from naive_bayes_classifier import NaiveBayesClassifer import sys model = NaiveBayesClassifer() if sys.argv[1]: model.train_all(sys.argv[1]) print 'Done' else: print 'Must provide a path to input'
preprocess = PreprocessData()
#vocab = preprocess.load_pickle_file('vocab')
vocab = preprocess.generate_vocabulary(train_data_test)
print(len(np.unique(vocab)))
print("Normalizing train data")
train_data = preprocess.normalize_text(train_data, 'train_data_normalized')
train_data_test = preprocess.normalize_text(train_data_test,
'train_data_normalized')
print("Normalizing test data")
validation_data = preprocess.normalize_text(validation_data,
'validation_data_normalized')
test_data = preprocess.normalize_text(test_data,
'test_data_normalized',
train=False)
naive_classifier = NaiveBayesClassifer(train_data_test, vocab)
naive_classifier.train(train_data_test)
alphas = np.linspace(0.09, 1,
20) # Best alpha submit 55.211% 0.09063157894736842
accuracies = []
for alpha in alphas:
preds = naive_classifier.test_accuracy(validation_data[:, :-1], True,
alpha)
accuracy = np.mean(preds == validation_data[:, -1])
accuracies.append(accuracy)
print("accuracy {0} for {1}".format(accuracy, str(alpha)))
print("Best alpha: ", alphas[np.argmax(accuracies)])
#vocab = preprocess.generate_vocabulary(train_data)
#naive_classifier_test = NaiveBayesClassifer(train_data, vocab)
#naive_classifier_test.train(train_data)
predictions = naive_classifier.test_accuracy(test_data, False,
from naive_bayes_classifier import NaiveBayesClassifer import sys model = NaiveBayesClassifer(load = True) if sys.argv[1]: model.predict_all(sys.argv[1]) print 'Done' else: print 'Must provide a path to input'
