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