def main():
df = pd.read_csv('./temp/ml-100k/u.data', sep='\t', header=None, usecols=[0, 1, 2], names=['userid', 'itemid', 'rating'])
table = pd.pivot_table(df, values='rating', index=['userid'], columns=['itemid'])
model = Apriori()
model.fit(table)
model.predict()
from fpgrowth import FPGrowth
from utils import read_data
import time
if __name__ == '__main__':
transactions, items = read_data('Online Retail.xlsx')
# transactions, items = {'T1': ['pasta', 'lemon', 'bread', 'orange'],
# 'T2': ['pasta', 'lemon'],
# 'T3': ['pasta', 'orange', 'cake'],
# 'T4': ['pasta', 'lemon', 'orange', 'cake']}, ['pasta', 'lemon', 'bread', 'orange', 'cake']
t1 = time.time()
apriori = Apriori()
apriori.fit(transactions=transactions, items=items, min_support=0.03)
# result = apriori.predict(['pasta', 'lemon'])
result = apriori.predict(['84029G', '84029E'])
print(len(apriori.rules))
t2 = time.time()
print(result)
print(t2 - t1)
print('--------------------------------------------')
t1 = time.time()
fp_growth = FPGrowth()
fp_growth.fit(transactions=transactions, items=items, min_support=0.03)
result = fp_growth.predict(['84029G', '84029E'])
# result = fp_growth.predict(['pasta', 'lemon'])
print(len(fp_growth.rules))
t2 = time.time()
print(result)
