92 lines
1.4 KiB
Python
92 lines
1.4 KiB
Python
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#!/usr/bin/env python
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# coding: utf-8
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# In[90]:
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import pandas as pd
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import csv
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# In[91]:
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tsv_data = pd.read_csv('in.tsv', sep='\t',header=None, quoting=csv.QUOTE_NONE)[0]
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# In[139]:
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expected = pd.read_csv('expected.tsv', sep='\t',header=None)[0]
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# In[94]:
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print(len(expected))
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print(len(tsv_data))
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# In[158]:
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male={'silnik', 'windows', 'gb', 'mb', 'mecz', 'pc', 'opony', 'apple', 'iphone', 'zwiastuny', 'hd', 'ubuntu', 'system', 'serwer'}
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female={'ciąża', 'miesiączki', 'ciasto', 'ciąże', 'zadowolona', 'antykoncepcyjne', 'ginekologia', 'tabletki', 'porodzie', 'mąż', 'krwawienie', 'ciasta'}
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male = {x[:6].lower() for x in male}
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female = {x[:6].lower() for x in female}
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# In[159]:
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trimmed_docs=[]
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for document in tsv_data:
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new_doc=[]
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for word in str(document).lower().split():
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new_doc.append(word[:6])
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trimmed_docs.append(new_doc)
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# In[160]:
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male_or_female=[]
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for doc in trimmed_docs:
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male_or_female.append((len(male&set(doc)), len(female&set(doc))))
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# In[161]:
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answers=[]
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for i in male_or_female:
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if i[0]>i[1]:
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answers.append(1)
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else:
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answers.append(0)
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# In[162]:
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result=[]
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for i in range(len(answers)):
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if answers[i]==expected[i]:
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result.append(1)
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else:
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result.append(0)
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# In[163]:
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print(f'Predykcja modelu wynosi {sum(result)/len(result)*100:.6f}%')
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# In[167]:
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df = pd.DataFrame(result)
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df.to_csv('out.tsv', sep = '\t')
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