From 5a71ff6fa14d3cff2a7e085396d8dbc7a8283ab7 Mon Sep 17 00:00:00 2001 From: Nandish Jayaram Date: Tue, 3 Apr 2018 14:32:03 -0700 Subject: [PATCH 1/2] MLP: Remove source table dependency for predicting regression models JIRA: MADLIB-1223 We were getting the type of the dependent variable from the input table to know if it was an array or not. With this commit, we now get this information from the model summary table. We also had to add a column in the minibatch preprocessor summary table called `dependent_vartype` in order to get the dependent type in the mlp code for minibatching. Closes #255 Co-authored-by: Nikhil Kak --- .../postgres/modules/convex/mlp_igd.py_in | 63 +- .../postgres/modules/convex/test/mlp.sql_in | 840 +++++++++--------- .../utilities/minibatch_preprocessing.py_in | 19 +- .../test/minibatch_preprocessing.sql_in | 1 + 4 files changed, 482 insertions(+), 441 deletions(-) diff --git a/src/ports/postgres/modules/convex/mlp_igd.py_in b/src/ports/postgres/modules/convex/mlp_igd.py_in index 9039ebe2e..80105792e 100644 --- a/src/ports/postgres/modules/convex/mlp_igd.py_in +++ b/src/ports/postgres/modules/convex/mlp_igd.py_in @@ -141,14 +141,7 @@ def mlp(schema_madlib, source_table, output_table, independent_varname, else: num_output_nodes = get_col_dimension(source_table, dependent_varname, dim=2) - - # This variable is used for creating the classes_str column in the model - # summary table. We append [] when we create this column in the create - # summary table command so we need to strip it out here. - dependent_type = get_expr_type(mlp_preprocessor.CLASS_VALUES, - mlp_preprocessor.summary_table) - if dependent_type[-2:] == '[]': - dependent_type = dependent_type[:-2] + dependent_vartype = pp_summary_dict["dependent_vartype"] else: x_mean_table = unique_string(desp='x_mean_table') tbl_data_scaled = unique_string(desp="tbl_data_scaled") @@ -160,12 +153,12 @@ def mlp(schema_madlib, source_table, output_table, independent_varname, dim=1) dimension = num_input_nodes # dimension is used for normalize normalize_data(locals()) - dependent_type = get_expr_type(dependent_varname, source_table) + dependent_vartype = get_expr_type(dependent_varname, source_table) if is_classification: # If dependent variable is an array during classification, assume # that it is already one-hot-encoded. - if "[]" in dependent_type: + if "[]" in dependent_vartype: # We are now using tbl_data_scaled, so change the dependent # varname accordingly. dependent_varname = col_dep_var_norm_new @@ -185,7 +178,7 @@ def mlp(schema_madlib, source_table, output_table, independent_varname, # dependent_varname should be replaced with one-hot encoded varname dependent_varname = "ARRAY[{0}]::integer[]".format(level_vals_str) else: - if "[]" not in dependent_type: + if "[]" not in dependent_vartype: dependent_varname = "ARRAY[" + col_dep_var_norm_new + "]" num_output_nodes = get_col_dimension(tbl_data_scaled, dependent_varname, dim=1) @@ -449,12 +442,10 @@ def _create_summary_table(args): if args['warm_start']: plpy.execute("DROP TABLE IF EXISTS {0}".format(args['summary_table'])) - - classes_str = PY2SQL([strip_end_quotes(cl, "'") for cl in args['classes']], - array_type=args['dependent_type']) - + classes_type = args['dependent_vartype'] minibatch_summary_col_names = '' minibatch_summary_col_vals = '' + dependent_vartype_colname = 'dependent_vartype' if args['is_minibatch_enabled']: # Add a few more columns in the summary table minibatch_summary_col_names = """ @@ -464,10 +455,16 @@ def _create_summary_table(args): batch_size INTEGER, n_epochs INTEGER, """ + dependent_vartype_colname = 'original_dependent_vartype' mlp_pre_dict = args['pp_summary_dict'] source_table = mlp_pre_dict['source_table'] independent_varname = mlp_pre_dict['independent_varname'] dependent_varname = mlp_pre_dict['dependent_varname'] + # This variable is used for creating the classes_str column in the model + # summary table. We append [] when we create this column in the create + # summary table command so we need to strip it out here. + if classes_type[-2:] == '[]': + classes_type = classes_type[:-2] batch_size = args['batch_size'] n_epochs = args['n_epochs'] minibatch_summary_col_vals = """ @@ -477,12 +474,16 @@ def _create_summary_table(args): {batch_size}, {n_epochs}, """.format(**locals()) + + classes_str = PY2SQL([strip_end_quotes(cl, "'") for cl in args['classes']], + array_type=classes_type) summary_table_creation_query = """ CREATE TABLE {summary_table}( source_table TEXT, independent_varname TEXT, dependent_varname TEXT, {minibatch_summary_col_names} + {dependent_vartype_colname} TEXT, tolerance FLOAT, learning_rate_init FLOAT, learning_rate_policy TEXT, @@ -491,10 +492,12 @@ def _create_summary_table(args): layer_sizes INTEGER[], activation TEXT, is_classification BOOLEAN, - classes {dependent_type}[], + classes {classes_type}[], weights VARCHAR, grouping_col VARCHAR )""".format(minibatch_summary_col_names=minibatch_summary_col_names, + dependent_vartype_colname=dependent_vartype_colname, + classes_type=classes_type, **args) summary_table_update_query = """ INSERT INTO {summary_table} VALUES( @@ -502,6 +505,7 @@ def _create_summary_table(args): '{independent_varname}', '{dependent_varname_backup}', {minibatch_summary_col_vals} + '{dependent_vartype}', {tolerance}, {step_size_init}, '{learning_rate_policy}', @@ -832,22 +836,32 @@ def _get_minibatch_param_from_mlp_model_summary(summary_dict, param, minibatch_param): """ Return the value of specific columns from the model summary table. - This is to be used only for three params: + This is to be used only for these params: source_table independent_varname dependent_varname - If the model was trained with minibatch, there would be three new + dependent_vartype + If the model was trained with minibatch, there would be four new columns introduced, that correspond to the above columns: original_source_table original_independent_varname original_dependent_varname + original_dependent_vartype This is because, when minibatch is used, the column names without prefix 'original_' will have the values from the minibatch preprocessed input table, and the column names with the prefix correspond to the original table that was input to the minibatch preprocessing step. + + Only dependent_vartype or original_dependent_vartype can exist. + This is used by predict regression to know if the dependent column + is array or not. """ - return summary_dict[minibatch_param] \ - if minibatch_param in summary_dict else summary_dict[param] + if minibatch_param in summary_dict: + return summary_dict[minibatch_param] + elif param in summary_dict: + return summary_dict[param] + + return None def mlp_predict(schema_madlib, model_table, data_table, id_col_name, output_table, pred_type='response', **kwargs): @@ -1038,12 +1052,17 @@ def mlp_predict(schema_madlib, model_table, data_table, id_col_name, """ else: # Regression - dependent_type = get_expr_type(dependent_varname, source_table) + dependent_type = _get_minibatch_param_from_mlp_model_summary(summary, + 'dependent_vartype', 'original_dependent_vartype') unnest_if_not_array = "" # Return the same type as the user provided. Internally we always # use an array, but if they provided a scalar, unnest it for # the user - if "[]" not in dependent_type: + # If the dependent_type is None, it means that the model was trained + # before this column was added (< 1.14). In this case, always unnest the + # output. Note that this will return a array of len 1 even if the input is + # scalar + if dependent_type and "[]" not in dependent_type: unnest_if_not_array = "UNNEST" sql = header + """ SELECT {grouping_col_comma} diff --git a/src/ports/postgres/modules/convex/test/mlp.sql_in b/src/ports/postgres/modules/convex/test/mlp.sql_in index a40d35a98..5f9e33800 100644 --- a/src/ports/postgres/modules/convex/test/mlp.sql_in +++ b/src/ports/postgres/modules/convex/test/mlp.sql_in @@ -207,6 +207,7 @@ CREATE TABLE iris_data_batch_summary( output_table text, dependent_varname text, independent_varname text, + dependent_vartype text, buffer_size integer, class_values text[], num_rows_processed integer, @@ -221,7 +222,7 @@ CREATE TABLE iris_data_batch_summary( -- scenario where the original source table is deleted and MLP is trained with -- the preprocessed table. INSERT INTO iris_data_batch_summary VALUES -('iris_data_does_not_exist','iris_data_batch','class::TEXT','attributes',30,ARRAY[1,2,3],141,0,'grp'); +('iris_data_does_not_exist','iris_data_batch','class::TEXT','attributes','text',30,ARRAY[1,2,3],141,0,'grp'); -- Create the corresponding standardization table for preprocessed data CREATE TABLE iris_data_batch_standardization( grp text, @@ -385,418 +386,418 @@ DROP TABLE IF EXISTS mlp_class_batch, mlp_class_batch_summary, mlp_class_batch_s DROP TABLE IF EXISTS lin_housing_wi CASCADE; -CREATE TABLE lin_housing_wi (id serial, x float8[], grp int, y float8); +CREATE TABLE lin_housing_wi (id serial, x float8[], grp int, y double precision[]); COPY lin_housing_wi (x, grp, y) FROM STDIN NULL '?' DELIMITER '|'; -{1,0.00632,18.00,2.310,0,0.5380,6.5750,65.20,4.0900,1,296.0,15.30,396.90,4.98} | 1 | 24.00 -{1,0.02731,0.00,7.070,0,0.4690,6.4210,78.90,4.9671,2,242.0,17.80,396.90,9.14} | 1 | 21.60 -{1,0.02729,0.00,7.070,0,0.4690,7.1850,61.10,4.9671,2,242.0,17.80,392.83,4.03} | 1 | 34.70 -{1,0.03237,0.00,2.180,0,0.4580,6.9980,45.80,6.0622,3,222.0,18.70,394.63,2.94} | 1 | 33.40 -{1,0.06905,0.00,2.180,0,0.4580,7.1470,54.20,6.0622,3,222.0,18.70,396.90,5.33} | 1 | 36.20 -{1,0.02985,0.00,2.180,0,0.4580,6.4300,58.70,6.0622,3,222.0,18.70,394.12,5.21} | 1 | 28.70 -{1,0.08829,12.50,7.870,0,0.5240,6.0120,66.60,5.5605,5,311.0,15.20,395.60,12.43} | 1 | 22.90 -{1,0.14455,12.50,7.870,0,0.5240,6.1720,96.10,5.9505,5,311.0,15.20,396.90,19.15} | 1 | 27.10 -{1,0.21124,12.50,7.870,0,0.5240,5.6310,100.00,6.0821,5,311.0,15.20,386.63,29.93} | 1 | 16.50 -{1,0.17004,12.50,7.870,0,0.5240,6.0040,85.90,6.5921,5,311.0,15.20,386.71,17.10} | 1 | 18.90 -{1,0.22489,12.50,7.870,0,0.5240,6.3770,94.30,6.3467,5,311.0,15.20,392.52,20.45} | 1 | 15.00 -{1,0.11747,12.50,7.870,0,0.5240,6.0090,82.90,6.2267,5,311.0,15.20,396.90,13.27} | 1 | 18.90 -{1,0.09378,12.50,7.870,0,0.5240,5.8890,39.00,5.4509,5,311.0,15.20,390.50,15.71} | 1 | 21.70 -{1,0.62976,0.00,8.140,0,0.5380,5.9490,61.80,4.7075,4,307.0,21.00,396.90,8.26} | 1 | 20.40 -{1,0.63796,0.00,8.140,0,0.5380,6.0960,84.50,4.4619,4,307.0,21.00,380.02,10.26} | 1 | 18.20 -{1,0.62739,0.00,8.140,0,0.5380,5.8340,56.50,4.4986,4,307.0,21.00,395.62,8.47} | 1 | 19.90 -{1,1.05393,0.00,8.140,0,0.5380,5.9350,29.30,4.4986,4,307.0,21.00,386.85,6.58} | 1 | 23.10 -{1,0.78420,0.00,8.140,0,0.5380,5.9900,81.70,4.2579,4,307.0,21.00,386.75,14.67} | 1 | 17.50 -{1,0.80271,0.00,8.140,0,0.5380,5.4560,36.60,3.7965,4,307.0,21.00,288.99,11.69} | 1 | 20.20 -{1,0.72580,0.00,8.140,0,0.5380,5.7270,69.50,3.7965,4,307.0,21.00,390.95,11.28} | 1 | 18.20 -{1,1.25179,0.00,8.140,0,0.5380,5.5700,98.10,3.7979,4,307.0,21.00,376.57,21.02} | 1 | 13.60 -{1,0.85204,0.00,8.140,0,0.5380,5.9650,89.20,4.0123,4,307.0,21.00,392.53,13.83} | 1 | 19.60 -{1,1.23247,0.00,8.140,0,0.5380,6.1420,91.70,3.9769,4,307.0,21.00,396.90,18.72} | 1 | 15.20 -{1,0.98843,0.00,8.140,0,0.5380,5.8130,100.00,4.0952,4,307.0,21.00,394.54,19.88} | 1 | 14.50 -{1,0.75026,0.00,8.140,0,0.5380,5.9240,94.10,4.3996,4,307.0,21.00,394.33,16.30} | 1 | 15.60 -{1,0.84054,0.00,8.140,0,0.5380,5.5990,85.70,4.4546,4,307.0,21.00,303.42,16.51} | 1 | 13.90 -{1,0.67191,0.00,8.140,0,0.5380,5.8130,90.30,4.6820,4,307.0,21.00,376.88,14.81} | 1 | 16.60 -{1,0.95577,0.00,8.140,0,0.5380,6.0470,88.80,4.4534,4,307.0,21.00,306.38,17.28} | 1 | 14.80 -{1,0.77299,0.00,8.140,0,0.5380,6.4950,94.40,4.4547,4,307.0,21.00,387.94,12.80} | 1 | 18.40 -{1,1.00245,0.00,8.140,0,0.5380,6.6740,87.30,4.2390,4,307.0,21.00,380.23,11.98} | 1 | 21.00 -{1,1.13081,0.00,8.140,0,0.5380,5.7130,94.10,4.2330,4,307.0,21.00,360.17,22.60} | 1 | 12.70 -{1,1.35472,0.00,8.140,0,0.5380,6.0720,100.00,4.1750,4,307.0,21.00,376.73,13.04} | 1 | 14.50 -{1,1.38799,0.00,8.140,0,0.5380,5.9500,82.00,3.9900,4,307.0,21.00,232.60,27.71} | 1 | 13.20 -{1,1.15172,0.00,8.140,0,0.5380,5.7010,95.00,3.7872,4,307.0,21.00,358.77,18.35} | 1 | 13.10 -{1,1.61282,0.00,8.140,0,0.5380,6.0960,96.90,3.7598,4,307.0,21.00,248.31,20.34} | 1 | 13.50 -{1,0.06417,0.00,5.960,0,0.4990,5.9330,68.20,3.3603,5,279.0,19.20,396.90,9.68} | 1 | 18.90 -{1,0.09744,0.00,5.960,0,0.4990,5.8410,61.40,3.3779,5,279.0,19.20,377.56,11.41} | 1 | 20.00 -{1,0.08014,0.00,5.960,0,0.4990,5.8500,41.50,3.9342,5,279.0,19.20,396.90,8.77} | 1 | 21.00 -{1,0.17505,0.00,5.960,0,0.4990,5.9660,30.20,3.8473,5,279.0,19.20,393.43,10.13} | 1 | 24.70 -{1,0.02763,75.00,2.950,0,0.4280,6.5950,21.80,5.4011,3,252.0,18.30,395.63,4.32} | 1 | 30.80 -{1,0.03359,75.00,2.950,0,0.4280,7.0240,15.80,5.4011,3,252.0,18.30,395.62,1.98} | 1 | 34.90 -{1,0.12744,0.00,6.910,0,0.4480,6.7700,2.90,5.7209,3,233.0,17.90,385.41,4.84} | 1 | 26.60 -{1,0.14150,0.00,6.910,0,0.4480,6.1690,6.60,5.7209,3,233.0,17.90,383.37,5.81} | 1 | 25.30 -{1,0.15936,0.00,6.910,0,0.4480,6.2110,6.50,5.7209,3,233.0,17.90,394.46,7.44} | 1 | 24.70 -{1,0.12269,0.00,6.910,0,0.4480,6.0690,40.00,5.7209,3,233.0,17.90,389.39,9.55} | 1 | 21.20 -{1,0.17142,0.00,6.910,0,0.4480,5.6820,33.80,5.1004,3,233.0,17.90,396.90,10.21} | 1 | 19.30 -{1,0.18836,0.00,6.910,0,0.4480,5.7860,33.30,5.1004,3,233.0,17.90,396.90,14.15} | 1 | 20.00 -{1,0.22927,0.00,6.910,0,0.4480,6.0300,85.50,5.6894,3,233.0,17.90,392.74,18.80} | 1 | 16.60 -{1,0.25387,0.00,6.910,0,0.4480,5.3990,95.30,5.8700,3,233.0,17.90,396.90,30.81} | 1 | 14.40 -{1,0.21977,0.00,6.910,0,0.4480,5.6020,62.00,6.0877,3,233.0,17.90,396.90,16.20} | 1 | 19.40 -{1,0.08873,21.00,5.640,0,0.4390,5.9630,45.70,6.8147,4,243.0,16.80,395.56,13.45} | 1 | 19.70 -{1,0.04337,21.00,5.640,0,0.4390,6.1150,63.00,6.8147,4,243.0,16.80,393.97,9.43} | 1 | 20.50 -{1,0.05360,21.00,5.640,0,0.4390,6.5110,21.10,6.8147,4,243.0,16.80,396.90,5.28} | 1 | 25.00 -{1,0.04981,21.00,5.640,0,0.4390,5.9980,21.40,6.8147,4,243.0,16.80,396.90,8.43} | 1 | 23.40 -{1,0.01360,75.00,4.000,0,0.4100,5.8880,47.60,7.3197,3,469.0,21.10,396.90,14.80} | 1 | 18.90 -{1,0.01311,90.00,1.220,0,0.4030,7.2490,21.90,8.6966,5,226.0,17.90,395.93,4.81} | 1 | 35.40 -{1,0.02055,85.00,0.740,0,0.4100,6.3830,35.70,9.1876,2,313.0,17.30,396.90,5.77} | 1 | 24.70 -{1,0.01432,100.00,1.320,0,0.4110,6.8160,40.50,8.3248,5,256.0,15.10,392.90,3.95} | 1 | 31.60 -{1,0.15445,25.00,5.130,0,0.4530,6.1450,29.20,7.8148,8,284.0,19.70,390.68,6.86} | 1 | 23.30 -{1,0.10328,25.00,5.130,0,0.4530,5.9270,47.20,6.9320,8,284.0,19.70,396.90,9.22} | 1 | 19.60 -{1,0.14932,25.00,5.130,0,0.4530,5.7410,66.20,7.2254,8,284.0,19.70,395.11,13.15} | 1 | 18.70 -{1,0.17171,25.00,5.130,0,0.4530,5.9660,93.40,6.8185,8,284.0,19.70,378.08,14.44} | 1 | 16.00 -{1,0.11027,25.00,5.130,0,0.4530,6.4560,67.80,7.2255,8,284.0,19.70,396.90,6.73} | 1 | 22.20 -{1,0.12650,25.00,5.130,0,0.4530,6.7620,43.40,7.9809,8,284.0,19.70,395.58,9.50} | 1 | 25.00 -{1,0.01951,17.50,1.380,0,0.4161,7.1040,59.50,9.2229,3,216.0,18.60,393.24,8.05} | 1 | 33.00 -{1,0.03584,80.00,3.370,0,0.3980,6.2900,17.80,6.6115,4,337.0,16.10,396.90,4.67} | 1 | 23.50 -{1,0.04379,80.00,3.370,0,0.3980,5.7870,31.10,6.6115,4,337.0,16.10,396.90,10.24} | 1 | 19.40 -{1,0.05789,12.50,6.070,0,0.4090,5.8780,21.40,6.4980,4,345.0,18.90,396.21,8.10} | 1 | 22.00 -{1,0.13554,12.50,6.070,0,0.4090,5.5940,36.80,6.4980,4,345.0,18.90,396.90,13.09} | 1 | 17.40 -{1,0.12816,12.50,6.070,0,0.4090,5.8850,33.00,6.4980,4,345.0,18.90,396.90,8.79} | 1 | 20.90 -{1,0.08826,0.00,10.810,0,0.4130,6.4170,6.60,5.2873,4,305.0,19.20,383.73,6.72} | 1 | 24.20 -{1,0.15876,0.00,10.810,0,0.4130,5.9610,17.50,5.2873,4,305.0,19.20,376.94,9.88} | 1 | 21.70 -{1,0.09164,0.00,10.810,0,0.4130,6.0650,7.80,5.2873,4,305.0,19.20,390.91,5.52} | 1 | 22.80 -{1,0.19539,0.00,10.810,0,0.4130,6.2450,6.20,5.2873,4,305.0,19.20,377.17,7.54} | 1 | 23.40 -{1,0.07896,0.00,12.830,0,0.4370,6.2730,6.00,4.2515,5,398.0,18.70,394.92,6.78} | 1 | 24.10 -{1,0.09512,0.00,12.830,0,0.4370,6.2860,45.00,4.5026,5,398.0,18.70,383.23,8.94} | 1 | 21.40 -{1,0.10153,0.00,12.830,0,0.4370,6.2790,74.50,4.0522,5,398.0,18.70,373.66,11.97} | 1 | 20.00 -{1,0.08707,0.00,12.830,0,0.4370,6.1400,45.80,4.0905,5,398.0,18.70,386.96,10.27} | 1 | 20.80 -{1,0.05646,0.00,12.830,0,0.4370,6.2320,53.70,5.0141,5,398.0,18.70,386.40,12.34} | 1 | 21.20 -{1,0.08387,0.00,12.830,0,0.4370,5.8740,36.60,4.5026,5,398.0,18.70,396.06,9.10} | 1 | 20.30 -{1,0.04113,25.00,4.860,0,0.4260,6.7270,33.50,5.4007,4,281.0,19.00,396.90,5.29} | 1 | 28.00 -{1,0.04462,25.00,4.860,0,0.4260,6.6190,70.40,5.4007,4,281.0,19.00,395.63,7.22} | 1 | 23.90 -{1,0.03659,25.00,4.860,0,0.4260,6.3020,32.20,5.4007,4,281.0,19.00,396.90,6.72} | 1 | 24.80 -{1,0.03551,25.00,4.860,0,0.4260,6.1670,46.70,5.4007,4,281.0,19.00,390.64,7.51} | 1 | 22.90 -{1,0.05059,0.00,4.490,0,0.4490,6.3890,48.00,4.7794,3,247.0,18.50,396.90,9.62} | 1 | 23.90 -{1,0.05735,0.00,4.490,0,0.4490,6.6300,56.10,4.4377,3,247.0,18.50,392.30,6.53} | 1 | 26.60 -{1,0.05188,0.00,4.490,0,0.4490,6.0150,45.10,4.4272,3,247.0,18.50,395.99,12.86} | 1 | 22.50 -{1,0.07151,0.00,4.490,0,0.4490,6.1210,56.80,3.7476,3,247.0,18.50,395.15,8.44} | 1 | 22.20 -{1,0.05660,0.00,3.410,0,0.4890,7.0070,86.30,3.4217,2,270.0,17.80,396.90,5.50} | 1 | 23.60 -{1,0.05302,0.00,3.410,0,0.4890,7.0790,63.10,3.4145,2,270.0,17.80,396.06,5.70} | 1 | 28.70 -{1,0.04684,0.00,3.410,0,0.4890,6.4170,66.10,3.0923,2,270.0,17.80,392.18,8.81} | 1 | 22.60 -{1,0.03932,0.00,3.410,0,0.4890,6.4050,73.90,3.0921,2,270.0,17.80,393.55,8.20} | 1 | 22.00 -{1,0.04203,28.00,15.040,0,0.4640,6.4420,53.60,3.6659,4,270.0,18.20,395.01,8.16} | 1 | 22.90 -{1,0.02875,28.00,15.040,0,0.4640,6.2110,28.90,3.6659,4,270.0,18.20,396.33,6.21} | 1 | 25.00 -{1,0.04294,28.00,15.040,0,0.4640,6.2490,77.30,3.6150,4,270.0,18.20,396.90,10.59} | 1 | 20.60 -{1,0.12204,0.00,2.890,0,0.4450,6.6250,57.80,3.4952,2,276.0,18.00,357.98,6.65} | 1 | 28.40 -{1,0.11504,0.00,2.890,0,0.4450,6.1630,69.60,3.4952,2,276.0,18.00,391.83,11.34} | 1 | 21.40 -{1,0.12083,0.00,2.890,0,0.4450,8.0690,76.00,3.4952,2,276.0,18.00,396.90,4.21} | 1 | 38.70 -{1,0.08187,0.00,2.890,0,0.4450,7.8200,36.90,3.4952,2,276.0,18.00,393.53,3.57} | 1 | 43.80 -{1,0.06860,0.00,2.890,0,0.4450,7.4160,62.50,3.4952,2,276.0,18.00,396.90,6.19} | 1 | 33.20 -{1,0.14866,0.00,8.560,0,0.5200,6.7270,79.90,2.7778,5,384.0,20.90,394.76,9.42} | 1 | 27.50 -{1,0.11432,0.00,8.560,0,0.5200,6.7810,71.30,2.8561,5,384.0,20.90,395.58,7.67} | 1 | 26.50 -{1,0.22876,0.00,8.560,0,0.5200,6.4050,85.40,2.7147,5,384.0,20.90,70.80,10.63} | 1 | 18.60 -{1,0.21161,0.00,8.560,0,0.5200,6.1370,87.40,2.7147,5,384.0,20.90,394.47,13.44} | 1 | 19.30 -{1,0.13960,0.00,8.560,0,0.5200,6.1670,90.00,2.4210,5,384.0,20.90,392.69,12.33} | 1 | 20.10 -{1,0.13262,0.00,8.560,0,0.5200,5.8510,96.70,2.1069,5,384.0,20.90,394.05,16.47} | 1 | 19.50 -{1,0.17120,0.00,8.560,0,0.5200,5.8360,91.90,2.2110,5,384.0,20.90,395.67,18.66} | 1 | 19.50 -{1,0.13117,0.00,8.560,0,0.5200,6.1270,85.20,2.1224,5,384.0,20.90,387.69,14.09} | 1 | 20.40 -{1,0.12802,0.00,8.560,0,0.5200,6.4740,97.10,2.4329,5,384.0,20.90,395.24,12.27} | 1 | 19.80 -{1,0.26363,0.00,8.560,0,0.5200,6.2290,91.20,2.5451,5,384.0,20.90,391.23,15.55} | 1 | 19.40 -{1,0.10793,0.00,8.560,0,0.5200,6.1950,54.40,2.7778,5,384.0,20.90,393.49,13.00} | 1 | 21.70 -{1,0.10084,0.00,10.010,0,0.5470,6.7150,81.60,2.6775,6,432.0,17.80,395.59,10.16} | 1 | 22.80 -{1,0.12329,0.00,10.010,0,0.5470,5.9130,92.90,2.3534,6,432.0,17.80,394.95,16.21} | 1 | 18.80 -{1,0.22212,0.00,10.010,0,0.5470,6.0920,95.40,2.5480,6,432.0,17.80,396.90,17.09} | 1 | 18.70 -{1,0.14231,0.00,10.010,0,0.5470,6.2540,84.20,2.2565,6,432.0,17.80,388.74,10.45} | 1 | 18.50 -{1,0.17134,0.00,10.010,0,0.5470,5.9280,88.20,2.4631,6,432.0,17.80,344.91,15.76} | 1 | 18.30 -{1,0.13158,0.00,10.010,0,0.5470,6.1760,72.50,2.7301,6,432.0,17.80,393.30,12.04} | 1 | 21.20 -{1,0.15098,0.00,10.010,0,0.5470,6.0210,82.60,2.7474,6,432.0,17.80,394.51,10.30} | 1 | 19.20 -{1,0.13058,0.00,10.010,0,0.5470,5.8720,73.10,2.4775,6,432.0,17.80,338.63,15.37} | 1 | 20.40 -{1,0.14476,0.00,10.010,0,0.5470,5.7310,65.20,2.7592,6,432.0,17.80,391.50,13.61} | 1 | 19.30 -{1,0.06899,0.00,25.650,0,0.5810,5.8700,69.70,2.2577,2,188.0,19.10,389.15,14.37} | 1 | 22.00 -{1,0.07165,0.00,25.650,0,0.5810,6.0040,84.10,2.1974,2,188.0,19.10,377.67,14.27} | 1 | 20.30 -{1,0.09299,0.00,25.650,0,0.5810,5.9610,92.90,2.0869,2,188.0,19.10,378.09,17.93} | 1 | 20.50 -{1,0.15038,0.00,25.650,0,0.5810,5.8560,97.00,1.9444,2,188.0,19.10,370.31,25.41} | 1 | 17.30 -{1,0.09849,0.00,25.650,0,0.5810,5.8790,95.80,2.0063,2,188.0,19.10,379.38,17.58} | 1 | 18.80 -{1,0.16902,0.00,25.650,0,0.5810,5.9860,88.40,1.9929,2,188.0,19.10,385.02,14.81} | 1 | 21.40 -{1,0.38735,0.00,25.650,0,0.5810,5.6130,95.60,1.7572,2,188.0,19.10,359.29,27.26} | 1 | 15.70 -{1,0.25915,0.00,21.890,0,0.6240,5.6930,96.00,1.7883,4,437.0,21.20,392.11,17.19} | 1 | 16.20 -{1,0.32543,0.00,21.890,0,0.6240,6.4310,98.80,1.8125,4,437.0,21.20,396.90,15.39} | 1 | 18.00 -{1,0.88125,0.00,21.890,0,0.6240,5.6370,94.70,1.9799,4,437.0,21.20,396.90,18.34} | 1 | 14.30 -{1,0.34006,0.00,21.890,0,0.6240,6.4580,98.90,2.1185,4,437.0,21.20,395.04,12.60} | 1 | 19.20 -{1,1.19294,0.00,21.890,0,0.6240,6.3260,97.70,2.2710,4,437.0,21.20,396.90,12.26} | 1 | 19.60 -{1,0.59005,0.00,21.890,0,0.6240,6.3720,97.90,2.3274,4,437.0,21.20,385.76,11.12} | 1 | 23.00 -{1,0.32982,0.00,21.890,0,0.6240,5.8220,95.40,2.4699,4,437.0,21.20,388.69,15.03} | 1 | 18.40 -{1,0.97617,0.00,21.890,0,0.6240,5.7570,98.40,2.3460,4,437.0,21.20,262.76,17.31} | 1 | 15.60 -{1,0.55778,0.00,21.890,0,0.6240,6.3350,98.20,2.1107,4,437.0,21.20,394.67,16.96} | 1 | 18.10 -{1,0.32264,0.00,21.890,0,0.6240,5.9420,93.50,1.9669,4,437.0,21.20,378.25,16.90} | 1 | 17.40 -{1,0.35233,0.00,21.890,0,0.6240,6.4540,98.40,1.8498,4,437.0,21.20,394.08,14.59} | 1 | 17.10 -{1,0.24980,0.00,21.890,0,0.6240,5.8570,98.20,1.6686,4,437.0,21.20,392.04,21.32} | 1 | 13.30 -{1,0.54452,0.00,21.890,0,0.6240,6.1510,97.90,1.6687,4,437.0,21.20,396.90,18.46} | 1 | 17.80 -{1,0.29090,0.00,21.890,0,0.6240,6.1740,93.60,1.6119,4,437.0,21.20,388.08,24.16} | 1 | 14.00 -{1,1.62864,0.00,21.890,0,0.6240,5.0190,100.00,1.4394,4,437.0,21.20,396.90,34.41} | 1 | 14.40 -{1,3.32105,0.00,19.580,1,0.8710,5.4030,100.00,1.3216,5,403.0,14.70,396.90,26.82} | 1 | 13.40 -{1,4.09740,0.00,19.580,0,0.8710,5.4680,100.00,1.4118,5,403.0,14.70,396.90,26.42} | 1 | 15.60 -{1,2.77974,0.00,19.580,0,0.8710,4.9030,97.80,1.3459,5,403.0,14.70,396.90,29.29} | 1 | 11.80 -{1,2.37934,0.00,19.580,0,0.8710,6.1300,100.00,1.4191,5,403.0,14.70,172.91,27.80} | 1 | 13.80 -{1,2.15505,0.00,19.580,0,0.8710,5.6280,100.00,1.5166,5,403.0,14.70,169.27,16.65} | 1 | 15.60 -{1,2.36862,0.00,19.580,0,0.8710,4.9260,95.70,1.4608,5,403.0,14.70,391.71,29.53} | 1 | 14.60 -{1,2.33099,0.00,19.580,0,0.8710,5.1860,93.80,1.5296,5,403.0,14.70,356.99,28.32} | 1 | 17.80 -{1,2.73397,0.00,19.580,0,0.8710,5.5970,94.90,1.5257,5,403.0,14.70,351.85,21.45} | 1 | 15.40 -{1,1.65660,0.00,19.580,0,0.8710,6.1220,97.30,1.6180,5,403.0,14.70,372.80,14.10} | 1 | 21.50 -{1,1.49632,0.00,19.580,0,0.8710,5.4040,100.00,1.5916,5,403.0,14.70,341.60,13.28} | 1 | 19.60 -{1,1.12658,0.00,19.580,1,0.8710,5.0120,88.00,1.6102,5,403.0,14.70,343.28,12.12} | 1 | 15.30 -{1,2.14918,0.00,19.580,0,0.8710,5.7090,98.50,1.6232,5,403.0,14.70,261.95,15.79} | 1 | 19.40 -{1,1.41385,0.00,19.580,1,0.8710,6.1290,96.00,1.7494,5,403.0,14.70,321.02,15.12} | 1 | 17.00 -{1,3.53501,0.00,19.580,1,0.8710,6.1520,82.60,1.7455,5,403.0,14.70,88.01,15.02} | 1 | 15.60 -{1,2.44668,0.00,19.580,0,0.8710,5.2720,94.00,1.7364,5,403.0,14.70,88.63,16.14} | 1 | 13.10 -{1,1.22358,0.00,19.580,0,0.6050,6.9430,97.40,1.8773,5,403.0,14.70,363.43,4.59} | 1 | 41.30 -{1,1.34284,0.00,19.580,0,0.6050,6.0660,100.00,1.7573,5,403.0,14.70,353.89,6.43} | 1 | 24.30 -{1,1.42502,0.00,19.580,0,0.8710,6.5100,100.00,1.7659,5,403.0,14.70,364.31,7.39} | 1 | 23.30 -{1,1.27346,0.00,19.580,1,0.6050,6.2500,92.60,1.7984,5,403.0,14.70,338.92,5.50} | 1 | 27.00 -{1,1.46336,0.00,19.580,0,0.6050,7.4890,90.80,1.9709,5,403.0,14.70,374.43,1.73} | 1 | 50.00 -{1,1.83377,0.00,19.580,1,0.6050,7.8020,98.20,2.0407,5,403.0,14.70,389.61,1.92} | 1 | 50.00 -{1,1.51902,0.00,19.580,1,0.6050,8.3750,93.90,2.1620,5,403.0,14.70,388.45,3.32} | 1 | 50.00 -{1,2.24236,0.00,19.580,0,0.6050,5.8540,91.80,2.4220,5,403.0,14.70,395.11,11.64} | 1 | 22.70 -{1,2.92400,0.00,19.580,0,0.6050,6.1010,93.00,2.2834,5,403.0,14.70,240.16,9.81} | 1 | 25.00 -{1,2.01019,0.00,19.580,0,0.6050,7.9290,96.20,2.0459,5,403.0,14.70,369.30,3.70} | 1 | 50.00 -{1,1.80028,0.00,19.580,0,0.6050,5.8770,79.20,2.4259,5,403.0,14.70,227.61,12.14} | 1 | 23.80 -{1,2.30040,0.00,19.580,0,0.6050,6.3190,96.10,2.1000,5,403.0,14.70,297.09,11.10} | 1 | 23.80 -{1,2.44953,0.00,19.580,0,0.6050,6.4020,95.20,2.2625,5,403.0,14.70,330.04,11.32} | 1 | 22.30 -{1,1.20742,0.00,19.580,0,0.6050,5.8750,94.60,2.4259,5,403.0,14.70,292.29,14.43} | 1 | 17.40 -{1,2.31390,0.00,19.580,0,0.6050,5.8800,97.30,2.3887,5,403.0,14.70,348.13,12.03} | 1 | 19.10 -{1,0.13914,0.00,4.050,0,0.5100,5.5720,88.50,2.5961,5,296.0,16.60,396.90,14.69} | 1 | 23.10 -{1,0.09178,0.00,4.050,0,0.5100,6.4160,84.10,2.6463,5,296.0,16.60,395.50,9.04} | 1 | 23.60 -{1,0.08447,0.00,4.050,0,0.5100,5.8590,68.70,2.7019,5,296.0,16.60,393.23,9.64} | 1 | 22.60 -{1,0.06664,0.00,4.050,0,0.5100,6.5460,33.10,3.1323,5,296.0,16.60,390.96,5.33} | 1 | 29.40 -{1,0.07022,0.00,4.050,0,0.5100,6.0200,47.20,3.5549,5,296.0,16.60,393.23,10.11} | 1 | 23.20 -{1,0.05425,0.00,4.050,0,0.5100,6.3150,73.40,3.3175,5,296.0,16.60,395.60,6.29} | 1 | 24.60 -{1,0.06642,0.00,4.050,0,0.5100,6.8600,74.40,2.9153,5,296.0,16.60,391.27,6.92} | 1 | 29.90 -{1,0.05780,0.00,2.460,0,0.4880,6.9800,58.40,2.8290,3,193.0,17.80,396.90,5.04} | 1 | 37.20 -{1,0.06588,0.00,2.460,0,0.4880,7.7650,83.30,2.7410,3,193.0,17.80,395.56,7.56} | 1 | 39.80 -{1,0.06888,0.00,2.460,0,0.4880,6.1440,62.20,2.5979,3,193.0,17.80,396.90,9.45} | 1 | 36.20 -{1,0.09103,0.00,2.460,0,0.4880,7.1550,92.20,2.7006,3,193.0,17.80,394.12,4.82} | 1 | 37.90 -{1,0.10008,0.00,2.460,0,0.4880,6.5630,95.60,2.8470,3,193.0,17.80,396.90,5.68} | 1 | 32.50 -{1,0.08308,0.00,2.460,0,0.4880,5.6040,89.80,2.9879,3,193.0,17.80,391.00,13.98} | 1 | 26.40 -{1,0.06047,0.00,2.460,0,0.4880,6.1530,68.80,3.2797,3,193.0,17.80,387.11,13.15} | 1 | 29.60 -{1,0.05602,0.00,2.460,0,0.4880,7.8310,53.60,3.1992,3,193.0,17.80,392.63,4.45} | 1 | 50.00 -{1,0.07875,45.00,3.440,0,0.4370,6.7820,41.10,3.7886,5,398.0,15.20,393.87,6.68} | 1 | 32.00 -{1,0.12579,45.00,3.440,0,0.4370,6.5560,29.10,4.5667,5,398.0,15.20,382.84,4.56} | 1 | 29.80 -{1,0.08370,45.00,3.440,0,0.4370,7.1850,38.90,4.5667,5,398.0,15.20,396.90,5.39} | 1 | 34.90 -{1,0.09068,45.00,3.440,0,0.4370,6.9510,21.50,6.4798,5,398.0,15.20,377.68,5.10} | 1 | 37.00 -{1,0.06911,45.00,3.440,0,0.4370,6.7390,30.80,6.4798,5,398.0,15.20,389.71,4.69} | 1 | 30.50 -{1,0.04590,52.50,5.320,0,0.4050,6.3150,45.60,7.3172,6,293.0,16.60,396.90,7.60} | 2 | 22.30 -{1,0.04297,52.50,5.320,0,0.4050,6.5650,22.90,7.3172,6,293.0,16.60,371.72,9.51} | 2 | 24.80 -{1,0.03502,80.00,4.950,0,0.4110,6.8610,27.90,5.1167,4,245.0,19.20,396.90,3.33} | 2 | 28.50 -{1,0.07886,80.00,4.950,0,0.4110,7.1480,27.70,5.1167,4,245.0,19.20,396.90,3.56} | 2 | 37.30 -{1,0.03615,80.00,4.950,0,0.4110,6.6300,23.40,5.1167,4,245.0,19.20,396.90,4.70} | 2 | 27.90 -{1,0.08265,0.00,13.920,0,0.4370,6.1270,18.40,5.5027,4,289.0,16.00,396.90,8.58} | 2 | 23.90 -{1,0.08199,0.00,13.920,0,0.4370,6.0090,42.30,5.5027,4,289.0,16.00,396.90,10.40} | 2 | 21.70 -{1,0.12932,0.00,13.920,0,0.4370,6.6780,31.10,5.9604,4,289.0,16.00,396.90,6.27} | 2 | 28.60 -{1,0.05372,0.00,13.920,0,0.4370,6.5490,51.00,5.9604,4,289.0,16.00,392.85,7.39} | 2 | 27.10 -{1,0.14103,0.00,13.920,0,0.4370,5.7900,58.00,6.3200,4,289.0,16.00,396.90,15.84} | 2 | 20.30 -{1,0.06466,70.00,2.240,0,0.4000,6.3450,20.10,7.8278,5,358.0,14.80,368.24,4.97} | 2 | 22.50 -{1,0.05561,70.00,2.240,0,0.4000,7.0410,10.00,7.8278,5,358.0,14.80,371.58,4.74} | 2 | 29.00 -{1,0.04417,70.00,2.240,0,0.4000,6.8710,47.40,7.8278,5,358.0,14.80,390.86,6.07} | 2 | 24.80 -{1,0.03537,34.00,6.090,0,0.4330,6.5900,40.40,5.4917,7,329.0,16.10,395.75,9.50} | 2 | 22.00 -{1,0.09266,34.00,6.090,0,0.4330,6.4950,18.40,5.4917,7,329.0,16.10,383.61,8.67} | 2 | 26.40 -{1,0.10000,34.00,6.090,0,0.4330,6.9820,17.70,5.4917,7,329.0,16.10,390.43,4.86} | 2 | 33.10 -{1,0.05515,33.00,2.180,0,0.4720,7.2360,41.10,4.0220,7,222.0,18.40,393.68,6.93} | 2 | 36.10 -{1,0.05479,33.00,2.180,0,0.4720,6.6160,58.10,3.3700,7,222.0,18.40,393.36,8.93} | 2 | 28.40 -{1,0.07503,33.00,2.180,0,0.4720,7.4200,71.90,3.0992,7,222.0,18.40,396.90,6.47} | 2 | 33.40 -{1,0.04932,33.00,2.180,0,0.4720,6.8490,70.30,3.1827,7,222.0,18.40,396.90,7.53} | 2 | 28.20 -{1,0.49298,0.00,9.900,0,0.5440,6.6350,82.50,3.3175,4,304.0,18.40,396.90,4.54} | 2 | 22.80 -{1,0.34940,0.00,9.900,0,0.5440,5.9720,76.70,3.1025,4,304.0,18.40,396.24,9.97} | 2 | 20.30 -{1,2.63548,0.00,9.900,0,0.5440,4.9730,37.80,2.5194,4,304.0,18.40,350.45,12.64} | 2 | 16.10 -{1,0.79041,0.00,9.900,0,0.5440,6.1220,52.80,2.6403,4,304.0,18.40,396.90,5.98} | 2 | 22.10 -{1,0.26169,0.00,9.900,0,0.5440,6.0230,90.40,2.8340,4,304.0,18.40,396.30,11.72} | 2 | 19.40 -{1,0.26938,0.00,9.900,0,0.5440,6.2660,82.80,3.2628,4,304.0,18.40,393.39,7.90} | 2 | 21.60 -{1,0.36920,0.00,9.900,0,0.5440,6.5670,87.30,3.6023,4,304.0,18.40,395.69,9.28} | 2 | 23.80 -{1,0.25356,0.00,9.900,0,0.5440,5.7050,77.70,3.9450,4,304.0,18.40,396.42,11.50} | 2 | 16.20 -{1,0.31827,0.00,9.900,0,0.5440,5.9140,83.20,3.9986,4,304.0,18.40,390.70,18.33} | 2 | 17.80 -{1,0.24522,0.00,9.900,0,0.5440,5.7820,71.70,4.0317,4,304.0,18.40,396.90,15.94} | 2 | 19.80 -{1,0.40202,0.00,9.900,0,0.5440,6.3820,67.20,3.5325,4,304.0,18.40,395.21,10.36} | 2 | 23.10 -{1,0.47547,0.00,9.900,0,0.5440,6.1130,58.80,4.0019,4,304.0,18.40,396.23,12.73} | 2 | 21.00 -{1,0.16760,0.00,7.380,0,0.4930,6.4260,52.30,4.5404,5,287.0,19.60,396.90,7.20} | 2 | 23.80 -{1,0.18159,0.00,7.380,0,0.4930,6.3760,54.30,4.5404,5,287.0,19.60,396.90,6.87} | 2 | 23.10 -{1,0.35114,0.00,7.380,0,0.4930,6.0410,49.90,4.7211,5,287.0,19.60,396.90,7.70} | 2 | 20.40 -{1,0.28392,0.00,7.380,0,0.4930,5.7080,74.30,4.7211,5,287.0,19.60,391.13,11.74} | 2 | 18.50 -{1,0.34109,0.00,7.380,0,0.4930,6.4150,40.10,4.7211,5,287.0,19.60,396.90,6.12} | 2 | 25.00 -{1,0.19186,0.00,7.380,0,0.4930,6.4310,14.70,5.4159,5,287.0,19.60,393.68,5.08} | 2 | 24.60 -{1,0.30347,0.00,7.380,0,0.4930,6.3120,28.90,5.4159,5,287.0,19.60,396.90,6.15} | 2 | 23.00 -{1,0.24103,0.00,7.380,0,0.4930,6.0830,43.70,5.4159,5,287.0,19.60,396.90,12.79} | 2 | 22.20 -{1,0.06617,0.00,3.240,0,0.4600,5.8680,25.80,5.2146,4,430.0,16.90,382.44,9.97} | 2 | 19.30 -{1,0.06724,0.00,3.240,0,0.4600,6.3330,17.20,5.2146,4,430.0,16.90,375.21,7.34} | 2 | 22.60 -{1,0.04544,0.00,3.240,0,0.4600,6.1440,32.20,5.8736,4,430.0,16.90,368.57,9.09} | 2 | 19.80 -{1,0.05023,35.00,6.060,0,0.4379,5.7060,28.40,6.6407,1,304.0,16.90,394.02,12.43} | 2 | 17.10 -{1,0.03466,35.00,6.060,0,0.4379,6.0310,23.30,6.6407,1,304.0,16.90,362.25,7.83} | 2 | 19.40 -{1,0.05083,0.00,5.190,0,0.5150,6.3160,38.10,6.4584,5,224.0,20.20,389.71,5.68} | 2 | 22.20 -{1,0.03738,0.00,5.190,0,0.5150,6.3100,38.50,6.4584,5,224.0,20.20,389.40,6.75} | 2 | 20.70 -{1,0.03961,0.00,5.190,0,0.5150,6.0370,34.50,5.9853,5,224.0,20.20,396.90,8.01} | 2 | 21.10 -{1,0.03427,0.00,5.190,0,0.5150,5.8690,46.30,5.2311,5,224.0,20.20,396.90,9.80} | 2 | 19.50 -{1,0.03041,0.00,5.190,0,0.5150,5.8950,59.60,5.6150,5,224.0,20.20,394.81,10.56} | 2 | 18.50 -{1,0.03306,0.00,5.190,0,0.5150,6.0590,37.30,4.8122,5,224.0,20.20,396.14,8.51} | 2 | 20.60 -{1,0.05497,0.00,5.190,0,0.5150,5.9850,45.40,4.8122,5,224.0,20.20,396.90,9.74} | 2 | 19.00 -{1,0.06151,0.00,5.190,0,0.5150,5.9680,58.50,4.8122,5,224.0,20.20,396.90,9.29} | 2 | 18.70 -{1,0.01301,35.00,1.520,0,0.4420,7.2410,49.30,7.0379,1,284.0,15.50,394.74,5.49} | 2 | 32.70 -{1,0.02498,0.00,1.890,0,0.5180,6.5400,59.70,6.2669,1,422.0,15.90,389.96,8.65} | 2 | 16.50 -{1,0.02543,55.00,3.780,0,0.4840,6.6960,56.40,5.7321,5,370.0,17.60,396.90,7.18} | 2 | 23.90 -{1,0.03049,55.00,3.780,0,0.4840,6.8740,28.10,6.4654,5,370.0,17.60,387.97,4.61} | 2 | 31.20 -{1,0.03113,0.00,4.390,0,0.4420,6.0140,48.50,8.0136,3,352.0,18.80,385.64,10.53} | 2 | 17.50 -{1,0.06162,0.00,4.390,0,0.4420,5.8980,52.30,8.0136,3,352.0,18.80,364.61,12.67} | 2 | 17.20 -{1,0.01870,85.00,4.150,0,0.4290,6.5160,27.70,8.5353,4,351.0,17.90,392.43,6.36} | 2 | 23.10 -{1,0.01501,80.00,2.010,0,0.4350,6.6350,29.70,8.3440,4,280.0,17.00,390.94,5.99} | 2 | 24.50 -{1,0.02899,40.00,1.250,0,0.4290,6.9390,34.50,8.7921,1,335.0,19.70,389.85,5.89} | 2 | 26.60 -{1,0.06211,40.00,1.250,0,0.4290,6.4900,44.40,8.7921,1,335.0,19.70,396.90,5.98} | 2 | 22.90 -{1,0.07950,60.00,1.690,0,0.4110,6.5790,35.90,10.7103,4,411.0,18.30,370.78,5.49} | 2 | 24.10 -{1,0.07244,60.00,1.690,0,0.4110,5.8840,18.50,10.7103,4,411.0,18.30,392.33,7.79} | 2 | 18.60 -{1,0.01709,90.00,2.020,0,0.4100,6.7280,36.10,12.1265,5,187.0,17.00,384.46,4.50} | 2 | 30.10 -{1,0.04301,80.00,1.910,0,0.4130,5.6630,21.90,10.5857,4,334.0,22.00,382.80,8.05} | 2 | 18.20 -{1,0.10659,80.00,1.910,0,0.4130,5.9360,19.50,10.5857,4,334.0,22.00,376.04,5.57} | 2 | 20.60 -{1,8.98296,0.00,18.100,1,0.7700,6.2120,97.40,2.1222,24,666.0,20.20,377.73,17.60} | 2 | 17.80 -{1,3.84970,0.00,18.100,1,0.7700,6.3950,91.00,2.5052,24,666.0,20.20,391.34,13.27} | 2 | 21.70 -{1,5.20177,0.00,18.100,1,0.7700,6.1270,83.40,2.7227,24,666.0,20.20,395.43,11.48} | 2 | 22.70 -{1,4.26131,0.00,18.100,0,0.7700,6.1120,81.30,2.5091,24,666.0,20.20,390.74,12.67} | 2 | 22.60 -{1,4.54192,0.00,18.100,0,0.7700,6.3980,88.00,2.5182,24,666.0,20.20,374.56,7.79} | 2 | 25.00 -{1,3.83684,0.00,18.100,0,0.7700,6.2510,91.10,2.2955,24,666.0,20.20,350.65,14.19} | 2 | 19.90 -{1,3.67822,0.00,18.100,0,0.7700,5.3620,96.20,2.1036,24,666.0,20.20,380.79,10.19} | 2 | 20.80 -{1,4.22239,0.00,18.100,1,0.7700,5.8030,89.00,1.9047,24,666.0,20.20,353.04,14.64} | 2 | 16.80 -{1,3.47428,0.00,18.100,1,0.7180,8.7800,82.90,1.9047,24,666.0,20.20,354.55,5.29} | 2 | 21.90 -{1,4.55587,0.00,18.100,0,0.7180,3.5610,87.90,1.6132,24,666.0,20.20,354.70,7.12} | 2 | 27.50 -{1,3.69695,0.00,18.100,0,0.7180,4.9630,91.40,1.7523,24,666.0,20.20,316.03,14.00} | 2 | 21.90 -{1,13.52220,0.00,18.100,0,0.6310,3.8630,100.00,1.5106,24,666.0,20.20,131.42,13.33} | 2 | 23.10 -{1,4.89822,0.00,18.100,0,0.6310,4.9700,100.00,1.3325,24,666.0,20.20,375.52,3.26} | 2 | 50.00 -{1,5.66998,0.00,18.100,1,0.6310,6.6830,96.80,1.3567,24,666.0,20.20,375.33,3.73} | 2 | 50.00 -{1,6.53876,0.00,18.100,1,0.6310,7.0160,97.50,1.2024,24,666.0,20.20,392.05,2.96} | 2 | 50.00 -{1,9.23230,0.00,18.100,0,0.6310,6.2160,100.00,1.1691,24,666.0,20.20,366.15,9.53} | 2 | 50.00 -{1,8.26725,0.00,18.100,1,0.6680,5.8750,89.60,1.1296,24,666.0,20.20,347.88,8.88} | 2 | 50.00 -{1,11.10810,0.00,18.100,0,0.6680,4.9060,100.00,1.1742,24,666.0,20.20,396.90,34.77} | 2 | 13.80 -{1,18.49820,0.00,18.100,0,0.6680,4.1380,100.00,1.1370,24,666.0,20.20,396.90,37.97} | 2 | 13.80 -{1,19.60910,0.00,18.100,0,0.6710,7.3130,97.90,1.3163,24,666.0,20.20,396.90,13.44} | 2 | 15.00 -{1,15.28800,0.00,18.100,0,0.6710,6.6490,93.30,1.3449,24,666.0,20.20,363.02,23.24} | 2 | 13.90 -{1,9.82349,0.00,18.100,0,0.6710,6.7940,98.80,1.3580,24,666.0,20.20,396.90,21.24} | 2 | 13.30 -{1,23.64820,0.00,18.100,0,0.6710,6.3800,96.20,1.3861,24,666.0,20.20,396.90,23.69} | 2 | 13.10 -{1,17.86670,0.00,18.100,0,0.6710,6.2230,100.00,1.3861,24,666.0,20.20,393.74,21.78} | 2 | 10.20 -{1,88.97620,0.00,18.100,0,0.6710,6.9680,91.90,1.4165,24,666.0,20.20,396.90,17.21} | 2 | 10.40 -{1,15.87440,0.00,18.100,0,0.6710,6.5450,99.10,1.5192,24,666.0,20.20,396.90,21.08} | 2 | 10.90 -{1,9.18702,0.00,18.100,0,0.7000,5.5360,100.00,1.5804,24,666.0,20.20,396.90,23.60} | 2 | 11.30 -{1,7.99248,0.00,18.100,0,0.7000,5.5200,100.00,1.5331,24,666.0,20.20,396.90,24.56} | 2 | 12.30 -{1,20.08490,0.00,18.100,0,0.7000,4.3680,91.20,1.4395,24,666.0,20.20,285.83,30.63} | 2 | 8.80 -{1,16.81180,0.00,18.100,0,0.7000,5.2770,98.10,1.4261,24,666.0,20.20,396.90,30.81} | 2 | 7.20 -{1,24.39380,0.00,18.100,0,0.7000,4.6520,100.00,1.4672,24,666.0,20.20,396.90,28.28} | 2 | 10.50 -{1,22.59710,0.00,18.100,0,0.7000,5.0000,89.50,1.5184,24,666.0,20.20,396.90,31.99} | 2 | 7.40 -{1,14.33370,0.00,18.100,0,0.7000,4.8800,100.00,1.5895,24,666.0,20.20,372.92,30.62} | 2 | 10.20 -{1,8.15174,0.00,18.100,0,0.7000,5.3900,98.90,1.7281,24,666.0,20.20,396.90,20.85} | 2 | 11.50 -{1,6.96215,0.00,18.100,0,0.7000,5.7130,97.00,1.9265,24,666.0,20.20,394.43,17.11} | 2 | 15.10 -{1,5.29305,0.00,18.100,0,0.7000,6.0510,82.50,2.1678,24,666.0,20.20,378.38,18.76} | 2 | 23.20 -{1,11.57790,0.00,18.100,0,0.7000,5.0360,97.00,1.7700,24,666.0,20.20,396.90,25.68} | 2 | 9.70 -{1,8.64476,0.00,18.100,0,0.6930,6.1930,92.60,1.7912,24,666.0,20.20,396.90,15.17} | 2 | 13.80 -{1,13.35980,0.00,18.100,0,0.6930,5.8870,94.70,1.7821,24,666.0,20.20,396.90,16.35} | 2 | 12.70 -{1,8.71675,0.00,18.100,0,0.6930,6.4710,98.80,1.7257,24,666.0,20.20,391.98,17.12} | 2 | 13.10 -{1,5.87205,0.00,18.100,0,0.6930,6.4050,96.00,1.6768,24,666.0,20.20,396.90,19.37} | 2 | 12.50 -{1,7.67202,0.00,18.100,0,0.6930,5.7470,98.90,1.6334,24,666.0,20.20,393.10,19.92} | 2 | 8.50 -{1,38.35180,0.00,18.100,0,0.6930,5.4530,100.00,1.4896,24,666.0,20.20,396.90,30.59} | 2 | 5.00 -{1,9.91655,0.00,18.100,0,0.6930,5.8520,77.80,1.5004,24,666.0,20.20,338.16,29.97} | 2 | 6.30 -{1,25.04610,0.00,18.100,0,0.6930,5.9870,100.00,1.5888,24,666.0,20.20,396.90,26.77} | 2 | 5.60 -{1,14.23620,0.00,18.100,0,0.6930,6.3430,100.00,1.5741,24,666.0,20.20,396.90,20.32} | 2 | 7.20 -{1,9.59571,0.00,18.100,0,0.6930,6.4040,100.00,1.6390,24,666.0,20.20,376.11,20.31} | 2 | 12.10 -{1,24.80170,0.00,18.100,0,0.6930,5.3490,96.00,1.7028,24,666.0,20.20,396.90,19.77} | 2 | 8.30 -{1,41.52920,0.00,18.100,0,0.6930,5.5310,85.40,1.6074,24,666.0,20.20,329.46,27.38} | 2 | 8.50 -{1,67.92080,0.00,18.100,0,0.6930,5.6830,100.00,1.4254,24,666.0,20.20,384.97,22.98} | 2 | 5.00 -{1,20.71620,0.00,18.100,0,0.6590,4.1380,100.00,1.1781,24,666.0,20.20,370.22,23.34} | 2 | 11.90 -{1,11.95110,0.00,18.100,0,0.6590,5.6080,100.00,1.2852,24,666.0,20.20,332.09,12.13} | 2 | 27.90 -{1,7.40389,0.00,18.100,0,0.5970,5.6170,97.90,1.4547,24,666.0,20.20,314.64,26.40} | 2 | 17.20 -{1,14.43830,0.00,18.100,0,0.5970,6.8520,100.00,1.4655,24,666.0,20.20,179.36,19.78} | 2 | 27.50 -{1,51.13580,0.00,18.100,0,0.5970,5.7570,100.00,1.4130,24,666.0,20.20,2.60,10.11} | 2 | 15.00 -{1,14.05070,0.00,18.100,0,0.5970,6.6570,100.00,1.5275,24,666.0,20.20,35.05,21.22} | 2 | 17.20 -{1,18.81100,0.00,18.100,0,0.5970,4.6280,100.00,1.5539,24,666.0,20.20,28.79,34.37} | 2 | 17.90 -{1,28.65580,0.00,18.100,0,0.5970,5.1550,100.00,1.5894,24,666.0,20.20,210.97,20.08} | 2 | 16.30 -{1,45.74610,0.00,18.100,0,0.6930,4.5190,100.00,1.6582,24,666.0,20.20,88.27,36.98} | 2 | 7.00 -{1,18.08460,0.00,18.100,0,0.6790,6.4340,100.00,1.8347,24,666.0,20.20,27.25,29.05} | 2 | 7.20 -{1,10.83420,0.00,18.100,0,0.6790,6.7820,90.80,1.8195,24,666.0,20.20,21.57,25.79} | 2 | 7.50 -{1,25.94060,0.00,18.100,0,0.6790,5.3040,89.10,1.6475,24,666.0,20.20,127.36,26.64} | 2 | 10.40 -{1,73.53410,0.00,18.100,0,0.6790,5.9570,100.00,1.8026,24,666.0,20.20,16.45,20.62} | 2 | 8.80 -{1,11.81230,0.00,18.100,0,0.7180,6.8240,76.50,1.7940,24,666.0,20.20,48.45,22.74} | 2 | 8.40 -{1,11.08740,0.00,18.100,0,0.7180,6.4110,100.00,1.8589,24,666.0,20.20,318.75,15.02} | 2 | 16.70 -{1,7.02259,0.00,18.100,0,0.7180,6.0060,95.30,1.8746,24,666.0,20.20,319.98,15.70} | 2 | 14.20 -{1,12.04820,0.00,18.100,0,0.6140,5.6480,87.60,1.9512,24,666.0,20.20,291.55,14.10} | 2 | 20.80 -{1,7.05042,0.00,18.100,0,0.6140,6.1030,85.10,2.0218,24,666.0,20.20,2.52,23.29} | 2 | 13.40 -{1,8.79212,0.00,18.100,0,0.5840,5.5650,70.60,2.0635,24,666.0,20.20,3.65,17.16} | 2 | 11.70 -{1,15.86030,0.00,18.100,0,0.6790,5.8960,95.40,1.9096,24,666.0,20.20,7.68,24.39} | 2 | 8.30 -{1,12.24720,0.00,18.100,0,0.5840,5.8370,59.70,1.9976,24,666.0,20.20,24.65,15.69} | 2 | 10.20 -{1,37.66190,0.00,18.100,0,0.6790,6.2020,78.70,1.8629,24,666.0,20.20,18.82,14.52} | 2 | 10.90 -{1,7.36711,0.00,18.100,0,0.6790,6.1930,78.10,1.9356,24,666.0,20.20,96.73,21.52} | 2 | 11.00 -{1,9.33889,0.00,18.100,0,0.6790,6.3800,95.60,1.9682,24,666.0,20.20,60.72,24.08} | 2 | 9.50 -{1,8.49213,0.00,18.100,0,0.5840,6.3480,86.10,2.0527,24,666.0,20.20,83.45,17.64} | 2 | 14.50 -{1,10.06230,0.00,18.100,0,0.5840,6.8330,94.30,2.0882,24,666.0,20.20,81.33,19.69} | 2 | 14.10 -{1,6.44405,0.00,18.100,0,0.5840,6.4250,74.80,2.2004,24,666.0,20.20,97.95,12.03} | 2 | 16.10 -{1,5.58107,0.00,18.100,0,0.7130,6.4360,87.90,2.3158,24,666.0,20.20,100.19,16.22} | 2 | 14.30 -{1,13.91340,0.00,18.100,0,0.7130,6.2080,95.00,2.2222,24,666.0,20.20,100.63,15.17} | 2 | 11.70 -{1,11.16040,0.00,18.100,0,0.7400,6.6290,94.60,2.1247,24,666.0,20.20,109.85,23.27} | 2 | 13.40 -{1,14.42080,0.00,18.100,0,0.7400,6.4610,93.30,2.0026,24,666.0,20.20,27.49,18.05} | 2 | 9.60 -{1,15.17720,0.00,18.100,0,0.7400,6.1520,100.00,1.9142,24,666.0,20.20,9.32,26.45} | 2 | 8.70 -{1,13.67810,0.00,18.100,0,0.7400,5.9350,87.90,1.8206,24,666.0,20.20,68.95,34.02} | 2 | 8.40 -{1,9.39063,0.00,18.100,0,0.7400,5.6270,93.90,1.8172,24,666.0,20.20,396.90,22.88} | 2 | 12.80 -{1,22.05110,0.00,18.100,0,0.7400,5.8180,92.40,1.8662,24,666.0,20.20,391.45,22.11} | 2 | 10.50 -{1,9.72418,0.00,18.100,0,0.7400,6.4060,97.20,2.0651,24,666.0,20.20,385.96,19.52} | 2 | 17.10 -{1,5.66637,0.00,18.100,0,0.7400,6.2190,100.00,2.0048,24,666.0,20.20,395.69,16.59} | 2 | 18.40 -{1,9.96654,0.00,18.100,0,0.7400,6.4850,100.00,1.9784,24,666.0,20.20,386.73,18.85} | 2 | 15.40 -{1,12.80230,0.00,18.100,0,0.7400,5.8540,96.60,1.8956,24,666.0,20.20,240.52,23.79} | 2 | 10.80 -{1,10.67180,0.00,18.100,0,0.7400,6.4590,94.80,1.9879,24,666.0,20.20,43.06,23.98} | 2 | 11.80 -{1,6.28807,0.00,18.100,0,0.7400,6.3410,96.40,2.0720,24,666.0,20.20,318.01,17.79} | 2 | 14.90 -{1,9.92485,0.00,18.100,0,0.7400,6.2510,96.60,2.1980,24,666.0,20.20,388.52,16.44} | 2 | 12.60 -{1,9.32909,0.00,18.100,0,0.7130,6.1850,98.70,2.2616,24,666.0,20.20,396.90,18.13} | 2 | 14.10 -{1,7.52601,0.00,18.100,0,0.7130,6.4170,98.30,2.1850,24,666.0,20.20,304.21,19.31} | 2 | 13.00 -{1,6.71772,0.00,18.100,0,0.7130,6.7490,92.60,2.3236,24,666.0,20.20,0.32,17.44} | 2 | 13.40 -{1,5.44114,0.00,18.100,0,0.7130,6.6550,98.20,2.3552,24,666.0,20.20,355.29,17.73} | 2 | 15.20 -{1,5.09017,0.00,18.100,0,0.7130,6.2970,91.80,2.3682,24,666.0,20.20,385.09,17.27} | 2 | 16.10 -{1,8.24809,0.00,18.100,0,0.7130,7.3930,99.30,2.4527,24,666.0,20.20,375.87,16.74} | 2 | 17.80 -{1,9.51363,0.00,18.100,0,0.7130,6.7280,94.10,2.4961,24,666.0,20.20,6.68,18.71} | 2 | 14.90 -{1,4.75237,0.00,18.100,0,0.7130,6.5250,86.50,2.4358,24,666.0,20.20,50.92,18.13} | 2 | 14.10 -{1,4.66883,0.00,18.100,0,0.7130,5.9760,87.90,2.5806,24,666.0,20.20,10.48,19.01} | 2 | 12.70 -{1,8.20058,0.00,18.100,0,0.7130,5.9360,80.30,2.7792,24,666.0,20.20,3.50,16.94} | 2 | 13.50 -{1,7.75223,0.00,18.100,0,0.7130,6.3010,83.70,2.7831,24,666.0,20.20,272.21,16.23} | 2 | 14.90 -{1,6.80117,0.00,18.100,0,0.7130,6.0810,84.40,2.7175,24,666.0,20.20,396.90,14.70} | 2 | 20.00 -{1,4.81213,0.00,18.100,0,0.7130,6.7010,90.00,2.5975,24,666.0,20.20,255.23,16.42} | 2 | 16.40 -{1,3.69311,0.00,18.100,0,0.7130,6.3760,88.40,2.5671,24,666.0,20.20,391.43,14.65} | 2 | 17.70 -{1,6.65492,0.00,18.100,0,0.7130,6.3170,83.00,2.7344,24,666.0,20.20,396.90,13.99} | 2 | 19.50 -{1,5.82115,0.00,18.100,0,0.7130,6.5130,89.90,2.8016,24,666.0,20.20,393.82,10.29} | 2 | 20.20 -{1,7.83932,0.00,18.100,0,0.6550,6.2090,65.40,2.9634,24,666.0,20.20,396.90,13.22} | 2 | 21.40 -{1,3.16360,0.00,18.100,0,0.6550,5.7590,48.20,3.0665,24,666.0,20.20,334.40,14.13} | 2 | 19.90 -{1,3.77498,0.00,18.100,0,0.6550,5.9520,84.70,2.8715,24,666.0,20.20,22.01,17.15} | 2 | 19.00 -{1,4.42228,0.00,18.100,0,0.5840,6.0030,94.50,2.5403,24,666.0,20.20,331.29,21.32} | 2 | 19.10 -{1,15.57570,0.00,18.100,0,0.5800,5.9260,71.00,2.9084,24,666.0,20.20,368.74,18.13} | 2 | 19.10 -{1,13.07510,0.00,18.100,0,0.5800,5.7130,56.70,2.8237,24,666.0,20.20,396.90,14.76} | 2 | 20.10 -{1,4.34879,0.00,18.100,0,0.5800,6.1670,84.00,3.0334,24,666.0,20.20,396.90,16.29} | 2 | 19.90 -{1,4.03841,0.00,18.100,0,0.5320,6.2290,90.70,3.0993,24,666.0,20.20,395.33,12.87} | 2 | 19.60 -{1,3.56868,0.00,18.100,0,0.5800,6.4370,75.00,2.8965,24,666.0,20.20,393.37,14.36} | 2 | 23.20 -{1,4.64689,0.00,18.100,0,0.6140,6.9800,67.60,2.5329,24,666.0,20.20,374.68,11.66} | 2 | 29.80 -{1,8.05579,0.00,18.100,0,0.5840,5.4270,95.40,2.4298,24,666.0,20.20,352.58,18.14} | 2 | 13.80 -{1,6.39312,0.00,18.100,0,0.5840,6.1620,97.40,2.2060,24,666.0,20.20,302.76,24.10} | 2 | 13.30 -{1,4.87141,0.00,18.100,0,0.6140,6.4840,93.60,2.3053,24,666.0,20.20,396.21,18.68} | 2 | 16.70 -{1,15.02340,0.00,18.100,0,0.6140,5.3040,97.30,2.1007,24,666.0,20.20,349.48,24.91} | 2 | 12.00 -{1,10.23300,0.00,18.100,0,0.6140,6.1850,96.70,2.1705,24,666.0,20.20,379.70,18.03} | 2 | 14.60 -{1,14.33370,0.00,18.100,0,0.6140,6.2290,88.00,1.9512,24,666.0,20.20,383.32,13.11} | 2 | 21.40 -{1,5.82401,0.00,18.100,0,0.5320,6.2420,64.70,3.4242,24,666.0,20.20,396.90,10.74} | 2 | 23.00 -{1,5.70818,0.00,18.100,0,0.5320,6.7500,74.90,3.3317,24,666.0,20.20,393.07,7.74} | 2 | 23.70 -{1,5.73116,0.00,18.100,0,0.5320,7.0610,77.00,3.4106,24,666.0,20.20,395.28,7.01} | 2 | 25.00 -{1,2.81838,0.00,18.100,0,0.5320,5.7620,40.30,4.0983,24,666.0,20.20,392.92,10.42} | 2 | 21.80 -{1,2.37857,0.00,18.100,0,0.5830,5.8710,41.90,3.7240,24,666.0,20.20,370.73,13.34} | 2 | 20.60 -{1,3.67367,0.00,18.100,0,0.5830,6.3120,51.90,3.9917,24,666.0,20.20,388.62,10.58} | 2 | 21.20 -{1,5.69175,0.00,18.100,0,0.5830,6.1140,79.80,3.5459,24,666.0,20.20,392.68,14.98} | 2 | 19.10 -{1,4.83567,0.00,18.100,0,0.5830,5.9050,53.20,3.1523,24,666.0,20.20,388.22,11.45} | 2 | 20.60 -{1,0.15086,0.00,27.740,0,0.6090,5.4540,92.70,1.8209,4,711.0,20.10,395.09,18.06} | 2 | 15.20 -{1,0.18337,0.00,27.740,0,0.6090,5.4140,98.30,1.7554,4,711.0,20.10,344.05,23.97} | 2 | 7.00 -{1,0.20746,0.00,27.740,0,0.6090,5.0930,98.00,1.8226,4,711.0,20.10,318.43,29.68} | 2 | 8.10 -{1,0.10574,0.00,27.740,0,0.6090,5.9830,98.80,1.8681,4,711.0,20.10,390.11,18.07} | 2 | 13.60 -{1,0.11132,0.00,27.740,0,0.6090,5.9830,83.50,2.1099,4,711.0,20.10,396.90,13.35} | 2 | 20.10 -{1,0.17331,0.00,9.690,0,0.5850,5.7070,54.00,2.3817,6,391.0,19.20,396.90,12.01} | 2 | 21.80 -{1,0.27957,0.00,9.690,0,0.5850,5.9260,42.60,2.3817,6,391.0,19.20,396.90,13.59} | 2 | 24.50 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+{1,3.77498,0.00,18.100,0,0.6550,5.9520,84.70,2.8715,24,666.0,20.20,22.01,17.15} | 2 | {19.00} +{1,4.42228,0.00,18.100,0,0.5840,6.0030,94.50,2.5403,24,666.0,20.20,331.29,21.32} | 2 | {19.10} +{1,15.57570,0.00,18.100,0,0.5800,5.9260,71.00,2.9084,24,666.0,20.20,368.74,18.13} | 2 | {19.10} +{1,13.07510,0.00,18.100,0,0.5800,5.7130,56.70,2.8237,24,666.0,20.20,396.90,14.76} | 2 | {20.10} +{1,4.34879,0.00,18.100,0,0.5800,6.1670,84.00,3.0334,24,666.0,20.20,396.90,16.29} | 2 | {19.90} +{1,4.03841,0.00,18.100,0,0.5320,6.2290,90.70,3.0993,24,666.0,20.20,395.33,12.87} | 2 | {19.60} +{1,3.56868,0.00,18.100,0,0.5800,6.4370,75.00,2.8965,24,666.0,20.20,393.37,14.36} | 2 | {23.20} +{1,4.64689,0.00,18.100,0,0.6140,6.9800,67.60,2.5329,24,666.0,20.20,374.68,11.66} | 2 | {29.80} +{1,8.05579,0.00,18.100,0,0.5840,5.4270,95.40,2.4298,24,666.0,20.20,352.58,18.14} | 2 | {13.80} +{1,6.39312,0.00,18.100,0,0.5840,6.1620,97.40,2.2060,24,666.0,20.20,302.76,24.10} | 2 | {13.30} +{1,4.87141,0.00,18.100,0,0.6140,6.4840,93.60,2.3053,24,666.0,20.20,396.21,18.68} | 2 | {16.70} +{1,15.02340,0.00,18.100,0,0.6140,5.3040,97.30,2.1007,24,666.0,20.20,349.48,24.91} | 2 | {12.00} +{1,10.23300,0.00,18.100,0,0.6140,6.1850,96.70,2.1705,24,666.0,20.20,379.70,18.03} | 2 | {14.60} +{1,14.33370,0.00,18.100,0,0.6140,6.2290,88.00,1.9512,24,666.0,20.20,383.32,13.11} | 2 | {21.40} +{1,5.82401,0.00,18.100,0,0.5320,6.2420,64.70,3.4242,24,666.0,20.20,396.90,10.74} | 2 | {23.00} +{1,5.70818,0.00,18.100,0,0.5320,6.7500,74.90,3.3317,24,666.0,20.20,393.07,7.74} | 2 | {23.70} +{1,5.73116,0.00,18.100,0,0.5320,7.0610,77.00,3.4106,24,666.0,20.20,395.28,7.01} | 2 | {25.00} +{1,2.81838,0.00,18.100,0,0.5320,5.7620,40.30,4.0983,24,666.0,20.20,392.92,10.42} | 2 | {21.80} +{1,2.37857,0.00,18.100,0,0.5830,5.8710,41.90,3.7240,24,666.0,20.20,370.73,13.34} | 2 | {20.60} +{1,3.67367,0.00,18.100,0,0.5830,6.3120,51.90,3.9917,24,666.0,20.20,388.62,10.58} | 2 | {21.20} +{1,5.69175,0.00,18.100,0,0.5830,6.1140,79.80,3.5459,24,666.0,20.20,392.68,14.98} | 2 | {19.10} +{1,4.83567,0.00,18.100,0,0.5830,5.9050,53.20,3.1523,24,666.0,20.20,388.22,11.45} | 2 | {20.60} +{1,0.15086,0.00,27.740,0,0.6090,5.4540,92.70,1.8209,4,711.0,20.10,395.09,18.06} | 2 | {15.20} +{1,0.18337,0.00,27.740,0,0.6090,5.4140,98.30,1.7554,4,711.0,20.10,344.05,23.97} | 2 | {7.00} +{1,0.20746,0.00,27.740,0,0.6090,5.0930,98.00,1.8226,4,711.0,20.10,318.43,29.68} | 2 | {8.10} +{1,0.10574,0.00,27.740,0,0.6090,5.9830,98.80,1.8681,4,711.0,20.10,390.11,18.07} | 2 | {13.60} +{1,0.11132,0.00,27.740,0,0.6090,5.9830,83.50,2.1099,4,711.0,20.10,396.90,13.35} | 2 | {20.10} +{1,0.17331,0.00,9.690,0,0.5850,5.7070,54.00,2.3817,6,391.0,19.20,396.90,12.01} | 2 | {21.80} +{1,0.27957,0.00,9.690,0,0.5850,5.9260,42.60,2.3817,6,391.0,19.20,396.90,13.59} | 2 | {24.50} +{1,0.17899,0.00,9.690,0,0.5850,5.6700,28.80,2.7986,6,391.0,19.20,393.29,17.60} | 2 | {23.10} +{1,0.28960,0.00,9.690,0,0.5850,5.3900,72.90,2.7986,6,391.0,19.20,396.90,21.14} | 2 | {19.70} +{1,0.26838,0.00,9.690,0,0.5850,5.7940,70.60,2.8927,6,391.0,19.20,396.90,14.10} | 2 | {18.30} +{1,0.23912,0.00,9.690,0,0.5850,6.0190,65.30,2.4091,6,391.0,19.20,396.90,12.92} | 2 | {21.20} +{1,0.17783,0.00,9.690,0,0.5850,5.5690,73.50,2.3999,6,391.0,19.20,395.77,15.10} | 2 | {17.50} +{1,0.22438,0.00,9.690,0,0.5850,6.0270,79.70,2.4982,6,391.0,19.20,396.90,14.33} | 2 | {16.80} +{1,0.06263,0.00,11.930,0,0.5730,6.5930,69.10,2.4786,1,273.0,21.00,391.99,9.67} | 2 | {22.40} +{1,0.04527,0.00,11.930,0,0.5730,6.1200,76.70,2.2875,1,273.0,21.00,396.90,9.08} | 2 | {20.60} +{1,0.06076,0.00,11.930,0,0.5730,6.9760,91.00,2.1675,1,273.0,21.00,396.90,5.64} | 2 | {23.90} +{1,0.10959,0.00,11.930,0,0.5730,6.7940,89.30,2.3889,1,273.0,21.00,393.45,6.48} | 2 | {22.00} +{1,0.04741,0.00,11.930,0,0.5730,6.0300,80.80,2.5050,1,273.0,21.00,396.90,7.88} | 2 | {11.90} \. @@ -829,6 +830,7 @@ CREATE TABLE lin_housing_wi_batch_summary( output_table text, dependent_varname text, independent_varname text, + dependent_vartype text, buffer_size integer, class_values text[], num_rows_processed integer, @@ -836,7 +838,7 @@ CREATE TABLE lin_housing_wi_batch_summary( grouping_cols text ); INSERT INTO lin_housing_wi_batch_summary VALUES -('lin_housing_wi','lin_housing_wi_batch','y','x',50,NULL,410,0,'grp'); +('lin_housing_wi_does_not_exist','lin_housing_wi_batch','y','x','float8',50,NULL,410,0,'grp'); -- Create the corresponding standardization table for preprocessed data CREATE TABLE lin_housing_wi_batch_standardization( @@ -846,7 +848,7 @@ CREATE TABLE lin_housing_wi_batch_standardization( ); INSERT INTO lin_housing_wi_batch_standardization VALUES -- TODO get real numbers by running preprocessor -(1,ARRAY[1,4.40216212195,7.51829268293,12.4008536585,0.0365853658537,0.57236804878,6.15941707317,72.44,3.53274926829,10.6170731707,436.743902439,18.8995121951,349.478317073,13.6464390244], ARRAY[1,9.37232721366,19.5307832574,6.88889668698,0.187741516077,0.116893738398,0.612794748753,26.8174708253,2.03272510382,9.29081378039,174.135271989,1.94044243285,99.8411145886,7.16704694791]), +(1,ARRAY[1,4.40216212195,7.51829268293,12.4008536585,0.0365853658537,0.57236804878,6.125941707317,72.44,3.5327492689,10.6170731707,436.743902439,18.8995121951,349.478317073,13.6464390244], ARRAY[1,9.37232721366,19.5307832574,6.88889668698,0.187741516077,0.116893738398,0.612794748753,26.8174708253,2.03272510382,9.29081378039,174.135271989,1.94044243285,99.8411145886,7.16704694791]), (2,ARRAY[1,4.40216212195,7.51829268293,12.4008536585,0.0365853658537,0.57236804878,6.15941707317,72.44,3.53274926829,10.6170731707,436.743902439,18.8995121951,349.478317073,13.6464390244], ARRAY[1,9.37232721366,19.5307832574,6.88889668698,0.187741516077,0.116893738398,0.612794748753,26.8174708253,2.03272510382,9.29081378039,174.135271989,1.94044243285,99.8411145886,7.16704694791]); SELECT setseed(0); @@ -874,6 +876,11 @@ SELECT mlp_predict( 'id', 'mlp_prediction_regress', 'output'); +SELECT assert( + __to_char(pg_typeof(estimated_y)) = 'double precision[]', + 'Estimated y should be an array. Actual ' || __to_char(pg_typeof(estimated_y)) + ) +FROM mlp_prediction_regress LIMIT 1; -- minibatch without grouping and without warm start DROP TABLE IF EXISTS mlp_regress_batch, mlp_regress_batch_summary, mlp_regress_batch_standardization; @@ -900,6 +907,11 @@ SELECT mlp_predict( 'id', 'mlp_prediction_regress_batch', 'output'); +SELECT assert( + __to_char(pg_typeof(estimated_y)) = 'double precision', + 'Estimated y should be a scalar. Actual ' || __to_char(pg_typeof(estimated_y)) + ) +FROM mlp_prediction_regress_batch LIMIT 1; -- minibatch with grouping and without warm start DROP TABLE IF EXISTS mlp_regress_batch, mlp_regress_batch_summary, mlp_regress_batch_standardization; diff --git a/src/ports/postgres/modules/utilities/minibatch_preprocessing.py_in b/src/ports/postgres/modules/utilities/minibatch_preprocessing.py_in index bb9fddd39..4a1c8ae06 100644 --- a/src/ports/postgres/modules/utilities/minibatch_preprocessing.py_in +++ b/src/ports/postgres/modules/utilities/minibatch_preprocessing.py_in @@ -172,6 +172,7 @@ class MiniBatchPreProcessor: self.output_table, self.dependent_varname, self.independent_varname, + dependent_var_dbtype, calculated_buffer_size, dep_var_classes_str, num_rows_processed, @@ -441,11 +442,17 @@ class MiniBatchStandardizer: class MiniBatchSummarizer: @staticmethod - def create_output_summary_table(output_summary_table, source_table, - output_table, dep_var_array_str, - indep_var_array_str, buffer_size, - class_values, num_rows_processed, - num_missing_rows_skipped, grouping_cols): + def create_output_summary_table(output_summary_table, + source_table, + output_table, + dep_var_array_str, + indep_var_array_str, + dependent_var_dbtype, + buffer_size, + class_values, + num_rows_processed, + num_missing_rows_skipped, + grouping_cols): # 1. All the string columns are surrounded by "$$" to take care of # special characters in the column name. # 2. We have to typecast all the string column names to ::TEXT because @@ -457,6 +464,7 @@ class MiniBatchSummarizer: $${output_table}$$::TEXT AS output_table, $${dependent_varname}$$::TEXT AS dependent_varname, $${independent_varname}$$::TEXT AS independent_varname, + $${dependent_var_dbtype}$$::TEXT AS dependent_vartype, {buffer_size} AS buffer_size, {class_values} AS class_values, {num_rows_processed} AS num_rows_processed, @@ -467,6 +475,7 @@ class MiniBatchSummarizer: output_table = output_table, dependent_varname = dep_var_array_str, independent_varname = indep_var_array_str, + dependent_var_dbtype = dependent_var_dbtype, buffer_size = buffer_size, class_values = class_values, num_rows_processed = num_rows_processed, diff --git a/src/ports/postgres/modules/utilities/test/minibatch_preprocessing.sql_in b/src/ports/postgres/modules/utilities/test/minibatch_preprocessing.sql_in index 7eaafb6a1..04c7fb5f5 100644 --- a/src/ports/postgres/modules/utilities/test/minibatch_preprocessing.sql_in +++ b/src/ports/postgres/modules/utilities/test/minibatch_preprocessing.sql_in @@ -138,6 +138,7 @@ SELECT assert output_table = 'minibatch_preprocessing_out' AND dependent_varname = 'length>0.2' AND independent_varname = 'ARRAY[diameter,height,whole,shucked,viscera,shell]' AND + dependent_vartype = 'boolean' AND buffer_size = 4 AND class_values = '{f,t}' AND -- we sort the class values in python num_rows_processed = 10 AND From 47eefc1c91a33db0a788c2c145b8016b892de5ad Mon Sep 17 00:00:00 2001 From: Nandish Jayaram Date: Wed, 4 Apr 2018 14:42:38 -0700 Subject: [PATCH 2/2] MLP: Fix bug in array dep var for regression MLP training for regression fails if the dependent var type is an array. This is because the variable dependent_varname was not updated to reflect the new column created in the standardized table. This commit fixes that issue. Co-authored-by: Nikhil Kak --- src/ports/postgres/modules/convex/mlp_igd.py_in | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/ports/postgres/modules/convex/mlp_igd.py_in b/src/ports/postgres/modules/convex/mlp_igd.py_in index 80105792e..2799355a2 100644 --- a/src/ports/postgres/modules/convex/mlp_igd.py_in +++ b/src/ports/postgres/modules/convex/mlp_igd.py_in @@ -155,21 +155,21 @@ def mlp(schema_madlib, source_table, output_table, independent_varname, normalize_data(locals()) dependent_vartype = get_expr_type(dependent_varname, source_table) + # We are now using tbl_data_scaled, so change the dependent + # varname accordingly. + dependent_varname = col_dep_var_norm_new if is_classification: # If dependent variable is an array during classification, assume # that it is already one-hot-encoded. if "[]" in dependent_vartype: - # We are now using tbl_data_scaled, so change the dependent - # varname accordingly. - dependent_varname = col_dep_var_norm_new num_output_nodes = get_col_dimension(tbl_data_scaled, dependent_varname) else: labels = plpy.execute("SELECT DISTINCT {0} FROM {1}". - format(dependent_varname, source_table)) + format(dependent_varname_backup, source_table)) num_output_nodes = len(labels) for label_obj in labels: - label = _format_label(label_obj[dependent_varname]) + label = _format_label(label_obj[dependent_varname_backup]) classes.append(label) classes.sort() level_vals_str = ','.join(["{0}={1}".format(