Apriori Algorithm

apriori.py

import numpy as np 
import pandas as pd 
from mlxtend.frequent_patterns import apriori, association_rules 
import os 
import requests

if not os.path.exists("Online_Retail.xlsx"):
	r = requests.get("http://archive.ics.uci.edu/ml/machine-learning-databases/00352/Online%20Retail.xlsx")
	with open("Online_Retail.xlsx","wb") as f:
		f.write(r.content)

# Loading the Data 
data = pd.read_excel('Online_Retail.xlsx') 

# Stripping extra spaces in the description 
data['Description'] = data['Description'].str.strip() 

# Dropping the rows without any invoice number 
data.dropna(axis = 0, subset =['InvoiceNo'], inplace = True) 
data['InvoiceNo'] = data['InvoiceNo'].astype('str') 

# Dropping all transactions which were done on credit 
data = data[~data['InvoiceNo'].str.contains('C')] 

# Transactions done in France 
basket_France = (data[data['Country'] =="France"] 
		.groupby(['InvoiceNo', 'Description'])['Quantity'] 
		.sum().unstack().reset_index().fillna(0) 
		.set_index('InvoiceNo')) 

# Transactions done in the United Kingdom 
basket_UK = (data[data['Country'] =="United Kingdom"] 
		.groupby(['InvoiceNo', 'Description'])['Quantity'] 
		.sum().unstack().reset_index().fillna(0) 
		.set_index('InvoiceNo')) 

# Transactions done in Portugal 
basket_Por = (data[data['Country'] =="Portugal"] 
		.groupby(['InvoiceNo', 'Description'])['Quantity'] 
		.sum().unstack().reset_index().fillna(0) 
		.set_index('InvoiceNo')) 

basket_Sweden = (data[data['Country'] =="Sweden"] 
		.groupby(['InvoiceNo', 'Description'])['Quantity'] 
		.sum().unstack().reset_index().fillna(0) 
		.set_index('InvoiceNo')) 

# Defining the hot encoding function to make the data suitable 
# for the concerned libraries 
def hot_encode(x): 
	if(x<= 0): 
		return 0
	if(x>= 1): 
		return 1

# Encoding the datasets 
basket_encoded = basket_France.applymap(hot_encode) 
basket_France = basket_encoded 

basket_encoded = basket_UK.applymap(hot_encode) 
basket_UK = basket_encoded 

basket_encoded = basket_Por.applymap(hot_encode) 
basket_Por = basket_encoded 

basket_encoded = basket_Sweden.applymap(hot_encode) 
basket_Sweden = basket_encoded 


print("For France -")
# Building the model 
frq_items = apriori(basket_France, min_support = 0.05, use_colnames = True) 

# Collecting the inferred rules in a dataframe 
rules = association_rules(frq_items, metric ="lift", min_threshold = 1) 
rules = rules.sort_values(['confidence', 'lift'], ascending =[False, False]) 
print(rules.head()) 


print("For United Kingdom")
frq_items = apriori(basket_UK, min_support = 0.01, use_colnames = True) 
rules = association_rules(frq_items, metric ="lift", min_threshold = 1) 
rules = rules.sort_values(['confidence', 'lift'], ascending =[False, False]) 
print(rules.head()) 

print("For Portugal")
frq_items = apriori(basket_Por, min_support = 0.05, use_colnames = True) 
rules = association_rules(frq_items, metric ="lift", min_threshold = 1) 
rules = rules.sort_values(['confidence', 'lift'], ascending =[False, False]) 
print(rules.head()) 

print("For Sweden")
frq_items = apriori(basket_Sweden, min_support = 0.05, use_colnames = True) 
rules = association_rules(frq_items, metric ="lift", min_threshold = 1) 
rules = rules.sort_values(['confidence', 'lift'], ascending =[False, False]) 
print(rules.head()) 

requirements.txt

numpy
pandas
requests
mlxtend

Cannot we download it from the link "http://archive.ics.uci.edu/ml/machine-learning-databases/00352/Online%20Retail.xlsx"?