forked from kubapok/retroc2
201 lines
3.1 KiB
Python
201 lines
3.1 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# # retroc2
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# In[1]:
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import lzma
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import csv
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from stop_words import get_stop_words
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import gensim
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import itertools
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from sklearn.feature_extraction.text import TfidfVectorizer
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import pandas as pd
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from sklearn.linear_model import LinearRegression
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# In[68]:
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def read_data(filename):
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all_data = lzma.open(filename).read().decode('UTF-8').split('\n')
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return [line.split('\t') for line in all_data][:-1]
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train_data = read_data('train/train.tsv.xz')[::250]
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# In[69]:
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train_data[0]
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# In[70]:
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stop_words = get_stop_words('pl') + ['a', 'u', 'i', 'z', 'w', 'o']
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print(stop_words)
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# In[71]:
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train_data_tokenized = [list(set(gensim.utils.tokenize(x[4], lowercase = True))) for x in train_data]
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# In[72]:
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train_data_tokenized[0]
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# In[73]:
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train_data_stemmatized = [list(set([w[:6] for w in set(i) - set(stop_words)])) for i in train_data_tokenized]
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train_data_stemmatized[0]
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# In[74]:
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vectorizer = TfidfVectorizer()
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vectors = vectorizer.fit_transform([' '.join(i) for i in train_data_stemmatized])
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# In[75]:
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feature_names = vectorizer.get_feature_names()
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dense = vectors.todense()
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denselist = dense.tolist()
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df = pd.DataFrame(denselist, columns=feature_names)
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# In[76]:
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len(train_data)
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# In[77]:
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df[:10]
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# In[78]:
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vectorizer.transform(['__ ma kota']).toarray()[0]
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# In[79]:
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train_Y = [(float(x[0]) + float(x[1])) / 2 for x in train_data]
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# In[80]:
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model = LinearRegression() # definicja modelu
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model.fit(df, train_Y) # dopasowanie modelu
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# In[81]:
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model.predict(df[:10])
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# In[82]:
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with open('dev-0/in.tsv', "r", encoding="utf-8") as f:
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dev_0_data = [line.rstrip() for line in f]
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dev_0_data_tokenized = [list(set(gensim.utils.tokenize(x, lowercase = True))) for x in dev_0_data]
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dev_0_data_stemmatized = [list(set([w[:6] for w in set(i) - set(stop_words)])) for i in dev_0_data_tokenized]
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dev_0_data = [' '.join(i) for i in dev_0_data_stemmatized]
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# In[83]:
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y_predicted = model.predict(vectorizer.transform(dev_0_data).toarray())
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# In[84]:
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y_predicted[:10]
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# In[92]:
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f = open("dev-0/out.tsv", "a")
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for i in y_predicted:
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f.write(str(round(i, 11)) + '\n')
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f.close()
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# In[86]:
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with open('dev-0/expected.tsv', "r", encoding="utf-8") as f:
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e = [line.rstrip() for line in f]
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# In[94]:
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import math
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t = []
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for i in range(len(y_predicted)):
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tmp = (float(y_predicted[i]) - float(e[i])) ** 2
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t.append(tmp)
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print(math.sqrt(sum(t)/len(y_predicted)))
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# In[88]:
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with open('test-A/in.tsv', "r", encoding="utf-8") as f:
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test_A_data = [line.rstrip() for line in f]
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test_A_data_tokenized = [list(set(gensim.utils.tokenize(x, lowercase = True))) for x in test_A_data]
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test_A_data_stemmatized = [list(set([w[:6] for w in set(i) - set(stop_words)])) for i in test_A_data_tokenized]
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test_A_data = [' '.join(i) for i in test_A_data_stemmatized]
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# In[89]:
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y_test_predicted = model.predict(vectorizer.transform(test_A_data).toarray())
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# In[90]:
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y_test_predicted[:10]
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# In[93]:
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f = open("test-A/out.tsv", "a")
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for i in y_test_predicted:
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f.write(str(round(i, 11)) + '\n')
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f.close()
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# In[ ]:
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