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# coding: utf-8
# In[41]:
import numpy as np
import scipy as sc
from pandas import Series,DataFrame
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
from collections import OrderedDict
from fractions import Fraction
get_ipython().magic(u'matplotlib inline')
mpl.rcParams['figure.figsize'] = (10.0, 5)
# In[42]:
# nice to have
def plot_matrix(matrix):
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(matrix, interpolation='nearest')
fig.colorbar(cax)
# In[106]:
transaction_df = pd.DataFrame({'Mozzarella' : [1,0,1,0,1,0,0,1],
'Balsamico' : [0,1,1,0,1,0,0,1],
'Pepsi' : [1,0,0,1,0,0,1,0],
'Pasta' : [0,1,0,1,1,1,0,1],
'Wine' : [0,0,1,0,0,1,1,1]})
transaction_df
# In[107]:
# calculate support for every product in all transactions
product_support_dict = {}
for column in transaction_df.columns:
product_support_dict[column] = sum(transaction_df[column]>0)
# visualise support
pd.Series(product_support_dict).plot(kind="bar")
# In[108]:
transaction_matrix = transaction_df.as_matrix()
transaction_matrix
# In[109]:
bool_index = (transaction_matrix>0)
bool_index
# In[110]:
plot_matrix(transaction_matrix)
# In[111]:
# get number of rows and columns
rows, columns = transaction_matrix.shape
# init new matrix
frequent_items_matrix = np.zeros((5,5))
# compare every product with every other
for this_column in range(0, columns-1):
print "this:", this_column,":",transaction_df.columns[this_column]
for next_column in range(this_column + 1, columns):
print "\tnext:", next_column,":",transaction_df.columns[next_column]
# multiply product pair vectors
product_vector = transaction_matrix[:,this_column] * transaction_matrix[:,next_column]
# check the number of pair occurrences in baskets
count_matches = sum((product_vector)>0)
print "\t", count_matches
# save values to new matrix
frequent_items_matrix[this_column,next_column] = count_matches
# In[112]:
print frequent_items_matrix
# In[113]:
plot_matrix(frequent_items_matrix)
# In[114]:
# combine matrix with names
frequent_items_df = pd.DataFrame(frequent_items_matrix, columns = transaction_df.columns.values, index = transaction_df.columns.values)
sns.heatmap(frequent_items_df)
# In[115]:
product_names = transaction_df.columns.values
# extract product pairs with minimum frequency(treshold) basket occurrences
def extract_pairs(treshold):
output = {}
# select indexes with larger or equal n
matrix_coord_list = np.where(frequent_items_matrix >= treshold)
# take values
row_coords = matrix_coord_list[0]
column_coords = matrix_coord_list[1]
# generate pairs
for index, value in enumerate(row_coords):
#print index
row = row_coords[index]
column = column_coords[index]
# get product names
first_product = product_names[row]
second_product = product_names[column]
# number of basket matches
matches = frequent_items_matrix[row,column]
# put key values into dict
output[first_product+"-"+second_product] = matches
# return sorted dict
sorted_output = OrderedDict(sorted(output.items(), key=lambda x: x[1]))
return sorted_output
# plot pairs with minimum frequency of 2 basket matches
min_frequency = 1
ax = pd.Series(extract_pairs(min_frequency)).plot(kind="barh", title="Frequent Pairs with Frequency >= " + str(min_frequency))
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