Case Study Insurance Amount Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(r'/home/awantik/Downloads/allstate_train.csv/train.csv', index_col='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 188318 entries, 1 to 587633\n",
      "Columns: 131 entries, cat1 to loss\n",
      "dtypes: float64(15), object(116)\n",
      "memory usage: 189.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>cont12</th>\n",
       "      <th>cont13</th>\n",
       "      <th>cont14</th>\n",
       "      <th>loss</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <td>A</td>\n",
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       "      <td>A</td>\n",
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       "      <td>A</td>\n",
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       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0.718367</td>\n",
       "      <td>0.335060</td>\n",
       "      <td>0.30260</td>\n",
       "      <td>0.67135</td>\n",
       "      <td>0.83510</td>\n",
       "      <td>0.569745</td>\n",
       "      <td>0.594646</td>\n",
       "      <td>0.822493</td>\n",
       "      <td>0.714843</td>\n",
       "      <td>2213.18</td>\n",
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       "    <tr>\n",
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       "      <td>A</td>\n",
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       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438917</td>\n",
       "      <td>0.436585</td>\n",
       "      <td>0.60087</td>\n",
       "      <td>0.35127</td>\n",
       "      <td>0.43919</td>\n",
       "      <td>0.338312</td>\n",
       "      <td>0.366307</td>\n",
       "      <td>0.611431</td>\n",
       "      <td>0.304496</td>\n",
       "      <td>1283.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.289648</td>\n",
       "      <td>0.315545</td>\n",
       "      <td>0.27320</td>\n",
       "      <td>0.26076</td>\n",
       "      <td>0.32446</td>\n",
       "      <td>0.381398</td>\n",
       "      <td>0.373424</td>\n",
       "      <td>0.195709</td>\n",
       "      <td>0.774425</td>\n",
       "      <td>3005.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0.440945</td>\n",
       "      <td>0.391128</td>\n",
       "      <td>0.31796</td>\n",
       "      <td>0.32128</td>\n",
       "      <td>0.44467</td>\n",
       "      <td>0.327915</td>\n",
       "      <td>0.321570</td>\n",
       "      <td>0.605077</td>\n",
       "      <td>0.602642</td>\n",
       "      <td>939.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.178193</td>\n",
       "      <td>0.247408</td>\n",
       "      <td>0.24564</td>\n",
       "      <td>0.22089</td>\n",
       "      <td>0.21230</td>\n",
       "      <td>0.204687</td>\n",
       "      <td>0.202213</td>\n",
       "      <td>0.246011</td>\n",
       "      <td>0.432606</td>\n",
       "      <td>2763.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 131 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9 cat10  ...     cont6  \\\n",
       "id                                                     ...             \n",
       "1     A    B    A    B    A    A    A    A    B     A  ...  0.718367   \n",
       "2     A    B    A    A    A    A    A    A    B     B  ...  0.438917   \n",
       "5     A    B    A    A    B    A    A    A    B     B  ...  0.289648   \n",
       "10    B    B    A    B    A    A    A    A    B     A  ...  0.440945   \n",
       "11    A    B    A    B    A    A    A    A    B     B  ...  0.178193   \n",
       "\n",
       "       cont7    cont8    cont9   cont10    cont11    cont12    cont13  \\\n",
       "id                                                                      \n",
       "1   0.335060  0.30260  0.67135  0.83510  0.569745  0.594646  0.822493   \n",
       "2   0.436585  0.60087  0.35127  0.43919  0.338312  0.366307  0.611431   \n",
       "5   0.315545  0.27320  0.26076  0.32446  0.381398  0.373424  0.195709   \n",
       "10  0.391128  0.31796  0.32128  0.44467  0.327915  0.321570  0.605077   \n",
       "11  0.247408  0.24564  0.22089  0.21230  0.204687  0.202213  0.246011   \n",
       "\n",
       "      cont14     loss  \n",
       "id                     \n",
       "1   0.714843  2213.18  \n",
       "2   0.304496  1283.60  \n",
       "5   0.774425  3005.09  \n",
       "10  0.602642   939.85  \n",
       "11  0.432606  2763.85  \n",
       "\n",
       "[5 rows x 131 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data = data.sample(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_col = sample_data.loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_data = sample_data.drop(columns=['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = list(filter(lambda x:x.startswith('cat'), feature_data.columns.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_feature_data = feature_data[cat_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cont_cols = list(filter(lambda x:x.startswith('cont'), feature_data.columns.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cont_feature_data = feature_data[cont_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder, MinMaxScaler, LabelEncoder, OrdinalEncoder\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.feature_selection import SelectKBest, f_regression\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.compose import make_column_transformer\n",
    "from joblib import dump, load\n",
    "\n",
    "class BuildMlPipeline:\n",
    "    \n",
    "    def __init__(self):\n",
    "        pass\n",
    "        \n",
    "    def set_estimators(self, *args):\n",
    "        estimator_db = {\n",
    "            'randomForestRegressor': RandomForestRegressor(),\n",
    "            'linearRegressor': LinearRegression(),\n",
    "        }\n",
    "        self.estimators = list(map( lambda algo: estimator_db[algo],args))\n",
    "        \n",
    "    def set_scalers(self, *args):\n",
    "        scaler_db = {\n",
    "            'standardscaler':StandardScaler(),\n",
    "            'minmaxscaler':MinMaxScaler(),\n",
    "        }\n",
    "        self.scalers = list(map( lambda scaler: scaler_db[scaler],args))\n",
    "        \n",
    "    def set_samplers(self, *args):\n",
    "        sampler_db = {\n",
    "            'smote':SMOTE(),\n",
    "            'smoteenn':SMOTEENN(),\n",
    "        }\n",
    "        self.samplers = list(map( lambda sampler: sampler_db[sampler],args))\n",
    "        \n",
    "    def set_encoders(self, *args):\n",
    "        encoders_db = {\n",
    "            'ohe':OneHotEncoder(handle_unknown='ignore'),\n",
    "            'oe':OrdinalEncoder(),\n",
    "        }\n",
    "        self.encoders = list(map( lambda encoder: encoders_db[encoder],args))\n",
    "        \n",
    "    def set_hyperparameters(self, params):\n",
    "        self.hyperparameters = params\n",
    "\n",
    "    \n",
    "    def create_pipelines(self, cat_cols, cont_cols):\n",
    "        self.model_pipelines = []\n",
    "        for scaler in self.scalers:\n",
    "            pipeline_num = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),\n",
    "                                           ('scaling',scaler)])\n",
    "            for encoder in self.encoders:\n",
    "                pipeline_cat = Pipeline(steps=[('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
    "                                               ('encoder',encoder)])\n",
    "                preprocessor = make_column_transformer((pipeline_num, cont_cols),(pipeline_cat, cat_cols))\n",
    "                \n",
    "                for estimator in self.estimators:\n",
    "                    pipeline  = make_pipeline(preprocessor, estimator)\n",
    "                    self.model_pipelines.append(pipeline)\n",
    "        \n",
    "    \n",
    "    def fit(self, trainX, trainY):\n",
    "        self.gs_pipelines = []\n",
    "        for idx,pipeline in enumerate(self.model_pipelines):\n",
    "            elems = list(map(lambda x:x[0] ,pipeline.steps))\n",
    "            param_grid = {}\n",
    "\n",
    "            for elem in elems:\n",
    "                if elem.lower() in self.hyperparameters:\n",
    "                    param_grid.update(self.hyperparameters[elem])\n",
    "            \n",
    "            gs = GridSearchCV(pipeline, param_grid= param_grid, n_jobs=6, cv=5)\n",
    "            gs.fit(trainX, trainY)\n",
    "            print (gs.score(testX,testY),  list(map(lambda x:x[0] , gs.best_estimator_.steps)), gs.best_params_)\n",
    "            \n",
    "            dump(gs, 'model'+str(idx)+'.pipeline') \n",
    "            self.gs_pipelines.append(gs)\n",
    "      \n",
    "        \n",
    "    def score(self, testX, testY):\n",
    "        for idx,model in enumerate(self.gs_pipelines):\n",
    "            y_pred = model.best_estimator_.predict(testX)\n",
    "            print (model.best_estimator_)\n",
    "            print (idx,confusion_matrix(y_true=testY,y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 130)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_col.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX,testX, trainY, testY = train_test_split(feature_data,target_col, test_size=.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9000, 130)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_pipeline = BuildMlPipeline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_pipeline.set_estimators('randomForestRegressor', 'linearRegressor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_pipeline.set_encoders('oe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_pipeline.set_scalers('standardscaler','minmaxscaler')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_dict = {}\n",
    "params_dict['randomforestregressor'] = {'randomforestregressor__n_estimators':[100]}\n",
    "params_dict['linearregression'] = {'linearregression__normalize':[True,False]}\n",
    "ml_pipeline.set_hyperparameters(params_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_pipeline.create_pipelines(cat_cols=cat_cols, cont_cols=cont_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found unknown categories ['G'] in column 101 during transform",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31m_RemoteTraceback\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py\", line 418, in _process_worker\n    r = call_item()\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py\", line 272, in __call__\n    return self.fn(*self.args, **self.kwargs)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 567, in __call__\n    return self.func(*args, **kwargs)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\", line 225, in __call__\n    for func, args, kwargs in self.items]\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\", line 225, in <listcomp>\n    for func, args, kwargs in self.items]\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 554, in _fit_and_score\n    test_scores = _score(estimator, X_test, y_test, scorer, is_multimetric)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 597, in _score\n    return _multimetric_score(estimator, X_test, y_test, scorer)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\", line 627, in _multimetric_score\n    score = scorer(estimator, X_test, y_test)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/metrics/scorer.py\", line 240, in _passthrough_scorer\n    return estimator.score(*args, **kwargs)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/utils/metaestimators.py\", line 116, in <lambda>\n    out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/pipeline.py\", line 598, in score\n    Xt = transform.transform(Xt)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/compose/_column_transformer.py\", line 512, in transform\n    Xs = self._fit_transform(X, None, _transform_one, fitted=True)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/compose/_column_transformer.py\", line 412, in _fit_transform\n    self._iter(fitted=fitted, replace_strings=True), 1))\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\", line 924, in __call__\n    while self.dispatch_one_batch(iterator):\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\", line 759, in dispatch_one_batch\n    self._dispatch(tasks)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\", line 716, in _dispatch\n    job = self._backend.apply_async(batch, callback=cb)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 182, in apply_async\n    result = ImmediateResult(func)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py\", line 549, in __init__\n    self.results = batch()\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\", line 225, in __call__\n    for func, args, kwargs in self.items]\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\", line 225, in <listcomp>\n    for func, args, kwargs in self.items]\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/pipeline.py\", line 695, in _transform_one\n    res = transformer.transform(X)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/pipeline.py\", line 540, in _transform\n    Xt = transform.transform(Xt)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py\", line 957, in transform\n    X_int, _ = self._transform(X)\n  File \"/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py\", line 122, in _transform\n    raise ValueError(msg)\nValueError: Found unknown categories ['G'] in column 101 during transform\n\"\"\"",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-70-e9e1999e16ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mml_pipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_col\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-57-090d7e29cc3b>\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, trainX, trainY)\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mgs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m             \u001b[0mgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m             \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtestX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtestY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    685\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 687\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    689\u001b[0m         \u001b[0;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1146\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1147\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1148\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params)\u001b[0m\n\u001b[1;32m    664\u001b[0m                                \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m                                in product(candidate_params,\n\u001b[0;32m--> 666\u001b[0;31m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    667\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 934\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    935\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    936\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    831\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    832\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 833\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    834\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    835\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    519\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    520\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mLokyTimeoutError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    430\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 432\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    433\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    382\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found unknown categories ['G'] in column 101 during transform"
     ]
    }
   ],
   "source": [
    "ml_pipeline.fit(feature_data, target_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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