uci_031_eeg_eye_state.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import urllib.request
import io
import pandas # install pandas by "pip install pandas", or install Anaconda distribution (https://www.anaconda.com/)

# Warning: the data processing techniques shown below are just for concept explanation, which are not best-proctices

# data set repository
# https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State

# if the file is on your local device, change url_data_train into local file path, e.g., '‪D:\local_file.data'
url_data_train = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00264/EEG%20Eye%20State.arff'

def download_file(url):
    resp = urllib.request.urlopen(url)
    if resp.status != 200:
        resp.close()
        raise ValueError('Error: {0}'.format(resp.reason))

    print('\rStarted', end = '\r')
    content_length = resp.getheader('Content-Length')
    if content_length is None:
        content_length = '(total: unknown)'
    else:
        content_length = int(content_length)
        if content_length < 1024:
            content_length_str = '(total %.0f Bytes)' % content_length
        elif content_length < 1024 * 1024:
            content_length_str = '(total %.0f KB)' % (content_length / 1024)
        else:
            content_length_str = '(total %.1f MB)' % (content_length / 1024 / 1024)

    total = bytes()
    while not resp.isclosed():
        total += resp.read(10 * 1024)
        if len(total) < 1024:
            print(('\rDownloaded: %.0f Bytes ' % len(total)) + content_length_str + '  ', end = '\r')
        if len(total) < 1024 * 1024:
            print(('\rDownloaded: %.0f KB ' % (len(total) / 1024)) + content_length_str + '  ', end = '\r')
        else:
            print(('\rDownloaded: %.1f MB ' % (len(total) / 1024 / 1024)) + content_length_str + '  ', end = '\r')

    print()
    return io.BytesIO(total)

# download data from UCI Machine Learning Repository
data_train = download_file(url_data_train) if url_data_train.startswith('http') else url_data_train

# eyeDetection is the original target variable, which will be converted into 0 or 1 later
columns = [
    'AF3',
    'F7',
    'F3',
    'FC5',
    'T7',
    'P7',
    'O1',
    'O2',
    'P8',
    'T8',
    'FC6',
    'F4',
    'F8',
    'AF4',
    'eyeDetection']

# convert flat files into pandas dataframes
df_train = pandas.read_csv(data_train, header = None, names = columns, index_col = False, skiprows = 19)

# the target variable, we insert target_eyeDetection into the dataframe as the first column and drop the original eyeDetection column
df_train.insert(0, 'target_eyeDetection', df_train['eyeDetection'])
df_train = df_train.drop('eyeDetection', axis = 1)

# save the dataframe as CSV file, you can zip it, upload it to t1modeler.com, and build a model
df_train.to_csv('uci_031_eeg_eye_state.csv', index = False)