87 lines
3.4 KiB
Python
Executable File
87 lines
3.4 KiB
Python
Executable File
#!/usr/bin/python3
|
||
from collections import defaultdict
|
||
import math
|
||
import pickle
|
||
import re
|
||
|
||
# in expected.tsv
|
||
def calc_class_logprob(expected_path):
|
||
paranolal_classcount=0
|
||
sceptic_classcount=0
|
||
with open(expected_path) as f:
|
||
for line in f:
|
||
line = line.rstrip('\n').replace(' ','')
|
||
if 'P' in line:
|
||
paranolal_classcount +=1
|
||
elif 'S' in line:
|
||
sceptic_classcount +=1
|
||
|
||
paranol_prob = paranolal_classcount / (paranolal_classcount + sceptic_classcount)
|
||
sceptic_prob = sceptic_classcount / (paranolal_classcount + sceptic_classcount)
|
||
|
||
return math.log(paranol_prob), math.log(sceptic_prob)
|
||
|
||
def clear_tokens(tokens):
|
||
tokens = tokens.replace('\\n', ' ')
|
||
# delete links, special characters, kropki, and \n
|
||
tokens = re.sub(r'\(((http)|(https)).*((\.com)|(\.net)|(\.jpg)|(\.html))\)'," ", tokens)
|
||
tokens = re.sub(r'(|\-|\_)([a-z]+(\-|\_))+[a-z]+(|\-|\_)', ' ', tokens)
|
||
tokens = re.sub(r'[\n\&\"\?\\\'\*\[\]\,\;\.\=\+\(\)\!\/\:\`\~\%\^\$\#\@\’\＞\″\±]+', ' ', tokens)
|
||
tokens = re.sub(r'[\.\-][\.\-]+', ' ', tokens)
|
||
tokens = re.sub(r'[0-9]+', ' ', tokens)
|
||
tokens = re.sub(r'œ|·', '', tokens)
|
||
tokens = re.sub(r' +', ' ', tokens)
|
||
return tokens
|
||
|
||
# ile razy slowo wystepuje w dokumentach w danej klasie
|
||
def calc_word_count(in_path, expected_path):
|
||
word_counts = {'paranormal':defaultdict(int), 'sceptic': defaultdict(int)} # dzienik zawierajacy slownik w ktorym s slowa i ile razy wystepuja
|
||
with open(in_path) as infile, open(expected_path) as expectedfile:
|
||
for line, exp in zip(infile, expectedfile):
|
||
class_ = exp.rstrip('\n').replace(' ','')
|
||
text, timestap =line.rstrip('\n').split('\t')
|
||
#print(f"text {type(text)}")
|
||
text = clear_tokens(text)
|
||
tokens = text.lower().split(' ')
|
||
#print(f"tokens {type(tokens)}")
|
||
for token in tokens:
|
||
if class_ == 'P':
|
||
word_counts['paranormal'][token] += 1
|
||
elif class_ == 'S':
|
||
word_counts['sceptic'][token]+=1
|
||
|
||
return word_counts
|
||
|
||
def calc_word_logprobs(word_counts):
|
||
total_skeptic = sum(word_counts['sceptic'].values()) + len(word_counts['sceptic'].keys())
|
||
total_paranormal = sum(word_counts['paranormal'].values())+ len(word_counts['paranormal'].keys())
|
||
word_logprobs= {'paranormal': {}, 'sceptic': {}}
|
||
for class_ in word_counts.keys(): # sceptic paranormal
|
||
for token, value in word_counts[class_].items():
|
||
if class_ == 'sceptic':
|
||
word_prob = (value +1)/ total_skeptic
|
||
elif class_ == 'paranormal':
|
||
word_prob = (value+1)/ total_paranormal
|
||
|
||
#print (token)
|
||
word_logprobs[class_][token] = math.log(word_prob)
|
||
|
||
return word_logprobs
|
||
|
||
def main():
|
||
#expected = './train/expected.tsv'
|
||
expected = './dev-0/expected.tsv'
|
||
#in_f = './train/in.tsv'
|
||
in_f = './dev-0/in.tsv'
|
||
print (f"expected {expected}")
|
||
print (f"in {in_f}")
|
||
paranormal_class_lgprob, skeptic_class_logprob = calc_class_logprob(expected)
|
||
wordcounts =calc_word_count(in_f,expected)
|
||
|
||
word_logprobs = calc_word_logprobs(wordcounts)
|
||
with open('naive_base_model.pkl', 'wb') as f:
|
||
pickle.dump([paranormal_class_lgprob, skeptic_class_logprob, word_logprobs], f)
|
||
# w predict.py bierzemy ten wzor argmax P(w) iloczynP(w|c)
|
||
|
||
main()
|