def test_predict_single_model_with_preprocess(self):
layer_model = Layer([LinearRegression()], [MinMaxScaler()])
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
layer_model.fit(X, y)
result = layer_model.predict(np.array([[3, 5]]))
assert result.shape == (1, 1)
assert np.allclose(result, np.array([[16]]))
def test_predict_single_model_with_2_class_proba(self):
layer_model = Layer([LogisticRegression(solver='liblinear')],
proba=True)
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.array([1, 1, 0, 0])
layer_model.fit(X, y)
result = layer_model.predict(np.array([[3, 5]]))
assert result.shape == (1, 2)
def test_predict_single_model_with_multi_class_proba(self):
layer_model = Layer(
[LogisticRegression(solver='lbfgs', multi_class='multinomial')],
proba=True)
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.array([1, 1, 0, 2])
layer_model.fit(X, y)
result = layer_model.predict(np.array([[3, 5]]))
assert result.shape == (1, 3)
def test_predict_multiple_model(self):
layer_model = Layer([LinearRegression(), LinearRegression()],
[None, MinMaxScaler()])
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
layer_model.fit(X, y)
result = layer_model.predict(np.array([[3, 5]]))
assert result.shape == (1,2)
assert np.allclose(result, np.array([[16, 16]]))
def test_using_proba_without_predict_proba_method(self):
with pytest.warns(Warning) as record:
layer_model = Layer([LinearRegression()], proba=True)
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
layer_model.fit(X, y)
result = layer_model.predict(np.array([[3, 5], [3, 5]]))
assert result.shape == (2, 1)
assert np.allclose(result, np.array([[16], [16]]))
assert record
