154 lines
6.7 KiB
Python
Executable File
154 lines
6.7 KiB
Python
Executable File
#!/usr/bin/python3
|
||
from collections import defaultdict
|
||
import math
|
||
import pickle
|
||
import re
|
||
import sys
|
||
import nltk
|
||
from nltk.corpus import stopwords
|
||
|
||
def calc_class_logprob(expected_path):
|
||
paranormal_classcount = 0
|
||
sceptic_classcount = 0
|
||
|
||
with open(expected_path) as f:
|
||
for line in f:
|
||
line = line.rstrip('\n').replace(' ','')
|
||
if 'P' in line:
|
||
paranormal_classcount +=1
|
||
elif 'S' in line:
|
||
sceptic_classcount +=1
|
||
|
||
paranol_prob = paranormal_classcount / (paranormal_classcount + sceptic_classcount)
|
||
sceptic_prob = sceptic_classcount / (paranormal_classcount + sceptic_classcount)
|
||
|
||
return math.log(paranol_prob), math.log(sceptic_prob)
|
||
|
||
def clear_post(post):
|
||
post = post.replace('\\n', ' ')
|
||
post = post.lower()
|
||
# delete links
|
||
post = re.sub(r'(\(|)(http|https|www)[a-zA-Z0-9\.\:\/\_\=\&\;\?\+\-\%]+(\)|)', ' internetlink ', post)
|
||
post = re.sub(r'[\.\,\/\~]+', ' ', post)
|
||
post = re.sub(r'(<|>|\@[a-zA-Z0-9]+)','',post)
|
||
post = re.sub(r'[\'\(\)\?\*\"\`\;0-9\[\]\:\%\|\–\”\!\=\^]+', '', post)
|
||
post = re.sub(r'( \- |\-\-+)', ' ', post)
|
||
post = re.sub(r' +', ' ', post)
|
||
post = post.rstrip(' ')
|
||
post = post.split(' ')
|
||
stop_words = set(stopwords.words('english'))
|
||
post_no_stop = [w for w in post if not w in stop_words]
|
||
return post_no_stop
|
||
|
||
#def calc_bigram_count(in_path, expected_path):
|
||
# bigram_counts = {'paranormal' : defaultdict(int), 'sceptic' : defaultdict(int)}
|
||
# with open(in_path) as infile, open(expected_path) as expected_file:
|
||
# num_of_bigams = 0
|
||
# for line, exp in zip(infile, expected_file):
|
||
# class_ = exp.rstrip('\n').replace(' ', '')
|
||
# text, timestap = line.rstrip('\n').split('\t')
|
||
# tokens = clear_post(text)
|
||
# #tokens = text.lower().split(' ')
|
||
# for index in range(len(tokens)-1):
|
||
# # if there is next token we append current and next
|
||
# bigram = tokens[index] + " " + tokens[index + 1]
|
||
# #print(bigram)
|
||
# #print (f"bigram constructed from ;;;;{tokens[index]}:{tokens[index+1]};;;;;;;")
|
||
# if class_ == 'P':
|
||
# bigram_counts['paranormal'][bigram] +=1
|
||
# elif class_ == 'S':
|
||
# bigram_counts['sceptic'][bigram] +=1
|
||
# num_of_bigams +=1
|
||
# #print(f"num of every added bigams with repetitions {num_of_bigams})")
|
||
# #print(f"num of bigams in paranormal {len(bigram_counts['paranormal'])} and sceptic {len(bigram_counts['sceptic'])}")
|
||
# return bigram_counts
|
||
|
||
def calc_bigram_logprobs(bigram_counts):
|
||
total_sceptic = sum(bigram_counts['sceptic'].values()) + len(bigram_counts['sceptic'].keys())
|
||
total_paranormal = sum(bigram_counts['paranormal'].values()) + len(bigram_counts['paranormal'].keys())
|
||
bigram_logprobs = {'paranormal' : {}, 'sceptic' : {}}
|
||
for class_ in bigram_counts.keys():
|
||
for bigram, value in bigram_counts[class_].items():
|
||
if class_ == "sceptic":
|
||
bigram_prob = (value + 1) / total_sceptic
|
||
elif class_ == "paranormal":
|
||
bigram_prob = (value + 1) / total_paranormal
|
||
|
||
bigram_logprobs[class_][bigram] = math.log(bigram_prob)
|
||
|
||
return bigram_logprobs
|
||
|
||
#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, True)
|
||
# tokens = text.lower().split(' ')
|
||
# #print(f"tokens {type(tokens)}")
|
||
# for token in tokens:
|
||
# clear_tokens(token,False)
|
||
# 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 launch_bigrams_and_words(in_path, expected_path):
|
||
word_counts = {'paranormal':defaultdict(int), 'sceptic': defaultdict(int)}
|
||
bigram_counts = {'paranormal' : defaultdict(int), 'sceptic' : defaultdict(int)}
|
||
with open(in_path) as infile, open(expected_path) as expected_file:
|
||
for line, exp in zip(infile, expected_file):
|
||
class_ = exp.rstrip('\n').replace(' ', '')
|
||
text, timestap = line.rstrip('\n').split('\t')
|
||
tokens = clear_post(text)
|
||
for index in range(len(tokens)-1):
|
||
# if there is next token we append current and next
|
||
bigram = tokens[index] + " " + tokens[index + 1]
|
||
#print(bigram)
|
||
#print (f"bigram constructed from ;;;;{tokens[index]}:{tokens[index+1]};;;;;;;")
|
||
if class_ == 'P':
|
||
bigram_counts['paranormal'][bigram] +=1
|
||
word_counts['paranormal'][tokens[index]] +=1
|
||
elif class_ == 'S':
|
||
bigram_counts['sceptic'][bigram] +=1
|
||
word_counts['sceptic'][tokens[index]] +=1
|
||
|
||
return bigram_counts, word_counts
|
||
|
||
def main():
|
||
if len(sys.argv) != 4:
|
||
print("syntax is ./train.py expected.tsv in.tsv model.pkl")
|
||
return
|
||
expected_file = str(sys.argv[1])
|
||
in_file = str(sys.argv[2])
|
||
model = str(sys.argv[3])
|
||
paranormal_class_logprob, sceptic_class_logprob = calc_class_logprob(expected_file)
|
||
#bigrams_count = calc_bigram_count(in_file, expected_file)
|
||
bigrams_count, words_count = launch_bigrams_and_words(in_file, expected_file)
|
||
bigram_logprobs = calc_bigram_logprobs(bigrams_count)
|
||
word_logprobs = calc_word_logprobs(words_count)
|
||
with open(model, 'wb') as f:
|
||
pickle.dump([paranormal_class_logprob, sceptic_class_logprob, bigram_logprobs, word_logprobs],f)
|
||
main()
|
||
|