def test_unfitted_models():
"""Calling predict before fit should raise exception
"""
bnn = BnnBinaryClassifier()
with pytest.raises(NotFittedError):
bnn.predict(np.random.randn(10, 3))
bnn.predict_proba(np.random.randn(10, 3))
bnn.predict_samples(np.random.randn(10, 3))
bnn.predict_proba_samples(np.random.randn(10, 3))
bnn = BnnScalarRegressor()
with pytest.raises(NotFittedError):
bnn.predict(np.random.randn(10, 3))
bnn.predict_samples(np.random.randn(10, 3))
def test_predictions_classification():
"""Binary classifier only predicts 0s and 1s for labels and values in
[0,1] for prbabilities. Also checks shapes
"""
n, p = 100, 10
n_test = 50
n_mc_samples = 15
#(X_train, y_train), (X_test, y_test) = toy_binary_classification_data(n, p)
bnn = BnnBinaryClassifier(verbose=0)
bnn.fit(np.random.randn(n, p), np.random.randint(2, size=n))
yhat_labels = bnn.predict(np.random.randn(n_test, p))
assert yhat_labels.shape[0] == n_test
assert np.sum(yhat_labels == 0) + np.sum(yhat_labels == 1) == n_test
yhat_proba = bnn.predict_proba(np.random.randn(n_test, p))
assert np.all([prob >= 0.0 or prob <= 1.0 for prob in yhat_proba])
yhat_labels_samples = bnn.predict_samples(np.random.randn(n_test, p),
n_mc_samples)
assert yhat_labels_samples.shape == (n_mc_samples, n_test)
assert np.all([val in [0, 1] for val in yhat_labels_samples.flat])
yhat_proba_samples = bnn.predict_proba_samples(np.random.randn(n_test, p),
n_mc_samples)
assert yhat_proba_samples.shape == (n_mc_samples, n_test)
assert np.all(
[prob >= 0.0 or prob <= 1.0 for prob in yhat_proba_samples.flat])