def run(transactions_file_name,
min_supp_count,
min_conf,
possible_class_values,
output_file_name=None):
transactions = util_functions.unzip_transactions_2(transactions_file_name)
start = time.time()
patterns = car_fpgrowth.find_frequent_patterns(transactions,
min_supp_count,
possible_class_values)
#rules = car_fpgrowth.generate_association_rules_with_one_item_consequent(patterns, min_conf, len(transactions))
rules = car_fpgrowth.generate_classification_rules(patterns, min_conf,
len(transactions),
possible_class_values)
end = time.time()
print('Total elapsed {}'.format((end - start)))
if output_file_name is not None:
print('Saving results to {}'.format(output_file_name))
rules_str = util_functions.rules_to_string(rules)
with open(output_file_name, 'w') as output_file:
output_file.write(rules_str)
else:
rules_str = util_functions.rules_to_string(rules)
print("\nPatterns")
print(rules_str)
def run(transactions_file_name,
classifier,
min_supp_count,
min_conf,
output_file_name=None):
transactions = util_functions.unzip_transactions_2(transactions_file_name)
start = time.time()
rules = apriori.generate_association_rules(transactions,
min_support=min_supp_count,
min_confidence=min_conf)
# rules = apriori.generate_classification_rules(transactions,
# classifier,
# min_support=min_supp_count,
# min_confidence=min_conf)
end = time.time()
print('Total elapsed {}'.format((end - start)))
if output_file_name is not None:
print('Saving results to {}'.format(output_file_name))
rules_str = util_functions.rules_to_string(rules)
with open(output_file_name, 'w') as output_file:
output_file.write(rules_str)
else:
rules_str = util_functions.rules_to_string(rules)
print("\nPatterns")
print(rules_str)
def run(transactions_file_name, min_supp_count, min_conf, output_file_name=None):
transactions = util_functions.unzip_transactions_2(transactions_file_name)
classifier = ['NO', 'YES']
inv = {"06", "07", "08", "09", "10", "11"}
var = {"01", "02", "03", "12"}
start = time.time()
contrast_rules = scr.generate_contrasting_rules(transactions, classifier, inv, var,
min_support=min_supp_count, min_confidence=min_conf)
end = time.time()
print('Total elapsed {}'.format((end - start)))
if output_file_name is not None:
print('Saving results to {}'.format(output_file_name))
rules_str = util_functions.rules_to_string(contrast_rules)
with open(output_file_name, 'w') as output_file:
output_file.write(rules_str)
else:
rules_str = util_functions.rules_to_string(rules)
print("\nPatterns")
print(rules_str)
def run(transactions_file_name,
min_supp_count,
min_conf,
output_file_name=None):
transactions = util_functions.unzip_transactions_2(transactions_file_name)
classifier = {'NO', 'YES'}
start = time.time()
rules = car.CAR_apriori(transactions,
classifier,
min_support=min_supp_count,
min_confidence=min_conf)
end = time.time()
print('Total elapsed {}'.format((end - start)))
if output_file_name is not None:
print('Saving results to {}'.format(output_file_name))
rules_str = util_functions.rules_to_string(rules)
with open(output_file_name, 'w') as output_file:
output_file.write(rules_str)
else:
rules_str = util_functions.rules_to_string(rules)
print("\nPatterns")
print(rules_str)
def run_census(file_name,
support_number_threshold,
confidence_threshold,
output_file_name=None):
"""
Generate SCR-patterns for census data files.
:param file_name: file with transactions
:param support_number_threshold: minimum support number
:param confidence_threshold: minimum confidence
:param output_file - a file to save resulting patterns, if none, the results are printed
:return:
"""
trans_info = {
"inv": {
"order": ["06", "07", "08", "09", "10", "11"],
"06": [
'06-Husb.:no school', '06-Husb.:school', '06-Husb.:no college',
'06-Husb.:college', '06-Husb.:bachelor', '06-Husb.:master',
'06-Husb.:associate', '06-Husb.:doctor'
],
"07": [
'07-Wife:no school', '07-Wife:school', '07-Wife:no college',
'07-Wife:college', '07-Wife:bachelor', '07-Wife:master',
'07-Wife:associate', '07-Wife:doctor'
],
"08": [
'08-Husb.:West Europe', '08-Husb.:Latino',
'08-Husb.:Other American', '08-Husb.:Afro-American',
'08-Husb.:Mexico', '08-Husb.:other Asia', '08-Husb.:Pasific',
'08-Husb.:North Africa and SouthAsia', '08-Husb.:East Europe',
'08-Husb.:Australia', '08-Husb.:other Africa',
'08-Husb.:Central America Islands'
],
"09": [
'09-Wife:West Europe', '09-Wife:Mexico',
'09-Wife:Other American', '09-Wife:Afro-American',
'09-Wife:East Europe', '09-Wife:Latino', '09-Wife:other Asia',
'09-Wife:Pasific', '09-Wife:North Africa and SouthAsia',
'09-Wife:other Africa', '09-Wife:Australia',
'09-Wife:Central America Islands'
],
"10": [
'10-Husb.work.class=PrivateWorker',
'10-Husb.work.class=GovernmWorker',
'10-Husb.work.class=NoWork', '10-Husb.work.class=SelfEmployed'
],
"11": [
'11-Wife.work.class=GovernmWorker',
'11-Wife.work.class=PrivateWorker',
'11-Wife.work.class=NoWork', '11-Wife.work.class=SelfEmployed'
]
},
"var": {
"order": ["01", "02", "03", "12"],
"01": ['01-H. not owned', '01-H. owned'],
"02": [
'02-Apart.', '02-Det. house', '02-No stat. home',
'02-Att. house'
],
"03": [
'03-Vechicl.=2', '03-Vechicl.=1', '03-Vechicl.>=4',
'03-Vechicl.=0', '03-Vechicl.=3'
],
"12": [
'12-Husb.Income=05', '12-Husb.Income=04', '12-Husb.Income=02',
'12-Husb.Income=03', '12-Husb.Income=06', '12-Husb.Income=07',
'12-Husb.Income=08', '12-Husb.Income=10', '12-Husb.Income=01',
'12-Husb.Income=09', '12-Husb.Income=11'
]
},
"class": ['YES', 'NO']
}
transactions = util_functions.unzip_transactions_2(file_name)
start = time.time()
patterns = scr_fpgrowth.find_frequent_patterns(transactions,
trans_info,
support_number_threshold,
confidence_threshold,
is_verbose=False)
end = time.time()
print('Total elapsed for patterns construction {}'.format(end - start))
str_res = util_functions.patterns_to_string(patterns)
if output_file_name is None:
# print the results
print('\nPatterns')
print(str_res)
else:
# print results into a file
print('Saving results to {} ...'.format(output_file_name))
with open(output_file_name, 'w') as output_file:
output_file.write(str_res)