84 lines
3.3 KiB
Python
Executable File
84 lines
3.3 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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import sys
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def calc_class_logprob(expected_path):
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paranormal_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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paranormal_classcount +=1
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elif 'S' in line:
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sceptic_classcount +=1
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paranol_prob = paranormal_classcount / (paranormal_classcount + sceptic_classcount)
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sceptic_prob = sceptic_classcount / (paranormal_classcount + sceptic_classcount)
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return math.log(paranol_prob), math.log(sceptic_prob)
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def clear_post(post):
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post = post.replace('\\n', ' ')
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# delete links
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post = re.sub(r'(\(|)(http|https|www)[a-zA-Z0-9\.\:\/\_\=\&\;\?\+]+(\)|)', '', post)
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post = re.sub(r'[\.\,\/]+', ' ', post)
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post = re.sub(r'(<|>)','',post)
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post = re.sub(r'[\'\(\)\?\*\"\`\;0-9\[\]\:\%]+', '', post)
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post = re.sub(r' \- ', ' ', post)
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post = re.sub(r' +', ' ', post)
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post = post.rstrip(' ')
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return post
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def calc_bigram_count(in_path, expected_path):
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bigram_counts = {'paranormal' : defaultdict(int), 'sceptic' : defaultdict(int)}
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with open(in_path) as infile, open(expected_path) as expected_file:
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for line, exp in zip(infile, expected_file):
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class_ = exp.rstrip('\n').replace(' ', '')
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text, timestap = line.rstrip('\n').split('\t')
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text = clear_post(text)
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tokens = text.lower().split(' ')
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for index in range(len(tokens)-1):
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# if there is next token we append current and next
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bigram = tokens[index] + " " + tokens[index + 1]
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#print(bigram)
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#print (f"bigram constructed from ;;;;{tokens[index]}:{tokens[index+1]};;;;;;;")
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if class_ == 'P':
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bigram_counts['paranormal'][bigram] +=1
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elif class_ == 'S':
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bigram_counts['sceptic'][bigram] +=1
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return bigram_counts
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def calc_bigram_logprobs(bigram_counts):
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total_sceptic = sum(bigram_counts['sceptic'].values()) + len(bigram_counts['sceptic'].keys())
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total_paranormal = sum(bigram_counts['paranormal'].values()) + len(bigram_counts['paranormal'].keys())
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bigram_logprobs = {'paranormal' : {}, 'sceptic' : {}}
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for class_ in bigram_counts.keys():
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for bigram, value in bigram_counts[class_].items():
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if class_ == "sceptic":
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bigram_prob = (value + 1) / total_sceptic
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elif class_ == "paranormal":
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bigram_prob = (value + 1) / total_paranormal
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bigram_logprobs[class_][bigram] = math.log(bigram_prob)
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return bigram_logprobs
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def main():
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if len(sys.argv) != 4:
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print("syntax is ./train.py expected.tsv in.tsv model.pkl")
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return
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expected_file = str(sys.argv[1])
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in_file = str(sys.argv[2])
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model = str(sys.argv[3])
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paranormal_class_logprob, sceptic_class_logprob = calc_class_logprob(expected_file)
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bigrams_count = calc_bigram_count(in_file, expected_file)
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bigram_logprobs = calc_bigram_logprobs(bigrams_count)
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with open(model, 'wb') as f:
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pickle.dump([paranormal_class_logprob, sceptic_class_logprob, bigram_logprobs],f)
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main()
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