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test_document_bayes_my_data.py
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test_document_bayes_my_data.py
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__author__ = 'sandip'
import codecs
import pickle
from os import listdir
from os.path import isfile, join
import re
from sensitivity_classifier_bayes import SensitivityClassifierBayes
def _split_text(text):
re_exp = '[\w]+'
return re.findall(re_exp, text)
#train
dir = '/home/sandip/neokami/sandipdocs/train/'
onlyfiles = [f for f in listdir('/home/sandip/neokami/sandipdocs/train/') ]
dataset = {}
for f in onlyfiles:
with codecs.open(dir+f, "r",encoding='utf-8', errors='ignore') as myfile:
data = myfile.read().replace('\n', '')
dataset[f] = data
#test
dir = '/home/sandip/neokami/sandipdocs/test/'
onlyfiles = [f for f in listdir('/home/sandip/neokami/sandipdocs/test/') ]
dataset_test = {}
for f in onlyfiles:
with codecs.open(dir+f, "r",encoding='utf-8', errors='ignore') as myfile:
data = myfile.read().replace('\n', '')
dataset_test[f] = data
print(len(dataset.values()))
keywords = ['credit card']
negative_emails = []
scb = SensitivityClassifierBayes()
model = scb.train(dataset.values(), keywords)
m = pickle.loads(model)
#print(m['word_with_target_word_count'])
for f in dataset_test.keys():
classification = scb.classify(dataset_test[f], model)
print(f, classification)