article-identify-hardbounce-ml_2_train-xgboost_1.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost with dataset oversampled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split dataset in Train / Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(533205, 9)\n",
      "(228517, 9)\n",
      "(533205,)\n",
      "(228517,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "    \n",
    "X_train, X_test, y_train, y_test = train_test_split(X_resampled, np.ravel(y_resampled), test_size=0.30, random_state=123)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extreme Gradient Boosting (XGBoost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "D_train = xgb.DMatrix(X_train, label=y_train)\n",
    "D_test = xgb.DMatrix(X_test, label=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
       "              nthread=None, objective='reg:logistic', random_state=123,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "              silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# XGBoost Classifier Train\n",
    "\n",
    "xgb_classif = xgb.XGBClassifier(\n",
    "    max_depth=5, \n",
    "    objective='reg:logistic', \n",
    "    random_state=123)\n",
    "\n",
    "xgb_classif.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(xgb_classif, open('models/xgb-classifier.sav', 'wb'))"
   ]
  }
 ],
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