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uci_015_qsar_biodegradation.py
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uci_015_qsar_biodegradation.py
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#!/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/QSAR+biodegradation
# 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/00254/biodeg.csv'
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
# experimental is the original target variable, which will be converted into 0 or 1 later
columns = [
'SpMax_L',
'J_Dz(e)',
'nHM',
'F01[N-N]',
'F04[C-N]',
'NssssC',
'nCb-',
'C%',
'nCp',
'nO',
'F03[C-N]',
'SdssC',
'HyWi_B(m)',
'LOC',
'SM6_L',
'F03[C-O]',
'Me',
'Mi',
'nN-N',
'nArNO2',
'nCRX3',
'SpPosA_B(p)',
'nCIR',
'B01[C-Br]',
'B03[C-Cl]',
'N-073',
'SpMax_A',
'Psi_i_1d',
'B04[C-Br]',
'SdO',
'TI2_L',
'nCrt',
'C-026',
'F02[C-N]',
'nHDon',
'SpMax_B(m)',
'Psi_i_A',
'nN',
'SM6_B(m)',
'nArCOOR',
'nX',
'experimental']
# convert flat file into pandas dataframe
df_train = pandas.read_csv(data_train, delimiter = ';', header = None, names = columns, index_col = False)
# the target variable, 1 = RB and 0 = NRB
# we insert target_experimental into the dataframe as the first column, and drop the original experimental column
df_train.insert(0, 'target_experimental', df_train['experimental'].apply(lambda x: 1 if x == 'RB' else 0))
df_train = df_train.drop('experimental', 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_015_qsar_biodegradation.csv', index = False)