lightgbmtree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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 joblib\n",
    "\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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/one_hot_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <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_o</th>\n",
       "      <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",
       "      <th>legal_ownership_status_w</th>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>590882</td>\n",
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       "      <td>418</td>\n",
       "      <td>10694</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\"data/train_labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>building_id</th>\n",
       "      <th>damage_grade</th>\n",
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      "text/plain": [
       "   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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     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "df2.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Unnamed: 0', 'building_id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <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",
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       "      <th>has_superstructure_mud_mortar_stone</th>\n",
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       "      <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",
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       "      <td>1488</td>\n",
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       "      <td>1</td>\n",
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       "<p>5 rows × 68 columns</p>\n",
       "</div>"
      ],
      "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = df2['damage_grade'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_train = lgb.Dataset(X_train, label=y_train-1)\n",
    "params = {'objective': 'multiclass',\n",
    "          'num_class': 3,\n",
    "          'metric': 'multi_logloss',\n",
    "          'boosting_type': 'gbdt', # dart\n",
    "          'max_bin': 4095,\n",
    "          'learning_rate': 0.001,\n",
    "          'num_threads': 6,\n",
    "          'num_iterations': 5000,\n",
    "          'num_leaves': 1200\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naman/.pyenv/versions/3.6.8/lib/python3.6/site-packages/lightgbm/engine.py:148: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    }
   ],
   "source": [
    "lite = lgb.train(params, d_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lite.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = np.argmax(y_pred, axis=1)\n",
    "y_preds += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = lite.predict(X_train)\n",
    "y_train_preds = np.argmax(y_train_pred, axis=1)\n",
    "y_train_preds += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8194311204911742\n",
      "0.7465129218549145\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_train, y_train_preds, average='micro'))\n",
    "print(f1_score(y_test, y_preds, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['decision_models/lite-7.model']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(lite, \"decision_models/lite-7.model\")\n",
    "# 1 gdbt - 100, 2 dart - 100, 3 dart-leaves-500-bin-1023, 3 (72.26) gdbt-leaves-500-bin-1023, 4 (73.33) gdbt-leaves-500-bin-4095, \n",
    "# 5 (73.87) gdbt-leaves-1000-bin-4095, 6 (73.48) dart-leaves-1000-bin-4095, 7 (74.65) gbdt-leaves-1200-bin-4095"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_leaves_vals(x):\n",
    "    accuracies = [None] * len(x)\n",
    "    accuracies2 = [None] * len(x)\n",
    "    for i in range(len(x)):\n",
    "        leaves = lgb.LGBMClassifier(n_estimators=1000, n_jobs=6, num_leaves=x[i], objective='multiclass', \n",
    "                                           boosting_type='gbdt')\n",
    "        leaves.fit(X_train, y_train)\n",
    "        accuracies[i] = f1_score(y_test, leaves.predict(X_test), average='micro')\n",
    "        leaves = lgb.LGBMClassifier(n_estimators=1000, n_jobs=6, num_leaves=x[i], objective='multiclass', \n",
    "                                           boosting_type='dart')\n",
    "        leaves.fit(X_train, y_train)\n",
    "        accuracies2[i] = f1_score(y_test, leaves.predict(X_test), average='micro')\n",
    "    return accuracies, accuracies2\n",
    "        \n",
    "xs3000 = [30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100]\n",
    "ys3000, ysdart = get_leaves_vals(xs3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80]\n",
      "[85, 90, 95, 100]\n"
     ]
    }
   ],
   "source": [
    "print(xs3000[:-4])\n",
    "print(xs3000[-4:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Lite Gradient Boosting Ensemble Validation accuracy for Number of Leaves')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "morexs = [81, 83, 87, 89]\n",
    "moreys3000, moreysdart = get_leaves_vals(morexs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 83, 85, 87, 89, 90, 95, 100]\n"
     ]
    }
   ],
   "source": [
    "print(xs3000[:-4] + morexs[:2] + [xs3000[-4]] + morexs[2:] + xs3000[-3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Lite Gradient Boosting Ensemble Validation accuracy for Number of Leaves')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "actualxs = xs3000[:-4] + morexs[:2] + [xs3000[-4]] + morexs[2:] + xs3000[-3:]\n",
    "actuays = ys3000[:-4] + moreys3000[:2] + [ys3000[-4]] + moreys3000[2:] + ys3000[-3:]\n",
    "actualysdart = ysdart[:-4] + moreysdart[:2] + [ysdart[-4]] + moreysdart[2:] + ysdart[-3:]\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(actualxs, actuays, '-ok', label='gbdt', color='black');\n",
    "plt.plot(actualxs, actualysdart, '-ok', label='dart', color='brown');\n",
    "plt.legend()\n",
    "plt.xlabel('Number of Leaves')\n",
    "plt.ylabel('F1 Score')\n",
    "plt.title('Lite Gradient Boosting Ensemble Validation accuracy for Number of Leaves')\n",
    "# plt.savefig('source_images/num_leaves_analysis.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(xs3000, ys3000, '-ok', label='gbdt', color='black');\n",
    "plt.plot(xs3000, ysdart, '-ok', label='dart', color='brown');\n",
    "plt.legend()\n",
    "plt.xlabel('Number of Leaves')\n",
    "plt.ylabel('F1 Score')\n",
    "plt.title('Lite Gradient Boosting Ensemble Validation Accuracy for Number of Leaves')\n",
    "plt.savefig('source_images/num_leaves_analysis.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getcounts(x):\n",
    "    accuracies = [None] * len(x)\n",
    "    accuracies2 = [None] * len(x)\n",
    "    for i in range(len(x)):\n",
    "        leaves = lgb.LGBMClassifier(n_estimators=x[i], n_jobs=6, num_leaves=80, objective='multiclass', \n",
    "                                           boosting_type='gbdt')\n",
    "        leaves.fit(X_train, y_train)\n",
    "        accuracies[i] = f1_score(y_test, leaves.predict(X_test), average='micro')\n",
    "        leaves = lgb.LGBMClassifier(n_estimators=x[i], n_jobs=6, num_leaves=80, objective='multiclass', \n",
    "                                           boosting_type='dart')\n",
    "        leaves.fit(X_train, y_train)\n",
    "        accuracies2[i] = f1_score(y_test, leaves.predict(X_test), average='micro')\n",
    "    return accuracies\n",
    "        \n",
    "xs3000 = [100, 1000, 2000, 3000, 4000]\n",
    "ys3000, ysdart = get_leaves_vals(xs3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(xs3000, ys3000, '-ok', label='gbdt', color='black');\n",
    "plt.plot(xs3000, ysdart, '-ok', label='dart', color='brown');\n",
    "plt.legend()\n",
    "plt.xlabel('Number of Estimators')\n",
    "plt.ylabel('F1 Score')\n",
    "plt.title('Lite Gradient Boosting Ensemble Validation accuracy for Number of Estimators')\n",
    "plt.savefig('source_images/num_estimator_analysis.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lite_ensemble = lgb.LGBMClassifier(n_estimators=3000, n_jobs=6, num_leaves=1000)\n",
    "lite_ensemble = lgb.LGBMClassifier(n_estimators=4000, n_jobs=6, num_leaves=65, objective='multiclass', \n",
    "                                   boosting_type='dart')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(boosting_type='dart', 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=4000, n_jobs=6, num_leaves=65,\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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lite_ensemble.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8380036454336147\n",
      "0.746110013238426\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_train, lite_ensemble.predict(X_train), average='micro'))\n",
    "print(f1_score(y_test, lite_ensemble.predict(X_test), average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['decision_models/lite-ensemble-11.model']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(lite_ensemble, \"decision_models/lite-ensemble-13.model\")\n",
    "#  1 (74.75) ensemble-leaves-31-n_estimators-3000, 2 (74.3) ensemble-leaves-100-n_estimators-3000, 3 (74.82) ensemble-leaves-32-n_estimators-3000,\n",
    "# 4 (74.70) ensemble-leaves-33-n_estimators-3000, 5 (74.79) ensemble-leaves-34-n_estimators-3000, 6 (74.68) ensemble-leaves-35-n_estimators-3000,\n",
    "# 7 (74.70) ensemble-leaves-30-n_estimators-3000, 8 (74.67) ensemble-leaves-32-n_estimators-5000, 9 (74.68) ensemble-leaves-32-n_estimators-4000,\n",
    "# 10 (74.42) dart-ensemble-leaves-32-n_estimators-5000, 11 (74.70) dart-ensemble-leaves-60-n_estimators-4000, \n",
    "# 12 (74.61) gbdt-ensemble-leaves-60-n_estimators-4000, 13 (74.55) dart-ensemble-leaves-50-n_estimators-4000,\n",
    "# 14 (74.53) dart-ensemble-leaves-40-n_estimators-4000,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-5d4cc2253786>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlitef_ensemble\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLGBMClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3000\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[0mnum_leaves\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobjective\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'multiclass'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlitef_ensemble\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\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/lightgbm/sklearn.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, init_score, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_metric, early_stopping_rounds, verbose, feature_name, categorical_feature, callbacks)\u001b[0m\n\u001b[1;32m    798\u001b[0m                                         \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeature_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    799\u001b[0m                                         \u001b[0mcategorical_feature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcategorical_feature\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 800\u001b[0;31m                                         callbacks=callbacks)\n\u001b[0m\u001b[1;32m    801\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    802\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.8/lib/python3.6/site-packages/lightgbm/sklearn.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, init_score, group, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_group, eval_metric, early_stopping_rounds, verbose, feature_name, categorical_feature, callbacks)\u001b[0m\n\u001b[1;32m    593\u001b[0m                               \u001b[0mverbose_eval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeature_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m                               \u001b[0mcategorical_feature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcategorical_feature\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m                               callbacks=callbacks)\n\u001b[0m\u001b[1;32m    596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevals_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.8/lib/python3.6/site-packages/lightgbm/engine.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(params, train_set, num_boost_round, valid_sets, valid_names, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, evals_result, verbose_eval, learning_rates, keep_training_booster, callbacks)\u001b[0m\n\u001b[1;32m    247\u001b[0m                                     evaluation_result_list=None))\n\u001b[1;32m    248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 249\u001b[0;31m         \u001b[0mbooster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfobj\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    250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0mevaluation_result_list\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~/.pyenv/versions/3.6.8/lib/python3.6/site-packages/lightgbm/basic.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, train_set, fobj)\u001b[0m\n\u001b[1;32m   1924\u001b[0m             _safe_call(_LIB.LGBM_BoosterUpdateOneIter(\n\u001b[1;32m   1925\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1926\u001b[0;31m                 ctypes.byref(is_finished)))\n\u001b[0m\u001b[1;32m   1927\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__is_predicted_cur_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__num_dataset\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   1928\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mis_finished\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "litef_ensemble = lgb.LGBMClassifier(n_estimators=3000, n_jobs=6, num_leaves=32, objective='multiclass')\n",
    "litef_ensemble.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>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",
       "      <th>has_secondary_use_hotel</th>\n",
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       "      <th>has_secondary_use_institution</th>\n",
       "      <th>has_secondary_use_school</th>\n",
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       "      <th>has_secondary_use_gov_office</th>\n",
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      ],
      "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": 102,
     "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": 103,
   "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": 106,
   "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": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.reindex(columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = test.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred = litef_ensemble.predict(test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_format = pd.read_csv('data/submission_format.csv', index_col='building_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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": 111,
   "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": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_submission.to_csv('data/submission2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=LGBMClassifier(boosting_type='gbdt', 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_estimators=3000,\n",
       "                                      n_jobs=6, num_leaves=31, objective=None,\n",
       "                                      random_state=None, reg_alpha=0.0,\n",
       "                                      reg_lambda=0.0, silent=True,\n",
       "                                      subsample=1.0, subsample_for_bin=200000,\n",
       "                                      subsample_freq=0),\n",
       "             iid='warn', n_jobs=6, param_grid={'num_leaves': [32, 50, 90]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='f1_micro', verbose=0)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_leaves = {\n",
    " 'num_leaves':[32, 50, 90]\n",
    "}\n",
    "gsearch = GridSearchCV(estimator = lgb.LGBMClassifier(n_estimators=3000, n_jobs=6), \n",
    " param_grid = param_leaves, scoring='f1_micro', n_jobs=6, cv=5)\n",
    "gsearch.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8355237912509593\n",
      "0.7482204869438422\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_train, gsearch.predict(X_train), average='micro'))\n",
    "print(f1_score(y_test, gsearch.predict(X_test), average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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=32, objective=None,\n",
       "               random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
       "               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "joblib.dump(gsearch, \"decision_models/gsearch-1.model\")\n",
    "# 'num_leaves':[32, 50, 90], n_estimators=3000\n",
    "# in the future, will use fewer estimators for figuring out optimal parameter, then run full training"
   ]
  }
 ],
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