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inputDatLibFM_converter1.py
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inputDatLibFM_converter1.py
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import pandas as pd
import numpy as np
import os
# The whole goal of this file is to now convert our data into the format that is used for
# ratings.dat in the movielens dataset which is:
# UserID::MovieID::Rating::Timestamp
# once we do this, we can use a pearl script built into libFM to convert these ratings
# and then make predictions
# http://files.grouplens.org/datasets/movielens/ml-10m-README.html
# https://github.com/srendle/libfm
# http://www.libfm.org/libfm-1.42.manual.pdf
# wrote this function knowing what was inside of our files with our columns
def fileToLibfm_MU(fileName, label):
# MU data
print('Loading data', label,'mu...')
df = pd.read_csv(os.path.join('data', fileName))
# modify data fram to get rid of data we're not using
del df['Unnamed: 0']
del df['Unnamed: 0.1']
del df['bin'] # from our bin stuff
#del df['Date Number']
df = df.astype('int32')
# move the ratings to the last column, and date number to the third column
cols = df.columns.tolist() # user, movie, date, rating -> user, movie, rating, date
print('cols list', cols)
# cols1 = cols[:2] # rearrange columsn
# cols1.append(cols[3])
# cols1.append(cols[2])
# df = df[cols1]
#df['Movie Number'] = ':' + df['Movie Number'].astype(str)
#df['Date Number'] = ':' + df['Date Number'].astype(str)
#df['Rating'] = ':' + df['Rating'].astype(str)
newFileName = label + '_libFM'
newFileLocation = "libfm/" + newFileName
print('Making new file', newFileName)
#df.to_csv(newFileLocation, sep=':', index=False, header=False)
# make a new file the old fashion way
f = open(newFileLocation, "w+")
stringToAdd = ''
count = 0
totalCount = 0
for index, row in df.iterrows():
stringToAdd += str(row['User Number']) + "::" + str(row['Movie Number']) + "::" + str(row['Rating']) + '::' + str(row['Date Number']) #+ '\n'
count += 1
totalCount += 1
if count % 10000:
f.write(stringToAdd)
stringToAdd = ''
else:
stringToAdd += '\n'
if stringToAdd is not '':
f.write(stringToAdd[:-1]) # need to take off last '\n'
print('Finished reading in data')
print('file processing done for', label, 'new file created', newFileLocation, '\n')
fileToLibfm_MU('mu_val.csv', 'mu_val')
fileToLibfm_MU('mu_train.csv', 'mu_train')
fileToLibfm_MU('mu_probe.csv', 'mu_probe')
fileToLibfm_MU('mu_qual.csv', 'mu_qual')
fileToLibfm_MU('mu_qual_val.csv', 'mu_qual_val')
fileToLibfm_MU('mu_qual_probe.csv', 'mu_qual_probe')