def test_k_folds_decision_tree(self):
folds = dt.generate_k_folds(self.dataset, 20)
for fold in folds:
tree = dt.DecisionTree()
features, classes = fold[1]
tree.fit(features, classes)
testf, testc = fold[0]
output = tree.classify(testf)
def accuracy_test(num_folds):
features, classes = loaddata()
folds = generate_k_folds((features, classes), num_folds)
for i in range(num_folds):
print('Testing model on fold %d' % i)
myClassifier = ChallengeClassifier()
training_data, test_data = folds[i]
training_features, training_classes = training_data
test_features, test_classes = test_data
myClassifier.fit(training_features, training_classes)
result = myClassifier.classify(test_features)
print('Accuracy test result - fold %d: %f' %
(i, accuracy(result, test_classes)))
def test_k_folds_training_set_count(self):
"""Test k folds returns the correct training set size.
Asserts:
training set size matches as expected.
"""
example_count = len(self.train_features)
k = 10
training_set_count = example_count - (example_count // k)
ten_folds = dt.generate_k_folds(self.dataset, k)
for fold in ten_folds:
training_set, test_set = fold
assert len(training_set[0]) == training_set_count
def test_forest_all_data(self):
"""Test random forest.
Asserts:
classification on average is >= 75%
"""
# Values specified in the assignment
tree = dt.RandomForest(5, 5, .5, .5)
fold_count = 10
ten_folds = dt.generate_k_folds(self.dataset, fold_count)
avg_accuracy = 0.0
for fold in ten_folds:
training_set, test_set = fold
tree.fit(training_set[0], training_set[1])
output = tree.classify(test_set[0])
avg_accuracy += dt.accuracy(output, test_set[1])
avg_accuracy = avg_accuracy / fold_count
print '\n\nRandom Forest K-folds:', avg_accuracy
assert avg_accuracy >= .75
def test_challenge_all_data(self):
"""Test challenge section.
Asserts:
classification on average is >= 80%
"""
dataset = dt.load_csv('challenge_train.csv', 0)
# Change as you see fit by adding parameters you have chosen or run
# it with defaults
tree = dt.ChallengeClassifier()
fold_count = 10
ten_folds = dt.generate_k_folds(dataset, fold_count)
avg_accuracy = 0.0
for fold in ten_folds:
training_set, test_set = fold
tree.fit(training_set[0], training_set[1])
output = tree.classify(test_set[0])
avg_accuracy += dt.accuracy(output, test_set[1])
avg_accuracy = avg_accuracy / fold_count
print '\n\nChallenger K-folds:', avg_accuracy
assert avg_accuracy >= .80
