77 lines
1.3 KiB
Python
77 lines
1.3 KiB
Python
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#!/usr/bin/env python
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# coding: utf-8
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# In[46]:
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import sklearn
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from sklearn.pipeline import make_pipeline
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from sklearn.feature_extraction.text import TfidfVectorizer
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import numpy as np
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.preprocessing import LabelEncoder
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# In[47]:
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def getInput(path):
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with open(path,encoding='utf-8') as f:
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return f.readlines()
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# In[48]:
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get_ipython().system('pwd')
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# In[49]:
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train_in=getInput('./train/in.tsv')
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train_expected=getInput('./train/expected.tsv')
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test_in=getInput('./test-A/in.tsv')
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dev_in=getInput('./dev-0/in.tsv')
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dev_expected=getInput('./dev-0/expected.tsv')
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# In[50]:
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pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
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encTransform = LabelEncoder().fit_transform(train_expected)
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model = pipeline.fit(train_in, encTransform)
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dev_predicted = model.predict(dev_in)
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test_predicted = model.predict(test_in)
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# In[ ]:
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# In[54]:
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with open('./dev-0/out.tsv', "w") as result:
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for out in dev_predicted:
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result.write(str(out) + '\n')
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with open('./test-A/out.tsv', "w") as result:
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for out in test_predicted:
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result.write(str(out) + '\n')
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# In[55]:
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get_ipython().system('jupyter nbconvert --to script Naiwny_bayes.ipynb')
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# In[ ]:
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