Created
February 25, 2013 12:47
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SVM
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#!/usr/bin/python | |
import sys | |
from numpy import loadtxt | |
import numpy as np | |
from sklearn.feature_extraction.text import CountVectorizer | |
from sklearn.feature_extraction.text import TfidfTransformer | |
from sklearn.pipeline import Pipeline | |
from sklearn.svm.sparse import LinearSVC | |
my_data = loadtxt(sys.argv[1], delimiter='\t', dtype='S') | |
my_test_data = loadtxt(sys.argv[2], delimiter='\t', dtype='S') | |
text_clf = Pipeline([ | |
('vect', CountVectorizer()), | |
('tfidf', TfidfTransformer()), | |
('clf', LinearSVC()), | |
]) | |
print("Training...") | |
my_clf = text_clf.fit(my_data[:,4], my_data[:,3]) | |
print("Done! \nClassifying test set...") | |
predicted = my_clf.predict(my_test_data[:,4]) | |
print(np.mean(predicted == my_test_data[:,3])) |
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