{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n",
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib as mpl\n",
"import random\n",
"import math\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"from tensorflow.python.framework import ops\n",
"from sklearn import preprocessing\n",
"import pickle as pkl\n",
"from pathlib import Path\n",
"from keras.datasets import imdb\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.layers import LSTM\n",
"from keras.layers import GRU\n",
"from keras.layers import Dropout, BatchNormalization, Bidirectional\n",
"from keras.layers import ConvLSTM2D\n",
"from keras.layers import Conv1D\n",
"#from keras.layers.convolutional import Conv1D\n",
"#from keras.layers.convolutional import MaxPooling1D\n",
"from keras.layers.embeddings import Embedding\n",
"from keras.preprocessing import sequence\n",
"from keras.callbacks import History\n",
"from keras.callbacks import EarlyStopping\n",
"from keras.callbacks import ModelCheckpoint\n",
"from keras.callbacks import TensorBoard\n",
"from keras.models import load_model\n",
"import keras.backend as K\n",
"from keras.losses import mean_squared_error\n",
"\n",
"import xgboost as xgb\n",
"from xgboost import XGBClassifier\n",
"from xgboost import plot_tree\n",
"from sklearn.metrics import accuracy_score\n",
"import graphviz\n",
"\n",
"import sklearn as skl\n",
"\n",
"#import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <td>4.760000</td>\n",
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" <td>26.880571</td>\n",
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" <td>-4.840000</td>\n",
" <td>19.684010</td>\n",
" <td>-11.173258</td>\n",
" <td>-5.000000</td>\n",
" <td>220.0</td>\n",
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" <td>26.880571</td>\n",
" <td>-9.817033</td>\n",
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" <td>19.684010</td>\n",
" <td>-11.173258</td>\n",
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" <td>3.036235</td>\n",
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" <th>3</th>\n",
" <td>-20.411108</td>\n",
" <td>-9.417887</td>\n",
" <td>4.760000</td>\n",
" <td>-27.813803</td>\n",
" <td>-6.944843</td>\n",
" <td>4.760000</td>\n",
" <td>-66.736946</td>\n",
" <td>22.903200</td>\n",
" <td>4.360000</td>\n",
" <td>-74.096100</td>\n",
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" <td>-3.080000</td>\n",
" <td>29.303265</td>\n",
" <td>-2.360749</td>\n",
" <td>-1.400000</td>\n",
" <td>22.469944</td>\n",
" <td>-4.447415</td>\n",
" <td>-1.080000</td>\n",
" <td>220.0</td>\n",
" <td>25.033289</td>\n",
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" <th>4</th>\n",
" <td>-20.411108</td>\n",
" <td>-9.417887</td>\n",
" <td>4.760000</td>\n",
" <td>-27.813803</td>\n",
" <td>-6.944843</td>\n",
" <td>4.760000</td>\n",
" <td>-66.736946</td>\n",
" <td>22.903200</td>\n",
" <td>4.360000</td>\n",
" <td>-74.096100</td>\n",
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" <td>-3.080000</td>\n",
" <td>29.303265</td>\n",
" <td>-2.360749</td>\n",
" <td>-1.400000</td>\n",
" <td>22.469944</td>\n",
" <td>-4.447415</td>\n",
" <td>-1.080000</td>\n",
" <td>220.0</td>\n",
" <td>25.033289</td>\n",
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" <td>-6.018011</td>\n",
" <td>21.819384</td>\n",
" <td>-23.240000</td>\n",
" <td>-10.009998</td>\n",
" <td>28.598572</td>\n",
" <td>-23.320000</td>\n",
" <td>-25.356653</td>\n",
" <td>66.079544</td>\n",
" <td>-23.639999</td>\n",
" <td>-28.309521</td>\n",
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" <td>-6.920000</td>\n",
" <td>25.644503</td>\n",
" <td>-13.621260</td>\n",
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" <td>20.724909</td>\n",
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" <td>-6.200001</td>\n",
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" <td>21.819384</td>\n",
" <td>-23.240000</td>\n",
" <td>-10.009998</td>\n",
" <td>28.598572</td>\n",
" <td>-23.320000</td>\n",
" <td>-25.356653</td>\n",
" <td>66.079544</td>\n",
" <td>-23.639999</td>\n",
" <td>-28.309521</td>\n",
" <td>...</td>\n",
" <td>-6.920000</td>\n",
" <td>25.644503</td>\n",
" <td>-13.621260</td>\n",
" <td>-6.200001</td>\n",
" <td>20.724909</td>\n",
" <td>-8.660306</td>\n",
" <td>-6.200001</td>\n",
" <td>312.0</td>\n",
" <td>27.834667</td>\n",
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" <th>7</th>\n",
" <td>-6.018011</td>\n",
" <td>21.819384</td>\n",
" <td>-23.240000</td>\n",
" <td>-10.009998</td>\n",
" <td>28.598572</td>\n",
" <td>-23.320000</td>\n",
" <td>-25.356653</td>\n",
" <td>66.079544</td>\n",
" <td>-23.639999</td>\n",
" <td>-28.309521</td>\n",
" <td>...</td>\n",
" <td>-18.360001</td>\n",
" <td>25.490572</td>\n",
" <td>-16.829796</td>\n",
" <td>-18.040001</td>\n",
" <td>19.622780</td>\n",
" <td>-11.321079</td>\n",
" <td>-18.040001</td>\n",
" <td>312.0</td>\n",
" <td>7.292468</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>6.590816</td>\n",
" <td>21.582119</td>\n",
" <td>-40.680000</td>\n",
" <td>9.941264</td>\n",
" <td>28.127028</td>\n",
" <td>-40.840000</td>\n",
" <td>33.714462</td>\n",
" <td>61.815155</td>\n",
" <td>-41.639999</td>\n",
" <td>42.568947</td>\n",
" <td>...</td>\n",
" <td>-53.320000</td>\n",
" <td>-6.150001</td>\n",
" <td>-28.598572</td>\n",
" <td>-54.439999</td>\n",
" <td>-5.408250</td>\n",
" <td>-22.071955</td>\n",
" <td>-54.520000</td>\n",
" <td>326.0</td>\n",
" <td>10.081528</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>20.043732</td>\n",
" <td>10.304811</td>\n",
" <td>-22.680000</td>\n",
" <td>26.009144</td>\n",
" <td>12.499014</td>\n",
" <td>-22.440001</td>\n",
" <td>68.274330</td>\n",
" <td>17.165602</td>\n",
" <td>-21.240000</td>\n",
" <td>80.994446</td>\n",
" <td>...</td>\n",
" <td>-33.239998</td>\n",
" <td>-20.296682</td>\n",
" <td>-20.603376</td>\n",
" <td>-32.279999</td>\n",
" <td>-17.410870</td>\n",
" <td>-16.661102</td>\n",
" <td>-32.119999</td>\n",
" <td>352.0</td>\n",
" <td>43.565620</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>20.043732</td>\n",
" <td>10.304811</td>\n",
" <td>-22.680000</td>\n",
" <td>26.009144</td>\n",
" <td>12.499014</td>\n",
" <td>-22.440001</td>\n",
" <td>68.274330</td>\n",
" <td>17.165602</td>\n",
" <td>-21.240000</td>\n",
" <td>80.994446</td>\n",
" <td>...</td>\n",
" <td>-10.840000</td>\n",
" <td>-21.332224</td>\n",
" <td>-19.851011</td>\n",
" <td>-10.440001</td>\n",
" <td>-17.870090</td>\n",
" <td>-15.552447</td>\n",
" <td>-10.440001</td>\n",
" <td>352.0</td>\n",
" <td>16.855164</td>\n",
" <td>1.0</td>\n",
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" <tr>\n",
" <th>11</th>\n",
" <td>-16.069820</td>\n",
" <td>17.655788</td>\n",
" <td>15.719999</td>\n",
" <td>-21.218962</td>\n",
" <td>19.933302</td>\n",
" <td>15.559999</td>\n",
" <td>-57.205452</td>\n",
" <td>42.342068</td>\n",
" <td>14.440001</td>\n",
" <td>-65.752090</td>\n",
" <td>...</td>\n",
" <td>11.000000</td>\n",
" <td>27.442982</td>\n",
" <td>8.086111</td>\n",
" <td>10.280000</td>\n",
" <td>20.747869</td>\n",
" <td>8.604874</td>\n",
" <td>10.120000</td>\n",
" <td>539.0</td>\n",
" <td>16.259047</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>12</th>\n",
" <td>-6.313652</td>\n",
" <td>21.696924</td>\n",
" <td>16.039999</td>\n",
" <td>-4.489999</td>\n",
" <td>28.598572</td>\n",
" <td>15.639999</td>\n",
" <td>15.484889</td>\n",
" <td>68.724678</td>\n",
" <td>14.120000</td>\n",
" <td>23.103405</td>\n",
" <td>...</td>\n",
" <td>28.520000</td>\n",
" <td>-7.870180</td>\n",
" <td>-29.631758</td>\n",
" <td>31.000000</td>\n",
" <td>-9.177679</td>\n",
" <td>-20.510605</td>\n",
" <td>31.799999</td>\n",
" <td>668.0</td>\n",
" <td>37.193771</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>-20.755524</td>\n",
" <td>8.586395</td>\n",
" <td>-10.680000</td>\n",
" <td>-24.584473</td>\n",
" <td>17.488115</td>\n",
" <td>-10.680000</td>\n",
" <td>-32.935036</td>\n",
" <td>62.265152</td>\n",
" <td>-10.600000</td>\n",
" <td>-32.243843</td>\n",
" <td>...</td>\n",
" <td>11.559999</td>\n",
" <td>4.249999</td>\n",
" <td>-28.598572</td>\n",
" <td>11.160000</td>\n",
" <td>0.726311</td>\n",
" <td>-24.612972</td>\n",
" <td>11.080000</td>\n",
" <td>891.0</td>\n",
" <td>19.876373</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>14</th>\n",
" <td>-20.755524</td>\n",
" <td>8.586395</td>\n",
" <td>-10.680000</td>\n",
" <td>-24.584473</td>\n",
" <td>17.488115</td>\n",
" <td>-10.680000</td>\n",
" <td>-32.935036</td>\n",
" <td>62.265152</td>\n",
" <td>-10.600000</td>\n",
" <td>-32.243843</td>\n",
" <td>...</td>\n",
" <td>11.559999</td>\n",
" <td>4.249999</td>\n",
" <td>-28.598572</td>\n",
" <td>11.160000</td>\n",
" <td>0.726311</td>\n",
" <td>-24.612972</td>\n",
" <td>11.080000</td>\n",
" <td>891.0</td>\n",
" <td>19.876373</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>15</th>\n",
" <td>-20.755524</td>\n",
" <td>8.586395</td>\n",
" <td>-10.680000</td>\n",
" <td>-24.584473</td>\n",
" <td>17.488115</td>\n",
" <td>-10.680000</td>\n",
" <td>-32.935036</td>\n",
" <td>62.265152</td>\n",
" <td>-10.600000</td>\n",
" <td>-32.243843</td>\n",
" <td>...</td>\n",
" <td>11.559999</td>\n",
" <td>4.249999</td>\n",
" <td>-28.598572</td>\n",
" <td>11.160000</td>\n",
" <td>0.726311</td>\n",
" <td>-24.612972</td>\n",
" <td>11.080000</td>\n",
" <td>891.0</td>\n",
" <td>19.876373</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>16</th>\n",
" <td>-20.755524</td>\n",
" <td>8.586395</td>\n",
" <td>-10.680000</td>\n",
" <td>-24.584473</td>\n",
" <td>17.488115</td>\n",
" <td>-10.680000</td>\n",
" <td>-32.935036</td>\n",
" <td>62.265152</td>\n",
" <td>-10.600000</td>\n",
" <td>-32.243843</td>\n",
" <td>...</td>\n",
" <td>11.559999</td>\n",
" <td>4.249999</td>\n",
" <td>-28.598572</td>\n",
" <td>11.160000</td>\n",
" <td>0.726311</td>\n",
" <td>-24.612972</td>\n",
" <td>11.080000</td>\n",
" <td>891.0</td>\n",
" <td>19.876373</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>17</th>\n",
" <td>-20.755524</td>\n",
" <td>8.586395</td>\n",
" <td>-10.680000</td>\n",
" <td>-24.584473</td>\n",
" <td>17.488115</td>\n",
" <td>-10.680000</td>\n",
" <td>-32.935036</td>\n",
" <td>62.265152</td>\n",
" <td>-10.600000</td>\n",
" <td>-32.243843</td>\n",
" <td>...</td>\n",
" <td>-3.800000</td>\n",
" <td>5.770000</td>\n",
" <td>-28.598572</td>\n",
" <td>-5.240001</td>\n",
" <td>1.287747</td>\n",
" <td>-23.778723</td>\n",
" <td>-5.400001</td>\n",
" <td>891.0</td>\n",
" <td>7.583350</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>-20.755524</td>\n",
" <td>8.586395</td>\n",
" <td>-10.680000</td>\n",
" <td>-24.584473</td>\n",
" <td>17.488115</td>\n",
" <td>-10.680000</td>\n",
" <td>-32.935036</td>\n",
" <td>62.265152</td>\n",
" <td>-10.600000</td>\n",
" <td>-32.243843</td>\n",
" <td>...</td>\n",
" <td>-3.800000</td>\n",
" <td>5.770000</td>\n",
" <td>-28.598572</td>\n",
" <td>-5.240001</td>\n",
" <td>1.287747</td>\n",
" <td>-23.778723</td>\n",
" <td>-5.400001</td>\n",
" <td>891.0</td>\n",
" <td>7.583350</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-20.755524</td>\n",
" <td>8.586395</td>\n",
" <td>-10.680000</td>\n",
" <td>-24.584473</td>\n",
" <td>17.488115</td>\n",
" <td>-10.680000</td>\n",
" <td>-32.935036</td>\n",
" <td>62.265152</td>\n",
" <td>-10.600000</td>\n",
" <td>-32.243843</td>\n",
" <td>...</td>\n",
" <td>-3.320000</td>\n",
" <td>7.690000</td>\n",
" <td>-28.598572</td>\n",
" <td>-2.840000</td>\n",
" <td>2.544224</td>\n",
" <td>-23.258274</td>\n",
" <td>-2.680000</td>\n",
" <td>891.0</td>\n",
" <td>4.611990</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>-20.755524</td>\n",
" <td>8.586395</td>\n",
" <td>-10.680000</td>\n",
" <td>-24.584473</td>\n",
" <td>17.488115</td>\n",
" <td>-10.680000</td>\n",
" <td>-32.935036</td>\n",
" <td>62.265152</td>\n",
" <td>-10.600000</td>\n",
" <td>-32.243843</td>\n",
" <td>...</td>\n",
" <td>-32.919998</td>\n",
" <td>11.049999</td>\n",
" <td>-28.598572</td>\n",
" <td>-35.720001</td>\n",
" <td>4.244162</td>\n",
" <td>-22.554136</td>\n",
" <td>-36.119999</td>\n",
" <td>891.0</td>\n",
" <td>35.642284</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>-3.412670</td>\n",
" <td>-22.898550</td>\n",
" <td>-22.360001</td>\n",
" <td>-5.110001</td>\n",
" <td>-28.598572</td>\n",
" <td>-21.880001</td>\n",
" <td>-25.453243</td>\n",
" <td>-66.053665</td>\n",
" <td>-19.160000</td>\n",
" <td>-33.054714</td>\n",
" <td>...</td>\n",
" <td>-10.040001</td>\n",
" <td>-6.569999</td>\n",
" <td>28.598572</td>\n",
" <td>-9.800000</td>\n",
" <td>-4.835445</td>\n",
" <td>22.309217</td>\n",
" <td>-9.720000</td>\n",
" <td>1026.0</td>\n",
" <td>36.178505</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>18.359924</td>\n",
" <td>14.369882</td>\n",
" <td>-43.239998</td>\n",
" <td>24.303370</td>\n",
" <td>17.748846</td>\n",
" <td>-43.480000</td>\n",
" <td>64.025154</td>\n",
" <td>29.886627</td>\n",
" <td>-44.200001</td>\n",
" <td>76.569237</td>\n",
" <td>...</td>\n",
" <td>-48.759998</td>\n",
" <td>-19.520025</td>\n",
" <td>-21.167650</td>\n",
" <td>-49.799999</td>\n",
" <td>-17.012880</td>\n",
" <td>-17.621937</td>\n",
" <td>-49.880001</td>\n",
" <td>1027.0</td>\n",
" <td>7.386358</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>18.359924</td>\n",
" <td>14.369882</td>\n",
" <td>-43.239998</td>\n",
" <td>24.303370</td>\n",
" <td>17.748846</td>\n",
" <td>-43.480000</td>\n",
" <td>64.025154</td>\n",
" <td>29.886627</td>\n",
" <td>-44.200001</td>\n",
" <td>76.569237</td>\n",
" <td>...</td>\n",
" <td>-56.200001</td>\n",
" <td>-17.319500</td>\n",
" <td>-22.766426</td>\n",
" <td>-56.680000</td>\n",
" <td>-14.425314</td>\n",
" <td>-18.336964</td>\n",
" <td>-56.840000</td>\n",
" <td>1027.0</td>\n",
" <td>16.478861</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>18.359924</td>\n",
" <td>14.369882</td>\n",
" <td>-43.239998</td>\n",
" <td>24.303370</td>\n",
" <td>17.748846</td>\n",
" <td>-43.480000</td>\n",
" <td>64.025154</td>\n",
" <td>29.886627</td>\n",
" <td>-44.200001</td>\n",
" <td>76.569237</td>\n",
" <td>...</td>\n",
" <td>-56.200001</td>\n",
" <td>-17.319500</td>\n",
" <td>-22.766426</td>\n",
" <td>-56.680000</td>\n",
" <td>-14.425314</td>\n",
" <td>-18.336964</td>\n",
" <td>-56.840000</td>\n",
" <td>1027.0</td>\n",
" <td>16.478861</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>-22.102570</td>\n",
" <td>5.334340</td>\n",
" <td>15.799999</td>\n",
" <td>-28.289688</td>\n",
" <td>5.480215</td>\n",
" <td>16.440001</td>\n",
" <td>-67.813629</td>\n",
" <td>18.884949</td>\n",
" <td>19.320000</td>\n",
" <td>-78.018410</td>\n",
" <td>...</td>\n",
" <td>43.400002</td>\n",
" <td>24.624746</td>\n",
" <td>16.759747</td>\n",
" <td>44.840000</td>\n",
" <td>18.849760</td>\n",
" <td>13.187316</td>\n",
" <td>44.919998</td>\n",
" <td>1067.0</td>\n",
" <td>14.580906</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-22.102570</td>\n",
" <td>5.334340</td>\n",
" <td>15.799999</td>\n",
" <td>-28.289688</td>\n",
" <td>5.480215</td>\n",
" <td>16.440001</td>\n",
" <td>-67.813629</td>\n",
" <td>18.884949</td>\n",
" <td>19.320000</td>\n",
" <td>-78.018410</td>\n",
" <td>...</td>\n",
" <td>43.400002</td>\n",
" <td>24.624746</td>\n",
" <td>16.759747</td>\n",
" <td>44.840000</td>\n",
" <td>18.849760</td>\n",
" <td>13.187316</td>\n",
" <td>44.919998</td>\n",
" <td>1067.0</td>\n",
" <td>14.580906</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>-22.102570</td>\n",
" <td>5.334340</td>\n",
" <td>15.799999</td>\n",
" <td>-28.289688</td>\n",
" <td>5.480215</td>\n",
" <td>16.440001</td>\n",
" <td>-67.813629</td>\n",
" <td>18.884949</td>\n",
" <td>19.320000</td>\n",
" <td>-78.018410</td>\n",
" <td>...</td>\n",
" <td>43.400002</td>\n",
" <td>24.624746</td>\n",
" <td>16.759747</td>\n",
" <td>44.840000</td>\n",
" <td>18.849760</td>\n",
" <td>13.187316</td>\n",
" <td>44.919998</td>\n",
" <td>1067.0</td>\n",
" <td>14.580906</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>-17.548027</td>\n",
" <td>17.043495</td>\n",
" <td>14.920000</td>\n",
" <td>-19.342041</td>\n",
" <td>21.296963</td>\n",
" <td>14.839999</td>\n",
" <td>-30.440884</td>\n",
" <td>63.705154</td>\n",
" <td>13.720000</td>\n",
" <td>-31.450989</td>\n",
" <td>...</td>\n",
" <td>-27.719999</td>\n",
" <td>16.429581</td>\n",
" <td>-23.412991</td>\n",
" <td>-29.959999</td>\n",
" <td>12.226481</td>\n",
" <td>-19.247751</td>\n",
" <td>-30.280001</td>\n",
" <td>1097.0</td>\n",
" <td>44.685917</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-17.548027</td>\n",
" <td>17.043495</td>\n",
" <td>14.920000</td>\n",
" <td>-19.342041</td>\n",
" <td>21.296963</td>\n",
" <td>14.839999</td>\n",
" <td>-30.440884</td>\n",
" <td>63.705154</td>\n",
" <td>13.720000</td>\n",
" <td>-31.450989</td>\n",
" <td>...</td>\n",
" <td>-27.719999</td>\n",
" <td>16.429581</td>\n",
" <td>-23.412991</td>\n",
" <td>-29.959999</td>\n",
" <td>12.226481</td>\n",
" <td>-19.247751</td>\n",
" <td>-30.280001</td>\n",
" <td>1097.0</td>\n",
" <td>44.685917</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109791</th>\n",
" <td>-2.969208</td>\n",
" <td>-23.082239</td>\n",
" <td>26.760000</td>\n",
" <td>-6.150001</td>\n",
" <td>-28.598572</td>\n",
" <td>26.760000</td>\n",
" <td>-33.783745</td>\n",
" <td>-61.775158</td>\n",
" <td>27.320000</td>\n",
" <td>-44.113327</td>\n",
" <td>...</td>\n",
" <td>53.799999</td>\n",
" <td>14.924809</td>\n",
" <td>24.506271</td>\n",
" <td>54.840000</td>\n",
" <td>13.834032</td>\n",
" <td>18.581881</td>\n",
" <td>55.160000</td>\n",
" <td>19090.0</td>\n",
" <td>23.234200</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109792</th>\n",
" <td>-2.969208</td>\n",
" <td>-23.082239</td>\n",
" <td>26.760000</td>\n",
" <td>-6.150001</td>\n",
" <td>-28.598572</td>\n",
" <td>26.760000</td>\n",
" <td>-33.783745</td>\n",
" <td>-61.775158</td>\n",
" <td>27.320000</td>\n",
" <td>-44.113327</td>\n",
" <td>...</td>\n",
" <td>53.799999</td>\n",
" <td>14.924809</td>\n",
" <td>24.506271</td>\n",
" <td>54.840000</td>\n",
" <td>13.834032</td>\n",
" <td>18.581881</td>\n",
" <td>55.160000</td>\n",
" <td>19090.0</td>\n",
" <td>23.234200</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109793</th>\n",
" <td>-19.622780</td>\n",
" <td>11.321079</td>\n",
" <td>12.679999</td>\n",
" <td>-26.435587</td>\n",
" <td>11.186554</td>\n",
" <td>12.599999</td>\n",
" <td>-67.523758</td>\n",
" <td>19.966785</td>\n",
" <td>11.960000</td>\n",
" <td>-78.462158</td>\n",
" <td>...</td>\n",
" <td>-21.480000</td>\n",
" <td>20.102518</td>\n",
" <td>20.744446</td>\n",
" <td>-22.520000</td>\n",
" <td>13.834032</td>\n",
" <td>18.581881</td>\n",
" <td>-22.520000</td>\n",
" <td>19109.0</td>\n",
" <td>29.602116</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109794</th>\n",
" <td>8.309232</td>\n",
" <td>-20.870329</td>\n",
" <td>10.360000</td>\n",
" <td>14.487940</td>\n",
" <td>-24.823675</td>\n",
" <td>10.440000</td>\n",
" <td>45.608902</td>\n",
" <td>-53.938618</td>\n",
" <td>11.000000</td>\n",
" <td>52.103645</td>\n",
" <td>...</td>\n",
" <td>-4.840000</td>\n",
" <td>-27.986851</td>\n",
" <td>-6.412251</td>\n",
" <td>-5.400001</td>\n",
" <td>-20.686640</td>\n",
" <td>-8.752694</td>\n",
" <td>-5.400001</td>\n",
" <td>19130.0</td>\n",
" <td>35.618042</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109795</th>\n",
" <td>15.478537</td>\n",
" <td>-17.900705</td>\n",
" <td>34.200001</td>\n",
" <td>19.212601</td>\n",
" <td>-21.391008</td>\n",
" <td>34.360001</td>\n",
" <td>46.061451</td>\n",
" <td>-53.486069</td>\n",
" <td>35.000000</td>\n",
" <td>51.978550</td>\n",
" <td>...</td>\n",
" <td>38.599998</td>\n",
" <td>-26.262537</td>\n",
" <td>11.719147</td>\n",
" <td>37.959999</td>\n",
" <td>-20.571835</td>\n",
" <td>9.029858</td>\n",
" <td>37.879997</td>\n",
" <td>19146.0</td>\n",
" <td>29.943666</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109796</th>\n",
" <td>15.478537</td>\n",
" <td>-17.900705</td>\n",
" <td>34.200001</td>\n",
" <td>19.212601</td>\n",
" <td>-21.391008</td>\n",
" <td>34.360001</td>\n",
" <td>46.061451</td>\n",
" <td>-53.486069</td>\n",
" <td>35.000000</td>\n",
" <td>51.978550</td>\n",
" <td>...</td>\n",
" <td>43.320000</td>\n",
" <td>-26.336700</td>\n",
" <td>11.490892</td>\n",
" <td>42.520000</td>\n",
" <td>-20.602449</td>\n",
" <td>8.955948</td>\n",
" <td>42.439999</td>\n",
" <td>19146.0</td>\n",
" <td>34.557800</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109797</th>\n",
" <td>15.478537</td>\n",
" <td>-17.900705</td>\n",
" <td>34.200001</td>\n",
" <td>19.212601</td>\n",
" <td>-21.391008</td>\n",
" <td>34.360001</td>\n",
" <td>46.061451</td>\n",
" <td>-53.486069</td>\n",
" <td>35.000000</td>\n",
" <td>51.978550</td>\n",
" <td>...</td>\n",
" <td>45.160000</td>\n",
" <td>-26.460308</td>\n",
" <td>11.110470</td>\n",
" <td>45.720001</td>\n",
" <td>-20.755524</td>\n",
" <td>8.586395</td>\n",
" <td>45.720001</td>\n",
" <td>19146.0</td>\n",
" <td>17.682192</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109798</th>\n",
" <td>0.308435</td>\n",
" <td>24.184366</td>\n",
" <td>19.080000</td>\n",
" <td>2.070001</td>\n",
" <td>28.598572</td>\n",
" <td>19.000000</td>\n",
" <td>40.157688</td>\n",
" <td>58.095158</td>\n",
" <td>18.439999</td>\n",
" <td>58.581963</td>\n",
" <td>...</td>\n",
" <td>16.680000</td>\n",
" <td>7.690000</td>\n",
" <td>-28.598572</td>\n",
" <td>12.679999</td>\n",
" <td>2.913775</td>\n",
" <td>-23.105200</td>\n",
" <td>11.880000</td>\n",
" <td>19171.0</td>\n",
" <td>15.471778</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109799</th>\n",
" <td>0.308435</td>\n",
" <td>24.184366</td>\n",
" <td>19.080000</td>\n",
" <td>2.070001</td>\n",
" <td>28.598572</td>\n",
" <td>19.000000</td>\n",
" <td>40.157688</td>\n",
" <td>58.095158</td>\n",
" <td>18.439999</td>\n",
" <td>58.581963</td>\n",
" <td>...</td>\n",
" <td>16.680000</td>\n",
" <td>7.690000</td>\n",
" <td>-28.598572</td>\n",
" <td>12.679999</td>\n",
" <td>2.913775</td>\n",
" <td>-23.105200</td>\n",
" <td>11.880000</td>\n",
" <td>19171.0</td>\n",
" <td>15.471778</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109800</th>\n",
" <td>0.308435</td>\n",
" <td>24.184366</td>\n",
" <td>19.080000</td>\n",
" <td>2.070001</td>\n",
" <td>28.598572</td>\n",
" <td>19.000000</td>\n",
" <td>40.157688</td>\n",
" <td>58.095158</td>\n",
" <td>18.439999</td>\n",
" <td>58.581963</td>\n",
" <td>...</td>\n",
" <td>16.680000</td>\n",
" <td>7.690000</td>\n",
" <td>-28.598572</td>\n",
" <td>12.679999</td>\n",
" <td>2.913775</td>\n",
" <td>-23.105200</td>\n",
" <td>11.880000</td>\n",
" <td>19171.0</td>\n",
" <td>15.471778</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109801</th>\n",
" <td>0.308435</td>\n",
" <td>24.184366</td>\n",
" <td>19.080000</td>\n",
" <td>2.070001</td>\n",
" <td>28.598572</td>\n",
" <td>19.000000</td>\n",
" <td>40.157688</td>\n",
" <td>58.095158</td>\n",
" <td>18.439999</td>\n",
" <td>58.581963</td>\n",
" <td>...</td>\n",
" <td>16.680000</td>\n",
" <td>7.690000</td>\n",
" <td>-28.598572</td>\n",
" <td>12.679999</td>\n",
" <td>2.913775</td>\n",
" <td>-23.105200</td>\n",
" <td>11.880000</td>\n",
" <td>19171.0</td>\n",
" <td>15.471778</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109802</th>\n",
" <td>0.308435</td>\n",
" <td>24.184366</td>\n",
" <td>19.080000</td>\n",
" <td>2.070001</td>\n",
" <td>28.598572</td>\n",
" <td>19.000000</td>\n",
" <td>40.157688</td>\n",
" <td>58.095158</td>\n",
" <td>18.439999</td>\n",
" <td>58.581963</td>\n",
" <td>...</td>\n",
" <td>16.680000</td>\n",
" <td>7.690000</td>\n",
" <td>-28.598572</td>\n",
" <td>12.679999</td>\n",
" <td>2.913775</td>\n",
" <td>-23.105200</td>\n",
" <td>11.880000</td>\n",
" <td>19171.0</td>\n",
" <td>15.471778</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109803</th>\n",
" <td>0.308435</td>\n",
" <td>24.184366</td>\n",
" <td>19.080000</td>\n",
" <td>2.070001</td>\n",
" <td>28.598572</td>\n",
" <td>19.000000</td>\n",
" <td>40.157688</td>\n",
" <td>58.095158</td>\n",
" <td>18.439999</td>\n",
" <td>58.581963</td>\n",
" <td>...</td>\n",
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" <td>-2.082284</td>\n",
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" <th>109816</th>\n",
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" <tr>\n",
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" <td>10.748274</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>109821 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" x1 y1 z1 x2 y2 z2 \\\n",
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"... ... ... ... ... ... ... \n",
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"109812 -19.921274 -10.600453 19.559999 -24.649467 -16.683662 19.400000 \n",
"109813 1.860553 23.541458 -11.480000 3.430001 28.598572 -11.320001 \n",
"109814 23.174084 -2.747478 11.320000 27.646933 -7.458413 11.559999 \n",
"109815 23.174084 -2.747478 11.320000 27.646933 -7.458413 11.559999 \n",
"109816 -12.374302 19.186522 -29.720001 -16.429581 23.412991 -29.799999 \n",
"109817 -20.939211 8.142934 -22.440001 -26.410866 11.262639 -22.760000 \n",
"109818 10.748274 -19.860044 -38.520000 13.322956 -25.670086 -38.439999 \n",
"109819 10.748274 -19.860044 -38.520000 13.322956 -25.670086 -38.439999 \n",
"109820 10.748274 -19.860044 -38.520000 13.322956 -25.670086 -38.439999 \n",
"\n",
" x3 y3 z3 x4 ... z6 \\\n",
"0 -21.724771 67.052704 -3.240000 -22.225971 ... 7.559999 \n",
"1 -66.736946 22.903200 4.360000 -74.096100 ... -4.040000 \n",
"2 -66.736946 22.903200 4.360000 -74.096100 ... -4.040000 \n",
"3 -66.736946 22.903200 4.360000 -74.096100 ... -3.080000 \n",
"4 -66.736946 22.903200 4.360000 -74.096100 ... -3.080000 \n",
"5 -25.356653 66.079544 -23.639999 -28.309521 ... -6.920000 \n",
"6 -25.356653 66.079544 -23.639999 -28.309521 ... -6.920000 \n",
"7 -25.356653 66.079544 -23.639999 -28.309521 ... -18.360001 \n",
"8 33.714462 61.815155 -41.639999 42.568947 ... -53.320000 \n",
"9 68.274330 17.165602 -21.240000 80.994446 ... -33.239998 \n",
"10 68.274330 17.165602 -21.240000 80.994446 ... -10.840000 \n",
"11 -57.205452 42.342068 14.440001 -65.752090 ... 11.000000 \n",
"12 15.484889 68.724678 14.120000 23.103405 ... 28.520000 \n",
"13 -32.935036 62.265152 -10.600000 -32.243843 ... 11.559999 \n",
"14 -32.935036 62.265152 -10.600000 -32.243843 ... 11.559999 \n",
"15 -32.935036 62.265152 -10.600000 -32.243843 ... 11.559999 \n",
"16 -32.935036 62.265152 -10.600000 -32.243843 ... 11.559999 \n",
"17 -32.935036 62.265152 -10.600000 -32.243843 ... -3.800000 \n",
"18 -32.935036 62.265152 -10.600000 -32.243843 ... -3.800000 \n",
"19 -32.935036 62.265152 -10.600000 -32.243843 ... -3.320000 \n",
"20 -32.935036 62.265152 -10.600000 -32.243843 ... -32.919998 \n",
"21 -25.453243 -66.053665 -19.160000 -33.054714 ... -10.040001 \n",
"22 64.025154 29.886627 -44.200001 76.569237 ... -48.759998 \n",
"23 64.025154 29.886627 -44.200001 76.569237 ... -56.200001 \n",
"24 64.025154 29.886627 -44.200001 76.569237 ... -56.200001 \n",
"25 -67.813629 18.884949 19.320000 -78.018410 ... 43.400002 \n",
"26 -67.813629 18.884949 19.320000 -78.018410 ... 43.400002 \n",
"27 -67.813629 18.884949 19.320000 -78.018410 ... 43.400002 \n",
"28 -30.440884 63.705154 13.720000 -31.450989 ... -27.719999 \n",
"29 -30.440884 63.705154 13.720000 -31.450989 ... -27.719999 \n",
"... ... ... ... ... ... ... \n",
"109791 -33.783745 -61.775158 27.320000 -44.113327 ... 53.799999 \n",
"109792 -33.783745 -61.775158 27.320000 -44.113327 ... 53.799999 \n",
"109793 -67.523758 19.966785 11.960000 -78.462158 ... -21.480000 \n",
"109794 45.608902 -53.938618 11.000000 52.103645 ... -4.840000 \n",
"109795 46.061451 -53.486069 35.000000 51.978550 ... 38.599998 \n",
"109796 46.061451 -53.486069 35.000000 51.978550 ... 43.320000 \n",
"109797 46.061451 -53.486069 35.000000 51.978550 ... 45.160000 \n",
"109798 40.157688 58.095158 18.439999 58.581963 ... 16.680000 \n",
"109799 40.157688 58.095158 18.439999 58.581963 ... 16.680000 \n",
"109800 40.157688 58.095158 18.439999 58.581963 ... 16.680000 \n",
"109801 40.157688 58.095158 18.439999 58.581963 ... 16.680000 \n",
"109802 40.157688 58.095158 18.439999 58.581963 ... 16.680000 \n",
"109803 40.157688 58.095158 18.439999 58.581963 ... 16.680000 \n",
"109804 -1.250000 -70.390724 -4.680000 -2.370000 ... -11.480000 \n",
"109805 -10.109999 70.390724 34.279999 -1.789999 ... 42.279999 \n",
"109806 -10.109999 70.390724 34.279999 -1.789999 ... 42.279999 \n",
"109807 -10.109999 70.390724 34.279999 -1.789999 ... 42.279999 \n",
"109808 15.929216 -68.605621 0.280000 18.170271 ... 42.759998 \n",
"109809 15.929216 -68.605621 0.280000 18.170271 ... 42.759998 \n",
"109810 15.929216 -68.605621 0.280000 18.170271 ... 42.759998 \n",
"109811 -56.025154 -43.743034 18.840000 -65.964081 ... 9.959999 \n",
"109812 -56.025154 -43.743034 18.840000 -65.964081 ... 9.959999 \n",
"109813 25.453243 66.053665 -10.040001 34.135880 ... -5.640000 \n",
"109814 53.698204 -45.849316 13.320000 58.983761 ... 40.439999 \n",
"109815 53.698204 -45.849316 13.320000 58.983761 ... 40.439999 \n",
"109816 -33.766418 61.785152 -30.760000 -34.045780 ... -45.639999 \n",
"109817 -59.615158 37.524971 -24.840000 -67.946777 ... -56.279999 \n",
"109818 25.279379 -66.100250 -38.040001 26.593647 ... -25.080000 \n",
"109819 25.279379 -66.100250 -38.040001 26.593647 ... -25.080000 \n",
"109820 25.279379 -66.100250 -38.040001 26.593647 ... -9.080000 \n",
"\n",
" x7 y7 z7 x8 y8 z8 \\\n",
"0 28.802656 3.901462 6.040000 21.421392 6.978845 5.640000 \n",
"1 26.880571 -9.817033 -4.840000 19.684010 -11.173258 -5.000000 \n",
"2 26.880571 -9.817033 -4.840000 19.684010 -11.173258 -5.000000 \n",
"3 29.303265 -2.360749 -1.400000 22.469944 -4.447415 -1.080000 \n",
"4 29.303265 -2.360749 -1.400000 22.469944 -4.447415 -1.080000 \n",
"5 25.644503 -13.621260 -6.200001 20.724909 -8.660306 -6.200001 \n",
"6 25.644503 -13.621260 -6.200001 20.724909 -8.660306 -6.200001 \n",
"7 25.490572 -16.829796 -18.040001 19.622780 -11.321079 -18.040001 \n",
"8 -6.150001 -28.598572 -54.439999 -5.408250 -22.071955 -54.520000 \n",
"9 -20.296682 -20.603376 -32.279999 -17.410870 -16.661102 -32.119999 \n",
"10 -21.332224 -19.851011 -10.440001 -17.870090 -15.552447 -10.440001 \n",
"11 27.442982 8.086111 10.280000 20.747869 8.604874 10.120000 \n",
"12 -7.870180 -29.631758 31.000000 -9.177679 -20.510605 31.799999 \n",
"13 4.249999 -28.598572 11.160000 0.726311 -24.612972 11.080000 \n",
"14 4.249999 -28.598572 11.160000 0.726311 -24.612972 11.080000 \n",
"15 4.249999 -28.598572 11.160000 0.726311 -24.612972 11.080000 \n",
"16 4.249999 -28.598572 11.160000 0.726311 -24.612972 11.080000 \n",
"17 5.770000 -28.598572 -5.240001 1.287747 -23.778723 -5.400001 \n",
"18 5.770000 -28.598572 -5.240001 1.287747 -23.778723 -5.400001 \n",
"19 7.690000 -28.598572 -2.840000 2.544224 -23.258274 -2.680000 \n",
"20 11.049999 -28.598572 -35.720001 4.244162 -22.554136 -36.119999 \n",
"21 -6.569999 28.598572 -9.800000 -4.835445 22.309217 -9.720000 \n",
"22 -19.520025 -21.167650 -49.799999 -17.012880 -17.621937 -49.880001 \n",
"23 -17.319500 -22.766426 -56.680000 -14.425314 -18.336964 -56.840000 \n",
"24 -17.319500 -22.766426 -56.680000 -14.425314 -18.336964 -56.840000 \n",
"25 24.624746 16.759747 44.840000 18.849760 13.187316 44.919998 \n",
"26 24.624746 16.759747 44.840000 18.849760 13.187316 44.919998 \n",
"27 24.624746 16.759747 44.840000 18.849760 13.187316 44.919998 \n",
"28 16.429581 -23.412991 -29.959999 12.226481 -19.247751 -30.280001 \n",
"29 16.429581 -23.412991 -29.959999 12.226481 -19.247751 -30.280001 \n",
"... ... ... ... ... ... ... \n",
"109791 14.924809 24.506271 54.840000 13.834032 18.581881 55.160000 \n",
"109792 14.924809 24.506271 54.840000 13.834032 18.581881 55.160000 \n",
"109793 20.102518 20.744446 -22.520000 13.834032 18.581881 -22.520000 \n",
"109794 -27.986851 -6.412251 -5.400001 -20.686640 -8.752694 -5.400001 \n",
"109795 -26.262537 11.719147 37.959999 -20.571835 9.029858 37.879997 \n",
"109796 -26.336700 11.490892 42.520000 -20.602449 8.955948 42.439999 \n",
"109797 -26.460308 11.110470 45.720001 -20.755524 8.586395 45.720001 \n",
"109798 7.690000 -28.598572 12.679999 2.913775 -23.105200 11.880000 \n",
"109799 7.690000 -28.598572 12.679999 2.913775 -23.105200 11.880000 \n",
"109800 7.690000 -28.598572 12.679999 2.913775 -23.105200 11.880000 \n",
"109801 7.690000 -28.598572 12.679999 2.913775 -23.105200 11.880000 \n",
"109802 7.690000 -28.598572 12.679999 2.913775 -23.105200 11.880000 \n",
"109803 7.690000 -28.598572 12.679999 2.913775 -23.105200 11.880000 \n",
"109804 -25.941158 12.708245 -10.840000 -21.857651 5.925623 -10.520000 \n",
"109805 28.314409 -5.404131 47.400002 22.531174 -4.299595 48.200001 \n",
"109806 28.314409 -5.404131 47.400002 22.531174 -4.299595 48.200001 \n",
"109807 28.314409 -5.404131 47.400002 22.531174 -4.299595 48.200001 \n",
"109808 -27.993032 6.393229 46.040001 -23.510843 1.934464 46.439999 \n",
"109809 -27.993032 6.393229 46.040001 -23.510843 1.934464 46.439999 \n",
"109810 -27.993032 6.393229 46.040001 -23.510843 1.934464 46.439999 \n",
"109811 -0.570000 28.598572 8.599999 -2.470313 23.288887 8.360000 \n",
"109812 -0.570000 28.598572 8.599999 -2.470313 23.288887 8.360000 \n",
"109813 0.570000 -28.598572 -6.120000 -0.382345 -24.153751 -6.200001 \n",
"109814 -11.704922 26.845655 41.320000 -4.835445 22.309217 41.480000 \n",
"109815 -11.704922 26.845655 41.320000 -4.835445 22.309217 41.480000 \n",
"109816 29.303265 -2.360749 -48.040001 23.847605 -1.121449 -48.279999 \n",
"109817 26.410866 -11.262639 -57.720001 20.173845 -9.990692 -57.959999 \n",
"109818 -20.053978 20.779713 -25.560001 -16.661102 17.410870 -25.560001 \n",
"109819 -20.053978 20.779713 -25.560001 -16.661102 17.410870 -25.560001 \n",
"109820 -20.765911 20.262461 -7.720000 -17.769758 16.951651 -7.560000 \n",
"\n",
" event trackchi2 matched \n",
"0 124.0 13.396379 1.0 \n",
"1 220.0 3.036235 1.0 \n",
"2 220.0 3.036235 1.0 \n",
"3 220.0 25.033289 0.0 \n",
"4 220.0 25.033289 0.0 \n",
"5 312.0 27.834667 0.0 \n",
"6 312.0 27.834667 0.0 \n",
"7 312.0 7.292468 0.0 \n",
"8 326.0 10.081528 1.0 \n",
"9 352.0 43.565620 0.0 \n",
"10 352.0 16.855164 1.0 \n",
"11 539.0 16.259047 0.0 \n",
"12 668.0 37.193771 1.0 \n",
"13 891.0 19.876373 0.0 \n",
"14 891.0 19.876373 0.0 \n",
"15 891.0 19.876373 0.0 \n",
"16 891.0 19.876373 0.0 \n",
"17 891.0 7.583350 0.0 \n",
"18 891.0 7.583350 0.0 \n",
"19 891.0 4.611990 1.0 \n",
"20 891.0 35.642284 0.0 \n",
"21 1026.0 36.178505 1.0 \n",
"22 1027.0 7.386358 1.0 \n",
"23 1027.0 16.478861 0.0 \n",
"24 1027.0 16.478861 0.0 \n",
"25 1067.0 14.580906 1.0 \n",
"26 1067.0 14.580906 1.0 \n",
"27 1067.0 14.580906 1.0 \n",
"28 1097.0 44.685917 0.0 \n",
"29 1097.0 44.685917 0.0 \n",
"... ... ... ... \n",
"109791 19090.0 23.234200 1.0 \n",
"109792 19090.0 23.234200 1.0 \n",
"109793 19109.0 29.602116 0.0 \n",
"109794 19130.0 35.618042 0.0 \n",
"109795 19146.0 29.943666 0.0 \n",
"109796 19146.0 34.557800 0.0 \n",
"109797 19146.0 17.682192 1.0 \n",
"109798 19171.0 15.471778 1.0 \n",
"109799 19171.0 15.471778 1.0 \n",
"109800 19171.0 15.471778 1.0 \n",
"109801 19171.0 15.471778 1.0 \n",
"109802 19171.0 15.471778 1.0 \n",
"109803 19171.0 15.471778 1.0 \n",
"109804 19219.0 32.846611 1.0 \n",
"109805 19260.0 9.661584 1.0 \n",
"109806 19260.0 9.661584 1.0 \n",
"109807 19260.0 9.661584 1.0 \n",
"109808 19518.0 11.021622 1.0 \n",
"109809 19518.0 11.021622 1.0 \n",
"109810 19518.0 11.021622 1.0 \n",
"109811 19551.0 7.543967 1.0 \n",
"109812 19551.0 7.543967 1.0 \n",
"109813 19617.0 8.062763 1.0 \n",
"109814 19682.0 17.139938 1.0 \n",
"109815 19682.0 17.139938 1.0 \n",
"109816 19721.0 11.005083 1.0 \n",
"109817 19787.0 7.796933 1.0 \n",
"109818 19826.0 19.236219 1.0 \n",
"109819 19826.0 19.236219 1.0 \n",
"109820 19826.0 20.784391 0.0 \n",
"\n",
"[109821 rows x 27 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"matched_unmatched_tracks = pd.read_pickle('matched_8hittracks2.pkl')\n",
"matched_unmatched_tracks"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x22bb2bc28d0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.hist(matched_unmatched_tracks['trackchi2'][matched_unmatched_tracks['matched'] == 1], label='matched', alpha=0.5, normed=True)\n",
"plt.hist(matched_unmatched_tracks['trackchi2'][matched_unmatched_tracks['matched'] == 0], label='not matched', alpha=0.5, normed=True)\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"matched_unmatched_tracks = np.array(matched_unmatched_tracks)\n",
"matched_unmatched_tracks = matched_unmatched_tracks.astype('float32')\n",
"\n",
"#print(matched_unmatched_tracks[0])\n",
"\n",
"track = matched_unmatched_tracks[:,:-3]\n",
"\n",
"#print(track[0,:])\n",
"\n",
"chi2 = matched_unmatched_tracks[:,-2]\n",
"chi = np.zeros((chi2.shape[0],1))\n",
"chi[:,0] = chi2[:]\n",
"chi2 = chi\n",
"#print(chi2[0])\n",
"\n",
"event_id = matched_unmatched_tracks[:,-3]\n",
"\n",
"#print(event_id[0])\n",
"\n",
"truth = matched_unmatched_tracks[:,-1]\n",
"\n",
"#print(truth[0])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5201008914506333\n"
]
}
],
"source": [
"true_event_dic = {}\n",
"idi = event_id[0]\n",
"chi2i = chi2[0]\n",
"chi2ic = 1\n",
"num_of_tracks = event_id.shape[0]\n",
"num_right_tracks = 0\n",
"best_tr = 0\n",
"\n",
"for i in range(event_id.shape[0]):\n",
" if event_id[i] == idi:\n",
" if chi2i > chi2[i]:\n",
" chi2i = chi2[i]\n",
" best_tr = i\n",
" elif chi2i == chi2[i]:\n",
" chi2ic += 1\n",
" else:\n",
" if truth[best_tr] == 1:\n",
" num_right_tracks += chi2ic\n",
" chi2i = chi2[i]\n",
" chi2ic = 1\n",
" idi = event_id[i]\n",
"\n",
"print(num_right_tracks/num_of_tracks)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#Reshapes the 2D-array to a 3D-array\n",
"\n",
"def reshapor(arr_orig, num_inputs=3):\n",
" timesteps = int(arr_orig.shape[1]/num_inputs)\n",
" number_examples = int(arr_orig.shape[0])\n",
" arr = np.zeros((number_examples, timesteps, num_inputs))\n",
" \n",
" for i in range(number_examples):\n",
" for t in range(timesteps):\n",
" arr[i,t,:] = arr_orig[i,num_inputs*t:num_inputs*t+num_inputs]\n",
" \n",
" return arr\n",
"\n",
"#The inverse transformation of the reshapor function (3D to 2D)\n",
"\n",
"def reshapor_inv(array_shaped):\n",
" num_inputs = array_shaped.shape[2]\n",
" timesteps = int(array_shaped.shape[1])\n",
" num_examples = int(array_shaped.shape[0])\n",
" arr = np.zeros((num_examples, timesteps*num_inputs))\n",
" \n",
" for i in range(num_examples):\n",
" for t in range(timesteps):\n",
" arr[i,num_inputs*t:num_inputs*t+num_inputs] = array_shaped[i,t,:]\n",
" \n",
" return arr"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-0.46716718]\n"
]
}
],
"source": [
"#Normalize the data advanced version with scikit learn\n",
"def correct_array_steps(arr, steps= 8, num_inputs= 3): #steps > array_steps\n",
" if arr.shape[1] != steps:\n",
" _ = np.zeros((arr.shape[0], steps, num_inputs))\n",
" _[:,:arr.shape[1],:] += arr\n",
" arr = _\n",
" return arr\n",
"\n",
"\n",
"#set the transormation based on training set\n",
"def set_min_max_scaler(arr, feature_range= (-.9,0.9)):\n",
" min_max_scalor = preprocessing.MinMaxScaler(feature_range=feature_range)\n",
" if len(arr.shape) == 3:\n",
" arr = reshapor(min_max_scalor.fit_transform(reshapor_inv(arr))) \n",
" else:\n",
" arr = min_max_scalor.fit_transform(arr)\n",
" return min_max_scalor\n",
"\n",
"\n",
"with open(\"scalor.pkl\" , \"rb\" ) as f:\n",
" scalor = pkl.load( f )\n",
"\n",
"min_max_chi2 = preprocessing.MinMaxScaler(feature_range=(-1,1))\n",
"chi2 = min_max_chi2.fit_transform(chi2)\n",
"print(chi2[0])\n",
"\n",
"#transform data\n",
"def min_max_scaler3D(arr, min_max_scalor= scalor):\n",
" num_inputs = arr.shape[2]\n",
" arr = correct_array_steps(arr)\n",
" arr = reshapor(min_max_scalor.transform(reshapor_inv(arr)), num_inputs=num_inputs)\n",
" return arr\n",
" \n",
"#inverse transformation\n",
"def min_max_scaler_inv3D(arr, min_max_scalor= scalor):\n",
" num_inputs = arr.shape[2]\n",
" arr = correct_array_steps(arr)\n",
" arr = reshapor(min_max_scalor.inverse_transform(reshapor_inv(arr)), num_inputs=num_inputs)\n",
" return arr"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(109821, 8, 3)\n"
]
}
],
"source": [
"track = reshapor(track)\n",
"print(track.shape)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"track = min_max_scaler3D(track)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(109821, 8, 4)\n",
"[[-0.08353733 0.85633283 -0.10962403 -0.46716718]\n",
" [-0.19387332 0.88403828 -0.10405403 -0.46716718]\n",
" [-0.2766562 0.85442146 -0.06179106 -0.46716718]\n",
" [-0.24266536 0.86545245 -0.0436893 -0.46716718]\n",
" [ 0.89736453 0.03339616 0.10172874 -0.46716718]\n",
" [ 0.89590581 0.00228753 0.10719893 -0.46716718]\n",
" [ 0.8453981 0.11812349 0.09198397 -0.46716718]\n",
" [ 0.7698897 0.25082128 0.08465644 -0.46716718]]\n"
]
}
],
"source": [
"track_and_chi = np.zeros((track.shape[0], track.shape[1], track.shape[2]+1))\n",
"print(track_and_chi.shape)\n",
"\n",
"track_and_chi[:,:,:3] += track[:,:,:]\n",
"\n",
"for step in range(track_and_chi.shape[1]):\n",
" track_and_chi[:,step,3] += chi2[:,0]\n",
" \n",
"print(track_and_chi[0])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1. 124.]\n"
]
}
],
"source": [
"truth_and_event_id = np.zeros((truth.shape[0], 2))\n",
"\n",
"truth_and_event_id[:,0] += truth[:]\n",
"truth_and_event_id[:,1] += event_id[:]\n",
"\n",
"print(truth_and_event_id[0,:])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(87856, 8, 4)\n",
"(21965, 8, 4)\n",
"(87856, 2)\n",
"(21965, 2)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
" FutureWarning)\n"
]
}
],
"source": [
"track_and_chi_train, track_and_chi_test, truth_and_event_id_train, truth_and_event_id_test = skl.model_selection.train_test_split(track_and_chi, truth_and_event_id, shuffle=True, train_size=0.8)\n",
"\n",
"\n",
"print(track_and_chi_train.shape)\n",
"print(track_and_chi_test.shape)\n",
"print(truth_and_event_id_train.shape)\n",
"print(truth_and_event_id_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"filepath = \"trained_models/keras_RNN.h5\"\n",
"model_4_to_4 = load_model(filepath)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"RNN_pred_input_train = track_and_chi_train[:,:4,:3]\n",
"RNN_pred_input_test = track_and_chi_test[:,:4,:3]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"Classifier_input_train = track_and_chi_train[:,4:,:]\n",
"Classifier_input_test = track_and_chi_test[:,4:,:]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"prediction_train = reshapor(model_4_to_4.predict(RNN_pred_input_train[:,:,:]))\n",
"prediction_test = reshapor(model_4_to_4.predict(RNN_pred_input_test[:,:,:]))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"Classifier_input_train[:,:,:3] -= prediction_train[:,:,:]\n",
"Classifier_input_test[:,:,:3] -= prediction_test[:,:,:]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-0.01692565 -0.06858504 0.07611917 0.02718735]\n",
" [-0.01577255 -0.06891568 0.07830505 0.02718735]\n",
" [-0.04065302 -0.1537843 0.09975575 0.02718735]\n",
" [-0.04712729 -0.18797903 0.10059148 0.02718735]]\n"
]
}
],
"source": [
"print(Classifier_input_train[0])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-0.01692565 -0.06858504 0.07611917]\n",
" [-0.01577255 -0.06891568 0.07830505]\n",
" [-0.04065302 -0.1537843 0.09975575]\n",
" [-0.04712729 -0.18797903 0.10059148]]\n",
"[[-0.00343056 -0.28258272 0.21165785]\n",
" [ 0.00093881 -0.23269528 0.21068021]\n",
" [-0.03962432 -0.23904903 0.21239483]\n",
" [ 0.01538819 -0.20903568 0.20812456]]\n"
]
}
],
"source": [
"min_max_diff = preprocessing.MinMaxScaler(feature_range=(-1,1))\n",
"\n",
"track_diff_train = Classifier_input_train[:,:,:3] \n",
"track_diff_test = Classifier_input_test[:,:,:3] \n",
"\n",
"print(track_diff_train[0])\n",
"\n",
"track_diff_train = reshapor(min_max_diff.fit_transform(reshapor_inv(track_diff_train)))\n",
"\n",
"track_diff_test = reshapor(min_max_diff.transform(reshapor_inv(track_diff_test)))\n",
"\n",
"print(track_diff_train[0])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"Classifier_input_train[:,:,:3] = track_diff_train[:,:,:]\n",
"Classifier_input_test[:,:,:3] = track_diff_test[:,:,:]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(87856, 4, 4)\n"
]
}
],
"source": [
"X_train = Classifier_input_train\n",
"y_train = truth_and_event_id_train[:,0]\n",
"X_test = Classifier_input_test\n",
"y_test = truth_and_event_id_test[:,0]\n",
"\n",
"print(X_train.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# truncate and pad input sequences\n",
"max_review_length = 4\n",
"filepath = \"trained_models/keras_model_classifier.h5\"\n",
"\n",
"callbacks = [\n",
" EarlyStopping(monitor='val_loss', patience=20, min_delta=0.0003),\n",
" ModelCheckpoint(filepath, monitor='val_loss', save_best_only=True),\n",
" History(),\n",
" TensorBoard(log_dir= filepath[:-3] + \"/logs\", histogram_freq=1, batch_size=32)\n",
"]\n",
"\n",
"#\n",
"\n",
"# create the model\n",
"model = Sequential()\n",
"#model.add(Dense(12, input_shape=(4,4)))\n",
"model.add(Bidirectional(LSTM(30, return_sequences=True, activation = 'relu'), input_shape=(4,4)))\n",
"model.add(BatchNormalization())\n",
"model.add(Bidirectional(LSTM(30, return_sequences=True, activation = 'relu')))\n",
"model.add(BatchNormalization())\n",
"model.add(Bidirectional(LSTM(30, return_sequences=False, activation = 'relu')))\n",
"model.add(BatchNormalization())\n",
"#model.add(LSTM(40, return_sequences=True, activation = 'relu')) \n",
"#model.add(Dropout(0.5))\n",
"#model.add(LSTM(4, activation = 'relu')) \n",
"#model.add(BatchNormalization())\n",
"model.add(Dense(100, activation='relu'))\n",
"model.add(Dense(1, activation='sigmoid'))\n",
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"print(model.summary())\n",
"model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=50, callbacks= callbacks, verbose = 1)\n",
"model = load_model(filepath)\n",
"# Final evaluation of the model\n",
"scores = model.evaluate(X_test, y_test, verbose=0)\n",
"print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pkl.dump( X_test, open( \"X_test.pkl\", \"wb\" ) )\n",
"pkl.dump( X_train, open( \"X_train.pkl\", \"wb\" ) )\n",
"pkl.dump( y_test, open( \"y_test.pkl\", \"wb\" ) )\n",
"pkl.dump( y_train, open( \"y_train.pkl\", \"wb\" ) )\n",
"pkl.dump( min_max_chi2, open( \"min_max_chi2_scaler.pkl\", \"wb\" ) )\n",
"pkl.dump( min_max_diff, open( \"min_max_diff_scaler.pkl\", \"wb\" ) )"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"X_test = pkl.load( open( \"X_test.pkl\", \"rb\" ) )\n",
"X_train = pkl.load( open( \"X_train.pkl\", \"rb\" ) )\n",
"y_test = pkl.load( open( \"y_test.pkl\", \"rb\" ) )\n",
"y_train = pkl.load( open( \"y_train.pkl\", \"rb\" ) )\n",
"min_max_chi2 = pkl.load( open( \"min_max_chi2_scaler.pkl\", \"rb\" ) )\n",
"min_max_diff = pkl.load( open( \"min_max_diff_scaler.pkl\", \"rb\" ) )\n",
"\n",
"filepath = \"trained_models/RNN_Classifier.h5\"\n",
"RNN_classifier = load_model(filepath)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# fit model no training data\n",
"\n",
"y_train_neg = y_train*(-1) + 1\n",
"y_test_neg = y_test*(-1) + 1\n",
" \n",
"Y_train = np.zeros((y_train.shape[0], 2))\n",
"Y_train[:,1] = y_train[:]\n",
"Y_train[:,0] = y_train_neg[:]\n",
"\n",
"X_train = reshapor_inv(X_train)\n",
"X_test = reshapor_inv(X_test)\n",
"\n",
"modelx = XGBClassifier(max_depth=3, n_estimators=100, learning_rate=0.05).fit(X_train, y_train, verbose = 1)\n",
"\n",
"predictions = modelx.predict_proba(X_test)\n",
"with open(\"trained_models/XGB_Classifier.xgb\" , \"wb\" ) as f:\n",
" pkl.dump(modelx , f )"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"predictions_true = predictions[:, 0]\n",
"predictions_false = predictions[:, 1]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"cut = 0.5\n",
"predictions_hard = (predictions_true > cut).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5,0,'cut')"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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L0lXqXgSyX90LZG5NclPv9WeSnGR7j6Q3SnpY0qeS7JhUZmAYXGGJqWP7Z9W958y5SZ6w/ZOSPrrM8KslXZXk4rEFBEaAZRNMo/Mk3ZTkCUlKstK9lIEqUd6YRtbSW2q+oN7Pu7s3ozl+3KGAUaK8MY2+Lumdtk+RpN6yySOSfr73+qXq3RhI0tPq3s4WqArljamT5ICkP5L0z7bvUXe9+xOS3mr7m5J+QdIPe8P3S3rB9j29u9wBVWC3CQBUiDNvAKgQ5Q0AFaK8AaBClDcAVIjyBoAKUd4AUCHKGwAqRHkDQIX+F9QFrBAYzD6gAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n_bins = 20\n",
"\n",
"plt.figure()\n",
"n, bins, patches = plt.hist(predictions_true[y_test == 1], bins=30, alpha=0.5, normed=True)\n",
"plt.hist(predictions_true[y_test == 0], bins=bins, alpha=0.5, normed=True)\n",
"plt.xlabel('cut')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ROC AUC: 0.8754601565187058\n"
]
}
],
"source": [
"fpr, tpr, _ = skl.metrics.roc_curve(y_test_neg, predictions_true, pos_label=1)\n",
"roc_auc = skl.metrics.roc_auc_score(y_true=y_test, y_score=predictions_false)\n",
"print(\"ROC AUC:\", roc_auc)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'true positives')"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.plot(fpr, tpr, label = 'XGBoost')\n",
"plt.xlabel('false positives')\n",
"plt.ylabel('true positives')\n",
"#plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8074208968814023"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"skl.metrics.accuracy_score(y_true=y_test_neg, y_pred=predictions_hard)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x28a7afc9208>"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"#plt.plot(RNN_pred_input_test[-1,:,0], RNN_pred_input_test[-1,:,1])\n",
"plt.plot(Classifier_input_test[-25,:,0], Classifier_input_test[-25,:,2], label = 'True track')\n",
"plt.plot(Classifier_input_test[-26,:,0], Classifier_input_test[-26,:,2], label = 'False track')\n",
"plt.plot(prediction_test[-25,:,0], prediction_test[-25,:,2], label = 'Predicted track')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"RNN_pred = RNN_classifier.predict_proba(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"RNN_predictions_true = RNN_pred\n",
"RNN_predictions_false = (-1) * (RNN_pred - 1)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"cut = 0.5\n",
"predictions_hard = (RNN_predictions_true > cut).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5,0,'cut')"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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g4ePbX2G0jK+0NAx0bRtVdQfw28A/JLmF0fz5HwPPT/JJ4IeBr42b3wocSXJLkksWUrA0Ic9ykaRGOEKXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNeL/AFXeu20gtVg+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n_bins = 20\n",
"\n",
"plt.figure()\n",
"n, bins, patches = plt.hist(RNN_predictions_true[y_test == 1], bins=30, alpha=0.5, normed=True)\n",
"plt.hist(RNN_predictions_true[y_test == 0], bins=bins, alpha=0.5, normed=True)\n",
"plt.xlabel('cut')"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ROC AUC: 0.9265346173526295\n"
]
}
],
"source": [
"y_train_neg = y_train*(-1) + 1\n",
"y_test_neg = y_test*(-1) + 1\n",
"RNN_fpr, RNN_tpr, _ = skl.metrics.roc_curve(y_test, RNN_predictions_true, pos_label=1)\n",
"RNN_roc_auc = skl.metrics.roc_auc_score(y_true=y_test, y_score=RNN_predictions_true)\n",
"print(\"ROC AUC:\", RNN_roc_auc)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'true positives')"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.plot(RNN_fpr, RNN_tpr, label = 'XGBoost')\n",
"plt.xlabel('false positives')\n",
"plt.ylabel('true positives')\n",
"#plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x22d07de4668>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.plot(RNN_fpr, RNN_tpr, label = 'RNN')\n",
"plt.plot(fpr, tpr, label = 'XGBoost')\n",
"plt.xlabel('false positives')\n",
"plt.ylabel('true positives')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
