def compute_quality_for_corpus(corpus_dir): truth_dic = methods.read_classification_from_file(methods.add_slash(corpus_dir)+"!truth.txt") pred_dic = methods.read_classification_from_file(methods.add_slash(corpus_dir)+"!prediction.txt") bc1 = BinaryConfusionMatrix('SPAM', 'OK') bc1.compute_from_dicts(truth_dic, pred_dic) dict_score = bc1.as_dict() fn=dict_score['fn'] tn=dict_score['tn'] fp=dict_score['fp'] tp=dict_score['tp'] return quality_score(tp, tn, fp, fn), tp, tn, fp, fn
def train(self,path_to_truth_dir): corpus = Corpus(path_to_truth_dir) #Read truth file truth = methods.read_classification_from_file(methods.add_slash(path_to_truth_dir)+"!truth.txt") #Make truth global self.truth = truth for fname, body in corpus.emails_as_string(): email_as_file = open(methods.add_slash(path_to_truth_dir) + fname,'r',encoding = 'utf-8') #Read email with EMAIL parser msg = email.message_from_file(email_as_file) self.extract_senders_list(msg,fname) self.check_subject(msg,fname) #Generate dict's methods.generate_file_from_dict(self.path_bl , self.black_list) methods.generate_file_from_dict(self.path_wl ,self.white_list) methods.generate_file_from_dict(self.path_ssl , self.spam_subject_list) methods.generate_file_from_dict(self.path_hsl ,self.ham_subject_list)
def is_spam(self,email_name): my_dict = methods.read_classification_from_file(self.path,'!truth.txt') if (my_dict[email_name] == 'SPAM'): return True return False