def test_ridge_regressor_predict_not_fitted():
"""Test of `RidgeRegressor` class with prediction without fit."""
lr = RidgeRegressor(learning_rate=0.1, iterations=1, standardize=False)
X = np.array([1])
with pytest.raises(NotFitted):
lr.predict(X)
def test_ridge_regressor_score_not_fitted():
"""Test of `RidgeRegressor` class with score calculation without fit."""
lr = RidgeRegressor(learning_rate=0.1, iterations=1, standardize=False)
X = np.array([1])
y = np.array([1])
with pytest.raises(NotFitted):
lr.score(X, y)
def test_ridge_regressor_dataset_inconsistancy():
"""Test of `RidgeRegressor` with dataset inconsistancy."""
rr = RidgeRegressor(learning_rate=0.1, iterations=1)
X = np.array([[1], [1]])
y = np.array([[1]])
with pytest.raises(InvalidInput):
rr.fit(X, y)
def test_ridge_regressor_normal():
"""Test of `normal` method of `RidgeRegressor` class."""
rr = RidgeRegressor(learning_rate=0.1, iterations=1, standardize=False)
X = np.array([1])
y = np.array([1])
rr.normal(X, y)
assert rr.score(X, y) == 1
def test_ridge_regressor_standardized():
"""Test of `RidgeRegressor` class with standardization."""
rr = RidgeRegressor(learning_rate=0.1, iterations=1, standardize=True)
X = np.array([1])
y = np.array([1])
rr.fit(X, y)
assert rr.score(X, y) == 1
def test_ridge_regressor_history_disabled():
"""Test of `RidgeRegressor` when history disabled."""
rr = RidgeRegressor(learning_rate=0.1, iterations=1, history=False)
assert rr.history is None
X = np.array([1])
y = np.array([1])
rr.fit(X, y)
assert rr.history is None
def test_ridge_regressor_history_enabled():
"""Test of `RidgeRegressor` when history enabled."""
rr = RidgeRegressor(learning_rate=0.1, iterations=1, history=True)
assert rr.history == []
X = np.array([1])
y = np.array([1])
rr.fit(X, y)
assert rr.history is not None