53 lines
2.0 KiB
Python
53 lines
2.0 KiB
Python
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from sklearn.metrics import recall_score
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from sklearn.metrics import precision_score
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from sklearn.metrics import accuracy_score
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from sklearn.metrics import f1_score
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male = ['windows', 'gb', 'mb', 'meczu', 'pc', 'opony', 'apple', 'iphone', 'zwiast', 'hd', 'ubunt',
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'system', 'serwer', "youtub", "sfd", "kfd", "elektr", "autoce", "dobrep",'merced', 'bmw',
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'audi', 'porsch', 'gry', 'gra','gram' 'cs', 'counte', 'piłka', 'mecz', 'gol', 'bramka', 'linux',
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'robota','felga','lagi' 'żona', 'żona', 'żony', 'żonie', 'żoną', 'zona', 'zony', 'zonie', 'komput', 'inform'
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'sserwer', 'ziom', 'ziomków', 'ziomkow', 'kumpel', 'kolega', 'kolegą', 'kolegi', 'pad'
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]
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female = ['ciąży', 'miesią', 'ciasto', 'ciążę', 'zadowo', 'ciąża', 'ciazy', 'antyko', 'gineko',
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'tablet', 'porodz', 'mąż', 'miesią', 'krwawi', 'ciasta', 'sukien', 'podpas', 'szmink',
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'maz', 'męża', 'męza', 'mąż', 'chłopak', 'szpilk'
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]
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def prediction(male,female, in_file):
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results = []
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with open(in_file, encoding='utf-8',) as file:
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for line in file.readlines():
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text = line.split("\t")[0].strip()
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text = text.replace(",","").replace(".","").replace("/","").replace("–","").replace(":","").lower()
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stem_words = [word[:6] for word in text.split()]
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man_score = len([w for w in stem_words if w in male])
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girl_score = len([w for w in stem_words if w in female])
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if man_score > girl_score:
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results.append('1')
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else:
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results.append('0')
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return results
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def out_file(result, out_file):
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with open(out_file, 'w') as file:
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for r in result:
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file.write(r + "\n")
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result = prediction(male,female,'dev-0/in.tsv')
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out_file(result, 'dev-0/out.tsv')
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result = prediction(male,female,'dev-1/in.tsv')
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out_file(result, 'dev-1/out.tsv')
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result = prediction(male,female,'test-A/in.tsv')
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out_file(result, 'test-A/out.tsv')
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