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_clf(params):
dataset = dt.load_csv('challenge_train.csv', 0)
# pdb.set_trace()
train_features, train_classes = dataset
folds = dt.generate_k_folds(dataset, 5)
accuracy = []
for idx, fold in enumerate(folds):
training_set, test_set = fold
clf = dt.ChallengeClassifier(**params)
clf.fit(training_set[0], training_set[1])
preds = clf.classify(test_set[0])
accuracy.append(dt.accuracy(preds, test_set[1]))
# print("Fold %d" %idx)
# print("accuracy %f" %(dt.accuracy(preds, test_set[1])))
# print("precision %f" %(dt.precision(preds, test_set[1])))
# print("recall %f" %(dt.recall(preds, test_set[1])))
print(params, np.mean(accuracy))