Autonomous_driving_antenna/lgbm+xgb+rf.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"lgbm+xgb+rf.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPjXERe6a+1aXi+i1LiMwrb"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"BANoEhcwZWpR"},"outputs":[],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":["!pip install flaml\n","!pip install catboost"],"metadata":{"id":"mtWIwz6vZiRA","executionInfo":{"status":"aborted","timestamp":1661932838977,"user_tz":-540,"elapsed":8,"user":{"displayName":"서영","userId":"08358393563291031311"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 0. Import Packages"],"metadata":{"id":"i1uUH-C5F7PO"}},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import matplotlib\n","import matplotlib.pyplot as plt\n","import sklearn\n","import sys\n","import seaborn as sns\n","import random as rn\n","import os\n","import scipy.stats as stats\n","from sklearn.preprocessing import LabelEncoder\n","from sklearn.preprocessing import OneHotEncoder\n","\n","from sklearn.model_selection import GridSearchCV, cross_val_score, RepeatedKFold\n","from sklearn import metrics\n","\n","from sklearn.linear_model import ElasticNet\n","from xgboost import XGBRegressor\n","from lightgbm import LGBMRegressor\n","from catboost import CatBoostRegressor\n","from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor\n","from mlxtend.regressor import StackingCVRegressor\n","from sklearn.multioutput import MultiOutputRegressor\n","\n","from collections import Counter\n","from flaml import AutoML\n","\n","import warnings\n","%matplotlib inline\n","warnings.filterwarnings(action='ignore')"],"metadata":{"id":"IST5EW-SZjHA","executionInfo":{"status":"aborted","timestamp":1661932838977,"user_tz":-540,"elapsed":7,"user":{"displayName":"서영","userId":"08358393563291031311"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["%cd '/content/drive/MyDrive/Autonomous_driving_antenna/'"],"metadata":{"id":"DEB1OuxfZxc-","executionInfo":{"status":"aborted","timestamp":1661932838978,"user_tz":-540,"elapsed":8,"user":{"displayName":"서영","userId":"08358393563291031311"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 1. Load dataset"],"metadata":{"id":"w9Yq69FiF-M-"}},{"cell_type":"code","source":["# reproducibility\n","def set_seed(seed_num):\n","  # tf.random.set_seed(seed_num)\n","  np.random.seed(seed_num)\n","  rn.seed(seed_num)\n","  os.environ['PYTHONHASHSEED']=str(seed_num)\n","\n","seed_num = 42\n","set_seed(seed_num)"],"metadata":{"id":"PVp2m91QZxe4","executionInfo":{"status":"aborted","timestamp":1661932838978,"user_tz":-540,"elapsed":7,"user":{"displayName":"서영","userId":"08358393563291031311"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train = pd.read_csv('dataset/train.csv')\n","\n","train.head()"],"metadata":{"id":"e7jgc__pZxgr","executionInfo":{"status":"aborted","timestamp":1661932838978,"user_tz":-540,"elapsed":7,"user":{"displayName":"서영","userId":"08358393563291031311"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_x_df = train.filter(regex='X')   # input\n","train_y_df = train.filter(regex='Y')   # output"],"metadata":{"id":"7TwM53bhaEuQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_x_df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"id":"vARqkbgLaRGs","executionInfo":{"status":"ok","timestamp":1661070535100,"user_tz":-540,"elapsed":21,"user":{"displayName":"서영","userId":"08358393563291031311"}},"outputId":"50fbff1d-d638-4adb-ce30-b6a1a08c3afc"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     X_01     X_02   X_03  X_04     X_05    X_06   X_07    X_08    X_09  X_10  \\\n","0  70.544  103.320  67.47     1  101.892  74.983  29.45   62.38  245.71   0.0   \n","1  69.524  103.321  65.17     1  101.944  72.943  28.73   61.23  233.61   0.0   \n","2  72.583  103.320  64.07     1  103.153  72.943  28.81  105.77  272.20   0.0   \n","3  71.563  103.320  67.57     1  101.971  77.022  28.92  115.21  255.36   0.0   \n","4  69.524  103.320  63.57     1  101.981  70.904  29.68  103.38  241.46   0.0   \n","\n","   ...  X_47  X_48      X_49        X_50        X_51        X_52        X_53  \\\n","0  ...     1     1   9706.03  137.043591  135.359219  147.837968  134.313475   \n","1  ...     1     1  10423.43  133.736691  135.979817  149.924692  123.630583   \n","2  ...     1     1  10948.53  132.805112  131.055355  146.814592  128.939070   \n","3  ...     1     1  15007.03  134.138760  133.239422  139.720132  132.260824   \n","4  ...     1     1  11051.03  142.728970  136.620022  134.853555  134.760252   \n","\n","         X_54        X_55        X_56  \n","0  125.605427  136.721425  125.028256  \n","1  127.893337  143.322659  124.877308  \n","2  127.012195  140.395688  122.238232  \n","3  130.723186  147.624829  134.875225  \n","4  125.647793  139.331105  123.272762  \n","\n","[5 rows x 56 columns]"],"text/html":["\n","  <div id=\"df-79999808-5c2e-4cc5-832f-7f9584ffcea3\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","    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<td>127.893337</td>\n","      <td>143.322659</td>\n","      <td>124.877308</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>72.583</td>\n","      <td>103.320</td>\n","      <td>64.07</td>\n","      <td>1</td>\n","      <td>103.153</td>\n","      <td>72.943</td>\n","      <td>28.81</td>\n","      <td>105.77</td>\n","      <td>272.20</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>10948.53</td>\n","      <td>132.805112</td>\n","      <td>131.055355</td>\n","      <td>146.814592</td>\n","      <td>128.939070</td>\n","      <td>127.012195</td>\n","      <td>140.395688</td>\n","      <td>122.238232</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>71.563</td>\n","      <td>103.320</td>\n","      <td>67.57</td>\n","      <td>1</td>\n","      <td>101.971</td>\n","      <td>77.022</td>\n","      <td>28.92</td>\n","      <td>115.21</td>\n","      <td>255.36</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>15007.03</td>\n","      <td>134.138760</td>\n","      <td>133.239422</td>\n","      <td>139.720132</td>\n","      <td>132.260824</td>\n","      <td>130.723186</td>\n","      <td>147.624829</td>\n","      <td>134.875225</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>69.524</td>\n","      <td>103.320</td>\n","      <td>63.57</td>\n","      <td>1</td>\n","      <td>101.981</td>\n","      <td>70.904</td>\n","      <td>29.68</td>\n","      <td>103.38</td>\n","      <td>241.46</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>11051.03</td>\n","      <td>142.728970</td>\n","      <td>136.620022</td>\n","      <td>134.853555</td>\n","      <td>134.760252</td>\n","      <td>125.647793</td>\n","      <td>139.331105</td>\n","      <td>123.272762</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 56 columns</p>\n","</div>\n","      <button 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"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["train_y_df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"Ol8RJ4GXaSMS","executionInfo":{"status":"ok","timestamp":1661070535101,"user_tz":-540,"elapsed":21,"user":{"displayName":"서영","userId":"08358393563291031311"}},"outputId":"d01aa99c-e0ae-4463-97a4-f2dd2597347d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Y_01   Y_02   Y_03    Y_04    Y_05    Y_06   Y_07    Y_08    Y_09    Y_10  \\\n","0  2.056  1.456  1.680  10.502  29.632  16.083  4.276 -25.381 -25.529 -22.769   \n","1  1.446  1.184  1.268  18.507  33.179  16.736  3.229 -26.619 -26.523 -22.574   \n","2  1.251  0.665  0.782  14.082  31.801  17.080  2.839 -26.238 -26.216 -22.169   \n","3  1.464  1.079  1.052  16.975  34.503  17.143  3.144 -25.426 -25.079 -21.765   \n","4  0.983  0.646  0.689  15.047  32.602  17.569  3.138 -25.376 -25.242 -21.072   \n","\n","     Y_11    Y_12    Y_13    Y_14  \n","0  23.792 -25.470 -25.409 -25.304  \n","1  24.691 -26.253 -26.497 -26.438  \n","2  24.649 -26.285 -26.215 -26.370  \n","3  24.913 -25.254 -25.021 -25.345  \n","4  25.299 -25.072 -25.195 -24.974  "],"text/html":["\n","  <div id=\"df-b949fc83-1faf-4df6-9cca-8cccf7031549\">\n","    <div class=\"colab-df-container\">\n","      <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>Y_01</th>\n","      <th>Y_02</th>\n","      <th>Y_03</th>\n","      <th>Y_04</th>\n","      <th>Y_05</th>\n","      <th>Y_06</th>\n","      <th>Y_07</th>\n","      <th>Y_08</th>\n","      <th>Y_09</th>\n","      <th>Y_10</th>\n","      <th>Y_11</th>\n","      <th>Y_12</th>\n","      <th>Y_13</th>\n","      <th>Y_14</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2.056</td>\n","      <td>1.456</td>\n","      <td>1.680</td>\n","      <td>10.502</td>\n","      <td>29.632</td>\n","      <td>16.083</td>\n","      <td>4.276</td>\n","      <td>-25.381</td>\n","      <td>-25.529</td>\n","      <td>-22.769</td>\n","      <td>23.792</td>\n","      <td>-25.470</td>\n","      <td>-25.409</td>\n","      <td>-25.304</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.446</td>\n","      <td>1.184</td>\n","      <td>1.268</td>\n","      <td>18.507</td>\n","      <td>33.179</td>\n","      <td>16.736</td>\n","      <td>3.229</td>\n","      <td>-26.619</td>\n","      <td>-26.523</td>\n","      <td>-22.574</td>\n","      <td>24.691</td>\n","      <td>-26.253</td>\n","      <td>-26.497</td>\n","      <td>-26.438</td>\n","    </tr>\n","    <tr>\n","   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    <td>3.138</td>\n","      <td>-25.376</td>\n","      <td>-25.242</td>\n","      <td>-21.072</td>\n","      <td>25.299</td>\n","      <td>-25.072</td>\n","      <td>-25.195</td>\n","      <td>-24.974</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b949fc83-1faf-4df6-9cca-8cccf7031549')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-b949fc83-1faf-4df6-9cca-8cccf7031549');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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Modeling - fastAutoml"],"metadata":{"id":"-ZfSVGiAGBPu"}},{"cell_type":"code","source":["# MODEL_TIME_BUDGET = 60*5\n","# MODEL_METRIC = 'rmse'\n","# MODEL_TASK = \"regression\"\n","# MODEL_LIST = [\"lgbm\"]\n","\n","# params = {\n","#     \"time_budget\": MODEL_TIME_BUDGET,  \n","#     \"metric\": MODEL_METRIC,\n","#     \"estimator_list\": MODEL_LIST, \n","#     \"task\": MODEL_TASK,\n","#     \"seed\":seed_num,\n","# }\n","\n","# auto_lgbm = MultiOutputRegressor(AutoML(**params))\n","# auto_lgbm.fit(train_x, train_y)"],"metadata":{"id":"-Ovk6BQAaiq0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# MODEL_TIME_BUDGET = 60*5\n","# MODEL_METRIC = 'rmse'\n","# MODEL_TASK = \"regression\"\n","# MODEL_LIST = [\"catboost\"]\n","\n","# params = {\n","#     \"time_budget\": MODEL_TIME_BUDGET,  \n","#     \"metric\": MODEL_METRIC,\n","#     \"estimator_list\": MODEL_LIST, \n","#     \"task\": MODEL_TASK,\n","#     \"seed\":seed_num,\n","# }\n","\n","# auto_cat = MultiOutputRegressor(AutoML(**params))\n","# auto_cat.fit(train_x, train_y)"],"metadata":{"id":"vqCAfaA8ais0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# MODEL_TIME_BUDGET = 60*5\n","# MODEL_METRIC = 'rmse'\n","# MODEL_TASK = \"regression\"\n","# MODEL_LIST = [\"xgboost\"]\n","\n","# params = {\n","#     \"time_budget\": MODEL_TIME_BUDGET,  \n","#     \"metric\": MODEL_METRIC,\n","#     \"estimator_list\": MODEL_LIST, \n","#     \"task\": MODEL_TASK,\n","#     \"seed\":seed_num,\n","# }\n","\n","# auto_xgb = MultiOutputRegressor(AutoML(**params))\n","# auto_xgb.fit(train_x, train_y)"],"metadata":{"id":"loTdpk0Aaiui"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# MODEL_TIME_BUDGET = 60*5\n","# MODEL_METRIC = 'rmse'\n","# MODEL_TASK = \"regression\"\n","# MODEL_LIST = [\"rf\"]\n","\n","# params = {\n","#     \"time_budget\": MODEL_TIME_BUDGET,  \n","#     \"metric\": MODEL_METRIC,\n","#     \"estimator_list\": MODEL_LIST, \n","#     \"task\": MODEL_TASK,\n","#     \"seed\":seed_num,\n","# }\n","\n","# auto_rf = MultiOutputRegressor(AutoML(**params))\n","# auto_rf.fit(train_x, train_y)"],"metadata":{"id":"VFtxgA3PdDdY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# MODEL_TIME_BUDGET = 60*5\n","# MODEL_METRIC = 'rmse'\n","# MODEL_TASK = \"regression\"\n","# MODEL_LIST = [\"extra_tree\"]\n","\n","# params = {\n","#     \"time_budget\": MODEL_TIME_BUDGET,  \n","#     \"metric\": MODEL_METRIC,\n","#     \"estimator_list\": MODEL_LIST, \n","#     \"task\": MODEL_TASK,\n","#     \"seed\":seed_num,\n","# }\n","\n","# auto_ext = MultiOutputRegressor(AutoML(**params))\n","# auto_ext.fit(train_x, train_y)"],"metadata":{"id":"0V65MtyydHsD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["lightgbm = MultiOutputRegressor(LGBMRegressor(colsample_bytree=0.5263478696871312,\n","              learning_rate=0.01522625637146587, max_bin=1023,\n","              min_child_samples=23, n_estimators=2950, num_leaves=434,\n","              reg_alpha=0.013442380441791843, reg_lambda=0.0440062359548784, seed = seed_num,\n","              verbose=-1))\n","\n","lightgbm.fit(train_x, train_y)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ngkPBnPUVqcN","executionInfo":{"status":"ok","timestamp":1661072610858,"user_tz":-540,"elapsed":2075770,"user":{"displayName":"서영","userId":"08358393563291031311"}},"outputId":"fba6ebc1-9017-4350-b2c4-f1ac475a12d8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["MultiOutputRegressor(estimator=LGBMRegressor(colsample_bytree=0.5263478696871312,\n","                                             learning_rate=0.01522625637146587,\n","                                             max_bin=1023, min_child_samples=23,\n","                                             n_estimators=2950, num_leaves=434,\n","                                             reg_alpha=0.013442380441791843,\n","                                             reg_lambda=0.0440062359548784,\n","                                             seed=42, verbose=-1))"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["xgboost = MultiOutputRegressor(XGBRegressor(colsample_bylevel=0.4192302659044743,\n","             colsample_bytree=0.7180716573534248, grow_policy='lossguide',\n","             learning_rate=0.013198731967539607, max_depth=0, max_leaves=140,\n","             min_child_weight=35.072279762160896, n_estimators=803, n_jobs=-1,\n","             reg_alpha=0.0009765625, reg_lambda=0.6342055478585719,\n","             subsample=0.827351358517848, tree_method='hist', seed = seed_num,\n","             use_label_encoder=False, verbosity=0))\n","\n","xgboost.fit(train_x, train_y)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GCx9htZOWBec","executionInfo":{"status":"ok","timestamp":1661072925486,"user_tz":-540,"elapsed":314631,"user":{"displayName":"서영","userId":"08358393563291031311"}},"outputId":"9784979c-3eea-4d83-e1a3-28249b001de0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["MultiOutputRegressor(estimator=XGBRegressor(colsample_bylevel=0.4192302659044743,\n","                                            colsample_bytree=0.7180716573534248,\n","                                            grow_policy='lossguide',\n","                                            learning_rate=0.013198731967539607,\n","                                            max_depth=0, max_leaves=140,\n","                                            min_child_weight=35.072279762160896,\n","                                            n_estimators=803, n_jobs=-1,\n","                                            reg_alpha=0.0009765625,\n","                                            reg_lambda=0.6342055478585719,\n","                                            seed=42,\n","                                            subsample=0.827351358517848,\n","                                            tree_method='hist',\n","                                            use_label_encoder=False,\n","                                            verbosity=0))"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["rf = MultiOutputRegressor(RandomForestRegressor(max_features=0.6347607006852164, max_leaf_nodes=1608,\n","                      n_estimators=136, n_jobs=-1, random_state = seed_num))\n","\n","rf.fit(train_x, train_y)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zJuYWV1pWBWr","executionInfo":{"status":"ok","timestamp":1661073817819,"user_tz":-540,"elapsed":892346,"user":{"displayName":"서영","userId":"08358393563291031311"}},"outputId":"13b825a7-164a-48ea-fbb7-bb38c4330f40"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["MultiOutputRegressor(estimator=RandomForestRegressor(max_features=0.6347607006852164,\n","                                                     max_leaf_nodes=1608,\n","                                                     n_estimators=136,\n","                                                     n_jobs=-1,\n","                                                     random_state=42))"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["# ext = MultiOutputRegressor(ExtraTreesRegressor(max_features=0.5788805596313885, max_leaf_nodes=4210,\n","#                     n_estimators=318, n_jobs=-1, random_state = seed_num))\n","\n","# ext.fit(train_x, train_y)"],"metadata":{"id":"BSNPmPtoWBMc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661074422463,"user_tz":-540,"elapsed":604647,"user":{"displayName":"서영","userId":"08358393563291031311"}},"outputId":"7803a601-ba68-41c8-984f-67b331b2cfaa"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["MultiOutputRegressor(estimator=ExtraTreesRegressor(max_features=0.5788805596313885,\n","                                                   max_leaf_nodes=4210,\n","                                                   n_estimators=318, n_jobs=-1,\n","                                                   random_state=42))"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["# stack_reg = MultiOutputRegressor(StackingCVRegressor(regressors=(lightgbm, xgboost, rf, ext),\n","#                                 meta_regressor = xgboost,\n","#                                 use_features_in_secondary=True))\n","\n","# stack_reg.fit(train_x, train_y)"],"metadata":{"id":"hLOy4xV7FLxW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test_df = pd.read_csv('dataset/test.csv')\n","\n","print(test_df.shape)\n","test_df.head()"],"metadata":{"id":"wNM2v3xMFbl8","colab":{"base_uri":"https://localhost:8080/","height":254},"executionInfo":{"status":"ok","timestamp":1661074424508,"user_tz":-540,"elapsed":2051,"user":{"displayName":"서영","userId":"08358393563291031311"}},"outputId":"b8fa6fd8-a0e8-43c7-9b78-6b0baac333ad"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(39608, 57)\n"]},{"output_type":"execute_result","data":{"text/plain":["           ID    X_01     X_02   X_03  X_04     X_05    X_06   X_07    X_08  \\\n","0  TEST_00001  68.504  103.321  76.67     1  101.867  73.963  30.51   63.57   \n","1  TEST_00002  67.485  103.320  69.37     1  101.992  67.845  28.03  116.99   \n","2  TEST_00003  69.524  103.320  68.97     1  101.884  77.022  29.65  205.68   \n","3  TEST_00004  69.524  103.320  65.87     1  101.866  73.963  28.15  103.38   \n","4  TEST_00005  73.603  103.321  66.67     1  101.891  74.983  29.92   71.20   \n","\n","     X_09  ...  X_47  X_48      X_49        X_50        X_51        X_52  \\\n","0  239.80  ...     1     1  17227.63  138.130429  129.460682  141.506570   \n","1  189.23  ...     1     1  17134.53  136.148839  128.266277  145.911745   \n","2  214.93  ...     1     1  14860.83  120.447446  119.988804  132.099908   \n","3  180.80  ...     1     1  15252.53  133.994695  125.069180  147.507669   \n","4  231.93  ...     1     1  10752.23  137.918202  135.116192  138.600473   \n","\n","         X_53        X_54        X_55        X_56  \n","0  133.427229  129.711498  133.138096  121.859684  \n","1  131.196417  132.411480  133.629025  124.178623  \n","2  120.450155  130.051708  128.252972  114.475628  \n","3  123.142653  125.963665  139.666592  126.589253  \n","4  127.173033  137.252712  134.411335  124.020016  \n","\n","[5 rows x 57 columns]"],"text/html":["\n","  <div id=\"df-1ccbdf9a-d1f6-4f4e-8378-c1486b933ba8\">\n","    <div class=\"colab-df-container\">\n","      <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>ID</th>\n","      <th>X_01</th>\n","      <th>X_02</th>\n","      <th>X_03</th>\n","      <th>X_04</th>\n","      <th>X_05</th>\n","      <th>X_06</th>\n","      <th>X_07</th>\n","      <th>X_08</th>\n","      <th>X_09</th>\n","      <th>...</th>\n","      <th>X_47</th>\n","      <th>X_48</th>\n","      <th>X_49</th>\n","      <th>X_50</th>\n","      <th>X_51</th>\n","      <th>X_52</th>\n","      <th>X_53</th>\n","      <th>X_54</th>\n","      <th>X_55</th>\n","      <th>X_56</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>TEST_00001</td>\n","      <td>68.504</td>\n","      <td>103.321</td>\n","      <td>76.67</td>\n","      <td>1</td>\n","      <td>101.867</td>\n","      <td>73.963</td>\n","      <td>30.51</td>\n","      <td>63.57</td>\n","      <td>239.80</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>17227.63</td>\n","      <td>138.130429</td>\n","      <td>129.460682</td>\n","      <td>141.506570</td>\n","      <td>133.427229</td>\n","      <td>129.711498</td>\n","      <td>133.138096</td>\n","      <td>121.859684</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>TEST_00002</td>\n","      <td>67.485</td>\n","      <td>103.320</td>\n","      <td>69.37</td>\n","      <td>1</td>\n","      <td>101.992</td>\n","      <td>67.845</td>\n","      <td>28.03</td>\n","      <td>116.99</td>\n","      <td>189.23</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>17134.53</td>\n","      <td>136.148839</td>\n","      <td>128.266277</td>\n","      <td>145.911745</td>\n","      <td>131.196417</td>\n","      <td>132.411480</td>\n","      <td>133.629025</td>\n","      <td>124.178623</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>TEST_00003</td>\n","      <td>69.524</td>\n","      <td>103.320</td>\n","      <td>68.97</td>\n","      <td>1</td>\n","      <td>101.884</td>\n","      <td>77.022</td>\n","      <td>29.65</td>\n","      <td>205.68</td>\n","      <td>214.93</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>14860.83</td>\n","      <td>120.447446</td>\n","      <td>119.988804</td>\n","      <td>132.099908</td>\n","      <td>120.450155</td>\n","      <td>130.051708</td>\n","      <td>128.252972</td>\n","      <td>114.475628</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>TEST_00004</td>\n","      <td>69.524</td>\n","      <td>103.320</td>\n","      <td>65.87</td>\n","      <td>1</td>\n","      <td>101.866</td>\n","      <td>73.963</td>\n","      <td>28.15</td>\n","      <td>103.38</td>\n","      <td>180.80</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>15252.53</td>\n","      <td>133.994695</td>\n","      <td>125.069180</td>\n","      <td>147.507669</td>\n","      <td>123.142653</td>\n","      <td>125.963665</td>\n","      <td>139.666592</td>\n","      <td>126.589253</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>TEST_00005</td>\n","      <td>73.603</td>\n","      <td>103.321</td>\n","      <td>66.67</td>\n","      <td>1</td>\n","      <td>101.891</td>\n","      <td>74.983</td>\n","      <td>29.92</td>\n","      <td>71.20</td>\n","      <td>231.93</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>10752.23</td>\n","      <td>137.918202</td>\n","      <td>135.116192</td>\n","      <td>138.600473</td>\n","      <td>127.173033</td>\n","      <td>137.252712</td>\n","      <td>134.411335</td>\n","      <td>124.020016</td>\n","    </tr>\n","  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