LGBM_forest.ipynb

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    "colab": {
      "name": "Untitled3.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPjhT9BLL+4dkaGBUwu1ZQb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Noxybot/tt_21/blob/master/Untitled3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDFQ8ZjyQimn"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from lightgbm import LGBMModel,LGBMClassifier\n",
        "from sklearn.metrics import precision_score, recall_score\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from np import random\n",
        "import time\n",
        "\n",
        "RANDOM_STATE=25\n",
        "ESIMATORS=5\n",
        "DEPTH=5\n",
        "MIN_SAMPLES_LEAF=1\n",
        "MAX_FEATURES='auto'\n",
        "CRITERION='gini'\n",
        "MIN_WEIGHT_FRACTION_LEAF=0.0\n",
        "MIN_SAMPLES_SPLIT=2\n",
        "MIN_IMPURITY_DECREASE=0.0\n",
        "MIN_IMPURITY_SPLIT=None\n",
        "MAX_LEAF_NODES=None\n",
        "MAX_SAMPLES=None"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wL4AlywL2chq"
      },
      "source": [
        "train_data = pd.read_csv(\"train.csv\", nrows=50000)\n",
        "test_data = pd.read_csv(\"test.csv\", nrows=50000)"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "MUIBZF2FEK5E",
        "outputId": "e2f90d23-e304-4467-9749-e69f9ca3dfa4"
      },
      "source": [
        "X_train = train_data\n",
        "X_train = X_train.drop(X_train.columns[0], axis=1)\n",
        "X_train = X_train.drop(X_train.columns[-1], axis=1)\n",
        "X_train.fillna(X_train.mean(), inplace=True)\n",
        "X_train.tail()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0.021663</td>\n",
              "      <td>5.085100e+09</td>\n",
              "      <td>0.129060</td>\n",
              "      <td>0.21112</td>\n",
              "      <td>1.3673</td>\n",
              "      <td>-0.288330</td>\n",
              "      <td>2.18760</td>\n",
              "      <td>44427.00</td>\n",
              "      <td>1.1473</td>\n",
              "      <td>1.388500e+16</td>\n",
              "      <td>6517.900</td>\n",
              "      <td>0.37186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49999</th>\n",
              "      <td>0.132170</td>\n",
              "      <td>0.478000</td>\n",
              "      <td>-304.97</td>\n",
              "      <td>0.102290</td>\n",
              "      <td>0.33439</td>\n",
              "      <td>1.90280</td>\n",
              "      <td>2089.4000</td>\n",
              "      <td>576650.0</td>\n",
              "      <td>4.090300e+14</td>\n",
              "      <td>0.61316</td>\n",
              "      <td>1.72960</td>\n",
              "      <td>-6.000100e+07</td>\n",
              "      <td>0.48607</td>\n",
              "      <td>4.6822</td>\n",
              "      <td>0.026517</td>\n",
              "      <td>2088.9000</td>\n",
              "      <td>5.3241</td>\n",
              "      <td>6.8651</td>\n",
              "      <td>34.4390</td>\n",
              "      <td>306.99000</td>\n",
              "      <td>5467.20</td>\n",
              "      <td>2.459100</td>\n",
              "      <td>68.93</td>\n",
              "      <td>-2.04660</td>\n",
              "      <td>631.28</td>\n",
              "      <td>7.541700e+11</td>\n",
              "      <td>1.847500e+12</td>\n",
              "      <td>-10525.0</td>\n",
              "      <td>-0.001129</td>\n",
              "      <td>5.4151</td>\n",
              "      <td>0.79379</td>\n",
              "      <td>135288.247861</td>\n",
              "      <td>1097200.0</td>\n",
              "      <td>-0.002034</td>\n",
              "      <td>2.598400e+16</td>\n",
              "      <td>1269.600</td>\n",
              "      <td>917.71</td>\n",
              "      <td>3.4718</td>\n",
              "      <td>3243.2</td>\n",
              "      <td>0.000932</td>\n",
              "      <td>...</td>\n",
              "      <td>-9.66160</td>\n",
              "      <td>0.166140</td>\n",
              "      <td>2.986300</td>\n",
              "      <td>7.930700e+10</td>\n",
              "      <td>3.4868</td>\n",
              "      <td>31017000.0</td>\n",
              "      <td>41.226</td>\n",
              "      <td>5.017300e+09</td>\n",
              "      <td>18.803</td>\n",
              "      <td>206.875932</td>\n",
              "      <td>4516.00</td>\n",
              "      <td>3.6973</td>\n",
              "      <td>0.48953</td>\n",
              "      <td>960.72</td>\n",
              "      <td>25.357</td>\n",
              "      <td>0.11151</td>\n",
              "      <td>26.63900</td>\n",
              "      <td>14140.000</td>\n",
              "      <td>0.99889</td>\n",
              "      <td>1.012800e+10</td>\n",
              "      <td>1.2663</td>\n",
              "      <td>0.34697</td>\n",
              "      <td>0.26626</td>\n",
              "      <td>321642.940175</td>\n",
              "      <td>546.629908</td>\n",
              "      <td>2179.80</td>\n",
              "      <td>0.202290</td>\n",
              "      <td>0.218730</td>\n",
              "      <td>0.014483</td>\n",
              "      <td>1.619100e+08</td>\n",
              "      <td>0.333570</td>\n",
              "      <td>-4.56050</td>\n",
              "      <td>1.6986</td>\n",
              "      <td>2.982800</td>\n",
              "      <td>5.23970</td>\n",
              "      <td>651.77</td>\n",
              "      <td>1.4250</td>\n",
              "      <td>2.445400e+17</td>\n",
              "      <td>-38.628</td>\n",
              "      <td>0.24177</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 118 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             f1        f2       f3  ...          f116      f117     f118\n",
              "49995  0.015200  0.345319  2082.90  ...  1.473300e+17  3355.300  0.53116\n",
              "49996  0.079473  0.278780  1109.00  ...  2.314800e+15  3363.600  0.25266\n",
              "49997  0.069434  0.468710  2801.60  ...  1.781100e+15  8572.500  0.13947\n",
              "49998  0.010675  0.429230   394.12  ...  1.388500e+16  6517.900  0.37186\n",
              "49999  0.132170  0.478000  -304.97  ...  2.445400e+17   -38.628  0.24177\n",
              "\n",
              "[5 rows x 118 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "rpStdLP3GsHG",
        "outputId": "1652fb16-07ea-4b5c-863c-8ddc826ac53a"
      },
      "source": [
        "Y_train = train_data\n",
        "Y_train.drop(Y_train.columns.difference(['claim']), 1, inplace=True)\n",
        "#Y_train.fillna(Y_train.mean(), inplace=True)\n",
        "Y_train.tail()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "       claim\n",
              "49995      1\n",
              "49996      1\n",
              "49997      1\n",
              "49998      0\n",
              "49999      1"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "9AET59WOrEXm",
        "outputId": "02e78f6e-f045-4620-d533-85944ff35c0b"
      },
      "source": [
        "X_test = test_data\n",
        "X_test = X_test.drop(X_test.columns[0], axis=1)\n",
        "X_test.fillna(X_test.mean(), inplace=True)\n",
        "X_test.tail()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>f95</th>\n",
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              "      <th>f118</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>49995</th>\n",
              "      <td>0.124820</td>\n",
              "      <td>0.480980</td>\n",
              "      <td>5654.40</td>\n",
              "      <td>0.026795</td>\n",
              "      <td>0.34938</td>\n",
              "      <td>-1.13400</td>\n",
              "      <td>1149.90</td>\n",
              "      <td>201920.0</td>\n",
              "      <td>2.664200e+14</td>\n",
              "      <td>6551.100</td>\n",
              "      <td>-0.64172</td>\n",
              "      <td>1.038900e+08</td>\n",
              "      <td>0.360560</td>\n",
              "      <td>4.6691</td>\n",
              "      <td>0.028125</td>\n",
              "      <td>-1.718800</td>\n",
              "      <td>6.9867</td>\n",
              "      <td>5.1558</td>\n",
              "      <td>4.3660</td>\n",
              "      <td>915.6000</td>\n",
              "      <td>163360.0</td>\n",
              "      <td>2.2943</td>\n",
              "      <td>64.0430</td>\n",
              "      <td>1.033100</td>\n",
              "      <td>792.3300</td>\n",
              "      <td>3.465800e+10</td>\n",
              "      <td>2.676400e+12</td>\n",
              "      <td>24549.0</td>\n",
              "      <td>0.014287</td>\n",
              "      <td>10.6990</td>\n",
              "      <td>1.204300</td>\n",
              "      <td>426320.0</td>\n",
              "      <td>1084900.0</td>\n",
              "      <td>0.001776</td>\n",
              "      <td>4.111100e+16</td>\n",
              "      <td>-36.962</td>\n",
              "      <td>52.2710</td>\n",
              "      <td>2.8491</td>\n",
              "      <td>2081.600</td>\n",
              "      <td>0.010407</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.00260</td>\n",
              "      <td>0.159200</td>\n",
              "      <td>0.98785</td>\n",
              "      <td>1.196800e+10</td>\n",
              "      <td>-1.8446</td>\n",
              "      <td>4274.1</td>\n",
              "      <td>531.66</td>\n",
              "      <td>-6.112700e+08</td>\n",
              "      <td>94.8300</td>\n",
              "      <td>125.83</td>\n",
              "      <td>5358.60</td>\n",
              "      <td>7.5634</td>\n",
              "      <td>0.002048</td>\n",
              "      <td>5444.4</td>\n",
              "      <td>10.074000</td>\n",
              "      <td>0.007878</td>\n",
              "      <td>49.2630</td>\n",
              "      <td>-431.740</td>\n",
              "      <td>0.99944</td>\n",
              "      <td>2.817900e+13</td>\n",
              "      <td>2.9183</td>\n",
              "      <td>0.50169</td>\n",
              "      <td>8.0171</td>\n",
              "      <td>849700.0</td>\n",
              "      <td>370.8800</td>\n",
              "      <td>-130.16</td>\n",
              "      <td>0.097532</td>\n",
              "      <td>0.086617</td>\n",
              "      <td>0.025816</td>\n",
              "      <td>3.263200e+08</td>\n",
              "      <td>0.270550</td>\n",
              "      <td>-15.9740</td>\n",
              "      <td>1.899400</td>\n",
              "      <td>-2.67740</td>\n",
              "      <td>-0.24917</td>\n",
              "      <td>212380.0</td>\n",
              "      <td>1.5432</td>\n",
              "      <td>9.023800e+16</td>\n",
              "      <td>6623.6</td>\n",
              "      <td>0.311690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49996</th>\n",
              "      <td>0.091221</td>\n",
              "      <td>-0.006127</td>\n",
              "      <td>2587.90</td>\n",
              "      <td>0.365930</td>\n",
              "      <td>0.45117</td>\n",
              "      <td>-0.34160</td>\n",
              "      <td>2364.40</td>\n",
              "      <td>443950.0</td>\n",
              "      <td>6.703700e+14</td>\n",
              "      <td>1747.200</td>\n",
              "      <td>-3.47650</td>\n",
              "      <td>7.146100e+06</td>\n",
              "      <td>0.015363</td>\n",
              "      <td>4.3478</td>\n",
              "      <td>0.068395</td>\n",
              "      <td>27.042000</td>\n",
              "      <td>6.7112</td>\n",
              "      <td>5.4184</td>\n",
              "      <td>3.4989</td>\n",
              "      <td>580.1400</td>\n",
              "      <td>19551.0</td>\n",
              "      <td>2.5098</td>\n",
              "      <td>65.5560</td>\n",
              "      <td>1.170400</td>\n",
              "      <td>1071.0000</td>\n",
              "      <td>9.332500e+11</td>\n",
              "      <td>6.693300e+11</td>\n",
              "      <td>4806000.0</td>\n",
              "      <td>0.929630</td>\n",
              "      <td>17.4890</td>\n",
              "      <td>0.091263</td>\n",
              "      <td>139450.0</td>\n",
              "      <td>1149500.0</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>3.486700e+16</td>\n",
              "      <td>182.700</td>\n",
              "      <td>921.9600</td>\n",
              "      <td>1.5305</td>\n",
              "      <td>2470.100</td>\n",
              "      <td>0.013835</td>\n",
              "      <td>...</td>\n",
              "      <td>0.77131</td>\n",
              "      <td>0.524440</td>\n",
              "      <td>7.12870</td>\n",
              "      <td>1.754900e+10</td>\n",
              "      <td>2.9977</td>\n",
              "      <td>61506.0</td>\n",
              "      <td>991.86</td>\n",
              "      <td>4.057700e+10</td>\n",
              "      <td>51.2180</td>\n",
              "      <td>303.30</td>\n",
              "      <td>2158.60</td>\n",
              "      <td>6.6325</td>\n",
              "      <td>0.372450</td>\n",
              "      <td>4685.2</td>\n",
              "      <td>132.189323</td>\n",
              "      <td>0.903100</td>\n",
              "      <td>25.5120</td>\n",
              "      <td>348.000</td>\n",
              "      <td>0.99788</td>\n",
              "      <td>2.966000e+13</td>\n",
              "      <td>3.1403</td>\n",
              "      <td>0.21356</td>\n",
              "      <td>23.2150</td>\n",
              "      <td>262250.0</td>\n",
              "      <td>173.5900</td>\n",
              "      <td>780.69</td>\n",
              "      <td>0.090668</td>\n",
              "      <td>0.065759</td>\n",
              "      <td>0.000542</td>\n",
              "      <td>3.781600e+09</td>\n",
              "      <td>0.368390</td>\n",
              "      <td>-14.6250</td>\n",
              "      <td>1.463600</td>\n",
              "      <td>142.77000</td>\n",
              "      <td>-7.37980</td>\n",
              "      <td>22526.0</td>\n",
              "      <td>1.1583</td>\n",
              "      <td>7.210400e+16</td>\n",
              "      <td>5538.2</td>\n",
              "      <td>0.361190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49997</th>\n",
              "      <td>0.079139</td>\n",
              "      <td>0.427740</td>\n",
              "      <td>1467.40</td>\n",
              "      <td>0.404310</td>\n",
              "      <td>0.40973</td>\n",
              "      <td>-1.77540</td>\n",
              "      <td>2348.70</td>\n",
              "      <td>540080.0</td>\n",
              "      <td>-6.412400e+12</td>\n",
              "      <td>101.730</td>\n",
              "      <td>1.15870</td>\n",
              "      <td>5.001800e+09</td>\n",
              "      <td>0.338690</td>\n",
              "      <td>2.7268</td>\n",
              "      <td>-0.000639</td>\n",
              "      <td>1586.000000</td>\n",
              "      <td>7.5503</td>\n",
              "      <td>7.8588</td>\n",
              "      <td>42.8900</td>\n",
              "      <td>852.0700</td>\n",
              "      <td>78621.0</td>\n",
              "      <td>1.7210</td>\n",
              "      <td>-2.7801</td>\n",
              "      <td>1.099400</td>\n",
              "      <td>508.3000</td>\n",
              "      <td>5.380800e+12</td>\n",
              "      <td>4.791500e+12</td>\n",
              "      <td>2108000.0</td>\n",
              "      <td>-0.000741</td>\n",
              "      <td>2.6742</td>\n",
              "      <td>0.488600</td>\n",
              "      <td>878010.0</td>\n",
              "      <td>123630.0</td>\n",
              "      <td>-0.001025</td>\n",
              "      <td>6.840400e+16</td>\n",
              "      <td>-537.610</td>\n",
              "      <td>899.6700</td>\n",
              "      <td>1.4362</td>\n",
              "      <td>224.510</td>\n",
              "      <td>0.908480</td>\n",
              "      <td>...</td>\n",
              "      <td>29.26700</td>\n",
              "      <td>0.124480</td>\n",
              "      <td>1.60650</td>\n",
              "      <td>5.335200e+10</td>\n",
              "      <td>7.1177</td>\n",
              "      <td>-93250.0</td>\n",
              "      <td>666.78</td>\n",
              "      <td>5.109500e+10</td>\n",
              "      <td>10.3040</td>\n",
              "      <td>139.25</td>\n",
              "      <td>635.21</td>\n",
              "      <td>5.1771</td>\n",
              "      <td>0.492140</td>\n",
              "      <td>5566.7</td>\n",
              "      <td>238.730000</td>\n",
              "      <td>1.025400</td>\n",
              "      <td>27.8900</td>\n",
              "      <td>7492.400</td>\n",
              "      <td>0.99914</td>\n",
              "      <td>8.354900e+12</td>\n",
              "      <td>1.8658</td>\n",
              "      <td>0.71251</td>\n",
              "      <td>20.2150</td>\n",
              "      <td>105330.0</td>\n",
              "      <td>27.1320</td>\n",
              "      <td>6829.20</td>\n",
              "      <td>0.185520</td>\n",
              "      <td>0.290300</td>\n",
              "      <td>0.002575</td>\n",
              "      <td>1.677712e+09</td>\n",
              "      <td>0.516550</td>\n",
              "      <td>-5.5605</td>\n",
              "      <td>1.710800</td>\n",
              "      <td>6.51690</td>\n",
              "      <td>-1.30590</td>\n",
              "      <td>28074.0</td>\n",
              "      <td>1.1287</td>\n",
              "      <td>2.637300e+15</td>\n",
              "      <td>9461.3</td>\n",
              "      <td>0.297950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49998</th>\n",
              "      <td>0.079515</td>\n",
              "      <td>0.414660</td>\n",
              "      <td>11618.00</td>\n",
              "      <td>0.384460</td>\n",
              "      <td>0.23680</td>\n",
              "      <td>-2.10700</td>\n",
              "      <td>346.21</td>\n",
              "      <td>275830.0</td>\n",
              "      <td>3.123300e+12</td>\n",
              "      <td>-72.064</td>\n",
              "      <td>-1.77350</td>\n",
              "      <td>6.057300e+06</td>\n",
              "      <td>0.007796</td>\n",
              "      <td>6.2890</td>\n",
              "      <td>0.030475</td>\n",
              "      <td>444.994539</td>\n",
              "      <td>8.5295</td>\n",
              "      <td>2.9968</td>\n",
              "      <td>31.5930</td>\n",
              "      <td>4.4667</td>\n",
              "      <td>92655.0</td>\n",
              "      <td>2.5305</td>\n",
              "      <td>106.8000</td>\n",
              "      <td>0.925330</td>\n",
              "      <td>3.1671</td>\n",
              "      <td>1.401100e+12</td>\n",
              "      <td>1.237900e+12</td>\n",
              "      <td>1194900.0</td>\n",
              "      <td>-0.000469</td>\n",
              "      <td>1.0148</td>\n",
              "      <td>0.800300</td>\n",
              "      <td>-21347.0</td>\n",
              "      <td>8097.2</td>\n",
              "      <td>-0.001656</td>\n",
              "      <td>6.083000e+15</td>\n",
              "      <td>150.830</td>\n",
              "      <td>8.0362</td>\n",
              "      <td>1.3285</td>\n",
              "      <td>-81.273</td>\n",
              "      <td>0.895350</td>\n",
              "      <td>...</td>\n",
              "      <td>-15.19500</td>\n",
              "      <td>0.194445</td>\n",
              "      <td>1.50590</td>\n",
              "      <td>2.031800e+11</td>\n",
              "      <td>347.9000</td>\n",
              "      <td>130220.0</td>\n",
              "      <td>699.31</td>\n",
              "      <td>7.160000e+10</td>\n",
              "      <td>3.2378</td>\n",
              "      <td>328.85</td>\n",
              "      <td>257.03</td>\n",
              "      <td>9.4548</td>\n",
              "      <td>0.498780</td>\n",
              "      <td>22259.0</td>\n",
              "      <td>11.632000</td>\n",
              "      <td>1.037000</td>\n",
              "      <td>4.2077</td>\n",
              "      <td>24.374</td>\n",
              "      <td>0.99853</td>\n",
              "      <td>4.400500e+12</td>\n",
              "      <td>1.1640</td>\n",
              "      <td>0.58874</td>\n",
              "      <td>15.7270</td>\n",
              "      <td>3929.1</td>\n",
              "      <td>8.7554</td>\n",
              "      <td>1933.20</td>\n",
              "      <td>0.331630</td>\n",
              "      <td>0.398460</td>\n",
              "      <td>0.033087</td>\n",
              "      <td>2.414200e+08</td>\n",
              "      <td>0.538270</td>\n",
              "      <td>-1.6511</td>\n",
              "      <td>2.073539</td>\n",
              "      <td>0.36797</td>\n",
              "      <td>-6.37680</td>\n",
              "      <td>17175.0</td>\n",
              "      <td>1.0769</td>\n",
              "      <td>4.323386e+16</td>\n",
              "      <td>6425.7</td>\n",
              "      <td>0.556961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49999</th>\n",
              "      <td>0.140930</td>\n",
              "      <td>0.320200</td>\n",
              "      <td>402.96</td>\n",
              "      <td>-0.002631</td>\n",
              "      <td>0.41900</td>\n",
              "      <td>-0.75916</td>\n",
              "      <td>3257.50</td>\n",
              "      <td>820620.0</td>\n",
              "      <td>9.884800e+13</td>\n",
              "      <td>-55.571</td>\n",
              "      <td>1.39470</td>\n",
              "      <td>4.293100e+08</td>\n",
              "      <td>0.293070</td>\n",
              "      <td>9.6356</td>\n",
              "      <td>0.007835</td>\n",
              "      <td>905.780000</td>\n",
              "      <td>8.2891</td>\n",
              "      <td>1.9736</td>\n",
              "      <td>11.1010</td>\n",
              "      <td>46.8850</td>\n",
              "      <td>18108.0</td>\n",
              "      <td>2.7739</td>\n",
              "      <td>120.6700</td>\n",
              "      <td>0.057844</td>\n",
              "      <td>607.2900</td>\n",
              "      <td>4.159800e+11</td>\n",
              "      <td>3.682300e+12</td>\n",
              "      <td>1884500.0</td>\n",
              "      <td>0.004632</td>\n",
              "      <td>10.1120</td>\n",
              "      <td>0.839360</td>\n",
              "      <td>51836.0</td>\n",
              "      <td>217780.0</td>\n",
              "      <td>-0.000763</td>\n",
              "      <td>2.929800e+15</td>\n",
              "      <td>-1416.300</td>\n",
              "      <td>830.6100</td>\n",
              "      <td>1.5307</td>\n",
              "      <td>1247.500</td>\n",
              "      <td>-0.003624</td>\n",
              "      <td>...</td>\n",
              "      <td>35.42000</td>\n",
              "      <td>0.105420</td>\n",
              "      <td>2.99650</td>\n",
              "      <td>1.831200e+11</td>\n",
              "      <td>193.5900</td>\n",
              "      <td>25818000.0</td>\n",
              "      <td>676.21</td>\n",
              "      <td>3.462500e+10</td>\n",
              "      <td>16.3360</td>\n",
              "      <td>362.48</td>\n",
              "      <td>442.09</td>\n",
              "      <td>4.1939</td>\n",
              "      <td>0.491910</td>\n",
              "      <td>11365.0</td>\n",
              "      <td>393.130000</td>\n",
              "      <td>1.194400</td>\n",
              "      <td>7.6621</td>\n",
              "      <td>4345.900</td>\n",
              "      <td>1.00280</td>\n",
              "      <td>2.595700e+13</td>\n",
              "      <td>1.1948</td>\n",
              "      <td>0.28375</td>\n",
              "      <td>29.5080</td>\n",
              "      <td>172810.0</td>\n",
              "      <td>-15.1400</td>\n",
              "      <td>1436.10</td>\n",
              "      <td>0.203730</td>\n",
              "      <td>0.146900</td>\n",
              "      <td>0.000553</td>\n",
              "      <td>1.248800e+09</td>\n",
              "      <td>0.008217</td>\n",
              "      <td>-12.6680</td>\n",
              "      <td>1.363900</td>\n",
              "      <td>90.93400</td>\n",
              "      <td>-4.08120</td>\n",
              "      <td>-3970.6</td>\n",
              "      <td>1.0737</td>\n",
              "      <td>4.494200e+16</td>\n",
              "      <td>2901.6</td>\n",
              "      <td>1.594700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 118 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             f1        f2        f3  ...          f116    f117      f118\n",
              "49995  0.124820  0.480980   5654.40  ...  9.023800e+16  6623.6  0.311690\n",
              "49996  0.091221 -0.006127   2587.90  ...  7.210400e+16  5538.2  0.361190\n",
              "49997  0.079139  0.427740   1467.40  ...  2.637300e+15  9461.3  0.297950\n",
              "49998  0.079515  0.414660  11618.00  ...  4.323386e+16  6425.7  0.556961\n",
              "49999  0.140930  0.320200    402.96  ...  4.494200e+16  2901.6  1.594700\n",
              "\n",
              "[5 rows x 118 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfFktynMJnLa"
      },
      "source": [
        "class MyRandomForestClassifier:\n",
        "  \n",
        "  \n",
        "  trees = []\n",
        "  def __init__(self, n_estimators=ESIMATORS, bootstrap=True,\n",
        "               random_state=RANDOM_STATE, max_depth=DEPTH,\n",
        "               min_samples_split=MIN_SAMPLES_SPLIT, min_samples_leaf=MIN_SAMPLES_LEAF,\n",
        "               min_weight_fraction_leaf=MIN_WEIGHT_FRACTION_LEAF, max_features=MAX_FEATURES,\n",
        "               max_leaf_nodes=MAX_LEAF_NODES, min_impurity_decrease=MIN_IMPURITY_DECREASE,\n",
        "               min_impurity_split=MIN_IMPURITY_SPLIT, criterion=CRITERION, max_samples=MAX_SAMPLES):\n",
        "    self.n_estimators = n_estimators\n",
        "    self.random_state = random_state\n",
        "    self.max_samples = max_samples\n",
        "    self.bootstrap = bootstrap\n",
        "    self.max_depth = max_depth\n",
        "    self.min_samples_split = min_samples_split\n",
        "    self.min_samples_leaf = min_samples_leaf\n",
        "    self.min_weight_fraction_leaf = min_weight_fraction_leaf\n",
        "    self.max_features = max_features\n",
        "    self.max_leaf_nodes = max_leaf_nodes\n",
        "    self.min_impurity_decrease = min_impurity_decrease\n",
        "    self.min_impurity_split = min_impurity_split\n",
        "    self.criterion = criterion\n",
        "  def fit(self, X, Y):\n",
        "    bootstraped_size = X.shape[0]\n",
        "    if isinstance(self.max_samples, int):\n",
        "      bootstraped_size = max_samples\n",
        "    elif isinstance(self.max_samples, float):\n",
        "      bootstraped_size = int(self.max_samples * X.shape[0])\n",
        "    print(\"size of sample: \",  bootstraped_size)\n",
        "    clf = DecisionTreeClassifier(max_depth=self.max_depth, min_samples_split=self.min_samples_split,\n",
        "                                 min_weight_fraction_leaf=self.min_weight_fraction_leaf,  random_state=np.random.RandomState(self.random_state),\n",
        "                                 min_samples_leaf=self.min_samples_leaf, max_features=self.max_features,\n",
        "                                 max_leaf_nodes=self.max_leaf_nodes, min_impurity_decrease=self.min_impurity_decrease,\n",
        "                                 min_impurity_split=self.min_impurity_split,\n",
        "                                 criterion=self.criterion)\n",
        "    if self.bootstrap is True:\n",
        "      print ('doint boots')\n",
        "    for i in range(self.n_estimators):\n",
        "      if self.bootstrap is True:\n",
        "        bootstrapped_sample = X.sample(n=bootstraped_size, axis=0, random_state=np.random.RandomState(self.random_state));\n",
        "        self.trees.append(clf.fit(bootstrapped_sample, Y.iloc[bootstrapped_sample.index.to_list()]))\n",
        "      else:\n",
        "          self.trees.append(clf.fit(X, Y))\n",
        "  def predict(self, X):\n",
        "    sub_res = []\n",
        "    for tree in self.trees:\n",
        "      sub_res.append(tree.predict(X))\n",
        "    return np.asarray(sub_res).mean(axis=0).round()\n",
        "  def predict_proba(self, X):\n",
        "    sub_res = []\n",
        "    for tree in self.trees:\n",
        "      sub_res.append(tree.predict_proba(X))\n",
        "    return np.asarray(sub_res).mean(axis=0)"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pkGXftBxHh-Q",
        "outputId": "55f1e4cf-b8e2-4e75-88a6-9589215307f0"
      },
      "source": [
        "#TASK 1\n",
        "\n",
        "my_forest = MyRandomForestClassifier()\n",
        "\n",
        "start_fit = time.time()\n",
        "my_forest.fit(X_train, Y_train)\n",
        "end_fit = time.time()\n",
        "\n",
        "my_forest_res_train = my_forest.predict(X_train)\n",
        "print ('my forest precision on train', precision_score(my_forest_res_train, Y_train.to_numpy()))\n",
        "print ('my forest recall on train', recall_score(my_forest_res_train, Y_train.to_numpy()))\n",
        "#pd.set_option('display.max_rows', None)\n",
        "\n",
        "start_pred = time.time()\n",
        "my_forest_res_test = my_forest.predict(X_test)\n",
        "end_pred = time.time()\n",
        "\n",
        "print ('my forest fit time: ', end_fit - start_fit, ', my forest predict time: ', end_pred - start_pred)\n",
        "\n",
        "df = pd.DataFrame({'id': test_data.id, 'claim': my_forest_res_test})\n",
        "df.to_csv('my_forest_result.csv', index=False)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "size of sample:  50000\n",
            "doint boots\n",
            "my forest precision on train 0.4480315594557604\n",
            "my forest recall on train 0.5267641629987221\n",
            "my forest fit time:  2.1636931896209717 , my forest predict time:  0.2944481372833252\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0t6VCDyCXWn_",
        "outputId": "0057a00b-a047-4384-ff2a-4ae20eb4d306"
      },
      "source": [
        "clf = RandomForestClassifier(max_depth=DEPTH,\n",
        "                             random_state=RANDOM_STATE,\n",
        "                             n_estimators=ESIMATORS,\n",
        "                             min_samples_leaf=MIN_SAMPLES_LEAF,\n",
        "                             max_features=MAX_FEATURES,\n",
        "                             criterion=CRITERION,\n",
        "                             min_weight_fraction_leaf=MIN_WEIGHT_FRACTION_LEAF,\n",
        "                             min_samples_split=MIN_SAMPLES_SPLIT,\n",
        "                             min_impurity_decrease=MIN_IMPURITY_DECREASE,\n",
        "                             min_impurity_split=MIN_IMPURITY_SPLIT,\n",
        "                             max_leaf_nodes=MAX_LEAF_NODES,\n",
        "                             max_samples=MAX_SAMPLES)\n",
        "\n",
        "start_fit = time.time()\n",
        "clf.fit(X_train, Y_train.values.ravel())\n",
        "end_fit = time.time()\n",
        "\n",
        "sklearn_res_train = clf.predict(X_train)\n",
        "print ('sklearn forest precision on train', precision_score(sklearn_res_train, Y_train.to_numpy()))\n",
        "print ('sklearn forest recall on train', recall_score(sklearn_res_train, Y_train.to_numpy()))\n",
        "#pd.set_option('display.max_rows', None)\n",
        "\n",
        "start_pred = time.time()\n",
        "sklearn_res_test = clf.predict(X_test)\n",
        "end_pred = time.time()\n",
        "\n",
        "print ('sklearn forest fit time: ', end_fit - start_fit, ', sklearn forest predict time: ', end_pred - start_pred)\n",
        "\n",
        "df = pd.DataFrame({'id': test_data.id, 'claim': sklearn_res_test})\n",
        "df.to_csv('sklearn_forest_result.csv', index=False)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sklearn forest precision on train 0.5701634328959021\n",
            "sklearn forest recall on train 0.546029298380879\n",
            "sklearn forest fit time:  1.2638556957244873 , sklearn forest predict time:  0.044704437255859375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkh_7Pa8Yyig",
        "outputId": "dab5f150-0326-4203-8c27-2bbf6f3f4834"
      },
      "source": [
        "Lgb = LGBMClassifier(boosting_type='goss',\n",
        "                     random_state=RANDOM_STATE,\n",
        "                     n_estimators=ESIMATORS,\n",
        "                     max_depth=DEPTH,\n",
        "                     min_child_samples=MIN_SAMPLES_LEAF,\n",
        "                     max_features=MAX_FEATURES,\n",
        "                     min_child_weight=MIN_WEIGHT_FRACTION_LEAF,\n",
        "                     min_split_gain=MIN_IMPURITY_SPLIT,\n",
        "                     subsample_for_bin=MIN_SAMPLES_LEAF)\n",
        "\n",
        "start_fit = time.time()\n",
        "fit_model = Lgb.fit(X_train, Y_train.values.ravel())\n",
        "end_fit = time.time()\n",
        "\n",
        "LGBM_res_train = fit_model.predict(X_train)\n",
        "print ('LGBM forest precision on train', precision_score(LGBM_res_train, Y_train.to_numpy()))\n",
        "print ('LGBM forest recall on train', recall_score(LGBM_res_train, Y_train.to_numpy()))\n",
        "\n",
        "start_pred = time.time()\n",
        "LGBM_res_test = fit_model.predict(X_test)\n",
        "end_pred = time.time()\n",
        "\n",
        "print ('LGBM forest fit time: ', end_fit - start_fit, ', LGBM forest predict time: ', end_pred - start_pred)\n",
        "\n",
        "df = pd.DataFrame({'id': test_data.id, 'claim': LGBM_res_test})\n",
        "df.to_csv('LGBM_goss_forest_result.csv', index=False)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LGBM forest precision on train 0.45165445616294986\n",
            "LGBM forest recall on train 0.541218465100574\n",
            "LGBM forest fit time:  0.3335893154144287 , LGBM forest predict time:  0.16802477836608887\n"
          ]
        }
      ]
    }
  ]
}