Eclat algorithm

# Importing the libraries
 
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
 
# Data Preprocessing
 
dataset = pd.read_csv('Market_Basket_Optimisation.csv', header = None)
transactions = []
for i in range(0, 7501):
  transactions.append([str(dataset.values[i,j]) for j in range(0, 20)])
 
# Training the Eclat model on the dataset
 
from apyori import apriori
rules = apriori(transactions = transactions, min_support = 0.003, min_confidence = 0.2, min_lift = 3, min_length = 2, max_length = 2)
 
# Visualising the results
results = list(rules)
 
print(results)
 
# Putting the results well organised into a Pandas DataFrame
 
def inspect(results):
    lhs         = [tuple(result[2][0][0])[0] for result in results]
    rhs         = [tuple(result[2][0][1])[0] for result in results]
    supports    = [result[1] for result in results]
 
    return list(zip(lhs, rhs, supports))
resultsinDataFrame = pd.DataFrame(inspect(results), columns = ['Product1', 'Product2', 'Support'])
 
resultsinDataFrame.nlargest(n = 10, columns = 'Support')
print(resultsinDataFrame)