examples/lgbm/3_lgbm_With_PreProcess .ipynb

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   "cell_type": "markdown",
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   "source": [
    "# LGBM → PMML \n",
    "\n",
    "### Exporter: LGBMRegressor\n",
    "\n",
    "### Data Set used: Auto\n",
    "\n",
    "\n",
    "### **STEPS**: \n",
    "- Build the Pipeline with preprocessing \n",
    "- Build PMML using Nyoka exporter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing, Model building (using pipeline) for Auto data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-13T17:23:34.023366Z",
     "start_time": "2018-08-13T17:23:33.043866Z"
    }
   },
   "outputs": [
    {
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       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;mapper&#x27;,\n",
       "                 DataFrameMapper(drop_cols=[],\n",
       "                                 features=[(&#x27;car name&#x27;, CountVectorizer()),\n",
       "                                           ([&#x27;displacement&#x27;],\n",
       "                                            [StandardScaler()])])),\n",
       "                (&#x27;lgbmr&#x27;, LGBMRegressor(n_estimators=22))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;mapper&#x27;,\n",
       "                 DataFrameMapper(drop_cols=[],\n",
       "                                 features=[(&#x27;car name&#x27;, CountVectorizer()),\n",
       "                                           ([&#x27;displacement&#x27;],\n",
       "                                            [StandardScaler()])])),\n",
       "                (&#x27;lgbmr&#x27;, LGBMRegressor(n_estimators=22))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DataFrameMapper</label><div class=\"sk-toggleable__content\"><pre>DataFrameMapper(drop_cols=[],\n",
       "                features=[(&#x27;car name&#x27;, CountVectorizer()),\n",
       "                          ([&#x27;displacement&#x27;], [StandardScaler()])])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(n_estimators=22)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('mapper',\n",
       "                 DataFrameMapper(drop_cols=[],\n",
       "                                 features=[('car name', CountVectorizer()),\n",
       "                                           (['displacement'],\n",
       "                                            [StandardScaler()])])),\n",
       "                ('lgbmr', LGBMRegressor(n_estimators=22))])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from lightgbm import LGBMRegressor,LGBMClassifier\n",
    "from sklearn_pandas import DataFrameMapper\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "auto = pd.read_csv('auto-mpg.csv')\n",
    "X = auto.drop(['mpg'], axis=1)\n",
    "y = auto['mpg']\n",
    "\n",
    "feature_names = [name for name in auto.columns if name not in ('mpg')]\n",
    "\n",
    "target_name='mpg'\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=101)\n",
    "pipeline_obj = Pipeline([\n",
    "    ('mapper', DataFrameMapper([\n",
    "        ('car name', CountVectorizer()),\n",
    "        (['displacement'],[StandardScaler()]) \n",
    "    ])),\n",
    "    ('lgbmr',LGBMRegressor(n_estimators=22))\n",
    "])\n",
    "pipeline_obj.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the Pipeline object into PMML using the Nyoka package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-13T17:23:36.956866Z",
     "start_time": "2018-08-13T17:23:36.319366Z"
    }
   },
   "outputs": [],
   "source": [
    "from nyoka import lgb_to_pmml\n",
    "lgb_to_pmml(pipeline_obj,feature_names,target_name,\"lgbmr_pmml_preprocess.pmml\")"
   ]
  }
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