import numpy as np
import csv
from bigram import Bigram
ham_validation_list = Bigram.processed_file("../ham/validation.txt")
spam_validation_list = Bigram.processed_file("../spam/validation.txt")
with open('eval_k_0.1_1.0.csv', mode='w') as report_file:
report_writer = csv.writer(report_file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
report_writer.writerow([
'k', 'ham accuracy', 'spam accuracy', 'precision', 'recall', 'F1_score'
])
for k in np.linspace(0.1, 1.0, 10):
ham_pro_dict = Bigram.bigram_probability_dict("../ham/train.txt", k)
spam_pro_dict = Bigram.bigram_probability_dict("../spam/train.txt", k)
ham_count_ham = ham_count_spam = 0
for message in ham_validation_list:
spam_pp = Bigram.perplexity_single_message(spam_pro_dict, message)
ham_pp = Bigram.perplexity_single_message(ham_pro_dict, message)
result = None
if spam_pp > ham_pp:
ham_count_ham += 1
else:
ham_count_spam += 1
spam_count_ham = spam_count_spam = 0
import numpy as np
import csv
from bigram import Bigram
k = 0.01
ham_test_list = Bigram.processed_file("../ham/test.txt")
spam_test_list = Bigram.processed_file("../spam/test.txt")
ham_pro_dict = Bigram.bigram_probability_dict("../ham/train.txt", k)
spam_pro_dict = Bigram.bigram_probability_dict("../spam/train.txt", k)
ham_count_ham = ham_count_spam = 0
for message in ham_test_list:
spam_pp = Bigram.perplexity_single_message(spam_pro_dict, message)
ham_pp = Bigram.perplexity_single_message(ham_pro_dict, message)
result = None
if spam_pp > ham_pp:
ham_count_ham += 1
else:
ham_count_spam += 1
spam_count_ham = spam_count_spam = 0
for message in spam_test_list:
spam_pp = Bigram.perplexity_single_message(spam_pro_dict, message)
ham_pp = Bigram.perplexity_single_message(ham_pro_dict, message)
result = None
if spam_pp > ham_pp:
spam_count_ham += 1
else:
spam_count_spam += 1