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| a | b/scripts/Multi-Lead-DataFrame-Update.ipynb | ||
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| 1 | { |
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| 2 | "cells": [ |
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| 3 | { |
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| 4 | "cell_type": "code", |
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| 5 | "execution_count": 1, |
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| 6 | "id": "f5a54e19", |
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| 7 | "metadata": {}, |
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| 8 | "outputs": [], |
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| 9 | "source": [ |
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| 10 | "import pandas as pd\n", |
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| 11 | "import numpy as np\n", |
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| 12 | "import cv2\n", |
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| 13 | "import matplotlib.pyplot as plt\n", |
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| 14 | "import seaborn as sn\n", |
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| 15 | "import os.path, sys, re\n", |
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| 16 | "import time\n", |
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| 17 | "from PIL import Image\n", |
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| 18 | "\n", |
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| 19 | "from sktime.utils.data_processing import (\n", |
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| 20 | " from_3d_numpy_to_nested,\n", |
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| 21 | " from_multi_index_to_3d_numpy,\n", |
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| 22 | " from_nested_to_3d_numpy,\n", |
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| 23 | " from_multi_index_to_nested,\n", |
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| 24 | " from_nested_to_multi_index,\n", |
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| 25 | ")\n", |
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| 26 | "\n", |
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| 27 | "from sklearn.model_selection import train_test_split\n", |
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| 28 | "from sklearn.pipeline import Pipeline\n", |
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| 29 | "import sklearn.metrics as metrics\n", |
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| 30 | "from sklearn.model_selection import StratifiedShuffleSplit\n", |
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| 31 | "from sklearn.model_selection import cross_val_predict\n", |
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| 32 | "\n", |
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| 33 | "from sktime.classification.compose import ColumnEnsembleClassifier\n", |
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| 34 | "from sktime.classification.dictionary_based import BOSSEnsemble\n", |
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| 35 | "from sktime.classification.interval_based import TimeSeriesForestClassifier\n", |
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| 36 | "from sktime.classification.shapelet_based import MrSEQLClassifier\n", |
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| 37 | "from sktime.datasets import load_basic_motions\n", |
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| 38 | "from sktime.transformations.panel.compose import ColumnConcatenator\n" |
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| 39 | ] |
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| 40 | }, |
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| 41 | { |
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| 42 | "cell_type": "code", |
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| 43 | "execution_count": 2, |
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| 44 | "id": "70e3cc1c", |
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| 45 | "metadata": {}, |
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| 46 | "outputs": [ |
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| 47 | { |
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| 48 | "name": "stdout", |
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| 49 | "output_type": "stream", |
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| 50 | "text": [ |
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| 51 | "/home/moise/Desktop/Data_Science/Erdos_Institute/ecg-proj/ecg-copy\n" |
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| 52 | ] |
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| 53 | } |
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| 54 | ], |
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| 55 | "source": [ |
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| 56 | "cd ~/Desktop/Data_Science/Erdos_Institute/ecg-proj/ecg-copy/" |
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| 57 | ] |
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| 58 | }, |
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| 59 | { |
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| 60 | "cell_type": "markdown", |
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| 61 | "id": "f0cdc5d0", |
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| 62 | "metadata": {}, |
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| 63 | "source": [ |
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| 64 | "### Data Preprocessing task" |
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| 65 | ] |
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| 66 | }, |
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| 67 | { |
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| 68 | "cell_type": "code", |
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| 69 | "execution_count": 3, |
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| 70 | "id": "96b63a75", |
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| 71 | "metadata": {}, |
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| 72 | "outputs": [], |
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| 73 | "source": [ |
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| 74 | "pathroot = \"CSV_data_v2/\"" |
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| 75 | ] |
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| 76 | }, |
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| 77 | { |
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| 78 | "cell_type": "code", |
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| 79 | "execution_count": 4, |
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| 80 | "id": "952c9fcc", |
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| 81 | "metadata": {}, |
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| 82 | "outputs": [], |
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| 83 | "source": [ |
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| 84 | "LeadDict={'Lead1':np.array([[]]),'Lead2':np.array([[]]),'Lead3':np.array([[]]),'Lead4':np.array([[]]),\n", |
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| 85 | " 'Lead5':np.array([[]]),'Lead6':np.array([[]]),'Lead7':np.array([[]]),'Lead8':np.array([[]]),\n", |
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| 86 | " 'Lead9':np.array([[]]),'Lead10':np.array([[]]),'Lead11':np.array([[]]),'Lead12':np.array([[]])}" |
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| 87 | ] |
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| 88 | }, |
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| 89 | { |
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| 90 | "cell_type": "code", |
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| 91 | "execution_count": 5, |
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| 92 | "id": "b25f602e", |
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| 93 | "metadata": {}, |
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| 94 | "outputs": [], |
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| 95 | "source": [ |
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| 96 | "leadMinLen = {'Lead1':0,'Lead2':0,'Lead3':0,'Lead4':0,'Lead5':0,'Lead6':0,\n", |
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| 97 | " 'Lead7':0,'Lead8':0,'Lead9':0,'Lead10':0,'Lead11':0,'Lead12':0}" |
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| 98 | ] |
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| 99 | }, |
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| 100 | { |
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| 101 | "cell_type": "code", |
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| 102 | "execution_count": 6, |
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| 103 | "id": "34d659e8", |
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| 104 | "metadata": {}, |
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| 105 | "outputs": [], |
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| 106 | "source": [ |
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| 107 | "ClassLabels={'ECGImagesofPatientthathaveHistoryofMI':0,'ECGImagesofPatientthathaveabnormalheartbeat':1,\n", |
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| 108 | " 'ECGImagesofCOVID-19Patients':2,'NormalPersonECGImages':3,'ECGImagesofMyocardialInfarctionPatients':4}" |
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| 109 | ] |
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| 110 | }, |
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| 111 | { |
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| 112 | "cell_type": "code", |
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| 113 | "execution_count": 7, |
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| 114 | "id": "4bfdbe9c", |
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| 115 | "metadata": {}, |
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| 116 | "outputs": [], |
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| 117 | "source": [ |
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| 118 | "\"\"\"\n", |
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| 119 | "For every \"x\" value of the signal, average the \"y\" values over duplicates.\n", |
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| 120 | "\n", |
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| 121 | "Inputs:\n", |
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| 122 | "-------\n", |
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| 123 | "df data frame of two columns containing the signal \"x\" and \"y\" coordinates corresponding to \"active\" pixels\n", |
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| 124 | "\n", |
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| 125 | "Outputs:\n", |
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| 126 | "--------\n", |
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| 127 | "signal numpy array of unique values (\"y\") of the signal\n", |
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| 128 | "\"\"\"\n", |
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| 129 | "def uniqValsSignal(df):\n", |
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| 130 | " xdf=df[0].to_numpy()\n", |
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| 131 | " ydf=df[1].to_numpy()\n", |
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| 132 | " unikVals = pd.unique(xdf)\n", |
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| 133 | " signal = np.zeros(len(unikVals))\n", |
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| 134 | " for i in range(len(unikVals)):\n", |
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| 135 | " mask = (xdf==unikVals[i])\n", |
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| 136 | " signal[i] = np.mean(ydf[mask])\n", |
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| 137 | " return signal" |
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| 138 | ] |
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| 139 | }, |
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| 140 | { |
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| 141 | "cell_type": "code", |
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| 142 | "execution_count": 8, |
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| 143 | "id": "3a10f67e", |
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| 144 | "metadata": { |
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| 145 | "scrolled": true |
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| 146 | }, |
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| 147 | "outputs": [ |
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| 148 | { |
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| 149 | "name": "stdout", |
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| 150 | "output_type": "stream", |
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| 151 | "text": [ |
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| 152 | "Processing ECGImagesofPatientthathaveHistoryofMI folder ...\n", |
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| 153 | "2064 files processed in this folder in 19 sec...\n", |
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| 154 | "\n", |
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| 155 | "Processing ECGImagesofPatientthathaveabnormalheartbeat folder ...\n", |
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| 156 | "2796 files processed in this folder in 29 sec...\n", |
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| 157 | "\n", |
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| 158 | "Processing ECGImagesofCOVID-19Patients folder ...\n", |
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| 159 | "3000 files processed in this folder in 14 sec...\n", |
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| 160 | "\n", |
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| 161 | "Processing NormalPersonECGImages folder ...\n", |
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| 162 | "3408 files processed in this folder in 33 sec...\n", |
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| 163 | "\n", |
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| 164 | "Processing ECGImagesofMyocardialInfarctionPatients folder ...\n", |
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| 165 | "2868 files processed in this folder in 28 sec...\n", |
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| 166 | "\n" |
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| 167 | ] |
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| 168 | } |
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| 169 | ], |
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| 170 | "source": [ |
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| 171 | "\"\"\"\n", |
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| 172 | "Note:\n", |
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| 173 | "-----\n", |
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| 174 | "\n", |
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| 175 | "1) For the \"time series\", only the second column of every lead is extracted as signal.\n", |
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| 176 | "2) Because of non-uniformity in signal lenght across both observations and Leads, the code\n", |
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| 177 | "uses the minimum signal length across observation and leads, in order to make the date \"proper\n", |
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| 178 | "for multivariate time series classification.\n", |
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| 179 | "\"\"\"\n", |
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| 180 | "\n", |
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| 181 | "labelArr = np.array([])\n", |
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| 182 | "for dirs in os.listdir(pathroot):\n", |
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| 183 | "# if dirs == 'ECGImagesofCOVID-19Patients':\n", |
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| 184 | "# continue\n", |
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| 185 | " t = time.time()\n", |
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| 186 | " count = 0\n", |
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| 187 | " print('Processing {0} folder ...'.format(dirs))\n", |
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| 188 | " if not os.path.isfile(dirs):\n", |
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| 189 | " for item in os.listdir(os.path.join(pathroot,dirs)):\n", |
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| 190 | " #print('Processing {0} file ...'.format(item))\n", |
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| 191 | " of, oe = os.path.splitext(item)\n", |
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| 192 | " if of[0]=='.':\n", |
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| 193 | " continue\n", |
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| 194 | " else:\n", |
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| 195 | " signal = pd.read_csv(os.path.join(pathroot,os.path.join(dirs,item)),header=None,sep=' ')#[1].to_numpy()\n", |
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| 196 | " signal = uniqValsSignal(signal)\n", |
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| 197 | " try:\n", |
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| 198 | " leadNum = int(of[-2:])\n", |
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| 199 | " except ValueError:\n", |
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| 200 | " leadNum = int(of[-1:])\n", |
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| 201 | " finally:\n", |
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| 202 | " leadKey = 'Lead'+str(leadNum)\n", |
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| 203 | " if leadNum == 13:\n", |
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| 204 | " continue\n", |
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| 205 | " if LeadDict[leadKey].shape[1] > 0:\n", |
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| 206 | " if len(signal)> leadMinLen[leadKey]:\n", |
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| 207 | " signal = np.reshape(signal[:leadMinLen[leadKey]],(1,leadMinLen[leadKey]))\n", |
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| 208 | " LeadDict[leadKey] = np.concatenate( (LeadDict[leadKey],signal) )\n", |
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| 209 | " else:\n", |
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| 210 | " LeadDict[leadKey] = LeadDict[leadKey][:,:len(signal)]\n", |
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| 211 | " LeadDict[leadKey] = np.concatenate( (LeadDict[leadKey],np.reshape(signal,(1,len(signal)))) )\n", |
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| 212 | " leadMinLen[leadKey] = len(signal) \n", |
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| 213 | " else:\n", |
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| 214 | " LeadDict[leadKey] = np.reshape(signal,(1,len(signal)))\n", |
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| 215 | " leadMinLen[leadKey] = len(signal) \n", |
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| 216 | " count = count+1\n", |
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| 217 | " labelArr = np.append(labelArr,np.repeat(ClassLabels[dirs],len(LeadDict[leadKey])-len(labelArr))) ##Add labels\n", |
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| 218 | " t = time.time()-t\n", |
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| 219 | " print('{0} files processed in this folder in {1} sec...\\n'.format(count,round(t))) " |
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| 220 | ] |
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| 221 | }, |
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| 222 | { |
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| 223 | "cell_type": "code", |
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| 224 | "execution_count": 9, |
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| 225 | "id": "e2823e5f", |
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| 226 | "metadata": { |
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| 227 | "scrolled": true |
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| 228 | }, |
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| 229 | "outputs": [], |
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| 230 | "source": [ |
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| 231 | "\"\"\"\n", |
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| 232 | "Post Processing:\n", |
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| 233 | "---------------\n", |
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| 234 | "\n", |
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| 235 | "1) Put all the leads on the same \"time\" scale/Length\n", |
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| 236 | "2) Pull all the leads(2D) into a 3D array of shape (n_obs,n_col,n_timepoints)\n", |
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| 237 | "3) Convert result from step 2 into a nested data frame.\n", |
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| 238 | "\"\"\"\n", |
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| 239 | "\n", |
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| 240 | "minLen = min(leadMinLen.values())\n", |
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| 241 | "for key in LeadDict.keys():\n", |
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| 242 | " LeadDict[key] = LeadDict[key][:,:minLen]\n", |
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| 243 | "\n", |
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| 244 | "dim1 = LeadDict['Lead1'].shape[0]\n", |
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| 245 | "dim2 = len(LeadDict.keys())\n", |
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| 246 | "dim3 = LeadDict['Lead1'].shape[1]\n", |
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| 247 | "X3d = np.zeros((dim1,dim2,dim3))\n", |
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| 248 | "for j in range(dim2):\n", |
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| 249 | " X3d[:,j,:] = list(LeadDict.values())[j]\n", |
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| 250 | " \n", |
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| 251 | "X3d_nested=from_3d_numpy_to_nested(X3d)\n", |
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| 252 | "X3d_nested.columns = list(LeadDict.keys())\n", |
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| 253 | "X3d_nested['Label'] = labelArr\n", |
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| 254 | "X3d_nested.to_csv('muti-lead-dataFrame.csv',index=False,float_format='%d')" |
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| 255 | ] |
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| 256 | }, |
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| 257 | { |
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| 258 | "cell_type": "code", |
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| 259 | "execution_count": 10, |
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| 260 | "id": "fdfd5abc", |
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| 261 | "metadata": { |
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| 262 | "scrolled": true |
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| 263 | }, |
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| 264 | "outputs": [ |
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| 265 | { |
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| 266 | "data": { |
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| 267 | "text/html": [ |
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| 268 | "<div>\n", |
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| 269 | "<style scoped>\n", |
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| 270 | " .dataframe tbody tr th:only-of-type {\n", |
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| 271 | " vertical-align: middle;\n", |
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| 272 | " }\n", |
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| 273 | "\n", |
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| 274 | " .dataframe tbody tr th {\n", |
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| 275 | " vertical-align: top;\n", |
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| 276 | " }\n", |
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| 277 | "\n", |
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| 278 | " .dataframe thead th {\n", |
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| 279 | " text-align: right;\n", |
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| 280 | " }\n", |
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| 281 | "</style>\n", |
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| 282 | "<table border=\"1\" class=\"dataframe\">\n", |
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| 283 | " <thead>\n", |
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| 284 | " <tr style=\"text-align: right;\">\n", |
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| 285 | " <th></th>\n", |
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| 286 | " <th>Lead1</th>\n", |
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| 287 | " <th>Lead2</th>\n", |
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| 288 | " <th>Lead3</th>\n", |
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| 289 | " <th>Lead4</th>\n", |
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| 290 | " <th>Lead5</th>\n", |
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| 291 | " <th>Lead6</th>\n", |
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| 292 | " <th>Lead7</th>\n", |
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| 293 | " <th>Lead8</th>\n", |
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| 294 | " <th>Lead9</th>\n", |
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| 295 | " <th>Lead10</th>\n", |
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| 296 | " <th>Lead11</th>\n", |
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| 297 | " <th>Lead12</th>\n", |
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| 298 | " <th>Label</th>\n", |
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| 299 | " </tr>\n", |
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| 300 | " </thead>\n", |
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| 301 | " <tbody>\n", |
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| 302 | " <tr>\n", |
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| 303 | " <th>0</th>\n", |
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| 312 | " <td>0 104.714286\n", |
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||
| 433 | "2 157.5\n", |
||
| 434 | "3 ...</td>\n", |
||
| 435 | " <td>0 145.5\n", |
||
| 436 | "1 145.5\n", |
||
| 437 | "2 145.5\n", |
||
| 438 | "3 ...</td>\n", |
||
| 439 | " <td>0 107.170732\n", |
||
| 440 | "1 104.000000\n", |
||
| 441 | "2 121...</td>\n", |
||
| 442 | " <td>0.0</td>\n", |
||
| 443 | " </tr>\n", |
||
| 444 | " <tr>\n", |
||
| 445 | " <th>3</th>\n", |
||
| 446 | " <td>0 116.5\n", |
||
| 447 | "1 117.5\n", |
||
| 448 | "2 126.0\n", |
||
| 449 | "3 ...</td>\n", |
||
| 450 | " <td>0 100.5\n", |
||
| 451 | "1 100.5\n", |
||
| 452 | "2 101.5\n", |
||
| 453 | "3 ...</td>\n", |
||
| 454 | " <td>0 104.5\n", |
||
| 455 | "1 104.5\n", |
||
| 456 | "2 104.0\n", |
||
| 457 | "3 ...</td>\n", |
||
| 458 | " <td>0 63.0\n", |
||
| 459 | "1 60.0\n", |
||
| 460 | "2 67.5\n", |
||
| 461 | "3 ...</td>\n", |
||
| 462 | " <td>0 130.0\n", |
||
| 463 | "1 123.5\n", |
||
| 464 | "2 129.5\n", |
||
| 465 | "3 ...</td>\n", |
||
| 466 | " <td>0 131.5\n", |
||
| 467 | "1 130.0\n", |
||
| 468 | "2 131.5\n", |
||
| 469 | "3 ...</td>\n", |
||
| 470 | " <td>0 140.5\n", |
||
| 471 | "1 150.0\n", |
||
| 472 | "2 144.5\n", |
||
| 473 | "3 ...</td>\n", |
||
| 474 | " <td>0 155.0\n", |
||
| 475 | "1 155.0\n", |
||
| 476 | "2 155.0\n", |
||
| 477 | "3 ...</td>\n", |
||
| 478 | " <td>0 169.500000\n", |
||
| 479 | "1 153.000000\n", |
||
| 480 | "2 145...</td>\n", |
||
| 481 | " <td>0 119.5\n", |
||
| 482 | "1 122.0\n", |
||
| 483 | "2 129.5\n", |
||
| 484 | "3 ...</td>\n", |
||
| 485 | " <td>0 154.055944\n", |
||
| 486 | "1 136.396396\n", |
||
| 487 | "2 44...</td>\n", |
||
| 488 | " <td>0 121.714286\n", |
||
| 489 | "1 144.000000\n", |
||
| 490 | "2 133...</td>\n", |
||
| 491 | " <td>0.0</td>\n", |
||
| 492 | " </tr>\n", |
||
| 493 | " <tr>\n", |
||
| 494 | " <th>4</th>\n", |
||
| 495 | " <td>0 157.000000\n", |
||
| 496 | "1 157.000000\n", |
||
| 497 | "2 148...</td>\n", |
||
| 498 | " <td>0 106.5\n", |
||
| 499 | "1 106.5\n", |
||
| 500 | "2 106.0\n", |
||
| 501 | "3 ...</td>\n", |
||
| 502 | " <td>0 107.0\n", |
||
| 503 | "1 106.0\n", |
||
| 504 | "2 106.0\n", |
||
| 505 | "3 ...</td>\n", |
||
| 506 | " <td>0 58.5\n", |
||
| 507 | "1 70.0\n", |
||
| 508 | "2 86.0\n", |
||
| 509 | "3 ...</td>\n", |
||
| 510 | " <td>0 141.0\n", |
||
| 511 | "1 136.5\n", |
||
| 512 | "2 138.0\n", |
||
| 513 | "3 ...</td>\n", |
||
| 514 | " <td>0 155.5\n", |
||
| 515 | "1 158.5\n", |
||
| 516 | "2 156.5\n", |
||
| 517 | "3 ...</td>\n", |
||
| 518 | " <td>0 131.0\n", |
||
| 519 | "1 130.5\n", |
||
| 520 | "2 130.0\n", |
||
| 521 | "3 ...</td>\n", |
||
| 522 | " <td>0 133.5\n", |
||
| 523 | "1 133.0\n", |
||
| 524 | "2 146.5\n", |
||
| 525 | "3 ...</td>\n", |
||
| 526 | " <td>0 139.333333\n", |
||
| 527 | "1 159.357143\n", |
||
| 528 | "2 156...</td>\n", |
||
| 529 | " <td>0 137.0\n", |
||
| 530 | "1 138.5\n", |
||
| 531 | "2 145.5\n", |
||
| 532 | "3 ...</td>\n", |
||
| 533 | " <td>0 137.5\n", |
||
| 534 | "1 138.0\n", |
||
| 535 | "2 146.5\n", |
||
| 536 | "3 ...</td>\n", |
||
| 537 | " <td>0 140.0\n", |
||
| 538 | "1 135.0\n", |
||
| 539 | "2 142.0\n", |
||
| 540 | "3 ...</td>\n", |
||
| 541 | " <td>0.0</td>\n", |
||
| 542 | " </tr>\n", |
||
| 543 | " <tr>\n", |
||
| 544 | " <th>...</th>\n", |
||
| 545 | " <td>...</td>\n", |
||
| 546 | " <td>...</td>\n", |
||
| 547 | " <td>...</td>\n", |
||
| 548 | " <td>...</td>\n", |
||
| 549 | " <td>...</td>\n", |
||
| 550 | " <td>...</td>\n", |
||
| 551 | " <td>...</td>\n", |
||
| 552 | " <td>...</td>\n", |
||
| 553 | " <td>...</td>\n", |
||
| 554 | " <td>...</td>\n", |
||
| 555 | " <td>...</td>\n", |
||
| 556 | " <td>...</td>\n", |
||
| 557 | " <td>...</td>\n", |
||
| 558 | " </tr>\n", |
||
| 559 | " <tr>\n", |
||
| 560 | " <th>1173</th>\n", |
||
| 561 | " <td>0 130.5\n", |
||
| 562 | "1 127.5\n", |
||
| 563 | "2 127.0\n", |
||
| 564 | "3 ...</td>\n", |
||
| 565 | " <td>0 100.5\n", |
||
| 566 | "1 100.0\n", |
||
| 567 | "2 99.5\n", |
||
| 568 | "3 ...</td>\n", |
||
| 569 | " <td>0 107.0\n", |
||
| 570 | "1 107.0\n", |
||
| 571 | "2 109.0\n", |
||
| 572 | "3 ...</td>\n", |
||
| 573 | " <td>0 57.0\n", |
||
| 574 | "1 67.5\n", |
||
| 575 | "2 82.5\n", |
||
| 576 | "3 ...</td>\n", |
||
| 577 | " <td>0 110.5\n", |
||
| 578 | "1 107.5\n", |
||
| 579 | "2 123.5\n", |
||
| 580 | "3 ...</td>\n", |
||
| 581 | " <td>0 142.5\n", |
||
| 582 | "1 145.0\n", |
||
| 583 | "2 156.5\n", |
||
| 584 | "3 ...</td>\n", |
||
| 585 | " <td>0 97.0\n", |
||
| 586 | "1 124.5\n", |
||
| 587 | "2 105.5\n", |
||
| 588 | "3 ...</td>\n", |
||
| 589 | " <td>0 127.5\n", |
||
| 590 | "1 126.0\n", |
||
| 591 | "2 127.5\n", |
||
| 592 | "3 ...</td>\n", |
||
| 593 | " <td>0 142.5\n", |
||
| 594 | "1 137.5\n", |
||
| 595 | "2 137.5\n", |
||
| 596 | "3 ...</td>\n", |
||
| 597 | " <td>0 133.0\n", |
||
| 598 | "1 132.5\n", |
||
| 599 | "2 132.5\n", |
||
| 600 | "3 ...</td>\n", |
||
| 601 | " <td>0 4.0\n", |
||
| 602 | "1 12.5\n", |
||
| 603 | "2 19.0\n", |
||
| 604 | "3 ...</td>\n", |
||
| 605 | " <td>0 138.5\n", |
||
| 606 | "1 143.0\n", |
||
| 607 | "2 143.5\n", |
||
| 608 | "3 ...</td>\n", |
||
| 609 | " <td>4.0</td>\n", |
||
| 610 | " </tr>\n", |
||
| 611 | " <tr>\n", |
||
| 612 | " <th>1174</th>\n", |
||
| 613 | " <td>0 92.5\n", |
||
| 614 | "1 78.5\n", |
||
| 615 | "2 92.0\n", |
||
| 616 | "3 ...</td>\n", |
||
| 617 | " <td>0 102.5\n", |
||
| 618 | "1 103.0\n", |
||
| 619 | "2 103.0\n", |
||
| 620 | "3 ...</td>\n", |
||
| 621 | " <td>0 87.5\n", |
||
| 622 | "1 87.5\n", |
||
| 623 | "2 87.5\n", |
||
| 624 | "3 ...</td>\n", |
||
| 625 | " <td>0 74.0\n", |
||
| 626 | "1 72.5\n", |
||
| 627 | "2 74.5\n", |
||
| 628 | "3 ...</td>\n", |
||
| 629 | " <td>0 71.5\n", |
||
| 630 | "1 74.5\n", |
||
| 631 | "2 83.0\n", |
||
| 632 | "3 ...</td>\n", |
||
| 633 | " <td>0 113.0\n", |
||
| 634 | "1 111.0\n", |
||
| 635 | "2 119.0\n", |
||
| 636 | "3 ...</td>\n", |
||
| 637 | " <td>0 141.600000\n", |
||
| 638 | "1 133.250000\n", |
||
| 639 | "2 122...</td>\n", |
||
| 640 | " <td>0 121.0\n", |
||
| 641 | "1 120.5\n", |
||
| 642 | "2 120.0\n", |
||
| 643 | "3 ...</td>\n", |
||
| 644 | " <td>0 87.0\n", |
||
| 645 | "1 86.5\n", |
||
| 646 | "2 86.0\n", |
||
| 647 | "3 ...</td>\n", |
||
| 648 | " <td>0 140.0\n", |
||
| 649 | "1 148.5\n", |
||
| 650 | "2 142.0\n", |
||
| 651 | "3 ...</td>\n", |
||
| 652 | " <td>0 137.909091\n", |
||
| 653 | "1 140.000000\n", |
||
| 654 | "2 137...</td>\n", |
||
| 655 | " <td>0 117.5\n", |
||
| 656 | "1 125.0\n", |
||
| 657 | "2 127.0\n", |
||
| 658 | "3 ...</td>\n", |
||
| 659 | " <td>4.0</td>\n", |
||
| 660 | " </tr>\n", |
||
| 661 | " <tr>\n", |
||
| 662 | " <th>1175</th>\n", |
||
| 663 | " <td>0 130.0\n", |
||
| 664 | "1 131.5\n", |
||
| 665 | "2 138.0\n", |
||
| 666 | "3 ...</td>\n", |
||
| 667 | " <td>0 103.0\n", |
||
| 668 | "1 103.5\n", |
||
| 669 | "2 103.5\n", |
||
| 670 | "3 ...</td>\n", |
||
| 671 | " <td>0 97.0\n", |
||
| 672 | "1 96.5\n", |
||
| 673 | "2 96.0\n", |
||
| 674 | "3 ...</td>\n", |
||
| 675 | " <td>0 54.5\n", |
||
| 676 | "1 40.0\n", |
||
| 677 | "2 70.0\n", |
||
| 678 | "3 ...</td>\n", |
||
| 679 | " <td>0 142.000\n", |
||
| 680 | "1 147.000\n", |
||
| 681 | "2 142.000\n", |
||
| 682 | "3...</td>\n", |
||
| 683 | " <td>0 135.0\n", |
||
| 684 | "1 145.0\n", |
||
| 685 | "2 138.5\n", |
||
| 686 | "3 ...</td>\n", |
||
| 687 | " <td>0 97.0\n", |
||
| 688 | "1 124.5\n", |
||
| 689 | "2 105.5\n", |
||
| 690 | "3 ...</td>\n", |
||
| 691 | " <td>0 137.5\n", |
||
| 692 | "1 144.5\n", |
||
| 693 | "2 153.5\n", |
||
| 694 | "3 ...</td>\n", |
||
| 695 | " <td>0 87.0\n", |
||
| 696 | "1 86.5\n", |
||
| 697 | "2 86.0\n", |
||
| 698 | "3 ...</td>\n", |
||
| 699 | " <td>0 156.5\n", |
||
| 700 | "1 156.5\n", |
||
| 701 | "2 155.5\n", |
||
| 702 | "3 ...</td>\n", |
||
| 703 | " <td>0 135.0\n", |
||
| 704 | "1 135.0\n", |
||
| 705 | "2 135.5\n", |
||
| 706 | "3 ...</td>\n", |
||
| 707 | " <td>0 128.0\n", |
||
| 708 | "1 124.5\n", |
||
| 709 | "2 136.5\n", |
||
| 710 | "3 ...</td>\n", |
||
| 711 | " <td>4.0</td>\n", |
||
| 712 | " </tr>\n", |
||
| 713 | " <tr>\n", |
||
| 714 | " <th>1176</th>\n", |
||
| 715 | " <td>0 127.0\n", |
||
| 716 | "1 133.5\n", |
||
| 717 | "2 137.5\n", |
||
| 718 | "3 ...</td>\n", |
||
| 719 | " <td>0 100.5\n", |
||
| 720 | "1 100.0\n", |
||
| 721 | "2 102.5\n", |
||
| 722 | "3 ...</td>\n", |
||
| 723 | " <td>0 91.5\n", |
||
| 724 | "1 94.5\n", |
||
| 725 | "2 96.0\n", |
||
| 726 | "3 ...</td>\n", |
||
| 727 | " <td>0 74.0\n", |
||
| 728 | "1 72.5\n", |
||
| 729 | "2 74.5\n", |
||
| 730 | "3 ...</td>\n", |
||
| 731 | " <td>0 113.875000\n", |
||
| 732 | "1 144.000000\n", |
||
| 733 | "2 152...</td>\n", |
||
| 734 | " <td>0 132.5\n", |
||
| 735 | "1 134.0\n", |
||
| 736 | "2 143.5\n", |
||
| 737 | "3 ...</td>\n", |
||
| 738 | " <td>0 119.0\n", |
||
| 739 | "1 118.5\n", |
||
| 740 | "2 118.0\n", |
||
| 741 | "3 ...</td>\n", |
||
| 742 | " <td>0 125.5\n", |
||
| 743 | "1 121.5\n", |
||
| 744 | "2 129.0\n", |
||
| 745 | "3 ...</td>\n", |
||
| 746 | " <td>0 137.0\n", |
||
| 747 | "1 136.5\n", |
||
| 748 | "2 138.5\n", |
||
| 749 | "3 ...</td>\n", |
||
| 750 | " <td>0 55.5\n", |
||
| 751 | "1 52.0\n", |
||
| 752 | "2 77.5\n", |
||
| 753 | "3 ...</td>\n", |
||
| 754 | " <td>0 116.5\n", |
||
| 755 | "1 116.5\n", |
||
| 756 | "2 117.0\n", |
||
| 757 | "3 ...</td>\n", |
||
| 758 | " <td>0 114.0\n", |
||
| 759 | "1 110.5\n", |
||
| 760 | "2 123.5\n", |
||
| 761 | "3 ...</td>\n", |
||
| 762 | " <td>4.0</td>\n", |
||
| 763 | " </tr>\n", |
||
| 764 | " <tr>\n", |
||
| 765 | " <th>1177</th>\n", |
||
| 766 | " <td>0 158.857143\n", |
||
| 767 | "1 158.857143\n", |
||
| 768 | "2 150...</td>\n", |
||
| 769 | " <td>0 103.0\n", |
||
| 770 | "1 103.5\n", |
||
| 771 | "2 107.0\n", |
||
| 772 | "3 ...</td>\n", |
||
| 773 | " <td>0 103.5\n", |
||
| 774 | "1 105.0\n", |
||
| 775 | "2 114.0\n", |
||
| 776 | "3 ...</td>\n", |
||
| 777 | " <td>0 74.5\n", |
||
| 778 | "1 74.5\n", |
||
| 779 | "2 75.0\n", |
||
| 780 | "3 ...</td>\n", |
||
| 781 | " <td>0 142.0\n", |
||
| 782 | "1 136.5\n", |
||
| 783 | "2 133.0\n", |
||
| 784 | "3 ...</td>\n", |
||
| 785 | " <td>0 150.0\n", |
||
| 786 | "1 144.5\n", |
||
| 787 | "2 154.5\n", |
||
| 788 | "3 ...</td>\n", |
||
| 789 | " <td>0 103.0\n", |
||
| 790 | "1 95.5\n", |
||
| 791 | "2 103.5\n", |
||
| 792 | "3 ...</td>\n", |
||
| 793 | " <td>0 111.5\n", |
||
| 794 | "1 100.0\n", |
||
| 795 | "2 104.5\n", |
||
| 796 | "3 ...</td>\n", |
||
| 797 | " <td>0 165.428571\n", |
||
| 798 | "1 165.875000\n", |
||
| 799 | "2 154...</td>\n", |
||
| 800 | " <td>0 146.5\n", |
||
| 801 | "1 145.0\n", |
||
| 802 | "2 144.0\n", |
||
| 803 | "3 ...</td>\n", |
||
| 804 | " <td>0 96.5\n", |
||
| 805 | "1 96.5\n", |
||
| 806 | "2 96.5\n", |
||
| 807 | "3 ...</td>\n", |
||
| 808 | " <td>0 137.0\n", |
||
| 809 | "1 134.0\n", |
||
| 810 | "2 129.5\n", |
||
| 811 | "3 ...</td>\n", |
||
| 812 | " <td>4.0</td>\n", |
||
| 813 | " </tr>\n", |
||
| 814 | " </tbody>\n", |
||
| 815 | "</table>\n", |
||
| 816 | "<p>1178 rows × 13 columns</p>\n", |
||
| 817 | "</div>" |
||
| 818 | ], |
||
| 819 | "text/plain": [ |
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| 820 | " Lead1 \\\n", |
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| 821 | "0 0 126.0\n", |
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| 822 | "1 119.5\n", |
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| 823 | "2 134.0\n", |
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| 824 | "3 ... \n", |
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| 825 | "1 0 139.0\n", |
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| 826 | "1 129.0\n", |
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| 827 | "2 127.0\n", |
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| 828 | "3 ... \n", |
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| 829 | "2 0 158.333333\n", |
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| 830 | "1 158.333333\n", |
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| 831 | "2 149... \n", |
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| 832 | "3 0 116.5\n", |
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| 833 | "1 117.5\n", |
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| 834 | "2 126.0\n", |
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| 835 | "3 ... \n", |
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| 836 | "4 0 157.000000\n", |
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| 837 | "1 157.000000\n", |
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| 838 | "2 148... \n", |
||
| 839 | "... ... \n", |
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| 840 | "1173 0 130.5\n", |
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| 841 | "1 127.5\n", |
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| 842 | "2 127.0\n", |
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| 843 | "3 ... \n", |
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| 844 | "1174 0 92.5\n", |
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| 845 | "1 78.5\n", |
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| 846 | "2 92.0\n", |
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| 847 | "3 ... \n", |
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| 848 | "1175 0 130.0\n", |
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| 849 | "1 131.5\n", |
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| 850 | "2 138.0\n", |
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| 851 | "3 ... \n", |
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| 852 | "1176 0 127.0\n", |
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| 853 | "1 133.5\n", |
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| 854 | "2 137.5\n", |
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| 855 | "3 ... \n", |
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| 856 | "1177 0 158.857143\n", |
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| 857 | "1 158.857143\n", |
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| 858 | "2 150... \n", |
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| 859 | "\n", |
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| 860 | " Lead2 \\\n", |
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| 861 | "0 0 105.0\n", |
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| 862 | "1 104.5\n", |
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| 863 | "2 104.5\n", |
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| 864 | "3 ... \n", |
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| 865 | "1 0 103.0\n", |
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| 866 | "1 103.0\n", |
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| 867 | "2 102.0\n", |
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| 868 | "3 ... \n", |
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| 869 | "2 0 103.5\n", |
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| 870 | "1 106.5\n", |
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| 871 | "2 110.5\n", |
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| 872 | "3 ... \n", |
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| 873 | "3 0 100.5\n", |
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| 874 | "1 100.5\n", |
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| 875 | "2 101.5\n", |
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| 876 | "3 ... \n", |
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| 877 | "4 0 106.5\n", |
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| 878 | "1 106.5\n", |
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| 879 | "2 106.0\n", |
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| 880 | "3 ... \n", |
||
| 881 | "... ... \n", |
||
| 882 | "1173 0 100.5\n", |
||
| 883 | "1 100.0\n", |
||
| 884 | "2 99.5\n", |
||
| 885 | "3 ... \n", |
||
| 886 | "1174 0 102.5\n", |
||
| 887 | "1 103.0\n", |
||
| 888 | "2 103.0\n", |
||
| 889 | "3 ... \n", |
||
| 890 | "1175 0 103.0\n", |
||
| 891 | "1 103.5\n", |
||
| 892 | "2 103.5\n", |
||
| 893 | "3 ... \n", |
||
| 894 | "1176 0 100.5\n", |
||
| 895 | "1 100.0\n", |
||
| 896 | "2 102.5\n", |
||
| 897 | "3 ... \n", |
||
| 898 | "1177 0 103.0\n", |
||
| 899 | "1 103.5\n", |
||
| 900 | "2 107.0\n", |
||
| 901 | "3 ... \n", |
||
| 902 | "\n", |
||
| 903 | " Lead3 \\\n", |
||
| 904 | "0 0 104.714286\n", |
||
| 905 | "1 117.250000\n", |
||
| 906 | "2 101... \n", |
||
| 907 | "1 0 80.5\n", |
||
| 908 | "1 80.0\n", |
||
| 909 | "2 79.0\n", |
||
| 910 | "3 79.... \n", |
||
| 911 | "2 0 103.5\n", |
||
| 912 | "1 103.5\n", |
||
| 913 | "2 103.5\n", |
||
| 914 | "3 ... \n", |
||
| 915 | "3 0 104.5\n", |
||
| 916 | "1 104.5\n", |
||
| 917 | "2 104.0\n", |
||
| 918 | "3 ... \n", |
||
| 919 | "4 0 107.0\n", |
||
| 920 | "1 106.0\n", |
||
| 921 | "2 106.0\n", |
||
| 922 | "3 ... \n", |
||
| 923 | "... ... \n", |
||
| 924 | "1173 0 107.0\n", |
||
| 925 | "1 107.0\n", |
||
| 926 | "2 109.0\n", |
||
| 927 | "3 ... \n", |
||
| 928 | "1174 0 87.5\n", |
||
| 929 | "1 87.5\n", |
||
| 930 | "2 87.5\n", |
||
| 931 | "3 ... \n", |
||
| 932 | "1175 0 97.0\n", |
||
| 933 | "1 96.5\n", |
||
| 934 | "2 96.0\n", |
||
| 935 | "3 ... \n", |
||
| 936 | "1176 0 91.5\n", |
||
| 937 | "1 94.5\n", |
||
| 938 | "2 96.0\n", |
||
| 939 | "3 ... \n", |
||
| 940 | "1177 0 103.5\n", |
||
| 941 | "1 105.0\n", |
||
| 942 | "2 114.0\n", |
||
| 943 | "3 ... \n", |
||
| 944 | "\n", |
||
| 945 | " Lead4 \\\n", |
||
| 946 | "0 0 35.957447\n", |
||
| 947 | "1 35.500000\n", |
||
| 948 | "2 46... \n", |
||
| 949 | "1 0 67.5\n", |
||
| 950 | "1 72.0\n", |
||
| 951 | "2 77.0\n", |
||
| 952 | "3 ... \n", |
||
| 953 | "2 0 97.5\n", |
||
| 954 | "1 97.5\n", |
||
| 955 | "2 97.0\n", |
||
| 956 | "3 ... \n", |
||
| 957 | "3 0 63.0\n", |
||
| 958 | "1 60.0\n", |
||
| 959 | "2 67.5\n", |
||
| 960 | "3 ... \n", |
||
| 961 | "4 0 58.5\n", |
||
| 962 | "1 70.0\n", |
||
| 963 | "2 86.0\n", |
||
| 964 | "3 ... \n", |
||
| 965 | "... ... \n", |
||
| 966 | "1173 0 57.0\n", |
||
| 967 | "1 67.5\n", |
||
| 968 | "2 82.5\n", |
||
| 969 | "3 ... \n", |
||
| 970 | "1174 0 74.0\n", |
||
| 971 | "1 72.5\n", |
||
| 972 | "2 74.5\n", |
||
| 973 | "3 ... \n", |
||
| 974 | "1175 0 54.5\n", |
||
| 975 | "1 40.0\n", |
||
| 976 | "2 70.0\n", |
||
| 977 | "3 ... \n", |
||
| 978 | "1176 0 74.0\n", |
||
| 979 | "1 72.5\n", |
||
| 980 | "2 74.5\n", |
||
| 981 | "3 ... \n", |
||
| 982 | "1177 0 74.5\n", |
||
| 983 | "1 74.5\n", |
||
| 984 | "2 75.0\n", |
||
| 985 | "3 ... \n", |
||
| 986 | "\n", |
||
| 987 | " Lead5 \\\n", |
||
| 988 | "0 0 158.000000\n", |
||
| 989 | "1 157.333333\n", |
||
| 990 | "2 149... \n", |
||
| 991 | "1 0 127.5\n", |
||
| 992 | "1 126.0\n", |
||
| 993 | "2 131.5\n", |
||
| 994 | "3 ... \n", |
||
| 995 | "2 0 118.363636\n", |
||
| 996 | "1 97.000000\n", |
||
| 997 | "2 94... \n", |
||
| 998 | "3 0 130.0\n", |
||
| 999 | "1 123.5\n", |
||
| 1000 | "2 129.5\n", |
||
| 1001 | "3 ... \n", |
||
| 1002 | "4 0 141.0\n", |
||
| 1003 | "1 136.5\n", |
||
| 1004 | "2 138.0\n", |
||
| 1005 | "3 ... \n", |
||
| 1006 | "... ... \n", |
||
| 1007 | "1173 0 110.5\n", |
||
| 1008 | "1 107.5\n", |
||
| 1009 | "2 123.5\n", |
||
| 1010 | "3 ... \n", |
||
| 1011 | "1174 0 71.5\n", |
||
| 1012 | "1 74.5\n", |
||
| 1013 | "2 83.0\n", |
||
| 1014 | "3 ... \n", |
||
| 1015 | "1175 0 142.000\n", |
||
| 1016 | "1 147.000\n", |
||
| 1017 | "2 142.000\n", |
||
| 1018 | "3... \n", |
||
| 1019 | "1176 0 113.875000\n", |
||
| 1020 | "1 144.000000\n", |
||
| 1021 | "2 152... \n", |
||
| 1022 | "1177 0 142.0\n", |
||
| 1023 | "1 136.5\n", |
||
| 1024 | "2 133.0\n", |
||
| 1025 | "3 ... \n", |
||
| 1026 | "\n", |
||
| 1027 | " Lead6 \\\n", |
||
| 1028 | "0 0 119.5\n", |
||
| 1029 | "1 116.5\n", |
||
| 1030 | "2 125.5\n", |
||
| 1031 | "3 ... \n", |
||
| 1032 | "1 0 130.0\n", |
||
| 1033 | "1 139.0\n", |
||
| 1034 | "2 130.5\n", |
||
| 1035 | "3 ... \n", |
||
| 1036 | "2 0 116.666667\n", |
||
| 1037 | "1 155.500000\n", |
||
| 1038 | "2 156... \n", |
||
| 1039 | "3 0 131.5\n", |
||
| 1040 | "1 130.0\n", |
||
| 1041 | "2 131.5\n", |
||
| 1042 | "3 ... \n", |
||
| 1043 | "4 0 155.5\n", |
||
| 1044 | "1 158.5\n", |
||
| 1045 | "2 156.5\n", |
||
| 1046 | "3 ... \n", |
||
| 1047 | "... ... \n", |
||
| 1048 | "1173 0 142.5\n", |
||
| 1049 | "1 145.0\n", |
||
| 1050 | "2 156.5\n", |
||
| 1051 | "3 ... \n", |
||
| 1052 | "1174 0 113.0\n", |
||
| 1053 | "1 111.0\n", |
||
| 1054 | "2 119.0\n", |
||
| 1055 | "3 ... \n", |
||
| 1056 | "1175 0 135.0\n", |
||
| 1057 | "1 145.0\n", |
||
| 1058 | "2 138.5\n", |
||
| 1059 | "3 ... \n", |
||
| 1060 | "1176 0 132.5\n", |
||
| 1061 | "1 134.0\n", |
||
| 1062 | "2 143.5\n", |
||
| 1063 | "3 ... \n", |
||
| 1064 | "1177 0 150.0\n", |
||
| 1065 | "1 144.5\n", |
||
| 1066 | "2 154.5\n", |
||
| 1067 | "3 ... \n", |
||
| 1068 | "\n", |
||
| 1069 | " Lead7 \\\n", |
||
| 1070 | "0 0 137.5\n", |
||
| 1071 | "1 137.0\n", |
||
| 1072 | "2 136.5\n", |
||
| 1073 | "3 ... \n", |
||
| 1074 | "1 0 129.5\n", |
||
| 1075 | "1 129.5\n", |
||
| 1076 | "2 129.5\n", |
||
| 1077 | "3 ... \n", |
||
| 1078 | "2 0 131.200000\n", |
||
| 1079 | "1 136.166667\n", |
||
| 1080 | "2 104... \n", |
||
| 1081 | "3 0 140.5\n", |
||
| 1082 | "1 150.0\n", |
||
| 1083 | "2 144.5\n", |
||
| 1084 | "3 ... \n", |
||
| 1085 | "4 0 131.0\n", |
||
| 1086 | "1 130.5\n", |
||
| 1087 | "2 130.0\n", |
||
| 1088 | "3 ... \n", |
||
| 1089 | "... ... \n", |
||
| 1090 | "1173 0 97.0\n", |
||
| 1091 | "1 124.5\n", |
||
| 1092 | "2 105.5\n", |
||
| 1093 | "3 ... \n", |
||
| 1094 | "1174 0 141.600000\n", |
||
| 1095 | "1 133.250000\n", |
||
| 1096 | "2 122... \n", |
||
| 1097 | "1175 0 97.0\n", |
||
| 1098 | "1 124.5\n", |
||
| 1099 | "2 105.5\n", |
||
| 1100 | "3 ... \n", |
||
| 1101 | "1176 0 119.0\n", |
||
| 1102 | "1 118.5\n", |
||
| 1103 | "2 118.0\n", |
||
| 1104 | "3 ... \n", |
||
| 1105 | "1177 0 103.0\n", |
||
| 1106 | "1 95.5\n", |
||
| 1107 | "2 103.5\n", |
||
| 1108 | "3 ... \n", |
||
| 1109 | "\n", |
||
| 1110 | " Lead8 \\\n", |
||
| 1111 | "0 0 156.0\n", |
||
| 1112 | "1 154.0\n", |
||
| 1113 | "2 153.5\n", |
||
| 1114 | "3 ... \n", |
||
| 1115 | "1 0 155.5\n", |
||
| 1116 | "1 155.5\n", |
||
| 1117 | "2 155.5\n", |
||
| 1118 | "3 ... \n", |
||
| 1119 | "2 0 108.5\n", |
||
| 1120 | "1 100.0\n", |
||
| 1121 | "2 112.5\n", |
||
| 1122 | "3 ... \n", |
||
| 1123 | "3 0 155.0\n", |
||
| 1124 | "1 155.0\n", |
||
| 1125 | "2 155.0\n", |
||
| 1126 | "3 ... \n", |
||
| 1127 | "4 0 133.5\n", |
||
| 1128 | "1 133.0\n", |
||
| 1129 | "2 146.5\n", |
||
| 1130 | "3 ... \n", |
||
| 1131 | "... ... \n", |
||
| 1132 | "1173 0 127.5\n", |
||
| 1133 | "1 126.0\n", |
||
| 1134 | "2 127.5\n", |
||
| 1135 | "3 ... \n", |
||
| 1136 | "1174 0 121.0\n", |
||
| 1137 | "1 120.5\n", |
||
| 1138 | "2 120.0\n", |
||
| 1139 | "3 ... \n", |
||
| 1140 | "1175 0 137.5\n", |
||
| 1141 | "1 144.5\n", |
||
| 1142 | "2 153.5\n", |
||
| 1143 | "3 ... \n", |
||
| 1144 | "1176 0 125.5\n", |
||
| 1145 | "1 121.5\n", |
||
| 1146 | "2 129.0\n", |
||
| 1147 | "3 ... \n", |
||
| 1148 | "1177 0 111.5\n", |
||
| 1149 | "1 100.0\n", |
||
| 1150 | "2 104.5\n", |
||
| 1151 | "3 ... \n", |
||
| 1152 | "\n", |
||
| 1153 | " Lead9 \\\n", |
||
| 1154 | "0 0 162.000000\n", |
||
| 1155 | "1 162.000000\n", |
||
| 1156 | "2 151... \n", |
||
| 1157 | "1 0 139.0\n", |
||
| 1158 | "1 131.5\n", |
||
| 1159 | "2 128.5\n", |
||
| 1160 | "3 ... \n", |
||
| 1161 | "2 0 169.500000\n", |
||
| 1162 | "1 153.000000\n", |
||
| 1163 | "2 145... \n", |
||
| 1164 | "3 0 169.500000\n", |
||
| 1165 | "1 153.000000\n", |
||
| 1166 | "2 145... \n", |
||
| 1167 | "4 0 139.333333\n", |
||
| 1168 | "1 159.357143\n", |
||
| 1169 | "2 156... \n", |
||
| 1170 | "... ... \n", |
||
| 1171 | "1173 0 142.5\n", |
||
| 1172 | "1 137.5\n", |
||
| 1173 | "2 137.5\n", |
||
| 1174 | "3 ... \n", |
||
| 1175 | "1174 0 87.0\n", |
||
| 1176 | "1 86.5\n", |
||
| 1177 | "2 86.0\n", |
||
| 1178 | "3 ... \n", |
||
| 1179 | "1175 0 87.0\n", |
||
| 1180 | "1 86.5\n", |
||
| 1181 | "2 86.0\n", |
||
| 1182 | "3 ... \n", |
||
| 1183 | "1176 0 137.0\n", |
||
| 1184 | "1 136.5\n", |
||
| 1185 | "2 138.5\n", |
||
| 1186 | "3 ... \n", |
||
| 1187 | "1177 0 165.428571\n", |
||
| 1188 | "1 165.875000\n", |
||
| 1189 | "2 154... \n", |
||
| 1190 | "\n", |
||
| 1191 | " Lead10 \\\n", |
||
| 1192 | "0 0 155.0\n", |
||
| 1193 | "1 154.5\n", |
||
| 1194 | "2 155.5\n", |
||
| 1195 | "3 ... \n", |
||
| 1196 | "1 0 153.5\n", |
||
| 1197 | "1 149.0\n", |
||
| 1198 | "2 151.0\n", |
||
| 1199 | "3 ... \n", |
||
| 1200 | "2 0 156.0\n", |
||
| 1201 | "1 156.0\n", |
||
| 1202 | "2 157.5\n", |
||
| 1203 | "3 ... \n", |
||
| 1204 | "3 0 119.5\n", |
||
| 1205 | "1 122.0\n", |
||
| 1206 | "2 129.5\n", |
||
| 1207 | "3 ... \n", |
||
| 1208 | "4 0 137.0\n", |
||
| 1209 | "1 138.5\n", |
||
| 1210 | "2 145.5\n", |
||
| 1211 | "3 ... \n", |
||
| 1212 | "... ... \n", |
||
| 1213 | "1173 0 133.0\n", |
||
| 1214 | "1 132.5\n", |
||
| 1215 | "2 132.5\n", |
||
| 1216 | "3 ... \n", |
||
| 1217 | "1174 0 140.0\n", |
||
| 1218 | "1 148.5\n", |
||
| 1219 | "2 142.0\n", |
||
| 1220 | "3 ... \n", |
||
| 1221 | "1175 0 156.5\n", |
||
| 1222 | "1 156.5\n", |
||
| 1223 | "2 155.5\n", |
||
| 1224 | "3 ... \n", |
||
| 1225 | "1176 0 55.5\n", |
||
| 1226 | "1 52.0\n", |
||
| 1227 | "2 77.5\n", |
||
| 1228 | "3 ... \n", |
||
| 1229 | "1177 0 146.5\n", |
||
| 1230 | "1 145.0\n", |
||
| 1231 | "2 144.0\n", |
||
| 1232 | "3 ... \n", |
||
| 1233 | "\n", |
||
| 1234 | " Lead11 \\\n", |
||
| 1235 | "0 0 193.545455\n", |
||
| 1236 | "1 3.500000\n", |
||
| 1237 | "2 3... \n", |
||
| 1238 | "1 0 80.166667\n", |
||
| 1239 | "1 77.071429\n", |
||
| 1240 | "2 82... \n", |
||
| 1241 | "2 0 145.5\n", |
||
| 1242 | "1 145.5\n", |
||
| 1243 | "2 145.5\n", |
||
| 1244 | "3 ... \n", |
||
| 1245 | "3 0 154.055944\n", |
||
| 1246 | "1 136.396396\n", |
||
| 1247 | "2 44... \n", |
||
| 1248 | "4 0 137.5\n", |
||
| 1249 | "1 138.0\n", |
||
| 1250 | "2 146.5\n", |
||
| 1251 | "3 ... \n", |
||
| 1252 | "... ... \n", |
||
| 1253 | "1173 0 4.0\n", |
||
| 1254 | "1 12.5\n", |
||
| 1255 | "2 19.0\n", |
||
| 1256 | "3 ... \n", |
||
| 1257 | "1174 0 137.909091\n", |
||
| 1258 | "1 140.000000\n", |
||
| 1259 | "2 137... \n", |
||
| 1260 | "1175 0 135.0\n", |
||
| 1261 | "1 135.0\n", |
||
| 1262 | "2 135.5\n", |
||
| 1263 | "3 ... \n", |
||
| 1264 | "1176 0 116.5\n", |
||
| 1265 | "1 116.5\n", |
||
| 1266 | "2 117.0\n", |
||
| 1267 | "3 ... \n", |
||
| 1268 | "1177 0 96.5\n", |
||
| 1269 | "1 96.5\n", |
||
| 1270 | "2 96.5\n", |
||
| 1271 | "3 ... \n", |
||
| 1272 | "\n", |
||
| 1273 | " Lead12 Label \n", |
||
| 1274 | "0 0 89.000000\n", |
||
| 1275 | "1 109.500000\n", |
||
| 1276 | "2 91... 0.0 \n", |
||
| 1277 | "1 0 109.000000\n", |
||
| 1278 | "1 100.000000\n", |
||
| 1279 | "2 111... 0.0 \n", |
||
| 1280 | "2 0 107.170732\n", |
||
| 1281 | "1 104.000000\n", |
||
| 1282 | "2 121... 0.0 \n", |
||
| 1283 | "3 0 121.714286\n", |
||
| 1284 | "1 144.000000\n", |
||
| 1285 | "2 133... 0.0 \n", |
||
| 1286 | "4 0 140.0\n", |
||
| 1287 | "1 135.0\n", |
||
| 1288 | "2 142.0\n", |
||
| 1289 | "3 ... 0.0 \n", |
||
| 1290 | "... ... ... \n", |
||
| 1291 | "1173 0 138.5\n", |
||
| 1292 | "1 143.0\n", |
||
| 1293 | "2 143.5\n", |
||
| 1294 | "3 ... 4.0 \n", |
||
| 1295 | "1174 0 117.5\n", |
||
| 1296 | "1 125.0\n", |
||
| 1297 | "2 127.0\n", |
||
| 1298 | "3 ... 4.0 \n", |
||
| 1299 | "1175 0 128.0\n", |
||
| 1300 | "1 124.5\n", |
||
| 1301 | "2 136.5\n", |
||
| 1302 | "3 ... 4.0 \n", |
||
| 1303 | "1176 0 114.0\n", |
||
| 1304 | "1 110.5\n", |
||
| 1305 | "2 123.5\n", |
||
| 1306 | "3 ... 4.0 \n", |
||
| 1307 | "1177 0 137.0\n", |
||
| 1308 | "1 134.0\n", |
||
| 1309 | "2 129.5\n", |
||
| 1310 | "3 ... 4.0 \n", |
||
| 1311 | "\n", |
||
| 1312 | "[1178 rows x 13 columns]" |
||
| 1313 | ] |
||
| 1314 | }, |
||
| 1315 | "execution_count": 10, |
||
| 1316 | "metadata": {}, |
||
| 1317 | "output_type": "execute_result" |
||
| 1318 | } |
||
| 1319 | ], |
||
| 1320 | "source": [ |
||
| 1321 | "X3d_nested" |
||
| 1322 | ] |
||
| 1323 | }, |
||
| 1324 | { |
||
| 1325 | "cell_type": "code", |
||
| 1326 | "execution_count": null, |
||
| 1327 | "id": "e3b5fedb", |
||
| 1328 | "metadata": {}, |
||
| 1329 | "outputs": [], |
||
| 1330 | "source": [] |
||
| 1331 | }, |
||
| 1332 | { |
||
| 1333 | "cell_type": "markdown", |
||
| 1334 | "id": "c2a71ae7", |
||
| 1335 | "metadata": {}, |
||
| 1336 | "source": [ |
||
| 1337 | "### Classification Task" |
||
| 1338 | ] |
||
| 1339 | }, |
||
| 1340 | { |
||
| 1341 | "cell_type": "code", |
||
| 1342 | "execution_count": 11, |
||
| 1343 | "id": "69ec3a5d", |
||
| 1344 | "metadata": {}, |
||
| 1345 | "outputs": [], |
||
| 1346 | "source": [ |
||
| 1347 | "split = StratifiedShuffleSplit(n_splits=1, test_size=0.25, random_state=42)\n", |
||
| 1348 | "for train_index, test_index in split.split(X3d_nested, X3d_nested['Label']):\n", |
||
| 1349 | " X_train = X3d_nested.loc[train_index]\n", |
||
| 1350 | " X_test = X3d_nested.loc[test_index]" |
||
| 1351 | ] |
||
| 1352 | }, |
||
| 1353 | { |
||
| 1354 | "cell_type": "code", |
||
| 1355 | "execution_count": 12, |
||
| 1356 | "id": "0df5b1b4", |
||
| 1357 | "metadata": {}, |
||
| 1358 | "outputs": [ |
||
| 1359 | { |
||
| 1360 | "name": "stdout", |
||
| 1361 | "output_type": "stream", |
||
| 1362 | "text": [ |
||
| 1363 | "Data set proportions:\n", |
||
| 1364 | "3.0 0.241087\n", |
||
| 1365 | "2.0 0.212224\n", |
||
| 1366 | "4.0 0.202886\n", |
||
| 1367 | "1.0 0.197793\n", |
||
| 1368 | "0.0 0.146010\n", |
||
| 1369 | "Name: Label, dtype: float64\n", |
||
| 1370 | "\n", |
||
| 1371 | "Test set proportions:\n", |
||
| 1372 | "3.0 0.240678\n", |
||
| 1373 | "2.0 0.213559\n", |
||
| 1374 | "4.0 0.203390\n", |
||
| 1375 | "1.0 0.196610\n", |
||
| 1376 | "0.0 0.145763\n", |
||
| 1377 | "Name: Label, dtype: float64\n" |
||
| 1378 | ] |
||
| 1379 | } |
||
| 1380 | ], |
||
| 1381 | "source": [ |
||
| 1382 | "#Ascertain the spits are balanced\n", |
||
| 1383 | "dataSetProp = X3d_nested['Label'].value_counts()/len(X3d_nested)\n", |
||
| 1384 | "testSetProp = X_test['Label'].value_counts() / len(X_test)\n", |
||
| 1385 | "print('Data set proportions:')\n", |
||
| 1386 | "print(dataSetProp)\n", |
||
| 1387 | "print('\\nTest set proportions:')\n", |
||
| 1388 | "print(testSetProp)" |
||
| 1389 | ] |
||
| 1390 | }, |
||
| 1391 | { |
||
| 1392 | "cell_type": "code", |
||
| 1393 | "execution_count": 13, |
||
| 1394 | "id": "c68b5285", |
||
| 1395 | "metadata": { |
||
| 1396 | "scrolled": true |
||
| 1397 | }, |
||
| 1398 | "outputs": [], |
||
| 1399 | "source": [ |
||
| 1400 | "y_train = X_train['Label']\n", |
||
| 1401 | "X_train.drop('Label',axis=1,inplace=True)\n", |
||
| 1402 | "y_test = X_test['Label']\n", |
||
| 1403 | "X_test.drop('Label',axis=1,inplace=True)" |
||
| 1404 | ] |
||
| 1405 | }, |
||
| 1406 | { |
||
| 1407 | "cell_type": "code", |
||
| 1408 | "execution_count": 14, |
||
| 1409 | "id": "3f727d3e", |
||
| 1410 | "metadata": { |
||
| 1411 | "scrolled": true |
||
| 1412 | }, |
||
| 1413 | "outputs": [ |
||
| 1414 | { |
||
| 1415 | "data": { |
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| 1416 | "text/html": [ |
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| 1417 | "<div>\n", |
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| 1418 | "<style scoped>\n", |
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| 1428 | " text-align: right;\n", |
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| 1432 | " <thead>\n", |
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| 1433 | " <tr style=\"text-align: right;\">\n", |
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| 1434 | " <th></th>\n", |
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| 1435 | " <th>Lead1</th>\n", |
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| 1436 | " <th>Lead2</th>\n", |
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| 1437 | " <th>Lead3</th>\n", |
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| 1438 | " <th>Lead4</th>\n", |
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| 1439 | " <th>Lead5</th>\n", |
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| 1440 | " <th>Lead6</th>\n", |
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| 1441 | " <th>Lead7</th>\n", |
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| 1442 | " <th>Lead8</th>\n", |
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| 1443 | " <th>Lead9</th>\n", |
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| 1444 | " <th>Lead10</th>\n", |
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| 1445 | " <th>Lead11</th>\n", |
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| 1446 | " <th>Lead12</th>\n", |
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| 1447 | " </tr>\n", |
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| 1448 | " </thead>\n", |
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| 1449 | " <tbody>\n", |
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| 1450 | " <tr>\n", |
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| 1451 | " <th>309</th>\n", |
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| 1452 | " <td>0 138.0\n", |
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| 1453 | "1 140.5\n", |
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| 1454 | "2 143.5\n", |
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| 1455 | "3 ...</td>\n", |
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| 1456 | " <td>0 100.0\n", |
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| 1457 | "1 89.5\n", |
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| 1458 | "2 88.5\n", |
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| 1459 | "3 ...</td>\n", |
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| 1463 | "3 ...</td>\n", |
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| 1464 | " <td>0 40.0\n", |
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| 1467 | "3 ...</td>\n", |
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| 1468 | " <td>0 108.50\n", |
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| 1469 | "1 98.00\n", |
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| 1470 | "2 113.00\n", |
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| 1471 | "3 ...</td>\n", |
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| 1473 | "1 155.0\n", |
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| 1474 | "2 156.5\n", |
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| 1475 | "3 ...</td>\n", |
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| 1476 | " <td>0 125.535714\n", |
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| 1477 | "1 147.666667\n", |
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| 1478 | "2 139...</td>\n", |
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| 1479 | " <td>0 133.0\n", |
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| 1480 | "1 135.5\n", |
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| 1481 | "2 131.5\n", |
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| 1482 | "3 ...</td>\n", |
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| 1483 | " <td>0 107.5\n", |
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| 1484 | "1 97.5\n", |
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| 1485 | "2 123.0\n", |
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| 1486 | "3 ...</td>\n", |
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| 1487 | " <td>0 140.5\n", |
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| 1488 | "1 139.5\n", |
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| 1489 | "2 146.0\n", |
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| 1490 | "3 ...</td>\n", |
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| 1491 | " <td>0 153.0\n", |
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| 1492 | "1 150.5\n", |
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| 1493 | "2 162.5\n", |
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| 1494 | "3 ...</td>\n", |
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| 1495 | " <td>0 101.0\n", |
||
| 1496 | "1 100.5\n", |
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| 1497 | "2 110.0\n", |
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| 1498 | "3 ...</td>\n", |
||
| 1499 | " </tr>\n", |
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| 1500 | " <tr>\n", |
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| 1501 | " <th>1108</th>\n", |
||
| 1502 | " <td>0 124.5\n", |
||
| 1503 | "1 115.0\n", |
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| 1504 | "2 116.5\n", |
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| 1505 | "3 ...</td>\n", |
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| 1506 | " <td>0 101.0\n", |
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| 1507 | "1 101.5\n", |
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| 1508 | "2 102.5\n", |
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| 1509 | "3 ...</td>\n", |
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| 1510 | " <td>0 107.0\n", |
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| 1511 | "1 107.0\n", |
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| 1512 | "2 109.0\n", |
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| 1513 | "3 ...</td>\n", |
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| 1514 | " <td>0 25.5\n", |
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| 1515 | "1 22.5\n", |
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| 1516 | "2 40.5\n", |
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| 1517 | "3 ...</td>\n", |
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| 1519 | "1 112.5\n", |
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| 1521 | "3 ...</td>\n", |
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| 1522 | " <td>0 135.0\n", |
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| 1530 | "1 111.0\n", |
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| 1531 | "2 114.0\n", |
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| 1535 | "2 154...</td>\n", |
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| 1536 | " <td>0 130.0\n", |
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| 1537 | "1 132.0\n", |
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| 1538 | "2 133.0\n", |
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| 1539 | "3 ...</td>\n", |
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| 1540 | " <td>0 89.5\n", |
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| 1541 | "1 92.5\n", |
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| 1542 | "2 102.5\n", |
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| 1543 | "3 ...</td>\n", |
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| 1544 | " <td>0 135.0\n", |
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| 1545 | "1 128.5\n", |
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| 1546 | "2 125.0\n", |
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| 1547 | "3 ...</td>\n", |
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| 1548 | " </tr>\n", |
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| 1549 | " <tr>\n", |
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| 1550 | " <th>647</th>\n", |
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| 1551 | " <td>0 65.000000\n", |
||
| 1552 | "1 56.000000\n", |
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| 1553 | "2 58.85...</td>\n", |
||
| 1554 | " <td>0 80.0\n", |
||
| 1555 | "1 80.0\n", |
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| 1556 | "2 80.0\n", |
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| 1557 | "3 81....</td>\n", |
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| 1568 | "1 50.200000\n", |
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| 1574 | "1 58.500000\n", |
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| 1576 | " <td>0 133.500000\n", |
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| 1577 | "1 133.500000\n", |
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| 1578 | "2 133...</td>\n", |
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| 1579 | " <td>0 73.000000\n", |
||
| 1580 | "1 75.000000\n", |
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| 1581 | "2 75...</td>\n", |
||
| 1582 | " <td>0 68.333333\n", |
||
| 1583 | "1 57.500000\n", |
||
| 1584 | "2 59.09...</td>\n", |
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||
| 1586 | "1 76.466667\n", |
||
| 1587 | "2 79...</td>\n", |
||
| 1588 | " </tr>\n", |
||
| 1589 | " <tr>\n", |
||
| 1590 | " <th>863</th>\n", |
||
| 1591 | " <td>0 157.666667\n", |
||
| 1592 | "1 157.666667\n", |
||
| 1593 | "2 149...</td>\n", |
||
| 1594 | " <td>0 105.0\n", |
||
| 1595 | "1 109.5\n", |
||
| 1596 | "2 102.5\n", |
||
| 1597 | "3 ...</td>\n", |
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| 1598 | " <td>0 104.0\n", |
||
| 1599 | "1 112.5\n", |
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| 1600 | "2 104.5\n", |
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| 1601 | "3 ...</td>\n", |
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| 1602 | " <td>0 52.0\n", |
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| 1603 | "1 76.0\n", |
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| 1604 | "2 58.0\n", |
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| 1605 | "3 ...</td>\n", |
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| 1606 | " <td>0 124.5\n", |
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| 1607 | "1 120.5\n", |
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| 1608 | "2 128.5\n", |
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| 1609 | "3 ...</td>\n", |
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| 1610 | " <td>0 118.333333\n", |
||
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||
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||
| 1614 | "1 120.0\n", |
||
| 1615 | "2 151.0\n", |
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| 1616 | "3 ...</td>\n", |
||
| 1617 | " <td>0 92.5\n", |
||
| 1618 | "1 94.0\n", |
||
| 1619 | "2 109.5\n", |
||
| 1620 | "3 ...</td>\n", |
||
| 1621 | " <td>0 120.0\n", |
||
| 1622 | "1 120.0\n", |
||
| 1623 | "2 126.5\n", |
||
| 1624 | "3 ...</td>\n", |
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| 1625 | " <td>0 150.0\n", |
||
| 1626 | "1 153.5\n", |
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| 1627 | "2 145.5\n", |
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| 1628 | "3 ...</td>\n", |
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| 1629 | " <td>0 144.0\n", |
||
| 1630 | "1 137.5\n", |
||
| 1631 | "2 145.5\n", |
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| 1632 | "3 ...</td>\n", |
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||
| 1634 | "1 126.962963\n", |
||
| 1635 | "2 133...</td>\n", |
||
| 1636 | " </tr>\n", |
||
| 1637 | " <tr>\n", |
||
| 1638 | " <th>207</th>\n", |
||
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||
| 1640 | "1 130.0\n", |
||
| 1641 | "2 130.0\n", |
||
| 1642 | "3 ...</td>\n", |
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| 1645 | "2 93.0\n", |
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||
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||
| 1651 | "1 33.0\n", |
||
| 1652 | "2 63.0\n", |
||
| 1653 | "3 ...</td>\n", |
||
| 1654 | " <td>0 83.50\n", |
||
| 1655 | "1 85.00\n", |
||
| 1656 | "2 94.04\n", |
||
| 1657 | "3 ...</td>\n", |
||
| 1658 | " <td>0 117.5\n", |
||
| 1659 | "1 113.5\n", |
||
| 1660 | "2 122.5\n", |
||
| 1661 | "3 ...</td>\n", |
||
| 1662 | " <td>0 157.818182\n", |
||
| 1663 | "1 144.714286\n", |
||
| 1664 | "2 122...</td>\n", |
||
| 1665 | " <td>0 104.5\n", |
||
| 1666 | "1 93.0\n", |
||
| 1667 | "2 113.5\n", |
||
| 1668 | "3 ...</td>\n", |
||
| 1669 | " <td>0 116.5\n", |
||
| 1670 | "1 106.5\n", |
||
| 1671 | "2 128.5\n", |
||
| 1672 | "3 ...</td>\n", |
||
| 1673 | " <td>0 147.0\n", |
||
| 1674 | "1 145.0\n", |
||
| 1675 | "2 149.0\n", |
||
| 1676 | "3 ...</td>\n", |
||
| 1677 | " <td>0 153.781022\n", |
||
| 1678 | "1 141.024793\n", |
||
| 1679 | "2 22...</td>\n", |
||
| 1680 | " <td>0 106.0\n", |
||
| 1681 | "1 101.5\n", |
||
| 1682 | "2 113.5\n", |
||
| 1683 | "3 ...</td>\n", |
||
| 1684 | " </tr>\n", |
||
| 1685 | " </tbody>\n", |
||
| 1686 | "</table>\n", |
||
| 1687 | "</div>" |
||
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| 1689 | "text/plain": [ |
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| 1693 | "2 143.5\n", |
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| 1694 | "3 ... \n", |
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| 1695 | "1108 0 124.5\n", |
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| 1696 | "1 115.0\n", |
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| 1697 | "2 116.5\n", |
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| 1698 | "3 ... \n", |
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| 1699 | "647 0 65.000000\n", |
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| 1700 | "1 56.000000\n", |
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| 1701 | "2 58.85... \n", |
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| 1702 | "863 0 157.666667\n", |
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| 1703 | "1 157.666667\n", |
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||
| 1705 | "207 0 122.0\n", |
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| 1706 | "1 130.0\n", |
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| 1707 | "2 130.0\n", |
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| 1708 | "3 ... \n", |
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| 1709 | "\n", |
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| 1710 | " Lead2 \\\n", |
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| 1711 | "309 0 100.0\n", |
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||
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||
| 1716 | "1 101.5\n", |
||
| 1717 | "2 102.5\n", |
||
| 1718 | "3 ... \n", |
||
| 1719 | "647 0 80.0\n", |
||
| 1720 | "1 80.0\n", |
||
| 1721 | "2 80.0\n", |
||
| 1722 | "3 81.... \n", |
||
| 1723 | "863 0 105.0\n", |
||
| 1724 | "1 109.5\n", |
||
| 1725 | "2 102.5\n", |
||
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||
| 1727 | "207 0 97.5\n", |
||
| 1728 | "1 92.0\n", |
||
| 1729 | "2 93.0\n", |
||
| 1730 | "3 ... \n", |
||
| 1731 | "\n", |
||
| 1732 | " Lead3 \\\n", |
||
| 1733 | "309 0 96.5\n", |
||
| 1734 | "1 96.0\n", |
||
| 1735 | "2 101.0\n", |
||
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||
| 1737 | "1108 0 107.0\n", |
||
| 1738 | "1 107.0\n", |
||
| 1739 | "2 109.0\n", |
||
| 1740 | "3 ... \n", |
||
| 1741 | "647 0 45.000000\n", |
||
| 1742 | "1 44.500000\n", |
||
| 1743 | "2 46.00... \n", |
||
| 1744 | "863 0 104.0\n", |
||
| 1745 | "1 112.5\n", |
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| 1746 | "2 104.5\n", |
||
| 1747 | "3 ... \n", |
||
| 1748 | "207 0 85.000000\n", |
||
| 1749 | "1 80.500000\n", |
||
| 1750 | "2 86... \n", |
||
| 1751 | "\n", |
||
| 1752 | " Lead4 \\\n", |
||
| 1753 | "309 0 40.0\n", |
||
| 1754 | "1 33.0\n", |
||
| 1755 | "2 66.5\n", |
||
| 1756 | "3 ... \n", |
||
| 1757 | "1108 0 25.5\n", |
||
| 1758 | "1 22.5\n", |
||
| 1759 | "2 40.5\n", |
||
| 1760 | "3 ... \n", |
||
| 1761 | "647 0 40.500000\n", |
||
| 1762 | "1 51.914286\n", |
||
| 1763 | "2 45.92... \n", |
||
| 1764 | "863 0 52.0\n", |
||
| 1765 | "1 76.0\n", |
||
| 1766 | "2 58.0\n", |
||
| 1767 | "3 ... \n", |
||
| 1768 | "207 0 34.0\n", |
||
| 1769 | "1 33.0\n", |
||
| 1770 | "2 63.0\n", |
||
| 1771 | "3 ... \n", |
||
| 1772 | "\n", |
||
| 1773 | " Lead5 \\\n", |
||
| 1774 | "309 0 108.50\n", |
||
| 1775 | "1 98.00\n", |
||
| 1776 | "2 113.00\n", |
||
| 1777 | "3 ... \n", |
||
| 1778 | "1108 0 106.5\n", |
||
| 1779 | "1 112.5\n", |
||
| 1780 | "2 117.5\n", |
||
| 1781 | "3 ... \n", |
||
| 1782 | "647 0 77.000000\n", |
||
| 1783 | "1 78.555556\n", |
||
| 1784 | "2 78... \n", |
||
| 1785 | "863 0 124.5\n", |
||
| 1786 | "1 120.5\n", |
||
| 1787 | "2 128.5\n", |
||
| 1788 | "3 ... \n", |
||
| 1789 | "207 0 83.50\n", |
||
| 1790 | "1 85.00\n", |
||
| 1791 | "2 94.04\n", |
||
| 1792 | "3 ... \n", |
||
| 1793 | "\n", |
||
| 1794 | " Lead6 \\\n", |
||
| 1795 | "309 0 156.0\n", |
||
| 1796 | "1 155.0\n", |
||
| 1797 | "2 156.5\n", |
||
| 1798 | "3 ... \n", |
||
| 1799 | "1108 0 135.0\n", |
||
| 1800 | "1 145.0\n", |
||
| 1801 | "2 138.5\n", |
||
| 1802 | "3 ... \n", |
||
| 1803 | "647 0 45.500000\n", |
||
| 1804 | "1 50.200000\n", |
||
| 1805 | "2 46.00... \n", |
||
| 1806 | "863 0 118.333333\n", |
||
| 1807 | "1 125.500000\n", |
||
| 1808 | "2 128... \n", |
||
| 1809 | "207 0 117.5\n", |
||
| 1810 | "1 113.5\n", |
||
| 1811 | "2 122.5\n", |
||
| 1812 | "3 ... \n", |
||
| 1813 | "\n", |
||
| 1814 | " Lead7 \\\n", |
||
| 1815 | "309 0 125.535714\n", |
||
| 1816 | "1 147.666667\n", |
||
| 1817 | "2 139... \n", |
||
| 1818 | "1108 0 145.148148\n", |
||
| 1819 | "1 6.000000\n", |
||
| 1820 | "2 6... \n", |
||
| 1821 | "647 0 78.000000\n", |
||
| 1822 | "1 82.000000\n", |
||
| 1823 | "2 76... \n", |
||
| 1824 | "863 0 113.5\n", |
||
| 1825 | "1 120.0\n", |
||
| 1826 | "2 151.0\n", |
||
| 1827 | "3 ... \n", |
||
| 1828 | "207 0 157.818182\n", |
||
| 1829 | "1 144.714286\n", |
||
| 1830 | "2 122... \n", |
||
| 1831 | "\n", |
||
| 1832 | " Lead8 \\\n", |
||
| 1833 | "309 0 133.0\n", |
||
| 1834 | "1 135.5\n", |
||
| 1835 | "2 131.5\n", |
||
| 1836 | "3 ... \n", |
||
| 1837 | "1108 0 123.5\n", |
||
| 1838 | "1 111.0\n", |
||
| 1839 | "2 114.0\n", |
||
| 1840 | "3 ... \n", |
||
| 1841 | "647 0 54.636364\n", |
||
| 1842 | "1 58.500000\n", |
||
| 1843 | "2 62.12... \n", |
||
| 1844 | "863 0 92.5\n", |
||
| 1845 | "1 94.0\n", |
||
| 1846 | "2 109.5\n", |
||
| 1847 | "3 ... \n", |
||
| 1848 | "207 0 104.5\n", |
||
| 1849 | "1 93.0\n", |
||
| 1850 | "2 113.5\n", |
||
| 1851 | "3 ... \n", |
||
| 1852 | "\n", |
||
| 1853 | " Lead9 \\\n", |
||
| 1854 | "309 0 107.5\n", |
||
| 1855 | "1 97.5\n", |
||
| 1856 | "2 123.0\n", |
||
| 1857 | "3 ... \n", |
||
| 1858 | "1108 0 165.428571\n", |
||
| 1859 | "1 165.875000\n", |
||
| 1860 | "2 154... \n", |
||
| 1861 | "647 0 133.500000\n", |
||
| 1862 | "1 133.500000\n", |
||
| 1863 | "2 133... \n", |
||
| 1864 | "863 0 120.0\n", |
||
| 1865 | "1 120.0\n", |
||
| 1866 | "2 126.5\n", |
||
| 1867 | "3 ... \n", |
||
| 1868 | "207 0 116.5\n", |
||
| 1869 | "1 106.5\n", |
||
| 1870 | "2 128.5\n", |
||
| 1871 | "3 ... \n", |
||
| 1872 | "\n", |
||
| 1873 | " Lead10 \\\n", |
||
| 1874 | "309 0 140.5\n", |
||
| 1875 | "1 139.5\n", |
||
| 1876 | "2 146.0\n", |
||
| 1877 | "3 ... \n", |
||
| 1878 | "1108 0 130.0\n", |
||
| 1879 | "1 132.0\n", |
||
| 1880 | "2 133.0\n", |
||
| 1881 | "3 ... \n", |
||
| 1882 | "647 0 73.000000\n", |
||
| 1883 | "1 75.000000\n", |
||
| 1884 | "2 75... \n", |
||
| 1885 | "863 0 150.0\n", |
||
| 1886 | "1 153.5\n", |
||
| 1887 | "2 145.5\n", |
||
| 1888 | "3 ... \n", |
||
| 1889 | "207 0 147.0\n", |
||
| 1890 | "1 145.0\n", |
||
| 1891 | "2 149.0\n", |
||
| 1892 | "3 ... \n", |
||
| 1893 | "\n", |
||
| 1894 | " Lead11 \\\n", |
||
| 1895 | "309 0 153.0\n", |
||
| 1896 | "1 150.5\n", |
||
| 1897 | "2 162.5\n", |
||
| 1898 | "3 ... \n", |
||
| 1899 | "1108 0 89.5\n", |
||
| 1900 | "1 92.5\n", |
||
| 1901 | "2 102.5\n", |
||
| 1902 | "3 ... \n", |
||
| 1903 | "647 0 68.333333\n", |
||
| 1904 | "1 57.500000\n", |
||
| 1905 | "2 59.09... \n", |
||
| 1906 | "863 0 144.0\n", |
||
| 1907 | "1 137.5\n", |
||
| 1908 | "2 145.5\n", |
||
| 1909 | "3 ... \n", |
||
| 1910 | "207 0 153.781022\n", |
||
| 1911 | "1 141.024793\n", |
||
| 1912 | "2 22... \n", |
||
| 1913 | "\n", |
||
| 1914 | " Lead12 \n", |
||
| 1915 | "309 0 101.0\n", |
||
| 1916 | "1 100.5\n", |
||
| 1917 | "2 110.0\n", |
||
| 1918 | "3 ... \n", |
||
| 1919 | "1108 0 135.0\n", |
||
| 1920 | "1 128.5\n", |
||
| 1921 | "2 125.0\n", |
||
| 1922 | "3 ... \n", |
||
| 1923 | "647 0 74.631579\n", |
||
| 1924 | "1 76.466667\n", |
||
| 1925 | "2 79... \n", |
||
| 1926 | "863 0 127.888889\n", |
||
| 1927 | "1 126.962963\n", |
||
| 1928 | "2 133... \n", |
||
| 1929 | "207 0 106.0\n", |
||
| 1930 | "1 101.5\n", |
||
| 1931 | "2 113.5\n", |
||
| 1932 | "3 ... " |
||
| 1933 | ] |
||
| 1934 | }, |
||
| 1935 | "execution_count": 14, |
||
| 1936 | "metadata": {}, |
||
| 1937 | "output_type": "execute_result" |
||
| 1938 | } |
||
| 1939 | ], |
||
| 1940 | "source": [ |
||
| 1941 | "X_train.head()" |
||
| 1942 | ] |
||
| 1943 | }, |
||
| 1944 | { |
||
| 1945 | "cell_type": "markdown", |
||
| 1946 | "id": "7fa8a41a", |
||
| 1947 | "metadata": {}, |
||
| 1948 | "source": [ |
||
| 1949 | "#### Column concatenator" |
||
| 1950 | ] |
||
| 1951 | }, |
||
| 1952 | { |
||
| 1953 | "cell_type": "code", |
||
| 1954 | "execution_count": 15, |
||
| 1955 | "id": "36fbcee6", |
||
| 1956 | "metadata": {}, |
||
| 1957 | "outputs": [ |
||
| 1958 | { |
||
| 1959 | "name": "stdout", |
||
| 1960 | "output_type": "stream", |
||
| 1961 | "text": [ |
||
| 1962 | "training time: 9 sec\n" |
||
| 1963 | ] |
||
| 1964 | } |
||
| 1965 | ], |
||
| 1966 | "source": [ |
||
| 1967 | "pipItems = [\n", |
||
| 1968 | " (\"concatenate\", ColumnConcatenator()),\n", |
||
| 1969 | " (\"classify\", TimeSeriesForestClassifier(n_estimators=100,n_jobs=-1))]\n", |
||
| 1970 | "concClf = Pipeline(pipItems)\n", |
||
| 1971 | "t = time.time()\n", |
||
| 1972 | "concClf.fit(X_train, y_train)\n", |
||
| 1973 | "print('training time: {} sec'.format(round(time.time()-t)))" |
||
| 1974 | ] |
||
| 1975 | }, |
||
| 1976 | { |
||
| 1977 | "cell_type": "code", |
||
| 1978 | "execution_count": 16, |
||
| 1979 | "id": "cd2aacae", |
||
| 1980 | "metadata": {}, |
||
| 1981 | "outputs": [ |
||
| 1982 | { |
||
| 1983 | "data": { |
||
| 1984 | "image/png": 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\n", |
||
| 1985 | "text/plain": [ |
||
| 1986 | "<Figure size 288x288 with 2 Axes>" |
||
| 1987 | ] |
||
| 1988 | }, |
||
| 1989 | "metadata": { |
||
| 1990 | "needs_background": "light" |
||
| 1991 | }, |
||
| 1992 | "output_type": "display_data" |
||
| 1993 | }, |
||
| 1994 | { |
||
| 1995 | "name": "stdout", |
||
| 1996 | "output_type": "stream", |
||
| 1997 | "text": [ |
||
| 1998 | "Column-Concatenator classifier score: 0.9254237288135593\n" |
||
| 1999 | ] |
||
| 2000 | } |
||
| 2001 | ], |
||
| 2002 | "source": [ |
||
| 2003 | "y_train_pred = cross_val_predict(concClf, X_train, y_train, cv=10)\n", |
||
| 2004 | "confmat = metrics.confusion_matrix(y_train,y_train_pred)\n", |
||
| 2005 | "plt.matshow(confmat)\n", |
||
| 2006 | "sn.heatmap(confmat,annot=True, annot_kws={\"size\":10}, fmt='d')\n", |
||
| 2007 | "plt.xlabel(\"Actual\")\n", |
||
| 2008 | "plt.ylabel(\"Predicted\")\n", |
||
| 2009 | "plt.show()\n", |
||
| 2010 | "print('Column-Concatenator classifier score: {}'.format(concClf.score(X_test, y_test)))" |
||
| 2011 | ] |
||
| 2012 | }, |
||
| 2013 | { |
||
| 2014 | "cell_type": "code", |
||
| 2015 | "execution_count": null, |
||
| 2016 | "id": "73c95904", |
||
| 2017 | "metadata": {}, |
||
| 2018 | "outputs": [], |
||
| 2019 | "source": [] |
||
| 2020 | }, |
||
| 2021 | { |
||
| 2022 | "cell_type": "code", |
||
| 2023 | "execution_count": 17, |
||
| 2024 | "id": "cb7021a3", |
||
| 2025 | "metadata": {}, |
||
| 2026 | "outputs": [], |
||
| 2027 | "source": [ |
||
| 2028 | "#### Column-wise ensembling" |
||
| 2029 | ] |
||
| 2030 | }, |
||
| 2031 | { |
||
| 2032 | "cell_type": "code", |
||
| 2033 | "execution_count": 20, |
||
| 2034 | "id": "4b2d8cdd", |
||
| 2035 | "metadata": {}, |
||
| 2036 | "outputs": [ |
||
| 2037 | { |
||
| 2038 | "name": "stdout", |
||
| 2039 | "output_type": "stream", |
||
| 2040 | "text": [ |
||
| 2041 | "training time: 1417 sec\n" |
||
| 2042 | ] |
||
| 2043 | } |
||
| 2044 | ], |
||
| 2045 | "source": [ |
||
| 2046 | "ensClf = ColumnEnsembleClassifier(\n", |
||
| 2047 | " estimators=[\n", |
||
| 2048 | " (\"TSF0\", TimeSeriesForestClassifier(n_estimators=100), [0]),\n", |
||
| 2049 | " (\"BOSSEnsemble\", BOSSEnsemble(max_ensemble_size=5,n_jobs=-1), [6])])\n", |
||
| 2050 | "t = time.time()\n", |
||
| 2051 | "ensClf.fit(X_train, y_train)\n", |
||
| 2052 | "print('training time: {} sec'.format(round(time.time()-t)))" |
||
| 2053 | ] |
||
| 2054 | }, |
||
| 2055 | { |
||
| 2056 | "cell_type": "code", |
||
| 2057 | "execution_count": 21, |
||
| 2058 | "id": "af8c34bc", |
||
| 2059 | "metadata": { |
||
| 2060 | "scrolled": true |
||
| 2061 | }, |
||
| 2062 | "outputs": [ |
||
| 2063 | { |
||
| 2064 | "data": { |
||
| 2065 | "image/png": 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\n", |
||
| 2066 | "text/plain": [ |
||
| 2067 | "<Figure size 288x288 with 2 Axes>" |
||
| 2068 | ] |
||
| 2069 | }, |
||
| 2070 | "metadata": { |
||
| 2071 | "needs_background": "light" |
||
| 2072 | }, |
||
| 2073 | "output_type": "display_data" |
||
| 2074 | }, |
||
| 2075 | { |
||
| 2076 | "name": "stdout", |
||
| 2077 | "output_type": "stream", |
||
| 2078 | "text": [ |
||
| 2079 | "Column-wise ensembling classifier score: 0.9084745762711864\n" |
||
| 2080 | ] |
||
| 2081 | } |
||
| 2082 | ], |
||
| 2083 | "source": [ |
||
| 2084 | "y_train_pred = cross_val_predict(ensClf, X_train, y_train, cv=3)\n", |
||
| 2085 | "confmat = metrics.confusion_matrix(y_train,y_train_pred)\n", |
||
| 2086 | "plt.matshow(confmat)\n", |
||
| 2087 | "sn.heatmap(confmat,annot=True, annot_kws={\"size\":10}, fmt='d')\n", |
||
| 2088 | "plt.xlabel(\"Actual\")\n", |
||
| 2089 | "plt.ylabel(\"Predicted\")\n", |
||
| 2090 | "plt.show()\n", |
||
| 2091 | "print('Column-wise ensembling classifier score: {}'.format(ensClf.score(X_test, y_test)))" |
||
| 2092 | ] |
||
| 2093 | }, |
||
| 2094 | { |
||
| 2095 | "cell_type": "code", |
||
| 2096 | "execution_count": null, |
||
| 2097 | "id": "82f302c7", |
||
| 2098 | "metadata": {}, |
||
| 2099 | "outputs": [], |
||
| 2100 | "source": [] |
||
| 2101 | }, |
||
| 2102 | { |
||
| 2103 | "cell_type": "code", |
||
| 2104 | "execution_count": null, |
||
| 2105 | "id": "21353e0e", |
||
| 2106 | "metadata": {}, |
||
| 2107 | "outputs": [], |
||
| 2108 | "source": [] |
||
| 2109 | } |
||
| 2110 | ], |
||
| 2111 | "metadata": { |
||
| 2112 | "kernelspec": { |
||
| 2113 | "display_name": "Python 3", |
||
| 2114 | "language": "python", |
||
| 2115 | "name": "python3" |
||
| 2116 | }, |
||
| 2117 | "language_info": { |
||
| 2118 | "codemirror_mode": { |
||
| 2119 | "name": "ipython", |
||
| 2120 | "version": 3 |
||
| 2121 | }, |
||
| 2122 | "file_extension": ".py", |
||
| 2123 | "mimetype": "text/x-python", |
||
| 2124 | "name": "python", |
||
| 2125 | "nbconvert_exporter": "python", |
||
| 2126 | "pygments_lexer": "ipython3", |
||
| 2127 | "version": "3.7.10" |
||
| 2128 | } |
||
| 2129 | }, |
||
| 2130 | "nbformat": 4, |
||
| 2131 | "nbformat_minor": 5 |
||
| 2132 | } |