def test_n_train_examples(n=500):
if python_version.is_compatible():
cnn = CNN(epochs=3, log_interval=1000, loader='train', seed=0)
idx = np.random.choice(X_train, n,
replace=False) # Grab n random examples.
cnn.fit(train_idx=X_train[idx],
train_labels=y_train[idx],
loader='train')
cnn.loader = 'test'
pred = cnn.predict(X_test[:n])
print(accuracy_score(y_test[:n], pred))
assert (accuracy_score(y_test[:n], pred) > 0.1)
# Check that dataset defaults to test set when an invalid name is given.
cnn.loader = 'INVALID'
pred = cnn.predict(X_test[:n])
assert (len(pred) == MNIST_TEST_SIZE)
# Check that pred_proba runs on all examples when None is passed in
cnn.loader = 'test'
proba = cnn.predict_proba(idx=None, loader='test')
assert proba is not None
assert (len(pred) == MNIST_TEST_SIZE)
assert True
def test_n_train_examples():
if python_version.is_compatible():
cnn = CNN(epochs=3, log_interval=1000, loader='train', seed=0,
dataset='sklearn-digits', )
cnn.fit(train_idx=X_train_idx, train_labels=y_train,
loader='train', )
cnn.loader = 'test'
pred = cnn.predict(X_test_idx)
print(accuracy_score(y_test, pred))
assert (accuracy_score(y_test, pred) > 0.1)
# Check that exception is raised when invalid name is given.
cnn.loader = 'INVALID'
with pytest.raises(ValueError) as e:
pred = cnn.predict(X_test_idx)
# Check that pred_proba runs on all examples when None is passed in
cnn.loader = 'test'
proba = cnn.predict_proba(idx=None, loader='test')
assert proba is not None
assert (len(pred) == SKLEARN_DIGITS_TEST_SIZE)
assert True