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my-brillia
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dca3122fab |
5272
dev-0/out.tsv
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5272
dev-0/out.tsv
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foo.py
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foo.py
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#!/usr/bin/env python
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# coding: utf-8
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# In[3]:
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import pathlib
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from collections import Counter
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from sklearn.metrics import *
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import pandas as pd
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# In[1]:
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import numpy as np, pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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from sklearn.datasets import fetch_20newsgroups
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.pipeline import make_pipeline
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from sklearn.metrics import confusion_matrix, accuracy_score
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sns.set() # use seaborn plotting style
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# In[5]:
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train_x = pd.read_csv('train/in.tsv', header=None, sep='\t')
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train_y = pd.read_csv('train/expected.tsv', header=None, sep='\t')
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dev_x = pd.read_csv('dev-0/in.tsv', header=None, sep='\t')
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dev_y = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t')
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test_x = pd.read_csv('test-A/in.tsv', header=None, sep='\t')
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# In[61]:
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print(dev_y.shape)
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print(dev_x.shape)
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# In[11]:
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print(train_x[:15])
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# In[27]:
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print(train_x.shape)
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# In[49]:
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print(train_y.shape)
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# In[8]:
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print(train_y[:15])
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# In[53]:
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print(dev_x[:4])
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# In[119]:
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.feature_extraction.text import TfidfTransformer
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vec = CountVectorizer(stop_words='english')
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x1 = vec.fit_transform(train_x[:20000][0])
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tfidf_transformer = TfidfTransformer()
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x1_tf = tfidf_transformer.fit_transform(x1)
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# In[120]:
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# Build the model
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#model = make_pipeline(TfidfVectorizer(), MultinomialNB())
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clf = MultinomialNB().fit(x1_tf, train_y[:20000][0])
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# In[121]:
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# Train the model using the training data
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#model.fit(x1[:][0], train_y[:289541][0])
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# Predict the categories of the test data
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X_new_counts = vec.transform(dev_x[:][0])
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# We call transform instead of fit_transform because it's already been fit
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X_new_tfidf = tfidf_transformer.transform(X_new_counts)
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#predicted_categories = model.predict(dev_x[:][0])
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# In[122]:
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predicted = clf.predict(X_new_tfidf)
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# In[125]:
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print(predicted[:10])
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# In[126]:
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print(predicted.shape)
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# In[123]:
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#mat = confusion_matrix(dev_y[:][0],predicted_categories)
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print("The accuracy is {}".format(accuracy_score( dev_y[:][0],predicted_categories)))
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# In[124]:
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print('We got an accuracy of',np.mean(predicted == dev_y[:][0])*100, '% over the test data.')
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# In[130]:
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np.savetxt("out.tsv",predicted, delimiter="\t", fmt='%d')
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# In[131]:
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X_test = vec.transform(test_x[:][0])
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# We call transform instead of fit_transform because it's already been fit
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X_tfidf_test = tfidf_transformer.transform(X_test)
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predicted_test = clf.predict(X_tfidf_test)
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np.savetxt("out.tsv",predicted_test, delimiter="\t", fmt='%d')
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# In[ ]:
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5152
test-A/out.tsv
Normal file
5152
test-A/out.tsv
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