soft-voting-dt.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naman/.pyenv/versions/3.6.8/lib/python3.6/site-packages/lightgbm/__init__.py:48: UserWarning: Starting from version 2.2.1, the library file in distribution wheels for macOS is built by the Apple Clang (Xcode_8.3.3) compiler.\n",
      "This means that in case of installing LightGBM from PyPI via the ``pip install lightgbm`` command, you don't need to install the gcc compiler anymore.\n",
      "Instead of that, you need to install the OpenMP library, which is required for running LightGBM on the system with the Apple Clang compiler.\n",
      "You can install the OpenMP library by the following command: ``brew install libomp``.\n",
      "  \"You can install the OpenMP library by the following command: ``brew install libomp``.\", UserWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import lightgbm as lgb\n",
    "import xgboost as xgb\n",
    "import joblib\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/one_hot_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>building_id</th>\n",
       "      <th>geo_level_1_id</th>\n",
       "      <th>geo_level_2_id</th>\n",
       "      <th>geo_level_3_id</th>\n",
       "      <th>count_floors_pre_eq</th>\n",
       "      <th>age</th>\n",
       "      <th>area_percentage</th>\n",
       "      <th>height_percentage</th>\n",
       "      <th>has_superstructure_adobe_mud</th>\n",
       "      <th>...</th>\n",
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       "      <th>plan_configuration_q</th>\n",
       "      <th>plan_configuration_s</th>\n",
       "      <th>plan_configuration_u</th>\n",
       "      <th>legal_ownership_status_a</th>\n",
       "      <th>legal_ownership_status_r</th>\n",
       "      <th>legal_ownership_status_v</th>\n",
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       "      <td>3</td>\n",
       "      <td>590882</td>\n",
       "      <td>22</td>\n",
       "      <td>418</td>\n",
       "      <td>10694</td>\n",
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      "text/plain": [
       "   Unnamed: 0  building_id  geo_level_1_id  geo_level_2_id  geo_level_3_id  \\\n",
       "0           0       802906               6             487           12198   \n",
       "1           1        28830               8             900            2812   \n",
       "2           2        94947              21             363            8973   \n",
       "3           3       590882              22             418           10694   \n",
       "4           4       201944              11             131            1488   \n",
       "\n",
       "   count_floors_pre_eq  age  area_percentage  height_percentage  \\\n",
       "0                    2   30                6                  5   \n",
       "1                    2   10                8                  7   \n",
       "2                    2   10                5                  5   \n",
       "3                    2   10                6                  5   \n",
       "4                    3   30                8                  9   \n",
       "\n",
       "   has_superstructure_adobe_mud  ...  plan_configuration_m  \\\n",
       "0                             1  ...                     0   \n",
       "1                             0  ...                     0   \n",
       "2                             0  ...                     0   \n",
       "3                             0  ...                     0   \n",
       "4                             1  ...                     0   \n",
       "\n",
       "   plan_configuration_n  plan_configuration_o  plan_configuration_q  \\\n",
       "0                     0                     0                     0   \n",
       "1                     0                     0                     0   \n",
       "2                     0                     0                     0   \n",
       "3                     0                     0                     0   \n",
       "4                     0                     0                     0   \n",
       "\n",
       "   plan_configuration_s  plan_configuration_u  legal_ownership_status_a  \\\n",
       "0                     0                     0                         0   \n",
       "1                     0                     0                         0   \n",
       "2                     0                     0                         0   \n",
       "3                     0                     0                         0   \n",
       "4                     0                     0                         0   \n",
       "\n",
       "   legal_ownership_status_r  legal_ownership_status_v  \\\n",
       "0                         0                         1   \n",
       "1                         0                         1   \n",
       "2                         0                         1   \n",
       "3                         0                         1   \n",
       "4                         0                         1   \n",
       "\n",
       "   legal_ownership_status_w  \n",
       "0                         0  \n",
       "1                         0  \n",
       "2                         0  \n",
       "3                         0  \n",
       "4                         0  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\"data/train_labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "   building_id  damage_grade\n",
       "0       802906             3\n",
       "1        28830             2\n",
       "2        94947             3\n",
       "3       590882             2\n",
       "4       201944             3"
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     "metadata": {},
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    "df2.head()"
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = df['building_id']\n",
    "test2 = df2['building_id']\n",
    "for i in range(len(test)):\n",
    "    if test[i] != test2[i]:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Unnamed: 0', 'building_id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <th>legal_ownership_status_a</th>\n",
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      ],
      "text/plain": [
       "   geo_level_1_id  geo_level_2_id  geo_level_3_id  count_floors_pre_eq  age  \\\n",
       "0               6             487           12198                    2   30   \n",
       "1               8             900            2812                    2   10   \n",
       "2              21             363            8973                    2   10   \n",
       "3              22             418           10694                    2   10   \n",
       "4              11             131            1488                    3   30   \n",
       "\n",
       "   area_percentage  height_percentage  has_superstructure_adobe_mud  \\\n",
       "0                6                  5                             1   \n",
       "1                8                  7                             0   \n",
       "2                5                  5                             0   \n",
       "3                6                  5                             0   \n",
       "4                8                  9                             1   \n",
       "\n",
       "   has_superstructure_mud_mortar_stone  has_superstructure_stone_flag  ...  \\\n",
       "0                                    1                              0  ...   \n",
       "1                                    1                              0  ...   \n",
       "2                                    1                              0  ...   \n",
       "3                                    1                              0  ...   \n",
       "4                                    0                              0  ...   \n",
       "\n",
       "   plan_configuration_m  plan_configuration_n  plan_configuration_o  \\\n",
       "0                     0                     0                     0   \n",
       "1                     0                     0                     0   \n",
       "2                     0                     0                     0   \n",
       "3                     0                     0                     0   \n",
       "4                     0                     0                     0   \n",
       "\n",
       "   plan_configuration_q  plan_configuration_s  plan_configuration_u  \\\n",
       "0                     0                     0                     0   \n",
       "1                     0                     0                     0   \n",
       "2                     0                     0                     0   \n",
       "3                     0                     0                     0   \n",
       "4                     0                     0                     0   \n",
       "\n",
       "   legal_ownership_status_a  legal_ownership_status_r  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   legal_ownership_status_v  legal_ownership_status_w  \n",
       "0                         1                         0  \n",
       "1                         1                         0  \n",
       "2                         1                         0  \n",
       "3                         1                         0  \n",
       "4                         1                         0  \n",
       "\n",
       "[5 rows x 68 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = df2['damage_grade'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "litef_ensemble = lgb.LGBMClassifier(n_estimators=3000, n_jobs=6, num_leaves=80, objective='multiclass', \n",
    "                                    boosting_type='gbdt')\n",
    "gbf = xgb.XGBClassifier(n_estimators=4000, objective='multi:softmax', num_class=3, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
       "               importance_type='split', learning_rate=0.1, max_depth=-1,\n",
       "               min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
       "               n_estimators=3000, n_jobs=6, num_leaves=80,\n",
       "               objective='multiclass', random_state=None, reg_alpha=0.0,\n",
       "               reg_lambda=0.0, silent=True, subsample=1.0,\n",
       "               subsample_for_bin=200000, subsample_freq=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "litef_ensemble.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'LGBMClassifier' object has no attribute 'tree_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-80-8c8f8664e8c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlitef_ensemble\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~/.pyenv/versions/3.6.8/lib/python3.6/site-packages/sklearn/tree/export.py\u001b[0m in \u001b[0;36mplot_tree\u001b[0;34m(decision_tree, max_depth, feature_names, class_names, label, filled, impurity, node_ids, proportion, rotate, rounded, precision, ax, fontsize)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0mproportion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproportion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrotate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrotate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrounded\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrounded\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         precision=precision, fontsize=fontsize)\n\u001b[0;32m--> 178\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mexporter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecision_tree\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\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    179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.8/lib/python3.6/site-packages/sklearn/tree/export.py\u001b[0m in \u001b[0;36mexport\u001b[0;34m(self, decision_tree, ax)\u001b[0m\n\u001b[1;32m    569\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\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    570\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis_off\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--> 571\u001b[0;31m         my_tree = self._make_tree(0, decision_tree.tree_,\n\u001b[0m\u001b[1;32m    572\u001b[0m                                   decision_tree.criterion)\n\u001b[1;32m    573\u001b[0m         \u001b[0mdraw_tree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuchheim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_tree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'LGBMClassifier' object has no attribute 'tree_'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(litef_ensemble)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbf.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_train = 0.5 * gbf.predict_proba(X_train) + 0.5 * litef_ensemble.predict_proba(X_train)\n",
    "prediction_train = np.argmax(predictions_train, axis=1) + 1\n",
    "predictions_test = 0.5 * gbf.predict_proba(X_test) + 0.5 * litef_ensemble.predict_proba(X_test)\n",
    "prediction_test = np.argmax(predictions_test, axis=1) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7997601688411359\n",
      "0.74743385583546\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_train, prediction_train, average='micro'))\n",
    "print(f1_score(y_test, prediction_test, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('litegbm',\n",
       "                              LGBMClassifier(boosting_type='gbdt',\n",
       "                                             class_weight=None,\n",
       "                                             colsample_bytree=1.0,\n",
       "                                             importance_type='split',\n",
       "                                             learning_rate=0.1, max_depth=-1,\n",
       "                                             min_child_samples=20,\n",
       "                                             min_child_weight=0.001,\n",
       "                                             min_split_gain=0.0,\n",
       "                                             n_estimators=3000, n_jobs=6,\n",
       "                                             num_leaves=32,\n",
       "                                             objective='multiclass',\n",
       "                                             random_state=None, reg_alpha=0.0,\n",
       "                                             reg_lambda=0.0, silent...\n",
       "                                            colsample_bylevel=1,\n",
       "                                            colsample_bytree=1, gamma=0,\n",
       "                                            learning_rate=0.1, max_delta_step=0,\n",
       "                                            max_depth=3, min_child_weight=1,\n",
       "                                            missing=None, n_estimators=3000,\n",
       "                                            n_jobs=6, nthread=None, num_class=3,\n",
       "                                            objective='multi:softprob',\n",
       "                                            random_state=0, reg_alpha=0,\n",
       "                                            reg_lambda=1, scale_pos_weight=1,\n",
       "                                            seed=None, silent=True,\n",
       "                                            subsample=1))],\n",
       "                 flatten_transform=True, n_jobs=6, voting='soft',\n",
       "                 weights=[1, 1])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eclf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7997601688411359\n",
      "0.74743385583546\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_train, eclf.predict(X_train), average='micro'))\n",
    "print(f1_score(y_test, eclf.predict(X_test), average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importances = litef_ensemble.feature_importances_\n",
    "# std = np.std([tree.feature_importances_ for tree in litef_ensemble.estimators_],\n",
    "#              axis=0)\n",
    "# indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# # Print the feature ranking\n",
    "# print(\"Feature ranking:\")\n",
    "\n",
    "# for f in range(X.shape[1]):\n",
    "#     print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))\n",
    "\n",
    "# # Plot the feature importances of the forest\n",
    "# plt.figure()\n",
    "# plt.title(\"Feature importances\")\n",
    "# plt.bar(range(X.shape[1]), importances[indices],\n",
    "#        color=\"r\", yerr=std[indices], align=\"center\")\n",
    "# plt.xticks(range(X.shape[1]), indices)\n",
    "# plt.xlim([-1, X.shape[1]])\n",
    "# plt.show()\n",
    "lgb.plot_importance(litef_ensemble, figsize=(20, 20))\n",
    "plt.savefig('importances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>geo_level_2_id</th>\n",
       "      <th>geo_level_3_id</th>\n",
       "      <th>count_floors_pre_eq</th>\n",
       "      <th>age</th>\n",
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       "      <th>height_percentage</th>\n",
       "      <th>land_surface_condition</th>\n",
       "      <th>foundation_type</th>\n",
       "      <th>roof_type</th>\n",
       "      <th>...</th>\n",
       "      <th>has_secondary_use_agriculture</th>\n",
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       "      <th>has_secondary_use_institution</th>\n",
       "      <th>has_secondary_use_school</th>\n",
       "      <th>has_secondary_use_industry</th>\n",
       "      <th>has_secondary_use_health_post</th>\n",
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       "      <th>has_secondary_use_other</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745817</th>\n",
       "      <td>26</td>\n",
       "      <td>39</td>\n",
       "      <td>633</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>t</td>\n",
       "      <td>r</td>\n",
       "      <td>x</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421793</th>\n",
       "      <td>17</td>\n",
       "      <td>289</td>\n",
       "      <td>7970</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>t</td>\n",
       "      <td>r</td>\n",
       "      <td>q</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             geo_level_1_id  geo_level_2_id  geo_level_3_id  \\\n",
       "building_id                                                   \n",
       "300051                   17             596           11307   \n",
       "99355                     6             141           11987   \n",
       "890251                   22              19           10044   \n",
       "745817                   26              39             633   \n",
       "421793                   17             289            7970   \n",
       "\n",
       "             count_floors_pre_eq  age  area_percentage  height_percentage  \\\n",
       "building_id                                                                 \n",
       "300051                         3   20                7                  6   \n",
       "99355                          2   25               13                  5   \n",
       "890251                         2    5                4                  5   \n",
       "745817                         1    0               19                  3   \n",
       "421793                         3   15                8                  7   \n",
       "\n",
       "            land_surface_condition foundation_type roof_type  ...  \\\n",
       "building_id                                                   ...   \n",
       "300051                           t               r         n  ...   \n",
       "99355                            t               r         n  ...   \n",
       "890251                           t               r         n  ...   \n",
       "745817                           t               r         x  ...   \n",
       "421793                           t               r         q  ...   \n",
       "\n",
       "            has_secondary_use_agriculture has_secondary_use_hotel  \\\n",
       "building_id                                                         \n",
       "300051                                  0                       0   \n",
       "99355                                   1                       0   \n",
       "890251                                  0                       0   \n",
       "745817                                  0                       0   \n",
       "421793                                  0                       0   \n",
       "\n",
       "            has_secondary_use_rental has_secondary_use_institution  \\\n",
       "building_id                                                          \n",
       "300051                             0                             0   \n",
       "99355                              0                             0   \n",
       "890251                             0                             0   \n",
       "745817                             1                             0   \n",
       "421793                             0                             0   \n",
       "\n",
       "             has_secondary_use_school  has_secondary_use_industry  \\\n",
       "building_id                                                         \n",
       "300051                              0                           0   \n",
       "99355                               0                           0   \n",
       "890251                              0                           0   \n",
       "745817                              0                           0   \n",
       "421793                              0                           0   \n",
       "\n",
       "             has_secondary_use_health_post  has_secondary_use_gov_office  \\\n",
       "building_id                                                                \n",
       "300051                                   0                             0   \n",
       "99355                                    0                             0   \n",
       "890251                                   0                             0   \n",
       "745817                                   0                             0   \n",
       "421793                                   0                             0   \n",
       "\n",
       "             has_secondary_use_use_police  has_secondary_use_other  \n",
       "building_id                                                         \n",
       "300051                                  0                        0  \n",
       "99355                                   0                        0  \n",
       "890251                                  0                        0  \n",
       "745817                                  0                        0  \n",
       "421793                                  0                        0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('data/test_values.csv', index_col='building_id')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def oneHot(df, column_name):\n",
    "    one_hot = pd.get_dummies(df[column_name])\n",
    "    df = df.drop(column_name,axis = 1)\n",
    "    df = df.join(one_hot)\n",
    "    for letter in one_hot.columns:\n",
    "        df[column_name + \"_\" + letter] = df[letter]\n",
    "        df.drop(letter, axis=1, inplace=True)\n",
    "    return df\n",
    "\n",
    "categorical_vars = [\"foundation_type\", \"land_surface_condition\", \"roof_type\", \"ground_floor_type\", \"other_floor_type\", \"position\", \"plan_configuration\", \"legal_ownership_status\"]\n",
    "for var in categorical_vars:\n",
    "    test = oneHot(test, var)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68 68\n"
     ]
    }
   ],
   "source": [
    "print(len(test.columns), len(columns))\n",
    "\n",
    "for i in range (len(test.columns)):\n",
    "\n",
    "    if test.columns[i] != columns[i]:\n",
    "        print(test.columns[i], columns[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.reindex(columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = test.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred = litef_ensemble.predict(test1)\n",
    "# predictions_test = 0.5 * gbf.predict_proba(test1) + 0.5 * litef_ensemble.predict_proba(test1)\n",
    "# prediction_test = np.argmax(predictions_test, axis=1) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_prediction = litef_ensemble.predict(test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_format = pd.read_csv('data/submission_format.csv', index_col='building_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_submission = pd.DataFrame(data=test_pred,\n",
    "                             columns=submission_format.columns,\n",
    "                             index=submission_format.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>damage_grade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>building_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>300051</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99355</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890251</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745817</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421793</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             damage_grade\n",
       "building_id              \n",
       "300051                  3\n",
       "99355                   2\n",
       "890251                  2\n",
       "745817                  1\n",
       "421793                  3"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_submission.to_csv('data/submission5.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.arange(4)\n",
    "# F1_baselines = [0.6549, 0.7137, 0.7365, 0.7226]\n",
    "F1_baselines_train = [0.9867, 0.9867, 0.7542, 0.7823]\n",
    "F1_baselines_test = [0.6549, 0.6911, 0.7046, 0.7119]\n",
    "F1_baseline_labels = ['Decision Tree', 'Random Forest', 'Extreme Gradient Boosting', 'Lite Gradient Boosting']\n",
    "plt.figure(figsize=(11,6))\n",
    "plt.bar(xs-0.375, F1_baselines_train, align='center', color='tab:blue', label='Train', width=0.375)\n",
    "plt.bar(xs, F1_baselines_test, align='center', color='tab:orange', label='Validation', width=0.375)\n",
    "plt.legend()\n",
    "plt.xticks(xs, F1_baseline_labels)\n",
    "plt.xlabel('Tree Type')\n",
    "plt.ylabel('F1 Score')\n",
    "plt.title('Baseline Tree Type F1 Score Comparison')\n",
    "plt.savefig('source_images/baseline_tree_comp.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "3.6.8",
   "language": "python",
   "name": "3.6.8"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}