Python DataSet.take_columns Beispiele

Beispiel #1

Datei: clustering1.py Projekt: maddymanu/machinelearning-project

# 	'fold',
# 	'murders', 'murdPerPop',
# 	'rapes', 'rapesPerPop',
# 	'robberies', 'robbbPerPop',
# #	'assaults', 'assaultPerPop',
# 	'burglaries', 'burglPerPop',
# 	'larcenies', 'larcPerPop',
# 	'autoTheft', 'autoTheftPerPop',
# 	'arsons', 'arsonsPerPop',
# 	'ViolentCrimesPerPop',
# 	'nonViolPerPop',
# ])
crime = crime.take_columns([
	'racePctHisp', 
	'racePctWhite',
	#'racepctblack',
	#'racePctAsian',
	'medIncome', 'NumStreet', 'NumImmig',
	'PctEmploy', "PctPopUnderPov", 'pctUrban'
	])
#crime = crime.fix_missing(fill_mean=True)
#crime = crime.standardize()
#crime = crime.normalize()
#crime = crime.drop_nominals()
#crime = crime.discretize('assaults', 3)
crime = DataSet(dataframe=crime.df[:200])
#print(crime.df.assaults)
print(crime.attributeNames)

# Variables of interestz
N, M = crime.N, crime.M
#C = len(crime.classNames)

Beispiel #2

# 	'rapes', 'rapesPerPop',
# 	'robberies', 'robbbPerPop',
# #	'assaults', 'assaultPerPop',
# 	'burglaries', 'burglPerPop',
# 	'larcenies', 'larcPerPop',
# 	'autoTheft', 'autoTheftPerPop',
# 	'arsons', 'arsonsPerPop',
# 	'ViolentCrimesPerPop',
# 	'nonViolPerPop',
# ])
crime = crime.take_columns([
    'racePctHisp',
    'racePctWhite',
    #'racepctblack',
    #'racePctAsian',
    'medIncome',
    'NumStreet',
    'NumImmig',
    'PctEmploy',
    'PctPopUnderPov',
    'pctUrban'
])
crime = crime.fix_missing(fill_mean=True)
crime = crime.standardize()
#crime = crime.normalize()
#crime = crime.drop_nominals()

crime = crime.take_first_n_rows(200)

crime = crime.discretize('racePctWhite', 2)
crime = crime.set_class_column('racePctWhite')
#crime = DataSet(dataframe=crime.df[:200])

Beispiel #3

crime = DataSet(
    datafile='../data/raw.csv',
    nominals=['state', 'communityname', 'countyCode', 'communityCode'])

#crime = crime.drop(['state', 'communityname']) 	  # Drop strings
#crime = crime.drop(['countyCode','communityCode']) # Drop nominals
crime = crime.drop_columns([
    'fold',
    'murders',
    'murdPerPop',
    'rapes',
    'rapesPerPop',
    'robberies',
    'robbbPerPop',
    #	'assaults', 'assaultPerPop',
    'burglaries',
    'burglPerPop',
    'larcenies',
    'larcPerPop',
    'autoTheft',
    'autoTheftPerPop',
    #'arsons', 'arsonsPerPop',
    'ViolentCrimesPerPop',
    #'nonViolPerPop',
])
print(type(crime.X))

crime = crime.normalize()

crime = crime.take_columns(['nonViolPerPop', 'arsons'])
#print(crime)

Beispiel #4

Datei: dataset_test.py Projekt: maddymanu/machinelearning-project

import pylab as pl


from Framework.DataSet import *

crime = DataSet(datafile='../data/raw.csv', nominals=['state','communityname','countyCode','communityCode'])

#crime = crime.drop(['state', 'communityname']) 	  # Drop strings
#crime = crime.drop(['countyCode','communityCode']) # Drop nominals
crime = crime.drop_columns([
	'fold',
	'murders', 'murdPerPop',
	'rapes', 'rapesPerPop',
	'robberies', 'robbbPerPop',
#	'assaults', 'assaultPerPop',
	'burglaries', 'burglPerPop',
	'larcenies', 'larcPerPop',
	'autoTheft', 'autoTheftPerPop',
	#'arsons', 'arsonsPerPop',
	'ViolentCrimesPerPop',
	#'nonViolPerPop',
])
print(type(crime.X))

crime = crime.normalize()


crime = crime.take_columns(['nonViolPerPop', 'arsons'])
#print(crime)