Added notebooks and models
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mayersn
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366
python/Step_32_ReadData-Evaluation.ipynb
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366
python/Step_32_ReadData-Evaluation.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## This notebook creates one dataframe from all participants data\n",
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"## It also removes 1% of the data as this is corrupted"
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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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"source": [
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"%matplotlib inline\n",
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"\n",
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"from scipy.odr import *\n",
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"from scipy.stats import *\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import os\n",
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"import time\n",
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"import matplotlib.pyplot as plt\n",
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"import ast\n",
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"from multiprocessing import Pool, cpu_count\n",
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"\n",
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"import scipy\n",
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"\n",
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"from IPython import display\n",
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"from matplotlib.patches import Rectangle\n",
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"\n",
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"from sklearn.metrics import mean_squared_error\n",
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"import json\n",
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"\n",
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"import scipy.stats as st\n",
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"from sklearn.metrics import r2_score\n",
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"\n",
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"\n",
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"from matplotlib import cm\n",
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"from mpl_toolkits.mplot3d import axes3d\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"import copy\n",
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"\n",
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"from sklearn.model_selection import LeaveOneOut, LeavePOut\n",
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"\n",
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"from multiprocessing import Pool"
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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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"source": [
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"def cast_to_int(row):\n",
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" try:\n",
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" return np.array([a if float(a) >= 0 else 0 for a in row[2:-1]], dtype=np.uint8)\n",
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" except Exception as e:\n",
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" return None\n",
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" \n",
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"def load_csv(file):\n",
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" temp_df = pd.read_csv(file, delimiter=\";\")\n",
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" temp_df.Image = temp_df.Image.str.split(',')\n",
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" temp_df.Image = temp_df.Image.apply(cast_to_int)\n",
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" return temp_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": 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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"['DataStudyEvaluation/2_studyData.csv', 'DataStudyEvaluation/12_studyData.csv', 'DataStudyEvaluation/5_studyData.csv', 'DataStudyEvaluation/1_studyData.csv', 'DataStudyEvaluation/10_studyData.csv', 'DataStudyEvaluation/6_studyData.csv', 'DataStudyEvaluation/3_studyData.csv', 'DataStudyEvaluation/7_studyData.csv', 'DataStudyEvaluation/8_studyData.csv', 'DataStudyEvaluation/9_studyData.csv', 'DataStudyEvaluation/11_studyData.csv', 'DataStudyEvaluation/4_studyData.csv']\n",
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"CPU times: user 1.35 s, sys: 786 ms, total: 2.14 s\n",
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"Wall time: 1min 43s\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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"pool = Pool(cpu_count() - 2)\n",
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"data_files = [\"DataStudyEvaluation/%s\" % file for file in os.listdir(\"DataStudyEvaluation\") if file.endswith(\".csv\") and \"studyData\" in file]\n",
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"print(data_files)\n",
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"df_lst = pool.map(load_csv, data_files)\n",
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"dfAll = pd.concat(df_lst)\n",
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"pool.close()"
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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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"data": {
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"text/plain": [
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"608084"
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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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"df = dfAll[dfAll.Image.notnull()]\n",
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"df = df[df.userID != \"userID\"]\n",
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"df.userID = pd.to_numeric(df.userID)\n",
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"len(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": 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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"loaded 610816 values\n",
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"removed 2732 values (thats 0.447%)\n",
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"new df has size 608084\n"
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]
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}
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],
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"source": [
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"print(\"loaded %s values\" % len(dfAll))\n",
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"print(\"removed %s values (thats %s%%)\" % (len(dfAll) - len(df), round((len(dfAll) - len(df)) / len(dfAll) * 100, 3)))\n",
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"print(\"new df has size %s\" % len(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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = df.reset_index(drop=True)"
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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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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>userID</th>\n",
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" <th>Timestamp</th>\n",
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" <th>Current_Task</th>\n",
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" <th>Task_amount</th>\n",
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" <th>TaskID</th>\n",
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" <th>VersionID</th>\n",
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" <th>RepetitionID</th>\n",
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" <th>Actual_Data</th>\n",
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" <th>Is_Pause</th>\n",
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" <th>Image</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>2</td>\n",
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" <td>1553593631562</td>\n",
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" <td>0</td>\n",
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" <td>34</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>false</td>\n",
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" <td>false</td>\n",
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" <td>[3, 3, 3, 2, 0, 0, 1, 0, 0, 0, 1, 2, 1, 0, 0, ...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2</td>\n",
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" <td>1553593631595</td>\n",
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" <td>0</td>\n",
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" <td>34</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>false</td>\n",
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" <td>false</td>\n",
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" <td>[3, 3, 3, 2, 0, 0, 1, 0, 0, 0, 1, 222, 0, 0, 0...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2</td>\n",
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" <td>1553593631634</td>\n",
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" <td>0</td>\n",
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" <td>34</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>false</td>\n",
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" <td>false</td>\n",
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" <td>[3, 3, 3, 2, 0, 0, 1, 0, 0, 0, 1, 222, 0, 0, 0...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>2</td>\n",
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" <td>1553593631676</td>\n",
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" <td>0</td>\n",
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" <td>34</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>false</td>\n",
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" <td>false</td>\n",
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" <td>[3, 3, 3, 2, 0, 0, 1, 0, 0, 0, 1, 222, 0, 0, 0...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>2</td>\n",
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" <td>1553593631716</td>\n",
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" <td>0</td>\n",
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" <td>34</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>false</td>\n",
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" <td>false</td>\n",
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" <td>[3, 3, 3, 2, 0, 0, 1, 0, 0, 0, 1, 222, 0, 0, 0...</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" userID Timestamp Current_Task Task_amount TaskID VersionID \\\n",
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"0 2 1553593631562 0 34 0 0 \n",
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"1 2 1553593631595 0 34 0 0 \n",
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"2 2 1553593631634 0 34 0 0 \n",
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"3 2 1553593631676 0 34 0 0 \n",
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"4 2 1553593631716 0 34 0 0 \n",
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"\n",
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" RepetitionID Actual_Data Is_Pause \\\n",
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"0 0 false false \n",
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"1 0 false false \n",
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"2 0 false false \n",
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"3 0 false false \n",
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"4 0 false false \n",
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"\n",
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" Image \n",
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"0 [3, 3, 3, 2, 0, 0, 1, 0, 0, 0, 1, 2, 1, 0, 0, ... \n",
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"1 [3, 3, 3, 2, 0, 0, 1, 0, 0, 0, 1, 222, 0, 0, 0... \n",
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"2 [3, 3, 3, 2, 0, 0, 1, 0, 0, 0, 1, 222, 0, 0, 0... \n",
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"3 [3, 3, 3, 2, 0, 0, 1, 0, 0, 0, 1, 222, 0, 0, 0... \n",
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"4 [3, 3, 3, 2, 0, 0, 1, 0, 0, 0, 1, 222, 0, 0, 0... "
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]
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},
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"execution_count": 8,
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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.head()"
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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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"data": {
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"text/plain": [
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"array([ 2, 12, 5, 1, 10, 6, 3, 7, 8, 9, 11, 4])"
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]
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},
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"execution_count": 11,
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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.userID.unique()"
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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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"source": [
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"df.userID = pd.to_numeric(df.userID)\n",
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"df.TaskID = pd.to_numeric(df.TaskID)\n",
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"df.VersionID = pd.to_numeric(df.VersionID)\n",
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"df.Timestamp = pd.to_numeric(df.Timestamp)\n",
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"df.Current_Task = pd.to_numeric(df.Current_Task)\n",
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"df.Task_amount = pd.to_numeric(df.Task_amount)\n",
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"df.RepetitionID = pd.to_numeric(df.RepetitionID)\n",
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"df.loc[df.Actual_Data == \"false\", \"Actual_Data\"] = False\n",
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"df.loc[df.Actual_Data == \"true\", \"Actual_Data\"] = True\n",
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"df.loc[df.Is_Pause == \"false\", \"Is_Pause\"] = False\n",
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"df.loc[df.Is_Pause == \"true\", \"Is_Pause\"] = True"
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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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"df.to_pickle(\"DataStudyEvaluation/AllData.pkl\")"
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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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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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