How to use the simpleai.machine_learning.precision function in simpleai

test = OnlineCorpusReader(input_files, lambda i: i in testindexes)


print "Training Naive Bayes..."
classifier = NaiveBayes(train, problem)
print "Testing..."
p = precision(classifier, test)
print "Precision Naive Bayes = {}".format(p)


print "Training Decision Tree (large data)..."
classifier = DecisionTreeLearner_LargeData(train, problem, minsample=500)
print "Final tree:"
print tree_to_str(classifier.root)
print "Testing..."
p = precision(classifier, test)
print "Precision Decision Tree = {}".format(p)

N += 1
    print "Corpus has {} examples".format(N)

    # Choose test set, either 10% or 10000 examples, whatever is less
    M = min(N / 10, 1000)
    testindexes = set(random.sample(xrange(N), M))

    corpus = ProConsCorpus(input_files, lambda i: i not in testindexes)
    test = ProConsCorpus(input_files, lambda i: i in testindexes)
    print "Corpuses created"

    problem = OpinionProblem(corpus)
    classifier = NaiveBayes(corpus, problem)
    print "Classifier created"

    p = precision(classifier, test)
    print "Precision = {}".format(p)

print "Corpus has {} examples".format(N)

# Choose test set, either 10% or 10000 examples, whatever is less
M = min(N / 10, 10000)
testindexes = set(random.sample(xrange(N), M))
print "Keeping {} examples for testing".format(M)

problem = LanguageClassificationProblem()
train = OnlineCorpusReader(input_files, lambda i: i not in testindexes)
test = OnlineCorpusReader(input_files, lambda i: i in testindexes)


print "Training Naive Bayes..."
classifier = NaiveBayes(train, problem)
print "Testing..."
p = precision(classifier, test)
print "Precision Naive Bayes = {}".format(p)


print "Training Decision Tree (large data)..."
classifier = DecisionTreeLearner_LargeData(train, problem, minsample=500)
print "Final tree:"
print tree_to_str(classifier.root)
print "Testing..."
p = precision(classifier, test)
print "Precision Decision Tree = {}".format(p)

dataset = IrisDataset(IRIS_PATH, lambda i: i not in testindexes)
    testset = IrisDataset(IRIS_PATH, lambda i: i in testindexes)
    problem = VectorDataClassificationProblem(dataset, dataset.target_index)
    # Distance without target
    problem.distance = lambda x, y: euclidean_vector_distance(x[:-1], y[:-1])

    classifiers = {
        "K-Nearest Neighbours": KNearestNeighbors,
        "Naive Bayes": NaiveBayes,
        "Decision Tree": DecisionTreeLearner_Queued,
    }

    print "Precision:\n"
    for name, method in classifiers.iteritems():
        classifier = method(dataset, problem)
        p = precision(classifier, testset)
        print "{:>20} = {:.2}".format(name, p)