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55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
import sys
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from lib import preprocess, train_naive_bayes, test_naive_bayes
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def print_test_results(results, test_docs):
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misclassified_docs = []
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for result, doc in zip(results, test_docs):
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if result != doc[1]:
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misclassified_docs.append(doc + tuple([result]))
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for doc in misclassified_docs:
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print('misclassified {}: actual: {}, expected: {}'.format(doc[0], doc[2], doc[1]))
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correct = len(test_docs) - len(misclassified_docs)
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incorrect = len(misclassified_docs)
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accuracy = correct / len(test_docs)
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print("\n")
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print("****************************************")
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print("* RESULTS:")
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print("* Correct: {}".format(correct))
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print("* Incorrect: {}".format(incorrect))
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print("*")
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print("* Accuracy: {}".format(accuracy))
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print("****************************************")
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def test(test_docs, prior, likelihood, classes, vocab):
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results = []
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for test_doc in test_docs:
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results.append(test_naive_bayes(test_doc, prior, likelihood, classes, vocab))
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print_test_results(results, test_documents)
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# Prevent running if imported as a module
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if __name__ == '__main__':
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classes = [0, 1]
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# perform test on the training data
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sys.stdout = open('output.txt', 'wt')
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vocab, documents = preprocess('trainingSet.txt', 'preprocessed_train.txt')
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_, test_documents = preprocess('trainingSet.txt', 'preprocessed_train.txt')
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prior, likelihood = train_naive_bayes(vocab, documents, classes)
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print("\n")
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print("testing on training data")
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test(documents, prior, likelihood, classes, vocab)
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# perform test on the testing data
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vocab, documents = preprocess('trainingSet.txt', 'preprocessed_train.txt')
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_, test_documents = preprocess('testSet.txt', 'preprocessed_test.txt')
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prior, likelihood = train_naive_bayes(vocab, documents, classes)
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print("\n")
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print("testing on testing data")
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test(test_documents, prior, likelihood, classes, vocab)
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