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Update main.py
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@AviAyano
AviAyano authored Nov 7, 2021
1 parent 290ef67 commit 0816acf
Showing 1 changed file with 3 additions and 81 deletions.
84 changes: 3 additions & 81 deletions main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,6 +57,7 @@ def openfile(minus_one,one):
for lines in table:
rows_number += 1
separator_line = lines[0].split(',')
col_number = len(separator_line)
line_without_index_col = separator_line[1:] #[1:] for phishing

if line_without_index_col[-1] == '-1' and counter_a < (minus_one*0.3):
Expand All @@ -67,9 +68,7 @@ def openfile(minus_one,one):
counter_b += 1
elif not(separator_line[0][0].isalpha()):
prob_table.append(line_without_index_col) #insert to phishing table
line = ''.join(line_without_index_col) #convert to string without a comma
if len(line) > col_number and rows_number != 1:
col_number = len(line) - line.count('-') +1

test_data = test_data_a + test_data_b
#print("The test data is :",test_data,'\n',"The prob table is :",prob_table)
return (rows_number-1,col_number,prob_table,test_data)
Expand All @@ -78,10 +77,6 @@ def openfile(minus_one,one):
finally:
return(rows_number-1,col_number,prob_table,test_data)

#[print(i,end=' ') for i in pishing_table[2] ]
#print( line,'\n',col_number,'=',len(line) ,'-',line.count(',') ,'-',line.count('-'))
#print(','.join(line))

def class_prob(rows,cols, prob_table):
one, minus_one = 0,0
for row in range(rows):
Expand All @@ -96,10 +91,7 @@ def class_prob(rows,cols, prob_table):
print("cell value is unexpected :",cell_value)
prob_class_one = one / rows
prob_class_minus_one = minus_one / rows
#prob_class_zero = zero / rows
#print("The class probability :","\nOne :",prob_class_one,"\nMinus_one :",prob_class_minus_one,"\nzero :",prob_class_zero)
return (one,minus_one,prob_class_one,prob_class_minus_one)
#print("Checking the probability rule equals to 1 :",prob_class_one+prob_class_minus_one+prob_class_zero)

def laplacian_correction( cols, class_list):
corrected_list = copy.deepcopy(class_list)
Expand All @@ -121,7 +113,6 @@ def item_set(rows, cols,prob_table):
set_table[col] = len(set(temp_list))
temp_list = []
max_set = max(set_table)
#print("The item",max_set)
return max_set

def attribute_prob(prob_dict, rows, cols, prob_table, class_one, class_minus_one):
Expand Down Expand Up @@ -155,58 +146,6 @@ def attribute_prob(prob_dict, rows, cols, prob_table, class_one, class_minus_one
prob_dict.update({col: [temp_list0,temp_list1]})
return prob_dict

# #corrected_zero_list = laplacian_correction(rows, cols, class_zero_list)
# corrected_one_list = laplacian_correction(rows, cols, prob_dict)
# corrected_minus_one_list = laplacian_correction(rows, cols, prob_dict)

# if phishing_table[row][30] == '0':
# if cell_value == '1':
# one_attribute += 1 # insert the class prob by attribute like under30 who buy..
# class_zero_list.insert(1, one_attribute / class_zero)
# elif cell_value == '-1':
# minus_one_attribute += 1
# class_zero_list.insert(2, minus_one_attribute / class_zero)
# elif cell_value == '0':
# zero_attribute += 1
# class_zero_list.insert(0, zero_attribute / class_zero)
# else:
# print("cell value is unexpected :", cell_value)
#
# elif phishing_table[row][30] == '1':
# if cell_value == '1':
# one_attribute += 1
# class_one_list.insert(1, one_attribute / class_one)
# elif cell_value == '-1':
# minus_one_attribute += 1
# class_one_list.insert(2, minus_one_attribute / class_one)
# elif cell_value == '0':
# zero_attribute += 1
# class_one_list.insert(0, zero_attribute / class_one)
# else:
# print("cell value is unexpected :", cell_value)
#
# elif phishing_table[row][30] == '-1':
# if cell_value == '1':
# one_attribute += 1
# class_minus_one_list.insert(1, one_attribute / class_minus_one)
# elif cell_value == '-1':
# minus_one_attribute += 1
# class_minus_one_list.insert(2, minus_one_attribute / class_minus_one)
# elif cell_value == '0':
# zero_attribute += 1
# class_minus_one_list.insert(0, zero_attribute / class_minus_one)
# else:
# print("cell value is unexpected :", cell_value)

#print("attribute_prob", class_zero_list, class_one_list, class_minus_one_list)



# class_zero_list = list(map(lambda x: x>0 , class_zero_list))
# print("wooosdsddsoow",class_zero_list)
# class_one_list = list(map(lambda x: x>0 , class_one_list))
# class_minus_one_list = list(map(lambda x: x>0 , class_minus_one_list))

def reduce_lists(rows,cols,table,prob_dict,prob_class_one,prob_class_minus_one):
prob_table = table
prob_x_given_class_one_list ,prob_x_given_class_minus_one_list = [0]*cols,[0]*cols
Expand Down Expand Up @@ -241,20 +180,9 @@ def reduce_lists(rows,cols,table,prob_dict,prob_class_one,prob_class_minus_one):
print(phishing_counter)

return (legit_counter,phishing_counter)
# #print("reduce_lists",corrected_zero_list, corrected_one_list, corrected_minus_one_list)
#prob_x_given_class_zero = functools.reduce(lambda x,y: x*y, corrected_zero_list,1)
#prob_x_given_class_one = functools.reduce(lambda x,y: x*y, corrected_one_list ,1)
#prob_x_given_class_minus_one = functools.reduce(lambda x,y: x*y, corrected_minus_one_list,1)
# #print("\nThe class probability:\n", corrected_zero_list, '\n', corrected_one_list, '\n', corrected_minus_one_list)
# #print("wooooow",prob_x_given_class_zero,prob_x_given_class_one,prob_x_given_class_minus_one)
# #naive_bayes_classifier(prob_x_given_class_zero, class_zero, prob_x_given_class_one, class_one, prob_x_given_class_minus_one,class_minus_one)


#print("The website is : "+str(row), "\nOne :", prob_class_one, "\nMinus_one :", prob_class_minus_one, "\nZero :",prob_class_zero)

def naive_bayes_classifier(prob_x_given_class_one, prob_class_one, prob_x_given_class_minus_one, prob_class_minus_one):
classifier = [0,0]
#classifier[0] = prob_x_given_class_z * class_z
classifier[0] = prob_x_given_class_minus_one * prob_class_minus_one # minus one
classifier[1] = prob_x_given_class_one * prob_class_one
#print('\n',classifier)
Expand Down Expand Up @@ -293,14 +221,8 @@ def final_porb(sum_of_minus_one,sum_of_one,legit_counter,phishing_counter):
class_one ,class_minus_one ,prob_class_one ,prob_class_minus_one = class_prob(train.getRow(),train.getCol(), prob_table)
prob_dict = attribute_prob(prob_dict, train.getRow(), train.getCol(), prob_table, class_one, class_minus_one)
legit_counter,phishing_counter = reduce_lists(train.getRow(),train.getCol(),prob_table,prob_dict,prob_class_one,prob_class_minus_one)
#print(Web.Phishing.name)
print_legit_trian,print_phishing_trian = final_porb(sum_of_minus_one,sum_of_one,legit_counter,phishing_counter)






print("The test dataset is: {} X {} ".format(test.getRow(), test.getCol()))
#test_data = test_data[1:]
print_size, sum_of_minus_one, sum_of_one = table_size(test.getRow(), test.getCol(), test_data,"test")
Expand All @@ -313,4 +235,4 @@ def final_porb(sum_of_minus_one,sum_of_one,legit_counter,phishing_counter):
print("\n***************** The training dataset results *****************")
print(print_legit_trian, print_phishing_trian, sep='\n')
print("\n***************** The test dataset results *****************")
print(print_legit_test, print_phishing_test, sep='\n')
print(print_legit_test, print_phishing_test, sep='\n')

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