def test_fit_predict_2_layers_clf(self):
model = Stack([layer_width2_clf, layer_width1_clf])
X = np.array([[1, 1], [1, 1], [0, 0], [0, 0]])
y = np.array([1, 1, 0, 0])
model.fit(X, y)
result = model.predict(np.array([[1, 1]]))
assert result.shape == (1,)
assert np.allclose(result, np.array([1]))
def test_fit_predict_2_layers_reg(self):
model = Stack([layer_width2_reg, layer_width1_reg])
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
model.fit(X, y)
result = model.predict(np.array([[3, 5],[3, 5]]))
assert result.shape == (2,)
assert np.allclose(result, np.array([16, 16]))
def test_fit_predict_stack_with_sklearn_folds(self):
model = Stack([layer_width2_reg, layer_width1_reg], folds=KFold(2))
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3], [1, 1], [1, 2], [2, 2],
[2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
model.fit(X, y)
result = model.predict(np.array([[3, 5], [3, 5]]))
assert result.shape == (2, )
assert np.allclose(result, np.array([16, 16]))
def test_stack_copy_function_only_model(self):
first_layer = Layer([LinearRegression(), LogisticRegression()])
second_layer = Layer([LinearRegression()])
model = Stack([first_layer, second_layer])
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
model.fit(X, y)
model2 = model.copy()
gotError = False
try:
model2.predict([1, 2])
except (NotFittedError):
gotError = True
assert gotError, "Model failed the copy Test: When copying, a deep copy was produced"