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AprioriSolution.py
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AprioriSolution.py
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from nltk.compat import raw_input
import numpy as np
class AprioriSolution:
attribute_names=[]
candidate_master_dict={}
#dictionary to store 1-frequent itemsets
attribute_column={}
candidate_one_itemsets={}
candidate_one_support={}
candidate_one_support_pruned={}
candidate_two={}
candidate_two_support={}
candidate_two_support_pruned={}
def calculate_candidate_one_itemsets(self,column,no_of_rows,attribute_name):
#read each column of the transaction matrix and create a dictionary that has the count of the occurence of 1
for each in column:
if each == 1:
if attribute_name in AprioriSolution.candidate_one_itemsets.keys():
AprioriSolution.candidate_one_itemsets[attribute_name]+=1
else:
AprioriSolution.candidate_one_itemsets[attribute_name]=1
def calculate_candidate_one_support(self,rows,support):
#creating a dictionary that has the 1-itemset and its support count
for key,value in AprioriSolution.candidate_one_itemsets.items():
calc_support=value/rows;
AprioriSolution.candidate_one_support[key]=round(calc_support,2);
#for key,val in AprioriSolution.candidate_one_support.items():
#print("candidate-one-before",key,"support",val);
#pruning the candidate one itemsets that do not satisfy the minimum support
for key,val in AprioriSolution.candidate_one_support.items():
if val >= float(support):
AprioriSolution.candidate_one_support_pruned[key]=val
#after pruning
#for key,val in AprioriSolution.candidate_one_support_pruned.items():
#print("candidate-one-pruned",key,"support",val);
number=1
AprioriSolution.candidate_master_dict[number]=AprioriSolution.candidate_one_support_pruned;
def calculate_candidate_two_support(self,row,support,DataMatrix):
candidate_one_pruned=[]
#generating candidate itemsets of length 2 from frequent one candidate itemset
for key in AprioriSolution.candidate_one_support_pruned.keys():
candidate_one_pruned.append(key)
for i in range(0,len(candidate_one_pruned)):
for j in range(i+1,len(candidate_one_pruned)):
AprioriSolution.candidate_two[candidate_one_pruned[i],candidate_one_pruned[j]]=0
for key,val in AprioriSolution.candidate_two.items():
column_first=AprioriSolution.attribute_column.get(key[0])
column_second=AprioriSolution.attribute_column.get(key[1])
list_one=DataMatrix[:,column_first]
list_second=DataMatrix[:,column_second]
count=0
for i in range(0,len(list_one)):
if list_one[i] == list_second[i] == 1:
count+=1
AprioriSolution.candidate_two_support[key[0],key[1]]=count
for key,value in AprioriSolution.candidate_two_support.items():
calc_support=value/row;
AprioriSolution.candidate_two_support[key]=round(calc_support,2);
#for key,val in AprioriSolution.candidate_two_support.items():
#print("candidate-two-before",key,"support",val);
#pruning the candidate two itemsets that do not satisfy the minimum support
for key,val in AprioriSolution.candidate_two_support.items():
if val >= float(support):
AprioriSolution.candidate_two_support_pruned[key]=val
#after pruning
#for key,val in AprioriSolution.candidate_two_support_pruned.items():
#print("candidate-two-pruned",key,"support",val);
number=2
AprioriSolution.candidate_master_dict[number]=AprioriSolution.candidate_two_support_pruned;
def calculate_candidate_k_1(self,rows,support,DataMatrix,num):
#for key,value in AprioriSolution.candidate_master_dict.items():
#print("master key",key,"value",value)
#get k-1 dictionary from the master dictionary
dict_k_1={}
dict_k_1=AprioriSolution.candidate_master_dict.get(num-1)
#generate candidate-k itemset by joining k-1 with k-1 only if first k-2 itemes match
itemset_list=[]
for itemset in dict_k_1.keys():
itemset_list.append(itemset)
dict_k={}
j=num-2
print("j",j)
for each in itemset_list:
for other in itemset_list:
if each == other: continue
else:
itemset_i=each
itemset_j=other
count=0
for k in range(0,j):
if itemset_i[k] == itemset_j[k]:
count+=1
if count == j:
dict_k[tuple(set(itemset_i).union(set(itemset_j)))]=0
#print(list(set(itemset_i).union(set(itemset_j))))
#pruning the k-candidate itemset using the support count
dict_k_pruned_support={}
for key in dict_k.keys():
list_of_key=[]
for each in key:
list_of_key.append(AprioriSolution().attribute_column.get(each))
list_of_data_columns=[]
for i in range(0,len(list_of_key)):
list_of_data_columns.append(DataMatrix[:,list_of_key[i]])
support_count=0
for i in range(0,rows):
count=0
for j in range(0,len(list_of_data_columns)):
if list_of_data_columns[j][i] == 1:
count+=1
if count == num:
support_count+=1
dict_k_pruned_support[key]=support_count
dict_k_support_final={}
for key,val in dict_k_pruned_support.items():
calc_support=val/rows
rounded_support=round(calc_support,2)
#print("support value",rounded_support)
if rounded_support >= float(support):
dict_k_support_final[key]=rounded_support
AprioriSolution.candidate_master_dict[num]=dict_k_support_final;
if len(dict_k_support_final) == 0:
return 0
else: return 1
def start_apriori(self):
#First, ask the user to input support and confidence
support=raw_input("Enter the support for the dataset:");
#confidence=raw_input("Enter the confidence for the dataset:");
number_of_attributes=raw_input("Enter the number of attributes for this dataset:");
for i in range(0,int(number_of_attributes)):
label=raw_input("Enter the attribute label:");
AprioriSolution.attribute_names.append(label)
AprioriSolution.attribute_column[label]=i
for key,val in AprioriSolution.attribute_column.items():
print("column name",key,"column no",val)
#reading the file into numpy array
DataMatrix=np.loadtxt("C:/Users/dell/PycharmProjects/Homework4/Output_Contraceptive.txt",delimiter=',')
(row,column)=DataMatrix.shape
#reading each column one by one
for i in range(0,9):
column=DataMatrix[:,i]
AprioriSolution().calculate_candidate_one_itemsets(column,row,AprioriSolution.attribute_names[i]);
for key,val in AprioriSolution.candidate_one_itemsets.items():
print(key,val);
AprioriSolution.calculate_candidate_one_support(self,row,support)
AprioriSolution.calculate_candidate_two_support(self,row,support,DataMatrix)
m=number_of_attributes
for i in range(3,int(m)+1):
val=AprioriSolution.calculate_candidate_k_1(self,row,support,DataMatrix,i)
if val == 0:
break
for k,frequent_item in AprioriSolution.candidate_master_dict.items():
print("k",k,"itemset frequent",frequent_item)
AprioriSolution().start_apriori()