Real_data_planet_search_xgboost_TSFresh_v2_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "YiJV1gMuKYZZ",
    "outputId": "1526b64f-b847-4fb1-bdba-0440add040bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "#Importing the required libraries\n",
    "##########################################################################################\n",
    "!pip install lightkurve\n",
    "import lightkurve as lk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, mean_squared_error, roc_curve, auc, roc_auc_score\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler, RobustScaler\n",
    "from xgboost import XGBClassifier, DMatrix, XGBRFClassifier\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import StratifiedKFold, train_test_split\n",
    "#import shap\n",
    "from ipywidgets.widgets.interaction import show_inline_matplotlib_plots\n",
    "from IPython.display import Markdown, display, clear_output\n",
    "import ipywidgets as widgets\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
    "\n",
    "def printmd(string):\n",
    "    display(Markdown(string))\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Khow24vF5CSI"
   },
   "source": [
    "# Data loading and preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "colab_type": "code",
    "id": "BPwdFjvOKYZe",
    "outputId": "94de0c41-2f58-42dc-8192-d6182764f7c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12589, 752)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th>Data__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"stderr\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"intercept\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"rvalue\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"slope\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"mean\"__chunk_len_10__attr_\"stderr\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"intercept\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"rvalue\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"slope\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"stderr\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"intercept\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"rvalue\"</th>\n",
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       "      <th>Data__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"stderr\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"intercept\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"rvalue\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"slope\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"min\"__chunk_len_10__attr_\"stderr\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"intercept\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"rvalue\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"slope\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"stderr\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"intercept\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"rvalue\"</th>\n",
       "      <th>Data__agg_linear_trend__f_agg_\"min\"__chunk_len_5__attr_\"slope\"</th>\n",
       "      <th>...</th>\n",
       "      <th>Data__ratio_beyond_r_sigma__r_7</th>\n",
       "      <th>Data__ratio_value_number_to_time_series_length</th>\n",
       "      <th>Data__skewness</th>\n",
       "      <th>Data__spkt_welch_density__coeff_2</th>\n",
       "      <th>Data__spkt_welch_density__coeff_5</th>\n",
       "      <th>Data__spkt_welch_density__coeff_8</th>\n",
       "      <th>Data__standard_deviation</th>\n",
       "      <th>Data__sum_of_reoccurring_data_points</th>\n",
       "      <th>Data__sum_of_reoccurring_values</th>\n",
       "      <th>Data__sum_values</th>\n",
       "      <th>Data__symmetry_looking__r_0.0</th>\n",
       "      <th>Data__symmetry_looking__r_0.05</th>\n",
       "      <th>Data__symmetry_looking__r_0.1</th>\n",
       "      <th>Data__symmetry_looking__r_0.15000000000000002</th>\n",
       "      <th>Data__symmetry_looking__r_0.2</th>\n",
       "      <th>Data__symmetry_looking__r_0.25</th>\n",
       "      <th>Data__symmetry_looking__r_0.30000000000000004</th>\n",
       "      <th>Data__symmetry_looking__r_0.35000000000000003</th>\n",
       "      <th>Data__symmetry_looking__r_0.4</th>\n",
       "      <th>Data__symmetry_looking__r_0.45</th>\n",
       "      <th>Data__symmetry_looking__r_0.5</th>\n",
       "      <th>Data__symmetry_looking__r_0.55</th>\n",
       "      <th>Data__symmetry_looking__r_0.6000000000000001</th>\n",
       "      <th>Data__symmetry_looking__r_0.65</th>\n",
       "      <th>Data__symmetry_looking__r_0.7000000000000001</th>\n",
       "      <th>Data__symmetry_looking__r_0.75</th>\n",
       "      <th>Data__symmetry_looking__r_0.8</th>\n",
       "      <th>Data__symmetry_looking__r_0.8500000000000001</th>\n",
       "      <th>Data__symmetry_looking__r_0.9</th>\n",
       "      <th>Data__symmetry_looking__r_0.9500000000000001</th>\n",
       "      <th>Data__time_reversal_asymmetry_statistic__lag_1</th>\n",
       "      <th>Data__time_reversal_asymmetry_statistic__lag_2</th>\n",
       "      <th>Data__time_reversal_asymmetry_statistic__lag_3</th>\n",
       "      <th>Data__value_count__value_-1</th>\n",
       "      <th>Data__value_count__value_0</th>\n",
       "      <th>Data__value_count__value_1</th>\n",
       "      <th>Data__variance</th>\n",
       "      <th>Data__variance_larger_than_standard_deviation</th>\n",
       "      <th>label</th>\n",
       "      <th>planet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.836808</td>\n",
       "      <td>180.368064</td>\n",
       "      <td>0.006931</td>\n",
       "      <td>-0.023346</td>\n",
       "      <td>0.009302</td>\n",
       "      <td>0.119372</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>7.399677e-07</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.204087</td>\n",
       "      <td>-0.358048</td>\n",
       "      <td>-0.001439</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.085393</td>\n",
       "      <td>0.034307</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>-0.009969</td>\n",
       "      <td>0.068441</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>-0.010621</td>\n",
       "      <td>0.273524</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>-0.009879</td>\n",
       "      <td>0.043619</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>-0.132618</td>\n",
       "      <td>0.008079</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>-0.240908</td>\n",
       "      <td>0.068830</td>\n",
       "      <td>0.001060</td>\n",
       "      <td>0.002461</td>\n",
       "      <td>-0.100776</td>\n",
       "      <td>0.010962</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004498</td>\n",
       "      <td>0.713643</td>\n",
       "      <td>-2.726371</td>\n",
       "      <td>0.075655</td>\n",
       "      <td>0.074039</td>\n",
       "      <td>0.102834</td>\n",
       "      <td>0.099449</td>\n",
       "      <td>11.439050</td>\n",
       "      <td>4.096960</td>\n",
       "      <td>-9.674587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000035</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000065</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009890</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135.747636</td>\n",
       "      <td>492.412377</td>\n",
       "      <td>0.252648</td>\n",
       "      <td>0.255756</td>\n",
       "      <td>0.001248</td>\n",
       "      <td>0.328603</td>\n",
       "      <td>-0.031177</td>\n",
       "      <td>-1.010275e-04</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.486178</td>\n",
       "      <td>-0.020211</td>\n",
       "      <td>-0.000246</td>\n",
       "      <td>0.001949</td>\n",
       "      <td>0.254273</td>\n",
       "      <td>-0.038063</td>\n",
       "      <td>-0.000066</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>-0.000598</td>\n",
       "      <td>-0.029762</td>\n",
       "      <td>-0.000082</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>-0.018329</td>\n",
       "      <td>0.072409</td>\n",
       "      <td>0.000936</td>\n",
       "      <td>0.002063</td>\n",
       "      <td>0.001826</td>\n",
       "      <td>-0.039588</td>\n",
       "      <td>-0.000059</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>-0.337234</td>\n",
       "      <td>-0.008767</td>\n",
       "      <td>-0.000030</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>-0.520181</td>\n",
       "      <td>0.065678</td>\n",
       "      <td>0.001297</td>\n",
       "      <td>0.003154</td>\n",
       "      <td>-0.261848</td>\n",
       "      <td>-0.003727</td>\n",
       "      <td>-0.000007</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.698651</td>\n",
       "      <td>-0.302504</td>\n",
       "      <td>0.441784</td>\n",
       "      <td>0.078115</td>\n",
       "      <td>0.038754</td>\n",
       "      <td>0.260232</td>\n",
       "      <td>50.600835</td>\n",
       "      <td>18.322207</td>\n",
       "      <td>-21.843300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000034</td>\n",
       "      <td>-0.000837</td>\n",
       "      <td>0.001145</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.067721</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AFP</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>158.534414</td>\n",
       "      <td>591.002920</td>\n",
       "      <td>-0.010915</td>\n",
       "      <td>0.004550</td>\n",
       "      <td>0.022779</td>\n",
       "      <td>0.443195</td>\n",
       "      <td>-0.045542</td>\n",
       "      <td>-1.495092e-04</td>\n",
       "      <td>0.000232</td>\n",
       "      <td>0.693278</td>\n",
       "      <td>-0.192560</td>\n",
       "      <td>-0.003648</td>\n",
       "      <td>0.002976</td>\n",
       "      <td>0.342300</td>\n",
       "      <td>-0.014722</td>\n",
       "      <td>-0.000023</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.001045</td>\n",
       "      <td>-0.061166</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.006963</td>\n",
       "      <td>-0.259066</td>\n",
       "      <td>-0.000697</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>-0.032258</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>-0.430247</td>\n",
       "      <td>-0.010670</td>\n",
       "      <td>-0.000031</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>-0.625731</td>\n",
       "      <td>0.048353</td>\n",
       "      <td>0.000702</td>\n",
       "      <td>0.002322</td>\n",
       "      <td>-0.334456</td>\n",
       "      <td>-0.041376</td>\n",
       "      <td>-0.000061</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.503748</td>\n",
       "      <td>0.020541</td>\n",
       "      <td>0.027526</td>\n",
       "      <td>0.021965</td>\n",
       "      <td>0.023308</td>\n",
       "      <td>0.281460</td>\n",
       "      <td>42.856309</td>\n",
       "      <td>7.014019</td>\n",
       "      <td>-5.546145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000054</td>\n",
       "      <td>-0.001124</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.079220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NTP</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58.897424</td>\n",
       "      <td>347.465642</td>\n",
       "      <td>0.009518</td>\n",
       "      <td>-0.006981</td>\n",
       "      <td>0.002419</td>\n",
       "      <td>0.228400</td>\n",
       "      <td>0.093086</td>\n",
       "      <td>1.720567e-04</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.401752</td>\n",
       "      <td>-0.200434</td>\n",
       "      <td>-0.001625</td>\n",
       "      <td>0.001272</td>\n",
       "      <td>0.163696</td>\n",
       "      <td>0.089594</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>-0.011863</td>\n",
       "      <td>0.075212</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>-0.010939</td>\n",
       "      <td>0.159539</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>-0.011987</td>\n",
       "      <td>0.054405</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>-0.240856</td>\n",
       "      <td>-0.029861</td>\n",
       "      <td>-0.000066</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>-0.380652</td>\n",
       "      <td>-0.051619</td>\n",
       "      <td>-0.000703</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>-0.182749</td>\n",
       "      <td>0.005407</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.924538</td>\n",
       "      <td>-0.356625</td>\n",
       "      <td>0.108820</td>\n",
       "      <td>0.139372</td>\n",
       "      <td>0.128269</td>\n",
       "      <td>0.171540</td>\n",
       "      <td>9.808013</td>\n",
       "      <td>4.791461</td>\n",
       "      <td>-5.718644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000226</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>-0.000260</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029426</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.843127</td>\n",
       "      <td>310.193344</td>\n",
       "      <td>0.008612</td>\n",
       "      <td>0.012611</td>\n",
       "      <td>0.001884</td>\n",
       "      <td>0.203791</td>\n",
       "      <td>0.013955</td>\n",
       "      <td>2.219449e-05</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.325072</td>\n",
       "      <td>-0.116735</td>\n",
       "      <td>-0.000829</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>0.150369</td>\n",
       "      <td>0.015326</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>-0.007628</td>\n",
       "      <td>0.020841</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>-0.011618</td>\n",
       "      <td>0.172277</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>-0.007047</td>\n",
       "      <td>0.008608</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>-0.224457</td>\n",
       "      <td>0.027517</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>-0.382699</td>\n",
       "      <td>0.110758</td>\n",
       "      <td>0.001532</td>\n",
       "      <td>0.002202</td>\n",
       "      <td>-0.166634</td>\n",
       "      <td>0.011655</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.594203</td>\n",
       "      <td>-0.411353</td>\n",
       "      <td>0.154716</td>\n",
       "      <td>0.051995</td>\n",
       "      <td>0.084257</td>\n",
       "      <td>0.144473</td>\n",
       "      <td>16.674524</td>\n",
       "      <td>3.559826</td>\n",
       "      <td>-12.437005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>-0.000402</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020872</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 752 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "variable  Data__abs_energy  Data__absolute_sum_of_changes  ...  label  planet\n",
       "id                                                         ...               \n",
       "0                19.836808                     180.368064  ...     PC       1\n",
       "1               135.747636                     492.412377  ...    AFP       0\n",
       "2               158.534414                     591.002920  ...    NTP       0\n",
       "3                58.897424                     347.465642  ...     PC       1\n",
       "4                41.843127                     310.193344  ...     PC       1\n",
       "\n",
       "[5 rows x 752 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the data from CSV file into a Pandas DataFrame\n",
    "##########################################################################################\n",
    "\n",
    "data = pd.read_pickle('/content/drive/My Drive/Exoplanet_Data/features_real_data.pkl')\n",
    "data_test = pd.read_pickle('/content/drive/My Drive/Exoplanet_Data/features_real_test_data.pkl')\n",
    "orig_data = pd.concat([pd.read_pickle('/content/drive/My Drive/Exoplanet_Data/train_data.pkl'), pd.read_pickle('/content/drive/My Drive/Exoplanet_Data/test_data.pkl')], ignore_index=True)\n",
    "data['planet'] = np.where(data.label == 'PC', 1, 0)\n",
    "data_test['planet'] = np.where(data_test.label == 'PC', 1, 0)\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "olEjMbK9ZA0t"
   },
   "outputs": [],
   "source": [
    "data = pd.concat([data, data_test], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VdjDgD76fsaB"
   },
   "outputs": [],
   "source": [
    "Planets = data.planet\n",
    "IDs = data.index\n",
    "df_final = data.drop(['planet', 'label'], axis=1)\n",
    "columns = df_final.columns\n",
    "df_final = pd.DataFrame(RobustScaler().fit_transform(df_final)) ## TODO: Save the Scaler!!\n",
    "df_final.columns = columns\n",
    "df_final['Planet'] = Planets #.reset_index(drop=True)\n",
    "df_final.index = IDs\n",
    "df_final = df_final.loc[:, (df_final != df_final.iloc[0]).any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WtoaVEuOji_1"
   },
   "outputs": [],
   "source": [
    "seed = 12345\n",
    "df_final = df_final.loc[:, (df_final != df_final.iloc[0]).any()]\n",
    "df_final = df_final.sample(frac=1, random_state=seed)\n",
    "y1 = df_final.Planet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Fn53fKubhMVZ"
   },
   "outputs": [],
   "source": [
    "# Splitting our data into test and training set\n",
    "##################################################################################################\n",
    "X1_train, X1_test, y1_train, y1_test = model_selection.train_test_split(df_final.iloc[:,:-1], y1, test_size=0.25, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "SJGFrPGbD4B9",
    "outputId": "b22fcb8a-084c-4af1-da3b-baebcc9ce7a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9395171537484117, 0.9786341736694678] \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.94      0.96      3060\n",
      "           1       0.82      0.93      0.87       875\n",
      "\n",
      "    accuracy                           0.94      3935\n",
      "   macro avg       0.90      0.94      0.92      3935\n",
      "weighted avg       0.94      0.94      0.94      3935\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgbc1 = XGBClassifier(objective = 'binary:logistic', n_estimators= 1000, scale_pos_weight=3.37, max_depth= 4, subsample= 0.8, colsample_bytree= 0.5, gamma= 1)\n",
    "xgbc1.fit(X1_train, y1_train)\n",
    "pred1 = xgbc1.predict(X1_test)\n",
    "y1_test_prob = xgbc1.predict_proba(X1_test)\n",
    "score1 = [accuracy_score(y1_test, pred1), roc_auc_score(y1_test, y1_test_prob[:,1])]\n",
    "report1 = classification_report(y1_test, pred1)\n",
    "\n",
    "print(score1, '\\n', report1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4K3zqPT2aHXV"
   },
   "outputs": [],
   "source": [
    "conf_matrix = confusion_matrix(y_true, y_pred2)\n",
    "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "Labels = [\"Benign\",\"Fraud\"]\n",
    "plt.figure(figsize=(12.5, 10))\n",
    "sns.set(font_scale=1.6)\n",
    "ax = sns.heatmap(conf_matrix, xticklabels=Labels, yticklabels=Labels, annot=True, annot_kws={\"size\": 20}, fmt=\".2f\", cmap=\"Blues\");\n",
    "# for t in ax.texts: t.set_text(str(round(int(t.get_text())*100,2))+'%')\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.ylabel('Predicted class')\n",
    "plt.xlabel('True class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eQdpthCIMUHC"
   },
   "source": [
    "## LGBM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_AzEPfGaK_xP",
    "outputId": "e774ee05-f588-45ea-a31a-1195faae1805"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_final.Planet\n",
    "X = df_final.iloc[:,:-1].values\n",
    "skf = StratifiedKFold(n_splits=5, shuffle = True)\n",
    "skf.get_n_splits(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "CgJxqw7bLFNN",
    "outputId": "225103e6-020f-406b-8522-c1c246fe2647"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/lightgbm/engine.py:118: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.4967531332106598\n",
      "0.7113808222659973\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.89      0.83     12137\n",
      "           1       0.22      0.10      0.14      3600\n",
      "\n",
      "    accuracy                           0.71     15737\n",
      "   macro avg       0.49      0.50      0.48     15737\n",
      "weighted avg       0.64      0.71      0.67     15737\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pp2 = {'num_iterations': 1000, 'num_leaves': 60, 'max_depth': 4, 'bagging_freq': 3, 'feature_fraction': '0.700', 'bagging_fraction': '0.800', 'objective': 'binary', 'metric': 'binary_logloss', 'scale_pos_weight': 3.37, 'seed': 12345, 'verbose': -1}\n",
    "\n",
    "y_pred = []\n",
    "y_true = []\n",
    "for train_index, test_index in skf.split(X, y):\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    d_train = lgb.Dataset(X_train, label=y_train)\n",
    "    d_test = lgb.Dataset(X_test, label=y_test)\n",
    "    clf = lgb.train(pp2, d_train)\n",
    "    y_pred.extend(clf.predict(X_test))\n",
    "    y_true.extend(y_test)\n",
    "\n",
    "y_pred = np.array(y_pred) >= 0.5\n",
    "target = roc_auc_score(y_true, y_pred)\n",
    "print(\"AUC:\", target)\n",
    "print(accuracy_score(y_true, y_pred))\n",
    "print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DU9oDHWza_ty"
   },
   "outputs": [],
   "source": [
    "df_pred = pd.DataFrame(index=df_final.index, columns=['idx', 'pred'])\n",
    "df_pred.loc[:, 'idx'] = df_final.index\n",
    "df_pred.loc[:, 'pred'] = xgbc1.predict_proba(df_final.drop(columns=['Planet']))[:,1]\n",
    "df_pred = df_pred.sort_values(by='pred', ascending=False)\n",
    "predicted_planets = df_pred[df_pred.pred>0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YICNU63LwU0n"
   },
   "outputs": [],
   "source": [
    "bst1 = xgbc1.get_booster()\n",
    "data_matrix = xgb.DMatrix(data=df_final.loc[predicted_planets.idx].drop(columns=['Planet']))\n",
    "df_temp = pd.DataFrame(bst1.predict(data_matrix, pred_contribs=True))\n",
    "df_temp = df_temp.iloc[:,:-1]\n",
    "df_temp.columns = df_final.drop(columns=['Planet']).columns\n",
    "df_temp.index = predicted_planets.idx\n",
    "predicted_planets_shaps = pd.concat([predicted_planets.set_index('idx'), df_temp], axis=1)\n",
    "\n",
    "predicted_planets_cases = df_final.loc[predicted_planets.index]\n",
    "predicted_planets_cases = pd.concat([predicted_planets[['pred']], predicted_planets_cases], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "guUqol5KwChq"
   },
   "outputs": [],
   "source": [
    "## False negatives: Sorted with prob\n",
    "case_interest = predicted_planets_cases[predicted_planets_cases.Planet==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "5a2tNlO_bhfy",
    "outputId": "09714e69-db22-49da-895b-01de5c9df46c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181, 716)"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Number of possible cases missed!\n",
    "case_interest.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall Results: Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "colab_type": "code",
    "id": "0hqg8-3ooIqZ",
    "outputId": "4a47c622-92c7-492b-fcf9-26dbfe00ba12"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "conf_matrix = confusion_matrix(y1_test, pred1)\n",
    "Labels = [\"Non-Planet\",\"Planet\"]\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.heatmap(conf_matrix, xticklabels=Labels, yticklabels=Labels, annot=True, fmt=\"d\");\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Real_data_planet_search_xgboost_TSFresh_v2.1.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  },
  "widgets": {
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       {
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        },
        "output_type": "display_data",
        "text/markdown": "### Global View for index: 0",
        "text/plain": "<IPython.core.display.Markdown object>"
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\n",
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         "tags": []
        },
        "output_type": "display_data",
        "text/plain": "<Figure size 848.5x400 with 1 Axes>"
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