import json
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
#from pprint import pprint
import csv
from collections import Counter
from sklearn.metrics.pairwise import cosine_similarity
from mlxtend.frequent_patterns import apriori
from mlxtend.preprocessing import TransactionEncoder
import pandas as pd
from scipy import sparse
import numpy as np
import time
import random
from scipy.interpolate import make_interp_spline, BSpline



data = pd.read_csv('lastfm.csv')

df = data.drop('user', 1)

conv_df = df.astype(bool)

start_time = time.time()

d = apriori(conv_df, min_support=0.01, use_colnames=True, max_len=2)
print((d['itemsets']))


print("--- %s seconds ---" % (time.time() - start_time))

interest_group_centroids = []                               # cluster centriods on which the interest groups are formed
interest_groups = []                                        # Most similar items for each centroid in the interest group
items_len = len(df.columns)                         # lengh of the items in the dataset
length = []  # stores the index of the centroids
print(items_len)