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b/Acceleration Features, Train = 10 users Test = 5 users, Random Analysis.ipynb |
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"cells": [ |
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"cell_type": "code", |
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"execution_count": null, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# train 1(14=0.01) test 14 (.99)" |
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] |
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}, |
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"cell_type": "code", |
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"execution_count": 1, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"CPU times: user 1.18 s, sys: 577 ms, total: 1.76 s\n", |
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"Wall time: 9.18 s\n" |
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] |
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} |
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], |
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"source": [ |
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"%%time\n", |
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"import pandas as pd\n", |
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"import numpy as np\n", |
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"from sklearn.ensemble import ExtraTreesClassifier\n", |
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"from sklearn.metrics import classification_report\n", |
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"from sklearn.model_selection import train_test_split" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 2, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"CPU times: user 1min 25s, sys: 13.7 s, total: 1min 38s\n", |
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"Wall time: 2min 26s\n" |
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] |
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} |
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], |
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"source": [ |
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"%%time\n", |
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"df = pd.read_csv(\"master_data.csv\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 3, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"Index(['target', 'subject', 'chest_ACC_x', 'chest_ACC_y', 'chest_ACC_z',\n", |
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" 'chest_ECG', 'chest_EMG', 'chest_EDA', 'chest_Temp', 'chest_Resp'],\n", |
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" dtype='object')" |
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] |
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}, |
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"execution_count": 3, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"df.columns" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 4, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"CPU times: user 533 ms, sys: 124 ms, total: 657 ms\n", |
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"Wall time: 698 ms\n" |
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] |
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} |
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], |
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"source": [ |
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"%%time\n", |
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"df=df[['chest_ACC_x','chest_ACC_y','chest_ACC_z','target','subject']]" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 5, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"CPU times: user 510 ms, sys: 43 µs, total: 510 ms\n", |
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"Wall time: 508 ms\n" |
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] |
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}, |
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{ |
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"data": { |
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"text/plain": [ |
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"[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17]" |
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] |
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}, |
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"execution_count": 5, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"%%time\n", |
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"df['subject'].unique()\n", |
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"list_of_subjects=list(df['subject'].unique())\n", |
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"list_of_subjects.sort()\n", |
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"list_of_subjects" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 6, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"CPU times: user 107 µs, sys: 18 µs, total: 125 µs\n", |
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"Wall time: 129 µs\n" |
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] |
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}, |
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{ |
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"data": { |
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"text/plain": [ |
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"['chest_ACC_x', 'chest_ACC_y', 'chest_ACC_z']" |
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] |
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}, |
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"execution_count": 6, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"%%time\n", |
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"features=df.columns.tolist()\n", |
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"to_remove = [fea for fea in features if \"target\" in fea or \"subject\" in fea]\n", |
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"feature = [x for x in features if x not in to_remove]\n", |
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"feature" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 7, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"2\n", |
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"subject_2\n", |
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"3\n", |
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"subject_3\n", |
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"4\n", |
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"subject_4\n", |
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"5\n", |
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"subject_5\n", |
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"6\n", |
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"subject_6\n", |
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"7\n", |
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"subject_7\n", |
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"8\n", |
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"subject_8\n", |
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"9\n", |
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"subject_9\n", |
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"10\n", |
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"subject_10\n", |
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"11\n", |
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"subject_11\n", |
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"13\n", |
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"subject_13\n", |
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"14\n", |
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"subject_14\n", |
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"15\n", |
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"subject_15\n", |
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"16\n", |
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"subject_16\n", |
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"17\n", |
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"subject_17\n", |
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"CPU times: user 16.3 s, sys: 2.31 s, total: 18.6 s\n", |
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"Wall time: 18.7 s\n" |
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] |
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} |
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], |
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"source": [ |
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"%%time\n", |
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"for i in list_of_subjects:\n", |
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" print(i)\n", |
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" globals()['subject_%s' % i]=df[df['subject'] == i]\n", |
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" globals()['subject_%s_train' % i],globals()['subject_%s_test' % i]=train_test_split(globals()['subject_%s' % i], test_size=0.3)\n", |
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" print('subject_'+str(i))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 8, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"(4165000, 5)\n", |
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"(2915500, 5)\n", |
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"(1249500, 5)\n" |
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] |
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} |
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], |
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"source": [ |
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"print(subject_2.shape)\n", |
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"print(subject_2_train.shape)\n", |
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"print(subject_2_test.shape)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 8, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"CPU times: user 614 ms, sys: 1.47 s, total: 2.08 s\n", |
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"Wall time: 2.08 s\n" |
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] |
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} |
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], |
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"source": [ |
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"%%time\n", |
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"train=pd.concat([subject_2,subject_3,subject_4,subject_5,subject_6,subject_7,subject_8,subject_9,subject_10,subject_11])\n", |
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"test=pd.concat([subject_13,subject_14,subject_15,subject_16,subject_17])" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 10, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"(40143599, 5)\n", |
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"(18981901, 5)\n" |
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] |
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} |
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], |
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"source": [ |
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"print(train.shape)\n", |
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"print(test.shape)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 11, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"Index 321148792\n", |
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"chest_ACC_x 321148792\n", |
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"chest_ACC_y 321148792\n", |
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"chest_ACC_z 321148792\n", |
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"target 321148792\n", |
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"subject 321148792\n", |
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"dtype: int64\n", |
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"Index 151855208\n", |
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"chest_ACC_x 151855208\n", |
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"chest_ACC_y 151855208\n", |
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"chest_ACC_z 151855208\n", |
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"target 151855208\n", |
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"subject 151855208\n", |
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"dtype: int64\n" |
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] |
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} |
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], |
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"source": [ |
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"print(train.memory_usage(index=True, deep=False))\n", |
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"print(test.memory_usage(index=True, deep=False))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 12, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"<class 'pandas.core.frame.DataFrame'>\n", |
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"Int64Index: 40143599 entries, 26710599 to 8382499\n", |
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"Data columns (total 5 columns):\n", |
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"chest_ACC_x float64\n", |
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"chest_ACC_y float64\n", |
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"chest_ACC_z float64\n", |
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"target int64\n", |
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"subject int64\n", |
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"dtypes: float64(3), int64(2)\n", |
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"memory usage: 1.8 GB\n", |
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"None\n", |
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"<class 'pandas.core.frame.DataFrame'>\n", |
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"Int64Index: 18981901 entries, 39094999 to 51311399\n", |
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"Data columns (total 5 columns):\n", |
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"chest_ACC_x float64\n", |
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"chest_ACC_y float64\n", |
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328 |
"chest_ACC_z float64\n", |
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"target int64\n", |
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"subject int64\n", |
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"dtypes: float64(3), int64(2)\n", |
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"memory usage: 868.9 MB\n", |
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"None\n" |
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] |
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} |
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], |
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"source": [ |
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"print(train.info(memory_usage='deep'))\n", |
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"print(test.info(memory_usage='deep'))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 10, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"['chest_ACC_x', 'chest_ACC_y', 'chest_ACC_z']" |
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] |
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}, |
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"execution_count": 10, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"feature" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 9, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"features=['chest_ACC_x','chest_ACC_y','chest_ACC_z']\n", |
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"target=['target']" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 11, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"['chest_ACC_x', 'chest_ACC_y', 'chest_ACC_z']\n", |
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"CPU times: user 98 µs, sys: 17 µs, total: 115 µs\n", |
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"Wall time: 120 µs\n" |
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] |
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} |
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], |
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"source": [ |
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"%%time\n", |
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"features=feature\n", |
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"print(features)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 12, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"['target']\n", |
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"CPU times: user 99 µs, sys: 0 ns, total: 99 µs\n", |
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"Wall time: 103 µs\n" |
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] |
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} |
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], |
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"source": [ |
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"%%time\n", |
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"target=['target']\n", |
|
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"print(target)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 13, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"2\n", |
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"16\n", |
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"17\n", |
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"CPU times: user 870 µs, sys: 127 ms, total: 127 ms\n", |
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"Wall time: 122 ms\n" |
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] |
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} |
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], |
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"source": [ |
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"%%time\n", |
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"for i in list_of_subjects[0:]:\n", |
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" print(i)\n", |
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" del(globals()['subject_%s' % i])\n", |
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" del(globals()['subject_%s_train' % i])\n", |
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" del(globals()['subject_%s_test' % i])\n", |
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"del df" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 14, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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|
461 |
"text": [ |
|
|
462 |
"ExtraTreesClassifier\t classification_report\t feature\t features\t i\t list_of_subjects\t np\t pd\t target\t \n", |
|
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"test\t to_remove\t train\t train_test_split\t \n" |
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] |
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} |
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], |
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"source": [ |
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"who" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 19, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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479 |
"[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17]" |
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] |
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}, |
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"execution_count": 19, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"list_of_subjects" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stderr", |
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498 |
"output_type": "stream", |
|
|
499 |
"text": [ |
|
|
500 |
"/home/sf/.local/lib/python3.6/site-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", |
|
|
501 |
" \n", |
|
|
502 |
"[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n" |
|
|
503 |
] |
|
|
504 |
}, |
|
|
505 |
{ |
|
|
506 |
"name": "stdout", |
|
|
507 |
"output_type": "stream", |
|
|
508 |
"text": [ |
|
|
509 |
"building tree 1 of 50\n", |
|
|
510 |
"building tree 2 of 50\n", |
|
|
511 |
"building tree 3 of 50building tree 4 of 50\n", |
|
|
512 |
"building tree 5 of 50\n", |
|
|
513 |
"building tree 6 of 50\n", |
|
|
514 |
"\n", |
|
|
515 |
"building tree 7 of 50\n", |
|
|
516 |
"building tree 8 of 50\n", |
|
|
517 |
"building tree 9 of 50\n", |
|
|
518 |
"building tree 10 of 50\n" |
|
|
519 |
] |
|
|
520 |
} |
|
|
521 |
], |
|
|
522 |
"source": [ |
|
|
523 |
"%%time\n", |
|
|
524 |
"et = ExtraTreesClassifier(n_estimators=50, n_jobs=10, verbose=2)\n", |
|
|
525 |
"et.fit(train[features],train[target])\n", |
|
|
526 |
"y_pred=et.predict(test[features])" |
|
|
527 |
] |
|
|
528 |
}, |
|
|
529 |
{ |
|
|
530 |
"cell_type": "code", |
|
|
531 |
"execution_count": 17, |
|
|
532 |
"metadata": {}, |
|
|
533 |
"outputs": [ |
|
|
534 |
{ |
|
|
535 |
"name": "stdout", |
|
|
536 |
"output_type": "stream", |
|
|
537 |
"text": [ |
|
|
538 |
" precision recall f1-score support\n", |
|
|
539 |
"\n", |
|
|
540 |
" 0 0.86 0.87 0.87 8295722\n", |
|
|
541 |
" 1 0.85 0.83 0.84 3698748\n", |
|
|
542 |
" 2 0.74 0.73 0.74 2092335\n", |
|
|
543 |
" 3 0.82 0.80 0.81 1170648\n", |
|
|
544 |
" 4 0.88 0.88 0.88 2480199\n", |
|
|
545 |
"\n", |
|
|
546 |
" accuracy 0.84 17737652\n", |
|
|
547 |
" macro avg 0.83 0.82 0.83 17737652\n", |
|
|
548 |
"weighted avg 0.84 0.84 0.84 17737652\n", |
|
|
549 |
"\n", |
|
|
550 |
"CPU times: user 34.9 s, sys: 6.72 s, total: 41.6 s\n", |
|
|
551 |
"Wall time: 41.6 s\n" |
|
|
552 |
] |
|
|
553 |
} |
|
|
554 |
], |
|
|
555 |
"source": [ |
|
|
556 |
"%%time\n", |
|
|
557 |
"print(classification_report(test[target],y_pred ))" |
|
|
558 |
] |
|
|
559 |
}, |
|
|
560 |
{ |
|
|
561 |
"cell_type": "code", |
|
|
562 |
"execution_count": null, |
|
|
563 |
"metadata": {}, |
|
|
564 |
"outputs": [], |
|
|
565 |
"source": [ |
|
|
566 |
"del train\n", |
|
|
567 |
"del test" |
|
|
568 |
] |
|
|
569 |
}, |
|
|
570 |
{ |
|
|
571 |
"cell_type": "code", |
|
|
572 |
"execution_count": null, |
|
|
573 |
"metadata": {}, |
|
|
574 |
"outputs": [], |
|
|
575 |
"source": [] |
|
|
576 |
}, |
|
|
577 |
{ |
|
|
578 |
"cell_type": "code", |
|
|
579 |
"execution_count": 16, |
|
|
580 |
"metadata": { |
|
|
581 |
"collapsed": true |
|
|
582 |
}, |
|
|
583 |
"outputs": [ |
|
|
584 |
{ |
|
|
585 |
"name": "stdout", |
|
|
586 |
"output_type": "stream", |
|
|
587 |
"text": [ |
|
|
588 |
"11\n", |
|
|
589 |
"14\n", |
|
|
590 |
"8\n", |
|
|
591 |
"15\n", |
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592 |
"9\n", |
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"10\n", |
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"16\n", |
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"4\n", |
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"13\n", |
|
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"3\n", |
|
|
598 |
"17\n", |
|
|
599 |
"5\n", |
|
|
600 |
"7\n", |
|
|
601 |
"CPU times: user 19.1 ms, sys: 306 ms, total: 325 ms\n", |
|
|
602 |
"Wall time: 318 ms\n" |
|
|
603 |
] |
|
|
604 |
} |
|
|
605 |
], |
|
|
606 |
"source": [] |
|
|
607 |
}, |
|
|
608 |
{ |
|
|
609 |
"cell_type": "code", |
|
|
610 |
"execution_count": null, |
|
|
611 |
"metadata": {}, |
|
|
612 |
"outputs": [], |
|
|
613 |
"source": [] |
|
|
614 |
} |
|
|
615 |
], |
|
|
616 |
"metadata": { |
|
|
617 |
"kernelspec": { |
|
|
618 |
"display_name": "Python 3", |
|
|
619 |
"language": "python", |
|
|
620 |
"name": "python3" |
|
|
621 |
}, |
|
|
622 |
"language_info": { |
|
|
623 |
"codemirror_mode": { |
|
|
624 |
"name": "ipython", |
|
|
625 |
"version": 3 |
|
|
626 |
}, |
|
|
627 |
"file_extension": ".py", |
|
|
628 |
"mimetype": "text/x-python", |
|
|
629 |
"name": "python", |
|
|
630 |
"nbconvert_exporter": "python", |
|
|
631 |
"pygments_lexer": "ipython3", |
|
|
632 |
"version": "3.6.8" |
|
|
633 |
} |
|
|
634 |
}, |
|
|
635 |
"nbformat": 4, |
|
|
636 |
"nbformat_minor": 2 |
|
|
637 |
} |