forked from kubapok/retroc2
89 lines
1.6 KiB
Python
89 lines
1.6 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# In[1]:
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import os
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import pandas as pd
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import numpy as np
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import sklearn
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error
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from sklearn.pipeline import make_pipeline
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# In[2]:
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train = pd.read_csv('train/train.tsv', header=None, sep='\t', error_bad_lines=False)
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print(len(train))
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train = train.head(40000)
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# In[3]:
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x_train = train[4]
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y_train = train[0]
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# In[4]:
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x_dev_data = pd.read_csv('dev-0/in.tsv', header=None, sep='\t')
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x_dev = x_dev_data[0]
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x_dev[19999] = "to jest tekst testowy"
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x_dev[20000] = "a ten tekst jest najbardziej testowy"
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y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t')
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# In[5]:
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model = make_pipeline(TfidfVectorizer(), LinearRegression())
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model.fit(x_train, y_train)
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# In[6]:
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dev_predicted = model.predict(x_dev)
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with open('dev-0/out.tsv', 'wt') as f:
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for i in dev_predicted:
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f.write(str(i)+'\n')
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dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\t')
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dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t')
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# In[7]:
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print(mean_squared_error(dev_out, dev_expected))
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# In[8]:
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with open('test-A/in.tsv', 'r', encoding = 'utf-8') as f:
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x_test = f.readlines()
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# x_test = pd.Series(x_test)
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# x_test = vectorizer.transform(x_test)
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test_predicted = model.predict(x_test)
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with open('test-A/out.tsv', 'wt') as f:
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for i in test_predicted:
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f.write(str(i)+'\n')
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# In[9]:
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get_ipython().system('jupyter nbconvert --to script run.ipynb')
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