LvZjy3qphxxLGbq6ClMPNekpx6oZH33nYP80jQDOQqrqB5P8aZJvSPKhJD/XWnv3alMB7J/92LJe6iHHjlmuu9fjrbVXzZSji5r0kqMXxkefOTh65jQCOGvpZdJFgDPKfmxZL/WQY+rWrbU9J8utqief3nPOBL3UpJccvTA++szBUdI0Ajhr6WXSRYAzyn5sWS/1kGOql6sw9VKTXnL0wvjoMwdHSdMI4Kzl1NO5DdA7+7FlvdRDjqlersLUS016ydEL42Pv5R728bE2zGkEcBbS26SLAEfLfmxZL/WQo1+91KSXHCzrZb30koOj50gjgLOW7Z9qzfEJFsCZyX5sWS/1kKNfvdSklxws62W99JKDo6RpBHDW0sukiwBnlP3Ysl7qIUe/eqlJLzlY1st66SUHR0nTCOCspZdJFwHOKPuxZb3UQ45+9VKTXnKwrJf10ksOjpKmEcBZSy+TLgKcUfZjy3qphxz96qUmveRgWS/rpZccHCUTYQMAAAAwcbZVBwAAAACgP5pGAAAAAExoGgEAnEFVdWJV/eKqcwAAHARNIwAAAAAmXD0NAGCbqvrNJDdOcs4kz0ryoiRPSnL28b77tdZetfD8yyZ5bWvt0uPtB2b4O+sBST6d5KFJfjzJOZI8LMkdk1SSO7XW/r6qXpPkJUmuNd7/wNbaMw78PwoAsAdHGgEALKiqa2Ro8FwnyQ8kOS7J45P8cWvtuknunORp+3mt1tpXk5wvyT+11n4gyf8luUlr7UZJfmd8rST5apILtNZunOR2Se5zpv2HAADOIEcaAQAsu0aGo4a+mqGZ8+NV9akkP50krbW3VdX5qurYo3jN143fP5Tk9Qs/X3DhOa8cv38gyYXPWHQAgDOPI40AAKZO72+kI0lOXbi9ue3x7R/MnbLLz0cWfv7KLvcDAKyEphEAwLI3JDm+qs5RVcdU1UlJ/jXJDZKkqq6W5BOttf9d+DefTHKBqjrnePv75gwMAHAQNI0AABa01t6Q5K+SvDbDaWUvTHKnJHepqlcleUySn9/2bz6T5KlJXlZVf5jkv+PvLABgzR3Z3Nx+NDUAAAAAh51PwAAAAACY0DQCAAAAYELTCAAAAIAJTSMAAAAAJjSNAAAAAJjQNAIAAABgQtMIAAAAgAlNIwAAAAAm/j+EakSkLmR6qgAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -30665,7 +30660,7 @@ "outputs": [ { "data": { - "image/png": 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Ja1prz0jypQxzyFw2ycOq6rNJUlXPHwsDd2qtXSDDvEMXHF/bkvxaVf3vAbb13vHrXcf3eW2SF2e4vObXW2tfyDC3z03G931Akscn+dXW2jFVdaQfqH8jw+Vjf9tae+T43j82vv9jq+qDC6+7ToYlyq+cYRWpy2Y4jk5P8pTxdW/LUBx6Ymvtu5O8KcOH6u9K8isZRom84ggzJskfZShYPaS19s0ZigzHJfnlDCun3fIAc/ns571Jvq+19sAkH6qqPz3Cf//QDOfb/cZV0v4xw6T1dxlz/uFB3qSqTm2t/b8MhbDP5MxC0EE8J8O8U3cdCyOnJPmOJPfIMJLnieM2zq5j9UjNeR5tU1Vva609Nsndk7y6tfaUDMftTZLcPMnTquq9e7/Dvr7WYweAcwAjjQDoQlV9LMNS6M9J8ksZJs+9bpL7Jbnt1oS54wera2YYufDzSZ6e5F5JXp/kmgeY1+MOGeZS+cUkr0zy2xlGPzx/fP5omtvoQRmWdf/VJH+Q4fKh+yf5nR2v+6kM++H7Mlxm9PsZJvq9WVU9/iAbqqrXZBgVctkMH/YvMy51fuMMK0rdP8Nk1J/KcAnTMzIUUK6foXB4RKrq3RmOk1cnuW+GJe1bhmLMvRZe919Jvj/D0vC3HTP+cobCxA9W1dvG1305yfEZiiQ3GL8+McOE2I9Ocp1xpa8jzfnF8f/4Bxna4uQMx+N7kly/qp6/5z8+mF9PcmqGosIP7f/SXfN9IskPZChu3TBD+/xmhvmGTqiqlx3B2508fv2rcZTXQTNsZpj/6iEZRhP+WYbz+NkZ2v0TCy//mo/Vs2C282g3VXWPDD/PNpM8Nsnjknxbhn3+tcwN9jUdOwCcMxza3Nw8/KsAgHOMceTAAzJ86D9ltWk4WrTW7pvk95Jctaresuo8XyvnEQBHAyONAABYqvHSu19P8vJzQsEIAI4W5jQCAGApWms3yDD/0L2SnDfJr602EQBwJJZaNGqtPSTDdfrnS3LnqnrTwnPvS/LBnLmaym2r6sPLzAMAwKzunmEOqLcn+dmqWsaS9wDAkixtTqPW2glJfrOqbjSugvNHVXXdheffl+QqVfV/SwkAAAAAwFm2zDmNTkjywiSpqncmuVRr7fxL3B4AAAAAZ5NlXp72TUnetnD/1CSXSPLehcee0lq7TJLXJrnP1nLKuzn99K9sHnvsuZYSFAAAAOAodWivJ5ZZNPrSLiEWi0L3T/LyJB9P8twkt0zy7L3e7JOf/PzZnW/tHHfcBXPqqZ9ddQw55JBDDjnkkEMOOeSQYw+9ZJFDDjnWL8eqHHfcBfd8bplFo48mufhijiQf27pTVU/f+r619rIkV15iFgAAAACOwDLnNHpJkpsmSWvtqkneU1Wnjfcv2Fp71cIcR9dO8s4lZgEAAADgCCxtpFFVvbm19rbW2r8kOT3JL7TWTkzy6ap6fmvt2Ule01r7fJK3JHnOsrIAAAAAcGSWeXlaqureSe698NA7Fp57YpInLnP7AAAAAJw1y7w8DQAAAIA1pWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMHLvqAEej+1/vrbNt68Gv+p7ZtgUAAACccxhpBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATxy7zzVtrD0ly/STnS3LnqnrTLq95WJJrVtXxy8wCAAAAwMEtbaRRa+2EJFerqmsluV2Sx+zymislue6yMgAAAABw1izz8rQTkrwwSarqnUku1Vo7/47XPCrJby0xAwAAAABnwTKLRt+U5NSF+6cmucTWndbaiUlemeT9S8wAAAAAwFlwaHNzcylv3Fp7YpJXVNVzx/tvSHLrqnpfa+0iSZ6d5EeTXDrJyYeb0+j007+yeeyx51pK1rn90pVePdu2/vjd15ltWwAAAMDaObTXE8ucCPujSS6+cP+4JB8bv79+hpFIr0ly3iTf1lr7g6q6x15v9slPfn5ZOc/RTj31s2f7ex533AWX8r5yyCGHHHLIIYcccsixzjmSfrLIIYcc65djVY477oJ7PrfMy9NekuSmSdJau2qS91TVaUlSVc+pqitX1Q8kuVmSf9mvYAQAAADAvJZWNKqqNyd5W2vtX5I8Kck9W2snttZutqxtAgAAAHD2WOblaamqeye598JD79jlNe9LcvwycwAAAABwZJZ5eRoAAAAAa0rRCAAAAIAJRSMAAAAAJhSNAAAAAJhQNAIAAABgQtEIAAAAgAlFIwAAAAAmFI0AAAAAmFA0AgAAAGBC0QgAAACACUUjAAAAACYUjQAAAACYUDQCAAAAYELRCAAAAIAJRSMAAAAAJhSNAAAAAJhQNAIAAABgQtEIAAAAgAlFIwAAAAAmFI0AAAAAmFA0AgAAAGBC0QgAAACACUUjAAAAACYUjQAAAACYUDQCAAAAYELRCAAAAIAJRSMAAAAAJhSNAAAAAJhQNAIAAABgQtEIAAAAgAlFIwAAAAAmFI0AAAAAmFA0AgAAAGBC0QgAAACACUUjAAAAACYUjQAAAACYUDQCAAAAYELRCAAAAIAJRSMAAAAAJhSNAAAAAJhQNAIAAABgQtEIAAAAgAlFIwAAAAAmFI0AAAAAmFA0AgAAAGBC0QgAAACACUUjAAAAACYUjQAAAACYUDQCAAAAYELRCAAAAIAJRSMAAAAAJhSNAAAAAJhQNAIAAABgQtEIAAAAgAlFIwAAAAAmFI0AAAAAmFA0AgAAAGBC0QgAAACACUUjAAAAACYUjQAAAACYUDQCAAAAYELRCAAAAIAJRSMAAAAAJhSNAAAAAJhQNAIAAABgQtEIAAAAgAlFIwAAAAAmFI0AAAAAmFA0AgAAAGBC0QgAAACACUUjAAAAACYUjQAAAACYUDQCAAAAYELRCAAAAIAJRSMAAAAAJhSNAAAAAJhQNAIAAABgQtEIAAAAgAlFIwAAAAAmFI0AAAAAmFA0AgAAAGBC0QgAAACACUUjAAAAACYUjQAAAACYUDQCAAAAYELRCAAAAIAJRSMAAAAAJhSNAAAAAJhQNAIAAABgQtEIAAAAgAlFIwAAAAAmFI0AAAAAmFA0AgAAAGBC0QgAAACACUUjAAAAACYUjQAAAACYUDQCAAAAYELRCAAAAIAJRSMAAAAAJhSNAAAAAJg4dplv3lp7SJLrJzlfkjtX1ZsWnrtTkjsk2Uzy9iR3qarNZeYBAAAA4GCWNtKotXZCkqtV1bWS3C7JYxaeO3+SWye5TlVdM8l3JLnmsrIAAAAAcGSWeXnaCUlemCRV9c4klxqLRamqz1fV9avqy+NjF0zy30vMAgAAAMAROLS5uZwrwlprT0ny0qp67nj/9UluU1XvXXjNfZLcI8kjq+pR+73f6ad/ZfPYY8+1lKxz+6UrvXq2bf3xu68z27YAAACAtXNoryeWOafRl3YJsa1CVVUPb609Psnfttb+qar+ca83++QnP7+EiOd8p5762bP9PY877oJLeV855JBDDjnkkEMOOeRY5xxJP1nkkEOO9cuxKscdd8E9n1vm5WkfTXLxxRxJPpYkrbWLtNaOT4ZL1ZK8OMkPLDELAAAAAEdgmUWjlyS5aZK01q6a5D1VddrCdp/aWrvAeP8aSWqJWQAAAAA4Aku7PK2q3txae1tr7V+SnJ7kF1prJyb5dFU9v7X2oCSvbK2dnuRtSf7fsrIAAAAAcGSWOadRqureSe698NA7Fp57epKnL3P7AAAAAJw1y7w8DQAAAIA1pWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADCx1NXT6Nv9r/fW2bb14Fd9z2zbAgAAAL52RhoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwceCiUWvtEq21a47fH7u8SAAAAACs2oGKRq212yV5dZInjw89rrX2S0tLBQAAAMBKHXSk0Z2TfHeS/xnv/3qSOywlEQAAAAArd9Ci0eeq6rStO1X1hSRfWE4kAAAAAFbtoHMTfaq19rNJvq61dtUkt0ry8eXFAgAAAGCVjuTytB9Ict4kf5rk65LccVmhAAAAAFitAxWNquoTSR5fVd9bVVdN8rTxMQAAAADOgQ66etojktxn4aF7j48BAAAAcA500MvTrlNVX10trap+Osm1lxMJAAAAgFU7aNFos7V2nq07rbULJDnXciIBAAAAsGoHXT3tKUne3Vp7S4Zi0fclecDSUgEAAACwUgcqGlXVSa21l2coFiXJ3arqQ8uLBQAAAMAqHXQi7K/LUDC6SJKLJvmR1tod9v9XAAAAAKyrg16e9rIkX07ygYXHNpP82dmeCAAAAICVO2jR6DxVdd2lJgEAAACgGwddPe3trbXjlpoEAAAAgG4cdKTRZZL8R2vtXzNcppYkMfoIAAAA4JzpoEWjRyb5yo7HNs/mLAAAAAB04kCXp1XVK5K8Ocl7x9tHkjx8ibkAAAAAWKEDFY1aa7+Z5ENJKslbk7wlyduWFwsAAACAVTroRNi3SHLxJG+oqoskOTFDAQkAAACAc6CDFo0+X1VfyjgHUlU9J8mNlpYKAAAAgJU66ETYH2ut/WKGFdSekeTtSS69vFgAAAAArNJBRxrdLsnLk9w9yX9kKBj99JIyAQAAALBiBx1p9MCqus/4/YOTpLX2J0nuvJRUAAAAAKzUvkWj1trNktw8yQ+31i618NS5k1x3mcEAAAAAWJ3DjTR6aZKPJ7lakn9YePyMJA9cUiYAAAAAVmzfolFVndZae12SP66qp82UCQAAAIAVO+xE2FW1meRHWmsXmiEPAAAAAB046ETY50ny/tZaJfni1oNVZV4jAAAAgHOggxaNfnepKQAAAADoymEvT0uSqnrV+O3Vklw1yZcWHgMAAADgHOZARaPW2u8meUSSSyS5VJIntNbuu8xgAAAAAKzOQS9POz7JNavqjCRprZ07yT8mediScgEAAACwQgcaaZTk0FbBKEmq6stJztjn9QAAAACssYOONHpTa+1FSV423v+RJP+8nEgAAAAArNpBi0Z3T3KrJNcY7z8jyV8vIxAAAAAAq3fQ1dPOSPL2JK9L8tokb6uqzWUGAwAAAGB1Drp62qOS/E2Sm2cYcfTicUU1AAAAAM6BDnp52g8lucI4AXZaa+dJ8sYkv72sYAAAAACszkFXT/ufrYLR6PQkH1lCHgAAAAA6cNCRRh9urb0xyT8mOZTkekne01p7cJJU1f2XlA8AAACAFTho0egDSd67cP9FS8gCAAAAQCcOVDQykggAAADg6HKgolFr7T5J7pnkQhkuTzuUZLOqzrPEbAAAAACsyEEvT7tdkh9I8uElZgEAAACgEwctGr07yfur6ivLDAMAAABAHw5aNHpakre31t6c5PStB6vqDktJBQAAAMBKHbRo9JgkT4/L0wAAAACOCgctGv1XVT14qUkAAAAA6MZBi0ZvaK09MMnrsv3ytFcsIxQAAAAAq3XQotEJGYpF193xuKIRAAAAwDnQMfs92Vp73PjtoSTn3nE7aMEJAAAAgDVzuMLPn41ff2fZQQAAAADox75Fo6p62/j1VfPEAQAAAKAH+16eBgAAAMDRSdEIAAAAgAlFIwAAAAAmFI0AAAAAmFA0AgAAAGBC0QgAAACACUUjAAAAACYUjQAAAACYUDQCAAAAYELRCAAAAIAJRSMAAAAAJhSNAAAAAJhQNAIAAABgQtEIAAAAgAlFIwAAAAAmFI0AAAAAmFA0AgAAAGBC0QgAAACACUUjAAAAACYUjQAAAACYUDQCAAAAYELRCAAAAICJY5f55q21hyS5fpLzJblzVb1p4bnrJXlYks0k/5nk9lV1xjLzAAAAAHAwSxtp1Fo7IcnVqupaSW6X5DE7XvLkJLccn/+6JDdeVhYAAAAAjswyL087IckLk6Sq3pnkUq218y88f42q+vD4/f8k+YYlZgEAAADgCBza3Nxcyhu31p6S5KVV9dzx/uuT3Kaq3rvjdd+U5FVJrllV/7vX+51++lc2jz32XEvJOrdfutKrZ9vWH7/7Ot3nAAAAAFbm0F5PLHNOoy/tEmJbhaq1dvEkL0pyt/0KRknyyU9+/uxNd5Q49dTPrjpCkuXkOO64C3bx/5NDDjnkkEMOOeSQQ44tvWSRQw451i/Hqhx33AX3fG6ZRaOPJrn4Yo4kH9u601r7hiQvTXK/qnrpEnMAAAAAcISWOafRS5LcNElaa1dN8p6qOm3h+UcneXxV/e0SMwAAAABwFixtpFFVvbm19rbW2r8kOT3JL7TWTkzy6SQvS/LzSb5jfCxJnllVT15WHgAAAAAObpmXp6Wq7p3k3gsPvWPh+/Muc9sAAAAAnHXLvDwNAAAAgDWlaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMDEsasOAPe/3ltn29aDX/U9s20LAAAA1pmRRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATxy7zzVtrD0ly/STnS3LnqnrTwnPnS/InSa5cVVdbZg4AAAAAjszSRhq11k5IcrWqulaS2yV5zI6XPDLJW5e1fQAAAADOumVennZCkhcmSVW9M8mlWmvnX3j+t5I8f4nbBwAAAOAsWmbR6JuSnLpw/9Qkl9i6U1WfXeK2AQAAAPgaLHNOoy/tuH8oyeZZfbNv/Mbz59hjz/W1JToKHXfcBVcdIck5P8c5/f93pOTYTo7t5NhOju3k2E6O7eTYTo7tesmR9JNFju3k2E6O7XrJ0ZtlFo0+muTiC/ePS/Kxs/pmn/zk57/mQEejU0/tY0DXOTnHccddsIv/nxxyyCGHHHLIIYccq9dLFjnkkGP9cqzKfgWzZV6e9pIkN02S1tpVk7ynqk5b4vYAAAAAOJssrWhUVW9O8rbW2r8keVKSe7bWTmyt3SxJWmt/neQvh2/bKa212ywrCwAAAABHZpmXp6Wq7p3k3gsPvWPhuVsuc9sAAAAAnHXLvDwNAAAAgDWlaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATCgaAQAAADChaAQAAADAhKIRAAAAABOKRgAAAABMKBoBAAAAMKFoBAAAAMCEohEAAAAAE4pGAAAAAEwoGgEAAAAwoWgEAAAAwISiEQAAAAATikYAAAAATBy76gDQi/tf762zbevBr/qe2bYFAAAAZ4WRRgAAAABMKBoBAAAAMKFoBAAAAMCEOY2gM73MrdRLDgAAAFbDSCMAAAAAJhSNAAAAAJhQNAIAAABgQtEIAAAAgAkTYQNdMyE3AADAaigaARxAL8WrOXMkCmkAAHA0c3kaAAAAABNGGgFwxIx4AgCAcz5FIwDWVi/Fq15yAADA2UnRCADOIXoqXvUyDxgAAGedohEAcI7VS/GqlxwAAEdiqUWj1tpDklw/yfmS3Lmq3rTw3DWTPHp87nlV9dBlZgEAONopXgEAR2JpRaPW2glJrlZV12qtXSXJHyW57sJLnpbkh5J8OMnrW2vPqqr/WlYeAAD6oHgFAOthmSONTkjywiSpqne21i7VWjt/VX2+tXb5JJ+oqg8mSWvtRUl+JMkfLzEPAAB8VS/Fq15yAMBOhzY3N5fyxq21pyR5aVU9d7z/+iS3qar3ttauleQ+VfXj43N3TnKpqnrAUsIAAAAAcESOWeJ7f2nH/UNJNg/wHAAAAAArtsyi0UeTXHzh/nFJPrbHc5dM8pElZgEAAADgCCyzaPSSJDdNktbaVZO8p6pOS5Kq+lCSc7fWvqW1dq4kPza+HgAAAIAOLG1OoyRprf1+khskOT3JLyT5viSfrqrnt9aum+RxGS5L+/OqeszSggAAAABwRJZaNAIAAABgPS3z8jQAAAAA1pSiEQAAAAATikYAwMq11i626gz0p7V2qLV2aNU5YF04Z1gnrbXrrzoDh2dOow611j6T5BlJHlpVH11hjicleWpV/fOqMow5zp3k5klOrapXtNZ+PMnVkvxHkmdW1Rkz5Tgmya2S/EiSSyT5YpIPJHluVb16jgxjjvtkmDz+Q3Ntc48c505y2yQ3THLJDJPafyTJ3yb5qxn3y6Gq2ly4f7UkV07yzqp68xwZxu3+QFW9Ya7t7ZPjkkl+PckFkzyrql618NwTququM+W4cJI7Jzk1yclJ7pphMYT/SPK4qvrsTDl6OU6/t6reMn5/3iS/mvE4TfKEqvriTDkum+Q+ST6e5FFJHpgz98sDqurDM+W4UZKfrKo7t9ZOyHCMfCbJ1ye5a1X97Rw59sj2yqo6YVXb36m19vCqus9M2+qln/vWJA9OcnzO/APnl5O8PMmDquojM+Xo5fegCye5R4afY5fI9p9jj6+qz60u3aC19tKq+tGZtvVvSZ6a5A+r6gtzbLP3LL2cM2OW47NLn1tVr58xw7cmuWFVPbm19i1JHpPkCkkqyb2r6j9nynHhOHcXt/XzOx46lOR3kjwkSarq6TPluGqS38vwu9ADkjw6w2fLf09y96p65xw51smxqw7Art6c5FlJntFa+1CSv0ryD1X1pZlzXDPJsa2138rwIe+Umbe/5WlJ/i/JhVtrv5TkK0lekeQHk/xwkhNnyvGkJJ9K8tcZVgX8vwwf+H6ztfZjVXXvmXLcPsm1W2vvTfKYqnrvTNvd6S+SvC/JEzP80D2U4ReEmye5UZKdHcOy/EOS6ydJa+2eSX46ySuT3KG19pKqevhMOV7YWvvXDL84Pnembe7mz5M8N8n/JHnQ+AH4QeNzV5o5x+uSXDHJa5O8OsMx8z1Jnp7kZjPl6OU4fXTG4zTDyqFfzvCz/doZPnD87Ew5njreLpLkn8csT0ryvUn+LMMvtnN4SJIfG79/YJIfqqr/bK1dPMMv07MUjVprZyT5aIbiyNZf5r9p/Pm6WVWXnynH+fd5+ppzZBj10s/9RZKHJfmFqjo9+WoB+MeTPDPDB+M59PJ70F8leWGSW2T6c+yvcua5tFSttRvv8dShJN80R4bRxzKct6e01l6a4Q8A/zrj9nvM0sU501p7QobiyAsz/C62dazeu7X2H1X1G3PkyPB//r3x+yckeXaSl2Q4p5+a5Hoz5XDubnf/JP+boY/f6nPPl+RyM2ZIksdm+APaRZP8Y4Y/ct46w+9CT8h8fczaUDTq02ZVvSbJD7fWrp7kjkke21o7LclHqmqvE//s9omq+sXW2kaSXxv/4vaaDL9AfqSqnj1TjktV1fHjUNt/q6q29URr7ZSZMiTJdyz89fklrbW/r6r7J3l2a23Ov0J+pKp+bBzO+YettYskeXHO3C//NFOOS1fVrXY8Vkle1Vp740wZkjM7nWQoRFyvqr7QWjtXho5grqLRu5PcNMm9xtFgr0jyNxlGPH1qpgxJcmxV/XGStNaem6H4/ODxWJ1zuPoFqur3xhzvrKrfHB//u9baK2bM0eNxeqWquu74/Utba6+cM0dVPTNJWmt3qqo/Gh//99banWfMkQy/OCbJ57b+6ltVH2+t/d+MGW6U4RfHP6yq5yVJa+31VTVnoSYZCjU7R3ltZjhuLjFjjl76uewcbVZVX07yvNbafWeM0cvvQV+/cK5ueX+SP2it3WamDElyUob//2d2ee64GXN8par+vLX2lxn6/ce11i6dYZTAR6rql4/GLJ2cM1evqmvs8vjTZv4ZcuxCe3x9Vf35+P2Lxj8wzsW5u91VktwvyXcluWdVfaC19qMLf9ycy+lV9bokaa09rKpePD7+xtaay7B2oWjUp69+uBiHRP9zkrTWvjnzVoM3xwz/nuRXWmtfl+RaSa6R5DoZqvZzOG9r7esz/FC7aGvt8lX1ntbaNyb5upkyJElaazfIMBLsRkm+ND524ySzXOIy2tovr0jyitbaZTKMDrhJhr9e/PhMOT7dWrtFkr/ZurSmtXa+JD+Z5PMzZUjG9hh9OMm5k3xhfPw8c+aoqs8kuX9r7XeT/FSSuyS5RmvtwlU1V6f8xdbaTyV5flWd0Vr7uSQntdb+LMMla3M5T2vtOzIck8e11q5ZVa8fHzvfjDl6OU7P31q7Yoaf7//TWvv2cWTNJTLvfjmmtbZ16dE3tNZ+JslLk1w9885z+Jgkr2ut/W2S97XWnpPk9RlGY/3FXCGq6mXjHx9+a/wF/p7Z/jNlLvdKcvGq+p2dT8xcVOyln3tna+2JGf5Cf+r42MUzfCh/14w5evk96MOttd/I7u0x5zQGt8pwqc0dFi8LT2Y/Tg8lyTii5q+T/PW4b66aeX9P7ilLL+fMaa21q1XVmxYfbK1dI8OVAnN59fh7z9OT/GNr7e4ZRjz/aIY/8s3FubtgvITzt1trLckTW2uvyrx/0Nzy5dbanTLsizPG0aQvyfC70CzTBawbRaM+PWO3B8e5JmaZb2L0Pzu2f1qSvx9vc3p0hpEBn8hw6dELxirwN2aYF2Qud8owB8h3JHlHkl8aH796kp+bMce2H65V9cEkfzre5vTzSR6Z5FFjUe/LGf6C8dIkPzNjjuu01raG/J5v3PaTkzwvQwcwl8Vi7xczDI1+5ozb33L7DJf+vCTJ58c5e27XWrttkrbvvzx7/VaG//+pGQoBj2+tXSXJfyeZ86/AvRynpyVZ/GvjlZP8Z4ZLkX93xhy/kuFysFMzfPD9vQx/9fvPnPkzbemq6pmttRdmKHhfJsMlhKcmueOcc2+MWb6Y5AFjQfMPk8w+IXdVPb619nOttQvsMsfFy2eMsrOfu8v4+Nz93C9nOHdvm+GD95cyzAPy0iQvmDFHL78HnZjkN5M8Jdvb4yWZ8edYVb2qtfbBJOfN8MeZRY+cK0d2OSfGffPaGTP0lqWXc+aOSZ7SWvu2DL+3H0py4SRvyfD7ySyq6l5tmFvp5hn6mPMm+c4kf5fhMr65nJjt5+4XMxSLjtZzdytPJfnx8Q+b75t7+xn2yz0z/N7x/Ul+I8PvYv+Z5HYryNM9E2GvmdbaeeeaMHWP7V+sqv7n8K9caoZDGUYd/c9ck9jukePcSS6V5MNb14/PtN1DO/9K0INxv1ysqk497Itn0Fr7tqr6rxm3d9Wq+pe5tnckejhvxxw/VFX/sOIMXR2nc2udTNi+n1X2c+PxcemxGL9SvZy3W1prl64ZF2BorZ1va2LhseB8xQyXqL9jrgx75FrJfhlHJX6uqv6vDXN/XSXJf1bVB2bOsbhfrpxhv9Tc+6WXHOP2L5nks1X1udba5TIsLrCKNlnMcfkxx0rOmfF35OMyFK8+PV4qN3eGlZ8zvfS5Oz87tBUtGrOfufv+1tqVknxDkjctfo5rw/x9L5orx7pQNFozrbVnVdUslenW2o9lGOXzwQwjev4qw+ULX5/kl6pqllEcbVhN7ukZVpP77zm2uUeOx1XVr43f/1CGifT+O8PQxl+qqpfNlKOX1fV+PMNfpBePj0MZLrWZ8/joYnWs/bTWrlFVs8yf0zpZlapNV8hIhhEtc6+Qsdgex+fM9rhg5m2PrfP2ISv+OfaxJD1M2L6nmfu5Xn6O7ZZjFf3tLZL8QYbLfF+cYRWZz4zPvaKqZlkaubX2Oxnm/rpNa+1uGUYovCbD6jYvrqqHzJSjl5+n987QBmdkGLF4pwzzKl0xyUlV9fiZcizul18bM7068++X305y5VXnGLPcL8MovC9nWL3svjnzWP1/Nc7tN0OO+2dYUGG3HH9TVbOMaG2t/WiGSYY/nmH0xhMy/IH1s0nuXAsrui45x85z5s4ZRk/Ofc500ecu/vxu2xeNuWaGc+b3V5Vty8x9/8MyTHb9iSSXTvJzVfX28bnZ+rp14vK0NTPXyTS6f4bVUy6T4ZfHm1fVW8bK/Qsz36U/b07yl0n+vK12NbnvWvj+AUmOr6r3tdYulqE9ZikapZ/V9e6XPo6Pv8gwqeCqV8faz/FJ5pp0uYtVqdLPChmL7fGgJD+8ovbYOm9X/XOslwnb9zRzP9fLz7Fecvxmhr7u00l+McnLW2s3HI+NOeed+Imq+v7x+1sl+cGqOq0NCxy8NmPxeQa9/Dy9aYYPu1+X5L+StKr6zNger04yywfgbN8vt0xyzRXtl5t2kiMZjo8rZFiR8p1JrlhVn1zIMkvRKMO8los5rlRVn1jIMddl0PfPcEn6RTNcxnmDqnp7G+ZlfXaGOcHm0Ms500ufe7hFY1ZeNJq57z++xgUvWmvfmeSZrbXbjVcMrGKOpe4pGnWotfbI7DMRZ525CtGyfX4cwvmB1trHquot4/Y/1lqb84NOL6vJLe6Tz1bV+5Kkqv6nzTvTfi/t0cvx0cvqWHtawV9weliVqpcVMpI+2qOX87aLCdv1c93mOL2qPjl+/+Txr+Qvb8NE2HP2c2e0My8x/mCGkU+nZRjhM+cE9kkfPz9SVZutta9kGDnxpfGxr4yXVM6ll/3SS45kWD3tjAwLHPzVwvkz9yjnnTk+saIcX6xhXrqPtNY+uTV6o6o+3Fqb9RK1Ts6ZLvrcdLJoTEd9/6HW2tdV1WlV9Y7W2s0zzJn7y/vlO5opGvXpnasOMPpYa+1eVfWoqvqBJGnDSl13z9BJz6WX1eSu0lp79pjnsq21W1fVX7bWHpBhhMtcemmPXo6PXlbHyrjtE5PcLcmFMuyrQxl+abj8TBF6WZWqlxUyumiP9HPe9jJhu36uzxyntNZelORWVfX5qnpha+0LGS5juPCMOe6Y5EnjX8FPS/LW1tq7Msw/8Rsz5ujl58crW2uvzzBq4o8zrAb1+gwrdP3djDl62S+95EiSl45Fmp+uM6cwuFqG0c/POQpzfLK19nsZpm74YGvtyTlzVao55wLr5Zzppc/tZdGYXvr+R2VYcfB7quqzVfUfrbUbZph65PsP82+PSopGfeplgtYTk/zEjscunmGW+/vOmKOX1eRuueP+f4xf/y3DD5+59NIeJ6aP42Pn6lhfynDt/NyrY225T4aC1Uom0q2OVqUa86x0hYyO2qOX8/bXZ9zWfvRzHeaoqt9qrV0nC6vrVNXLxg9bPz1jjnckuUFr7aIZ5ps4b5L/rpknfe7l50dV/XYbJnv+1Dhi45kZ5qp5es04iW1H+6WLHGOWB7dh8utF/53kF2vGCah7yZFhfqcTk7ytqv5q7PtvkOESsdlWperlnEknfW5VnXuPp369Zlw0Jp30/VX1nNbaV//YPD72oSQ3bMNKquxgIuwOtdZO2ufpzaq6w2xh9tBWvIqbHHIcMMeFa+Z5WlprL6yqm865zYPqaL/I0WeOOSds18/JcVZzzHacHiaH9pBjX71k6SiHc+Yoz7EmfX8X+6U3Rhp1qKpuv9vjbVgl6g9njrOXk7OaURw7nRw5Fp0cORY9L8NlBHP6+PhX+dcn+eoSnjNep72fk9PHfjk5ciw6OX3kOD4zTdiunzsiJ0eORcdnvoUF9nNytMei4yPHTsenjyzHp48cJ8c5s+j4HGU51qTvPz597JeuGGnUsdbaHTIsm3lchktujknygqq67UqDQUfGSet2cyjJ3aqqzZxna4nXbarqaXPmgHWgnwOAo4u+f/0YadS3uyT59gwTlF0/w4z7l5lr473McC+HHIdxzwzLun50l+f2uoZ7aarqpNba8Um+J0Px6E1V9bq5tt/LfpGjzxxbOpiwfYt+To49rfo41R5yrFuWVedwzshxQCvt+7d01B7dUzTq2xer6guttWOTHBon7fr7JH8w0/Z7meFeju3k2O4nkzw+ya/tvFZ+LN7MqrX2B0m+LcmrM3Q+92utvbmqfmemCL3sFzm26yXHlpVO2L5APzeQY3erPk61hxwH1UuWVedwzshxEKvu+7f00h7dUzTq2xtaa/dI8qokr2qtvT/JBWbcfhcz3EeOneRYUFXvbK39WJIv7/L0Klat+L6quu7WnfGvbq+ecftd7JfIsVMvObZUVf3bqkNEP7dFjt2t+jjVHruTY6qXLKvO4ZzZnRzbrbrv39JLe3RP0ahjVfUbrbXzVNWXxhETF0ny8mS2md13LjG/aDPJi5e8fTnkOJCq+vweT71rrgwLztNaO/9Cpgtk3p+1vewXOfrMsaWLCdv1c3IcxqqPU+0hx7plWXUO54wch9VB37+li/ZYB4pGnauqL41fT9nx1MOy5FWhepnhXg45vgYnZ/6VOh6T5O2ttX/LMLHfdyS571wb72W/yNFnjgWvS/KaFWx3Qj8nxz5Wepxqjz3JMdVLFufMdvbLdr3kWGnfv6Cb9uidotH6OjTXhvaa4X6u7cshx1lVVbMv7VpVz26t/W2SjQx/Vfv3fUZCLU0v+0WOPnOsesL2A9LPHeU5ejlOtYcc65KllxzOGTm+BrP1/WvSHl04ZtUBOMv2XJlgCbZmuH9dkm9Icrskb5px+3LIsafW2iNba4/Y6zZjjgeMX/86yUkZRhf9VpKTW2vPnivHAseHHHsaJ2y/Z4YVBs+XYcL2h86d4zD0c0d5jo6OU+0hx1pk6SVHnDNynHWz9f1r0h5dUDTiIL5YVV/IMDLtUFU9J8lN5JCjkxzvzDB30V63ubxg/PqEJE/ccXvCjDm2rHq/yNF3ju+rqp+oqkdW1SOS3DjJ8SvI0Yte9osc2/VynGoPOdYlSy85nDNyrAPtcUAuT1tfsw3dSz8z3Mshx266WKmjqt42fvurVXWLxedaa69Ncq2ZI616v8jRd45VT9h+EPo5OXo5TrWHHOuSpZcczhk5zqo5+/51aI8uaJQ101q7b1U9LMkz59pmdTLDvRxy7KGLlTpaaz+V5D5Jvru19vGc2ekdk+Rf5siwqIP9IkfHObLiCdv3o5+TY0EXx6n2kGONsnSRwzkjx5FaRd+fjtujN4c2N+ecMoAj0Vq7cYZJ5C4yPnSeJO+rqmuvLtV2rbVXVNVcM9zLIceRbPvcSf6wqu4y83bvVVWP2vHYVarqnXPm2I/jQ45xexfI6ids18/Jcbjtrfw43c/R2h5y9Jullxx7cc7I0VPf30N7rAMjjfr2gCQ/leQZSW6R5FZJPrHSRFNzDiHcjxzbHXU5WicrdSR5amvtV5JcdLx/niS3TXK5FWTZy1F3fBzGUZOjtfaAqnpQGyZs39zxXKrqVsvOsIN+7uCOmhwdHqf7OWraQ45+s/SS44CcM0dpjgUr7fs7bI/uKRr17bSqev948H48yRNaa3+XeYftHU4vQ9Xk2O5ozLG1UsdLklw/Q2d0mRm3v+XZSV6T5DZJnprkh5PcbQU59nM0Hh/7OZpyvGD8utvk7KtoB/3cwR1NOV4wfu3lON3P0dQecky9YPy66iy95DgI58zRm2PLqvv+F4xfe2mP7ika9e0DrbVfTPLO1tqzkvxXkouvOBP06otV9YXW2ldX6mit/X2SP5g5xzHjXy+Or6pHtNYen+RZSf5m5hww0eGE7fo5Jjo8Tleql/aQo98sveToRS/tIceeVtr3d9ge3VM06tvtM1zr+fQMoxYukuTHV5po6qgZLn9Acmx3NK5+dN7W2vcn+WJr7YYZOsJvX0GO/RyNx8d+jpocrbMJ26OfOxJHTY4Oj9P9HDXtIUe/WXrJcUDOmaM0x4KV9v0dtkf/Njc33Tq9bWxsPGeXx1676lxjjvuOX+8ohxy95NjY2DjP+PX4jY2Nm29sbFxwvH+NGTNcZWNj4/obGxvfvbGxccrGxsbbNjY2fmGV+2PV+0WO/nJsbGzca5fHrrKC/7t+To79ttnFcao95FiXLL3k2CObc0aOrW120ff30h7rcLN6WocWq59JPpWh+rmZofr5lqq6wcx5upjhXg45zqo5VuporZ1/j6cOJdmcezWGXvaLHN3m+MYMf93bNmF7Vc0yYbt+To4D5ljpcbqQQ3vIsRZZOsrhnJFjt+331vd3sV/WwTGrDsBUVT23qq6e5Leq6uJVddz49WJJ7rGCSFsz3H8oyQ8keXiSP5JDjs5y7GeOyzreleSd49d3Ldzfus2tl/0iR585np3kYhlW9vtCkutmxgnb9XNyHNBKj9MF2kOOdcnSSw7njBwTHfb9veyX7ika9e2prbVfaa3df7w9NKuZTPe0qnp/klTVx6vqCUlOlEOOznLsZ+lDKqvqclV1+fHr5Xbcv/yyt7+LXvaLHH3mOKaqHpTkI1X1iCQ/kuQOK8ihn5NjP70cp9pDjnXJ0ksO54wc++ml7++lPbpnIuy+9bJ0dy+r28ghR/daa6/MtEh1TFUdP3OUXvaLHH3m6GXCdv2cHPvp5TjVHnKsS5Zecjhn5NhPL31/L+3RPUWjvvWydHcvq9vIIcdZNeeqQ3dd+P7YJN+X5Ftm3P6WXvaLHH3muEuGX+DvneRxSb4xyWNXkEM/J8d+ejlOtYcc65KllxzOGTn200vf30t7dE/RqG+9VD//qqpuMX5/cpK01l6b5FpyyNFRjonW2n2r6mFJnjnXNqvqXTseeltr7aS5tr+gl/0iR0c5FiZsf894S5Ib58zJKOemn5NjosPjVHvI0XWWXnIscM7IsZ+V9v0dtkf3FI36ttLq5+IM9621j2fHDPdyyNFDjoU8u67UkeRhVfWUGXP88o6HLpbkyjNuv4v9IkefOTJM0L6Z7aPvtu5vJpl7/i39nBy76eI41R5yrFGWLnI4Z+Q4oFWP8OmtPbp3aHNTMa03rbX9LmU5pqreN1eWJGmt3auqHrXjsatU1awrQskhx2FyvDHJrZI8I8ktxu8/UVWzjTIaczxg4e5mkk8neX5VfWDmHL3sFzk6zLFq+jk51on2gCPjnGE3vfX9HJyRRn16boYPm+dJcoUk/5mhQv9tSf4lyTVnzvPU1tqvJLnoeP88GZYmvJwccnSU47Sqen9rLVX18SRPaK39XWa8NG30u0l+NElLckaSSvLBmTMk/ewXOTrM0VY/Ybt+To7D6uA43aI95FiLLL3kiHNGjt111fd30B5rQ9GoQ1V19SRprT0jyY2r6iPj/cskecgKIvUyw70ccuynl5U6/irJl5O8KcPP2DtkmBDyp2fO0ct+kaPPHCudsF0/J8cB9bKwgPaQY12y9JLDOSPHRId9fy/7pX+bm5tund42NjbeuMtjr1tBjn8Yv75y/Hq+jY2N58shR2c5zrWxsXHcxsbGeTY2Nk7c2Ni458bGxmVWkOO1uzzmvJWjqxx7ZDtpBdvUz8lxpNlO0h6rbQ851iuLc2b17SHHZJtd9P29tMc63Iw06tsbW2tvSPLPGYbOXTXJ21aQo5fVbeSQYz+9rDr0ptba91bVW8YM35vkzTNnSPrZL3J0mGPVE7Yv0M/JsaeOjlPtIcdaZOklR5wzcuyvi76/o/bonqJRx6rqbq21Kya5UobZ3J9SVe9YQZRVz3Avhxx7av2s1LHllkl+tbX2uTHL+ZP8b2vt1kk2q2quS+YcH3Ls57iF77cmbL/FHq9dGv2cHIfRxXEa7SHH+mTpJYdzRo49ddT3d9Ee60DRqHNV9a9J/nUV216Y4f4z4y1Jfn78eowccvSQo6qem+S5e63UMVeOBZeuqjNWsN0k/ewXOfrMsaCXCdv1c3LsZ6XHqfaQYw2zOGe2s1/6zLHSvn9BN+3RO0Uj9tPLDPdyyHEQXazUkeQ7W2uPT/IdGdrnXUnuVlX/NtP2e9kvcvSZY0svE7avWi/7RY7drfo41R5yrFuWVedwzsixTrTHASkasadeZriXQ44D6mWljj9Mcveq+pckaa1dM8kfJzlhjo33sl/k6DPHgktW1bb5vlprr1tBjpXqZb/IsaeVHqfaQ441zOKc2c5+6TNHL7THASkadai19k+HecmhJGdU1TXmyJNkY+uHfpJU1QdbaxszbVsOOQ7qmKp6UGvt+Kp6xDja51lJ/mbmHGdsFYySpKpe31o7NHOGpJ/9IkefOVY6Ybt+To4D6mVhAe0hx7pk6SWHc0aOiQ77/l72S/cUjfr0lSS33uf5Qxk+DM+lixnu5ZDjMLpYqSPDpNf3TvIPGc7VH07yvyvI0ct+kaPPHKuesF0/J8dBrPo43aI95FiXLL3kcM7IsZve+v5Vt8faOLS5ubnqDOzQWrvq4kiFHc9draretN9rlpRpcYb7WtEM93LIsV+Gq2RYqeN/c+ZKHY+vqqfOnOMbkvxaku/L8IvSPyd5QlV9Zt9/uJwsK98vcvSZo7V2zIonbNfPyXGQDCs9ThdpDzn200uWXnIkzhk5dt1+V33/qttjnSgarZnW2iuq6vqrzgG9aGeu1LGbY6rqfXNlSZLW2rmS3CjDSgybSd6d5GVV5Yct3WitfXeSVU7Yvif9HFt6Pk5XoZf2kKPfLL3k6EUv7SHH4a2i7++5PXrj8rT1s4q5UaBnva3U8eycuRLDMbESA31a6YTth6GfY0vPx+kq9NIecvSbpZccveilPeQ4vFX0/T23R1eOWXUAjpjRCrCgqq5eVd+f5B1JLl9VV6mqK2WYz6hWEOmSVXXrqnpUVT2yqm6V5DIryAH7mUzYnn6KNfo5tvR8nK5CL+0hR79ZesnRi17aQ47DW0Xf33N7dMVIow611rYmjdvpUJLZVh7oZYZ7OeQ4ICt1pJ/9IkefORasdMJ2/ZwcB7Tq41R7yLFuWZwz29kvHeXope9f0Mt+6Z6iUZ9usc9zc1Zhe5nhXg45DqK3lTr+L8NozqN1ZQo5+syx5fYZJmz/nZw5YfvtZ9y+fk6Og1j1cao95Fi3LKvO4ZyRYz+99P1bVt0ea0PRqENV9f6dj7XWrpDkNhk+kF5xpii/sluWMc/WDPe/IoccK86RJKmqu+1YqeMpK1p16NIrXomhl/0iR585tnwuyVuSfD5nTtj+2bk2rp+T44BWepxGe8ixfllWncM5I8eeOur7t/SyX7pn9bSOtda+NUO1/jYZZnX/vSRPr6oPrDRY+lndRg45OLhe9oscq8/RWntuzpyw/VCSq2cYCTfrhO36OTkOs80ujtPdHM3tIUe/WXrJsRvnjBwLObro+3tpj3VgpFGHWmt3y3AiXSrDSky3T/LUqnroSoNt18skYXJsJwf76WW/yLHdKnJcsqqutfhAa+11c21cP3dEjuYcKz1OD+Nobg85+s3SS47dOGeO8hwd9v297JfuWT2tTw9K8g1JfiPJ/cZZ3XsbEtZLHjm2k4P99LJf5NhuFTneNE7SnmT+CdujnzsSR3OOVR+n+zma20OOfrP0kmM3zhk5euv7V90ea8PlaR1qrZ03yU0yDNm7TpKXJfnBJN9RVbPtsMPNcF9VF5JDjg5ydLFSR2vt4wfMcYkl5+hlv8jRYY6FPB9Jcskk2yZsH59e+oTt+jk5Dphn1cep9pBjrbKsOodzRo7DbL+Lvn8hTxf7ZR24PK1DVfXFJM9L8rzW2gWT/FSGA/oDrbVnVdVvzhSllxnu5dhOju16WanjXVV1wn4vaK29coYcvewXObbrJceWlU7Yrp+bkGN3q15YQHvsTo6pXrKsOodzZndypKu+f0sv+6V7RhqtkdbaJZPcsqr+cIUZvjrDfVXNPcO9HHLsts2rjsNbd3tua6WOPV9zNub4pqr66GFec6mq+sgyc+yx3aP2+JBjvejn5Oid9oAj45zhcHro+9mfkUYdaq1dIMndM8wm/6YkfzRWQTeTXCPJrCfUHjPc33DODHLIsZfDFIMekeT6yy4YjZ7ZWtvM9okeN5OcL8NEe5efs2C06v0iR985Vk0/J8c60R5wZJwz7Ka3vp+DM9KoQ621v07yziRvTHLzJJ9O8qEkd03y+1X1pzPl2DnD/V9mmOH+e/f9h3LI0YnW2isPd8nYErd9TJLbJblHhmVEHzXTdrvYL3L0maMX+jk51oH2gCPjnGE/vfT9HDkjjfp0yaq6ZZK01l6W5L+TPC3J91XVZ2bM8aAkH84ww/3/q6ovjiMp5iaHHGfVSvK01m6S5AFJXpnkulX1qRk338t+kaPDHL1M2B79nBz76Og41R5yrEWWXnLEOSPH/rro+ztqj7WhaNSn07e+qarN1tq7VzAxWDJMTLY1w/0TxpP7G1prh2ae4V4OOfZ0uJU65soxZvn+JA9P8r4kN6+qD825/VEX+0WObnP0MmG7fk6O/fRynGoPOdYlSy85nDNy7KeXvr+X9lgbx6w6ALva+UN1JaMlquqLVfW8qrpFkm9P8ook78kww/0j5JCjhxwZVuq45S63WyT5zrlCtNaek+RPkjw6yQOTHNNa+5at21w5etkvcvSZI8Mv8odz26Wn0M/Jsb8ujlPtMSHHVC9ZusjhnJmQY7su+v700x5rw5xGHWqtfTnJJ8e7h5JcKMmnxu83q+riK4qWpJ8Z7uWQY58Mq1jF7aR9nt6sqjvMkWMvPewXOVafY/zL2b4Tts+UQz8nx37b6+I43cvR2h5y9Jullxx7cc7IMeboou/vpT3WicvTOlRV5151hqSfGe7lkOOAeVa9itvtF7LMPQz7q3rZL3L0mWPncOy2fcL2P5ojw5hDPyfHnno5TrWHHOuSpZcczhk5DpOji76/l/ZYJy5P61Br7dY77n/rwve/M2OUkzMUFv8yyXcl+f3W2q8leU2SU+SQo4ccrbW7tdZel+RVSS6a5PZJqqoeWlUfmCvHmOXOrbV3Jfnv1tqprbW3tdZuf9h/ePY6OR3sFzm6zfFVbZiw/Q1JrpBhwvZZVvgbt62fk+NAVnmcRnvIsYZZnDPb2S/95Oio71/M0MV+6Z2RRn26U4YftltOSnL98fvrJ3noTDm6mOFeDjkOo5eVOu6e5NpJfrSqPjg+dtkkj2qtnb+qnjhTlF72ixx95uhlwnb9nBz76uQ41R5yrE2WTnI4Z+TYTy99fy/tsTYUjfp0aJ/7O59bpl5muJdDjv30slLHbZL8YFUttsv7Wmu3SfLqJHMVjXrZL3J0mKMNE7Z/W5LfSfKOjBO2L2Sba3Sefk6OPXV0nGoPOdYiSy854pyRY39d9P0dtcfaUDTq034zy8/5IbiXGe7l2H+7R3WOqvpikucleV5r7YJJfipDIekDrbVnzfjLypcXC0YL+b7Uhon/5tLFftllu3Lsf38un03y1gyrC95ix3ObSeaasF0/t/92j/Yc63CczqmX9pCj3yy95HDOyLGfXvr+XtpjbVg9rUOttTcmOTFnVlxPGu8fk+TPquoaM+XoZYZ7OeQ4Ym3+lTpem+Sndw5vba19W5KTquq6M+XoYr/I0WeOHZlWOWG7fk6Og2Za5XGqPeQ4sF6yOGcmmY76/dJLjl76/h2ZutgvvVM06lBr7ZTsU22tHTO+w9Gs7bFSR2vtEkkeXVU/O1OOH07y+CSPTfK2JOdKcvUkv5LkF6rq1XPkgMNprd05yd2SXCzDL2ofSfLYqjppxgynRD/HPno4TnvSS3vI0W+WXnL0opf2kGNbhlPSSd/fQ3usE6undaiqjq+qE/a6zZWjlxnu5ZDjME5OByt1VNXfJ7lRkssluU+S+ya5RIaJsWcrGPWyX+ToNsfdk9wgw3F5iao6LslNk9yktfYrc+XQz8lxmBx3TwfHqfaQY12ydJTDOSPHnjrq+++eDtpjnSgadai19oj9bjNGudOO+4uV1+tnPnLIsZ9LVtWDquqlSe6c5OeTfHOGlTr+dMYcqar3V9V9q+pmVfXjVfVbVfWeOTOkn/0iR585bpPk1jWu8JcME7aPj//8XCH0c3IcRhfHabSHHOuTpZcczhk59tRR399Fe6wTE2H36V0L3987ye+vKEcXM9zLIcdh9LJSxz9n9yG3W9fxf/9MUXrZL3L0maOXCdv1c3Lsp5fjVHvIsS5ZesnhnJFjP730/b20x9pQNOpQVT1t6/vW2omL92fWywz3cshxVnPMaefqC6uyDvtFjtXlSGvt0rX7hO1nzJVBPyfH4fRwnEZ7yLFGWTrJ4ZyRY08d9f1dtMc6UTTq3ypnKr9Aa+2KOfMvA1v3j0lyATnk6CTHdVprHx+/P5TkQuP9uVfq+I2quutM29pPL/tFjj5zPCDJ37XWHptdJmyfMcci/ZwcO/VynGoPOdYlSy85nDNyHNQq+/4e26Nrikbs57Qkf7Rw//ML9z8vhxw95Kiqc8+1rcO40qoDjLrYL3L0maOq/r61dqMkd8kwcfuxSd6RYTLIueff6kEX+0WO7To6TrWHHGuRpZcccc7IsQa0x5E7tLm5yiIfu2mtnZqh+nooyYWSfGp8au6RE9C91tqtq+ovF+5/a1W9f/z+d6rqoTPl+Pckj93r+ar6o72eg6ONfg4Aji76/vVlpFGHxmX/Vu5ws9jPNdmwHHIcxp2S/OXC/ZNy5god108yS9EoybmTXCzzTvQ40ct+kaPbHF1M2K6fk+MwObo4TrWHHOuSpaMczhk59tRR399Fe6wTRaMOtdYOZVjy79uT/HNVvXhFUXqZ4V4OOfbTy0od76uqB8+4vb30sl/k6DNHFxO26+fkOIwujtNoj53kmOolSy85nDPbybGgo76/i/ZYK5ubm26d3TY2Nv5kY2PjqRsbG3fe2Nh4wcbGxn06yPTKVWeQQ449tv2Kve7vfG7JOf58j8cvt7Gxcd+jbb/I0WeOjY2NJ6z6/z/m0M/Jsd+2uzhOtYcc65Kllxw7Mr3yaG8POSY5uuj7e2mPdboZadSnq1TVtZKktfbUJP+Q5OGrjbTSGe4XybGdHJ2s1FFVP7v1fWvtm5L8dJJbJ7lIkl6WE18VObZbZY5eJmzXz+1Njn6O00XaQ47d9JKllxyLnDNy7NRL399Le6wNRaM+fXHrm6o6vbX2lVWGgc51sVJHa+0iGYa7bg27fW6SC1fVxlwZ4AAu3Vr75b2enHHCdv0c++nlOO1FL+0hR79ZesnRi17aQ47teun7e2mPtaFo1KcLtNautOP+V0dSVNW75wixc4b71trHx6dmneFeDjn2U1XHz7GdA/jvJP+e5F5J/q6qzmitvWXuEL3sFzn6zJFOJmyPfk6O/XVxnGoPOQ6glyxd5HDOyHEYXfT96ac91sahzc1eRkGzpbV2SvYe0rlZVdff4zk46nS0UsfPJPmZJFdP8jcZVnR7dFV97xzbh4Norb2yqk7oIMcp0c+xh16O01700h5yTPWSpZccveilPeSY5DglHfT9vbTHOjHSqEO9jJzoZYZ7OeQ4jC5W6qiqZyV5VmvtG5PcMsn9k1yhtfbIJCfNOHKii/0iR585knx4twdba5dLcuuqetgcIfRzchxGF8ep9pBjjbJ0kcM5I8d+eun700l7rBMjjTq03zWWyXzXWbbW/iRDYfGfktwoyRuqavbJyuSQ4wgydfWXg9baN2cYfXTrqrraTNvsYr/I0WeOHZkmE7ZX1e/OtG39nBwHzbTK41R7yLF2WZwzk0xH/X7pJUcvff+iXvZL74w06tNxqw4w6mWGeznkOKiuquBV9eEkj2qtfd2Mm+1lv8jRYY7Wz4Tt+jk59tTRcao95FiLLL3kiHNGjv110fd31B5rQ9GoQ1X1oL2ea62dZ8YovcxwL4cc6+6EJA+ZaVu97Bc5+szRxYTt+jk5DqOL4zTaQ471ydJLDueMHHvqqO/voj3WiaJRh1prV0jyhAyVzzcl+aWqOrW1duMkj05yxZmi9DLDvRxy7Kn1s1JHL7rYL3J0m+N2GS6ZPCnJ37TW/nKm7W6jn5PjMLo4TqM95FifLL3kcM7IsaeO+v4u2mOdmNOoQ22YWf6BSd6QM4fOfTHJ+ZLcs6r+dcYcPcxwL4cc3dvxS9JOJ1XVNWbKcUo62C9y9JljSztzwvbbJLlGhl/i5pyw/ZTo5+Q4jE6OU+0hx9pkWXUO54wch9n+Kemg71/I08V+WQeKRh1qrZ1SC7PLt9YqyT1qdSsQQLdaJyt1tNZeud/z1dEE3bCorWbCdv0cR2QVx2nPemkPOfrN0kuOXvTSHkdzjp77/l72S69cntanM3bc/8gqTqbWyQz3cshxGE/KmSt13Km19l21gpU6eikK9bJf5Ogzxx7bXsWE7fo5OY7IKo5T7SHHOmdxzky2fdTul45ydNH376aX/dIrRaM+bbv+N8n5V3Q9cBcz3EeOneTYrpeVOh6x46HNJJ9O8qKqevuMUXrZL3Js10uO/cw5Ybt+bjs5Dm7O41R7HJwcU71kcc5sdzTul/0cjX3/fnrZL11RNOrTaUkWK/GfX7i/mWSW64F7meFeDjkOo5eVOt61y2MXTfKnrbU/rKpnzBGil/0iR585OqKfk6N72gOOjHOGw+ii7+fIKRp1aPFaz1XqZYZ7OeQ4jC5W6qiqp+32eGvtT5K8IsksRaNe9osc3ebYb8L2C8yRIdHPyXHYHF0cp9pDjsPpJUtHOZwzcuypo76/i/ZYJ4pGHWqtPbyq7rNw/6ZV9cLx++dU1S1mivKkbJ/h/mmtta0Z7m8+UwY55Dic05I8ceF+V3+1qKrPje0yl172ixx95njiPs99fq4Q+jk5DqOL4zTaYyc5pnrJ0ksO58x2cizoqO/voj3Wyubmpltnt42NjVfsdX9jY+OVM+Y4Zcf92tjYuPEK2kMOOdb2trGx8YMbGxt/f7TtFzn6zNHLTT8nxzrctIeb25HdnDNu+9166fvdjvxmpFGfDu1zf3PGHL3McC+HHHvqZaWO1to/Z3p+XjjJJ5Pcdo4Moy72ixx95uhownb9nBx76ug41R5yrEWWXnLEOSPH/rro+ztqj7WhaNSnnSfNnL9AL+plhns55NhPLyt17Dak9rNV9YmZc/SyX+ToM0cXE7ZHPyfH/no5TrWHHOuSpZcczhk59tNL399Le6yNQ5ubq9pX7KW19u9JHrvw0N3H+4eS3K2q2kw5TsneJ/NmVc0yV4wccpxVrbXzVNWXZtrWoSS3yTj5Y1X97Rzb3SXHKelgv8jRZ469tNYukOQVVXWNmbann5PjiK3gOD0l2kOONc7inNnuaN0vveTope/fSy/7pUeKRh1qrT1gv+f3W84SjjaHW6mjquZaqeNPMoze/KckN0ryhqp6+BzbhrNDa+0fq+q6M21LP8dZMudxug56aQ85pnrJ0kuOXvTSHkdjjnXo+3vZL705ZtUBmKqqB40nzYO3vl+8zZWjtfbwHfdvuvD9c+SQo4ccGVbqeGiSKyR5QYaVOp6f5Fcz70odV6mqX6iqP8lwqdqNZtz2V/WyX+ToM8deWms/mGSWUXmJfk6Os2bu41R7yHFW9ZLFObPd0bpfesnRS9+/l172S4/MadSh1tq1kjw1yde31j6c5Oeq6t9XEOX7d9z/tSQvHL+/qBxydJIjVXXK+O2ft9bul+QeK5h48YsLeU5vrX1l5u1v6WW/yNFhjtbJhO36OTn208txGu0hx5pk6SVHnDNy7J+ji76/l/ZYJ4pGfXpkkh+tqve11q4x3r/pYf7NMnQxw70cchxGFyt1ZJj88Uo77q9i8sde9oscfeboZcJ2/Zwc++nlONUechxOL1l6yeGckWM/vfT9vbTH2lA06tOXqup9SVJVb2ytXWhFOXqZ4V6O/bd7tOfoZaWOzyd54o77fzR+v5lkrskfe9kvcuy/3VXl+EA6mLA9+rmd5Niul+NUe8ixLll6yeGckWM/vfT9vbTH2lA06tPOkRM778/l0q21X97l/qEk3yyHHJ3kOC1nFmeSFRVrquqEObZzAL3sFzn6zPGknDlh+x1ba9+5ognb9XNy7KeX41R7yLEuWXrJ4ZyRYz+99P29tMfaUDTq01Vaa88evz+0436q6lYz5fiLJMftcf+ZM2WQQ459VdXxc23rcFprN8wwAfeVMhSs3pnkcVX1ihljdLFf5Og2x1Wq6lpJ0lp7apJ/SLKKX5T0c3Lsp5fjVHvIsS5ZesnhnJFj3xyd9P29tMfaUDTq0y133H/CKkJszWLfWjtUVasaXiqHHPtqrT28qu6zcP+mVfXC8fvnVNVu1y0vI8dtkvxykvsmeWuG1Sm/N8lDWmsXraq/niNHL/tFjj5zpJ8J2/Vzcuyni+NUe8ixRlm6yOGckeMwuuj70097rA1Foz7dtqrutN8LWmtPPtxrvlYdzXAvhxz76WKljgwjjH6kqj678NgprbUfS/KSJLMUjXrZL3L0mSP9TNiun5NjP10cp9pDjjXK0kUO54wch9FF359+2mNtKBr16Sdbaxfe5/lDSa6bZNknVC8z3Mshx356WanjKzsKRkmSqvr0zH/B6GW/yNFnjl4mbNfPybGfXo5T7SHHumTpJYdzRo799NL399Iea0PRqE87h+7tZo7hfL3McC+HHPvpZaWO87bWLlRVn158sLV2XJJzz5ijl/0iR4c5qp8J2/Vzcuypo+NUeyyQY6qXLL3kiHNmGzkmuuj7O2qPtaFo1KGqetWqM4x6meFejv23e7Tn6GWljsckeVlr7f5J3pbkXEmunuSBSR48Y45e9osc+293VTm6mLBdPzchxw49HKfRHnKsUZZOcjhn5NhTR31/F+2xTg5tbq5yjjJ61lr7eJJTxruHklxv4f5sM9zLIcdhcjxgv+e3JmWcKcsPZJhT6QoZivJvT/LEqnrdjBl62S9y9Jljzwnbkzy+ZpqwvRcd7Rc5tufo4jjVHnKsS5aOcjhn5Oie9jhyRhqxn15muJdjOzkWVD8rdaSq3tBae+OKc3SxXyLHTr3k6GLC9o70sl/k2K6X41R7yLEuWXrJ4ZyRYx1ojyO1ubnp5rbrbWNj48lnx2vkkGPJOa61sbHxbxsbGx/a2Nh448bGxsayt7lHjmvvyPEdK8rRy36Ro88cr9nnuVcve/u93TraL3Js30YXx6n2kGNdsnSUwzkjR/c37XHkNyON2E8vM9zLIcd+elmp4xE7cjxqRTl62S9y9Jmjlwnbe9HLfpFju16OU+0hx7pk6SWHc0aOdaA9jpCiEfvpYob7yLGTHNt1sVJHRzl62S9ybNdLjl4mbO9FL/tFju16OU61hxzrkqWXHM4ZOdaB9jhCJsIG1lpr7RVVdf297h9tOeBwepiwHQ7HcbpdL+0hR79ZesnRi17aQ44+aY8jo2gErLWOVuroIgccRA8Tx8PhOE6366U95JjqJUsvOXrRS3vI0SftcXCKRsBaa61db7/nq+pVR1MO2E9r7dpJ/jTJ1yf5cJKfrar/WG0q2M5xul0v7SFHv1l6ydGLXtpDjj5pjyNnTiNg3d22qvadULG19uTDveYclAP208uE7bAfx+l2vbSHHP1m6SVHL3ppDzn6pD2OkKIRsO56Wamjlxywn14mbIf9OE6366U95Og3Sy85etFLe8jRJ+1xhBSNgHXXy0odveSA/ZxxmPvQA8fpdr20hxxTvWTpJUcvemkPOfqkPY6QOY0A4ChhwnbWgeN0u17aQ45+s/SSoxe9tIccfdIeR85IIwA4euwcEWf0Gz1ynG7XS3vIMdVLll5y9KKX9pCjT9rjSG1ubrq5ubm5ubkdBbeNjY0nnx2vcXNb5s1x2md7yNFvll5y9HLrpT3k6POmPY78ZqQRABw9TNjOOnCcbtdLe8jRb5ZecvSil/aQo0/a4wgpGgHA0cOE7awDx+l2vbSHHFO9ZOklRy96aQ85+qQ9jpCJsAEAAACYOGbVAQAAAADoj6IRAAAAABOKRgAAZ1Fr7eTW2i+uOgcAwDIoGgEAAAAwYfU0AIAdWmu/neTGSc6T5FlJXpTkSUnONT52n6p61cLrL5vkNVV16fH+AzP8nvWAJJ9O8tAkP5Hk3EkeluSOSVqSO1XV37fWXp3kpUmuNT7+wKp6xtL/owAA+zDSCABgQWvtBzIUeK6b5AeTHJ/kD5P8SVVdL8mdkzztIO9VVV9JcoEk/1RVP5jk/5LcpKpulOR3x/dKkq8kuVBV3TjJ7ZPc62z7DwEAnEVGGgEAbPcDGUYNfSVDMecnWmufSnKrJKmqt7fWLtBaO+4I3vN149cPJ3n9wvcXXnjNK8evH0hykbMWHQDg7GOkEQDA1OF+RzqU5IyF+5s7nt/5h7nT9/j+0ML3X97jcQCAlVA0AgDY7g1JTmitnbu1dmxr7ZQkb0lywyRprV0tySeq6n8X/s0nk1yotXae8f7V5wwMALAMikYAAAuq6g1J/jrJazJcVvbCJHdKcpfW2quSPDbJz+34N59JclKSv2ut/VGSD8bvWQDAmju0ublzNDUAAAAARzt/AQMAAABgQtEIAAAAgAlFIwAAAAAmFI0AAAAAmFA0AgAAAGBC0QgAAACACUUjAAAAACYUjQAAAACY+P/+tHKcHpT95AAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -31186,7 +31181,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:1m:36.437623\n", + "Time taken: 0h:1m:15.978068\n", "\n" ] }, @@ -31197,26 +31192,26 @@ " feature_learners=['Multirel', 'Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['data_all'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['container-LSE4VK'])
url: http://localhost:1709/#/getpipeline/robot/EQdxaT/0/
" + " tags=['container-v9Wmdm'])
url: http://localhost:1709/#/getpipeline/robot/bBt7cf/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['Multirel', 'Relboost'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['data_all'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['container-LSE4VK'])\n", + " tags=['container-v9Wmdm'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/robot/EQdxaT/0/" + "url: http://localhost:1709/#/getpipeline/robot/bBt7cf/0/" ] }, "execution_count": 22, @@ -31242,6 +31237,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "Multirel: Building features...\n", "[========================================] 100%\n", "\n", @@ -31326,7 +31324,7 @@ " 0\n", " \n", " \n", - " 2022-03-24 21:32:19\n", + " 2022-07-16 23:55:38\n", " \n", " \n", " \n", @@ -31355,7 +31353,7 @@ " 1\n", " \n", " \n", - " 2022-03-24 21:32:19\n", + " 2022-07-16 23:55:38\n", " \n", " \n", " \n", @@ -31384,7 +31382,7 @@ " 2\n", " \n", " \n", - " 2022-03-24 21:32:19\n", + " 2022-07-16 23:55:38\n", " \n", " \n", " \n", @@ -31413,7 +31411,7 @@ " 3\n", " \n", " \n", - " 2022-03-24 21:32:30\n", + " 2022-07-16 23:55:47\n", " \n", " \n", " \n", @@ -31442,7 +31440,7 @@ " 4\n", " \n", " \n", - " 2022-03-24 21:32:30\n", + " 2022-07-16 23:55:47\n", " \n", " \n", " \n", @@ -31471,7 +31469,7 @@ " 5\n", " \n", " \n", - " 2022-03-24 21:32:30\n", + " 2022-07-16 23:55:47\n", " \n", " \n", " \n", @@ -31501,12 +31499,12 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-24 21:32:19 train f_x 0.4488 0.5888 0.9961\n", - "1 2022-03-24 21:32:19 train f_y 0.5262 0.6887 0.989 \n", - "2 2022-03-24 21:32:19 train f_z 0.2722 0.3572 0.9988\n", - "3 2022-03-24 21:32:30 test f_x 0.5642 0.7379 0.9949\n", - "4 2022-03-24 21:32:30 test f_y 0.5689 0.7563 0.987 \n", - "5 2022-03-24 21:32:30 test f_z 0.2955 0.3838 0.9986" + "0 2022-07-16 23:55:38 train f_x 0.4488 0.5888 0.9961\n", + "1 2022-07-16 23:55:38 train f_y 0.5262 0.6887 0.989 \n", + "2 2022-07-16 23:55:38 train f_z 0.2722 0.3572 0.9988\n", + "3 2022-07-16 23:55:47 test f_x 0.5642 0.7379 0.9949\n", + "4 2022-07-16 23:55:47 test f_y 0.5689 0.7563 0.987 \n", + "5 2022-07-16 23:55:47 test f_z 0.2955 0.3838 0.9986" ] }, "execution_count": 23, @@ -31552,6 +31550,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "Multirel: Building features...\n", "[========================================] 100%\n", "\n", @@ -31591,7 +31592,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 27, @@ -31600,7 +31601,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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+Rj5tmwNsBbyIIcb8E0PgehLYMhwOp1WuynKcV4AdMcLELgZuwahYd1A4HL6xBH/HmxhCz0jg/kxJy8Ph8FkYQmQcuBbjup4CnGP+pnPfSzAq8NUBNwF/xRCjtgiHwx85dj0bw+X2BwzxiHA4vML8m+7FyC30T4xr913g/7oRGAVBEARhQKPoej5FIQRBEAShPAmFQuMwyivfaE4oy5pQKDQBw7XwfDgc3rOfmyMIgiAIgiAMEMThIwiCIFQbf8PIyXJdfzdEEARBEARBEEqF5PARBEEQKh4zcfA2GLk/DsRIsDy3XxslCIIgCIIgCCVEBB9BEAShGvg/jMTFyzFysFzav80RBEEQBEEQhNIiOXwEQRAEQRAEQRAEQRAqjD5z+Cxf3loRytKQIbWsXt3R380QhKpG+qEg9C/SBwWh/5F+KAj9i/RBYSAxYkSDkmm7JG3OEa/X099NEISqR/qhIPQv0gcFof+RfigI/Yv0QaEcEMFHEARBEARBEARBEAShwhDBRxAEQRAEQRAEQRAEocIQwUcQBEEQBEEQBEEQBKHCEMFHEARBEARBEARBEAShwhDBRxAEQRAEQRAEQRAEocIQwUcQBEEQBEEQBEEQBKHCEMFHEARBEARBEARBEAShwhDBRxAEQRAEQRAEQRAEocIQwUcQBEEQBEEQBEEQBKHCEMFHEARBEARBEARBEAShwhDBRxAEQRAEQRAEQRAEocIQwUcQBEEQBEEQBEEQBKHCEMFHEARBEARBEARBEAShwhDBRxAEQRAEQRAEQRAEocIQwUcQBEEQBEEQBEEQhIrn0ksv4LDD9u+X3/744w/ZbrvN+eab2X32myL4CIIgCIIgCIIgCIJQ9axcuYLtttu8KMd66aXnmTnzxKIcK19E8BEEQRAEQRAEQRAEoer56qsvB+Sx8kUEH0EQBEEQBEEQBEEQes2SJYs566xfM336thxwwN48+uiDXHfd1a5wqe2225wHHriHU089genTtyEajQLw2GMPc8QRB7HTTluzzz67cvHF57Nq1Ur7ewcfPIPLL7/Y9XtXXvlXDj54hmuf22+/mfvuu5sDD/wZu+22PaeffjILFy5wtfHMM09l+vRt2X//vbj77lnd/k3PPPMUf/zjb+22X3rpBVn/jpkzT+SMM051ff/uu2fZ7qBLL72Ahx++n08//ZjtttucZ555yt6vtbWF8847h1133Y699prOnXfe3sPZzh9vyY4sCIIgCIIgCIIgCEJGLrjgPJ566vF+bcOMGftzwQWX5Py988//A6tWreTKK/9OY+MgbrnlehYtWpi233/+8whHHnkMf/7zxfh8Ph5//BGuu+4qzjjjt2y55dYsW7aUK6/8K+eccxZ33HEXiqL0ug2vvPISm2++JddccyPNzU2cf/4fuO66q7jiir8D8Oc/n8vq1au59tobaWwcxAMP3MN7771DTU1NxuPtsstuLFgwn3//+06eeOI5AoFg1r+jJ84447csXbqEaDTKpZdeQX19ve34ueOOWznssCM5+eTTeOKJx7jzztvZbLMt2GijTXr9t/cWcfgIgiAIgiAIgiAIgtAr5s+fx9dff8VJJ/2azTbbgsmTp3DJJX9j9epVafuOHr0m++57AGusMRpFUXjggfuYPn03DjzwEMaOXYtNN92cs846h2++mZ1zCJSiwG9+cw4TJkxko402YYcddmb27K8AmDfvJ2bP/pKTTjqVjTbahIkTJ3HOOX/C4/FkPV4gEKSmphaAYcOGU19fn/Xv6In6+nq8Xh9er5dhw4a7xKPtt9+RXXbZjbFj1+LYY48H4Ouvv8rpb+8t4vARBEEQBEEQBEEQhD7mggsuyctd099YYVOTJq1jb6utrWODDTZi/vx5rn2nTJlqv25vb2PBgnkccshhrn2mTVsPgDlzvmH99TfodTtCoWmoatLDMmTIEFpbWwCYO/dHACZPDtmfezwepk5dlx9++K7Xv5Hp7yiUqVOn2a/r6urx+Xx0dHQU7fhOxOEjCIIgCIIgCIIgCEKvaGlpBnA5YAAaGwel7VtbW2u/bm9vN7fVZdzH+ry3BIPBrJ91dBjHcjprjN/KHM7VE86/o1D8fnebFEVB1/WiHd+JCD6CIAiCIAiCUGbMmDGD/fffu2STBEEQhGz4/X4AotGIa3tzc3O336urM4Se9vY213brvSUgZRJAOjs7c2qjlacnEulybW9tbcu0e84YYV2pbSyNS6cQRPARBEEQBEEQhDJC13Wefvpp3n77LWKxWH83RxCEKmPs2HEAfPPNN/a2pqYmPv/8k26/V1dXz1prjeOLLz5zbf/yyy8AmDZtXcBwALW2ttqfa5qWc46btdYaD7hz40QiXWm/nY2exPS6ujo7fMxi9uz+L8OeiuTwEQRBEARBEIQywinyiMNHEIS+Zu2112HcuPH861+3M3r0aILBGm666e+MHDmKeDze7XePPPJorrzyMh566D623XYHFi5cwN//fiUbb7wpU6cagk8oNJVXX32ZDz54j1GjRvHIIw/mVL0LYNKktVl77cnMmvUPxowZS11dHffcc1e3YWAADQ0NALzxxmtMnDiRceMmZNwvFJrG//73Jq+88hJTpoR4+eUXWLZsadqxPv10Dt98M5shQ4bm1P5iIQ4fQRAEQRAEQSgjnGEDmqb1Y0sEQahGFEXhkkv+RkNDI6effjLnn/979t57BlOnTrPDvbKxzz77c+aZv+M//3mEI444iIsv/jObbbYll112tb3Pr351CuuvvwF//ONvmTnzJIYNG84uu+yeczsvvvhyhg8fwemnn8xZZ81kwoSJ7LTTLt1+Z8cdpzNlylT+8pdzufXWm7Lud+ihP2ennXbhiisu4Ve/OoampiZ+/vOjXPsceOChKIrKqaeewKuvvpRz+4uB0lerAsuXt1bE8sOIEQ0sX97a846CIJQM6YeC0L9IHxSE/mXx4kVstJFRMebHHxfbeTEEQeg7qv1Z2NnZSSKRcCVuPvHEYxk8eAhXXHFtP7asOhkxoiGjBUpCugRBEARBEAShjHA6fCSkSxCE/uD0008iFovz29+ey5AhQ3j99VeYPftL/vrXq/q7aYIDEXwEQRAEQRAEoYzo6EhWq9F1CekSBKHv+etfr+KGG67lD384i66uLsaMGcu55/6ZHXbYqb+bJjgQwUcQBEEQBEEQyohEIpkUVRw+giD0ByNGjOSiiy7r72YIPSBJmwVBEARBEAShjHBW6ZKkzYIgCEI2RPARBEEQBEEQhDIiHk/Yr0XwEQRBELIhgo8gCIIgCIIglBHukK5+bIggCIIwoBHBRxAEQRAEQRDKiHg8KfiIw0cQBEHIhgg+giAIgiAIglBGOB0+IvgIgiAI2RDBRxAEQRAEQRDKCKfDBySmSxAEQciMCD6CIAiCIAiCUEZI0mZBEAShN4jgIwiCIAiCIAhlhDtpszh8BEGofC699AIOO2x/+/12223OrFn/yPt4Bx88g8svv7gILRvYePu7AYIgCIIgCIIg9B5J2iwIQrXzxBPPUVtb16t9E4kEe+65E//+94OMHr0mAHfc8W/8fl8pmzggEMFHEARBEARBEMoIEXwEQah2hg0b3ut9f/jhOzo7O13bhgwZUuwmDUgkpEsQBEEQBKEA2tpaueCC8/jss0/6uylClZBIJHP4SEiXIAj9wXbbbc5DD93HlVf+lT322JHddtueCy74Ex0dHQAsXryI7bbbnKeffoJjjvk5Bx20D2CI1PfcM4tf/OJQpk/flkMO2Zd77pnlupctWbKYM888lenTt2X//ffi7rtnZfx9Z0jXJ598xIknHsv06dty8MEzuO22m4jH43z88Yccd9yRABxyyL7MnHkikB7S9e23YX7zm5nsttsOTJ++LSeeeCzvvfeO/fkzzzzFdtttzrx5cznjjFPZbbftOfDAn3Hfff8u3kktAeLwEQRBEARBKIAnn3ycm2++nv/+90k++ODz/m6OUAWIw0cQKoPPLljN/Kfa+7UNa82oY6ML8nO73H//PRx44CH84x93M2dOmL/+9QIaGxv5zW9+b+/zwAP3cNJJvyYUmgbArFn/4N//vpPTTz+bLbfcms8++4RrrvkbiqJw5JHHAPDnP5/L6tWrufbaG2lsHMQDD9zDe++9Q01NTcZ2/PjjD5x99ukcfPBhnHfeBSxatIiLLjqfeDzOiSeeym9/ey5XXXUZd9xxF2PGjE37/ooVKzjttJPZZJPNuPnmO/D5/DzwwD2cc86Z/OMf/2by5JC975VXXsbPf34U48dP4JFHHuTmm69n0003Z+rUdfM6h6VGHD6CIAiCIAgF0NzcDMBPP83t34YIVYNT8NF1EXwEQegf1lhjDY466jjWWmscu+yyG7vvvjevvPKia58NNtiI7bffiZEjRxGPx3nggXs54IBDOPDAQxg7di1+9rN9OeCAQ7j//nvQNI15835i9uwvOemkU9loo02YOHES55zzJzweT9Z2PPbYwwwfPpxTTz2dceMmsPXW2zBz5pnouo7P56O+vh6AwYOH0Ng4KO37zzzzJNFolPPOu4DJk0NMmDCRc875E8OGDefxxx917Ttjxv5ss812jBkzlqOPPg6A2bO/KvRUlgxx+AiCIAiCIBSAc/ItCH2B09UjIV2C0H90dHTw9NNPsM8++1FbW5vz9ze6YEje7pqBwLrrbuB6HwqFeOqp/9DV1WVvmzJlqv167twf6ehoZ7PNNnd9b5NNNuOBB+5hxYrlzJ37I4DLVePxeJg6dV1++OG7jO345pvZrt8B2HvvGb3+O7755msmTpxEXV29vU1VVUKhqcyZ841rX6eTZ/Bg4/9da2tLr3+rrxHBRxAEQRAEoQCcJbIFoS9wuno0TQQfQegvLr30Au6441a++upLLrzw0v5uTp9jOWcsamoM0autrdXe5hTC2tuN8LU///mPeDzJYCNLxF61aiUdHcY+gUDQdeza2szhXACtra1MmDAxnz/BblddXXrFr9raWrvNFsFgsl2KogADW3gXwUcQBEEQBKEAxOEj9DVOh4/k8BGE/uPjjz8C4OuvB25ITylJrXxlJWxuaGhg1apVaftbAtHZZ/+ejTbaJO3zESNGsHTpEgAikS7XZ62tbVnbMWTI4DRhJhfq6+tYvHhx2va2tjaX66cckRw+giAIgiAIBSCCj9DXSEiXIAwMYrEYAD6fr59b0j98/vmnrvfh8NeMHDkqzZ1jMX78BOrq6lixYjljx65l/9fQ0EBNTQ2BQJC11hoPuEW0SKSLL774LGs7pkyZyldffe66N/73v09yzjlnuvbLdr+cOnVd5s79gdbWpDMpHo/zzTezmTZtYCZj7i0i+AiCIAiCIBSANcD0esU4LfQNzjAucfgIQv8Ri0UB8Pn8/dyS/mHx4kXMmvUP5s+fx8svv8gLLzzHHnvsnXV/r9fLIYf8nHvv/TfPPvs0ixYt5PPPP+V3vzuT884zKntNmrQ2a689mVmz/sEXX3zGDz98x9/+dqkrlCqVgw46jJaWFq666jIWLlzABx+8y2233cTYsWsB0NDQCMA777zF99+n5wHaZ5/9CAZruPDCP/Hdd9/yww/fcdllF9La2sZBBx1WyCnqd2RkIgiCIAiCUACWw0cEH6GvcK5Si8NHEPoPy+Hj91enw2fGjP1ZtWolJ554LPF4jJ133oVjjjm+2+8cf/xJBAJB7rzzDpYtW0JDQwPbb78TJ588097n4osv54orLuX000+msXEQBx10KIMGDebtt9/MeMxx48ZzxRXXcsstN3LUUYcyePAQ9tzzZ5xwwsmAkRR6s8224IYbrmXSpHW48857XN8fMmQo119/KzfeeC0nn3wcuq4zbdp6/P3vNzF+/ITCTlI/o/TVQ2L58taKeBqNGNHA8uWtPe8oCELJkH4oCP2L9EE3f/7zH7n11hupq6vnxx8X9XdzhCrgxhuv46KLzgfg5ZffYoMNNuznFglC9TFiRAPjx09k3ry5HHLI4dx00+393aQ+ZbvtNueEE07m2GNP6O+mCMCIEQ1Kpu0S0iUIgiAIglAAVpUucfgIfYU7h4+EdAlCf5EM6apOh48w8BHBRxAEQRAEIU+WLVvGHXfcCoDX6+nn1gjVglPkkZAuQeg/rJAur1cEH2FgIktRgiAIgiAIefLUU4/brz0eGVYJpePjjz9k+PARjBs3XsqyC8KAwRBcFSVjNE1F89ZbH/Z3E4ReICMTQRAEQRCEPBk6dKj9WkK6hFKxYMF89txzOgDLlrW4XD0i+AhC/6EoRsCMOO2EgYqEdAmCIAiCIORJY2Oj/VoEH6EULFy4gE03Xc+1zZ3DRyaagtBfWM4eyaUlDFRE8BEEQRAEQcgT52Tb45EcPkLxmT37y7Rt7pAuEXwEob9ICj7SD4WBiQg+giAIgiAIeRKPJ+zXIvgIpSCToOMUfCKRrr5sjiAIDlRVQrqEgY0IPoIgCIIgCHkSj8ft1xLSJZSCTDl6nOEjBx00g6effrIvmyQIgonl8JFcWsJARQQfQRAEQRCEPInHY/ZrqdIllILMgo/7/V13/bOPWiMIghMJ6RIGOiL4CIIgCIIg5Ik4fIRSk0nwSd3m8/n6qjmCIDiQkC5hoCOCjyAIgiAIQp64BR/J4SMUn0zVf9IFH39fNUcQBAcS0iUMdETwEQRBEARByJNEwpm0WRw+QvFxXmPZJpfi8BGE/kJCuoSBjQg+giAIgiAIeSIhXUKpcYo72QQfcZcJQv+gqiL45MK7777DhReeL+erDxHBRxAEQRAEIU+cSZutXA6CUEyc4k4yX0iq4CMOn+548sn/MHfuj/3dDKECSSZtlpCu3rDvvntw003X8fXXs/u7KVWDjEyEXvH222/R1tbW380QBEEQhAGF0+EjK5ZCKcjk8Em91vx+yeGTjTlzwpxwwjHsuOPW/d0UoQKRKl35Ieer7xDBR+iRV155kf3335sTTzy2v5siCIIgCAOKeDyZX0UGsEIpcF5XlsMnPaRLwgmzsWDBPAA6Ozv7uSVCJZLsk3L/z4XOzo7+bkLVIIKP0COfffYpAC+99EL/NkQQBEEQBhjOibcIPkIp6E0OH2fS5hdffI7TTz9FqgaZNDc393cThAommwgrpON0xIoA23eI4CP0SCTSBSQHGYIgCIIgGDjzNsiAXygFvanS5cwfdeSRh/LAA/fy+eef9kn7BjpPPfVEfzdBqGAkpKv3LFq00H4tDp++QwQfoUe6uiIABIPBfm6JIAiCIAwsnIN8GfALpcDt8MkWPpK+KBeNxtK2VSO1tbXmv3X93BKhEkkmUpf7f0888cR/7NcdHSL49BUi+Ag9Yjl8AoFAP7dEEARBEAYOuq5z2203u94LQrHJXKXLfa1lcmEnEvG0bdXM8OHD+7sJQkWS2XUnpLN8+TL7tTO8SygtvcrwFgqF1geeAK4Nh8M3hkKhWcBmwEpzlyvD4fB/S9NEob+JRqMA+HxSAUIQBEEQLN59921WrFhuvxfBRygFvSnLnlnwSaRtEwRB6C+i0Yj9OtUdK6lDSkePDp9QKFQH3AC8nPLRueFweCfzPxF7KhirQzrjwwVBEASh2pk37yfXexF8hFLgFHey5fDJhKygC0LpcebwiUQi/OEPZzN79lf93KqBSSyWDDO1npdtba2MGjWI888/t7+aVfH0ZgYfAfYGFpW4LcIAxYoXl4GsUGqi0Shtba393QxBEIRe0drakrJFR9d17r//HhYsmN8vbRIqD3cOH/e2TdmDiWyU5Xvi8BGEUuN0pjzwwL3ceecd7LffXv3YooFLJJLu8JkzJwzAbbfd1C9tqgZ6DOkKh8NxIB4KhVI/mhkKhX4DLANmhsPhFd0dZ8iQWrxeT94NHUiMGNHQ303oU2prjVAuRam+v13oW0aPHs2SJUt6JS7KtSgI/Yv0QWhsrHW993hUZs/+mDPOOJVRo0axZMmSfmqZUElY4zAAj8fDiBEN+P0exrMBv+QqAObW3ZXWJ+vrA9JPgUDAmO6oqiLnQyg6Xq+xMO73e4hG2wFobm6Say0DipIUr6370+jRw+xtcs5KQ69y+GTgbmBlOBz+NBQK/QG4AJjZ3RdWr66MTNwjRjSwfHl1ORA6Ow37XSKhVd3fLpSe9vZ26uqMyhnW5Kin66wa+6EgDCSkDxp0dLirIMViccLhHwBYunSpnCOhKLS0dLreL1/eyooVqxnJeHtbYkFd2vW2cmWrXINAe7tRfETTdDkfQlEZMaKBRMJYpIxG46xalXR9yrWWTktLu+N1J8uXt9LWlnyOyjkrjGyCWV5JWcLh8MvhcPhT8+2TwAb5NUsoB5JWRQnpEorLNddcwdprj+F//3uzv5siCIKQM6lJJj/55GM6Ozuz7C0I+eEO6VL44ovPeOaZp1iHzeztNUtHpn1PkjYbJM+DJIUVSoeu63aOGp/P18+tGZhkStosKUNKT16CTygUejQUCk0y3+4EfFm0FgkDDkmaLpSK6667Gk3TeO21V/q7KYIgCDmTaUJ97bVX9UNLhErGmYtHUVS++eZrALblYHu7qqWb9iWHj4EIX0Kx6erqsoVYS/ifP38+n332CSCVjbMRjaYnbZbk8qWnx5CuUCi0GXA1MAGIhUKhgzGqdj0YCoU6gDbguFI2UhgYiAIrFJtAIEBnZyft7W2u7VKeURCEciDThHrevLl93xChokl1+ASDNdQxyLWPEk/Pk+msiFPNiOAjFJtx40ay3nob8OWXn9vbZs9O+h8CARF8MhGPJ+9J1n1N+mfp6U3S5o8wXDypPFr01ggDEqscuwg+QrGpq6unqamJtjYRfARBKD9kZVLoC5yCjzEm0xmEO4RLTaQP6aPRaKmbVhaI00koJtZ86KuvvgDSQ3tBHD7ZcM4lrdfSP0tPXiFdQnVh3cisjhmJRDj66J/zyisv9WezhArAStacKviI2i8IQjmQSGhZP1PxyEKJUBRSBR9N0whS79pHSaQ7fDo6KqNgSqHImEIoJr25njyeyqhMXWwyCT7SP0uPCD5Cr1m9ejVffvkFr7/+Cs89918OP/zA/m6SUOYEAkEAYjH3KqRzcCsIwsDgvPN+z2OPPdzfzRhQJBKZHT6T2Zzr+ZQf72vL+Lkg5EJqSJch+BgLJp/yIgBqBsFHEogbyIRSKCapoZKZHD4yjs2MU/CRkK6+QwQfISd+9atjxKYoFA0rXDD1Zn/UUYdx331390eTBEHIQFtbG7fffgsnn3x8fzdlQJFtoHoG/wLgw7NW9WVzhAoldfKoaRoBU/BpYzWQOYdPR0d72rZqJB6XCaVQPJx5aLIh7s7MZHb4iDhWakTwEXrEOaD9/vvvqKur72ZvQeg9Ho9xC0odzL722iuceeav+6NJgiBkINWFJxhYOXx8BNmRI6il0fV5w9o9pkoUhB5JfUbquk6QWgBaTcFHjSevNb/fWJiTkC4DyREiFJPU3G2ZUk6K4NMzksOn7xDBR+iRVOVVMs8LxUJRjFvQK6+8xJ///Md+bk15s2TJYv7zn0ekKotQEiT5a2askK7dOYFDOJef8xcAVrIQgNqxIvgIheOcYOq6jqZp+KkBoJ0mwJ3Dp6bGEIPa28XhA8mFS10XJ4FQOLFYsj+ecsopfPrpJ2n7yLWWGbcQJmXZ+woRfIQeSb1piWotFAtnUrtbb70x7fOjjz5cHgS95JRTTuCkk37Jc8/9t7+bIlQgXV1d/d2EAYm1IDKScQCsyWQAO7+KjPmFYuB0WluCj8cstNuFkSdKdQk+phjUXt05pJ5//lkuv/xiexwhuUKEYuAM6br11lsz7iNzpcw4z8vcuXMB6Zd9gQg+Qo+kdkSntfjTTz/u6+YIFURPVQyee+4ZFiyY30etKW/+9783AVi2bGk/t0SoRCKRSH83YUCSTZD2Yjhh9bgM+oXCcYY8WGMw6xrrpBUAxVGWPSn4VLfD56ijDuOaa67kww/fB2DRooU8+uhDMhkXCqI3C5GStDkbyb532203EYlEJKSrDxDBR+iRVMFnzz2n26/feeftvm6OUEFYSZu7Qxw+gtD/RCLi8MlEtpVJDz4AdBnHCkUgHo+zJTM4lVsYFV0bTdNswacLQ9RxJm22QrokaXM6p5xyAl9++UV/N0MoYyRpc/6knpdYLCoOnz5AgsuFHumuI1a7XVgoDFXt3uEDkjtEEAYCEtKVGSuHz2bs5drutQUfGfQLhfPGa69zBo8AsM7qzdAS79nXWJROEsRdgo/Xawzvq93hkw05L0IhOHP4ZEP0nsykCj6KokqVrj5AHD5Cj3RntRPLolAIqpqhtEEKUh1IEPoXXddFeM1CIpFgPba33/upRXWspWki+AhFoDExwn7tpwY+HWY7fOLEiBGxc/h89dWXfP75p4BUv8mGJNQVCqE3xTHE4ZOZ1POi67o4fPoAEXyEHulOeZUbmlAIPeXwAXH45Ir0SaGYnHbayYwfP0r6YRbi8QQT2Mh+P4RRtvMCoKOtsz+aJVQYnri7Oqr/7g2YztEAxIkSI2I7fM477/f2frIolxmZYAqFYDk7u0PGYr1FF2G6DxDBR+iRb7+dk/UzWSURCqE3OXykzLgg9B8PPngfXV1dtLQ093dTBiTGQNU9sPc4HD6RTkl2LRSBmPGsfJfH0z5KECNO1BZ85s+fZ3+maTLpzIQIYUIh9EYwbG1t6YOWlB+pOliqw0eEstIggo/QI4sWLcz6mQwmhFJz4YXn9XcTygp5WAqlQBw+mcmUVH4NJiU/j8rKpVA4eswIf25mWdpnMaLE6EJJpDtmRdjIjDh8hELobb/66KMPStyS8iN1jKppmmub9M3SIIKP0C26rtPW1pr1c03TePrpJ9l88w3kxibkTG/EiU8++Vgmmzkggo9QCqQsezovvfQ877zzPyOnioOfMdN+LUmbhaJgCj5R0pOnN7OcmMPh40QEn8yIO10ohO7GWeuzA4fyJwC++OLzvmpS2ZAph48IPqVHBB8hK7/73Vmsv/7kbjufrutcdNH5zJv3E/fdd08ftk6oBHo7GJVBa++RagdCvtx00/X88Y+/yziY7eqSXDROrr76bxxxxCH89NNc/AQBmMdsAFayILmj1nNiekHoCSVuhAnGcAuvy9f/BI04MSIkIjpbbLEhCxbMtz8XYSMzMqkUCiGb3uPFx8ncxA4czjDG9CptQbWRSfBxjvFlvF8a5EoUsnLXXf9k+fKkfXhdtudo/upazdQ0zS7XKwMLIVeyrZJMYSvO4E6C1APyAMjE559/ykMP3Z+2PVOIiSD0hgsvPI9//OM2mppWp33W2Sll2Z387W+X2q+35zAAXuEuAIazVnJHEXyEImC5d+Ik3a7/4SqadvnI3u4nyE8/zXU9L0XYMPgZM9mE3e33sjAiFEK2seuaTEnugy6CTwbSQ7rE4dMXeHveRRAM9ucs1mQyP/IZb/IgIBNxoTCyPTRP5x8AbMZe/I+H5TrLwK677gDA3nvvQ319g71dqh0IhRKPp19DnZ0d/dCS8uI7jMl3iK2SG3URfITC8STSHT4B6qirqzO3G4KsFx9xkoUO5NkJPoLsxUkAzGQDQM6LUBjZxq7OkEsfARF8MtBTSJeMYUuDXIlCr1mTyQDUMdjeJg9NoRB6SvptrWaKeyw7qVXMxOEjFEqmAVdnp4R0WTz44H0AKKjswrH29lPOPTF9Z7l1CQWi6zp12lAAWllpCzpe/NTW1gJG4mZjWwAPXnwEABmjAdTSkLZNXARCIWQbk3rxOV77RfDpBemCj9yzSoFciUKvsVaWAtQ6tkpCSqEQur9+rNUSeQBkJxp1Cz4ykBUKJZNoKA6fJKeddjIAW7M/B3A2AN/wDrvsspu9z3PczlJ+BE2GWUJhRLui7MeZADSzwl4I8eF3OHwi5rYAf+BhruVDQJ6dADU0pm2TRSShELI5fLz47dc+AiiKODxTyVSly4mEW5YGGYkIvaaLdgCC1NnbZDAhFEJP149Oolf7VTPRqDuJZyIhDh+hMDKJhpny+lQ7dQyyX8eJ0tCQnFh+z0doaCi6IpXzhILoWJ28xzdM9ZAwHT4efNTXG3nunILPaNYxP/f26KKtBmrMXIAAgxgByMKIUBjZ7ukeh8PHR4DTTjuZhQsXZNy3ekk9d5LDpy8QwUfokRoa+S330YBhKQ6kCD4ymBXypadrZyrbAD2HflUz6YKPiGNCYWRy+CxYIIPWVKxFEICJbATAqs2+oJVVLGQOOhqxSJxRowbx5Zdf9FczhTIn1mUIPItHfUGw1s8KxeiLETpsh0/cIfhYBKmreidLA8NcSdQHMRKQRSShMLKNXX0pDh+Aiy/+c5+0qVzoqUqXzClLgwg+Qo9M4/+YYCa6A3dIV3NzM0uXLumPZgkVQE+Dru04pFf7VRtWZTxID+mSHD5CoWRaYZP7fDrOyfXr3Ieu6ww6uolz2ZEWM/RG1Y3qSvfcM6ufWimUO5EO857u1VEUlX97f0/bZt/yAv9g6NCh7LDDzg6HT9D+XpD6qn92XsZrHMNl9nuPWatGXARCIWQP6XI7fABiMRmTOckU0uXcVu0idakQwUfIiKvzpdjvnILPI488aL+WWFUhV3qr5Ivi7+ann+bar9MdPjKQFQoj0zXU2trSDy0Z2FjPwke4nGe4BdBdz8EoXa4JuCDkg+XwwWNcXyv1RTTt9jGdtOLxeLjoor/aSZsbGW5/b0TdmlUv+KSimoKPnBehEHob0gUyN0ql5ypd0jdLgQg+Qkacnc9ZhQTcOXwEoRAyPTRVPGnbfvjhu75oTtngfCBGIlHXZ5LDRyiUTC6x5ubmfmjJwMYa3M/nawBGjx7jqsoSJ4qKarsKUivqCUJviHYY143iNSaPzhAIRVFQFMUuyz6EUfb3QjWbVfXkSUukjy88IvgIRSDb9eNM2ux8LWRHBJ++QQQfISPODueuypV8YApCoWS6sTsfkiswchXsu++efdamcsB53mIxt+Azf/48cUQJBeEsy15TUwOIc8yira3Nfm3Z9+PEWLRoFTU1NS7BZyijARjP+syb9xNjxgzjyisvQxBy4fNPPgNA8eqoquqaIKmqago+xnNgsEPwqdUGVeXk6YsvPuf000+hZVlb2mcS0iUUyvfff8v111+T8TM/NY7X4u7MRE8OHxm/lgYRfISMOB+GzSxzfZbJgSEI+ZDpxu5MemflJRDcuAUft2vg+eeflUmlUBBOh48l+AgGhxyyHwDbcgi78UsAEsTweo2JpFPwGckEAH7JVbz99lsA0jeFnPjqqy+57NJLgKTDR9M0+xlgCT5W0man4OOnpirzYRxxxME88MC93H3nXWmfeczxazUKYUJx2Gef3XnrrTcyfjaD0+zX1uKlpmm0t7dn3L8a6SmHj/TN0iCCj5ARZ4dLzUEgDh+hUBKJBFdeeRnffjsn7bNpbGe/bmQ4ChL/nIpzEJ9JNLv77ll92Bqh0nAK/h6P3O8tmppW89FHHwDwc5KVV+IkRVen4GNRx2BXyXZB6C1LliyyQwctwQeSz4CkwyeD4KPXVuXkySpqsGje0rTPVHH4CAWycuXKrJ85c2gdyO8AePrpJ5g6dQItLRIWDT1X6arGe1ZfIIKPkBHnw9DvqEQC7qRkgpAP//3vk1x55WUZH4DHcrn9uo5BbMDOfdm0ssBdwjL94bh06RI233xD3nzz9b5sllDGOAdhzvu/JJw0uPnmG5gyZTyQvugRJxlW6c7hYwhBq1lMY6MIPkLuDBo02L7eVK9iX1+JRDKHj8ej2oLPENawv+vXglU5eRo6dCgALSvSE81b49dqdD4JpSXTWHVvTgUgEomwYsWKvm7SgKTnkC7pm6VABB8hI84O58Nt6ReHj1Aoq1at6vW+o1mnhC0pT9yCT+Z453nz5nLTTdf1VZOEMsd5HWVK2lzt/POft9mva2hwfZZwOHwUJTmsuoojAJjH7IzOH0HoiUAgYIsUqi8pwFqirKqqqKrHzuEzgnH2dw2HT/XlwwgGDVd6rDPdxSM5fIRSMJyxnMT1adv35hT7tQgZBumCjwY4Q7qq757VF8gIRMiIc0KZmnjMKw4foUByWXV0rlgKBr21v0pVIKG3OK8jazJ03313s2xZelhENeIUbNZgbddn2UK6mjDOnQdvVTothMLRdd0ecyk+JaPg4/V67Rw+zgU5X5U6fLDCwGPpUxzJ4SOUgl3NfG4Wn/Oq/doK87JcedWOJG3uH0TwETJiDSY2ZLpdacRCQrqEQulupWMViwF4jtsBWJtN+qRN5YRzBaS7h6M8OIXe4hZ8DIfPmWf+OuO+1XhdeTzJYgVn8i/XZ4ksgo8lBHnwiWtKyAtd1+3KP2ow6SC75ZYbACuky5OxwIFfr07BR1EURjCOXT87O+0zO4dPvPrOi1A6tuMQ+/UF7M0dnGmLPkHqABEZs/Hkk4+7xhTt7W2S5LoEiOAjZMSaUFrxp048eNmKfbmRL9JWOgWhN2R78NUzxBYYn8MIoRjNOoxhSp+1rRxwO3yyT75lgCH0lkwOn2xUo+BjVeHKRCxLDp8EhsjjwcePP/5QusYJFYumaQSoBUCp0UhNqeXxePB4PLTiTiTrrVfwacGqDCNRVZWt2C/jZ9ttvSNnMoshN0tuQKH4fMO7rGA+OhqrzcVLn5kHVcZjBqnjh8suu9i1bffdd2LixNGpXxMKRAQfISOJRIIhjGYsIVpYQYQO+zMPPg7lTwD8jvsc35LknkLvyPTg8+DlOK603zsToTorjwhuh1R3g4hqnJgL+eG8juJxEXxSsaqV+VKKGADE6LJfe71JJ1BS8JG8d0J+6LpuCz6e2vQk6obg42UpP7q2++rVqg3pUlXVdtc9wEWuz6ZN2JB12AzvCkmiLhSf/zjGsFHzueAVwcdFpuGDnJvSI4KPYNPa2sIbb7xmxlNqjGICAG/xMNdwtL2fD789ALH+FYRcyORK2ZBdCLGVa9uL3AkkLbGCQWrS5mwT8GqcmAv5IQ6f7rFCulJz2qXvlxR3NBF8hALRdd1+/qm1eprgo6oqHo8hcNzKTHu7t17Bl6hOwUdRFPbBCEf9ia9cn3U8kCybnYhW331MKD6WA30x37GQOfZ2K8zSqnRcjW67zKT3u3vuuSt9ryocZ5QSEXwEm6OOOpyDD96X119/FU3T7EFqlE6WMbd/GydUFJkmlGsy2X79FW8BsIhvAQiI4OMiNWlztgm6PDCF3uIcjCYS8W4TflfjdWU5d3w9CD6poV9xYlLoQMgbw+FjPP+8dWpatTePx2NfcytYYG/31ql4q9Th49TErMTpmYiurr5zIxSfEFsD7v4HScHHemZIZTiDTOOHOXPCadskj09xEcFHsHn7bWOS/d13c0gkEnjxA0ZoTaaEgIKQL5kGoXtxkv26iSUAdGHc8MVJ5sbt8NGyJoStxom5kB+pZdm7ujp7tW+1YDl3duDnaZ9ttdX/2a+dgo+qqiSIicNHyBtN0wmazz9vXeaQLlU1xMiVLGA+X/MfrkKL6vi0AL5E9wJlJaKqKq2sAqCVlczhfZpZlrZfvF3j+uuv5e67Z/VxC4VKooFhQLLQiEVS8JGQLie9HT+0traUuCXVhQg+QkY0TbNXJZ0lZ1Pp7jNByEZ31tblzOc/XAVAO00ANDC0L5pVNlgDhz05iei/18q6ciQDDKG3pIZ0RSLRrPtWp+BjTKp35VgAVrGId3mc65TjuOmm29P2s1578TGO9Xp0BglCJpw5fHz1apYcPsY1FyPC3ziUl7mL5q+NsdlenFJV/VXTNL744nNAZwlGovQb+BXnsVvavokOnUsu+Qtnn316H7dSqCQmswUAEdyOFBF8MqPrOiNH9pyXUypbFhcRfHJk6dKlPPLIgxX9AFUUJc3hA/AS/2J1ij1WdV1ClXtOhOLS3YPvRf5pO3tWm06fIUjGfieWYLYPM9HeGIGmSUiXUBipgo8kA3djOXesakhX8wvu4XzW2nkw48aNT9sPzMm4uXBiTQqgOs+fkB+ukK769JAuVVW7rSCXIF5V19u99/6bWCyGB5+9IKmjoaOlhdzEO2QCLuRGV1eX6/1gRjGBDYzP0gQfY9+k4FM9/bAngsEaTjzxlG73EYGsuIjgkyPTp0/n1FN/xUsvPd/fTSkZuq6TSCTsgapVaeRxruF8dnXtq+JxfU8QekN3N/KEwzXWZtqy6xhkb5O43vTzl30lRPqk0Ducg9FEItGtC68a7/Uej5c1WJtBjGQ+X9PMciA9xMYd0uXhaW4CoIZ6e3s1nj8hd3RdJzJXpRajopS/oXuHj5MN/zwYgBXMr6qJ0yeffASAF7+r0ifAvZzvep/olH4o9J53332bSZPWdG07nqvt185qxgAx8/qz3J3ZFuaqDev5l3ovS6Wa7lt9gQg+OTJ79mwAFixY0MOe5c0OO2yV5vCxeIrrM35HOqfQW1KvFWdSU2eYYIwICeK2pf2pp55g7bXHcNttt/VNQwcoqecvZq5UrrnmmG73E4RsuMuyx8Xhk4LHo3IejwOgBmHIkCFZ9nM7fFYwH3DnIZN+KfSGhU93sPT0NZjGNrTTjKdWBdIFn0wTp7rxxnXoI1BV15uVz8iLz7V4BNA0Yh7ekclJd1wEHyEH3njjNdfi2jjWZSIb2e+zO3yMuVQ19cPu0HUdRQFF6V6CqMZxRikRwSdPKrnjJkO6MufwyZbAuZLPiVBc0gUfv+O1u6JNhA57svTEE4+haRrXX59ZdKwWUq3BsQ5jELvhhhu7tuu6zqJFC/uqWUIZ43T0nHPOWZx//rnd7Ft9AzGnc2fsmLUYP34C0L3DJxLpsld9g45Kg1KtRegNy99LjrWW8D319fUZq3RlwhMwrksvvqoam3k8KioeVDxpi5WPP/4MzjlmtFVyhAi9J7WvjSFkv36Ne9FJWYhLqdJVTf2wO3RdR8XDGu9u5TqH6fvJ+SomIvjkTeUPeL12SJdb8Inirt6imJeR3MyE3uK8ka/PjlzFu/b7VSx27Ruh0xZ8VNUYxFZ7MrfUvnb04UbloNRcDp9++gkbbzytokNQheKQek09+eR/su5b7YKPV/GR7RRY5dsBotGonchTHD5Crnhrk2KiGoQtt9w6TWBMFYDs7X5L8PFX1fXmzJsVJzlOGDFiJJMnT0HXkufvzF+f1uftE8qX1L5mLYY/xKU8wuVp+6fn8Kmeftgduq4zsWtThn+6MefySNb9JOdRcRHBJ08qrePut99eadsaGQFAi5mk0qKTNtd7uZkJuZJIJK+Vk7nR9dm3fOB6bzh8jNVx64Fb7dda6t8/dP4UAPx+X6bdeeWVl0reJqG8qUYRJxesUBEAD17bpZMqsnq97j6YWqkF5P4l9A5fQ3KIPmrccFQ1cw6fTCQdPtUW0qVmTEdg9dPE6uT581PTt40TyprUvmbd01PnRBbpOXyqpx92h67r+PWe+97KlStlXFJERPDJk0q7CN95539p24ZiJCdbhTskpINm13u/3MyEHMnWf1KraADEidhuMyvmt9L6X66kWl13MUtF+3z+DHsLQs/kdv+uvv7nSsase0gkDPeAM2dP6n6QHPQ7w1bFqi70Bo/D4dO0y6dAussgm+CjOkK6qul6U1UPAVPIcbrRrX454YpkmJwIPkIuOEV/SN7Ts6W5SM/hI6G8YI7flZ7HEPvuuwe33npTH7SoOhDBJ08qWdywJtNWVZFU9To1MZmlclfToEIoHs7KBtdxXNrncWIOwccYxFZy/+sNd989y/VewxhI+P2ZBZ9qF8iEnsmlT1Xj9eTxJIdLiu6xz1e648It+Fgh0U7BR3L4CL0h0WX0s1v5NfpgY/LYW4eP6qvWHD4eW8hxCj7WeRq0BdzOGUBysVIQekOq2OrLUtjGQnL4ZEdTevcMvOqq9FA5IT9E8MmTSh7vdnYaA4sAdUToSEtE9iOfMe2SAF9juIKSN7MKPilCyWg1S68DrGZJ2ucJYnZMvoR0Gbz22ivU0GC/92BWZPFlDumqxgm6kBsi+HSP629OKFnvRamruHHb4eNz7FPd9y+hZ5qbm7jnugcAwyVmCT2pBbky5fDZdtvtUU3dUcVbVdebx+Ox82U5F5Msh4/Ho9JkjjOcDp/LL7+Y2bO/6sOWCuWGU/QHI1wSsgs+UcnhkxFd1xkeHd+rfYNBEWWLhQg+eVLJHTcWM25eAWpcD0wnax0aYAk/AnIzEwrDWoW7kJ9l/DxODA9evvziS1atMvJJybWGqxyoJfikhpNYVOMEXciNVKFiMKOy7luN11MslkwAu/Utw217f+q9aI01RvOLXxzDnXfeAzgFn6TDRxZHhJ5499132LBpT8BwCiQFn+4dPocddgQPPfQ4iunw8eBx5cyrdDwe1RZyIg6Hj7UYoiiqvd2ZSP2aa67klFNO6MOWCuWONffJFtIVt3P4WHMkue8b6Gyz6gj73SnczEbsknHPjTfepK8aVfFknh0IPVLJA17Lbh6gNqvgoyiKfZOTHD5CIXjx08wyljMv4+eaWWlj9112tKsiyLWWDLkEaGQ4J3ANY98ay56KxnP6ba59K/l+JRTOO+/8j6OP/rn9fmeO4iDO4Tlu52lusLcrqOhoVXk9dbQak8Th2/kZtnnAdlakhjIrisI11yTPmXXP8ojDR8gBZ5Lv+cxGUfYHknnsLAzBJ3k9jRkzFp/PR8RjXHfV5vDRND2jw8cKtfR4PPZ2p+AD8PXX4vARspMq2GzCbkAyV08qUqUrM6njh/XYnvXYnpeZxee8xvd8lHVfIX/E4ZMnldxxk4JPXVbBR1VVx81MBB8hN5w3cS9+O7FpJjJNmKr9IbALx3IcV7q2bcxuDJ09jX30mfipYQwh+7MqP11CDxx66P40NzcBhqhzEOcAsCcnMoqJ9vYb+IwTuKYq+1+s2fibg0ONiaMl+PSUjyeeIYePJO8UeqJ9hTG++pgXunX4pIZ01dcbob6KN+nwqab8ipqmZUnabDihdF23x7UbsnPfN1AoW5xzHBWv/WzsyjJPsu7909gGkNxtFtnGD7twLGcxi0GMsB3G1TjWKBUi+ORJJT9AnQ6f1ATNFoqiUEMjAL/gIqCyz4lQOoYymiHdhI8kTIfPmky2t1W7uHgAZ3f7+XH8jXN5hPXYvo9aJJQzzrCQbTnI9dn5PEkNDQSpAwxhsdoGYT/9NJdvP/segMAQY9ikqj0nkJ84cVKWkK7qvn8JPdP5nfFvF60AvQ7pGjZsmLFflebwSSQSGR0+e+21D2D0PafDZ3127PtGCmWJU7BxioXZFsadePHLHMmkp/HDpbzCJbwEyLOymIjgkyeVfBEmEgnGsz4qqisG2o3CONYFkuXbK/mcCKXBWiFxundSmcAGAJzNPfY2udaSZCplv4E5GJnCloCskgjd46wsFXQkA7eYwpYpZcX7pFkDhuuvv5Y6BgHgG2wJPplz+Dh5+OEnbMHH7wjRkfuX0CM3rQdABy1AUuhJdfRY73fZxQgvmTDBeKaqtsOnugQfLaGxBYa4Yzl87rrrfk4//TeAsTBpVbUEGEnvkscKgiXYbMW+nMA19vZIloVxgE9N4aKGhqrqh9mIt2uc1HJbzzuayDkrHiL45Ek8Hu95pzIlkUhwJncBZHVeKIrimgCAdEwhd4YzFoBFfJt1n4DpLHAi11oSyzacCSt5pQg+Qnf4fEnBp4u2tM9/xd9d+S6q6XpatWold9/9L+oYDIDfFHz22GMvAHbeedes362pqUXHcBQ4z5/cv4Te8g3vAElhJ9XhY72/+eY7ePLJ59h6ayN8JOnw8VTV9eZfMZgQWwFJ58VWW21tO6FS87BYgpog9ITVj8abi5AW2RfGsaMkhjOWE088rqqenZlY+EwHYxPTer1/lZ+uoiKCT44EAsYqXWdn9g5e7iQScXymmNNdSNfr3OfaVk2DCqE4nMLNACzjp6z7zOWztG1yrRmsYpGr3HMqybKz8tQUsuPxeNmK/TiCCxnGmIz77MGv7NfVNGj96qsvAdibUwFQ/cYE+7TTzuLFF1/nxBNPyfrdQMB6jrbZIXEguRyEntEVnVZW2YKPpfM4BZ9bb/2n/XrIkKFsvfU2SSeQI4dPNT0vvU3JYgaWeO10RY0evaZr/0wCtyBkwrpvt7DS3vYDn6KTvX9ZDvXdOB6A5cuXl7CFAx81qGTcfhunp23zEZQwuCIigk+O1NUZg7b29sp9SPzj9jvs1//lpoz7KIrCF7wGwBe8CsgkXMifbTeezmabbZHxswe5FHCLj9U04cxEhA5aWMkV/LxX4XCCkI2uFQlG62tzFJewDQeyPYcB8BYPJffxtPF/HGC/r6b+F4sZDrq1MFYlg8MMp4DH42GjjTZJy6HixO83Foi6aKeBYfZ2eVYK3aHFdBRdYRFz7G2WkGNdO2PHrsWBBx6S9RiKeVmq+KrqevO2G066+czmRz4H3HmO6uvr2X77HXmYywAjGb0g9EQsFrMjO2IOR8/1/BKAK6/8e8bvvcwsAFaaofednT3n+6lkfHWZ+9tqFqdtm8zmVXXvKjVyp8sRS/Dp6KjcTtvAUAA+4lnCvJtxH0VR7GS6qjnhTLXKCkJ3OAda9Y21DBkyJON+K5jPD3yKjwAKPSdKrXR0XceLjxXMp41VNLEEgNe4l85DPnfta+VIuvvuWTz00P193lZh4PPkugs4ccXt9nvLifIB/+US9iM6qBldSfA9n9j7VJPgE48bgk9T/UIA1jqgtrvdXfj9hsOngxZqaLBDWGXVUuiOeIfRv5yhIpZLJRqNAEm3eTZUX3VW6fK2Gfevh7ncdl6klrIPBoN2bq3uFkwEwWLMmGFcc80VAOgOx7QVUj9x4iR8vvRraR6zzVdGf2xpqe4QQiUZPU7Cm0xHEM0QFrceO7Bs2VK7gqhQGCL45EglCj6pg3drJbKZFVm/Ywg+RmcdboYAVNOgQigcK+k3JMMkstHKSjx4qTUTp1az4BOLxvDgswesL3MXs3mL17gXpT49t5glks2ceVKftlMY+MTasvejGF0s4Qd0X4Ka+CDWZhPA6IvVFCIYjRrPuUbvEGrHeNJyqHSHNUn/mv8BMIENAVkcEbqnaWkz4K7+Y113XV2W4BPs9hhJh4+nqq43X4cxRm9mmb0tNdG1c8HyF1yMSnaXniCkkpq/FNKvMQsrofPO/AKA1tbqFnx0c4j6/siHmXSp8Wx9nfszpnUIUse3385h8uRxfdnEikUEnxyprzfigysppCt18tzIcABauhF8VFW1Kx2MYiLrs0NVT8KFnvn++2/5298u5aCDZvD222+xLUk7uuKB2tr05MyKojB+/ARzkpkUI6v5Wot2GE9MS3D9lBe5mVNYwXz0UFPa/s5ksYLgJNacvR+txHC06Kp7nzjxqnT4KB1+/EPzmxguIAxAvemelRw+QjbmzfuJ6dsZpcKdq96W4JN0+KRPOp0oqoKuaHaVrkQiwdlnn85dd91ZopYPDLxdhhDWyip7W2rYpaIotjPDi481mdx3DRTKHkvw+ZBn7G0eT+bFAKdLbzhrEYlEaGurnPljrmhxY+ygqXG2Om4aj+58Jg/z14z7BjMUbBHyRwSfHLEs2pVky0sdfDaak+rWHhw+Trbl4KqehAs9s8suO3D11X/jzTdfZ/VHcbbhQPszXYfTTz8rTfQZPnwEjz32tC0+DmENoMoFn05joJqpOpfqU1ikuiueBalP208QIBk6kol2DJdBcNlw13Yv3qoSfGKxmBFOGvUSGJrfkMmauAeoYTCjqvr+JXTPokULbZHe7fCxQroMZ2dPDh8wxFovfjRN46ef5nL33bP43e/OLH6jBxCeSJA4MZdY1p3DB4yS2YLQWyzBx1m4JjVs0KLNITwOYiQ33ngdkyatydtvv1XaRg5QLIePrhjPQGfffJW7XftmqtAr5I8IPjliJe1qa2vt55YUj9TB5yBGAO5M9KmkCj4jmSCDWKFbOjqSSZetEscWY39Wy4YbbszTT7+Q9j1VVfnRrNS1LtsC1S34xCOWwyc9fEtVVXTFLeA6V0nOPPPX/PjjD6VtoFA2xNuNfvQeT3IJ+/MxRv/7iuRgNFHrjq334Ks6wccuyZ6H4PPyy29x+m/PAGB3TuASXmLpv709fEuoVhKJhEPwSXf4dHV1AcmE4N0eK9BFDQ1ommaPXSEpGlUi3kiQdppc29IFH9V2yAIcyp/6omlChWAJPjGS/ShbSJeObicIb2QYb775GgDPPPNUKZs4YNESpsNHSR+//o9HXO+D4k4vKiL45Ij1oKwkS17q5NlyUTSxNOt3UgWfUUxkWPOk4jdOqEhSxYoJhxuiRCbrtaqqfMsHAKzJFKC6BZ9o1Dh3mUqBGqGW7u3n8QST2RyA++67mxNOOKb0jRTKgni7MfhayUKW8D33cB7/5LfcQrLUePs0d2y9h+oSK2KxGH4MN4U3S4WR7thggw0Zv46Rg8CayLe8IolihcxomoafGiBzDp9IxAjpCgZ7FnzigQh1DEoTfCrJoZ6KN1pDB81sssmm9rZMDh/NMQYZzdp2KgNB6Ik6GgHoJNmPPJ7szwarH1vPEeidQ68S0U2d1XL4OOeSVpoQi0GMYneOt++HQmGI4JMjluBTSRNOTXN3ssGm4JOpTJ5FJjV772/+YK8YC0J3OB98D6//O/umn2lgpqoqcWLEMSZeDQyrKodBKomYJfiknwNFUewHqZMz+Jf9evXqVWmfC9WJdb+2BqRROvmE5137KKpb3K8+h08UL8bk2hPsfcJmJ2qN+3tKnTwnhcxommavbGfK4ROJ9N7howUi1NCIltDsXFSQzANUaeiaji8apJ1mjjrqOHt76gKloihpRUlO444+aaNQfqQ+76xcbM48UaqqZk3on6xonFzQDAarU/BJ5vBJz2OXKvgMYRT7ciZ7cmKftK3SEcEnRypT8HH/LTU0kCBOF+1ZvpHkfHZnFr+337f+kG7TE4RUAqZifw/n0xZIhg6mOnwgGRsdo4uJbMRlvMa6ie37pqEDkFiPDp/uE8LmUmVIqGwsh4/TSZBK+6bfu9578VXU868nYrE4PkvwCeTXdzwpC5RzvvtaEjcLGUkkEnbetU6SqQOsxRDL4dNTWXYAzZdARaXrJ1wOHyssrBL44ovPOOigGXzyyUfEWjQUVNppprY2eziIoijMZzbXc7ydH3A06/RVk4Uyw3mvDlLPemxP3BNhztwf7e3ZQrogKWSoDndsb/pvJaKbpzLTwmQi69hVxqzFQASfHKkGwSdAbbcTACerWcyHPGMn2+pcLIKP0DOWRTNGl0uAUNX0lTjrQRojuSq5jX5QH7RyYBLvxuFj5PDp/t4kgo9gkerwyYQ+PPnZChYAoMWqyeETSwo+eTp8vLXu78UjGp999knBbRMqD13X7LxrXSRTB1j3ba/XmDTW1PQc5tAwfy0AFp7fQDyenExZolElcPXVV/Dmm69z+OEHsu0m2wDQQTM1Nd0LPgBzeJ+/Ur1jCaF3OAWfSWwMQDTY5hIVVTVzlS5ICj4e8qvyWElYYwddNf51nrNMi5gAXVROztz+RASfHEkKPpWzOpdIJDuZiocANXaywJ12ms6ECRN7PMYyjDwP1oqxIDhJtcTW2CuYba6VEVVNfyBaIlCU5KqkZamtRizBJzVXD5iCT4btznMnCBbRVuM51p3g46w+stISfKpI19e0RMGCT2pIlw8/Pp/k8RHS0TTNrhqVSfD54x//zNixa3HkkUf3+pjxVSqJRLLTWmFhlUBrq5FHZfXq1ejtxr2qnSa83uyTa+cks41VLOEHV3iOIDhxLoo3mFWMF677nmuf7hw+mUK6qtXhmazS1XNIl0VAkjcXBRF8cqSSHT5jmcrVvM8IxhGhg2uuuYGHHnqc99//rMdjWO4LLSKCj5DOsmXLXO9rzKR3HbSkOHzSb0lWmFdMRAsA4jHjoaijsfbabhu6qqpoDofP5RwKgOKyxIrDRzDoajbyejhzhaTi7JOWcKhFq+c+r2la4Q6fgPu+5sWfUdwWhEQi6fDpdITVW/1wxoz9+fjjr9hssy16PNbSnd8GoGH3jpSQrspx+FhswT7M5HbAcPh0NwFPdWJ00WaH0QlCKpY448XPUVxibGyMufZRVTWriJMM6Ure8ytpDtkTr732Cr/85VFEIhG7SpeupI8hnILPEn7gE7NqqF8En6Iggk+OVKLg09S0GoDN2AufWW4wSgdrrTWu18eImyUuE1U0ERB6z4IF8+zXNTSwDQcChuDjHJhZdnUndu4C16S0eq8zZ0jXxRdfxm9+c479WWpIVzPL+JYP7QkrgER0CRYdqw0BpzuHj7N/xs0ytNUU0uUUfNQ8c/ikTjC9BFxJdAXBwkjabIgPzlCGfEJxI2sa+Wk0XXcJPpXk8LHcw8dwGbXmQtIy5rmciamknsoYUXvsKwipWBEdVrVTALXRLe6oqpp1XmgJGU6nSiXNIXvi0EP35+mnn+DVV1/GMqBnSj3gFHxWsognuQ5I5vwUCkMEnxxJCj6VM+DddlvjJjaSCfa2Fla4QrkuvPCvTJu2btZjxE2HT0IcPkIGVq5MVsS4krepYzAALSx3DWRHj17T9T1FUeyBWzvN9vZM+WuqhWSVLg2Px+uKI1cUd0iXjmb3Ta8MaIUUls5fDrgFn1Sh3+dLirCWNb3aBB8rBNXfmN+QKV3w8dtjCUFw4rzeelM4ozsUv1kRJ0LFVunSdT3t2TZpxzW6dfikfmYJ2V4kzFJIx3LuOMed3qFuwSJTwRELS8j4Gb/m9zxkHrOK4qJNVFWhrdW4p0Uy3IM019g1YY9LAtRWVWXQUiGCTw5omlaRDh+LIYyyX69isWvgf8opM7nhhluzftde+RXBR8hAS4sRZ+/B7eDpot01Gcq0imkNzsazXglbOHCZP38eBx+8H99//y0AsWgypGvMmLGulcxUh49Ggqgp+FguBUnaLFgs+H4RkBR8XnrpDdZff0PXPl5vchKUMJ2c1RbSFTRzqvjyFHxSk9H78BOLicNHSEfTEg6HT3oOn1zw1RiT0ESX7kra7MzbWO7ous5QRru27XrAzt0KPqlhzbIoInSHtcDvFASHTmh07ZN6j3diLZQArMU0FNSK6oO9JRKJ8PhjjwIwb/6PaZ87HT4amkvwcToUhfwQwScHVq9ebb+upKTNFlaiQIDv+DDtgRkMZrfVxatwIiD0nuZmw53jjMW1qmN0NzA74ICD7c+txOBQXQ6fs88+nTfeeJW99toFXddJmAP3ESNHMmVKyLWylC74pDt8RPARLCItxn3bGlhtuOHGafs4kwtbA1c9Xj39T9eTjgtfQ759Rxw+Qu+wQrpiROxxFXT/nMyGauac0rrcIV2VljDWOXYF8NWpOZ2vmHmevVRnqWyhe6z+YuWe1Egwei23yKgoao9Vuixqaai4Ptgbli1bxrKlRj5Py83jPGduwSdh5wwUwac4iOCTA86wlEp0+DiT1n1PesnY+vrsSe2spM0JGcMKGbAqaVixuJ/xMouYY36a+SF5//2PcP75F9oDt+e4zf6sWgQfXdd57bVXAGhqauLtt9+yBZ/RaxoDDufKUmqVLh2NmOm+8xPsq2YLZYI3YYiA3eXw8Xq9xBRj4GUL+1VkTtE03U6i663P1+GT/F4nbabDRx6WQjqW4NPpcPdAng6foHHdaTF3CEkljV91XU8TfDweb44hXdaiiIR0CelYC/zWdfYvfk9dXZ1rn46Ojl4LPnUMqag+mI2FCxdw8snH2+/nzPnGHr9mqiarpYxdddPlE6C2KkPgio0IPjnQ3Nxkv67EzlpDAwv4hj8xnWaWpX2eTfBZb70NkoJPR+WdF6FwrNUMK2ldKyvtz7JZYddZZwoej8cenLlLi1eH4NPV5U6uuXDhAr4NG0KZYp43d1l7xVX9QEOzq5uJXV1IxZMIoJGw79+Z8Pv9XLfm4ZzNVo4cPsZnb731BnvttQsrV67M+v1yR9M0u+/kW6XLORGI0oEHH5GuKlLNhF6TSCSooc4VzgXd5wjJhjdofGf+j/N5+OEH7O2V5FDXdd0ulW3h9Xq7FchSP4ulhD0LghNrvmcJPp202BEPZ5xxNnV1dUyYMNG+rrba6v+YOfPM5PdTBB9DwKicPpiNl156gccee9h+v3jxIhJx41xmWrTVHefJGmtE6MRPjeTwKQIi+OSAs4NWmuDTwDC8+FjJQppZnnGf2tq6jNtvvPE26kca7oHo6so6L0JxsPqLJfg4HQXZBmZWksmk4JOs0qWj29XlKpnUVY3W1hZamg23lOrJJPioaEryPqWj2efNcilISFd189JLz/OrXx3L6tWr8CYCae6e1OvD5/MRV6JE6Ejm8DFDuo488hA++ugDbrnlhr5pfD+gaRoec+Vf9eUv+FzHL3mRO1lgOhujHSL4COlYDp/UhM01NbmXJvYFjOu2tbmVF1983t5eSZNNRVc5hssAeJ+nuYLD8Xo9PVTpSs3hY7jtfOKCFTJg9ZdaW/BppabGuFb+9Ke/0NbWRl1dnX1dbbHFVpx77vn291MX23wE0PXKnyulJodftmyp7fDRMjh89pmxX/K75kJllE5J2lwkRPDJAedDspIemACncgvgroSUilUye8iQIa7tqqrS5TXKh0ZWVv5NTMgdS/AZbCYGb2WV/Vm2XBYxsxqV9RB1Onw6aWXKlPG88spLJWnvQMGOHa8xVpOam5uxnnuj1jDOpXNgqygqMTV5nuJEaaMJwK6MJoJP9fL+++9xxBGH8MQTj/Hii8/jyyD4pOL1+uxrxnb4mLnaLFv7qlWV7vAxBZ88DQCqqvItH/AE19rhI1LgQMiEHtcJUEunoyQ74KrG2Ft8AWPM5kkJVaqk8WsgmnSev8Y9zOMrvF5ftyFdqc9Ay9HuLFwiCBZWfxnESADaWJ0xp6l1Xem67sp9F8C9WO4jUHGmgUx0dbkFn48//sg25+spOXxGjBjJ9Om72vvW1BuCmhXSJYJP4YjgkwNGucxGBjECXdcr6gJci2lAMsdKNr755kc++eRr1zZVVYl7IsSJEllZOQMJoXhYqxnDGQvAcubZn7W2uge2N910O7vttgeTJ08Bkg+EiGPF01qJO/row0vX6AGAVVll0KDBAFx++SXcf989QDKkKzVpc4enyXWMNgwnlCX4CNXLPvvsZr+Ox+MZHT6p+Hxe+1mXTNpsfBYIGP2wkitOaZpGI8OB/B0+zj5quQn0mAivQjqJduO6SHf4dD82y4TX5yVOzK6OOYJx+KmpKMFn/MrN7dfzMcamXq+326pJqYLPchYAcAo3E++s/Im4kBtWCOQoJhKhg1UsYtiw4Wn7OQUfJwtwz5l8BCqqD2Yj1eEDoGI8C1NDutZYYzS6rtNipntobBgEWIKPcc+qpDl3fyCCTw4kEgku5FkuxUiiWikX33DWsl9/yRvd7jt06DB7penyy69mxoz9mThxEoqq0KE207Gw8m9iQu5YZS2tKgdOJ5mV0NnikEMO5957H07LWbCaJdzHBQBMZWsAxo+fUKIWDwysygQhZUs2ZQ8AFPO2rWQJ6er0uc+ntVJca557cfhUJ52dna73uq7j1QJE6MzyDQPnKl01lmX3tAeYwpYAqP78+k5DQzKprHW+9U7ph0I6WqdxP+9Kcfg4HQO9xe/3kyCGBx/jWI+/8F/25zcVM3YFGNO0AQB38yfbNWAIPr13+LgWoL6T5LCCG2v8WksjnkadJ558lmHDhqXtl03waWEFz3OH/d5H0D5mJROJpAs+1vg1Na8RGOfNyuOz3TY7GMcwc97tvP327LXX9BK2tvIRwScHNE2zJ021NFaMJe+A2jPs1x/wdK+/98tf/op//vPf+P1+VFWlXhtK17IEV+5xF8uWpSd9FqoXq69YeWScdvV5837K+J1MvMNj9msVD15vZVfVSCTiKKgcsvgSfslV1DMU1axqli2HT8zvdmykJm0Wwac6iUTcCcDRwKf17PBxCrJ2SFcVzYmCS0far/MVfOrqkmEn7WaIpdaaexJeofLROyzBx+3wyacssdfrI0At41iXnTgSgB04vGLcBdFmjTVb1kVD4z2etLc7iz1kIltIF0Ciq/In4kJu3Hff3YBRydhTB1tvvU2WPTMLPgBPcwOPcy0Ae3JiRSVOz0YkEqGR4UznaFTTZahY5yglh48VNWPl9gn4kiFdAM3LW4yQMCFvRPDJgUQs2UFHMbFiBJ8W1UjS/D5P5X0MVVVtq974T3bioovO7+EbQjWRWuXAOZjNpR85baCGNb2yZ57xeJzBJCech3N+0uGjZBZ81BRnVEwSUgqkr7bpUQUFtcekzc7qjFZZdiuky3IdVHJIl68l+fereerLznNqhVjSVtlitZAfWmfmkK5NN9080+7d4sz7syUz7NfOsWw58+p+SwBQU6YyPTl8Uj9zLkDFWipjXC8Ujxtv/DtgLljWZB9z3nbbnQwdOpTjjjsBgJ9+Wsqvf20sqOvozOFdAAYxomLmj90RiXRxPNdwIL9jBw4Dkg4fayxv5aC0BB+raJAVPm2NT6yCL0L+iOCTA50vDLJfT2Sjilgl0eI627f9AoBXuSfv46Q+QFtasid/FqqPdMGntbvdu+VDngGMB0AlTzTBEHys/AsAG7Or/T5bSNeSoV/xJa9zM6cASYePTxw+VU1Xl9vho3W5B1SpNDQ0ctFFf2XvvZMTRc3K4WN2u2DQEBFTw8Uqibqlo+3XhfQdKwdLhxnOqreLw0fIQML6J/lsC4WmditgZGPzzbfIuL1S3GXNszM//2tqavOq0gUQXVX+43qh+ASoNcZewezXxx577MU338xl4sRJgHHPd4qu85jNYr5HQakKwScajTKatYFk6pBUh491X9M0I0fPk1zHN7zLeucMNo5hhkCL4FM4IvjkgPMhWcugiuiwi55PDtQX823ex1FVlRZlhWOLTCqFJFZfaWAoCWJ00NLDN7KTfADU5GVzLycSiYS9ImIRNEUz1WNsd04EFEUFn86tzGQ2bwHi8BEMUqvh6V3GdZNN8Bk7diwnnzwTj8djW9Qth48WM96rqvFMjMcrV3htWGwMVOs2LOx5/913C7j88quTzo0uGX4J6VjuuYQjx0WmXBi9wefzZ9yud1XW+OxT3NU66+vrexBns38WaSr/cb1QfBoYCoDSmNuYM3WeuPZ6EwlSZ5cnr2Ti8TgxsyqlD6PEZTJps3FerFydmqah6zpzeI8b+RU1o4zt4vApHjLiyAHdk+ygtTTalYfKGW9d8sEXd6woXXDBpTkdR1VVrtaPAoyEvOIiEJxYfaWBYbSyyhWatc46k3M6lpX0dE9OqgqHT6rgU4fhNLSqdLkdPkpack/L4eM3H7jSN6uTNIdPp9EHoz0kbXZi5fCJdRn9zrqWKlV41XUdX6SGn/iSqbcV9jf6fD58Pp8t+Ogi+AgZsMRUq69B/iGTXq8343a9szKuvbEzjEng41zt2l5XV5dTDh+Ad3kCgOiq8h/XC8WnASNJc66Cj3OeuP/+B+IfpOLBB/HK6IPdkUgk0gSfZNJma9FItffNlPvIEnz8PVSQFnqm8q+4IqJus9wWRWpoqAiHj8VT3GC/Xrq0mVNPPS2n7yuKykoWMJ/ZeJHcBIIbq680MswuuzhkyBDeffcTXnjh9V4fZ5tttqMVw0m2JTMY1rVWD98obxKJRFp+AitxfLakzamJrOO2w0cEn2omNWlzwjT26N4EG2+8CWed9dsej2GFmSy7vp6VX3Q5BJ/KXK384d9tAIxn/W5DRHqLrut0YRxT76yMsBqhuOhmV9Icgs/MmWfmdazUSpcWWoUIPoqpZ1mTSou6uno73NTvT3c5ZXoGvsa9xrFaK2dcLxSPRowy7MqgXAWfpIhx223/wj/UuPZGzl2veI0boDgFn/XZEUiGdF1z7fUAeDxWSJfWreAjDp/CySz/CxnRG6L8ien8jTfx4a8Mwcf8E3SHfTifCaE16YwSMSaWUuhAcKBpGgFqCVBLiynYbLrp5kyatHZOx9lpp+m89elXWFEovnhlq/5WlS4nAXOlwxJ8nIN6j8eDz+e+rVsTzCD1CNVLakhXwpwjKX4to+iaafD1I58ZL+IqD2z4HcoGxjVYqcnT5z2eTJybTw6VTNghdF0i+AjpWCFdGgkaGwfx+edhVx6QXMg2ltM7K0T016x/3GNxv9/PxImTuPbaG5k2bd20r6U+IwEipvMu2lqZ4rVQGJbgow7ONaTLeI56vV4URaFmrAokmPrJjO6/WAFoWoJWVjKatZMLleZ4duNNNgGSSZuNnLiZBB/J4VMsKkPm7yMSiYRts/XgJZEof8FHtx+YRkfLtiLUE9ZgOEYEFQ+KLpeWkETTNOoZAiSr1AwaNDjn4yiKwmz97eRxK3OeaROPx9McPtaDL1PSZkVR0mz8HWaC7ForFEwcPlVJapEB3dR/4mrvw0WCYxRXMlnrmJUa0mWFTUJxBB9d1+k0BVgRfIRM6IlkSJfP581b7LGwihwYx3RX2St3LDeUs8TzddfdDBjPuSOPPDpjdbNf/OJY1l13fdc2S4j99svvStRaoZypM8evamNugqBlDLCeH6o/+RzJtKhSSSQSGvP52n5/I18kkzebp8Gac+q6bucEdJLq8Kn0c1ZKZFaeA4bgYzwwPfgqwuGjm+qz5fDJ1XFhoZoDYytfiFfLnCxQqE5aWlrskCIrZ0hdXV3Ox1EUlZboKvv9qFh+12u5EI+nJ23egxOBZNJmZ6iJqnrSQrp0NDposXP/SEL16iT1eZXoMieWOQg+a6892Z1wPWFUrNTilTcI+/bbOXz/fbKQQbEEHztpc0QEHyGdZEhXAo+ncBP+i9xpv7b6rh6vjGeAJY4FagL2tsmTp/T4vXXXXY/XXnvb1aetiWXbysxJ7IXqxq5yGuhhxxQsgcIWfByLCB0LKttNlqnoyHpsD6TnoEwkEhxyyOHssstuPP54UqROrdJVqYtLfYEIPjmgaZrL4VMRgo95v1FUhY033oRLL70ir+NYrgGrIpBHlzw+gsErr7zEo48+hNcUfKycMlaZ4lxQVZVoIhmvv13i0OI0coDiDOlKta1nyuHj9Xoz2tVXs4ThjEWVKN6qJfV5pUfMcKwUwSeTA8watNbUBF2Cz8YrZnA29/B/Px5d7Ob2O3/726UsXLTQfl8swccKHVG6pC8K6VhiTIJ43o5rJ84qfMuYZ/5GwYcdENiL/Y6umS1RdSac97qouVjpiclipZCOXWUqx8vDeu5a15rzmlv4bGWLi5qWnoPSxtycLMuuUV9fz/33P8o222xn72alJBjOWCD/BPaCCD45oWkaGgk0NNPhU/7qrCX4jF5zNC+88Do77TQ9r+MkQ7rMh2ZCBB/B4I03XgOSKyRWEreamtyt6qmTrhiRihBes+EM6bL6lkWmKl1WnHgqS/gePzU0MERCuqqU1OeVHjMnlko00+4ZCQSCzOVz+/3QiDEIm7x62yK0cGDR1tbKMuba74sl+GgkjMmlOHyEDOjxZEhXLuJFNppZZr+2+q5VCazcscavHm/ymZavSKajEaEDNS5jVyEdrzl+VQO5jZ+Sgo/pyHY4fBKdldEPs6Fpml2GPRUlg+CTibl8AcAaTAIgFuv9eEVwI4JPDlgXZIIYHrxEIpEevjHwseMhC7wSrJuZNZlXNVm9FNxYKySxghw+7oetF39FWzydZdnjKRNzK6TLqnJgvY5G01dApm04FTD+H4jgU51kdfh4Mq+YOWPlrWvG7/fzKEkXqKfNTCCOp+Ji64cNG84K5gNwJ78tSpWu5uYmwMylkpB+KKSjm9eFRqIoImOMCN/yIZ/zKgv4xthYYSFdHq/zPPX+b0t9FkboxCOCj+DAeq4lHT65Peessux2SJdDnIy3V+5iJVghXZn7o/U4tcYl2YTaKJ0kiOHDqLoXi1XueL/UiOCTA1aCSo0EXnx0dXX18I2Bj7VCglrYYF0cPkJPJEO6CnP4BINBXuAfgGHzXP5R5dpiE4mkwyc1FtrjS3f4eDxeNt98y7TjNAwzKnT5qeyqZkJ2rGohFpbDJ672vGJmD3p9PjqVVr4b8RYAk9nC3ieyvLIGr6qq2iGQXXQUZfK9YsVywHBvkJDhl5CBhPVPcRw+ANdxHLdzOnEzB2WlFDuwio4ojkWPQhY0InSgSkiX4MCa91kOH0+ODh/r2Wldl+OPqKXTLKTx9d9b6FhUIZ0xA4lEohuHj3E+rOqhqbknncSI2oJbPC4hXfkiI44csDp+QonjwUdXV2c/t6hwrBWSQhcvk4KPmcNHE8FHcJMa0hUMBnM+hqqq/Pe/LzHmlOQk9X/7NRPvqKzJpoUzaXOHd7XrM29NetJmr9fL8cefyG233cneeyfLfqpmokFx+FQvaZbpqGmlVns/4FQUhUAgQFRPX+yINlVWHzQGq8Y50ovktjj66F8CxqIRmvRDIR2nw+eqq64r6rGtoiOJSGX0VWvB0spnB7kJPhtttInrfYQO9C6F9vb2orRPKH8sB7nPDunK7fupVbqCQ7z8jcPsz5/eeGHG71UCiYSW1eFjqQ+WgOP3dyf4dCUjBCSHT96I4JMDljVPVxN48FaEw0eLmw/+ogk+psNHqnQJJqmW2EKSNiuKwgYbbMjNN9/s2r7888ocoDknnT/UfmhMFE08Qavqg1vwCQaDHHDAwYwYMdLerpramo+gCD5VSlpIlyn4ZAvpykYgEKRTT+9viUhlhXTF4zF7dTJBIi2cNB9Coance+9DJIijiOAjZMASMc7/ywVsu+32RT22ZhYdqZSqeq0thlNi2fIl9rZchNnrrrvZ9TyM0IGfGs79w2+L10ihrLEEH68d0pVfDh/r+eHxeGinqXgNHMAYSZuNZ+iyCV+5PrPWKa0UBD5fd4JPRASfItCrO2MoFFo/FAp9HwqFZprv1wqFQq+FQqE3Q6HQQ6FQKEfNszyxHD66aiRt7uwsX4fPxRf/hUMP3Z/WFiMDulLgYDYp+EgOHyEz3hSHT21t7iFdTjfLt+s/b7++Z9a/C2zdwMRVpcsT4zXutT/z+KwcPknLrKomXzurdXlswSfAF198xuWXXywPziojVfBR4tZ11XuHj67r+Hw+flg5O+0zq8x7pRCJRO3Bqk6iKBWTwBCtjZAuEXyEdJSIcd/21hX/+rBCuvQKSdr8XfhbAKLxpOM3lwWNKVNCnHfehfb7KB148PLhux8Wr5FCWWMVO8g3afOGG24MwPTpuxnfV1U7pKvScZZln7/RW66/W0lx+Ph82U0CUSJ2Dp9zzvlNiVpb+fQo+IRCoTrgBuBlx+aLgJvC4fD2wHfAL0vTvIFFImE5fDQzpKt8HT433HAtr732Ct/M/hpITh7zJTVps+TwEVIpTtLm5HW6cOoHPM41APw4+6citHDg4UzajOrO42OV73U7fJyCT/IBqgaNfa3/B9dccyUffvh+ydotDDwshyoYSZZHhzcD0gWfww8/AoBf/eoUe5tzEtXe3uaq/GOhVZjDJxqN2IKPhobfX5x1LVVVDaeFOHyEDKhRY+zkqSv+sROWw6dCCt0k4gmX6xVyr6bnFHKtEvY1nvrCGydUBM6QrgTxnOdKhx9+JA888BhXX309kMxD2exf0sM3yx+n4KMrCWposD/zmCkJrBw+zgXKVOJE7JC6N954tVTNrXh6c+VGgL2BRY5tOwFPmq+fAnYtbrMGJiNHGiESnoCKF29F5PBZttQYuHu8ha1eWnbFqBnSpSbE4SO48RUpabN9PJ+PTkyHWqQyBUZnWfaElrDPIQDRzGXZLfz+pODjNcO//CTzJlVydTMhHcvh48XH33gzuT0lpGv33ffixx8Xc/TRx9nbrGsskUiw994z6KDF/uwl/mV8VmGCTywWtwWfAw46OK+cY5lRSJCQpM1CRmyHT33xBcFEhYV0eT0+NNzOxVwFH+ciiS34KCL4CAbxuCEo+ggQJ5rzQqWqqkyfvqvtaFcUha6uLiLR8q/y3BNGSJfRH7fcemvXZ74G4/525JFHA/DLX56Y9Tgxh8NHyJ8eZ+XhcDgOxEOhkHNzXTgctq7WZcDono4zZEit68Zajpx00i/Zf/+f8dgW82hviePzwYgRDT1/cQDz8ksv8nO2IVjjK+hvCQaNyaWVnyWgBsr+3AjFobbWuDZSQ7pGjx6W8zUyaFCt/Z2Ghlq6WAGAL1GZ11ttrc9eIalvrCW+KjnYqA0Yf/OwYcm/e9SowdTVGUvDgwcnB60NwwI0gUswGjy4tiLPmZCZ+nrj//26bO9aadMC0bTrIPW99ez2+z14PH66TKEVoMXsg3X+yuiDbW1ttLe34/FgD1Y33WyDov1tQ4fWk6AFRVcq4nwJxcWXMPrp8LH1vb4+eruflbRZ1dWKuPYUXUUjwZ/+9Cc23HBDHn/8cbbaauOcRJ9Bg5JWqgjGIm69r7Eizo9QONGosbjhxU+cKGPHjsx4beRyvey0004889otHMvlOX+3nFBVBa/qAw1mzNiDJ/6w3P5s5KhGAE444RgOP/wg6uuzi6xRuvBijIV1tIo9X6WmGDaMXi1DrF5dGaWT11hjDVB/woOXZctWs3x5+cViWkl0AYfdjoL+lriZ/NlK2kzMU5bnRig+HR1R1mZTpmMo+VZIV3398Jyvkfb2KMuXtzJiRAPxuJ50+HT6K/J6W726zZ50jlpjNA/P/SebsScAjf9n9LFIxLl/Jx1mxbKYI09DRDPjpB2rJKtXt1fkORMys3q10VeiuJ2pETp7cR0Yj/mOjgi1tbV0kUzabK2Kr17awfLl5e9a2WOPnfjkk48BOJDNAeiKxovWV1pauowwlIQi/U9Iwwq3au3sTb80Jou92e9XvzqZ/TY9irmnQDyiV8a1pyugwBln/B6AnXfei5Urcyvg0NmZdLpa9zKfFqyM8yMUREdHB59/biQb9hEgRoSurvS+09s+aHHLLf9i2rSJ7N/4a4Z0rMWyZS0VWUwjEonhUYzFoqaWDlazhCGsAaTPNzs7089fY+MgWlqa7UViHwGivRqvVDfZBLF8BZ+2UChUEw6HO4ExuMO9Kh7Fr+DBR2uZ5vCxkk9DcgXT4ys0pMuMxzQ75tdfhEkkipfoUihjYipncZf99k9/Pp8F3tmMGjUq50Olh3QZN35vrDLzxjtz+KgehTDvchoboaKyOLQKwHb0gDuky5nDx1tjDCaGsAZ1DKadpvQy3UJF4wzpcqJ6ehZprH6n6xoej5dWVgOGeGQNxqKdiazfLycssceD1xapfcHihSgbSZtjKFr5i2NCCTCTeXv8xb0+QqFpTFxnInNZiV4h0byKrqBT2HPM+cy0hOwAEtIlwO9+dybfPbyKG/nC3lZXl3sqglSs8K6Ipx09boi8ngocwiYSCRRT8PH4PFzJz/krrxKtb+nhmwYvv/wmO+74f8Q73IKPkB/5PlFeAg4yXx8EPFec5pQHHp+CFx9dZVqly5m7w5pMFprDx3INWQ4fPwGOOuowTj75lzKxrHJ8q91q89ZbbcvJJ88s+Lgej9d2+PgTuSeALgecVbpUrzER0NHsXAwAdXXJwalTEPP7kxN7b63Rv3fjl3b+FqfTT6h8rP/fXkdY33Llp16tLFrXlabpeL0eOmnh7xzHlRzBsFFDAIi2VUgmWJNhjLVfewtcEHGiqipB6lESHtrbc3MjCFWAKQR6/MVdLPN6vQTqjEUALVoZ935FV9GVwsaXmZI2D4qPLOiYQmXw8MMPcDB/cG0bOnRowccNBIxncFQ15pDxtsqcI2laAo89flVpYQWXcyjfHvhwr74/fvwEDjvs57aRYH12JChibN70pkrXZqFQ6DXgWOAM8/WFwDGhUOhNYCg4lu+rAI/fGCBHOstzgJtR8ClwQNvU1GQc2wzX2ZQ9eemlF3jssUdYsWJFQccWyhu9M3mbGbShh2GbZS+/2BOpDh8rl0istTIGsKkkEsmkd9mcGE6Hj3Py7nb4pH/XWbVJqHws4d2Zx+m/3hvthPvd4Uza7PUaQuJ3fMhivmP0uDUBaF1dWeJFI8Pt115/cR0+o1kbgAfOfbZoxxUqA8Ws3ubxFTfEw+/3o5rHTETL/97/7T9bGJWYBBT27HcKPkGMZ+lWP/yC6dO3Y+XKlQUdWyhvRo1agwV8Y79X/RQlakFVVXP8ajwzYxUq+CQSCYboRopfa/y6gK/RamLdfc2Foii2keAoLuEgzuH000/p4VtCJnoUfMLh8EfhcHincDg8IRwOTzZfLwyHw7uFw+Htw+HwL8LhcO//71UAqmm17WovT8EnkUgKPtZk0lfggHbZsqVAcoVkDSaxNpsCEI9X1eUhpNJlXFuPcy3bPj4IpRcTzGykVqSyQrqC1PPcc88U1s4BSDyecJVlz0RtbS2KolBb667j6/MlHT6B4emDFHHeVRfJkK6kEKjGelfdTlU99jGcIRAAC5fNA+Cpx55M+145U0uj/dpXV1yHj0W0S56NQgqagobWq1DLXAgEArbg09ZSvuLs8uXLefTRh/jkXCOsNKgVlsDVeT9rYJj9+ssvP2f27C8LOrZQ3owZM8YWZb7b63H2+XRsD9/oPX5/gIhuzJfibZW5YFnbMZQ1EusA7hDVXPIVKYriyhk4jDE88MC9xWtkFSFB5HngDZj5ajrKU/CxygwCdtnZocMKsynOm/cTAEv4wd42hikAFVG+Xsgfq8xshHbbHZAvqQ6fCB1oJKihnuee+29Bxx6IJBJxJrMFAF2dmXOGqarKxx9/xTvvfOTa7hR8aka7b/UKioR0VRmZHD5zeJ8ffvgh21dskiFd6YLP6LWMXFyrlzUVqaUDgxqHdVxtLF5fURSVV7jbeOMT0VVIQVNJEC96/kOfL+nw8eBj7twfi3r8vuLQQ/fnlFNOKNrxnOf5ZWbZrwPUEouV5xhfKA4dHR226ysyegXBDAtn+RII+G0h47P3Pi/acQcS9V0j7Nf+2uS4IZcqeoqi0Mwy+/1CwsVpXBUigk8eWLHV0c7yXJ1zhnR5zLzdkyZPKsqxNRLcyIkA1DIIgK6uSHdfESqdiNFfNtl6Y4LBYA87d4/b4WMIGl20U0MDq1evLujYA5FFixYyjW2NN7XZM22OGTOW0aPXdG3z+5NODl+Nl4e5LPmeIJomgk81kRR8jOviVmbSxFJXSGA2koJPeiL+I445EoDJE6ba2+644xZ+97uzylpUrGeI/TqmFK9Ag6IorGQBAKomRQ0EN4qmohHPaVLUG4yQLuO1By9NTeX5vPzqqy9c77UCkzZ7PMmJ6CoW8R6GU7GOwUQiIvhUMx0dHfgx8kOqhQ1d0wgEgnRohkP9z384r7gHHyDo0aSTx3k/y+Xepqoq7/EE3/IhYIjVQn6I4JMH/npjkBZvL8/VOSuka889f0Y9hrNn8KjG7r7SIxdffBnjxk0AII4hhFlikjh8qhurIsiMA/Yt+FipDh+ATloJUl/0AXJ/c++9/+Zf//oHVo6C4289IKfvu3L4eL28zn18zAsA+KmRkK4qIxnSZTh8rHxrvek3ToeP0zkGEGwwHUPxpHjxpz/9nrvu+ieLF5dvAc+9MPIErGAB0WjxBB9VVexnpJIQwUdw07y6GY1E0Z5n//rXveyyy25st90OKGbify8+V7XWcmZp/ZyCvp8qYFsFETx4iUZlsbKaSSQSePERJ1ZwYZtU/H4/nabgE6DnRZdyZK9WozjL5tcOzVvwURSFdpq5nwuB5LxSyJ3KmiH1EXVjjQGv2lR4eb7+wHL4rNW1PjvzCwCGjx3S3Vd65KSTfs2HHxq2xESa4FOe5euFIhE3B5mBwh+YiuJ0+BjH66SNGurTQk3KncceewQfQUYwDt+kGA0NjRx99C97/X1nlS5rUJusohcsa/eFkDtWkm4rpMsqp94brMTOmUK6fDXGtaXE04cTkUj5TphWsxiAyziIDTbYuGjHtcqyAyji8BEczJv3EyqeooZ0/exnM7j//kcNh4/fCunyukL7y5FEvbGQ+MraNxV0HK83VfBJjl9l7FrdaJqGBx8JYsRixY3oCAaDtCeM8uRB6ipyAW6wvgYAa+xUk7fgA+bYwxRirTQkQu6I4JMHQ9Y1vH2Dlo63tz3zzNO89NLz/dWknLAEn8HNyQRkDSOKpzA7V0hAHD5VjyX4BIsh+CQtosmQrjZqaCCQKE8BNhuBgJ//w3D1+NcwBue9qahk4XT4WLZ152C2EgcYQnasFX2vaYnOTfDx2MdwhkAAeGstwcf41zlJikbLLyRi8ODBAETpIkIH3y+Yx8iRxSvTrKqq7fCRkC7BSXNzk3FvJuFa3CgWqg80X4xGhqNp5S34kFBZzPckAoWJMqn3M7fDp/zuX0Lx0HXddPhEiz6P8fsDtMcNwSdALZ2dlTdPamEFzYEl1I7xusbuuYxjre+lziuF3BHBJw+GbWDEdHq6kskvjz32CI444pCyWBGwrbyOfuMbVJxLYYcddrY7pmr+gEwsqxdN05jzzbdAMtl5sbBCS9ZhMwA2fP+woh6/v/H7AwzCSHo3eIYx8Mxl1deZw8daxUyGW/qkLHuVkZq02Qrp6g3JkC49bUXcEnKVhHG/b25utj8rx5CIaDSG3+/HT5AoXa5+VAwMwcc490pChmBCkra2NlS8JIoY0uVEURRiw5sZxQTisfIWfOKdGjEiBTuhJKRLyIau64YbjljR53Z+v98l+LS3l2/lvExomoaPIAlvujMqFzE7VfARh0/+yGgjD7w15mmLpZ++jo6B22ljsRjnnfd7Pv/8UwA8mjFAH3dQHf7G4lwKjzzyBKf8+tfG8UXwqXoWL15kOwqs0I9ikRpaMnjZhKIev78JBPwEMFxL/mHGQy+XSYAz14p1riyHjxefhHRVGVaSbqsse24OH+O60/X0HD4+83momg6flhan4FN+K+TxeIza2lp8BHM6R73FGdIlDh/B4oMP3uPDDz/Ag4cEMTvXYrHRGrrw4CPWVL7jMhUPAWqI0F6w4JM6jkgKPj5J2lzlOEO6iu3wMUK6jGfl5uw9oOeO+RCNRvETRPMUFgpnCT4ahkAtDp/8kTOXB54a4wK0chY4BY2BXPnm0Ucf4vbbb7Hf+3RjpXfSkfXZvpIXinlVJQWfgXtOhNLi8XjsCaY3UNzbTTm46QrB7w/gNQUfn5ko3gqt6Q3OibllW49LSFfVkurwscSM3gh/1qQqU0iXr850+JjihbP6TzkKPrFYjLq6evxNQTpoLfrxFSUZ0hXtLM2kXig/fvaz3QC4hJeIE2Pw4MLyKmZDqzf6fXx178MqBhIej4dTErcChnhdqOCz0UabuN47HepSlr26sUK6onSx6657FPXYfr+fCB0ArMlkXnrpWU444eSi/kZ/0tUWMcaZvuQzrra2jo6Odtra2np9nPSQLqnSlS/i8MkDT8B8UGbIWTCQJ1FLlix2vffoxkTcEyzugz8p+BgdcyCfE6G0KIpqXwe+2sIFH+fktLW1+JOxgYTfH0g6fBoswScXh4+7She4H5rSL6uL9CpdvV95u+KKa9lss8254opr05M2B3wkiKPGje1Oh0+5JW1OJBLouu5w+BRfVHaGdP3vjbdYuHBB0X9DKF88+Bg+ajjBYJHrQFsEjZXyeHt5LsR5vV6msjUAgxhZsOBTX+9e8HSGdFVKJTMhP3RdI+AJMmHtCRxyyOFFPXYgELAFH4A//vGcoh6/v2lZYvxtuj8p+AwZYojYzc1NvT6ONeaVkK7CEcEnD1RT8FHNnAXOZFsDORFeqqrq0c1qY4HiCj6qKcBaDh95aFYvuq7ZIV3FqNLlpK3NiH/+J2cDsHrNH4p6/P4mEPDTwFA0NPyDjHOXfw4ft8NHQrqqj2SVLuO6iOcQrjRlSohnn32FUGiqS/DxeDx4PB7iRO3wpHLO4XP33bMAqKurw0+AaAkEH7/f7wit9PPFF58X/TeE8sVPEDVQunuzYo7PEpHyvP87n4EROopWzcxCM/vmIEbI2LXK0XUdVffhryl+MEwgEKSL3jtdyoVYLMbTTz/Jfb//r/F+RJP9WUNDA5DbYm0ypMudtPnyyy+xCxAJvUMEnzywHDGW4OOMvRzIq+aRiHvw6o0aK72+hiJbe82ragt+ho+AJIetYjRNS4Z0BfO/3ey003QARo4cZW9rbBwMwLd8CECiwFjhgcagleOYzBY0sRS/6Y7KxeGz1lrj2HTTzTjttLMcqyTJpM0D+V4lFJ9sIV254hR8brvtTjweDzEiqAljJtnU1GR/Xk45MObO/ZFzzjkLgLpgg5HnpAQ5fPx+v0t4lUGrYKGgGK7OQOnuzarfEHq0MhV8nGHNHbQUXfCxzsoxXFayPEpCeaBpGh7di+orfvij3++nnWY6MBYulQqZjp911kzO/uVvmfSBEaLK0OQz1O83xh65LARlS9p8zTVXcM89dxWjyVWD5PDJA0VRiCtRvJoxkV25coX92cCeRLlvWv7mRhQP1I4p7mWgOEIs12TKAD8nQilJJBLUY9g4A0Pzf6Ddcccs5swJs/nmW9rbjjvuBJqbm7jl7zcDoMQqy+o58T/7AjCU0fYk2+Pp/TkMBoM899yr9vv99juQzv/qEDcmmtIvqwstrnM5b1DPEDQ0ewCVq9PL7fDxmoJPFJ+WHtI1kB2vqTjLxjYGjXtWjOKXynULPn6ZVAo2VrilGiyhGOMzjl0JDp9OWl2hy/myxhqj7ZQHY5hib08k5BlZzaiaD6/ux9dQfDEmEDD6+o98xnpsby+MljsPPXQ/E9jQfq8OTj7fLNd5Lrn9rOeyjk6CmL1gBelpSoTuqQxJsR+I+jqo0RvQNI2vv55tby+XSVQNjfgXD2Pwev6iq9fOwUoN9WVzToTik0gkaGSYMSEclP/tZtCgwWyxxVauSVltbS2/+9259ip82+r2irRgz+MrO1FuLuUsU7njjlkcdoQRh+6RkK6qQ+9SbfE1l3CuVJxJm1VVNXPSJB0+zpCucnKvOPuDFfYWLYnDJ+By2lXiPUvIj0aGAaDES7d4oZrzSq28oi1tJmgb2K8f5BJ8vsIXLD/66Es+++wbAL7nY3u79M3qpkYzQpD8w0on+FjjVx/+ipkrOUWZ/c/a3X5tCT6xWO/d+I4hPxE68VPj+FTGsLkggk+exHwd1DGYrq4uPvjgPXv7QH5AOCfL41kPRVNZY9eabr6R5+/44EXuBKCOwRVzExNyJx5JmGq/7rr+ioXH40EjQYIYWgQefviBov9GfxGZsAwwchRZq5qF2tcVU9z14qO9vZ1FixYW1kihbNDiyftwjOQKW6790u3wUVEUxQjpMh0+zso25ST4ON1IS+YZfa8USZv9fp8rpGsgjxmEvmUHDEE+8XVxK6c6sQWfaHlOljaPzrBfr2YxXm/hVXt8Pp+dX+QtHqaFlQDEo9I3q5maRCMAgSHFF2Ct8CYrgb+PYNkVOciGJfg8wd+ZMG1ccrtZOTaXv9M5PonSScAh+NTU1Bba1KpCBJ88iQci1NJIZ0eHKwHVQM5X4+w4NRgPt+CI4l8CqqqymO8A8FMjA9oqZvnjxjXnVPyLiXVNR4ngI8CcOeGS/E6/oBr3khZW4PXmXqUr4yHthOo+zj33t2y88TRX0nmhgokn7/91DLJf5+r0cq6oW9djXInaRQCc9/tyuvc7wzdWLmkCYOzEMUX/Hb8/YA/yjZCu8jlHQmlpwUgP4N9rRQ975o9qPoq18kmv5UJJSU1Q6DPRPq7pntXRWMDXxut4eYpiQnHwWpWMa4u/WBkMGh2xCyMHbJC6inkWWOFpqQsmtqspJ4dPsn9HUxw+YibIDRF88sVrdMyOli7a2pKCj6aVxwPCKvfsqyuN4JMsbemR0JEqRiuxAGoJPjFT8ClZKdv+wMxJFCNCIGD8XYUObhVzru5xpG/r7OzIsrdQSejx4tzrU0O6wJgk+bQAnUvjrkFrOTl8nO3WzXHqLnvvUvTf8fl8jpAuKf0sJEVXnzmZ8WzQ+yo2uaI0mrm7VpRmEabUdCrGubmfiwD47LNPi3Jc57PVGr/OuvNOrrvu6qIcXyg/VN0sluEvRdJmo/+1sxqAeoYMaMNAb2hvN8QrP8Z4NbXogZVvK5+kzWBU5XMKPvLszA0RfPLESkzc2RZxOXwG8gUYjydV1SB1AHjrin8jUxTFIfhIcthqRgka/+/nbfJmSX8nRhc+gq5wk3LlgQfu5euvZ6PEVaJ0MXPmmdTUGA+5gh0+/mRIl0UpQu2EgYfuWFT7gmQy7yFDhuZ0HGcfs1bfxutGXo0PzljpcsqUq+Dj04z+5q0vft8wch4Z9go/NWV1joTSYI2RasxxGcHSjSM9Y41rT19enoKPNeH7zqzOmVp9Nl+c4dIaxvn34OXSSy8syvGF8sNjCT6FRw2mYQk+bTQBhuAzkOePPfHMM08zceJowOnwcQs7M2eeAcB5513Q6+O6Q7q6bDEJBvZ8eyBS/rOjfkIxKx10tUZoa2uztw9kcSMaTY74LYePt774mp9b8PEO6HMilJZ4l/H/Xh9U/FwYTmJEWINJNHWVd2n2xYsXcfrppwBwzfA3iRNl552TLoNCJ4fOkK7kMeWhWRU4Qrru4o/264svviynw1hx+JAUIGd732Dd+A6AOxdOOVWgcrbbfj6WwAELho0/QgeDGEk8PrckvyGUD9YYKYiZu6emdP3GE1TQAMr0UWlN+KJmBb1iPb8yOXxUmSJVNYpuXBOlKMtuhXStxMijOILxZV0V7p57Ztmvszl8NttsC5Yubc5pkdG5b5woHrwoqOhoZVUFdCAgDp88STp8uspG8HE6fAKWw6cEgo+qqmj2A9MzoM+JUFo0q/Srr7RhfQ2YLoWl5Z3EzSnoeNqDtLLSNRC1rLD5Jm+2Bi5OwUcemtWBHncnP7TYbrsdcjqO89qzrs3n6m4CIDjCU8Y5fJJtPTB4FgDxttI8u2644Va8+BhLCJZUUBiqkBeapjGYUYTYGgA9UELBJ2DeB4oU4tnX+PRUwac4ylU2h49QvSQdPqUL6VrOPACGsWZZPS9TcbY9m8MHcneUuwWfuOv45SyQ9QfleccfAFiVDqIdMdrbnTl8Bm6HjUaTWfo2ZGcAfCWwrKc6fMr5JiYUhl361VvaG/O7PA7AI/c/xLvvvl3S3yolVoUjD148nUGaWe6OYY4Yn1vJ73IlU0iXhJRUCbHkdWRNaPLBXaXLzDPlMRx87fPiLoG/nNxj1nNqjTVGM7xrAgD1E0vg5QeGDRtmi66192xWkt8QygdN0ziHBxnOWAD0YOnsN56g0Wd1834wc+ZJXHllbi6//sQSfCK24FOc55fzOWvl2FIpfnUmoXywBR9/8Y9tjeGiZmJjL/4BPX/sCedz3yrSEqfwzPCpDh9wCj7le776AxF88sVrOBYSEc2Vw2cgu1mslZA1WJs1mASUzuEjIV0CQMdsY8Ck+Evr8ImaKwk+Ahx++IEl/a1SYoVdWlX02mnK6PDx+/MbgSjedIePPDSrhERy4LT77nvmfRin4GNdm1FvJ11qG01fRVMcPuUjJlrPqUMOOZxROxiTyrH7lMYxqKoevuQN43frSxvuKgx8NE2jkWH2+2123LZkv+X1eYgTg5hKV1cXDz10P1deeRkffPBeyX6zmAT0WhLE7clfsRw+ANtvvyMACXH4CICqlc7hYwk+ccfYtZzHYm7Bx3LhFf5sq6urs18nBZ/0iqBCz4jgky+m4NPVFnE5ZwayuJGcTNbb20qVlNIZA13umeeF/EmsNm4x2qDSlv52PjQ7Osq36pQl6Fg5RCJ0uspSWlUOxo2bkNfxLYfPRkxnJOMBcfhUDWYIh/eAhVx0Uf4r+l6vM+G3cUyPV2WVdyGJiO5apSyna8uyh3tUD0vfMAaqdvhLkVFVlfu5AACtrvcVS4TKxDlGaqlZyogRI0r2W16vx5g4xRUWLVpob7dyxw106rVhdvl6MATaYvHoo0+x22572CkJRPCpbqwcTqUM6bLCnrz4y1rAcBU9MB048QwhXbmy/fY72a9TBZ9ydkT1ByL45Ilq5iRZ8tNy1/aB3GGtlRBnOIevoTRJm50x0ANZBBNKi64Z/UQb3tbDnvnz009LmXnm6UDS6lmuRCKmg8esRBKhA1VNDjaOO+4E9t57BpdfflVex/eYp2dNJvNnngYGtkgtFA+rSteINUYwadLa/POf/+a99z7N+Thuh49ib0sQR49RtlW6rMGjr6v0ecA8Hg8RDGFaiUvYSLWjaRo/8SUAr29wU0l/S1U9xiQzrroEn++//66kv1sMdE2nITGMJpYC8Jvf/I4LLri0qL/h9frE4SMApa3SFQhYeW4MEaOGhrIei7kFHytpc+EhXQ0NDcnfMEMtz+VRSReSB3I3yxPNrDq06J1m93attKErhWA5kXzmZLJuvyZU7/ii/46EdAkWelShkzZUtXSTmpqaGoINfiBqxw6XK1YfTTp8OlwhXRMmTGTWrHvzPr7l8HFSTpNyoQBMh49qulZmzNg/r8NkStps3PNj6Bo0LB/DjXzBbZxeVgMyq63ehHEPCQwv3XqYqqrJhJZlmjxXKB6apuHBRwctdAabSvpbHo/h8FFi9dx//z329jXXHFPS3y0Gy/7XhQcvOsY4OxSalncBg2x4PB6HQ13E2GqltbWFTdkDAMVTfIePtTBiuVZCbEX79zGYUPSf6hMyJ20uPKTLKfjETcGnlkZO4x80Jd4s+PjVhIw08iQxvgmA4BdGkr3DtjuRNZg0YMWNRx55kFdffRlIlszzjSyNOKUoquuBKZnUqxctYlhWnaJFKdDbjYHZLhxT0t8pNdFolOGsxSAMS3+UzqKeO08GwaecJuVCASRMcSbDNZALToePVaLd4/GQ0I17/trf7gTA/pxVVmKi1Q/qflgLgHEH1HW3e0FYk0qNBEpMJpXVjqZpePETJ4qul3bR0Ov10sIK1LYatITGFLbk9zzIpGHTSvq7hdL8dZTXD1oGQD2DAQgEil/hzuPxSEiXwM9/fjCdGM70oRsX3zne0mKYBXSS86PmD4v+M32GM7zKShsSofBUDvX1TsEn6Rhah80GtMFiICJ3s3wZaiiXY/Vp/Ia7mfTWxmzPaWja/H5uWGZOPfVX9utpbAOAx1+aSbhRpctQYsXhU93oMYU4kaKvwqVSO9IPdDKBDUv6O6XmrUc+4AKesd+3sKKogk9XrBMY4tpWTol1hfxRTMHHU+DY1RJ5IJnPx7kqriSMvp4gXlZiojVgHfzKJgCUct5tOR5jRFAlpKvqSSQ0vPjsFexSoqoeVrKQ8Yn12fmx8816rbDJov1K/tuF8Obp88BMR3AzpwLJsJhi4vGoxMx7WX3Ks1KoHt5//102MxcQg6OKf4+eOjVdYFVqyneulEhojGIiO3IEY5hClC5aWVnwcZ0LTNYYw0Jy+OSGOHzyxNOoM4f3AZjExvb2chA31mU7AHy1pfnfr6oqHbQAsD2H2bkjhOpDjypE+0DwmXBwchWgniG88sqLvPjicyX9zVJQ//Uk1/uVLCyq4JNQ3A9Mw4EnD82qIG44e9QChX6PJzkAs6rFaZpmiongSZgVNIiXlZiYSCTYwJ7+QmRF6fqFlfuog1bU9vLOOyYUjuHw8RWljHFPeDwqK1iQvj2eDIdevXoVn332Scnbkgsdnxn3lbs4lxUYC6ulcfh47fHrKdxc9OML5YMVDeEJFj+ka4MNNuL//s+oxvcYVwKw+JYSJAvqIxKJBH/kUXbgcEYygVUsKvpvWNVrnb8p9B4RfPJEVT38k7OZx1eu7QNV8PF6vdTSyJ6cxBDWAKBxu9K0VVVVl7Jb8/6kbvYWKhk9ajh8SpnDB8BXn7yV7cuZHH74QRx55KEDtj9mI9bifoCtZIGrSleh/N92/+d6X+6lQIUcsBw+BVae8nqTfdly+3z33be2q3Noi5EXbi2mEY+Vk+CjcRLX2+8n/aK+m70LwxLAF/MtnpZaIqulD1Yzum6EdBl9qPQhXd/yQdp2Z76NY489kt1225Fvv51T0rbkw9f8z35tVToqJh6PhwV8U/TjCuWHjwBROlHU0lRrPOWU0wBYyo8AxFeqJQ/pLBWdnR14HAWBVrKwm73zY89df+Z6L2PX3BDBJ088Hg/tNHE9J7i2OyeYX389m+eeeyb1q/1CfX09WzKDfZhpbwsMLo2arCgpN8eYXGZVS0zpkxw+zgdyI8Pt11bVq3Ih3pF82GtorGJJcXP4BNzH8hEgHpeHZjWgxK2QrsKuJ6fF2nL4QLrdGkBtMQoEdHV18bOf7cajjz5U0G+XEueze+T2QUbtUFOy37L69EKMCXXLN2KDrWYSiQQBaouS86InPB4PX/M/uoascm2P6EnB5513DFHlxx+/L3l7estqlrCCBbSx2t4WDJZG8LGqgAnVzRimFKXSVDb23HNv3n33Y3Y6akt7W7y9PAWf9rYO1/tPebFoY9f11tuAQCDA8OEj7G1f8KoIPjkiM/E8sVboumhjUU1yNcAZU7jjjltz9NGH25V3+pOGhsY0O5zfX5oUTpZdfRHfAhAPFJ6pXSg/dE2HuFGNxuMp/a3GejB78bMvZ+IjSDRaXoIP0aR74nqORyNeVMFHSenyPgISB13hLFq0kKam1UV0+CQXCny+7gUfK5z37bff4oMP3uOUU05I22egEG9NDrTXPXtQSX/Lcjxak9dYS3k5EYXiEm/T8BFwiRmlwrr2wvs9yMNb/sbeHtXTxaZly5aVvD29xU8N0RRBrBQOH1X1sJxkLk5FpklVyTpsjo8AdZT2WTBp0jroDTG+5HUAPn7j05L+XqnQ2939ZNNTxvPKK//LsnduvPzym/zwgztEzCtj15yRO1meuBJJ1SYFDUtxdK4WDoRJZ319PX6zHPv36se8zn3U1zeW5LesCepTpj1e73+9S+gHEl3GBCpGtOQ5fAAuZgYAU9ma3TmevTiJrq7+73u5oMaM+8r7PMV3GCUbiin4pFZo8hFk4cL0fA5C5bDxxtOYMmV8MmlzoHgOH58vc0JFy86tldG9P3LDRAC0Ye2M3Kb4uUGcWPdDK2dLIlqeq7pCcYisNP7/t7O6T6p0gZEwvF1r5m71TwDEtXSXWUtLS0nbkguBDIJPIFB8wcfrNap0fckbgLEoIlQfVuqL+Xxd8t/yeDx2WNf7v11G6w/l5/hMtCVf62Pa+MuFF7HuuusV5diqqhrh445boxe/VIDOERF88sQ5CWvdJGy/jkSMAVxra/JBGYv1f+cdPXpNAtQCsPs/Qux3z8aMGjWqJL9lhXR1mSUNo03SKauRSJtx3cfoKnkOH4AmlqKRVPyD1BOJlJe7zBM3HBN38yd7W1dX8Wz+qidV8PHz9dezi3Z8YeCi2GXZiyn4JB0+mkPwWWSGKllzyLQw3wHATz/N5ZFHHrTf63MMB6zSVLpQLgtr/GBVZdIiIvhUM7E2Y4zUQWvJf8sSGxMJjXg8RpPPWDnXMwzT4vH+H7sCaHEdL/60kLeGhuIvWo4aZUz04xiLRT4kqXo1YiVsfplZJf8tj8dDq+nuG7ViGs9uXfyEx6XGa/aTObzP4EvnleQ3nFq4D7+EdOWICD554nQsDN5SRRvZTjtN9uSsubnZ/jwa7f+HphUjDjB1oynsvvteJfstK8nsUuYC0PK5VraJyIT8efo/TwHGKnapc/gAaCRYTDLnQBdtZZfDx5vwE6ED3bGUsdZa40r2ez6Crgm8UFk477tWuXRvsLC+6OzLzhw+MZJ9rY0m44VZGSwWG1hWn+eff5YtttiQU0/9FS+88CzxuCMcbe3m7F8sEukOn5L/pDCAeeKhxwF34uRSYV17mpYgGo2heE0xVksXZQdCOgJI5rZLdfgMGzas6L91+OFH4vf7mWhW392IXYv+G8LAx2cKPtE+6JOqqtLCcte2p556vOS/Wyy6urrwmk64BYTx15ReJPXiHzCCdLkggk+eOAWfcePGo/qMWN+uLuPm0NqaXKkZCBdlPB4nSB0A3rrSrrZaE8gWVrCUH5nEJnzzTeltkcLA4oc5hkU1SlefhHQB3Mdf7Mp523KI3R/LAV3X8WpBIhjJ79Zdd32WLGliyJChRf2doWet4ns+BoxVkoHgQBRKg2sFzArp8hfv/u/s150kPd1W0lM9apYf73AndOxvbr89WW75F784jDNOPxW9zugHyvHflvz3rTx3luAjDp/q5tknnwWMZ2WpF8dssTEeJx6P4fGZ04AMi+UDYewKkOgw7EdOwee3v/1DSZyDa645hgULVhChHYAdOLzovyEMbDo6OmyHT1+IsIFAgE95iZf4FwBdtPP666+V/HeLRVdXp+2EixN1LQQVFVdIV4Dnn3+We+/9d2l+qwIRwSdPUgUfxaOg4rEdPs5Qkmg0ysKFCzj00P154YVn+7ytVhtswae+tP/bnedGaUzgJzjgBvxC6fn6c0Pki/dRDh+An/iSR7kSgDoG0dVZPoJPNBolQI1tWw8GAyVxRtXv3OXITxAcMIN6ofg4xTzVdPh4CnT4ANx00+385S9/cU24FByTL68xMtNjxrb29vaCf7OYdHS42/PYI4+gtPsI8y7e0uboBJKJc23BR3L4VDVWnpgonX0m+CQSCaLRKB6vcT/IFNI1ENzpkHT4OEO6zjnnjyX9zVn8HjAcC0J1ce65v+1Th099fT1ROnmcawAIUocnWj65oyKRCF6zJHuciKuwQ1FJCekCOOusmVl2FlIRwacITJ26LqpXQUXlj388B3BbYWOxGJtssi6vvfYKZ511Wr+0sbOzk6Bah+or7gpvJpz5WuoGGfkQYp3pFVyEymZQ7RCAPinL7sRZUjXSWT4hXR0d7fipsR0+gUBpEseqqmqH3/gI8NFHH0rIZYWSSCTvu4pmhnQVmLQZ4JBDDueCCy5I2Wo8V5bxE4rPvJ7Mn08VWPqbYNCdp8fKPxAjYiSHLDFpIV3i8Klq/H04ubSeK5FIhFgshmo5fDIIPpZg/MILzxIOf5O+Qx9hlaq2HD59kROs2QyxsSayQvXw5puvEzCL3PRFn3QuiC/hBwCG/LROyX+3WDhDumJE8ftL32e8klsrZ0TwyZOVK1cCEAwGqa+vR/UoKCSFDqfg096etLovX94/ZS5bWpqpVRtK7u4Bo8qBjTnwj7YPjJUi4f/ZO+94Oary/793ZrbelnvTC4EkhNASEor0jlRpShOkg/BFUURQRFGxIEoTwZ8dFZAuIoL0Jr33Emp6T27fOuX3x9mZnd17k9wk9+5sed6vV17ZnW3n7uyZc87nfJ7nKR+G4w4AmbI5fABWsoAVqMpT1SX4JImS8ASfobLFhkIUCT6vvPIS11571ZB8lhAsfoePK/iE44PfF1944XX+x628yoP8lrO88K7GTpUAtdIcns3NxTaewu5kDl0f+pxWfZI2i8OnrnHzK+bKENLV2NgIQE9PN7lcDj0/Xwuh9UmCmstlmT9/Hl/5yrEcdNC+Q9quNWGlikO6yjGfcPumjuS4qzfC4bBX1bg0b9RQsPHGm3i33bAuzRy8393LL784pNVYM5kMW7MnoPrLUDl8ipM2FxxQkpZgYIjgs54cddQxHHfcCTz55HMAJBoTaPmvU1llCwvNgSaONU2TBQvmr/V59913L1/4wv7Mmzd3wO3t6uokSmLI8/dA8WCsRfKCT1IcPvWGG85RbocPwGI+BiDdWxlJJwdCb2cKHcObYAxFyVlwHT5q18odNC+77CdD8llCsORyheuuNkhVuvpj/PgJdLOSv3IhK1nI+9GnsLEZtXIaUHkOH7c9F130A6CwW2iSLSo1P1SUOnxE8KlvWhgFqLyHQ00sFlNVgbq7yeWy6GE1TusYfRZOuZzpFSDp6ekuTm5eRkpDusrhwrPygo84CeoPXdc9wcfdgBtKDj/8i9x4420A9KL6m5np29dM0+Tkk4/nv/+9b8Dv3dnZwSGHfJ7tt58+OI3th0wmzZ58GYApbDuEOXwK46TfeVcpyeUrHRF81pPGxiZ+85vfMXmyst2Fo2FvJyCbzXrl2UGFU7mMGDFite/5ta+dybbbbsUnn6w5aeS5557NSy+9wEMP/Xet7TRNk7vvvpOOjg4iTqIsDp+iHD75fp8Twaf+yLqCT/mSNru45dmryeHTvUK5IgoOn6EUfNT1yY1TF2oTf34mzVITJGMIHD6lC7Ap0yer6nx5V1G5cvhcfvlPByReuhPEb33rQrbeegZ62R0+pSFdQ/6RQgXTinLCtbOEY489fkg/KxQK0djYxCuvvEQuZ6KHCw4f08wVuXxyuWzRNaSra+gr2PWHVVKlKxweehHG9AQfCemqNzKZjC/McugdPqFQiAMPPBgAOx8HnetH8HnllZd44IH7OOWUgV8jli1TUSVDWcI8lUrzPs8CcC/XlmXTxPA5fGxbyrMPBBF8BolQfg4dIsTixYu47babvceKKwWt3mHzr3/9E4C5c/s6dz78cLa3++KGiPlz5ayOq6/+FWeffToAESdWJodPobNrUdfhIx2ynvj618/i/bdVzH85kza7WHnBJ5taN+V/+fLl/P3vN9DT0732Jw+ATCbDr3512YDceM88pgZMd4IRj8fX9PT1JhQK+Rw+sntZy/h37MO5ODmyhBODP+yXOvhGjRqFgw1Oeat0XX31Ffz611cyalQz8+fPW+3zLMtC13VCoRDhsOHt4lvkyuIeKIR0icNHgOaIqsT46LOPc/zxJw7557mLv56eboyIGps1dLLZbNE1I5fLFSVu9lefLSemV6UrP26VYUFpeSFdIvjUG6lUypfDZ+gFHz/uZuVHsz/sZx667uu3ZcuWrv1JG0g6nfLGsuXMGzJBduuLhrGAD0jRjYaGljdZDKWYVUuI4DNIhPJlVkPoHHPMkTz0UKEalz+8ayDW9tKEdM8++zS77bYD559fnPB5IKFir732CqDsupptEG4os8Mnbxe20tIh6wXHcbjjjlu9cCEV0jX0gs9zz73K1VdfBxR2SdIDEHz+/vcbOOaYI+jp6eGSSy7iwgvP43e/u55cLscLLzy3QYPJjTfewJVXXs4JJxy91ue+/Izqq67Dp6VlaMoFaZrmDc6lDp8nnniMRx55cEg+Vyg//qTNYTNOks6yiK8nnniKmrjaruAz9A6f0twnd911+2qfa1kmhqEmi+FwxJfDJ1uWBOa6Ljl8hAIRWy0uW8cOK8vn7b13IR+Pm8NHQyOXM8nliguO+O8HJ/gUO3zKgYODRU5CuuoQ5fCJY2N51+hy4W5Wahjcd9+9RY+548a6UI6xN5lMMizvUsySGrJNk8ZNwlzO0XzMq0Bhw9K2+8k4L/RBBJ9BwnX4aGjMmzen6DH/jkkymVzrj9MfAgYq4RbA7bffUnR8bR35P//5t2fnc5MCljuHj5533eVSIvjUC+7vvVjwGfpLzaabTuVLXzoGKOySZNNrF3x++tMf8eSTj/Pwww94Auknn3zMVVf9ksMOO5Df//63692mpUvV7sqnn36y1ueuXNQOFPIUNDU1rffnrolQSCvslJZMZo899khOOOEYGUBrBH8On0guQZLOIe+LBx30BXbZZTfA8Sr/uA6fNYlNpmlu0O/OXxwB1rwhYpqW15ZIJFKUtLl0/B0KSh0+UqWrvok4Sng3EkM/PwOYMWMb77bf4WOauaL5qm3bRfcHy/m6LqTTaf7x138Ahc0Q0xz6+WQkEsEkJyFddci+++6Xr5iaYvz4CWX9bNsTfPQ+46F/7PabCtaEP73I6nAch8sv/9mA37OUnq4eNmILLHLYWEOXwyePO256zlxL5qsDQQSfQaIg+PSd0JYmlEomkyxdupQDDtiLV155qc/zTznleB599KHCe/scP/6keX6bfG9vb9Fj//3vfZx++om8885bADSjcgdFhg/97m5R0uaoaruZlg5ZL7gTRFdkzJBar52J9cFNdOwOmqmeNIsWLVzjrr2bl2DBggXeb9e2LR5+WDldnn/+mfVuj+sOGoirQsspx8G06VMBmDFj1np/7ppQIV1ula7+c/iI4FMbuGNPiBBhM0GSriEXfMaOHQuAE3Ig3+3cHD6rS0RuWRY77bQtp5xywmrf98knH+ecc86ks7Oj38fd5LIua9plNE3TCz3+8Y9/xkw+r46TLcuOqJufS0K6BICwEyenpT2n+FAzatRo77Zbll1Dzzt6CgKPZVlFDh9/33AcpyzjxPXX/ZrJ7+4NQDcrvXYNNffd9zAWpjh86pBJkyYTJU6k0eCRR/5X1s925646ep+xyD+PPPHEYwf0fv7+W8rZZ5/OjjvOZN68uVx99a8G/J6lJFeoz0jSBUBLy7D1ep+BUshBmZ/vSw6fASGCzyAR0tVArfXzlZYKPh0d7fzhD7/l9ddf40tfOrTf9zv++EIISChUeM+VKwtVHNyLQWdnB1ttNYUvf/lL3mN33HFr0fuNZhIAzZsO/W6F/6LkJgjN9EgW9XrBTfIYowGANN1ly+HjLmatfEjXk48/wcyZW3DppZcUPc+2bV5//dUiIai3t9BOy7K9gXJD4pHdiWl/i+x0Os0rr7zktSGUU33zgMMO4LHHnubgg7+w3p+7JlTS5kJZ9v4Qwac26O5WE7AoDWhoJOnsEzI82GyzjRIqnZDdT0hX38/+yU9+yCWXXMS8eXN48MH7VyvOXnDBN7nrrtu9XHcAF174LbbeeiqmafYRfIpz5xVj2xZGPpRl2oTp7M8ZALzOw0yYsNHA/tANIJFIcNFFP/AEn/RSmbDWMxEnjqmVL3P3yJEjvdvhiBI+NTTefvtNjjrqMO8xx7GLXIL+26eccgJjxgwbcvFl1ftJL4HuPN7Nt2voBdKZM7dFM0KEZJlUdzgORIgTbTLWWGhnsInH4z6Hj0FPT89aXrF21lTB6u677+Szzz4dkAN9TaRXqTn/WzwBDGyDc0ModfjIfHVgyJVskNBjaiIbo7HPY6UK66effuIlZE2lUmtNaOmfoH/22afebfd1H344m2QyyVNPPeE9tnz5sqL3cAWfpqnlKzkLkGhWC8rezvImPhOCw50UxlAhSSl6y5LDx487aH78oap4d8MNfyx6/PLLf8YBB+xdFCaZyWS9XX+1s6kGsUhk/UVSd+eh9O/v6upk4sRRHHzwfjzwwP0A6Kb6nFhLhOnTtxmyhbmmaYVKYBQSQ/sn0eWYUAtDz6pVqwBoQOWDSoU2fAK5NrbeegaQd/jYxUmbS3fiuru7uP76X/PnP//BO7a6sJFFixYCMHfuHO/Y3//+F5YtW8qSJYv7VBC6887b+PnPL+33vfwOn7l3KTEqpMNv7r6SHXbYcUB/54ay//4HeTuVy5/PcOsfVp9zSKhtwk4UUyvfptiIEQXBR4spcbaBVk4//SRmz/7Ae6zU4eN3/zzwgCoNvXjxoiFtq9Gu5hEvcZ+3kVOu8ckJ2WhosqCsM2zbJkIcLVbeedAzz7zMFVdeDSgBtlTw8UdxwMD6Qaljz8VfOfP4449ar/a6ZFap903Sxe6777lB7zUQ+oZ0WXz44Wx22WU7Ly2D0BcRfAaJ5s3UYm0sm/Z5zF0Au4Psc889TTRaCKWYMWMaq1atXO17+xd+zzxTsBe6k+iFCxf0eU1pHoIxTFbtLIPDx02GCRBvVgvK+/71nyH/XKEyCNLh41KwxarfYunAeOONNwDwyisve8dyuWxRSJeXi2gDHD7uRLH077/ppr97t19/XSWg28FUO6tDncchFAqRzQs+btidv62lt4XqxR1XWhgFQHdoxZqePiiMGeOGdNk4tspj1durfm+lbgD/ZNTFFal+/vNLuemmv3nHGxvVZoor7Pj79MKFC/s4fBYtWsi1117Vr9NHCT6qT3Z/rNqwyw0j2W23PQb+h24gymlXaNsNl9xWts8WKoswUawyCj7Nzc3e7eGj2si0dHpzRD+WZRU5BNyx/cYb/+odc8evgZJOp7nxxr+uNjSzlDEfKcfgOzzpO1pOwceQKkB1hm3bRIkT6j/ifcjYaKOJzJg1EyiEWPopzVUzkHxzfiOA//0+/LBY2N0Qcp2qPx57ytH8859Dv9ZzHeq7fk6JS5ZlcemlP+Djjz/iwgu/NeSfX62I4DNIDNtKLQrHM63PY26VrqlTNwNgxYqVRdVTuro6ee21V4qEneK8PYVO6i+xl0qpSXSpCmzbNgsWFJekHc0kMBwSE4fe4eN3M7SOVDvLEgddP7iDShOq1GxvGRLFlmL5Et9BXwHDXVS6wg9ANpvzFoGmaXoT3fVNQPfpp594C9bSHEZdXR3ebdu2eembK9jM+Zz6vGFD+11pmkaafE4VEXxqFsuyWLhwPgBtKBGmQxv6Eq1tbarf58wsIUJcddXlJJNqjCrdoewv+eqqVSsxTZNrr72Kb3/7G97xWCyef1z1Xf9ENpnspaOjvd/2dHZ28PDDDxQJRJZlYRgGZsom162OD5te3jFK0zQcHK7nqwBsxJZl/XyhcogQw9LLVw2ouXmYd3vMmLFkh7eToNkThl1s2y7qs+6YePnlP/WO3XPP3ev02b///fVccME3+da3zl3rc23TYcQyldOunSXe8bI5fDQHHb3PdUuobewc6ITL7vAB0PJLNCU0Fv/uSu8feeTB3nzbsiyee+6ZornbnDmf8e9/F/qnfy3pd/KtD3/60++4/vprsTIOLa9sAUBseHnGUDMv+IxoVrnILMvyNnyGqrptLSCCzyARH6MWiq513p30QmGQdBNZdXd3kskU7zouX768aBAbPXqMd/yyy35S9DwX1+HjT+yVzWb5/e9/S0dHh3csRIgJTCM83kTThz4poJsbAaBluLLjholy6qlfGfLPFoInl8uhYTCdvQBI0T1kFadWh1uWfVh+ApvL5fj2t7+5xonikiWLvJwnlmV5g+P6lJh0HIeddprlVQoqDelqaCiEftrpEHNuLfTh4dv3n1dnsNC0EBYmFjk2ZXvvuH9S6zgi+FQzc+Z8xtixrVxzzZVAIXQvG1pz+PCGsO++n2fs2HGeaJpoSKCh09DQ6I1VjuOUCC99F1Lt7auKxi8XN9GxK+z84heFcTGbzfYJ6XI5+eQv85WvHMs99xRy/1iWRRMjuHfLBcz7p+p7Rrw8CXNdXBF8MR8DMJKJZf18oTKwLIsI8TILPgWHzx577IXdqMYpd/5aaJtd4vBR/dWflHXRor4O89WxYsUKbz67OmeQbdtce+1VnHrqV/ja8V/3js/lHe+2P6/lUKIcPnq/1ymhhsmq35ceX8vzhoCQocahCWyOVbIhUurEef3113j/fZXX6s47b+OIIw7mkksu8h53qzS7uEnZHcehvb3/DZKB8v3vf5ef/OQS3ru6g6bFKu9dYkR5BJ9O1Dq4ZaWqoOY4tjd3b24WwWd1iOAzSOj5alRuEtSjjjqWX/5SxWK6CmxraysA3d3dfXY2ly5Vuxd7770v0WjUWyDfc89dRc974onHvNtuGVp/LGYy2cvPf/7jotecyhXohDFay7OIK0ra3Kh+YjEauP/+e8vy+UKwmKbJZuwAqKoaN954GzNmzCxrG9yy42dyrZd08aab/sqqVat4/vln+33NQw894CWvUxPd9Rd8XBeCixvm4hKLFbzCentD0WND7fBx3YMhNMJEPJeP60QEcfhUO4888mDRfc/pFhq683rLLXfx+uvvefcbmxvR0NA0rShPnb//9bdzvmrVqj4OVSjkwnOdrUuWFHb8s9lsn5Aul9deUwtLf+4f0zTZyNwSs7cgPullKont4go+3aiJt+uIFOqLXK+aC5ZT8IlEImy55dbsscfebL/95whF1HWhtGqjStpcEHxc8WeTTVROSE3T+iwqe3q6Wbq0fyfhxx9/6N1enWvm7bff5Oc/v5T777+XN55U15OH+JMXpg0QjZbJjae5go+EdNUToawaL7UgBJ/88mkKsxg2uzhipL8+092tct59/LHKV/mnP/3ee8xdI7osWbKE8eOHc/HFF/Z5zE9PT89qc+kBRZsnHe8Vrg+tU8vzha1A5fNrXqaEJsuyvc3Vsl0bqhARfAYJLVYs+ESjMW9C5wo+zc3NhEIhuru7+wwgS5YsBqCxsYm2tuHea0odCa6bZ9iwYbzxxutcdtlPiibTyWSSkSOLbbnbcgAADduVZ5fCzU+kaRqRVvUdNDCsLJ8tBE8ul6MZVdngv/pvOfDAg4e8MlApz3AH6VAvOgaj2cQ7nkz28vLLL6719SqHjxrIBjrZW7JkMffe+y9M0+xTTnPq1KlF9/39et5ri4seG+rSvO7u6Ieo/EWujT+TKQzcIvhUN6Xnz8tlpQ3deQ2FQkWhm6rCjc6zzxaXtfU7VvubwHZ1dfH88895993+505sUykl5vrHvWw2s9acIH5XgmWZTE0WJ2d2Cy+Ui9ZWJfDYmPTSKYJPnZLtVX3A1svrInn88We4445/ARCKqvEoUiL4qKTNhXa5rld3M2SrraazbNnSovHs0EMPZJttphU57tzH/fm0/K6ZBx/8Lx988D5AkXCbQDmReih2I2xIXr11wdEcNPR+Q0+F2sVJ56sulzmHD4CdK/Sl+PIRRYV6+is/7uXp66cUut8MAPCrX10GwF/+8sc1FguaPHkckyeP73Pc7cdf/eqp3rG33nnDuz16+/I4+T/ldXVDU+2xLKvsa4xqRASfQcKdLLq5agxD95wu7sJR1w2VN8A0+0x03R2RaDSKYRSSxLkd9txzC4mo4vG4N1n89a+vLFpcdnZ20tHRwRZbbMXEiRsDhcFyxLHlmVDEYjEefPBxnnzyeaLD1XfQSGtZPlsIHtPMEc9X6EqHetfy7MGnubmFLlbwxiiVPG4EE7zHksmkJ66uCcuyvB2D+fPncdJJx7Fgwfw+z7nnnn8yf/48nnrqCWbMmMYZZ5zMo48+zL333lP0XP+kWb22sPDunVfeyaS7KJ/DWwCeOCcOn9rBtos3ClyHjzOEDp9SQnoIDa2PddyfE8svpl5/varUlclkmDv3M+94KpUkl8t5O46uw8c/7uVyOd59txDy0R+JRIJsNssf/vBbUitMtuzep7i9ZZ4wtrW1edU60/QUVcwT6oeC4FM+hw+occAdC9yFbangY9t2SZUuM/9/llAoxNixY8lkMp64Y9s27777NrZt88EHKkfI888/y6RJ4/jb3/5SNMa4fb+jo52TTjqOPfZQAqw/J2Wh8EOxG2F98+qtMyGHRlq54orLZEysE0477UTuzSceDkLwadwkjN6gxu/5Hy5mxx1n8vjjjwCFnHfHH3+i93x3nhqP921sqYvHH2Xhd+n4+eY3z+n3+F133c7o0S28/fabRcdTC1W/uIg9vfFsqHGr9WmOun75BR+pMLt6RPAZJNyQro3ZGgDDCHuCj7tzrus6hmFg21YfpdZdhMZiMXS9kCTOHfw23ngT77kNDY1FcYpdXV3e7VdeeYlkspdZs7b1QlEixJnD24SjQ5+w2WXbbbdn8823INzklqtvWMsrhFrBNE1iqBw16TKUgS7l2Wdf4e677yM+0s2rNcx7LJVKek6BNWGapjdwPPzwgzz44H+54IJvFj3nueee4atfPZXTTz+Riy++0Du+bNlSLr30B4AK7XTfz49/8jgirvJ1/YVv8/xhvx7gX7n+uANjCnXdcPtmseAjg2Y1UzrpKYfDpxRNDxFC61NJxB/O6PaLU089w3OmptMpVq4sVK3s7U0W5fRJp9NeLgKXVCpVVMGyPy688Dx22mkWl1zyPTaj4O7Z9e8j2eE3w9f9D9xAQqEQ222nQl9zZD13sFBfmEk1F7SN4PLEuMlpS0XH0ipdrviTy+WIRCJerskf/vBiFi1ayKRJhdDlpUvVnPbqq39FMtnLHXfc6i1OoTAv9uelhOJFahvjALwiAy7bbDNrPf7KdaepS/19/7vhdZ588rG1PFuodjKZDPfd928a83NGo7ypJwG1lpx5v+qP7sbpO++ozQx3vNxyy634zW9+B8DixYu54YY/8etfX9XnvVzDQCKR6PNY6Qamy6233tzvcTc3kL9yJigXXjtL6GFV2TZNnHx4Z8jdyHJsn+BTliZUJeVTAGoc1+EzhsmMZxqGYXg/QHeQNAwdTVP2ULfj3nLLnRx//NFeDh/X4TNnzmf85jdXe4Of69YBVZ7Wn3Svc06KGI2k6fFKtE+YsBGvv/4aGjoRYmRIFpVLLxe6F+oWgFQuBIJpWp6IkBnCJLGrY/To0YwePZprPv0r23B00ULq5ZdfHJDg88knH/U55jgOlmXx3nvvsPnmW3rJY9944/WiPD9uEj0oLG5Lkz76Bd+wrSbZK1hAZPjQJ251d3XdPEfurq7fti67mdWNX/D5KY/Siqpm4Vqgy4GWd/iUCj4jRoz0brvjoGEY3u5gJpMmnS68JpnsLconkEql+oRMun26oaGRL3zhMLbeejrt7e1cffWvvOek02lvkhvL562a9YtWxh/UdzJcLoYPV+46kwwG5RedhOBxc/iUO6TLj1t+uoWRRccdxy6q7OOGW+VyOcLhiLfxeOutN3P44UcW9fVUKsWll17iVdNrbGwsqgyUTPbSO99k0ZsFcReKHT6Hoip55UIZJm60CXvuuRcTJ27sbaSUi63Zo2hjVahN3PFoHCoEPz4pmHZEmnQssl5IIzisXLmS9naVG1LXdW/O+dOf/nC17+MXfNYUwjUQdF2tH11n3jimspiPidFAN6vW9NJB5bzzLuD631wLNoTE4bNOiMNnkNCiBWVzDJNKHD5qV8Mf0uWGdGy88SR0XfcJPjGvI//sZz/2OunYseO8929qavYmx4fyDb7w4k+4kucZwQRWzOkAoKWphUnZWUxipmoDyaJkyuVCj6ufmOxe1g+2bXmJgDOUX/BxiTUp27d/1/L73//ugASf0qTLAIlEAzfc8Ef23Xd3rr32qqIB1O82eOmlQo6gaDTa53EoFlTCtpptZ+ilsXHot5TcgbEg+MT7tFGqdFU37u8rhFYQeyhvSJdmaGjoXgjWnnvuDRSXTXWFUF03vL6SSqW9PD3qfopPPvnYu9/T0+05gIYNGwbAiy+qnD+7774n1133e84662tekQSXcUzlaC4mRqM3kU6MD3bPyx3rc2QIE5XJah2S61V90g4H6PAZp/rbUVyEQWHzQjl8CuPCddddw1NPPUEulyUSCRfliywVRFKpFP/973+8+/Pnz/NyiIC6Nt2/3UKWnjOOycxkHJsBapHawDCu5iXvuR87r/Dyy29y1VW/4Zvf/DbjxxfCtMtBiJDkCKkD3OuvK/gkNg2mHYZhkKTbc/jYts2OO87k/POVAKrrxoDCGt988/VBbROoa8JW7M7F3M11vEWMRjKUL3XDxRf/kAWLlCtQBJ91QwSfQUKPhAiNVINmjCYMw/AEFnd3MpFowDB0bNvyTXQ1WltbvcVWW9vwol2QOXNULoNYLOYl5WpsbCQeVwvqz3Oa99wf8wDTHjoKgKZPJnPwJ9/lW/wNAJNsIA4fLQIOtgg+dYRt24UwoVBqLc8eOkL5n1ypu8xdgK4r999/L9///ncBlZdgdTsmK1YULOpucsnSECl/7pIt03sAyrbe0FC+0Mcs6ty4Dh9/rgZx+FQ3rmBXGkpb1pAuQ4V0uSEh7qaF/7fld/jEYvl8NulUH4fP22+rfFMjRozEsiyvpPPEiZsA8MgjDwEFgRXUWOrnfG5iT77MlTzPkVwADH1FvLXhjslmPqRrdZWLhNollw/pcozgEgOHJhTGsnheDA0RYouln2fEkztwEpcxgS0AeOedt8lms4TDEU488WTvdaVV8tLpVJHo6hdtATZuLhQyOJ+buJh/suyZND093fyCp4o2atL0Biy4iNhTD7jj5jimkiNDfOgN1/2iaTopuryNiWw2W5QEPRwOrzVx+e2338K996qk7CeffPoGt8ldz5qmycEU8vwYhEmXeWNX0zRCuj+Hjy2CzwAQwWcQiX1FuXRiNBAOG2iaThPDSbZnGM80Gp22fEiX6S34QjmD4U2FXZKtNp1OxI4To4EYDbQv7lST9rROo95MjAaGxYYTDzUSo4HlFJevbelVMdSRnmKnwCe8FojgEwqFyIWykpCyjrAsn8MngJAurx2aElEjJWJjqdtmfRgzZuxqBR//Tqe7g1+as8u2bb7AuXyPfxJ11KK8h45ABB+3Slexw0cGzWrGPX99cqeVM6Qr7/BxcfvCP/5xI93dqo+4YYS6rntiTTqdLqrmk0wmvbCQo48+DoBbb/0HAAcddEhRZTB/aKXfBTCVHfrNIxe8w0dN2k2UKOYm8BXqBzOlFplOgDl8tttxey9PzqU8wFW8yHW8xa6LT2TEOzP4HIdy3lZXE6MBK2eTzWaJGnFiNLLjzF1pDDfTtayHGA1EiBMhTroni9WrMaJpDFtMno5BGA3De84O4/fp0465d/XQ29uL5lua3Muvy/U1rJaQLJXqAnfcbGMcK1mIFg7mvOu6TpJuT/BZtmxZ0ePxeJxIJNzfSwE1v7zllpu8+0cddQy/+tU1fZ63xRZberdnzJjZ53H/PNAVfKys7eWqdRm5bZwHHihvjivHgvji0USIY9si+AwEyeEziESadFLA/pyB/tEn5P6wOb/gSfLVj+HqHNPD+zBj0X40vBDhUk7k5d1DnMvtAKxiEStPGceVvFB407nqvxd3t/ke96s7au7LbqhEsStYwLPcxeGch51PZuW8qqp4tbOUd/kfT3ELun7pEP71q8cMZZnobLn2Jwo1gV/wyQXo8LF0tYjamS8yiZncwo9YwQJP2GikjTbGMJ8PcFg358Odd97Gzjvv2u9j/qST4bC6xPpdDb0LTNru25EDmeIde5PHsTE9x8JQ4g6MPXQAcABn8gL3FLkLxOFT3RQEn8bi42VP2lzYGXc3HJLJJN/73oVcf/0ffE5X3bOo53K5EodPkkWLFjJ58hS23/5zAPzvf2oQ3GOPvUgmk1x3nZrM+h0+O+64s3f7MIoTrrvEx5Y/zNlPNJr/m13BJ5mlYZhsjtQTbtJmJxycw2fYsGFo+ST+q9uci707Qc1NL4MLOQwNnX9Nmc+J/F494Qq4ksMLL/glnMkX1e3+oqhVFXYeGXU945fNZEt2w0w69OYLPdjYnM8OnhgaJBPZUkK66gB33hOlgRXML9pMKCe6rrGShWzCdL7L7cy98d2ix+PxxBodPo8//giffvqJd7+xsZmJE4vtSg0Njey11768//57gAqHfuutN7zbTz/9FFtsMYmnnnqR0aNHe4JP28KplPJ/Dx62Xn/nYPALnsS2V+K68ETwWT0i+AwiRoNbFagF/rotfX52vWG+yPfV7Tl9X+9WJHiXZ7ApdiHsvfd+PP30U5hmjgkTJmKaOa+y12s8zMvcxzR2YnN2IkYj1mdq0L6aE2lHPS8Ihw9At76MhN3MFLYN5POF8uLm8DHJYbLhbpr1pSu6jGXMoZVxbMbnmMbOrOBOLwnl5TwFwAP8gfu5vt/3MAhzON+iieF8wms8nRdnAbTnxzGDfVjBfFaysN98RZFcAzpGUQjXMycso+n9KUXP+xCV92fXXXfbsD96ALgD4lzeRouFsNMOW7OnCD41hHv+DEomhWV0+IT0kFcdDIpLKbsTS39IlzuhtG2ryOHT09PNqlUrmTJlU5qaip2rW289w3P/lH6GP2ddUz4h8hUcz0JmM529Gf+5YRxj/GlD/8wNwjAKOXxAHD71SC6p+mSQIV2GES4Sei7lEFawgL02Ooyd4ofxwYfv8fltDmfxm+2MZpLn3Atp4KZ7s7QcXfaqopxhy8KfMio3ud/PzGopNjtmBM8/eC9d/IMreR4r5aC1K1eDhlYRYg/AMEYTCi1f+xOFqsZxHHQMwkRIkwxM8DEMgzd4hC3YhfFMYyO25N9cQyqvnMZisSI3aynHH3900f22tjYSiWKHazhsFI2R/tx6hmGwOTuzfNV89ttvd8aMGet9F9u8rBKm/5f/xwc8z9jJYziGOzbsD94AoiTI9SwXh88AEMFnEIk0FX+dkYNW8MsHLmAE44kQ54QJ38FcUJiQzuEtjrhxex545N/cf9MjpOjmgZfv5/2/fcDvfntt0Xtd8Md5nDHrO/T0dHPmQWczZ85nPLLkoaLnzOc9NmcnxjPNO+aKPVC8+1lOnm2+lS+uvISpfC6QzxfKg+M4vPTSi/T29hKliQy9aFpwO+imkeYnHMoM9uGrXOvlkcplTMZQmIQexFk8zo3sztEcxnkAfMTL/Jaz2IuvsDcnArA9B7P9nrO45qmL2IQZfJlCdYRPeI1rUPkMGmllG/YjQowJ132Ja/kS/+r9gffcro/6imBv8hi/+c3vvKon5WK3v4/kf8cuo5kRRSFdIvhUN+7584dUQXkdPnq+kIFBJJ9Dru8EtVjwcZNC2kXVflauXIFt27S0tBSFPI4ZM5Z4PF40kS1NZPnMMy+TyWR4eV+bxXzMXN4G4HUeYnjz/oP0l64/Xl6EvOCT7Eyv6elCDWKlbECDSHDX3EgkjIXpCbRuqoD50Xdp3TrMvz68k+Mu3pUrjv0x5zT/hg+6XmHltPf449PXcM5R3+CF/71I82Ya789+j3///ilOPet4vvKl0/jHU/+Psc0bs/nUrXjhoVdoZjgHfGsn/vOnRwlvZPLo1U+x7LbFaPnPNZMOdKpx2r+5EhSffukeJv/zCNoYh9PeEXRzhCHGtp1ChdkA80Zpms7rPMzrPMyX+RG7chTNjCBFNyOYQDjZQPd9TXyOQ3mJ/6z1/cLhcJ/1XygUKhJ83EJAAFvM358vcTAAP1p6EG8ufZ2NN96kyLG7isV8yhvE49M39M/dYFIrCsKwCD6rRwSfQSQ2oqAGh075iPCeKT554FU+QSWY/P6fz+JrX/sq3d3d7LDDjtx//72c8bnP+MoOR/Pzm74DQOPGYX70o59ywAEHcdhhB3rvZxhhL/lzY2Mje+65N4888hBnnHEWhx56BAsXLuCmc/4LwNh8qEjP+AWwsNC+cuYH8bM0oqyFYwioxqFQFh599CFOOOEYNE3jR/w3sMpwLmPGjOGttwq5aqaxIyuYx6nzrvBCzlyu4Nmi+1PZgV/zWp/3nPLUIWzJfZzD74qP591r+3EqR3B+n9cd+c7P+O53z2eLLbYiau7lTaw/4mVuNL5Hh7k0EAdepFVdsz7PaeSS73vHRfCpbtxJj14i+JQz96ibEDlBM12sKNqRdNtnmjlCaERXDif5odpJN02zyOGzapWqyBWLxWloUCFqYWJMb92Z9rezNHSM8jY5mjrH0v52lqYpBkZCY7PNpuE4Du/rc1luFee7C8rx6sddULghXZ0r115BUKgtPMEnwJAuwwhzDSdzAf/wSixHo1Fs2/Y2Apqbm/mMN3l2n2u555672XOMqrqXHdbJAt6HD2DUqNE0TwuzkgX0DFtCOp3BHp0lHeliPip05PD43rTHFtBqtnrVMG1M5QhORjA6VB+fzYulzSw7uXGr+A/XcSjnYs9LrP0FQlXjOA7RvOCTpjfAkK7CuN2JcpZtzR6cwTWMZQqLTlWPncRlJGjmKW6FfEyJg0OIkPf/qaeegeM4hI1o0XHLtOnt6fFEnFg0TogQBlG2/Phg7/Mv5QF+xIGYpslGFFJzvMnj6vMqQGDpum40Wtw9V8G3p1IJfsZTQ8SGh7mSE4iS4IJdz+bTT4snmI2NTei6jmWZRbkLWlqGscceezN+/HjvuaUVRgzDYMaMmbz11huMHDmK008/i002mcQuu+xOY2Mjn376CVfzBwCO4xIApm03he+ccLFXCjMotbpLX4GNRStjsCwrUBFAGDree0/FGdu2TZQE3axE14NLdnjllddy7bVX8cRfVBKt6ezFdPba4PGgVOxxuT7vHlgdf/3rnwkR4jre8o7dxk9pN5cCwSxAw02F85PrKnwxUpa9unnuuWcAvJ1zjzKGjbiCTytj6WIFzR9PZkcO412e5qCF3+aBXRZifsPmC3yd4X/Yh5f+kOJnPMrK5z5lbM+e2PmOOuzuSZzGlWz02kQW/ayB07iSbTkA3odH9l1MnJ34HnepD70NHrlNuVonHKYWaNl2G6wQoyYPh08L7XMdRZWAG9LVvapnLc8Uag0zlV+ohYN0+ESYw1t8l93JosTWtrbhWJblCT6u2OoWJXArxfrHrWHDhnn302lVcaulpYVsNuM9R9M0wuEwH330YVFFyywpVr0aZlu+BCgHfCQS8ar8BYFh6KzM75o67asPoRFqA9u2aWAYAL10VoTgs4gPAbzKkqUcxUUcxUWrf7O/wp1/nQc0Fc096Qb+AtfxdXX/OxQ/7mMLduGDzJNMyZsJXuTfpPI5v0oLkgTCJ82EpktI19qonBlPDRCNRpmT7zDh8Dc46qhj+OlPC2EfjY2N6LqBaVpeTg+3Y99117+L3qu1ta3ofjgc5rbb7uaGG/7ICSecTCgUYv/9D/IeHzVqNEv5rOg1qfkOF/z5IpYsWdIn90FZCTlkSBEhTi6XE8GnRvEvoKL5CnLBOnzG8otfXMkOfykkV36MvzE5NJM2fSyLzc+4iYv56eZ3EvqkhSdyt9JACwuZTStj2ZuvDEo7MiRJoxZyMVQ/XMYcruQEkhQqepXru/Jbd5umFCaxZsafw0cGzWplyZLFPPvs00DfkK6u6LL+XjIkhAw1AducndmYrRh13+6cmF9QRnpidH9soj3UxgGcCcDwXQ1WPmsyvH0yw30hl8yF4WwGC2DVApTYA+SMFFueNooVK1Zw990qh8DOO+1K8webku2wWXBvcU6t7BaLSwSf4MchdxPGzVXS3S6CT71h5QUfIkE6fNTY3ZtP5A+qfziOQy6nfpuJhBJ4Hn/8UUDlBYHiyng/+MGlGIbqV26OyeHDR3jH1GeFPQHo5ZcLLp65vMMW7AIod0UHSxndOoalS5cwevSYwftj1wFdNwq5+bJSqavWcRyHJtTvuoeVFSH4vM2T3MM1JGhiFgcQHabRlpvAsOkRHn3hfmay35C1435+yyF8jWGMIZfLEc/PX9+ikDfPn/sxKD7OR9GACD5rQgSfQcQ/8I0ZM4axY8fx1a/+H3/8o3IENDQ0YBgqgWtB8On/FLS2tnq3Q6EQmqYxYsQIvvOdi/t9fmNjI1YixWPJv7EvpwCQ2Ei995VX/npD/7QNJkeaCLF8wtxY0M0RhgC3IlWMBsJESNIVaA4fF3+J139xFTgQM2KkzXzYyDnvc+xxJ/Dqd1byt7/9ynvuP/kln8xeTGNLgvFjRmJj8atfXc3xR5/MY088zGmnnYiFiY3F3rvsz803384mk8dikcPG4om7X2HatptyzeT/MsHeHICGfJnNT3mjSOyB8jkO2tqG85e/3MTmm28BwKqp79P20RbksoUFh4R0VS+ZTGE33Q3p+oAXuJUfM1xrXN3LBp2xn4/z8V+6MYhwjFusAIj4rv/2c8rJ6hg2O/+jlc03mcKO2+3Gq6++zPDhI1i5coX33BNPPIVvnXch2263FQBHHHsoJ/zsOiyrhRPuVv129+N+y+HH7UVmZfHvVwvD3OUZLrm/cMy/CA2KQkiXOmc97X0Tvwu1jSv4hCLBLVT6c39rmoZlWWSzxQ4fl9NOU0Ktf947ffoMbwH42GOPADB8+HBOPPEU/vWvfwIqX9DJJ5/G1VdfwVVX/dJ77f/j//j7Lq/R9ZzOr1ExK7FYjOeee5WRI0cO1p+6TjiOQy7veBLBp/ZxnGKHTygUzDn3C00WJo9yA1/60jEcf9mWxOMJYjE1hh436ltEiGOSRUMjlP9nkUMnjI3J/PkrsLMOKzuXM3PbLTAIq5x6RDjplJO54W9/orWljZ//7Aq+fu6ZODgce/hJ/Ovfd9JAS17wGUU2m/EEn5Sv7J6/IEkQ2Ngk4glJ2jwA5Ao2iLg7IAAbb6zy1fgTVTY0NKLrGpZlYprFDp9SDMNg5MhRAGvMxu7n7bc/5F9cxTeYycQLLLa7om3tLyoDjuM6fBJFiWGF2sL9rW+e36XrYGlF7KJ/xpvcz2+5nELlAn+OEHeA8B9zaRyWIKSFMMliY9HQ1IDRoDFs5DByZLBR/djUs4QbdW667RbvWGK8yiOS1DrQCZOgmQQqKbN/J9WlnAvQQw89nKlTN1N38h/7+COPeY+L4FMbuA6f93mWlSws645lbKT67IM4yzuWJc2rPEBG61UH2vOJJL8wF13XSdPLysxielhFbIRGD6u8f+FWaJmQ8O435l2rRdW4mpoJaSFiI/Wif5FhOlOnbsaCBQUBqZJCugpJm1NreaZQa9h5fTYUYNLmUvbf/0AaGhrp7u72qlr6naGbbjqVGTNmAsUhXaNGje4TmnzKKWcwffo23v1wOMLkyZsCBRfQxhtvgoPNiB+s4OtMVzmBUIvJTTedSkvLsEH/GwfCFlts6eUAdLJSlr3WcRzH25DIBFilqz/C4TCtrW2e2OOSJYWNhUmOttGtZElhYZIlRThuoEdDhJs04k1R7PxxG4ssKRzdxsbCCuUIJ3RMcliYZIweUnR7+YN24gjsNMTzG5ZJn+ATtMBiYRLRY147KumcVRryzQwio0eP4bLLfsUNN9zM8OFq59Iv1riVSEzTJJNJEwqF1ijmbL21yn4+0BjmpibVGW0sxn/ZINoa/GLbxXX45HLB2/+EocH9LQ9H5aJK01sRgg/AA/yeBXzQ72OuCNmfNdXdNXD/Dvdv3GST0gTkarDZZ5+CvdatGNSrdwBwGN8s2j0qJRaL9zlWFvKlup/53/+8QyL4VC/+c+fm8HFFyHLuWIabihdIuRmLOZ8d+Cvf4Q+bnszBL40jc/zb/JFvYuyzwlsoujlC3A0Pl2g0WuRE8IcpH3WUKhXrutZWh7+KVyUmbRbBp/6w0m5IV2XsTJ911jnceONtDB8+nK6uTpLJZJ8qP/4QK/8cNhwO96nGN23a5kX3I5GIFw4GsOOOO7PddjsA0NNTHNIYlNDjMnnypl5OIzstgk+t4zgO4bzgkyUdWN5TgCuu+HXR/f7Wit6mHXD++d/hxhtvLXrc3/5odM2RFf73d0u058hgoubHOzhfINGPw2f33fdc4/sONTYWyZ4kH300O9B2VAMi+AwyZ5xxNl/4wmHe/dJJZTQaJZfL0dnZoXYj13BBcQWcdeGEE05i+vRtGDVq9Dq/dijJkFTWQ1McPrWKO2C4Faje51l+8pNfBNmk1fKvfxViO1zB58QTT2HcuPHsvvtefZ7/17/+g0MOOcwb3EaPHs3773/GtttuB/S/yzF27DgAXovdB8BuHMM+nATQJ5wLCKzPhnS3olNhwBfBp3rx/xbdkC5X8IlGI/2+ZiiIlGw4aIni31TjJmHSW83nLR4n0hD2dua6ujoAlYPLT6kg6hd8fvnLq3jppTeLJsBroxLE6NKQrkyvjI/1woUXfouLLvo2Vl7jC0UrQ/AZMWIkmqYxfPgIAN5883XC4chqSziXCjz+OW9TU7P3up12Us7fzTffoihH5YQJG3likiv4HHXUsRxzzJf585//Noh/2bqTSMS9vulk1vJkoar5y1/+wMyZWxBF/bazpAIN+y0VePoTfO66696ix0tFnSOO+JJ3u7QsO8C2224PwMEHH+qlZAA4++yvc/TRxwHw27xDd3/O9IV0dXHooUfw05/+gssuu2Kd/q7BYqc/jGD7a9qwyKGhew79oMLwqoHgt7hqnNJO6k5SlyxZvNZEygsXzl/nz7vmmuvX+TVDjaZpyl5IhGxKJrS1ijuxM/LCwde+cS5f+MIXgmxSEXvvvS+hUIjDDjuSXXfd3TvuipA777wrb7zxPvfddy9PP/1k0WsPPPBgDjzw4KJjw4cPJxxWC2j/Ivuhh57AsixvAbso/CEv8R8+x6FeUkrXKutn7NixfY6VhbzgU5zgtzIWH8K646+w5p5TC+VeK6eLLNKiMeYbaZb8Rk1CtQZ/FTh123V8RiIRQqEQuq7T0dEBwLhx4/nud7/PL3/5cwDi8eLJrH9DpKmpeZ03SCorpEs5fKyM9Lt6wHEc/v73vwCwy3ZfBSpH8HHHtH322Y9///tuQOXd8eNfWPb2qvBM17XjXyQPGzbMu33LLXfy/vvvMWvWdnz66Sfe8ba2Nm/TpadHOQdGjRrNj3/8s8H6k9abeDwhIV11wve+dyFQyDGXJd1HzCwnpRsS/Qk+Y8eOQ9M0bNtm9OgxReFejzzyFJtuWtgA8ZsLHn74ScaMGcvo0WPYaKOJzJy5LbNnv+89b5NNJvHb3/6RO++8jY94mQxJ2hhLG2qOmqaX1tY2zjrra4P6N68LE49sAODJb1neJjMEV426GhApbIgpvWA0NiqRp6OjY62CT3e3GvxOPfWMoWlcmXAcx7PFZmUHs2YpOHzU/1q4Mi68N910O6B2LW677W6OP/7EosdLwww32mgjAC8sc030N7jMmrUd22//Oe++4zg8zJ+9+z208x5PF73mBz+4NDj7uu7+Vxg0xeFTvfgrrJWGdPW3yzeUjD66YP/WRvS99rsVgNzdRf8kN5NJc8wxX/bul4pVG/q3VEbSZvW/6yKwxUVQF/gTndq9KvGoFq2Ma64r7uyxx17esdJ5rN8p2NnZAcCkSaqynl9I9Y+PjY1N7LDDjgBFIV2trW0kEmrx1tHRnm9D+ZyIayKRSHhzVycjy6V6IOw5fNIDzp86JO0Ilzrn+m/LbbfdzTHHfJmjjz6uyHm3zTazaGho6Pc1M2duy5gxYwmFQuy00y7EYjG22WYWV155Lc8++0qf57/J497tbCiJjdVHBA4KC7Nos1IEn9VTOVtcNcrqHD4Aw4a1lj69iOuv/wNXXnk53/3u99f4vEpHCT5qlyTTLTl8ahV3QHIdPlqkMi68BxxwEIsXt/fZMUkkGkgme/ss/GbMmMnll1/FrFnbrvW9BzK4OI7DEj7lxxzMFuzCi9zrOS6++MWj2Wqr6Zx77nkD/4MGmZDhhnQVvgcRfKqXNYV0+SeE5SAc1vkZhzCccVy093lwe75d+b7o7uy7rgJ/H9111z2IRAqijivwnH/+d7juumvYY4+9N6htlRjSJYJPfeAXfDLLbXpYhVYBv0co9EX/YrH0uuF3+Jx88mm8885bXHzxj4DikK558+b2+xnNzS3e7WnTNueDD5S7wBV8glxo+2loaCwIPuLwqQtiqOI7WZKB/g5Lx6fVCSx77bUPe+21D7D2TZDHHnvac9D2x0knndrv8dd5iPFsxvs8y5sT7oX5hetE0NhYRekIRPBZPSL4DDGliWD9Is/EiRuv8bUzZ27LzTffMSTtKidFDp+eYEv4CUOHuyvnXnz1SOXsiPW3uPv3v//L9ddfy1e+cnLR8VAo5JWcHShrqlTgiidb7rEp5513Il/84u3eY5deehmjRwecbyt/msThUxsUJ20uDulaW+LGwUbXDTpZRifLiMQKE0TXel5w+IS957uPH3jgwd4CUB1Ti86LLvoBF1xw0QYnXa6UCSsUQrocMcDWBX7BJ7tShfhWggAJhXHcdd1A34VkZ2eh6MBuu+3B88+/5t0fSL/UNI2f/vQXLFgwn/33P4j581X6gvb29n4/LygMw+CXV14BF4jDp17wV1KtJPFgIOFlaxvf/dXyBsIf/nADZ511Gm/zJG/zpDqYzzTiOvqCptThE3TVsEpGBJ8hJpst3rLbaKOJ3u1KS6w8VIwbN57sQuXwWbloVcCtEYYK90JbaQ6f1bHNNrP405/+tkHv0djYCKzZOeGG2DQ1NbHbbnsQDoc9Z0M5k+iujlB+FNBE8KkJ/OfOFfEc1LHSkq5DjX/x5/+tRyJRenp6vAVeQfBRE7e2NrfKZeE1/hw+g1FhqxL6HhQ7fMRFUB/Ydj7EkgRREnSxgtYKEXzcvhiJRLyxyhVbZ83altdff81LN9AffuHqiSeeW+3z/Pk/XDfRW2+9kW9DJfRNRWNzIz2k0LMi+NQDDXnBx196PAjcpOkuA3EbufPQtZkJBsqRRx5FV1cXF154Xp/H9t5730H5jA3FxiRMQSAWwWf1iOAzxLiK64gRqvOOGVMoZ+kuFmud3//+L1y5262Qgk/e/xTYI+gmCUOAu9AsOHxqf/Fy+eVXEYlEufTSn6/2Oe734uYpiUZjnuDjD1kJikKVLr/gI4NmteI4DmOYwkS2LCoxC+V3+PiFGX/1jGg0ys47b8vSpUsAnztQV89xJ7f+nX6/42AwqIi+l99Bds9PKFsZi35haHEdPs2oeWEnyxmuVca59+fPaWhooKOjw+uHEyZM5PXXXytKDF+K+5tuaGhkq622HtBnjhgxEoBXX30l34bKCOkCJWDlyKDnKuP8CENLjEZS9HibJEExc2ZxSoGBhGNrmsYHH3xGQ8PgrS1Xl09rwoSNBu0zNgQLSxw+A0QEnyHmkEMO5emnn+R737sEgKamQuxyvQg+G200kUOPOpRVN0HXit6gmyMMEe6Ftp4En402mshf/3rzGp/jfS+6K/hEyFefrQzren4U8MdBr2lCL1Q2tm3zDf5CM8N5H7XDbukZsPpWuhpq/AlcNU2jsbGJnp5u2tvbPbEHCnb1Qh+J5o8XXr+2nHfrSiW5CDpZBkAkuW6VxoTqxBV84qg5YIpudH1YgC0q4A8dUc6eDs8Z+LWvfYMlSxZz5ZW/XuN7vPPOx8RiAx/bDjrokKL7ldQ3NU0nS5pYds1FVoTaIEwUk+CTqTU2NjJ//nI22kiJoQMVcVx37GDRn+DT0NBYMSGoFmbRZqXMXVePCD5DzKRJk7n99n9595ubCxM6t2JXPdDYmmAV0LUyWJukMHSUhnTpUbFAQ8G+7y5eXZeFYRgVMWiGwuq8hSkM7BLSVb04jk0zatK3MWqHPWkphbH8Dp/C71vTNN58832mTJnAsmVLi57n7ui7k8v+Fnz+yj6DgaZVzvWpk+XY2ETT9TMnqGdMMz8m5K+5JlnP3RY0/jGpMGYp8Wbbbbfn/vsfWet7jBo1ap0+U9M0Jk2azGeffQpUTpUuUN9BlhTkhgXdFKEMGEQwqYziMv4Nj9VV3Bpq+psLrq3CdDlRSZv9go84fFZHZYwwdYRf8HHDvOqBtrHDAPjonU+kQ9YorrLulWWvoKTNQZJKqfxVpQN2aUL3oAhFXcGnIAaI4FO9+K+vCdR4kyEJBJvDJxwO09TUTCwWY9WqlSXPU9cM1/Xqz68zduw4NE0b9J3LSkjI6bbBwcYkg2YGLwALQ4+3CVAk+FTGuR8+vNDPXFHUzeEzlPgrd1VKlS5QYaZZ0oQkpKsu0Al7SfSDxr8pMdghzQOldKyG8lf7XBMWuaL8k7K+XD2yIisz48aN54gjvsiBBx6ywWVlq4lIg+qQ6e6Mt4sj1A52zsF8vwENo+DwEcGnCFf4WbRoYcAtKSGczzHkS3wngk/10t+5cwWfcjt8NK2vW8Awwn3ETndH3xV8/Pl1nnjiWd5884OK2vUfLDbffAsA9tlnP0yyaLaYrusBN6TL3RwxyRX1lSBw56PTpm3uHXOvJYlEYsg/3+8aqKS+7oZ0ieBTHxgVJPj4NyWCcvhYlp3//EJIWSX1T+XwkRw+A0FmF2VG0zT++Me/Bd2MsqM3qAtXlAQ77TSLzz5bHNgFTBh83v9NJ7krN+M3vM4iPgLqI4fPurBgwYKgm9AvWt7hE/E5fGTQrF6sdH+CjxIb1yWvxmBQ6vBR//eddrghXK6TwP/7G2xnj0tvb8+QvO+6cOihR3DLLXey4447c/umH6JZMiWrB1zBx3X4WOQCd/j84x93kEolaWoquNDdPmuauSH//OLPrZwFpUranCZka9g5By0s85paw7Ycvs3NvMGjGEQqoj+WMnbs2EA+95RTTmf27Pf5+tfPY599diWTyQyoRHy5cMuyhwjh4Mhm5RqQLXihLBhx9VOLoCb01113TZDNEQaZ5c8VktyNYyoAeqSyBsyg+NnPLgdg330/X3T873+/NYjm9MXL4SMOn1pg1e19d+Ndh0+5K1P5BR83IXN/izlXBHJDzlw33FDwox/9DIC99tpnyD5joIRCIfbb7wCampoxQzl0u3Im0sLQURB8XIdP8CFd0Wi0T2J0tz+m00OfxLZSHT4qh4+qomelZSOkFkkvtZjENhzJtzEI0zK8mY8/rowNOjesa/z4YKpiJRIJrr32/zF16mZsvvmWAKxYsTyQtvSHjbqWupW6ZLNy9YjgI5QFPaF2RVwXwbPPPh1kc4RBpjQdRq4CJrCVwpln/h8PPfQEZ5/9NQA23VQJYiNHjgyyWR5aXgMIFzl8RPCpVrKf9nWJ5PILFr8AUw78OQgKDp++ooYrArnlmYdyQvm1r32D2bPnsMMOOw7ZZ6wPtpZDd8JkMpl+8yYItUOpw8esQEcBwJe//BUADjjgoCH/LH9+y0oSfFyHD4CZlMVkLRLSCxPYMFG0cHAhVKU88sj/eOCBx8qef68/9thjL4CiCptBY+UTbLt5fETwWT3iHxbKguEJPsrhU3F5TIQNo0Q6VpZY0ZNB7eLPmrWdd/+hh57g6af/x3bb7RBgqwoUBB9x+NQCDquf8AS5qHRdPK7o1Nzcwqabbsrs2bO9KkDDh6tCBkMteLS2Dm7Fr8HA0kwMK8LOO2/LggXzWbKko6IqiQmDh2VZjGMqJ/MLQDl8KvFcn3XW1zj88C8yduy4If8sv+BTSSFdbg4fACtlA5UnzAkbhmMWj5m2Xjnzn+nTZwTdBI9JkyYH3YQ+2HnBR8cghwg+a6LyRhihJtHjxQ6fnh4pz15LlDp8KjEGulJoamrm4IO/UBFVgqBQpauBQpUUEXxqkyB/c27cv+vwaW5u5j//eZgPPvjMu1a4O/vZbGUkzSwntm4SceIkFqjFdS439HlThGAwTYuj+Z53v1LHy1AoVBaxB2DMmMLnZLNDH0I2UNwqXQBWShaTtYidKzmvFST4VBKVVI7dxXX47Mhh/Ij/YmQqp4JYpSGCj1AWjERxDh/TtIJsjjDYlFxJGhhWkRNYoS96VIkAO3EEv+EN9uEkbFsmttWK3+HzF77NrF8Xds6DFHwKIV1K1GlqaiIcDnvuHii4f+pRcLR1NXH9Bn8GCmE/Qm1h9tq8u3cjUyk4PFP01P14+eUvf4WTTjqNLbbYilmztg26OR66rpPNJ703RfCpSeziopE4ulx7+6PcOQAHwrjmTQA4mu8xko0Yv3J6sA2qYCSkSygLpQ4f26y/CX0tE9L6LiSDLjMrDAzX4QMq8d0XuRDLfDvAFgkbhG9N8joPM/nYZjhP3Q8ybMR1+Liijr8qj0t/+X3qBq1w4sYwBcsy1/BkoVpJLu67mOyhve4Fn2g0ypVX/jroZvRB1w0vh484fGoTp4/DR85zf0SjlRNq6bJRZDMyvknPmkLa6x1x+Ahlwc3hM2vLz/GD8L/4furegFskDCahkitJpSahFPqi9ZML0E6Xvx3C4FA64fH3wyAFHzeHj1viuT97eCWVey03MRq92z/gHpYvr5xKKMLg4YoGT3CTd6yXTtkgqVCUw0eFmIngU5uUOnw0U7wQ/RGNBp84uhQ9VjynsREzweqQX7VQFvSYEnxGtoyG3LBgGyMMPj6Dz/fYCwebB/WHg2uPMGC0KLh7zm/wCDP5PI4IPtWLb03yl7/cWPRQsIKPEnPckuv9CT477bQzAMcc8+XyNaxCiFvF38fOO23H0mUdwTRGGDKspFqQZEnxJP+gieGsZIFskFQouq5j5gUfOyuCTy1SmsMn5Ehf7A83afOMGTODbYgPLVIcXSAVZlePCD5CWQhpIfREqKispWVZMsmpEfwOn25UhR2p0lUd6FENC0jSRZpeQBw+1Yzf4XPooUcUPVYJSZvTafXjSiT6lr3dYYcdeeGF15k4ceOytq0SiGSLv48YlVEWWBhc3DlQljT/4TrvuMyFKhMl+ChXYi4tYZa1SKmQF7Jl7tof48aN57nnXi1bIvcBUaLvJDKtwbSjChDBRygbRjyEmSz0zlzKRG+USU4tIDl8qhdd1/gB+5Gmh0P5JgB2ujIqiAnrjpNd/bkLwuHz3HOvMmfOp8Riyg7uOnzi8f6raUyePKVsbaskjFyxXV4En9rEDQvK5BMBu8gGSWWi6zpWXvBZubSdTeibe0yobl4+b2XRfRF8Vs+mm04NuglF6Ini+c6I7sorHV8pyK9aKB8h6P6osEOS6ZGyszVDP1cS2bGsDjRNp4OlpOn1qpE4aRkaqpVQdPWW5lBpsq0ysOmmU9lvvwO8+52dHQC0tLSUvS2VjDNdLTqW8BlQnNNHqB2stBJ8chSXHpcNkspkwoSNPIdPb1cvjiNhXbVGz6fFzi0tW7+55KqN2Kji66YjOXxWi8zqhbKRWVHcEUXwqR1iI/tOVoPMFyIMHL8wt9O+nwPA7pZzV62EmtXkddUpT/V5rJL6ZHPzsKCbUFGMvaiXK/gyb/E4IA6fWsU2lWDgukZcZIOkMgmHwxx6+GEALLmqgTtHz2PlK5m1vEqoRlayEID0xKUBt0QYKDtcO5x3eYbL+CIAulV5lcQqhfWe/U2bNm2vadOmLZ82bdqT+X/Xrf1VglAgtVwEn1pBjytb5e/5WuGYTGCrAs0XjhcfraJ8nU6J9q1WHHezsrHv9bUSBJ9LLvkJTU3N7LXXPkE3paJItEaZyzuk6QFE8KlVXMHHprg8u4yXlUu0oXgR+eal7QG1RBgKhm2lHD2XcghXcxKdu78VcIuEgZIYa/A7/o9FfIRJDsMWwWd1bOjs76nZs2fvlf937qC0SKgbOt6WBHi1gpOfu3ZRiIUWi3p14F9ohBpVn7R75NxVK46r8/RzCoNM2uxy7rnn8dFH89hyy62CbkpF4Za8dROnS0hXbXLHbbcBfQWfcFgWKpVKJFEc4tOxrIsTTzyWRYsWBtQiYTCxc9BDOzYWn/I6WuVVHxcGQJYUhh0NuhkVS/DbfULdsWjj1wDIdVlreaZQLTi2u2upwvamTductra2IJskDBB/XpdQOJ+fwAzx1ltvsHSpWJurDcdSoo53Ln1o/SRXD4JKcBpVGm7ZenH41DYvvfAiAJZP8Nlnn/3YdtvtgmqSsBYi8YLg08UKFn22mIceeoC77rojwFYJg4WVcTDJevdlfKpOMiQJW6LWrY4N9e1vOW3atHuBNuDS2bNnP7K6J7a2JjCM2tg1HjmyKegmVDX2sCTMBSNnyHdZI8Qi3QA4WNx5550cddRRQ/6Z8tsZHIYPL3yPjcOiJIGQBfvttweAJKmsMgx0TKCpJdqnj0yaNGFQ+430wcFj8eK+Dh/5fmsPPT/ttik4nK+55irGjVv/DRL5nQwtbaOaPO9yml6ieTFW12357quc9vZ2Fs9fTM4n+DQ3J9b5vMrvIHh6WMWo7CY89tj9HHfccUE3p+LYEMHnI+BS4A5gMvDEtGnTNp09e3a2vye3tyc34KMqh5Ejm1i+vDvoZlQlU89s4qM/ddM5fCETgM4lafkua4Rkj+r2Njbd3ZkhP6/SDwePrq60dzuVVeexp7NwTL7n6iKbUi673kzh+vrcc6/y1FOPM2XKVoN2PqUPDi7NzaMAyKH6Xpgoy5Z1VUQYnjB4aPlYS39IVzJprndfkn449NhtWbKkeJn7mciWtDASgO7ulHz3Vc6rr75OyDaKHD6p1Lr1R+mDlUEXq9iILTn5y6fxxBP/46c/vbwux8/ViY/r7VubPXv2wtmzZ98+e/ZsZ/bs2Z8AS4Dx6/t+Qu0z6+dtHLV4ImajKv1sJsU5UCu4OXxsrLq8wFYz/hw+mutcN+UcVi35vqiFC+dw002ncvrpZ0nfrGAaGhr4/vd/5JWANghj21JittZwBR9/SFckInknKpnGCRG+w67cyqV5h0+CECEcR/pntZNMJjEIF1XNk5Cu6iRFFwBxmvjjH3/Hyy+/FHCLKosNqdJ1wrRp0y7I3x4DjAYkg5mwRjQ9hJ5fVNqmDJa1guOVmjVlUVll+Cc3oXzeUEcEn6rFPXchKbRWdSQSCW/hoRPGsiTPXa3RX0hXS0tLUM0RBsA228zyhFg35DJKAsuSOWy109vbi0GkyOGj6yL4VCPuOTRQi8xcrt+Ao7plQ6aE9wK3TJs27XAgAvzf6sK5BMGPHslbmnPi8KkVrIz63yQruyNVRjweB2DEiJEFkUAK6FUv+XMXqo2UeXXF5MlTiiat4vCpPTRP8CmIeU1NzUE1RxgAiUSCSCRCNpslg0pPEaNR+mcN0NvbS5ioJ+hBsetZqB5M32YJSP7JUtZb8Jk9e3Y3cOggtkWoEzRDCQK2KZ2xVnDFO3H4VB/Tp2/D5ZdfxQ47fI5PX1kAgGOKaFetfPbJHCawBXqNFEmoJ/bdd38mT/07fAQGEXH41CB6PyFdMmZWPuGwEnz8Dh8RfKqfVHeKCJRU6ZKxsxopbJYoq7oIPsWI6VsoO3rYFXxksKwV7Ky6sCqHj0xeqwlN0zjttDMBmPNmPirXknNYbXz00Yc88MB9aPbmWORkl7JK+fxBn4eP1C6l5AipPQpJm03a2tr4y19uCrhFwkBwr6cZr4peA5YlVthqJ5PM5QUfyeFT7Vhe/jslbYjgU4wIPkLZ0fPJRG0ZK2uGbFKdTJOs7FZWMVokf+4kh0/VceSRh7Bs2VIu4i4sTMIi+FQlelQtNgzJ4VOTtDYPhy5V0fLww7/IrrvuHnSThAHQ06OqMGVR8esGUbJZyWJR7dhZJapvs+0MeE0dk82S6qRQ8EAcPv0hMqZQdvSwupg6EtJVM7z47AuAuuCK4FO9iOBTndi2zbJlSwG8iiOyS1md9KRVpRFdcvjUJC1NwwCVw8e2ZQ5ULbh90Sqqoifnr9px80+GwoVjIvhUF+ec8w0ikULibcnh0z8yIxTKjuEmbbakM9YMloaFiYNNKCSXlWpFi6g+KVW6qotVq1Z5t3UMTEwRfKoUI6qM18rhI4JPzZEPl7UxRdCrQqx8VnwdA9sWB161Y+dNWlqkcEw2LauLH//4ZyxYsKJIjAURfEqRGaFQdjTX4SOCT80Q9pW1NAyJFK1W4o0JAJzcWp4oVBRdXR0AtDKGkUzExpRdyirlpFNPBqRKV61i5GIAJOlm5sxZAbdGWFcsXyUg6Z/Vj5t/0i/4yNhZnUhI15qRlZlQdox80mZHcvjUDAaRQklEGSyrlsbGBkxyXtU1oTro7OwE4BvcAMBwxqPrS4NskrCexJuUIKAWlOIgqDUiOSWq//Eff2K3fXYNuDXCulLs8BHBp9px8g6fULjg6pE5bHVSWpYdZB7rRwQfoewYkXyVLnH41Aw6RmHnSwbLqqWxsQmLHE5OLM3VRFeXyvsyko28Y1Jatjpxc0lIWfbaJJprJEeGPfbbXUJHqhDTFzYi/bP6cTe3/A4fCYeuTkpDuoRi5FctlB09nC+ZJ2NlzWD4QrpkoVm9NDQoh48kba4ustlMn2MivFYnmh7CCdkSMlKjxMwmkqFOEXuqlEJIlzh8agE7H77uFaxAxs5qRUK61owIPkLZCYdVFRkJ6aod/IKPrstlpVpJJBJY5AjZcg6riVyu78VU+mH1YmuWOAhqlJjVSDLUGXQzhPWkENIlgmwt4GSV0OMXfGTTsjqRKl1rRmaEQtkxDAMLSxw+NYRBuKCuS9LmqiUcDmOSJWTLhKeaMM0cGsXnTGzp1YujW7KgrEHsnEPETpDSuoJuirCeFDt8ZBJb7bgFKvSoX/CRsbMakSpda0Z+1ULZ0XUdGwtkLlsz+JM2y+5I9WIYSrjTHRHtqolsNssIX/4ekH5YzTiahUGE//u/04NuijCIWBm1ADFDUgaxWjGLqnTJgrLacUO6dJ/DR8ItqxNTBJ81IoKPUHYMw8DGkpCuGqI4pEsWmtWKct/l0ETwqSpM02QKxSWepR9WL45mYxDm9ddfk0lrDeG6mu2QOEOqFVuqdNUW+XyFWlREnmrnG986DyiEdFmW9E8/IvgIZccwDNL0QEoGzFrAsR2p0lUj6LqOSU6qHFQZuVyOMNGiY9IPqxdbs7xJa09Pd8CtEQaLJY+lAJTDWahKpEpXjZGvSKpHZTlc7Ww9c2ugkLRZ+mcx8gsXyo6uG/TQQa7DYcyYYSxbtizoJgnryTnnnMkpX/kKgDh8aoBQKISN6S02heogm80SIV50TPph9WKFcoTzk9bOTknwWyu8cPYKAGI0BNwSYX2xfA4fx5ENy2rHzgs+hk/waWpqCqo5wgbgnkPDc/hIGIkfEXyEshMOh+mlgygJDMK89NILQTdJWE/uuut2nnj0CQBfWXa5rFQzVr5CkFA9mGaOXfhi0THJ4VO9ZEMpT8Dr6pIEv7WGIwkMqxY314skVa8NQmbB4XPooUcAsM02s9bwCqFS0WOu4KPczuLwKUZWZkLZMQydLMraHCZOIhFfyyuESsa1T+akSldNYIdMNHRCMjxUDbmkwyg2KTqmaZKToFrJaWkixNDQ6eoSh0+tEQrJtbVq0d3/DFlQ1gCOJ/iE+OMf/8qcOUtobGwMuFXC+hBOqM4ZEcGnX2TUEcqOrhvkyAAQJkI8ngi4RcKG4NknJYdPTWCHlA1WXD7Vg5XuO7GRfli9mLoaHyPESSaTAbdGGAwyqwp9NISIsdVKyFBJ1MXhU/04joOWUfMcI6aj6zqJhKxHqhU9riSNsAg+/SKCj1B2DMPwwn/CRIlEIgG3SFhfxjCFi7gTkBw+tYKtKcGngWHBNkQYMLl+BB/J/VK9uIJPlASmKSW8a4HFD6e82+KerF5CeQOzVOmqfl7+xko2fm93oBAOJFQv4bhae4SJAUj/LEF+4ULZ8Qs+BhHplFXM7hxLI61AQfARu3p1Y2uqP/6MRwNuiTBQ7Gzfa2hLS0sALREGg5xWEHxyOUk8WQuYace7LQ6f6iXnqL4pgk/1M+f2Xu+2m5tJqF6MBrX2iOQFH3H4FCMrM6HsGEYhpEsJPs5aXiFUKnEK1QzccqUg57OacTQZJKsNM9N34bHtttsH0BJhMBCHT+1h5wrjoiabIlXHKaecDsCW07cCVMizCD61gyEOn6rHkJCuNSK/cKHsKMGnENJl29Ipq5WorxS06/BxHBF8qhkRfKqP/hw+QvVi+hw+2Ww24NYIg4GdFYdPNfPLX17Nhx/OZfzEcYDk8Kk1QmHpk9WOEdGxyHkhXSL4FCOCj1B2dN3A9JI2R6VTVjGtjPVuuw6f5mYJJalmLEdCSKqNXEZdQ5cxB4DuzT4LsDXChpLT0wDESGCa0h9rAf9l1QnJpki1EQqFGDaslbZRbQC0MkbmrlVMelnxuROHT/WjaRpZ0l5Il5gJipFfuFB2DMOgkxUAjGSi7JJUMW0+wWeLrbfg/fc/kyoHVU5PsjvoJgjriBsu8g5PcznHsPTzTwfcImFDsEJKPD+NK/nmN88hl5OwrmrH7/B5rfXeAFsibAinf+M0rIYUY5jCW2+9wVZbbcrNN/896GYJ60j3p8XXVE1y+FQ9oVCIHBkJ6VoNIvgIZSeRSLCYjwEYzjjplFWK4zhoFCpyaXGH4cOHB9giYTCwnYIAO4qNA2yJMFDcxaRFjgW8j63LNbWaWdr8IQBdrATgkUceCrI5wiDgirK3NH6f+c1vBtwaYX1paGhg5LQWosRJp9MsX76MSy75XtDNEtYRK1nssgs3ynK42nEFn9FMIkKcH/7wYpYuXRJ0syoG+YULZWf8+Amk6QEgRpPY7qoU27bRMbz7Tkx2oWsBm4LgcxF3ku0UB16lY+XUObJQcSOSR6u6aU8sYAULPEHdsiSsq9qx86mYOlkmlSyrHKNBwyDi9c9UKhlwi4R1xUwVj5GRZumTtcBwxgNwLN8H4Pbbbw2yORWF/MKFsmMYBsecdCwAMRrE4VOlmKbpJUcDICaLklrA8Qk+EeL0zpPzWum4Dh83j5YIPtWN4zhkSXm5CJAkv1WP6/DJOTlCITmf1YzRoM5fxFe0QqgurGTxRpYekeVwLTGGKQAMGzYs2IZUEPILFwLhgksuAGBHDpOy7FVKttdE811CnEYRBmqB5cwrul86MRIqDztvrrNF8KkJHMfBJEcjrUE3RRgkPFHWyaJpMvWuZoyEEnwmsDmACHhVSKnDR/pkbdHOYkCFYAoK+YULgRBuUgNklrQ4fKqUbI9aXH7Gm9zKpaTGLA24RcJg8Bz/LLpfOjESKg/bdHP4SEhXLeA4DhPZEoDN2VkWlDWA6/AxnZwsLqucxk3CAMzi84AIPtWIlZ/XfLTJE1zNiXIOawTjuPkAzOM9AKly6UNGHSEQQloIc5NVRIiJ4FOl5NLqQrqKxTzLXXT1dAbcImEwyJFhHu9690uTGwqVh+secDTlxhLBp7rxn7/tOVjOZw3guvBMJ4OmyeKymtn4GOUacKsBiVhQfVgZdU1dPuxjPuUNyatVIxgT1IU2lA+DzmQyQTanopBfuBAcEbU4sTMyma1GsnnBx8350tUlgk+t4FDok6aEdFU8VjZ/jvJF80QgqG4cx+G77AbARLYil8sG3CJhQynk8JGQrmpHM9Ri0i1aIYJP9dG+vAOAOfM/ASSkq1aIRJX7LpSfDGUy6SCbU1HIL1wIjnC+skxaFifVSHdnN1AII5kwYaMgmyMMIv7EzRLSVfmYGeWSdLT8/46IdNWM4zj00sl83mM448lmRfCpdlwXXs7OiJugytEi6n+d/OJSBJ+q49UXXwFgycpFgJzDWiGWUIUOtLzDJ5USwcdFRh0hMEJ5h4+VCrghwnrR06UEn00mbcIvf3k13/jGtwJukTBY+B0+Ti7AhggDwl1MWiHJ4VMLzJw5C4AMKaIkSMuktepxQ7qydkYWl1VOqMThI1QfZlptjlj5Qgfi8KkN4okEAKG8vGFZksPHRa5WQmCEompRYovDpyrp7uoFhtPY1Mippx4adHOEIcINRRAqFzekSwuHII04CKqcSy75CdtttwP29VPgPcj2iupa7UiVrtpBiyjBxxCHT9VihJRNy0S5J+Uc1gabTNqEuSz3BB/bFrezi4w6QmCEIvlEo1m50FYj6V6166xH5DJSS4TDYWxfSJeVFcGn0rHyoty5532TPfbYm3PPFbddNZNIJDjqqGMJN6hr6713/SfgFgkbipfDxxbBp9rR8lvlmgg+VYvuqHNnisOnptANdR7dpM0i+BSQX7gQGKH8oCkOgurEzCmrpJvAUKgNLr3050WTH9f6LFQubtjd5KmTueuufzNq1KhgGyQMCnpcXVs/fu8TVq1aGXBrhA0h1ZMhpDs4OLK4rHJch89W7MaW7B5wa4T1wR0zXcFHRLvaIJSvgHjg/ocAIvj4kVFHCIyQEtg9q7NQXZhZJQRohlxGaokzzjibHT73Oe++mxBYqEx6enq8c6SFZdJaSxgJdT4jxFi2bFnArRE2hHffeod0PmGhCD7VTUgv3D6TX4tYUIW4ObUsEXxqi/yltbGhCZACFn5k1BECwxN8TLnQViNmJu/w0eX81RrTzmn2bqd7MgG2RFgbc+fO4RDOAYoXIkL1o0fVFE0nzMqVKwJujbC+2JbDxmyNjRJmJcdWdeMXB3R0QOZAVUdOnTPJ4VNbeHMgxw3pEkOBi4w6QmC4cdCOhHRVJVYunyhWQrpqjvEHJnhm1+sB+PsNfw24NcKamDPnM5YzH4DWGZGAWyMMJo6urrEGYbq6ugJujbC+mD1qjhNH7TrL4rJ20BCVvSqx1PLXDekSagM3pCvkSA6fUqRKlxAYnsNHrrdViZmzCANaWHTjWkTL908pPVvZLFu2lDQhCFtE22TxUUvYIeUI0QmTzYrTrlpxTCX4vM7DABiG9NNaQgS86iNku4JPNuCWCIOJ2xUlaXNfZKUmBIabb0IEn+rEyuXzhkhIV00SCqtFSiOtAbdEWBOpVIoIMa/qoVA7jN94PKAcPul0OuDWCOuLbeb/z1c/PP30rwbYGmGwkZxM1YdmqY0sSxw+tUW+K3b+O8HW7CGCjw+5SgmB4ToIYs9NCrYhwjrjOA5WNh/SJQ6fmsRsSmJjM47Ngm6KsAbS6RRhYoRiQbdEGGwicbUo0QmTyYjDp1px8nkk3Bw+U6ZMDbI5wiAjDp/qI2RrWORwkI2SWsKfHu1sfouxvBnHkXMMIvgIARLKp5uIzBtB91yZzFYLt932D7baagqffPQxALoIPjVJKOKwikXEaAi6KcIacB0+WjTolgiDjeuC1THIZMThU604nsNH3Zg4ceMAWyMIgmYbkr+nBnFz+Lg8cucTHHnkIQG1prKQlZoQGP4Swh+9/WmALRHWhX/840ZWrFjB8889B4AuZdlrEsdxyJIijCgJlUw6lSJKAj0edEuEwUbLb4qokC7ZFKlW3Bw+Vt7hYxiSF62WEIdP9RGydRF8apDSSqVhojz33DPBNKbCkJWaEBi6zziwYunK4BoirBPxuFpZhvLVKcThU5vouk6ODBEkVqiSybVrxGggOk4WHbVGweETFodPFWObhZCuZ599JeDWCION6D3Vh2brkr+nFinpixFkJ8xFVmpCYBhthWRaPR09AbZEWBfceFg9L/ho4vCpSaLRKFnSRIhjW5L4rlJx2pVbIDFBKv/UGlpEzV4NyeFT1WTTamE5ZuwYpk6VnGiCEDSarUuFrhokVLIcEYd6AVmpCYERbivcTnXIhbdacLPeu+W69Yhsb9Ui0WiMRoYB0PmJuAsqFTOpBNhok4SJ1BpuYQODCJZlBdsYYb3JpVXuntJwA6F6eY9CmIiEdFUfksOnNinN4SMO9QIi+AiBoScKP79Ml1x4qwXTVJNXHbUa0UTwqUkaGxtpYRQAK99KBdwaYXXYqbzg0xwOuCXCYGM0qDEySkIEnyrGzOWzNutSLaZW+DPnm2IUbAAAkbVJREFUA7CKxSL4VCGaY4jDpxYpUTW252CgsG6pZ0TwEQLDSBRuZ7qkM1YL7sLDyAs+ITEW1CRHHXUs93EdALmM9M9KxcprcbHmSLANEQYdo0EtJKMkPGelUH1Yufy500TwqRWypFjCpxIyUqXotoGFzGtqjdKQrgxqgpROy6alCD5CYOjxws8v2S4hI9WCK/iIw6e2mTx5ClvM2AIAKyMLlUplm0+PACDSJPEitYbRWHD42LY4fKoVK5sXfKSL1hQ50oSJisOnCtEcHSskkQW1RqngE80nbRaHLMjevBAYhlGY/XQtl6TN1YK78HAdPm4lGaH2cHR1rq20CD6VSlN2pPp/ioR01RquwydGA5bVHXBrhPXFzAs+IXH41BRZMuLwqTKef/5Z4vEEutOGrZkgXbKmKM3hE0WFkojgIw4fIUD8FvVl81aIZb1KcKt0aXm9WBw+tYtjiOBT6XTrK0iHehi5kyQnrDWMvAs2TBRLKuVVLYsXLFQ3JIdPTZEjo4pXWDIHqhYOP/wgDtp/XwBJ2lyLlKgabll225Zrrwg+QmDYts1/+R0AZsZmzpxPA26RMBDyek/B4SOCT83iCj7pRbLYrFQ026DXaA+6GcIQoOXTMumEJaSrirn3kmcBCOcSa3mmUE3kUKkINEvcldWEgbqw2iHJ4VNrhJuKZQ1x+BQQwUcIDNt2eIsnADWhXbVqVcAtEgZCoSy7muRIWfbaRXNU2OWCG2WwrFQ0x8DRRZCrRUKGurbqGDJhrVIsyyJNLwCZxq6AWyMMJrl8lSddBJ+qwt2stDRx+NQaerSwHuliJU20MZwJOI7MkUTwEQLDtm2svKVSxyCTyQTcImEguILPluwGiOBTy/S2LQu6CcIasCxLTV4NmczUIq570iAsgk+V8vvf/xYbde56Ri8MuDXCYOI5fGxJh1pNbMZOAGyV2yvYhghDSoYkAJuxg4yfiOAjBIhtW15ZRIOwCD5Vgiv4TGEWAOEWEXxqlVxjDx/zKgC2JTHQlcY777ytqsQYcm5qES1vHNBF8KlaLr30B14ISSgs/bSWyKHmrLolgk81MZbJAHRoS7jssl/xk59cFnCLhKHgZf4DqPHzppv+FmxjKgARfITAsCzL5/ARwadacK2Rq1gMQHxKkK0RhhJN08iQAsDOymKl0pj9/mwAtKiIrrWI6/DRMSSHTxXjhpAgwmxNUXD4SEhXNdGNSh/xaOOfOeOMszn77K8H3CJhKLBRaxWDCFdf/auAWxM8IvgIgbHVVlt7WfJVSFc64BYJA8Gt0qWjs4y5aJpcRmoVTdM8UVYEn8pj5RI1cR0xenjALRGGAk0PgeY6fCRsr1ox8qW7xeFTW7gOH80y6OrqDLg1wkCJoCpaWlo24JYIQ4k/gkQQwUcIkLa24bz+1ruAmtCm0yL4VANuSFecJlJ0i+BTw4RCIU+U7e7oDbg1QimdS3sAiDTIhKZW0Qw1Yf3Pf+7h/vv/E3RzhPVAQrpqE1fwCRNj0003Crg1wkAJ5wUfW5ekzbXI5x8by9QLYsxDrS/d66+7WV2vyEpNCJRIXC1UoiTIZkVtrwZs28YgTIS4CD41jt/h09spgk+lEf5UOXsatqzviUwtEwq7ZdltTj31hKCbI6wjs1r2ZFv2V3dE8KkpCoKPWlBKnq3qwHP4iOBTk7ROj7Dp/8Ux3Sp6blW2Ou+fslITAsVNSrkVu0lIV5Vg2zZxmgFIiuBT04RCBcGnu6Mn4NYIpTR8NgGA1j0CbogwZGjhEBuxRdDNENaT/Xq+6t3OOZKnsJbwO3wAentljKxkXIdHlAQAli6bzLWKYYQ9d7ob0mWaZpBNChxZqQmBooULyUbTKZkMVQNK8GkCIEWXCD41jKYVQrqWzl8ecGuEUsLdjVjkaN4sEnRThCEi16EWKY20BtwSYV1xHIcWexQAj/JXco4sMGuJbD5p88ZsDUB3d3eQzRHWgiv4tDEWgJ7wqiCbIwwh0WjUc/gUBJ/6dnTJSk0IlJCvmmU2Vd+dsVpwHMcn+IjDp5bRNI3tOAiARTdL0tiKw9TJkiYWiwbdEmGIGH+4OrduHgKhesgstxnmjOEdnuIerq77BUet0UM7AE2o0FoRfCobN//kMMaQIUnWkDD1WsWfjmBWPqRWHD6CECChUIj459QuSTZZ352xWrBtm4RP8AmF5DJSq2iaxjPcAUC2QwSfSuLJJx8n3Z0lR4ZIRASfWkWPqeurVBqpPnI96prZxUoAyVNYY8zhLaCQw0fSElQ2ruDTQAu9dKAbesAtEoaSVSwGIEKcECFyufpeY8pKTQgcPabCurJJ2f2qBvwOnyRdhEKhtbxCqFY0TeNxbgQgnUsF3BrBzzHHHIFhR8iSJhoV90etYniCj1QaqTaspDpXGZLqvlXfC45aI5cP6XKTAIugV9nYtk2UBG2MIynpCGqeLCle5F4SNDOKTUinU3VdDVp+7ULguBPaXLK+M6hXCyFL53McCkhIV60TCmlkUUKPkxFhr9IIEyVHWhw+NYwedQUfdY7rvdJINWH2KkeBew2t9x3mWiPrJW2OAxIyUunYts1VvAhADx1omjh8ap0lfArACCZw5JGHMHHiKM/pVW/ISk0IHCOmLrpWuj47YbWxY/eX2Jo9yZDkM95C00QIqFU0TfMSU4ayMjmqBN566w06OzsAJfgoh48IPrWKHlXXVzdsRFwE1UMuqeY04vCpTWxMLEzP4ZPLiUu9kjEzhf73Mv+Rzco6wK2kpxNm3ry5ACxatDDIJgWG/NqFwAlZaiEZXzA64JYIA2FUbjIA13AKK1kgg2YN4zgODjY5MvS0JyWcJGDmzPmM/fbbg7333hUNgygJsqGUhFXWMFpe8HFz+ORyIvhUC9lu5cZyHT5jxowNsjnCILLNNrMAMENZYjQA0jcrndRS1R+XMZcXuVfmrnWAVVKaHWDJksVBNSdQ5NcuBI5uq1Jdn74+n95eyZpf6bTYIzHJsZAPAGTQrGHcHeksKSLEeeedtwJuUX2zYsVyABYsmE8DLQCk9K4gmyQMMa7D5//4f4TQyGRkUVkt5HrUAnPY6GbOP/9Czj//OwG3SBgsbr75dm655U5yTT2MYTIR4hKyV+Gklqrz8zZPAqDrMnetZTRN85VmL+Q5NM36DIuWX7sQOJt/tRWAEBq33faPgFsjrI2IEydDLw7K7SFVumoXN19IJi/4SNnZYGlsbCrcRl0307qck1pmxI4qXC9CnC3Zla6uzoBbJAyUbLdaYDaPaOSiiy4Rh08NMXr0GPbb7wB625aiodNIq4R0VTjppSrEspNlgGxW1jptbcMx8w4f3efwMc367KfyaxcCJ9ykfoYxGpgz57OAWyOsjbAdI0OhYpMMmrWLuxOSJU2EOO3t7QG3SHAZzSQAuiMrAm6JMJSM3ClGd76sdytjWbx4UcAtEgbCp59+zJsvvw2AExHnR61iRVWOuwQtEtJV4WTbleDTSwcgCfBrnXg87gk+YZ/Dp16FWVmpCYFjNKqf4d6cSGdHR7CNEdZKhLiXkwBE8KllbLuQgyJKXCa0AeOfoE5kKwCWxT4JqjlCmfg9XweUyCeCT3Ww++478tC9DwJgGyL41Cq5sJoLbcO+dbuQrBbMlHKlu9XV3CS+Qm0SjUa9kC5x+IjgI1QA8TGF6j+P3P4U8+fPC7A1wtpQIV1J774kjK1d3DKzWVKEiZHN1udAWSm4AhzAKCYCsDKyIKjmCGViFSrJZAsj6zbhZLWRy+WIklB3IuIkqFW6WpUA28YYKcte4dgZ5fDJ5SuPdnZKeGwtE43GfEmb/Q6f+uynIvgIgWMkNKae1QioQfOee+4OuEXC6rAtJ18KWhw+9YDrKMmSQkPDStkBt6i+se3C9x+nGYCs0RNUc4Qy4S5QwkRZvnx5wK0RBkqEOACOCD41y6qRn5AjyzR2EodPhWPlHT5uqW4J6aptYrGoL2mzOHxkpSZUBA0TVGfckcOl9HMFk1mR3yGJiuBTD/irdAHkkjJBChL/BDVBM2l6QZfrZa3jhiBEiJFOpwNujbA2kknlgPUcPtH63FGuB+yQzXLmMozRWIvCa3+BEBhmqtjhI9Q2s2Ztp+ZI+K7FSA4fQQiUCYepzjiSjQFZwFQq6WVqwek0ZbxjIvjULpalJkjZ/ATJSq3p2cJQ4xd8GmghRTeJREOALRLKgY2JRY4wMVKp5NpfIATKypUqkbrr8LHDIpTXKo5j8wEvqDuftATbGGGNWPlpaw7JRVgP/OAHl3LBJRcAKqm6iwg+ghAgibEG+iZpJrIltmVLLHSFkl6uJq7pSJd3THL41C7+HD4AVlLE2CBxBbgQGi2MpIOlIrjWCVkyhIlyxx23cvjhB5HNyqKlUjFNEw2dmewHQCgmobC1im3bvMfT6k6XOHwqGTekKxSW/lgPJBIJjvzKEYDaIHOp1/WlzBSFikFvsQkT5Ypf/JJx49rIZDJrf5FQVlyHTyrSxT//+R+uueb6gFskDCXRaBTwO3xE8AkSN2lzE23ohGlnKfF4POBWCeUgR5oIMQCef/5ZXn75xYBbJKwO27bYnF28+5roADWL40BPvsy302ME2xhhjWSXqflLMizJmuuFcLOGE3KKHD7nn39ugC0KDrk6CRVDKKouxhFi5Mgwb95cpk7dLOBWCX4yK/JJfMM97L77ngG3RhhqzjnnXObPn8csbSa8CLaEvgeKm7Q5jBLibD3Lz3/+yyCbJJSJFN00MMy7bxiiIlQqpmkxhkkA/IMfsqk+LNgGCUOGZVn05gWfUFKWVJVMdhmk6MGMpEAiY+sCTQ9BzCSRag66KYEjDh+hYtBiSvAJ53cxFy1aGGRzhH6ws+oc2UZ9WiLrjRkzZnL//Y+w0aYTAHj1+deYPHk877//XsAtq0/cHD56vuLE0V8+lhkzZgbYIqFcrGABjbQSpwkAw9ADbpGwOizL8soAd7AUXZepdq1i23YhCXBWznMlY2eUU1LE8jojYRY5fOoVuToJFYOmNq0923p7+6oAWyP0h5UXfBxNYqDrCSOh8jTN/3gRPT3d/OY3V/Pgg/+t2+R3QeEKPm6JUT0iQ3i90Ikqx95IKwC6LoJPpWJZpif4mGQxDHF+1CqO43hlvp2cXI8rGScHJjkikUjQTRHKSYNJo88dW6/I1UmoGNyQLtfh87//PRlga4T+cB0+ji5VR+qJ8HD1fzPqxj//eQcnnXQcd9xxa4Ctqj/cHD6uw0dyg9QPKVSi/ATKmq7rIiJUKqbpF3xyIs7VMI5je4JPSASfisYxwcIUh0+doQ0zCRMlnh87hw0bFmyDAkKuTkLF4Dp8ovlSpgsWzA+wNUJ/mJm8s0eX5L31xLCNVenvFkYVHZ87d04Aralf3Bw+rsNHi0iFvHohWSL4SHW2ykWFdKk+miOLpongU6vYto2NhYUJppznSsY2wSJHOCxieT2ht6l5UwsjgUK103pDZgxCxaC3KBHBTUwp5b4rDysrgk89MnmrjQGI0VB0XNOkj5YTd6Jy6MFHAqCF5fuvFzpYCsDofDJgx5FrcKVimlZRSJc4fGqXmTO3BSBHxnNAC5WJ6/CRkK76ItyipI44jUAhNL7eEJlTqBiMVjVYNtIGyIS2ErHyDp9QWM5NPWE0qAGzVPAJhWTPoJy4E5Vxo8YDoMm8tW74jLcAGM80oBDeJ1Qetl1w+EgOn9rm618/jzFjxpI5P0OqMxN0c4Q14ORCWJiEwzJw1hORRp0UECUB1O/YKbN1oWJwBZ+mvOBj2yIqVBquwyckIdB1hdGonCTugOkiYSXlxZ2oaI5aQIrDpz546KEnuPqvVwAwHCX2ueF9QuVRnMMnK9fJGiYWi/GVr5xMlhRxvVE2KisZS4V0bbnlVgAceeSXAm6QUA6iLWq+5M5fTbM+qwzLKCRUDMawYsHHcWRCW2l4lmUJ6aorXIdPtMThI6EK5cV1+ITfH6MOyAheF8yatR37H3IA6WinCD5VgGVZhFFJCSWkqz7IRHtosFoZPbqF2277R9DNEUpwHMdz+IwYMZI5c5bw+9/fEHSzhDIQa1E71DEaCIfDdRvSJdNFoWIIK52HpnwlINkpqTzcsuzi8Kkv9EgI28jRQEvRcdm5Li/uIl9Lqw4Y0sXhU0/kmntoRYl9IvhULpZlesm1k3SL4FMHpIwODMI00ML3vndh0M0RSnDya3yLHJqmkUgkJE9onZAYocT3BobR1NSkxL86XF/KbF2oGFwXQSRfpUsmtJWHlcoLPkb9XSzrnWxjN22MKzomgk958XamYuraOP7AeICtEcqNngANHYNI3VYaqQYsyyZBM2l6sTElh08dYOpZAMLE6O3tCbg1QimuO93ElHlLnTFuqxEAtDKGxkYlxNejy0d+9ULFoBlKbdfzucRF8Kk8XMGHaP1dLOudbKyXBloI+YYN6aPlxf2+Qzl1jTQSMoTXE+GEOu9hohLyXMGYpkmcZlJ0A0hZ9nogH+auSy2cisSdu+ZIS3XROiMxRjmimxhOW1srAP/4x41BNikQZLYoVAxaRP0c3QGzHi13lY6VcsiSRtPl0lFvjJuoQknc3BRQn7skQeJ+36GsWkDqCZm41hNGXF13DSIcdtiBTJ++WcAtEvrDti0SNJOkC5BcZ/WAo6trs47Eu1ciuS4lkKfpkeqidYYeU/OknbffjbY2lTLkwgvP44knHuPKKy8PsmllZb1/9dOmTbtm2rRpz0+bNu25adOm7TCYjRLqE71E8BH3QOVgmia33HITme4cOdIyga1DWkaq/D1hCiVNRfApL7ZtE6eZ0Lsq4ZkeF8GnnpgwSSVsdkXXpUuXSB+sQHJZs0jwiUSkDHTNo4nDp1JxHIfb/34HACm6JaSrznAFn+Eto4rWLsceeyS/+tVlrFy5MqimlZX1+tVPmzZtT2Dq7NmzdwZOB34zqK0S6hJNUxn0NRF8Ko5XXnmJ8877GovnLSVDSgSfOkTPG3vCxLxjllWf5S2DwrIsTudKAIxECE2SNtcV0QYlHPhddu3t7UE1R1gNZrvqlyL41BG6mq+6Dp9MJhNkawQfTz75ODf8P1WRK0WPzF/rDC2qrsdWuv+okWy2Pvrq+sqc+wL3AMyePft9oHXatGnNg9UooT7RNA0L07dDIiFdlUIy2QtAnCYyJNF12cWqN9xdEqPI4SOibDmxTIuNmQ7Avg+OCbg1QrnR8xPXnTnSO7ZqVX3sTlYT9uxGAFYwH4BwWMJ8ah0nn8PHyAs+S5YsDrI5go9Vq1bSwkgAulkpDp86Q9NDaGGw006/RoJcLhdAq8rP+q7axgCv+u4vzx/rWt0LWlsTGEZtqKojRzYF3YSapL29CYtVnuCjaSH5riuEhoYICZppoIXPeIPGxljg5yboz683Glu7gV4SRiPkjT3RqC7noQy89dZbnHzyyey0zW5szQFEZvSy6e7Dg26WnPsy09TWC3SzH6fyBDfTyTIiEUfOQ4URXtmIBbzHMwCMGNEypOdIzn/wuGKsO38dNiwu56VCaG6O08pYAFaxiKammYN+buRcVzZGXANTwzD6in2JRH3MYwdrm36tvvL29uQgfVSwjBzZxPLl3UE3oyZpb09i+xw+uZwp33WFsGJFF42ovCGdLCeRtQI9N9IPy0/GVirPd8w7+CmHsZTP6O5OyXkoA+ec83XeeOMNlr2RZGvOwhmRDvx7lz5YfiJTCrejJABYtGiFnIcKI9PuYADdrAIglRq68VL6YWVgU5y0edmyTlpa5LxUAt3daWI0ACqHTzKZG9Q+I32w8glFYOWbabZdcBIP8VDRY4sWrWDEiNo5f6sTr9bX17YI5ehxGQeIf1HYIEpDumxbQroqBcsyvVAek6zEQNch4/aPe7c3ZXtAVaMRhp54XH33I5gAgDZCcifVI/4+6IaOuOG2QvB0dnbw859fSvvyDkCNlSA5fOoCozhps2nKNbpSCIVCvvlrTkK66pBJX1ZhtuNXziBGY9FjqVQ6iCaVnfX91T8MHAUwbdq0bYFFs2fPrh15TAiEUCiUF3zURFaSNlcOpml6CwyTLJomgk+9MWrXGHv+cxSAFw8vFYLKQ0NDI1/hZ3yVawEIT6mPCYpQjJHQSO3yCVBwEiSTteGergXuuusOrr32Kj75UJ0jKx/7Kjl8ap9QfkrkzpNkbKwcRPARZlzSypRTlNDTxriix9LpVBBNKjvr9aufPXv2c8Cr06ZNew5Voetrg9oqoS4pdfg4jjh8KgUl+BQcPjJg1ifx0apviuBTXsa3TGInDgdUmEh0en1UlRD6EjKKk8OK4FM55HLK0eNWGrVQyUDF4VMHhNUGpVtBTypYVg5K8PFvWEp1y3okMUFdl4eXCD6mKUmb18js2bMvGsyGCIKmaZjkiKBs6+LwqRwsy/IEnxw5urrqQxEXiomNVtuYk9gGEMGnXIxZviWgFpDXcBLXG9cG3CIhKFwngScqSB+sGNyNkMLiUi0kwmERfGqeqOqH7vxV+mXl0FfwkQ3LeqRhIzVmljp8crn6EGeltrJQMWiaRo4UUUYD4Dgi+FQKpQ4fmczUJ+HmEFoUxmWmEiEuv4MyYWeUq+MWfswy5koOrXqmxOEjfbBySKeV884Nt9tm1jYs713E1KlTg2yWUA6iar7qJlM3TemXlYJ//mpJSFfdUhB8xhYdr5ey7PKrFyoGTdPIkCJMDJCQrkoil8vJDolAKBRi5I6qf+7AIbLYLBP5Ammk6AGQ/lfHhPLpYCTXXeWRyajcWu5Y+Y1vncczz7xMW9vwIJsllIOIGgtdwUdCuiqHbDZbkpJANkzqkYaJSvDZgl29azTUT0iXzBqFCiJElhQaGmGiMpGtICzLJOwbMEMhiYGuV6acqko+fpkfse0DZ5DtlH461NhZJX67OUHE4VO/aPl5qiGCT8WRybgOH7Ww0KMyxa4bYqWCj2yGVAp+h09ONizrltgone7IcsYxlat5mSaUEJ/NZgNuWXmQX71QMWiaRhaVGyZCXCayFYRpWgxnPAA9tBMKyaWjXhl3QJyNzlTnv6FrJM+etCzgFtU+tukKPmrXWHYo6xctosR21+Ejoc+VQ0HwUefGiErWhLqhUYnxzYwApCx7JZHrtdiK3cmQJEdGBJ86Zu5BD5KkCw2dXfgiUD99VX71QsXghnSBEnwkpKtyME2TUUwCYAHvy4BZx2hGiMnfKCQhXf68VIwaapycWuQXHD7S/+oVI67OfSQf+mxZFj/60fd54YXngmyWQHFIl0lOnHh1hN2izr2bH8S2xeFTKdiL1HwlSRcOtsxf65iLr7uAq/gKAPtzBiA5fASh7Oi6homy1oWJiMOngrAskwTNAPTShUR01Te6rvN3vgfApBMaA25N7eP0cfjI0F2vGE3q4htHhVa+9tqr/O5313HYYQcG2SyBwk5xmBg50hiGCD71ghHXyJL2qnTVi2ugGrDyRWVf4N+AjJ/1TCwWY5vPTwMK8ynJ4SMIZcYwwl4ZU4MIti0On0ohl8uRoBkHmzTdzJgxM+gmCQGi6xoLma1ux0T9G2qc/HzElBw+dU+4RfU3V4BPJpNBNkfw4S7yYzSQogfDkJCuekHXdSxyheTAUqWrYrCV+YpcPoKgsbEpwNYIQaMbBnN5x8uDJ2XZBaHMhMPhQsgCYaA+EmlVMpZloWkauVyOUWyC1uhw4+9uY7/99g+6aUKAGIbhiQ9uQmFh6HAsN6RLcvjUO5EWjV4KDh+pBlQ5uIl64zTRyTI0LRFwi4RyEQ6rDUt3ESlJmysHK6+JuykjWlpaAmyNEDSqrxYqt0lIlyCUGbcTgoqBl5Cu4Nl004045pgjiHw4mmaGoxkhDjjgIHEY1Dmapnvi7D3/vJubbvpbsA2qdczSHD7S/+qVyDB17l2Hj4SOVA6WZdLCKBpoEYdPnWEYRpHDR4TYymHkvbsDEEOFn4vgU98Yho5JFg0dDV1CugSh3GiaVghZEMEncCzLore3h6eeeoLIQlV5omWf+rgwCmtG13Wvr2aSOS655KKAW1TbOGaxw6e5uTnI5ggBEh2mRISC4CNOgkqhdf5Ufs5jgCpuIMJs/eCmJNDF4VOxdLAEgDFjxgbcEiFIStOHiMNHEALA8jphGMdxaG9fRVdXZ8Ctqk96e3sKd7LqUjHyC5KvRXDzFSjxQScseUSGGD2ndo3T9AIyYa1noi0GNpaEdFUgIxepZKBJuvgnV0jS5jpCuQYKIV3ivKsczKhK4tOz5RwOOOAgRo8eE3CLhCBRKQlUNIlOuG76qgg+QkXhqq4tjMQ0Tbbddmt23nm7gFtVn3R3dxfu5AWfSJNY1IVCgkpQ4mw4HB6U9+3oaGflypWD8l7Vxvnnn8v220+np6enz2NGVpXgTtHFnnvuLc6BOibRkCBFt+fwyWQyAbdIcNFMdR28khOwMSXXVh0RjcawyBGnCQ1dHD4VhGbqzOUdfvv//sBNN90edHOEgNF1oyh9iDh8BCEA3EXkifycrbr3pbe3h+XLlwXcqvrkzTffKNzJqolrrGlwFvZCdVMcfmmQy+W4667bcZwNS+C8yy7bM2vWFnU5Wb755r8zb95cFi9e1OexMR2bkyXFsy++zM033xFA64RKYbPNppH0CT6pVCrgFgkurhOvncUAksOnjojFYugYhIlyMf/CykhKgkrAsR00K0yW1KBtTAnVTTjsF3wiksNHEILA9FXmmpbd2bst+XzKz6JFC7zbWk5NXKNNkaCaI1QYjqZEmQhxAM4550weeOD+DXrPFSuWk06n62bHpT9Kxa7e+fnKXBhMmjSZaDQaRLOECiEUCjFh1ESGMZrhjCeVknDKSkE3o1iY5FCuK3Hi1Q+xWIwWRgEwhklYK0RcqASslNqEypIWwUcAVBn2LCrML0q8buabIvgIFcVsXqQLFdIRsQslTeulQ1YS/rjWUC5fGWaYCD6CQg9rpOgmnq98AfDRR7PX+/38Qkc9Onxc/P3u0Ucf4pfnXwXAU8bNQTVJqDCap6rr8PncRE9X3xBAIRgMM0qGggCn6+LwqRdisTj3cZ1330lLHp8g+f73v8MRRxyMmVSCT0YcPkKe5557mhQqZUWcprrppyL4CBXFcuZxMXthhrJEiHnHRfApP/7qL6GcgYVJtFHcBYJC13VSdDOBzb2dzWSyd73fz5+LxLbrV/Dx/+3HH380rz71JgBd4eVBNUmoMGb+pA1Que4inVJiuFIwrKiXWB3E4VNPRKNRnuIWHubPANxzx7+ZPHkcL7zwfMAtq0/+9Kff89xzz/gcPikMQwQfAZYtW0YyL/jsytHkMiL4CELZGTFiJABmKEu4SPDJru4lwhDhr/7iZDRypInH4wG2SKgkNE2nhw4AzuQagA2q1pXNFgSfenP4+HeY/LcTNHMMFwPQnl1a9nYJlUnr9AgtRythIWIm1vJsoVwoh09B8JEqXfVDLKbmqxlUTq32pR2k02keeOC+IJtV9+R61VxC5fARx50ABx54MD2sAmAnDmfJHXD33XcG3KqhRwQfoaJ47bV3+eyzxVhascMnmxWHT7nxLzydTAhTl2owQgFd17mbKwDYhBlo6PT2bojDpyDqWlZ95exKpwuJd/1i19Fc7JXfnrzNxmVvl1C5GPlISvf3IQRP2I6VOHxkgVkvjB49GlDCAhRy22Uy6cDaJECmW81js6SIRCQlgQBXXPFrLrnrm4w7Vgny09iRa6+9KuBWDT0i+AgVRSwWo6GhAVMvFnzqJYt6JeFfeEaIeUnOBAFUX/2YV3iRfwMwgo02MKSr8PuqtyTtxfmLCn97A4VwnQnbDy9rm4TKRs/rPG4OLakIFSwP3vcgumNISFedMnnypjz22NMcc8IxAERRzjt/qLJQfjLdau2QJS0hXQIAiUSCHfbYji0vjdNLJyOYQCRS++kqRPARKhJLyxWFdGWzEtJVbvwhXRHiJIbF1vBsod5oa1MCxHJUNbfdOYaPP/54vd/P38frLYdPseCj+p3jOCxGfZ8v8R+aRkvojlDAdfjExOETOK+88hL/d9pXAUpCukSEqyemT9+GphENAN6GZSqVWtNLhCEm21Nw+EjSZsFPOBymncW0MpZEvPbnVyL4CBWJpeckaXPAuEmbQ4SI0YgTq4/EZsLAGDFiBADv8BQATQznrbfeWO/38++E1lsOH9t2vNvu3/7ee+96ovfD/IVoVOzoQgG9MQQUQrpM05SNkYBYuHABMdRCP11UpUscPvWGnlD9shDSJQ6fIMl0qnlrJpSU/igUEYvFaWcxMRpoDrcF3ZwhRwQfoSKx9RxhooTyP1EZNMuPm8MnSgMaGk5MRDehwJZbbgXAQmZjYdLKmA2Kkfc7yupN8OmvJP0nn3zkLSIz9BIOi+AjFDBKQroAUqn1T5ourD+aphH19VX/caG+0OOlgo+EwgdJtiOftFmXa6NQTDgcpieskje3hsYG3JqhR0YjoSKxdbX4C6PiKjek+o+wfrgLcHdBEYrXV14VYc1MmTIVAAebND3EacJxnLW8avX0J3rUC45T6Ftuv8tkMt4iMk1S7OhCEeGmYocPwMqVK4NqTl0TCmk+h8/65zETqp9wg+qX0bzgk06L4BMkGU/wkdA6oS+hhPp9RG0J6RKEQLANtehxw7p6e7uDbE5d4i664zQDEGqor0W4sGZaWgoJhVN5wWdDki37RZ56y+Hj/97cpM25XI6Ym/iTXhF8hCLCTWr65i4sAebNmxtUc+oaTdOK3HhC/WIkVL+MiOBTEbgOHzMigo/Ql1CDmm+5v5NaRjLKCRWJK/i4OSx6enqCbE5d4ubwcR0+WmL93RtC7dHc3OzdTtHFSDbGsiwcxyEUCq3z+/mrU/lz2lQSb7zxGmPHjvdK8A4WfrHrww9n89prLxOLxYmyBVnS2FhSUlYowg0dCfsEn+5u2RgJglAo5HPjieBTzxgNKk+MCD6VQa7LBjRyhpwHoS9OVKWqCGVqf0NNBB+hMgmrBVBEBJ/AKIR0qZABvbEyF+FCMDQ0FEJJUvQQowENHdM018uNUuk5fHp6utl//73QdZ3Fi9sH9b39Dp+f/vSHAIwaNZqv8kfPMSAlZQU/RkItLKPE0dCxsTBNybMWBMrho9x4IvjUN/EWJcy7zrveXpm7BkmuS81bDSlmKPSDFVGFDoxs7VchFsFHqEgcQy34XIdPd3dXkM2pS9ykzc2oaky6DJiCj1gs6t1OoZwFMRrIZDLrKfhUdg6f3l6VR2wo2tbfe3Z0tBOlwav6Iw4fwU+sKQzYbMbn+A1v8CoPSpWuIeSRRx6kt7eXI474Up/Hco+3cgI/ASDaZIAYreqWRGsMcDyHj2xWBktuhfq/YWTtL+iFdccMq4JAhln7vw8RfISKxAmrHW9x+ARI0uDznM5W7A5AZGMpyy4UiET8go/qn3GayGYz4KscNFAqXfBZnzC1geJP2uySzWaJkWAlyk0kOXwEP+FIhCzt3sJyOw4kZz4VcKtqlxNOOAagj+DTO98k+ddChZfosDB0wy677FbW9gmVQWNbAuj1+qU4fIJhY6azKduSetdgOfNoHdmy9hcJdYcVUaF+YRF8BCEgIhLSFTQj527BbhwCgI1NdKoIPkKB/hw+cZq49957OPbY44nH46t7ab/4EzVXYtLmoRR8/PmL/ERJeCEi4vAR/EQiYbKkvYUlqETfQnnp/KDgqnqNh1k27CPmP7ccXdcDbJUQFInGBDlWeSFdyWSSXC4ngn2Z+TI/YgLTwIGlzGGTTSYH3SShAjn4SweTfg2mT5kVdFOGHKnSJVQmEbUAKoR0iUe63IRyBT14OXOJJmTCIhQodvi4gk8j3/nOtzj33LPX+f3q2eHTX3WzSD43SyYf0iU5fAQ/4XCELMWJSCWHT/mx83rPXVzODXwbI6wRjUYxDNlPrUdisRhZUkVC7PPPPxtgi+qTUWzs3f6MNxk/fnyArREqlc8fth8AE0bUviAogo9QkYzfZBwAu/BFQGyxgZBTl4cH+SO/5tSiBb4gRKMFC2zaC+lSlbvuv//edX6/4ipdNo7jVFROEscZuqTl/Qlcifx36YppkYgIPkKBSKSv4JPLiQuz3Dimui5YqO9e10XoqWc0TSNTIvjIhmX56WAJAL/jHB7lBhoa1j3MXKh9ws1qI09Vc6ttRPARKpIxG48CYDp7cRBnk8vlsG2b//f/ruPTTz8OuHX1QchSl4f3eJpuVorgIxQRjRZCjJKopOrxfO6e9XHouEnC1ettzjnnTDbbbGNWrly5gS0dHPpz4awOy7J49dWXByxYlb73FLbjZzwKwHLmA8rRIQgu4XCYHKmiY7lc5Qik9YKdU4KPjbrmibNHaB7eQIwGDuYcJrA5qVQy6CbVHQZRVrCAd3kaC3OdQ8yF+kCPhdAiIvgIQmCM32K0d/sQvoaZsbn11pv58Y+/z8knHx9gy+oIU+UgyKEWEf4FviD4HT5HnnA4AAfwVUKoHZOPPvpwnd6vNIfPP/95B8lkLwsWzAMKDptvf/sbnHTScUPquOmP/hIrr44//vF3HHTQvlxxxS8G9PzSnEVTKMSTv85DgCRtFooJhUJkyRQdy2UlpGuoKRVn7bxOXXD4SO6eeieSbKKRVg7m/7iIO0mlUmt/kTBoOI5DmAg53/VRhFihP0KhELGROk4dmGNF8BEqkjGbtRXdH754Cu+88xYAixcvDqJJdUfIE3zUoCkOH8FPNBrlH/+4g6effokTLj0SUHHzbahY+V133Z6enoFb2VeXwyeTyfLoow8xenQLr7zyEjfd9DcefPC/6/Teg8G6OHxeeOE5AB5++MH1eu8YDQBcxVdYxEeAhHQJffELgwBmqvZ3KYOm1L3YN6RLBJ96x04V53tLdYvgU05s28YgiukTfJJJcVkJ/bPzn0ay3VVta39ilSOCj1CRtGweYYffDCfSpn6iI5dN83b0DUMmVOVAs9T37A6a4vARSvn85w9k2rTNiTRrdM1Sjp4YCe/xd999d8DvtTrBJ5vN8P3vfxeAq676pXc8nS52Nww16yL4FBI8D8yFVPre0bzg4yZsBgnpEvryAc8X3bey5XW91SP+0FMAO2+qsvOCjzgJhEkXhPiMN737mfbKK0JQy1iWRZio506HoS26IFQ3w7eP0rZN7W9oi+AjVCyTjmvkwKfHAmDkar8zVhohS01c3UFTFpzCmtDiSrRwxQqAxYsXDvj1axJ83ISLmlYYsjKZ4oS1Q836CD4DDTsrLcvuimZuSXaQsuxCX67nq1zEHiwZ+w4AZlocPkNNaSU0u8Th4w91FeqTjU4KcxVf4VnuAiDbZfLZZ5/yxhuvBdyy+sDMmhiEi0K6vvSlYwJskSAEj2xFCBWNHlcLvJApP9VyU+rwkZAuYU1oCbXwiZJg7NhxLF68iKVLlwz49X6Rx58vJ5PJEoupRVRXV5fveOULPuv73m61M7f6GUhZdqF/emjHDCkRwsqIk2CoKXX4OHn9xxV83GuVUL+412pXsM92Ouy440wA5s1bJr+RISaXUn3RzG9W/uAHl0oOPKHuEYePUNHoUbVw0iwRfMqNVuLwkZAuYU3oeWNPnEYuvviHALS3tw/49Wty+MTjyvHiF3zS6QyO43DVVb/k1Vdf3pCmD4ihdPj4kzYbhJnB3mRJe9XPQHL4CH1xFzGWJ/gE2Zr6wDSLRbU3f6yucVa+SpdUAxJGjBjBT35yGXvsvzsAZk9hHPjkE6kyO9Rkk0rwcR0+EmYpCCL4CBWOFg5hYaJZetmr8tQzK1euJNmlEg26uyTi8BHWhDZMLXhGMIHm5hYAMpmBr0CLBR+/wyfj5e3q7OzwHU/zwQfv88tf/pyDDtp3Q5o+INZWpWv58uXeNWrdBZ/Ce2/NXgBFdnSQkEqhL1tuuTUAsQb127Alh8+QY1n9l3OJoFwb4t4QAM4+++tM3nwSUJxMPZ2WBM5DTS6p5hLu3FXyfgqCCD5CFWCRQ7cLu9uSfG3oueeeuzCIYpHDzu9cRqMi+AirJzxa/U4O4zwS2VZg3Sa3q3f4ZL0wis7OTu94JpMhmx0aS8PSpUv7VPWw7dUvph9//FG22mpKnzLsA8/hU/h7WxgJwIP8oeg5YkkXSrn55ju44IKLmLHtDAA+eOcDdthhBm+//eZaXimsL7lcrt/jcZoAyeEjFIgmlBCbSxWu7+viFBXWDzOpvmN300TXxeEjCCL4CBVPljRWxuF//3sy6KbUDaZp9qlyIOVmhTUR2dTkA14AwHlVlbhcm8Pn6aef4qijDufjjz8qCmvy31bCjvod9vYWctqkUkk0bfB/k6tWrWT69KkcddRhRcfXNFF/8MH7Afjb3/7Mq6++zJIli4H1c/g0MAyABXwAwNtvf8hjjz0j/U/ow+jRo/nOdy4m0qAWNAvnLmTu3DmcffbpAbesdvELPrZV6N89qNAuEWYFl1ij+i2Y6f7dq8LQ4H7fEtIlCAWkFwgVT44MBlGJfS4jjuMQJtInrEQQVkc0GuU+rmZzdkLrUW6wVGrNDp+zzjqNFSuW85//3EMsVsh90dfh03dX/bjjvsQBBxw0SK0vsGDBfABeeeWlouNrEnzc9mazufUKL/MLXK7g04tyM40ePYbRo8es83sK9YMe1QEbA+UoSKfLm9C8nnDFZ4DuD9V1yQzleMd5ku23/xwHHXRIUE0TKoxoYwRIYqULwuDaQoOFDccNoTO9CrMiwgqCOHyEiifWFCWChBOVk/vuuxeDqDdgCsLaiEYjJPMiRSilFp5rc/isWLEcUFVN/CKPX1zJZjPkcv3nzXjooQf6HFu8eBH33vuvdWu8j9Ulml6T4OOGnPWp4DNAh4/ZBXtyPLtxDMMYBUAvHQN6rSCE89UsXcHH/d298MJznHPOmasNQxLWHTdMdd68uXzwmtqEer7hDiZuvDH//e+jbLvt9kE2T6ggwnG1p25lC2OHf5wThoZVz6rv2K2cJ+5YQRDBR6gCWke2YIjgUzZs2+all14gSoIMKSKRiDgMhLUSDkc8kcLuVnm2UqnkGl5RIJNJF7lc/JPiTCZDLjdw4fGgg/bljDNO7uPQGSgdHWsXfO6663YmTRrHCy88V9Te0p3EgQo+6UeGcTTf4zguYRuUQ+h7P/0ed9xxz7o2X6hD3IVlOD9OLlgwn6VLl3LYYQdy112389hjjwTZvJoinVYi9vbbT+eSb/0AgF6zi0QiEWSzhApEi6hx0MoUxgHJ4TP0mBn1HS9jDiCCjyCACD5CFaDFIIxUqCkXyWQvADESjNtkNB98MIfnn38t4FYJlU4kEiVJNzY2VpfGBDbns8dW0Nvbu9bXptPpNTh8suvkUFi0aCEACxcuIJfL8Yc//JbDDz+I7u6utbxSsTqHj9+Kf845Z9Lb28N3v3s+UHD26HrxkGqaJj/84cU8/HBfJ5IfO1n8upyR4rSzTmevvfYZUJuF+ibWpoTGBM3esdmz3/duS8L9DWc8mzGT/bww1f05nTO4BoDO9Eopxy70QY/mqzX6queJw2foMXvU972Q2YB854IAksNHqAL0mEYEmUyVi97eXjR0IsRpHAGNjY1BN0moAqLRCA42GhorXshwEXcCcM+f7+eEbx6zxtdmMumicsb+PBnK4bPuISmvvPISZ555inf/vffeY8cdd1rr6wbi8CmgJvSu4KNpxcLNvHlz+f3vr+f3v7+eZctWLzg5+T/vEW6gh1WMntXICfxitc8XBD/xEWoqN5wJ3jG/K26gTjNh9ZzIZUxgGj0vzcXew+EwzvMey5DktddeDa5xQkWi5XVWJ1cYFySHz9Bj9qrrXRrlMBZXlSCIw0eoAtxdEoNCuMTtt9/CN795jkxkh4De3h6iNAAQaRYrrDAwJk7cuN/j3XPWnvg7lUoX5b/JZApJZ1UOn3UXfB555KGi+/73XBN+h8/qXEf+toE/d09oHVupcPIvf5F7eYy/0zFy3nq9j1CfxMcowWcW+3vHstlCn1lb8nRh7UxgGgDZhSE+fWdu0WNL+SyIJgkVTrhJLbG0TMGhLuLD0GPlTcUZetlqq+mSSF0QEMFHqAKMfELKiWzlHTv33LO59dabaW9fFVSzapaenh4a8qEB8TapbiAMjLa24Vx++VXEvz6fpqkGoaia2KaW92+n9k980+lU0f1UqiDOuA6fKVM2Xaf2xOPFOTUGKvh0dnYUfXZ/7S20Uy2kLUspNqHQugk+H330Iel02nP4WKgbknNAWBdat1ELyhwFYccVI6EQpiusH/6NpUy7xStPqhDnZ7mL/33hV8zlnSGpGChUN5FWNXeN5PwVKEXwGWpMVTuCY045hieeeJZhw1qDbZAgVAAi+AgVj7tLcj439XlMys8OLr29vbzxz4/5Et8FoHGC5E4SBs5pp53JoT/cjYOeHc8mlyihJ7eqfxdeMllI6GyauSI3jT/Zs5u02Z8Qub8k4q74Eomo3+y7775d9PhArxV+h49fJLLtvn+HW7HHbfu6CD6vvPISu+66Peeff64n+Jh5wccwRPARBk4oFKKTZZ4zE4rFSnH4bBh+sTe5MsPKeR2AqqS3MrUEgDPOODuIpgkVTKRVXcd35HBiqNB4cfgMLR/8tpPeVw166UCLSgSAILiI4CNUPFPOXH31i4FWARIGxi/OvprY73ZgOnsB0LqNJPsU1o/GcfmcPN39i4b+RWguZ/apzOWikjabhMOF97nooh/Q2NhU9H6u22+jjSb2+3kDFXz8OXwG6vBxQ7rWRfB5/nlV4euuu27HMfPVXDyHj6TXE9aNNL1EKYyV/jxY/nBJYd3x9/1MV47MKnW/lw46O5WdQHLdCaWEmwrjwShUyLMkEB4abNPh2VOW8dalHQDM4100TTZOBMFFBB+h4mmbEWMVi1iJqr5TtNuWlJ3LwaT7IbVgaGcJPRc/wYRDpNSssH40jFECjZGKsXjxIh5//NGix/1ibS5X7PBxnTOgQlNMM0c4XBBBIpEIkUhxuKEr6MRi/Sd494s3a2J1gk9/yTZTqRT/+tddXttLkzavCX/ITcHhoxbpEtIlrCujmUQLIxnGaABee+0V7zHblkXmhuC/NjmWQ65DOQd66fSqEK7uuiPUL6FQiFU7vwFALO++k6TNQ0Pn+zkW/lfNG1pP7uZ3fB3DkI0TQXARwUeoeMLhMGl6vQEzkynsXIpVff1xHIfvfOdb/O5313vHhumjALico2gYK+FcwvrTNE65w4xkgp133o7jjvsi8+cXkhH7+65lmUWLUv9jmUyGbPb/t3ffcU6UWwPHf2nbK7D03qLSBKQI0sResIG9IfaCvV7rtWLvYsEXxY5iQa4IIiJFBFGqEqR3FpbtNe39Y3YmM2mbXbYlnO/7uR+TyUzyhDezM3PmPOdUYLX6AjzJySmGjB/wBXz8s2zUYtL6IFI4xild4TN8AK677qoaZfjEx/u6kuFSDsW+KV1yoiqqx5Ko/Pae4CcGcAYfffSB9ppk+BwaY8DHhLtA+bcuJk8X8JFsWBHIlKAcN9TplpLhUzfKK2sFHnlbGlu6/YoHV7VuwAgR62RvEI2exWKhnFLtgKm/cCsqCt3qWIS3f/9+pk6dwiOPPKAtSzVl4sZFCQUkJSWH2VqI8JKaJlBBGXFlKVrR2NWrV7F580YANm3aqK3rdDoNF6X6gM/mzZvxer1abR5Qatz4B0XUejv6zBmA88+/CICyskPL8AlXe6EmAR99IVi1S5c6pUtS0UV1nbmmLVtR6lZ1Y4DhNSkUe2jcFb5/P68bKFaCz8XkUVJSBEiGjwjOlKj8nVdvWEoNn7pRnqMEfJLaWrXzB5lmKYSPBHxEVCg25WLFxklMoF1qN235li3SDrWmXK7AVteppqYUk4cXL4MGHdsAoxKxIi4ujlIKsDl9mSzjx1/C4MH9APjmmy+15W6323BRqq+3s337VsCY9WKxWALq3OzYsZ29e/cYapcApKenA5F16SopKTF8trFoc+gT9Zp06TJMs9Fq+CjvI0WbRXXFpZl5E6VwcJP45obX1N+nqBl3uW7fd5uwlirBHf2ULkPGnhCVzMnKbyeDFnSij2T41JGyHOXfOd99gPfeexuA/v0HhNtEiMOK5I2LqJDHPgDGcBvk38bN9AJg584dDTiq6OZ0GgM+l19+Icc7/0Me+/j77800a9asgUYmYoHJZMJtcmLxBF4Ide/eXrtQAuW3aJzSVUIKmZzJRHbwDyuYbcjwSUlJCwiKXH31FQC0bt3GsDw9PQOIrGjz3r27Dc/1waNQJ+rx8fH8+ecKoHoBH31Gk9dpwonvs6SGj6iJUorw4KFtZmfY61suF5mHRp/hg9tE1oHuuHFRQI6WTSBTukQwpuZKluhZ3AaAZ+dfDTia2KVm+Nz7xC3sK1T++PlP+xbicCYZPiIq/O79LujynJwD9TyS2KHP8PF6vcydPYck0ijkoAR7RK1wm1yYPYH3FfLy8nA6nTRp0gSbzRZYtLmogtv5gKGM5UIe4jkWM2TFtXw4cgVPD/ySVnt70b6iF6YghzD/KV36gM/Gjf+ydu2agG1U06d/bniunz4arNhm585dDNO+TKaqD6nPP/8MRxzR0TClDbdZm84F0qVL1IwXD7nsIWVva0ZwsbZcavgcGrdTFzDbl0SyO5NV/EwpvinlkuEjgjG3KWMfvkx0b4H8ba8L5ZUZPnsKfXUC4+Ml4COESgI+IipsYRV3MBCAAnK05QcO7G+oIUU9l8t3EltSomRUABSRG2oTIarFY3Jj9obOVsnMVAI+brfSlv0sbmcEF5O8rxUt6GRYt1lONwp+iSN1mZ2l1xxg7M7HuYfPuJQn6MYxAHRnICfl3kQ3jsGMhXO5h/jdSvCyvLyMIUP6c/zxQ3njjVe57LILKCwsYM+8Uop3KhfEhYXKBdwJJ5wEwMH9eSy5aj+Lr8im/M9EXuB3XmIFL7GCW3iPpDhja/hIEnyeffYpDh48yNq1q30LXSatQxdI0WZRc3vZDMA47tc6dh1qhs+SJYv44otPD3ls0crt8tXbakprAPbwr7bsqquuwWazBWwnRFyijSc5lzXMB8BTFnkWqIhc+QHlb1wRB7VlkuEjhI+cVYqoUUEp21hLK7pqy/ynaeTmHiQzs0l9Dy0q6ad05eXlkk4WIAEfUXvcZhcWV+gLoYSERKxWG06nC5fTyYlcBcBXhY8DcPC4v/hj0QoGciZl7fdx7HHHsuUT31SwdhxJO45kMGexm39pTTdwwxDGauscuB/uYzoZ32RyLycDUPYYHE13vum1nfgSJWhj6VBGp7xzuJdRtF7Xhv5cjeeWI9lJZfv4H9oTj1Jnx4MbO4Owrx9EIQdJpQm57GN7zkre4x68BGYDud1uw1St9ev/0b1o8svwkSldombK8O0fTWlLHvsOOeBz9tmnAXDaaWeQkpJaxdqxRz+ly4YydSsf382mZ555od7HJKKD1WrFg4u1/EovRuEtlfvsdaF0rxuTBUrdhdoy/TRwIQ538pdHRJVSiogjAUtlrFIftFiwYD52e0deeUVOviKhn9J18GAOfTgBgANIXSRROzwmFxZv6PsKu3fvxGq14HI58ZTqghyVXXCad83kO17hQU5g09AfGPByM876py3n7WjP5oxlhovbTFoaPxvfRW5T2pBQlE4z2hr+5y313W0t2ubEmp9CM9piK0ilGW0ptxTjbVqGg6Us5kvmMIUnMk/heS7RtksirfLzW9Cn6GRDQFrPv2aWgcustWQHKdosauayy8bTrn177XlGZRC/tmr4GKYhHkY8Qbqc5ZHdACMR0UbN/FKPVd4yE6WlpUybNtUwHVjUXMluF7krK0juYMWLLxtPAj5C+EiGj4gqZVS2QCWFYvJwOn3TIGbO/BaAd955i1tvvbNBxhdN9HUdcnJy6Ep/PLj5ja+BNxpuYCJmeMwuzFgwYQ6a9ZKfn0+zps1pUtweS7GvBsawCqWVekITX3aQ1ao8jm+qBEN+7fo2f/yxrMoxbN+eTfv2vRh27EgWLvzF+KI3cP2kpCS2btnL0KHHsGvXLs4YPcYwneW2K+7i5Zef557EwRw76Dh+/mUOAKdxI6dyPek0YzcbAt7X5XIF7fTVnI6YnBZDho+koouaeOGFV1h0eTa7tyu1pxJRsnFqq4ZPYWFh1SvFII8zyN+uykYSZrPcNxWhqb+PYvIA8BbauPnm65g58xtWrVrJ88+/3HCDi2K7du1kypR3uOOOu8lbofwbd7wgBZ72rSMBHyF85Egloop6lySBFACcTt+JrNp6VupfREZ/EXDwYA5ZdCCHXVpQTYhD5TUrF0pWgk/r8ng8nFJ2A5fsfIFea87RljenAwBxqb5Ml7g443tEup8nJCSQkZHBhg3rI1r/ww8/A6BNm7aUlBQH1C7JyspizpxfWLR4GXGJNryV/3eAnQA0o13Q93W5nAFTUI/mRB5mJuaSeEOGT0pKSkRjFcKfu8wXxYwnWVl2CG3ZvV7f+5WUFIdZU7FixXLGjj2L3NyDVa7bkD75ZBpHHNGRnJycKtd1u4wBHxdOrRBvQkJinYxPxAY14KN2mq3Y52XmzG8A+P33JQ01rKh33XVX8frrL/Pqqy9Rnqvsn0ltjJmxcuNECB8J+IioUopyhzGh8kS2tLSENWtW4/V6tQBGJBeC5eXlLF26xNB2+XCjn2KS6yghjaZks60BRyRijcesTCWxYOPGGycGXWdg4bkANMvrEvCaLc13Aqdm+PieV72f33nnvQD07duffft8faqvuGICLVu20p6/8sqb2uPhw0cCkJsbvJaV2Wzm6KP70bZtOxITfVlJW1gJQDcGBN3O6XQZun4B9OVE3+v40vsPxzoponZ0HJesPU5FqWd3KFO69FlpxcVVB3zOOed0fv11PlOmvFPjz6wPt912EwcPHmTu3NlVruvxC/gUk6sFaKUduwhH7dxYWNlspHCP7xjQvn2HBhlTLNi5Uyk9sG/fXnbPUf5N45sYAz6ybwrhIwEfEVXUDB81VX3jxn8ZPfo4ZsyYrgV8Iil4etddtzJmzCmN/qS0LrlcLoZwHlcyCfeC5gDksKuBRyViiceqXBQ9zhx6/XM2e/fm8fLLb3DH+Y9xEY/wX34Mu31cuu8Q5d8FJ9x+3qVLV+Li4pg48Q4Axo27UHvtwgsv4bnnXmLQoGMB6NWrD61atQ54j0ceeTzoe+tbr+tbMWezjRJTAa3pHnS7YBk+TfB97jbWao8lw0fUVIdxKRSe8xcAJzCeNtgNHRmrSx/wKSkpqXJ99TeuzwxqzCKZkuV2Gr9Lqa52mGT4iHDU31d5ZfF/fZeuuDgJSNSUesPH5XJRtEk5zyhNN2brJSYm1fu4hGisJOAjooonTsnISSDZMD93yZLF1ZrSpabU6u/6H25cLicX8yjHcBoZfx4FwFzeb+BRiVjisioXf4mk4p6fxZctd2C9bTidvziXoYw1BDyCiU+3aIEek1/P83D7+WuvTWbnzgMkJioXY507+7KH1GDKuHEXkJqaxn/+8whNmzYNeI/jjhvOY489FbBcf4EYH288YS8yHSSFDGU9LJX/tWLCjNPppLTUd8GcSBqd6KM9z8P3t0gCPuJQeI/wXfgczYl4PDUP+OizgyKZ0qXy318bq0huELmdxn+/Moq45ZbbARg6dFidjEvEBvV44cKJGxemCt9xyz/jU0RO/Xf1OD0UbnaxjbWccskQwzrS7VIIHyl2IqJCp06d2bJlM8lNE2GPcgGZlpbGgQMHAKWrTWmpcvD0zwQIRu2OcDjX+9HXPwKlq1G+dB4Rtchlrd6UyQrKcFJOMukAxGfYiIuLx+l0UlFh7GgSbt9NT88wPM/MbKI9Tk5WgiknnXQq//67HbPZzN69e4K+T5s2bQKW6QM+/nf3i015NKcjHejJ3XzKFlbRgk4UcIDdn7n5ZfrvXM/rFHCAIZxn3JYC7XGrVoGfK0SkzOke3uN2ruYl4ojH6ax5PR19ho//MSPoZ5vNeDweQ0OFxiySDB+v25jhU0YxN1x/M7169eaEE06uq6GJGKD/fZVTgsXlOz9dtOhXrrnmSh555HHatg1e+00Epx7/U7Pbghf2sYW8vLyGHZQQjZhk+Iio8PHH03n77ffpdKTScvZc7uLRA/M5htMAJZK/c6dSNLV166ovltS7ltGSdl4XnKXGk/cyiknPTOPVV99qoBGJWKNm5AG0uMJFp0tTyDw6joRrd7KKeco6Vt/vsIADuCpr2TgpJz7ZRny8kslXVmYM+IQrtpqWlm54npqapj3WB4TVk/GWLVvx2WczWLZslWG7oUOH061bd849d2zANgDNmmUZx+RV/gadyAQAOtGHJNJoSWe2P2+h87Zh9GSEIdiTxz48uNnNBm1sUttBHAqr1co+tgJgI4Hy8rLwGwRRXl7OsmW/Gwo+R1ILSP0NRxIcagwiyQLwuIznCeUUk5iYyNlnnyfZeCIss9mX6VZOKVZ3PCO4mMt4goryCr79dgYffvh/DTjC6JREGhfwIMcsGQ9ANtsbeERCNG4S8BFRoWvXbpxzzliSmilTKFJRpmCchZJWbbFYKCxU7pCnp6cHf5MggrVJjmYul4tFi36NqBi164Bx9/+Rd5g69RMuvPCSuhqeOMwUpCqZMyUU0PPGZgx4sSknzmnFwJs78y0vs6XXfHLO8nUqKeAA6Sj1pJyUY7PZtDo5/hk+p58+JuTnpqWlGZ6npvqKIB9//AlBtzn++BPo2LGTYVnTpk1ZvPgPHnjgEW2ZPuDTvn17w/ozva8DcDTBP6OUQqZyL18yCQ8ePuFRHuZk7mU4W1jF99/P4Z9/tshFpDgkVqsVJ0qQx0Z8RMWW/d19922cccaJfPXVdG1ZJFPDLBblzrv//tpYmc0RTOnyK9pcRpFMFxER0R8vijhIM9oxjvsZxFm040igelMla2r9+n9o27YZP/74Q51/Vn0YbrqQYVygPZ+HMWiWlJTsv4kQhzUJ+Iio0qK38UJObXVpNlu04E117ixWp3uJ1+slLy94557GYu7cHzn33DO48cZrqlzXtcM49W0Dy2nSJLCWiRA1tTllObfRn3sYStNOvkBsy5at+G3TIu756Qqs3Xx1bQrYrz1OIg2bzcYll1wOwLHHDjW8t39QRy8hIcHwXF9rp1OnwG5gVdHXC9OfwI8cebxhvTzvvrDvs4qf+YP/8QsfMZE+LOErPLgprZzOlZnZJGg9ISGqIz4+Xuv6lkwGxUXVv6CcOfNbAP788w9tmdoYIRx1qoV/Rl5jFVGGj9+ULiflEvAREdEX+ffvgqp2equPTPN3351MRUUFd9xxS51/Vn3ILPFNgZtv/tDQ5bJbt+6sWLE22GZCHLYk4COiSpY9w/BcLYxqtVp1AZ/Iawd4vZFn+Pzyy890796BadOmRrxNfdu2bQsA3333dZXrusuNJxkJyXF07dqtTsYlDk8FBfm4qOCUU04PeC01NQ2TyURSE19wZj87mM80AGbwHDZbHHfddR+//76SCy642LC9PgjjL1jB2HHjLuSII44kMzOz2t/DZvN9lv69MzKM7+XGxZ/MwYMxkFzAAfaxhcVMJxzpKiJqQ2JiEhWVF0C9GcXgv8dX+z3UY6P+YjSyKV1KwKcm08gaQiQ1fDx+Xbqy6CABHxER/e/LP+Bjpep6k7UlLk75LJfLWW+fWZdMLt+/a77HOL27TZu2cuNECD8S8BFRRe26o7KiXIhZLBbtZDSS6Uyq6kzp+v575Y7no48+GPE29U0tSBsJj1+aekpmkpzEilqltnEOV2A5OS2JtSwgn/2s5Ve+4lluphc/8yE2mxWz2UynTp0Dgjj+HbKq8vrrb7NgwdKILvD8qSfLUPUF4vvcyV0cy7/4MiNm8QaPM4YtrAqzJSQnS8BHHLrExESc+DoANSmpfkHYYMfG6kzpipaATyRBLPVYuYSvWM9vfM9rNfo7Ig4/+t/JPrYYXlPPX+sjw8dqVY5hFRWxEfDxunznA2tZYHjNP8NXCCEBHxFlEhMT+A1f9op6hyQxMVGX4RP5Aa06AR+18099nMgWFhbw3/8+zJo14S8Q/UWScq9yO43fPS4pdMaEEDXRp09fAHr06BlyneTkFCZzM//heDaxwvCafxcsvbi44AGfli1bBV1uMplq3Cpan+ETyYVeBaWU45tGc/lVVxhev/zyq4JuJxk+ojYkJibhwslkblYWuE2ceupoPvjg/YjfQz026i9GXS5fcGTz5k2sWbM6YLtom9IVUcDHrfxb5LCb17mW7Za1UdN2XjQs/fFiO8ZpRmrApzqlBWpK3S/1RdijmlvZ/27nGPayyfBSSkpqsC2EOKxJwEdElYSERL7gKZ7lQpLaWLQDps0Wp6WqVifDpzoHWjWjoDrvX1Pffvs1r7/+MtdeW71U/Oqk6/oHfOJT6i+9WBwennvuJZ544hkmTrwj5DrJycGLKyYlJYW9U5eQEBjwOe20M+tk7n6wzl5VKdMFfJIzfYGcs846l+eeeynoNpJhJ2qDGoxYywLybXvxekysWLGcu+++LeL3CHYzRN/dcvDgvowefZz2mhoYUjMJ6uPGiMvlYtWqvw4pQyKSC2C1S5e3cqqm7KciUvrfSjbbeJrzWFQ5tVc9fy0rM+4r27ZtpaiosFbH4eueF10ZPh988D5Tp04JfMGj/I3T1+5RhavvJ8ThSgI+IqokJCTgpIztrMMcZ6IpbbiYx3A5nZSXV7ZzrkYNH48n8hPFcDVDaltZmZKOv2nTxmptp78DWxX/VrMJKZLhI2pXZmYTrr32RkPAxF+LFi2DLvevj+MvWIZPWlpa2M+qKf1Ju3/AZ9KkF7VMJj19wCetnS9wZbFYgmYH9O59dC2MVAjIysoCoF+//mDxYsEYoPjzzz+46KLzyM09GPI9gmf4KMER/+2eeeZxOnVqRXFxMRaLsn+ox+O69OSTj3HiiSP47LOPq7WdPhNWfTxt2lR++GFW0PXVY6UH5d9EAj4iUvqizQC72KBN7bqRt+jPqYbf4969exgwoDfjxp1Vq+NQj4v1kU1UU2vWrOLbb2cYlt19923cc8/tgUFdtwk3wYNXqakS8BHCnwR8RFTR1wIp2qIcJIdwLuxK1u6SVGeOcnWKNldnutShUusgVFe1prP51fBJSJF5z6L+devWneuvvzlgeWZmk7DbBbuLF65WUG3xD/iMH381c+cuCFivFN8d2ozOvqlp6hjPP/8iLrvsSm357Nk/1/JIxeGqc+cuLFiwlC+/nAlmr9bcQDVu3NnMmzeX9957O+R7hKvhs2vXTsN6L774HCUlJTgc/2jb1UeGz+zZSoDm559/qtZ2+mO5egF8550TueKKi4Ku7wv4KOtG0spdCAieEerCd1PyUh43ZGbn5CgFiFes+CNgu0iEykCPhppTo0cP45prrtQCyvogj38A2eQx4yZ48EoKNgsRqPH/BRBCR43ct2rVmrgM3c93X2KNunRVp4ZPfXY3qGmWQvWmdCkH05/5kGk8SFKaBHxEw7jiCmXqYlJSsnZi2qRJ+IBPp06dA5bVNFBaHf53bEPJZa/2OKODb9qaGvB5/fW3eeGFVwOWC1EbjjzyKFJSUsBCQMBHnS7idrsYP/5Sjj76yIjeUw2O7NzpC/jMmDHd8Lp6TK2PGj5JScp+VVJSvbbz/gEf/YVlsOlhXrdk+IiaMZsDszn105BsxBsys6tzTqpSb3auXr2STp1a8cwzTwSsUx+FoWuLw7EeMGYj+QeQlYBP8JuwXbp0rbvBCRGlJOAjokpKSgpLl/7FokXLGPNPW5pMKADAleP7Kdd2l67y8nL+/nsdRUVF1R9wDdX0hLI6WUjqSexm/uJ3vg1ZS0WIutalSzc++uhz5s9frF3EVTWlKzU1jZdeep1XX31LW2a11v2FWKg7pW+++a7h+Q7+BmAra0hM8mX41EdQSgiN2YsF429OvfgzmczMmvUdu3fvCjkFK1jR5s8//0RbduON12iPnU6ndgyKNMPH4VjPb78tjmhdf0lJSm2s0tLSKtY00tftcbvdhnOG4uLA4JEv4KPW8JFTZxGZYMcLb5zxPM1Z4WTmzG+48cZrql1U+YcfZtGpUys++ugDvvpqOk6nk2nTpgZ+ZhQFfH799RfAGPDxr3Nk8pjxhAj4jBp1Qp2NTYhoJUctEXU6d+5CamoaZouJjH7Kiawn13ehV51pTW63m7lzZ9O8eRrjx18acFAsKSmhXbssRo48lnffnRywvcvlqvbJZiRqcpcHqhfw8VRm+KhpsdVp6S5EbTvppFPp1Kmz9ttPT0+vcptLLrmc4cNHas8bYkqXauzYCwzPt7CK+xnJm3HXEh/vy57zD0rVZ20wcRgyewIyfLSXdL/l3NyDFBUVsWzZ7yGPP+qUrlA3P4qKCnUBHyWAlJ+fx9tvv8G+fXuDbjNs2EDOOuvUGh3zEhOVQGr1M3zcuscurWYeKB0y/Xn8Aj5CRCrY8UJtAKLyurxMmHA5X375OTt27KjyPb/77mstKDJjxnTcbjdvvvmqlrkXrEZcTc8pa2L//v2ceOIIbYzVpQa99AEf/Xm21+vF7fQEzfDp33+AZMsKEYQEfERUy+ig3OHz5vumQO3cuSPgbkAoHo+HSy45H4BZs75j584dLFmyiJUr/wRgx47tYbc/77wz6d3bXutBn9oM+BQWFjB16hRKSkqMn+HXeUQyfERjoJ6sRTqtUZ8xUx/ZM8FS9EMpJAesXsMJvv/3+vffHWzdGvxiWIhD5Q1Sw0elvxjNycnh7rtv44wzTmTWrJm+7XU3QdQLsFABlpKSEi0opB6DX3zxOR566H6efvrxsOOsSVciX+fM6k23Nk7pchmmnxUUBAn4aMdKddp4jLS2FnUueMDHOH2+8KDv3Kyq4GVu7kGuvvoKxo4dU/leyj5QVlam7XPBulvWZ4bP1KnvsWrVX9oYq/LGG69y/PG+jn/q/qjPdtJnIG7fvg0z1qA1fMJ19hTicCZhUBHVmnRMBQqxFicaln/11RcsXbqEF198LeyFo39gZf/+bM4++zQAsrML2L8/O+znq6noubkHSUxsU4NvEFxNOynos5tmzvyGb76ZQUJCAtOnf8bOnTt48MFHtdfVNHW3BHxEI6JOlwh2lzL4+r6L2YbM8AnFarUavot/wVc1S0GIOmEKnNKlvaT7XebmHuSrr74AYMuWTUHXVwMlwaY9gTKdWs2eUS/Qfv99CUDQzAX9jZLc3FzS0qrO6tNTW8C7XE6Kiop48MF7OfPMsxg9+qSw2/lP6dJn+CxatIDVq1eyZ89uJk68Awgs2lyf9fxEdAtW821/0w243SVYcpUblmv/Wqu9FmrfUm3dusXwXB/wUW/q+WcQKdRpnJHfsKgvjz32oOG5Oh3UOKXLt486nU4sWIN26ZLpXEIEJwEfEdXSmqRRxHbiy40de26/Xen6c8opp3P66WeG3N6/S9fGjf9qj0tLSzl4MCeicdR2C9qaZvjoT2QnTLjc8NqWLZuNn+F3EhuszbUQ9U0N4Ljdke0D+ilSDVnDJxT/MeXl5dbmcIQIy2tR9iMzFjy4mT79M+01fZ2dc889Q7eV76KwOhk+SsDHWMMnPz8fCF6EfdmypdrjvLxcOnToGMlX0qgBXpfLxfz58/jkk2l88sk0srN9WTpTprzD+vX/8OyzL2oXu8a27G7D8TsnJ4f7778bgBtvnIjVasXr15a9OnUCxeEt2PFiT94Ocm/9iY2PehnMWSSQTAEHAGPAx+v1BgRo/M9JfVluFVqWnH8GEYDH4w05nsZG3R/15wD6fc7pdGLFhtvmRY35bNmyhzVrVjNgwMB6HasQ0aLx7/lChGG1Wik05ZDiCd7Rp6r02A0bNhie6wM+5eVlAdOgQqlO3aBIVKddvF64Gj7+tUK8lavKXUvRmKjTsiItXqnP6mlMXbpU6pj69esPwK5du2p9TEKEZKq80Kuc1vXYYw9pF5F79wafShgqC0CdrhUuw0ddp7y8nM8++5hNmzYCvqmMU6dO4ZZbrmfr1i3MmzdX27a8vPpBFH3AZ+/e3UHXuf/+u/jggyns2eN73b9Ll/64rz/mZ2fvA8BbebFsqozd1mc9FBHdggVYxo27EJfLqWXedcMXpCgu9tXHCnZe6Z/9rWa5VVSUa7W1gn2m+putjwyfQ/0MdWqa/rsag7Qu4kkiPt3KwoXL2Ls3j+TkZAYPPlY66AkRggR8RNRzWktJIHjB4aqmRq1bt8bw/ODBg9rjigpnlem1vnUrtM9buHDBIZ8Q1nRKl3oXJxj/uc3+hSjro42uEFXRX8RFQh/kaaxTugDatm0PwK5dVRflFKLWWIwBn8GDh2gF+vVBkNACu3SFuhFSUVFu2G8nTrxBt61y8XrPPbfz+eefMHr0MCZPfl173emsfsBHDSK5XC5ycg6EXVd/8aw/vrrdLsO/Q2mp77up5wNqNmzrtq2rPUZxeAt2vLDZrDidTrayGtDn0ylFzlXBjoH+53jqjRGTyaQFi9Tfenl5OX/8sQyv16tl/yQmJtX4u0TqUAM+aoaPGjxWl40YMZjnn38GZ7mTeJLwxrmw24+IiqwlIRqa7CUi+lnAjBkT5oALvh9+mMWuXTsjfiv9dAuXyxm2GLM+1V09WZ02bSrnnXdmlQUq/d9n8eKFhrTymgaMwhXmS0lJNa5beSzNyMwAoFWrVjX6TCFqky/DJ7KgZ31n+ERixIhR2mN1fCecoNQVGTTo2AYZkzg8eSszfNRsgvz8PK1u1N69e4Juo7/Q0nO73Xg8noimdPkrLze+5t8NK9Q0Ka/Xy3XXjee8884Mkt2gfCen01llhpA+Y9A/W0B/7NUHs9R/B3dlwKf/McfQq1cfHnjg4bCfJYQqWDDCYlECPvnsV9bRVddYufIv7XGwLFf/c0N9IFPN8FHPRz/99CNOO+0EXnrpOfLy8gBISan7bqyHHvAJzPDZvXsX//zzN88++xQVhcpyb7wUTxciUhLwEVHPZPOd0KamGoMaP/zwvdaFKxK5ub6AT0VFRcgTW4/HYzjQqiebixcvBODrr7+M+DM//vhDzjnndEPhOv1dnOp0Vwg3Fcy/zbW1VMn4eerFZ3jllTc599xxEX+OEHVFLdoc6ZQufQp3Xaar9+59NEDA35hgrr32BoYOHVY5JuX7XHjhJUyf/i1PPfVcnY1RiAAW9fioZMPk5+dpNzL27Ake8NEHX/THH4/HrW07cODggO2eeOLRkMeriopyrZ5PMMEyfLxeL2vXrubrr79i4UKlmLKefvpnRUX4DFV9Zy399GW3223Y1hjwUY6nHpfyX1u8lXnzFnLbbXeF/SwhwrHZbIwadYKWXa3voqef0hXspoc+4OP1erXgpclkorBQyeJRz01nzvwWgDlzfqCgQNn3Ijl+HapDzbhRv5M+MKv/u1JxoLJrngR8hIiYBHxE1FPn1SsBn8AuH3//vTZgWSgLF/6iPXY6nSFT16+//ipmz56lW1c5WVUP1unpGRF/5k8/zQFg0aJftWX+c5edTifbt2+r8r3CZQZVVFSwYYODCRMuZ82a1SQVZOHCSbsBzbjooksbZfcGcfhR79pHWrRZf3J54okn18mYAKZN+4yPP/6Cvn37V7mux+PVunHpd6sRI0ZJNzxRr7xW5VhiQ6nhtnLlX9r0Dv30Eb1Q2TYul0ub5tyiRUv69OkLEFHdjOLiYrZt2xKwvGfP3pWf6WT+/HncfPN1bNjg4NFHH6RFi3RGjx6mresfoEoszuAMbqHsgMeQ4ePxeNi1a6eh1bt/Vo9K6dLlK16t7wakHoc9zsppcVY5ZRbVE6yhh9VqY9Cgwbzx1tsAWHQBH/1vMdgxUH9Tz//Gozod0T/Lzmq1adk/wQo617ZDbQGvTh3Vnwfr/x1LNyj7oatV3iF9jhCHk8aR/y7EIVC7kJzJRNyufLbzVsA6l19+Effc8wA9e/aK+H2dTqdhPr/eN9/M4JtvZujWVU421YBLVd2C/vnnb6xWK926dde6LmRm+gpP6w/q5eXlvPjis7z++st8+eV3DB8+MuT7VhXwufHGa/CuzmTOXxtolzeAXPYRn9g87FiFqE/VreGjV5d3L1u1ak2rVuFreAwcOJhly5aSkZFZ7fbyQtQJmxrwifxCTx/wMbts3MXHePHyp3MqJSXFtKQLA1ZcQSu3g1YMorW5C23cdjx4+JVPcVLObv5lO+u091m6dAmnnHK84XMuvvgyjj66H/fccztOZwWXXHIlLpeLL774NOi4DhzYT8FGJ7OHKDV3enIlPYH+nMpXH05iAGewnO/Zs2c3ffsexYABg7Rt1ayevLxcHnzwPm252+0OWRzWP8PHYpOAj6gefSc8lXqMS8tMA8pIoxmX8gSzeN0Q2Ahew8d3jud2u3UBH5O2vrpMzZK1WCxaFtuh1pd86KH7yM/P55VX3gxT3N04xuoWUlbHrX+fsrIyTJgYw22U/Fw5LS1NGo0IESkJ+IjoZ1YOCiO5BHbD10ECPrNnz2L27FlkZxdEfPfh+uuvYv36fyJaV727qB6g/vrrT9q3b87y5atp0aJlwPojRijp8NnZBVoWkX5utf5Ap3Q7+QhQpoqFC/iE+24VFeU4nRVcy3PYdiqtPDNpoXV5EKIxqG6XLr36qE8Qzv/938esW7eGgQMHaZlHUlBSNCQ1wycugoBPdwbSkd64in3Hn7ScNnREycLZse0oZsyYzkU8QpPdXWlCV+UV3XXXBfimJu9ig9ZuOpi2C9qRsCKFm3ibipe6cp3rDTJoTjIZ7OZfvHjJQil2vp/t8HpnZt8VWGg6i3Zcj1IA2oWTvn2PAmD58t+1ddSL4U8++ShgubG+j76gs/Lv4HZ5MCMZPqL6kpICMzptNuUYV5kEymiuVJYTx3cVk7T1gtXS0p8bulwuXVDIq73mdFawdesWlixZBCgBJjUzT33PHTu28+abr3LNNdfTuXPXiL5LWVkZb7/9JgCPP/50yEx2/RgrKir45ZefOeqoHnTo0DGiz1G/kz4QW1ZWSnM6ciJX4azstWJKOLRMIiEOJxLwEVHPa4n8j77T6Yz4bkOkwR7lfZWAj/7OaFlZGd9//y0TJlwXdlv1DpA+1da/WF3btu04cOAAO3aE7/AT7u5NeXkFaWnp2Ig3LK+PzkZCRErdP2uS4aN2H2ooWVlZjBypZDGo30MyfERD8lZm+DSjHYM4i//xFqUUBF13IlMA+HfrTG2ZtcR3wTp82zVMf+ZpxnFBwLbrhk5n1eJ1jOQSWtMNgDZ0pw3dQw9ul/KfIxkC6yv/WymNZoZVm9EWtvqeZx4dxz+lSyl0eOjGMdry47mMeBKpoJQ/+RGABFJYufIvFi9eZGjMYMKM2+0yTAfT1/NRM23VBgcWq+zLonqOPPIoXnnlTQYNGszgwf0A300Nk9/vyYLN0C012DFQf27o8fgyfIxNRFzcc8/tvve1WLT11HPEd9+dzJQp77Bhwwa++uq7iL7L1q2+KZkFBQURBXw2bdrIFVdchNlsZu/evIg+Rw3AGgM+ZcSRaFjPUvez04SIGXKlJ6KfOfIU1ZKS4oBuVbUhPz+fzZs3BhR5XrcufP2g0tJSSkuVgM/u3Tt5+OEHuOOOuw0HzK1bt9C2bXtWrvyLnTu3h32/8FO6yoO+LgEf0ZioU6GqE/D58cf57Nq1q1H9liXgIxqFyoDP1bwIQA+O47+cSVJSkqFGnUnXHNpb6ntsLTNeZI3jfgAqjtrDjLSnyF5aQrt+zbn9/lt564z/soSvSE5OobS4FBMmJk9+n+uvvyro0D788FOKi0u44YYJ3Hvvf5g06Unl8/Fq41EvY03AaaeN4aELXmLG7E/4vw1fscLxBwBdOYZEUriIR+hEHzrRB4AtKSs4u+he+nMKz99/CVtZrWUZHMc4zuNetm7/HwUZ67Ux6bN/nLmw+eNCPMVKWV2zrXpTU4QAuOiiSw3P1eOUKeDn5DVMAXO73fz881yeeeYJXn75TY46qkdA9swPP3yvratyuZyG+lxWq1Wb0uh2u9m7d49WSmDFiuXae02ceD1XXnk1gwf7Aq96mzZt1B4XFAQPGoOxJMHu3UqX3OpMJQtWw6e0tFSrQ6YyJ8qxVYhINZ6zYyFqyhp5hk9JSUnQFNuqpKdnhCxwCcq85mAFnvV3RFT6OzErVizXikT++ecK/vxzBW63i7Q0X/HpsrJSrcPW5s2b8Hg8IaeJVDWly78Ypwe3XJCKRsU3pSvyE8S+fftHVEy5PvmKNsv+JRqON944LaQ5HbmHzyh3FZHNDvpxCiuZSyK6KcW6w4TJFfj73cNGOt5egOlD2MAyMqyDDXXrUlNTtQYGXbp3wU3w4G2Hzh3Ztm0Lblw8NemxKr/LzP/NIDElPqDGz0aUwE83BnI8l2nLx5U+SG9GA9CeHmxlNdu2bQXgbO7ERjxpWzuxs6fvxozH4yGVpnhwkzs5k71LD2KjAwBWqeEjaoEayDAHyRgzFhR38cQTj7F27Wref/9dnn/+ZUPgZO7cH7XH+uX+DUf0GT4lJSX07m3XbaeMZd68ucyY8SUzZnxJdnbwYM6ePbu0x4WF4QI+vvNQtVh0dfjXIAIlE97ql51uSZIpXUJEqkYBH7vdfiXwOLCpctFch8PxZG0NSohqqcaUrpKSYrzeZiFfTyCFUVzKEQyhmDy2s475TCMlKTVswCdUN69Fi37l/fff5aqrrtGW6bsqLFz4i9bmVvXuu5NJS0sngxbEk8StN93MiNEjtNf//XcDdvsRPPfc02za9C+TJ7+vvRauLXt5eQUVFRWUU0I8SQA8ymlcyPqQ2whR304++TSWL/+d448/oaGHckikho9oDNzpvmNTSUIu1rIE2tMDKqAbSlHjYznHsM3aVesYyjjKKQa3clE6jf9gxkIBB9jCKn4fvIKPP/5A+Qy3yzBVOjU1lb17lY5a4RolZGVlhewIFsoffywL+do3vEhX+infD+jtHq295p8doE4PsZQmGC4srdh4mO9JJIXypcb3N8fJviwOnfp7M/ldgVn8Lsncbo8WOC0sVNqq64Mp+huKbrebNtg5kqEscH/Mv45/tXbvZpNVCyT5Z+a43W4OHDjA/Pk/VTnu3FzfdEi1zbsqOzubiROvZ9iwkXhc0JwO5LBLqx2k5/V68Xq9IY+Noad0+QI+hRwksW31/nYIcTg7lAyfzx0Ox121NhIhasov4NOBniSTQS9Gsop5FJDDcYxjFw5KSkqCZsH06NGLv9et42G+J42m2vLejOIMboY9sJEVrOVXw3YmTHShHxtZQXM6UE4JyYkpmFLc7NqvTL/ael88q3MPYLMpxZHLK8o5ASXFfc1LBxnEWO39BnMWLenM9oJ12knrAXbywrxLSSGTEgrIz88nJyeH5557GoC33pqiZRF4PFVl+JRrqfIf8wgHCSyAKURDuummiYwcOYoePSLvqNcYqX9nJMNHNCR9wMczYC93LrwQCxYG9jiOpHWdOBtfrQ8PbsxYsBHPWO4F4G/3bABy2K1l0gA0a9ZMC/IonXh8p5MZGZkRjS0tLV07LgaTmZmpXWTGx8dTXl7O/v37Q64/9vxxPPvFhcoYKm+YdKIPl/I4J3Mt81ACVCbMmKkMyDpthkLNreluyHbSs6VF9LWECKpPn76sWvWXVlbAZDEeG5LIAGAAZ+DGSUWxi9H511BIMd59+wBjJo++HlVTbxvu50sAwz4N4J3toVnSN1hIoLD4AK3pzjoW8jMf4vV6ufLKi1m2zBfd/OWXnxk2bIS2fxcWFpCdvY/c3IPaOm+//Rbff/8dTz45idLSMh5++H5+//kP9i4o5Ub3ZB7mCmXb5csxYyGORN59azLjr7maQYOOZsCAQUyePCXov5Ov25gvEFtaWmrI8HmUU3kh7qWQ/9ZCCCOZ0iWin9WY1XI3n5LPftLJYphfccn963fw4Yf3GZZZsPL8mM+4fd3VhmBPBWXsi9/IEUceRfFKM13pT1eCTxvpyXDfk1LlfwN0r6+fZLzL4X9A9qcGe0ApVvk0v1S+dREH/lfIRhbzMDOZx4e4XC5sNhs//DCLffv2hnzP8vIKnBVOrMSzkRX8xgxmzpwTdhxC1DeLxULv3kc39DAOmQR8RGPgTfEVgU1qFo8XDy48eFLL+Yn3+Yn3GTx4CEuXLqE7A5nIFPpyorZNm/1Khy43xhbIFotFu0PvdnsM9bO6du3GySefxlFHHRV2bBaLhZSUVE4++VR+/PGHgNf1+05SUhLl5eVhp5Log0d5KBfIxSiZCEmkYcGKG5ehcYG5wobb7SaeJI40DWWY93zttWXMpBcjSUS5QLelyb4sau7tt6cwZ85sxoxRMurMfrHOLvTlNG7kNG4A4O+T4WjOAGDvP0oTEX3Ap7CwkI70xo1L6VJbaT2+4E1rupJGMwaVnKssqLwnaGcwQxjLVtdqBi87i8uB73iFdhzJuvNtZLfYQJfTmuMq8vLvt9msrVjMX0f8hQUr43kWFsJnPMEJJ5zE7bffwqmFtzCJh8GvsVjqZwN4lZXKk0dgZflueuw4jeQdbXG+7MKWEHgZqgZ81LpDoHSrVffbwjNXUj6zpFHV7BOisTuUvWWE3W6fDdiAuxwOx1/hVs7MTDLM8Y5mWVm1X/RX1Jw1KXBZOlkA/IUS0OjLSQBsuzORD8qmYMVGPEkUk89E3mfT0x5u5h0A5jGVr3kBgNK8UhISEji539ns/Csn4HP6MJqhjGU273AK1+LBgxkzCc0sfJs2Cc/mFE7lepbwFU98fxtms5n9+/dzxRVXBLxXB3pyOjcB8BJXsJfNpJPFSVyNGStHMoREUih6MwUrrWgOnMCV7N69mdWrVzNhwoSw/05erwtThQUzZsopYc2aNfTs2TOyf+RGSPZD0ZjFxSnHO6vVErO/1Vj9XrEkuYVv2kTr/inwtfI4Pd33/7ujjjqCpUuX4EQJDulvOKSXtgYCAz5ZWak0a9YEgCZNMmje3Fd3LiUlkccffzjoeEwmkxYMVX8/SqbAkzz66KMA3HXXXTz//POGqdLJycmGKSXBpKQkBiwrwpeVEE8SJRQYAj5Wdzxt9vXiBZ72VYgG7mM4ReRyJZM4htMAaNo2uVH+5hvjmESgrKy+DBrUV3tuPVgesI4a7PHXMvdINr5VwN6iHcSRiAc3v834k3srs3pUE+mLx69m1ri+V7NnfQ4Xlv7X+J50oiWdtOdjuNX34j7Y9H/KdLI4kunHSfRbf5Jh+6M5EedTxVxV+DLdDLc4YSr3YmdwwHTRzU95OaPyPHfnsj0MPC8wKOz1esjKSiU52RcRM5k8JKCk2HlsSmHrpk3TGs1vv7GMQ4hQqgz42O32q4Gr/RZ/CjzqcDhm2e32Y4EPgbD597m5wWucRJusrFT27y9s6GEIHafZN4/X3KqCXXt20IouLOZLPkUpBNmMdjzK/0guU+r33ME02tODz3mCLvQ1vN8Wy0ruuPUeLrvsSgoLnRQWOtlS9A//siHgs/9mEV/yDE7K+S39U8aefwH33PoQyZmJTD7rH/7YvIw5vIeTcm7KPINu3bpT5sjhbxYFfa8fmGxYVkweUytT69txFPfyueH1LNozZsA57MHXPeEW3qOMIt7lNhJI5mzuZAX/Y9GiRXRLV+7W9jjWTosWHaL2tyz7oWjsysqUv0seDzH5W5V9MDqUOiv4iIfIpCU39DxXW75nzz7tcZcuShFXl19QR8/ldxG5f38hV199Ixs3buaOO+4jL89Xi87p9AT9bRx1VE+mTfuM/v17au+hOvZYX5261q2VIskjRx7P7Nn/A4iou6bTGbyG3TJmMpAzgwZ8zBU22m4abFh/Pb9RhBJc2lNZqtJJOSWuikb3m5f9MHoV5fv2NzdO5lje41S3EvD5gPsZn/okP7Z9iRP/uROAPx/J5UVe4nF+ooQCyl3GzPE1/BIQ7AHYZlvLGlaxkVU8yLfa8oc5mTbYuY5Xa/wd3JuTA4I9M0fczx8L/scf/A/b2N388uUSSijgaE4wBJV+/GY+nYa3C3jPigon+/cXsve3ckZzJfOYSlFRCWl0ASDHmQ1AcbGzUfz2ZR8UjUmo4GOVAR+Hw/Ee8F6Y13+z2+1Zdrvd4nA43KHWE6LOxPl+di3vL+DGiWeTRjMK8WXkHGAHa1lAT0bQgk7aHcz+nKqt8y63YcLMSvc85tz3teEjxow5hxdemBT049W7oiNGj+LxJ5/WlhcVFRpeHzr0GHbuPMB5551p2L5Hj16sW7emyq+5h3+DLr+MJ3lWN3XNXlmIE6AXoziOcfRiBP9hNMPzlcyiHsd1q/LzhBA1J1O6RGNgsVhYyjcA3JXqaw+dkqLUqRk1ajSpqcqd83J8N+bKKcGFk2SUzB3/DB+AXr36MGvWXAC2b99m+MxgrrxyAu3atQcImI4RH5+gPR437kISEhIYNmykFvBRxxhOqHpAZSgXxvEkcxTH0Y9TtNeSSpqQSAY7+Ie22DFh5lc+015fzJckk84/LOaTjiFPhYWoNmuqL/sum20sSJxGWlEWCSSznO95+JOJHHhmE3vYRKvKYEcyGaTRlDSaavvkD0xmFw7+ZXnQz3G5nDidTvaymUc5jXgSyWE3ZRRxkN08yAk8yLckkEwJBWSzjY66e/hzeZ8TK+tOVmUq9/HHglna85zyvWxlNQBzeI+jOE4rjbBxXWAXW2W8StCq8NFOnMOdrOEXvPlWbdqaWnvSZpMpXUJEqqZduu4Bdjgcjk/tdntPYL8Ee0RDMcX77uolpCknfAUcCFivsDK1ezS+6VTqgaf3wxms+u+8kJ8RSacd/5PN9ev/CVhn3ry5ZGfvMyxLSIgPWC8YF04e4VSuvvQGSpck0X2z0sUoAV+b+QRdsckeDOMKlABUOs15HV9QqfPlkn4qRF2SgI9oDEwm37ErMdE35WnixDto1iyLSZNe1Dr05JOtve5C6egYLuCjpw/ghKqtoe4TGzZsw2o1Hi/1x8/4+HjGjjXW30tKCjJ324++cLSeGshqRhuu542A102Ymc808ttupWSnmx38rb1WxEFm8Bwmk4k2bdpWOQYhIhXfxKwVSk9DKYKuZqWD0jLd7XbzBtfxBMo+2kNXL9KCjd38y6wgv2lVcnIKFRVOLYhygB0AnHPOeQB8/fVX5LGP17ia8TzHW9xIOSXa5z3HRbSme8D7/spnDOfCgOV/MMvwPD/f2M1LrakFkPlXT2bMmK77PlYSSMbtdrH1c18794eZSWXMiL9ZxNSZyvf1/xsihAitpj0mPwGutdvtC4C3gfDFQ4SoQ54mvlTy+KzQdaK+RanoP4TzAl6Lbxq+vlRNAj7BOByBQSCbzdcu9q67jAWl77//IcPzHHYy6aP/8Orm2/mCJwEl/VyVUtnlAeDUEHPB/+j4OYktYqOelhCNlXqCHSu160R0ChWIGTVqNO+99wFNmzb1dZCkhIMo7dTXsEDLjAFwB5kqoqfP6gkVeFEDPhkZmVqGkW+b8MfYhISEsK9D6EDT3sppWcGCPQB/tfua5abv8SSWG4I9enFxcUGXC1FTJrOJElseoGTumM3GmwMLFy7gn3/+Jo99rG6iFDW/RBcQAijFOJXIbj/C8DwuzkZpaWBJjR49epGU5LtZuI21PMqp7GMLeexjHYuwZLnYxlpDCYKKhGK8TUqZztPcTC+e7ziGP/kx5HfMz88zPP+Rd8hFaS7SjWOYeP3N2msX8yiTWER6aWuW3RJYM7OCUj7hUe25FG0WInI12lscDsdOYFQtj0WIGjF3KeYRTqGCMn7MDN11qohciskjWRcUUVmTTbz00uvcfvvNgRsSOkXd8B5+B58pU6YxYcJlhmVbtyoprGPGnMPixb+Sk5ODzRbHhRdewtatW7j11jvZvXsXn3wyDYBWrVpr2yYlJRmKWDr4HUBrLwtKUUpVR3pRShGvMJ4s2uPFSz7ZtGvXpMrvIoQ4NE6nkhGhD+gKUd/i4nw3IvTHKH3mmf5O+bNcQCpNyWYr1/AyrVGm/3bu2pmOfYYzcOBg+vbtF/A5+iBPqOOl1xu8xg4omQjhJCT4spPuuus+vvrqCy6//CoSEhLYunUzo0aNZtmy34Nuu4LZXFZ5gyQYt8eN1WoNe5yXbAJRF7474lEuXvMauea9Ab+/559/Rnu83byO3roSBKpSigzP+/cfgMOxXntus8VRWlrqvxkpKanEx4fOLn+LG2C/8rjfqF6c/3kHDh7MITW5NbN/+AHvtcq+vHXrFt7nLrIuruDcs87HrzEua9asMjzfzjoe4kT+wze0ogupNOExZhvWuavsM8PzvWzmu6QX2F2yWeu+p3w32SeFiJSER0XUc7lc5LALqDriHyzYA5B1bAIDcgcFfQ1qluFz5plnsXjxHwwdeoy2bNkypV3mSSedwpIlCwGIj4/j1Vff0tZp3ryF9lhfqDIjIxOz2aLVBlLvvnbA12krDmOXknyy2cl6duI7AegSb+y0IISofb6Aj5yUioYTF6frSBXi+KhfXkSuVrC4RJc9MP6mKzn/ksApHL73sAR9rKdm+ATTokVLXnzxNfr0OdqwfOzYC/jyy88NmQv33PMA99zzQMB7/PnniqDv7aKCpXzDYM4O+rrZbalsMx864CP1QkRdKI9TblimNUvGTOjf39oDyyobtBuV+QV89EGjq666hh9//IHi4iL/zUhLSzP8bQinqEjZvkmTpgAkpQZm2500YTidejVn4sQ7ePXVF7Xl+jbyeutZQiu6kEJmlZ//KhMoKFHKNHz55XeMHTsGkAwfIaqjplO6hGg0rr/+JsaOvYDly1cbLq7MZjNPP/08AD179g554VVOCQlZlrAp2+FOBFXB7gB269adrVv3csoppwOwcaNSeFlJpVXusPpnAHTvbtce69PebTab4UQ6n2y2soZ2HKWdyOozfADeYWLAmCI9yAshas7pVLp0ScBHNCT9XfxQU61CBTPUxgdrWYAlIfzpov7iK9TxMlzAB+DSS6+gV68+hmVvvPEOO3bs1y42Ix2Dvx+YzDe8yNe8oC2bzzT2p27in4z5WCzWsDd2JMNH1AWLxUIOu3AnVITNMNvFekN2i6pN15accILvJp5+33vggYex2WxBM3zS0tLCZvjode7cxfA82P6t3qiM9HhXQgEA43ku6OuL+AKAH3nXUJOzZ09fMWkJ+AgROQn4iKiXkZHJm2++S4cOHQ0nZR6Ph/Hjr2bu3AXMnbuA1NRUslE6ifyPtyioPJlVizmHO9hGkuETqjhrUlISt9xym2FZSkqKtr5/oEl/J7Np02ba4/j4+ICTznlMBeBSHudqXgzI8FG/r54+xV8IUbc8nvAXuULUJf1FXajMm1DBjFm8waKUT/iQB6o8BuovAqsq2lwdJpOJ+Pj4iC5OwwVlctjFT/wfDpZqyzawnNlHTSLfvB+rNXzARwK3oi6ov7m4OJvhHPT++x8yFFn34uUbXgzY3hvvNPxu9bWwrFYbxcXFlJeXB2zXtGmziOpSXXDBxQG1JIcOHabdxFQ1a9as8nv43jPcNM0NlR3FmhFYCH0tv/I5T3AzvZipaxmfkpJqCPxKEFaIyEnAR8QU/zuVZrOZPn36YrFYSEtL53Wu5UfeZTbvkIZy4Nhs+ROAzEwltTRYJw7/YnrBuN2hi1o2a5alPW7ZshXHHDNAC/j4n0jqp3GlpaXRu/fRAFRUVAR0KvkLX82iozmRa3lFe76dddpj/WdIho8QdS85WdmPi4sLq1hTiLpT3SldehWUMi9xCiUUVFnHLlyXrjvuuBuAkSNHRzTmYJKTk6tcJ5IC6btwMJ9p/MbX/MsyXC4XbrcLq9US9jgvAR9RF9Rgjc0WZwiadujQkVNPNU7iUm9S6pVn5PsFfCwsW7aKL7/8jqSkJPbvzw7YBpRM8nBB1COP7MFll13Ja69NpnXrNobX4uLi+PDDTxk2bKThc9XvobriitCt3DexglcYH7B8B38zmZvwEhgcTk9XOgaeeebZZGZmBmQeCSFCk3w4EVPC3bHo3t3OnK2zDXcMAA6adwNKoOX331eSlZUVsG0kRZvVrjzBqMWXzzvvfF555U3DOP1PJNPS0g2f+8QTk7jppmsYO/YCVq36i+3bA7N2/C3gUxbyufY8OTmZvLw8ABITq25vK4Q4NB06dAAgNTWtgUciDmfx8b5jjcViZdWq9VRUVBjWCXenXL2RoW/vHky4KV333fcQt956lyFjoboiuZuvH4PFYsHtdges48XLVzyrPfd43LhcLsxmSxVTuuR0WdQ+9TenFA33/f4SEhIDzjs38DsfcD9FHOQm3gagolkeZqexQ17Hjp3o2LFT2M+tqmjzggW/VZmRd9VV17Bw4S+GZfoMcv9OfP62B+mIlxtk2poqLU05lr777lScTmfEU9KEEBLwETEm3EnhkUf2YM4cXzeAZXzPQM5gp9nXKr1Tp85Bt41kSpfLFXhyqUpISCA7uwCv1xsw9cv/oK4/SJrNZgYPPpYVK9YCsGXLZrKzs1m16i9tne94hX6cTBpZWtbSXKYY5nsnJfkCPm3aGO/WCCFq32OPPUVcXBy33XZ3Qw9FHMb8M3z0nR9964Q+bqrHtapueuiPkcGCI4cS7IHwGbS+z/V9j8TEJK3BQTgul5uysjISEhLCBrWkLbuoC+r5oNVqIS8vV1uekBAftODxcr4nEd9NBG9qBeY83+82MdFYUHnIkONYsmQR/kwmU5XZ3qHKFKgyMjIClukzfMJN6Roz5hxmzfqOj90P04cT6MlwAPazPeQ2aoa72WyWYI8Q1SRTukRMCZd23bx5c8PzD7mfuxjMRtvyKt83kqLNkdQn0B9AQx1M9SeW/oGmTp0688orbxqWzeE9nmEcu3AASqFN/+J++hPwLl26VjlOIcShSUlJ4emnn6dFixZVryxEHdFPAw6VpaK/MJs5c47hYkrNXI3kpofvc6o+XlZX587KcevYY4eG+Vzf90tICOwkdMYZZwUcP9esWcWuXTtJSUnRvmOLFi154olnePLJSVrGrdQLEXVh1aqVAOTm5hqKK8fHJzB+/DUceWQPWrRoadimlAK+5gXWsgDSKgz7Zny88XcfrJuduv6hBk2OOOIoAM4//yJtmbGGT+hpmKeffiZvvPEOv/E1C/G1Yd/GmpDbhCo6L4SomgR8REwJdxcy2N2MMoqrvIsBkZ3sNmnSpMp19NTP9Q8U6ccTLNDUvn0HmjZtapj6BZCIctK+szLwE0q/fseEfV0IIURs0Gf0hDqO6Y8lgwYNNtTecLmcgLEYbFUiuUFSXb169WbevEV8/PH0kOu0bOm7MA6WUZSVlcW4ccFby6ekpGrH4iZNmnLttTdyzTU3aIEjacsu6kJBQT4AJ554MmVlZdpymy2OgQMHsWDBb5x00qkB281jKpO5GVu8sfaUf8AnWGaar1C077XXXpvMuHEXMmDAID7++IuIxt60aVO2bdtnCKLq/5aEO7e2Wq1aoFltnAKQr+vIFWwbIUTNSMBHHDZC3c2IJJgTbJ327TvQp09frrxyAgCjR58UsE44oQI+VX1uSkoKf/+9mUmTXjAsn83b5LKXr5gUsI3+IBxJe1shhBDRr3nzFlrTglDU2hgq/YWaWu+nehk+dXNh1qtX77B1Qbp393W4DBbwSUtLD3kRmpKSwo4d27XPUak3kSTDR9Qlta25Sn9emJQUejqkxWI1BFgTEoznucHOe9XftL6+3AUXXMwbb7zDrFlzOfHEUyIed2KisdaQ2q0LYOjQ4xg1ajQzZnwfsJ3ZbNGyD/Vt14t0wZ+vv57Fvff+R3su+6AQNSfhUnHYGD36JEwmU5CMmqq3DZY59O67U+nbtz8VFRXcdNOtdOjQsVrjiSzgE3xwJpOJhATjScBafmUtJwZdv337Dqxduxo49FoKQgghooPFYmHFirVhs1/VLBb1jnuwWjbVydppqDvx+oyFYM0J0tLSQwau0tPTKSkpAXwdO0HfRUkuNkXd8Z+CqK9Z5X+up2ez2cJO6QqW2a7+LUhPz6jJUMNq2tQX8ElJSePzz78Oup7ZbNYCPnnsYx4fYCOebLZq63Tu3IXMzCZMmvQkUDdTRYU4XEiGj4g5/ndKVFlZWezdmxfQYrKmGT7qSW1cXFy1gz164QI+4U7Shw8fwXXX3cR33/0Ycp1vv/2BW265nfHjr67x+IQQQkSv1q3bBNQB0TOZTKxatZ7Vq9drz/1VJ8Mnkq6WdUGfARCshk9WVlbIDJ+ioiLKykortw28wJbsAlGX9MWOAbp27a49DneTTsnw8e2b/oWeg0/pUvZPfWCztugDPvpxLVmygunTv9Wel5eXGWqHfc3zfMGThnbsVqsxmCVBVyFqTgI+IubMn78k5GsmkyngxLWmNXxq6wSwulO6VKmpaTz++NMMHnxsyHWOOOJIHnroMUPhTiGEEEKvVavW2hSPYMfE6gRxqhMcqk36OjtqpoP+Ytn/ZtDJJ/tqo1x00aU4nUq9In2wSP23kBo+oi4MGzYCgK5du2nLHnnkCUOTkWDZaiqlnbtv39RPa4TwU7ratWtP27btOPfcsTUbfBDG7Djf35GuXbsxYsQo7Xl29r4qz0ttNmMwS4o2C1FzEvARMaeq4pL+qenhWrH6tglcJ1wHgkhcfPFlABx33PCQ60QyNn/6OzrqHRFpKSuEECIShxrwSUlJrc3hREyfJaG2mtcHb9q372BY/4wzziI7u4AtW/Zw5plna8v1GT6+gI9kF4ja9/HH0/nss68M54H+HWWDZaup/IMi/jcAgwVJ1HPkpKQkVqxYy+TJ79do7MHoxxLsvPmcc84DlG6xSUnhz6FttjjDe0jRZiFqTvYeEXMyMjLp3ftow907Pf+6ODXN8Ak1dSxSd911HxdccHHASaheTVLjrVabVmhTzUIKNo9bCCGE8BfsmFidmw8ZGRm1OJrI6YMy6rHPbDbTtm078vLy6Nixk2F99Xv637zRF75V15EpXaIuJCQkcPzxxtqL/lk5VU3pUqdx+ZcrUF4PPIfUn89Gcv5bU8He+7XX3ubaa2+kf/8Bhjb0wSj1iXzvIQEfIWpO9h4Rc8xmMz/99GvI1/0PgJGknwc7aIa76xIJs9lcZe2fmqTGB5vzrN7tFEIIIcIJnuET+bGoLorBRkJ/QagP/ixY8BtutzvggjHU8VWfFSFTukR9KywsNDwPN/XJZvPd4AuWyR2s0PGBA6Fbn9cm/3pCoIyxf/8BQNXn0DabzRBoloCPEDUnU7rEYedQa/gMHz6K++57sNbHFUxNMnz030fdXjJ8hBBCROJQiza3bt26NocTMf241QCNyWQmNTWNjIzAArWhjv365erjmkyvFqI6Ro48HiDgRmC4wIjVag0b8NGfQ9rtSn2fuszqAbj77vvp27dflYHfqsbhX3NTAj5C1JzsPeKw43/iGlmXLt9B88svvw2zZu2qSYaP01lB795Hs3r1Sm2Z1PARQggRiSFDhvLqqy8alkVy8+Gbb/5HSUkxmZlN6mpoEUtOVuoIhbuoDHV8DT7lJXRzBSFqw//938esWbOKwYOHGJZXVbS5uLgICF5XUp+tNmHCdaxZs5qLLrqklkYc3N1338/dd99fK+9lDPhIproQNSUBH3HYqa0uXfWhJndiysrKmDPnF1wul+595O6kEEKIqh1//IksXLiMYcMGassiOQYOGXJcXQ6r1oU6vgYLboXrpilEbUhOTg4I9gCkpaVV/jedgoJ8w2tWq1WbApaSkhawrT4rJikpiRdeeKU2h3zIrr32BlwuF++//27Q140Bn+pnvAshFHIVKA47NTlxi6aADyjj1Wf1qHd+GqqYphBCiOjh3ynIv7tlY/Xeex/w2WczUDNyDjXDR+XxSMBHNIxevfrw1FPPBs0ut1ptFBWpAZ+UgNf1wcvG2GnuiScm8cwzL4R8PVhNSiFE9UnARxx2/AM+bre7ym2ifUpUcnIyixf/wbJlqxp6KEIIIRq5SAscNzZjxpzD8cefoD0PF/CJpIbPwIGDAeWiW4iGYDabufrq6zn66H4Br1mtVkaOHA3AsGHDA16P1ilRaqBKP/5gLeaFEJGRvUccdmoS8DnUjlx1bcCAQSxf/nvYdbp1615PoxFCCBHN/DN6atJAoCGpx/lwGb2hpjrrLzLvvvt+jjtuOOPGXVi7AxSiFlitFh544GFOP30M/fr1D3hdH7yMj4+eG5dqwFm6dAlRO6Ljlo0Qtci/VWQkU7zi4xt3l6vp07/ljjvubuhhCCGEiAH+F1fRFvBRp3+4XM6Q64TK8NEHfNq378AFF1wcNRlO4vCSkJBIfHw8AwcOqjIgEk3dWn0dZn1ZSRLwEaLmZO8Rhx3/AE8kJ7Lx8fWb4bNhwzaKi4sjXj8pKYkePXrV4YiEEEIcLuLi4khKSqakRDkOmc1128q5tqnH7LKyspDrVKeGjxCN0THHDKx6pUqN/cYlQGZmJrm5uVqGoX4amgR8hKg5OaqJw14kJ3dJSaHbYtaFjIxM2rRpW61tIpmaJoQQQlTFZDLRunVr7Xm0FG1WJSYqAZ/S0tKQ61SnS5cQjVF1yg1EQy3K5GSl8LQvw8c3Zgn4CFFzsveIw05NilF27NiJG264heHDR9TVsA5Zy5atABg1anQDj0QIIUS0S01N1R5HW9ZLYqJykybclO1QWUuhavsI0dhUp5NrY57S9dFHnzNjxnTcbg87d+7QWrDrO3NFU9FpIRobOaqJw47/QSOSE1mTycRjjz3J6NEn1dWwDtngwUP47rvZTJv2eUMPRQghRJTTd8WJtqyXs88+DyBsy+dIavgIESsa85Suk046lcmT39cyetSMQv0+qgaBhBDVJxk+4rBjsxl/9rF0N2/w4CENPQQhhBAxQB/kibYgSMeOndi3Lz9sBkSojIFo+65CRCIapnSp+16wALNM6RKi5uSoJg47gRk+0VWMUgghhKhrxoBP9N1dr2q6i366CEC3bt0BaNeufZ2NSYiG0pgzfFTqPhs84CNTuoSoKQmXisOO/0me3M0TQgghjPRTumLxOOmfMfD993P4++919OwpHS9F7HjwwcfYsGG9VuexMVObj+j/9qgkw0eImpO9Rxx2LrvsSn77bbH23Ol0NuBohBBCiMbHYvEFefynQscC/yyCzMwmDB06rIFGI0TdmDjx9oYeQsR8AZ/AALMEfISoudi7ZSNEFcaOvYB//93O448/DUC/fsc08IiEEEKIxkV/gRWL0ynkAlKIxsXjUQM+UsNHiNoke484LKWnZ3D11dcTH58gd/SEEEIIP/qLrli82IrFIJYQsebcc8fy9ddf0b59x4YeihBRSzJ8xGHLYrFw5ZUTtEKNQgghhFDo62j4176LBbEYxBIimqm1wjwej7bsrbemsH79FgYOHNRQwxIi6knARwghhBBCGERzW/ZISMBHiMbFZAoM+JhMJjIzmzTUkISICbF3BBdCCCGEEIdEXzi1qhbn0ShYnRAhGrNff/2d66+/uaGHUWeCZfgIIQ6dBHyEEEIIIYRBsNbIsSQWs5ZEbDviiCM5/vgTGnoYdUbt0iX7phC1S/YoIYQQQghhIBkwQjQ+LpezoYdQZyoqKgCIi4tr4JEIEVsk4COEEEIIIQykxo0QjU9yckpDD6HOVFSUAxAXF9/AIxEitkjARwghhBBCGMR6hk+sfz8RmwYPHsKDDz7GwoXLGnoota68XA34xF5XQCEakty+EUIIIYQQBmZzbAZEPvjgU375ZR5t27Zr6KEIUW0mk4mJE29v6GHUidtvv5sFC+Zz330PNfRQhIgpEvARQgghhBAGMdiYC4BTTz2dU089vaGHIYTwM2TIcezblx+TXQGFaEgypUsIIYQQQhgcOHCgoYcghDjMSLBHiNonAR8hhBBCCGHgcrkAsNuPaOCRCCGEEKKmJOAjhBBCCCEMHnnkv3Tu3IXXX3+7oYcihBBCiBqSGj5CCCGEEMKgS5duLF36V0MPQwghhBCHQDJ8hBBCCCGEEEIIIWKMBHyEEEIIIYQQQgghYowEfIQQQgghhBBCCCFijAR8hBBCCCGEEEIIIWKMBHyEEEIIIYQQQgghYowEfIQQQgghhBBCCCFijAR8hBBCCCGEEEIIIWKMBHyEEEIIIYQQQgghYowEfIQQQgghhBBCCCFijAR8hBBCCCGEEEIIIWKMBHyEEEIIIYQQQgghYowEfIQQQgghhBBCCCFijAR8hBBCCCGEEEIIIWKMBHyEEEIIIYQQQgghYowEfIQQQgghhBBCCCFijAR8hBBCCCGEEEIIIWKMBHyEEEIIIYQQQgghYowEfIQQQgghhBBCCCFijAR8hBBCCCGEEEIIIWKMBHyEEEIIIYQQQgghYowEfIQQQgghhBBCCCFijAR8hBBCCCGEEEIIIWKMBHyEEEIIIYQQQgghYozJ6/U29BiEEEIIIYQQQgghRC2SDB8hhBBCCCGEEEKIGCMBHyGEEEIIIYQQQogYIwEfIYQQQgghhBBCiBgjAR8hhBBCCCGEEEKIGCMBHyGEEEIIIYQQQogYIwEfIYQQQgghhBBCiBgjAR8hhBBCCCGEEEKIGGNt6AFEE7vd/hIwGPACtzocjuUNPCQhYordbu8JfAu85HA4Xrfb7e2AaYAF2ANc5nA4yu12+yXAbYAHeMfhcEyx2+02YCrQAXAD4x0Ox+YG+BpCRDW73f4sMAzlHOFpYDmyHwpRL+x2exLKPtQCSAAeB1Yh+6AQ9cputycCa1H2wXnIPiiilGT4RMhut48AujkcjmOBCcCrDTwkIWKK3W5PBl5DOaiq/gu84XA4hgEbgasq13sYOAEYCdxut9ubABcDeQ6H4zjgSZQLVSFENdjt9lFAz8pj3SnAy8h+KER9OhP4w+FwjADOB15E9kEhGsKDwMHKx7IPiqglAZ/IjQa+AXA4HP8AmXa7Pa1BRyREbCkHTgN265aNBL6rfDwT5aA6CFjucDjyHQ5HKbAYGIqyj35due5PlcuEENXzKzCu8nEekIzsh0LUG4fD8bnD4Xi28mk7YCeyDwpRr+x2+xHAUcCsykUjkX1QRCkJ+ESuJbBf93x/5TIhRC1wOByuygOmXrLD4SivfJwNtCJwXwxY7nA4PIDXbrfH1e2ohYgtDofD7XA4iiufTgD+h+yHQtQ7u92+BPgEZbqI7INC1K8XgDt0z2UfFFFLAj41Z2roAQhxmAm1z1V3uRCiCna7/SyUgM/Nfi/JfihEPXA4HEOAMcBHGPcj2QeFqEN2u/1y4DeHw7ElxCqyD4qoIgGfyO3GmNHTGqVolxCi7hRVFs0DaIOyH/rviwHLKwvmmRwOR0U9jlWImGC3208G/gOc6nA48pH9UIh6Y7fb+1c2LMDhcKxEKZ5eKPugEPXmdOAsu92+FLgaeAg5DoooJgGfyM0BxgLY7fZ+wG6Hw1HYsEMSIub9BJxX+fg8YDbwOzDAbrdn2O32FJS50QtR9lG19siZwPx6HqsQUc9ut6cDzwFnOBwOtVil7IdC1J/hwJ0Adru9BZCC7INC1BuHw3GBw+EY4HA4BgPvoXTpkn1QRC2T1+tt6DFEDbvd/gzKgdgD3ORwOFY18JCEiBl2u70/ypzpjoAT2AVcgtLaMgHYhtLa0mm328cCdwNe4DWHw/Gx3W63oByYu6EUgL7S4XDsqO/vIUQ0s9vt1wKPAht0i69A2bdkPxSijlVmEUxBKdicCDwG/AF8iOyDQtQru93+KLAV+BHZB0WUkoCPEEIIIYQQQgghRIyRKV1CCCGEEEIIIYQQMUYCPkIIIYQQQgghhBAxRgI+QgghhBBCCCGEEDFGAj5CCCGEEEIIIYQQMUYCPkIIIYQQQgghhBAxRgI+QgghhBBCCCGEEDFGAj5CCCGEEEIIIYQQMeb/AYsGvSVJ5Bq5AAAAAElFTkSuQmCC\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -31867,7 +31868,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.4" } }, "nbformat": 4, diff --git a/seznam.ipynb b/seznam.ipynb index 54380d5..a55f80a 100644 --- a/seznam.ipynb +++ b/seznam.ipynb @@ -92,27 +92,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220322202946.log.\n", + "getML engine is already running.\n", "\n", "\n", "Loading pipelines...\n", "[========================================] 100%\n", "\n", "\n", - "Connected to project 'seznam'\n" + "Connected to project 'seznam'\n", + "http://localhost:1709/#/listprojects/seznam/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/seznam/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -3620,7 +3609,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostRegressor'],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['dobito', 'probehnuto', 'probehnuto_mimo_penezenku'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", @@ -3632,7 +3621,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostRegressor'],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['dobito', 'probehnuto', 'probehnuto_mimo_penezenku'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", @@ -3695,8 +3684,7 @@ "[========================================] 100%\n", "\n", "\n", - "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and PROBEHNUTO__STAGING_TABLE_3 over 'client_id' and 'client_id', there are no corresponding entries for 0.022066% of entries in 'client_id' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", - "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and DOBITO__STAGING_TABLE_2 over 'client_id' and 'client_id', there are no corresponding entries for 2.250854% of entries in 'client_id' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", + "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and DOBITO__STAGING_TABLE_2 over 'client_id' and 'client_id', there are no corresponding entries for 2.228789% of entries in 'client_id' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and PROBEHNUTO_MIMO_PENEZENKU__STAGING_TABLE_4 over 'client_id' and 'client_id', there are no corresponding entries for 26.543966% of entries in 'client_id' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n" ] } @@ -3720,9 +3708,11 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", - "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and PROBEHNUTO__STAGING_TABLE_3 over 'client_id' and 'client_id', there are no corresponding entries for 0.022066% of entries in 'client_id' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", - "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and DOBITO__STAGING_TABLE_2 over 'client_id' and 'client_id', there are no corresponding entries for 2.250854% of entries in 'client_id' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", + "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and DOBITO__STAGING_TABLE_2 over 'client_id' and 'client_id', there are no corresponding entries for 2.228789% of entries in 'client_id' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and PROBEHNUTO_MIMO_PENEZENKU__STAGING_TABLE_4 over 'client_id' and 'client_id', there are no corresponding entries for 26.543966% of entries in 'client_id' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", "\n", "\n", @@ -3746,7 +3736,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:38m:26.899936\n", + "Time taken: 0h:32m:12.617095\n", "\n" ] }, @@ -3757,26 +3747,26 @@ " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostRegressor'],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['dobito', 'probehnuto', 'probehnuto_mimo_penezenku'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-JIJ8mk'])
url: http://localhost:1709/#/getpipeline/seznam/hyYQRe/0/
" + " tags=['fast_prop', 'container-eXtd9P'])
url: http://localhost:1709/#/getpipeline/seznam/ZhdfPY/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['FastProp'],\n", " feature_selectors=['XGBoostRegressor'],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['dobito', 'probehnuto', 'probehnuto_mimo_penezenku'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-JIJ8mk'])\n", + " tags=['fast_prop', 'container-eXtd9P'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/seznam/hyYQRe/0/" + "url: http://localhost:1709/#/getpipeline/seznam/ZhdfPY/0/" ] }, "execution_count": 16, @@ -3894,7 +3884,7 @@ " 0\n", " \n", " \n", - " 2022-03-22 21:09:00\n", + " 2022-07-03 21:06:22\n", " \n", " \n", " \n", @@ -3906,15 +3896,15 @@ " \n", " \n", " \n", - " 3090.3262\n", + " 2948.0223\n", " \n", " \n", " \n", - " 22460.0288\n", + " 14811.5555\n", " \n", " \n", " \n", - " 0.8579\n", + " 0.9389\n", " \n", " \n", " \n", @@ -3923,7 +3913,7 @@ " 1\n", " \n", " \n", - " 2022-03-22 21:10:06\n", + " 2022-07-03 21:06:46\n", " \n", " \n", " \n", @@ -3935,15 +3925,15 @@ " \n", " \n", " \n", - " 3160.0215\n", + " 3007.4299\n", " \n", " \n", " \n", - " 24479.1577\n", + " 19059.5235\n", " \n", " \n", " \n", - " 0.7822\n", + " 0.8696\n", " \n", " \n", " \n", @@ -3953,8 +3943,8 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-22 21:09:00 train kc_proklikano 3090.3262 22460.0288 0.8579\n", - "1 2022-03-22 21:10:06 test kc_proklikano 3160.0215 24479.1577 0.7822" + "0 2022-07-03 21:06:22 train kc_proklikano 2948.0223 14811.5555 0.9389\n", + "1 2022-07-03 21:06:46 test kc_proklikano 3007.4299 19059.5235 0.8696" ] }, "execution_count": 17, @@ -4000,10 +3990,10 @@ "data": { "text/markdown": [ "```sql\n", - "DROP TABLE IF EXISTS \"FEATURE_1_39\";\n", + "DROP TABLE IF EXISTS \"FEATURE_1_44\";\n", "\n", - "CREATE TABLE \"FEATURE_1_39\" AS\n", - "SELECT EWMA_1H( t2.\"kc_proklikano\", t1.\"month_year_datum_transakce\" - t2.\"month_year_datum_transakce, '+1.000000 days'\" ) AS \"feature_1_39\",\n", + "CREATE TABLE \"FEATURE_1_44\" AS\n", + "SELECT EWMA_1H( t2.\"kc_proklikano\", t1.\"month_year_datum_transakce\" - t2.\"month_year_datum_transakce, '+1.000000 days'\" ) AS \"feature_1_44\",\n", " t1.rowid AS rownum\n", "FROM \"POPULATION__STAGING_TABLE_1\" t1\n", "INNER JOIN \"PROBEHNUTO__STAGING_TABLE_3\" t2\n", @@ -4014,7 +4004,7 @@ "```" ], "text/plain": [ - "'DROP TABLE IF EXISTS \"FEATURE_1_39\";\\n\\nCREATE TABLE \"FEATURE_1_39\" AS\\nSELECT EWMA_1H( t2.\"kc_proklikano\", t1.\"month_year_datum_transakce\" - t2.\"month_year_datum_transakce, \\'+1.000000 days\\'\" ) AS \"feature_1_39\",\\n t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"PROBEHNUTO__STAGING_TABLE_3\" t2\\nON t1.\"client_id\" = t2.\"client_id\"\\nWHERE t2.\"month_year_datum_transakce, \\'+1.000000 days\\'\" <= t1.\"month_year_datum_transakce\"\\nAND t1.\"sluzba\" = t2.\"sluzba\"\\nGROUP BY t1.rowid;'" + "'DROP TABLE IF EXISTS \"FEATURE_1_44\";\\n\\nCREATE TABLE \"FEATURE_1_44\" AS\\nSELECT EWMA_1H( t2.\"kc_proklikano\", t1.\"month_year_datum_transakce\" - t2.\"month_year_datum_transakce, \\'+1.000000 days\\'\" ) AS \"feature_1_44\",\\n t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"PROBEHNUTO__STAGING_TABLE_3\" t2\\nON t1.\"client_id\" = t2.\"client_id\"\\nWHERE t2.\"month_year_datum_transakce, \\'+1.000000 days\\'\" <= t1.\"month_year_datum_transakce\"\\nAND t1.\"sluzba\" = t2.\"sluzba\"\\nGROUP BY t1.rowid;'" ] }, "execution_count": 18, @@ -4050,7 +4040,7 @@ "source": [ "# Creates a folder named seznam_pipeline containing\n", "# the SQL code.\n", - "pipe1.features.to_sql().save(\"seznam_pipeline\")" + "pipe1.features.to_sql(size_threshold=None).save(\"seznam_pipeline\", remove=True)" ] }, { @@ -4059,7 +4049,7 @@ "metadata": {}, "outputs": [], "source": [ - "pipe1.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"seznam_spark\")" + "pipe1.features.to_sql(dialect=getml.pipeline.dialect.spark_sql, size_threshold=None).save(\"seznam_spark\", remove=True)" ] }, { diff --git a/sfscores.ipynb b/sfscores.ipynb index e5f049b..18542c0 100644 --- a/sfscores.ipynb +++ b/sfscores.ipynb @@ -104,27 +104,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220324212004.log.\n", + "getML engine is already running.\n", "\n", "\n", - "Loading pipelines...\n", - "[========================================] 100%\n", - "\n", "\n", - "Connected to project 'sfscores'\n" + "Connected to project 'sfscores'\n", + "http://localhost:1709/#/listprojects/sfscores/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/sfscores/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -1691,7 +1677,7 @@ "\n", "

\n", " 6358 rows x 16 columns
\n", - " memory usage: 1.58 MB
\n", + " memory usage: 1.57 MB
\n", " name: businesses
\n", " type: getml.DataFrame
\n", " url: http://localhost:1709/#/getdataframe/sfscores/businesses/\n", @@ -1728,7 +1714,7 @@ "\n", "\n", "6358 rows x 16 columns\n", - "memory usage: 1.58 MB\n", + "memory usage: 1.57 MB\n", "name: businesses\n", "type: getml.DataFrame\n", "url: http://localhost:1709/#/getdataframe/sfscores/businesses/" @@ -4224,7 +4210,7 @@ "\n", "

\n", " 6358 rows x 16 columns
\n", - " memory usage: 1.37 MB
\n", + " memory usage: 1.36 MB
\n", " name: businesses
\n", " type: getml.DataFrame
\n", " url: http://localhost:1709/#/getdataframe/sfscores/businesses/\n", @@ -4261,7 +4247,7 @@ "\n", "\n", "6358 rows x 16 columns\n", - "memory usage: 1.37 MB\n", + "memory usage: 1.36 MB\n", "name: businesses\n", "type: getml.DataFrame\n", "url: http://localhost:1709/#/getdataframe/sfscores/businesses/" @@ -5744,7 +5730,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['businesses', 'inspections', 'violations'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", @@ -5756,7 +5742,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['businesses', 'inspections', 'violations'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", @@ -5840,6 +5826,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "INFO [FOREIGN KEYS NOT FOUND]: When joining POPULATION__STAGING_TABLE_1 and VIOLATIONS__STAGING_TABLE_3 over 'business_id' and 'business_id', there are no corresponding entries for 5.685426% of entries in 'business_id' in 'POPULATION__STAGING_TABLE_1'. You might want to double-check your join keys.\n", "\n", @@ -5864,7 +5853,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:4.968643\n", + "Time taken: 0h:0m:4.187508\n", "\n" ] }, @@ -5875,26 +5864,26 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['businesses', 'inspections', 'violations'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-9NdMaJ'])

url: http://localhost:1709/#/getpipeline/sfscores/y7Fy1T/0/
" + " tags=['fast_prop', 'container-LMLODZ'])
url: http://localhost:1709/#/getpipeline/sfscores/RoS7ps/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=False,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['businesses', 'inspections', 'violations'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-9NdMaJ'])\n", + " tags=['fast_prop', 'container-LMLODZ'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/sfscores/y7Fy1T/0/" + "url: http://localhost:1709/#/getpipeline/sfscores/RoS7ps/0/" ] }, "execution_count": 16, @@ -6014,7 +6003,7 @@ " 0\n", " \n", " \n", - " 2022-03-24 21:20:16\n", + " 2022-07-04 22:17:22\n", " \n", " \n", " \n", @@ -6043,7 +6032,7 @@ " 1\n", " \n", " \n", - " 2022-03-24 21:20:17\n", + " 2022-07-04 22:17:22\n", " \n", " \n", " \n", @@ -6073,8 +6062,8 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-24 21:20:16 train score 4.8865 6.5247 0.3608\n", - "1 2022-03-24 21:20:17 test score 5.3218 7.0532 0.2889" + "0 2022-07-04 22:17:22 train score 4.8865 6.5247 0.3608\n", + "1 2022-07-04 22:17:22 test score 5.3218 7.0532 0.2889" ] }, "execution_count": 17, @@ -12872,7 +12861,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Imputation'],\n", @@ -12884,7 +12873,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Imputation'],\n", @@ -12948,7 +12937,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:1.426279\n", + "Time taken: 0h:0m:1.289732\n", "\n" ] }, @@ -12959,26 +12948,26 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Imputation'],\n", " share_selected_features=0.5,\n", - " tags=['featuretools'])
url: http://localhost:1709/#/getpipeline/sfscores/7e6eZc/0/
" + " tags=['featuretools'])
url: http://localhost:1709/#/getpipeline/sfscores/BVbP9n/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Imputation'],\n", " share_selected_features=0.5,\n", " tags=['featuretools'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/sfscores/7e6eZc/0/" + "url: http://localhost:1709/#/getpipeline/sfscores/BVbP9n/0/" ] }, "execution_count": 36, @@ -13079,7 +13068,7 @@ " 0\n", " \n", " \n", - " 2022-03-24 21:20:41\n", + " 2022-07-04 22:17:46\n", " \n", " \n", " \n", @@ -13108,7 +13097,7 @@ " 1\n", " \n", " \n", - " 2022-03-24 21:20:41\n", + " 2022-07-04 22:17:46\n", " \n", " \n", " \n", @@ -13138,8 +13127,8 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-24 21:20:41 featuretools_train score 5.1093 6.7794 0.3122\n", - "1 2022-03-24 21:20:41 featuretools_test score 5.4396 7.1771 0.2649" + "0 2022-07-04 22:17:46 featuretools_train score 5.1093 6.7794 0.3122\n", + "1 2022-07-04 22:17:46 featuretools_test score 5.4396 7.1771 0.2649" ] }, "execution_count": 37, @@ -13212,22 +13201,22 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# Creates a folder named sfscores_pipeline containing\n", "# the SQL code.\n", - "pipe1.features.to_sql().save(\"sfscores_pipeline\")" + "pipe1.features.to_sql(size_threshold=None).save(\"sfscores_pipeline\", remove=True)" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ - "pipe1.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"sfscores_spark\")" + "pipe1.features.to_sql(dialect=getml.pipeline.dialect.spark_sql, size_threshold=None).save(\"sfscores_spark\", remove=True)" ] }, { diff --git a/stats.ipynb b/stats.ipynb index 81ddcb6..8546fdb 100644 --- a/stats.ipynb +++ b/stats.ipynb @@ -102,27 +102,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220323112335.log.\n", + "getML engine is already running.\n", "\n", "\n", - "Loading pipelines...\n", - "[========================================] 100%\n", - "\n", "\n", - "Connected to project 'stats'\n" + "Connected to project 'stats'\n", + "http://localhost:1709/#/listprojects/stats/\n" ] - }, - { - "data": { - "text/html": [ - "http://localhost:1709/#/listprojects/stats/" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -3118,7 +3104,7 @@ "\n", "

\n", " 91976 rows x 21 columns
\n", - " memory usage: 130.43 MB
\n", + " memory usage: 128.41 MB
\n", " name: posts
\n", " type: getml.DataFrame
\n", " url: http://localhost:1709/#/getdataframe/stats/posts/\n", @@ -3155,7 +3141,7 @@ "\n", "\n", "91976 rows x 21 columns\n", - "memory usage: 130.43 MB\n", + "memory usage: 128.41 MB\n", "name: posts\n", "type: getml.DataFrame\n", "url: http://localhost:1709/#/getdataframe/stats/posts/" @@ -4719,7 +4705,7 @@ "\n", "

\n", " 40325 rows x 14 columns
\n", - " memory usage: 10.79 MB
\n", + " memory usage: 10.32 MB
\n", " name: users
\n", " type: getml.DataFrame
\n", " url: http://localhost:1709/#/getdataframe/stats/users/\n", @@ -4757,7 +4743,7 @@ "\n", "\n", "40325 rows x 14 columns\n", - "memory usage: 10.79 MB\n", + "memory usage: 10.32 MB\n", "name: users\n", "type: getml.DataFrame\n", "url: http://localhost:1709/#/getdataframe/stats/users/" @@ -6725,7 +6711,7 @@ " \n", " \n", " \n", - " NULL\n", + " nan\n", " \n", " \n", " \n", @@ -6932,7 +6918,7 @@ " 1 2 59 24 1 ... 8198 7 1 8 \n", " 2 3 5 18 1 ... 3613 19 4 36 \n", " 3 4 135 23 1 ... 5224 5 2 2 \n", - " 4 5 NULL 23 2 ... nan nan 3 nan \n", + " 4 5 nan 23 2 ... nan nan 3 nan \n", " ... ... ... ... ... ... ... ... ... ...\n", "\n", "name LastEditorUserId\n", @@ -8602,7 +8588,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['badges', 'posts', 'votes'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -8614,7 +8600,7 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['badges', 'posts', 'votes'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", @@ -8651,7 +8637,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['badges', 'posts', 'votes'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", @@ -8663,7 +8649,7 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['badges', 'posts', 'votes'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", @@ -8776,7 +8762,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:56.273306\n", + "Time taken: 0h:0m:35.011992\n", "\n" ] }, @@ -8787,26 +8773,26 @@ " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['badges', 'posts', 'votes'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-9c7hnb'])

url: http://localhost:1709/#/getpipeline/stats/HcoKQ9/0/
" + " tags=['fast_prop', 'container-SMMC9W'])
url: http://localhost:1709/#/getpipeline/stats/neDYxa/0/
" ], "text/plain": [ "Pipeline(data_model='users',\n", " feature_learners=['FastProp'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['badges', 'posts', 'votes'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=[],\n", " share_selected_features=0.5,\n", - " tags=['fast_prop', 'container-9c7hnb'])\n", + " tags=['fast_prop', 'container-SMMC9W'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/stats/HcoKQ9/0/" + "url: http://localhost:1709/#/getpipeline/stats/neDYxa/0/" ] }, "execution_count": 20, @@ -8868,6 +8854,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "\n", "WARNING [COLUMN SHOULD BE UNUSED]: All non-NULL entries in column 't3__posttypeid' in POSTS__STAGING_TABLE_3 are equal to each other. You should consider setting its role to unused_string or using it for comparison only (you can do the latter by setting a unit that contains 'comparison only').\n", "WARNING [COLUMN SHOULD BE UNUSED]: 93.147125% of all entries in column 't3__favoritecount' in POSTS__STAGING_TABLE_3 are NULL values. You should consider setting its role to unused_float.\n", @@ -8912,7 +8901,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:5m:12.404315\n", + "Time taken: 0h:2m:15.471851\n", "\n" ] }, @@ -8923,26 +8912,26 @@ " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['badges', 'posts', 'votes'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['relboost', 'container-9c7hnb'])
url: http://localhost:1709/#/getpipeline/stats/ZrKVmd/0/
" + " tags=['relboost', 'container-SMMC9W'])
url: http://localhost:1709/#/getpipeline/stats/gVkByO/0/
" ], "text/plain": [ "Pipeline(data_model='users',\n", " feature_learners=['Relboost'],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=['badges', 'posts', 'votes'],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Mapping'],\n", " share_selected_features=0.5,\n", - " tags=['relboost', 'container-9c7hnb'])\n", + " tags=['relboost', 'container-SMMC9W'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/stats/ZrKVmd/0/" + "url: http://localhost:1709/#/getpipeline/stats/gVkByO/0/" ] }, "execution_count": 22, @@ -8982,6 +8971,9 @@ "Staging...\n", "[========================================] 100%\n", "\n", + "Preprocessing...\n", + "[========================================] 100%\n", + "\n", "FastProp: Building subfeatures...\n", "[========================================] 100%\n", "\n", @@ -9060,7 +9052,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 11:24:45\n", + " 2022-07-04 22:27:01\n", " \n", " \n", " \n", @@ -9089,7 +9081,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 11:30:03\n", + " 2022-07-04 22:29:19\n", " \n", " \n", " \n", @@ -9119,8 +9111,8 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-23 11:24:45 train Reputation 31.8897 43.6771 0.9974\n", - "1 2022-03-23 11:30:03 test Reputation 33.6064 65.3332 0.9777" + "0 2022-07-04 22:27:01 train Reputation 31.8897 43.6771 0.9974\n", + "1 2022-07-04 22:29:19 test Reputation 33.6064 65.3332 0.9777" ] }, "execution_count": 23, @@ -9232,7 +9224,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 11:29:59\n", + " 2022-07-04 22:29:17\n", " \n", " \n", " \n", @@ -9261,7 +9253,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 11:30:06\n", + " 2022-07-04 22:29:22\n", " \n", " \n", " \n", @@ -9291,8 +9283,8 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-23 11:29:59 train Reputation 29.318 42.6186 0.9975\n", - "1 2022-03-23 11:30:06 test Reputation 31.1388 60.7643 0.9809" + "0 2022-07-04 22:29:17 train Reputation 29.318 42.6186 0.9975\n", + "1 2022-07-04 22:29:22 test Reputation 31.1388 60.7643 0.9809" ] }, "execution_count": 24, @@ -50386,7 +50378,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Imputation'],\n", @@ -50398,7 +50390,7 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Imputation'],\n", @@ -50462,7 +50454,7 @@ "\n", "\n", "Trained pipeline.\n", - "Time taken: 0h:0m:12.126025\n", + "Time taken: 0h:0m:11.597333\n", "\n" ] }, @@ -50473,26 +50465,26 @@ " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Imputation'],\n", " share_selected_features=0.5,\n", - " tags=['featuretools'])
url: http://localhost:1709/#/getpipeline/stats/SaJv2V/0/
" + " tags=['featuretools'])
url: http://localhost:1709/#/getpipeline/stats/UsbEIx/0/
" ], "text/plain": [ "Pipeline(data_model='population',\n", " feature_learners=[],\n", " feature_selectors=[],\n", " include_categorical=True,\n", - " loss_function=None,\n", + " loss_function='SquareLoss',\n", " peripheral=[],\n", " predictors=['XGBoostRegressor'],\n", " preprocessors=['Imputation'],\n", " share_selected_features=0.5,\n", " tags=['featuretools'])\n", "\n", - "url: http://localhost:1709/#/getpipeline/stats/SaJv2V/0/" + "url: http://localhost:1709/#/getpipeline/stats/UsbEIx/0/" ] }, "execution_count": 36, @@ -50593,7 +50585,7 @@ " 0\n", " \n", " \n", - " 2022-03-23 11:32:41\n", + " 2022-07-04 22:31:55\n", " \n", " \n", " \n", @@ -50622,7 +50614,7 @@ " 1\n", " \n", " \n", - " 2022-03-23 11:32:41\n", + " 2022-07-04 22:31:55\n", " \n", " \n", " \n", @@ -50652,8 +50644,8 @@ ], "text/plain": [ " date time set used target mae rmse rsquared\n", - "0 2022-03-23 11:32:41 featuretools_train Reputation 32.7876 50.2289 0.9966\n", - "1 2022-03-23 11:32:41 featuretools_test Reputation 34.7433 81.4984 0.9656" + "0 2022-07-04 22:31:55 featuretools_train Reputation 32.7876 50.2289 0.9966\n", + "1 2022-07-04 22:31:55 featuretools_test Reputation 34.7433 81.4984 0.9656" ] }, "execution_count": 37, @@ -50779,7 +50771,7 @@ "source": [ "# Creates a folder named stats_pipeline containing\n", "# the SQL code.\n", - "pipe2.features.to_sql().save(\"stats_pipeline\")" + "pipe2.features.to_sql(size_threshold=None).save(\"stats_pipeline\", remove=True)" ] }, { @@ -50788,7 +50780,7 @@ "metadata": {}, "outputs": [], "source": [ - "pipe2.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"stats_spark\")" + "pipe2.features.to_sql(dialect=getml.pipeline.dialect.spark_sql, size_threshold=None).save(\"stats_spark\", remove=True)" ] }, {