3_model_training.ipynb

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   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
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   "source": [
    "import pandas as pd \n",
    "from pickle import load\n",
    "import os.path as path\n",
    "from pickle import load, dump\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "id": "useful-lawyer",
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   "source": [
    "# load dataframes\n",
    "X_train_scaled_resamp = pd.read_csv(path.join(\"3_X_fitted_dataframe\", \"X_train_scaled_resamp.csv\"))\n",
    "y_train_resamp = pd.read_csv(path.join('4_y_dataframe', 'y_train_resamp.csv'))\n",
    "X_test_scaled = pd.read_csv(path.join(\"3_X_fitted_dataframe\", \"X_test_scaled.csv\"))\n",
    "y_test = pd.read_csv(path.join('4_y_dataframe', 'y_test.csv'))"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "# We limit the depth of the models intentionnaly to take less time to fit\n",
    "## We remind that we are not focused on the model accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "standing-waste",
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     "output_type": "stream",
     "text": [
      "[17:54:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
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     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=1, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=12,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit xgboot model\n",
    "XGB_clf = XGBClassifier(max_depth=1)\n",
    "XGB_clf.fit(X_train_scaled_resamp, y_train_resamp)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "id": "direct-cricket",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit random forest model\n",
    "rf_clf = RandomForestClassifier(max_depth=1)\n",
    "rf_clf.fit(X_train_scaled_resamp, y_train_resamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "polished-jefferson",
   "metadata": {
    "execution": {
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    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(max_depth=1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit Gradient boostinf model\n",
    "GBoost_clf = GradientBoostingClassifier(max_depth=1)\n",
    "GBoost_clf.fit(X_train_scaled_resamp, y_train_resamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ongoing-uruguay",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# save models\n",
    "dump(rf_clf, open(path.join('6_models', 'randomForest.pkl'), 'wb'))\n",
    "dump(XGB_clf, open(path.join('6_models', 'xgBoost.pkl'), 'wb'))\n",
    "dump(GBoost_clf, open(path.join('6_models', 'GradientBoosting.pkl'), 'wb'))"
   ]
  }
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