def test_partial_dependence_easy_target(est, power):
# If the target y only depends on one feature in an obvious way (linear or
# quadratic) then the partial dependence for that feature should reflect
# it.
# We here fit a linear regression_data model (with polynomial features if
# needed) and compute r_squared to check that the partial dependence
# correctly reflects the target.
rng = np.random.RandomState(0)
n_samples = 200
target_variable = 2
X = rng.normal(size=(n_samples, 5))
y = X[:, target_variable]**power
est.fit(X, y)
averaged_predictions, values = partial_dependence(
est, features=[target_variable], X=X, grid_resolution=1000)
new_X = values[0].reshape(-1, 1)
new_y = averaged_predictions[0]
# add polynomial features if needed
new_X = PolynomialFeatures(degree=power).fit_transform(new_X)
lr = LinearRegression().fit(new_X, new_y)
r2 = r2_score(new_y, lr.predict(new_X))
assert r2 > .99
def test_huber_equals_lr_for_high_epsilon():
# Test that Ridge matches LinearRegression for large epsilon
X, y = make_regression_with_outliers()
lr = LinearRegression()
lr.fit(X, y)
huber = HuberRegressor(epsilon=1e3, alpha=0.0)
huber.fit(X, y)
assert_almost_equal(huber.coef_, lr.coef_, 3)
assert_almost_equal(huber.intercept_, lr.intercept_, 2)
def test_transform_target_regressor_invertible():
X, y = friedman
regr = TransformedTargetRegressor(regressor=LinearRegression(),
func=np.sqrt, inverse_func=np.log,
check_inverse=True)
assert_warns_message(UserWarning, "The provided functions or transformer"
" are not strictly inverse of each other.",
regr.fit, X, y)
regr = TransformedTargetRegressor(regressor=LinearRegression(),
func=np.sqrt, inverse_func=np.log)
regr.set_params(check_inverse=False)
assert_no_warnings(regr.fit, X, y)
def test_omp_reaches_least_squares():
# Use small simple data; it's a sanity check but OMP can stop early
rng = check_random_state(0)
n_samples, n_features = (10, 8)
n_targets = 3
X = rng.randn(n_samples, n_features)
Y = rng.randn(n_samples, n_targets)
omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_features)
lstsq = LinearRegression()
omp.fit(X, Y)
lstsq.fit(X, Y)
assert_array_almost_equal(omp.coef_, lstsq.coef_)
def test_transform_target_regressor_2d_transformer(X, y):
# Check consistency with transformer accepting only 2D array and a 1D/2D y
# array.
transformer = StandardScaler()
regr = TransformedTargetRegressor(regressor=LinearRegression(),
transformer=transformer)
y_pred = regr.fit(X, y).predict(X)
assert y.shape == y_pred.shape
# consistency forward transform
if y.ndim == 1: # create a 2D array and squeeze results
y_tran = regr.transformer_.transform(y.reshape(-1, 1)).squeeze()
else:
y_tran = regr.transformer_.transform(y)
_check_standard_scaled(y, y_tran)
assert y.shape == y_pred.shape
# consistency inverse transform
assert_allclose(y, regr.transformer_.inverse_transform(
y_tran).squeeze())
# consistency of the regressor
lr = LinearRegression()
transformer2 = clone(transformer)
if y.ndim == 1: # create a 2D array and squeeze results
lr.fit(X, transformer2.fit_transform(y.reshape(-1, 1)).squeeze())
else:
lr.fit(X, transformer2.fit_transform(y))
y_lr_pred = lr.predict(X)
assert_allclose(y_pred, transformer2.inverse_transform(y_lr_pred))
assert_allclose(regr.regressor_.coef_, lr.coef_)
def test_transform_target_regressor_functions_multioutput():
X = friedman[0]
y = np.vstack((friedman[1], friedman[1] ** 2 + 1)).T
regr = TransformedTargetRegressor(regressor=LinearRegression(),
func=np.log, inverse_func=np.exp)
y_pred = regr.fit(X, y).predict(X)
# check the transformer output
y_tran = regr.transformer_.transform(y)
assert_allclose(np.log(y), y_tran)
assert_allclose(y, regr.transformer_.inverse_transform(y_tran))
assert y.shape == y_pred.shape
assert_allclose(y_pred, regr.inverse_func(regr.regressor_.predict(X)))
# check the regressor output
lr = LinearRegression().fit(X, regr.func(y))
assert_allclose(regr.regressor_.coef_.ravel(), lr.coef_.ravel())
def test_transform_target_regressor_functions():
X, y = friedman
regr = TransformedTargetRegressor(regressor=LinearRegression(),
func=np.log, inverse_func=np.exp)
y_pred = regr.fit(X, y).predict(X)
# check the transformer output
y_tran = regr.transformer_.transform(y.reshape(-1, 1)).squeeze()
assert_allclose(np.log(y), y_tran)
assert_allclose(y, regr.transformer_.inverse_transform(
y_tran.reshape(-1, 1)).squeeze())
assert y.shape == y_pred.shape
assert_allclose(y_pred, regr.inverse_func(regr.regressor_.predict(X)))
# check the regressor output
lr = LinearRegression().fit(X, regr.func(y))
assert_allclose(regr.regressor_.coef_.ravel(), lr.coef_.ravel())
def test_plot_partial_dependence_fig(pyplot):
# Make sure fig object is correctly used if not None
(X, y), _ = regression_data
clf = LinearRegression()
clf.fit(X, y)
fig = pyplot.figure()
grid_resolution = 25
plot_partial_dependence(clf,
X, [0, 1],
target=0,
grid_resolution=grid_resolution,
fig=fig)
assert pyplot.gcf() is fig
def test_subsamples():
X, y, w, c = gen_toy_problem_4d()
theil_sen = TheilSenRegressor(n_subsamples=X.shape[0],
random_state=0).fit(X, y)
lstq = LinearRegression().fit(X, y)
# Check for exact the same results as Least Squares
assert_array_almost_equal(theil_sen.coef_, lstq.coef_, 9)
def test_theil_sen_1d():
X, y, w, c = gen_toy_problem_1d()
# Check that Least Squares fails
lstq = LinearRegression().fit(X, y)
assert np.abs(lstq.coef_ - w) > 0.9
# Check that Theil-Sen works
theil_sen = TheilSenRegressor(random_state=0).fit(X, y)
assert_array_almost_equal(theil_sen.coef_, w, 1)
assert_array_almost_equal(theil_sen.intercept_, c, 1)
def test_theil_sen_1d_no_intercept():
X, y, w, c = gen_toy_problem_1d(intercept=False)
# Check that Least Squares fails
lstq = LinearRegression(fit_intercept=False).fit(X, y)
assert np.abs(lstq.coef_ - w - c) > 0.5
# Check that Theil-Sen works
theil_sen = TheilSenRegressor(fit_intercept=False,
random_state=0).fit(X, y)
assert_array_almost_equal(theil_sen.coef_, w + c, 1)
assert_almost_equal(theil_sen.intercept_, 0.)
def test_ransac_stop_score():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
stop_score=0,
random_state=0)
ransac_estimator.fit(X, y)
assert ransac_estimator.n_trials_ == 1
def test_theil_sen_2d():
X, y, w, c = gen_toy_problem_2d()
# Check that Least Squares fails
lstq = LinearRegression().fit(X, y)
assert norm(lstq.coef_ - w) > 1.0
# Check that Theil-Sen works
theil_sen = TheilSenRegressor(max_subpopulation=1e3,
random_state=0).fit(X, y)
assert_array_almost_equal(theil_sen.coef_, w, 1)
assert_array_almost_equal(theil_sen.intercept_, c, 1)
def test_regressormixin_score_multioutput():
from mrex.linear_model import LinearRegression
# no warnings when y_type is continuous
X = [[1], [2], [3]]
y = [1, 2, 3]
reg = LinearRegression().fit(X, y)
assert_no_warnings(reg.score, X, y)
# warn when y_type is continuous-multioutput
y = [[1, 2], [2, 3], [3, 4]]
reg = LinearRegression().fit(X, y)
msg = ("The default value of multioutput (not exposed in "
"score method) will change from 'variance_weighted' "
"to 'uniform_average' in 0.23 to keep consistent "
"with 'metrics.r2_score'. To specify the default "
"value manually and avoid the warning, please "
"either call 'metrics.r2_score' directly or make a "
"custom scorer with 'metrics.make_scorer' (the "
"built-in scorer 'r2' uses "
"multioutput='uniform_average').")
assert_warns_message(FutureWarning, msg, reg.score, X, y)
def test_ovr_multilabel():
# Toy dataset where features correspond directly to labels.
X = np.array([[0, 4, 5], [0, 5, 0], [3, 3, 3], [4, 0, 6], [6, 0, 0]])
y = np.array([[0, 1, 1], [0, 1, 0], [1, 1, 1], [1, 0, 1], [1, 0, 0]])
for base_clf in (MultinomialNB(), LinearSVC(random_state=0),
LinearRegression(), Ridge(), ElasticNet(),
Lasso(alpha=0.5)):
clf = OneVsRestClassifier(base_clf).fit(X, y)
y_pred = clf.predict([[0, 4, 4]])[0]
assert_array_equal(y_pred, [0, 1, 1])
assert clf.multilabel_
def test_transform_target_regressor_1d_transformer(X, y):
# All transformer in scikit-learn expect 2D data. FunctionTransformer with
# validate=False lift this constraint without checking that the input is a
# 2D vector. We check the consistency of the data shape using a 1D and 2D y
# array.
transformer = FunctionTransformer(func=lambda x: x + 1,
inverse_func=lambda x: x - 1)
regr = TransformedTargetRegressor(regressor=LinearRegression(),
transformer=transformer)
y_pred = regr.fit(X, y).predict(X)
assert y.shape == y_pred.shape
# consistency forward transform
y_tran = regr.transformer_.transform(y)
_check_shifted_by_one(y, y_tran)
assert y.shape == y_pred.shape
# consistency inverse transform
assert_allclose(y, regr.transformer_.inverse_transform(
y_tran).squeeze())
# consistency of the regressor
lr = LinearRegression()
transformer2 = clone(transformer)
lr.fit(X, transformer2.fit_transform(y))
y_lr_pred = lr.predict(X)
assert_allclose(y_pred, transformer2.inverse_transform(y_lr_pred))
assert_allclose(regr.regressor_.coef_, lr.coef_)
def test_classes_property():
iris = load_iris()
X = iris.data
y = iris.target
reg = make_pipeline(SelectKBest(k=1), LinearRegression())
reg.fit(X, y)
assert_raises(AttributeError, getattr, reg, "classes_")
clf = make_pipeline(SelectKBest(k=1), LogisticRegression(random_state=0))
assert_raises(AttributeError, getattr, clf, "classes_")
clf.fit(X, y)
assert_array_equal(clf.classes_, np.unique(y))
def test_ransac_no_valid_model():
def is_model_valid(estimator, X, y):
return False
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
is_model_valid=is_model_valid,
max_trials=5)
msg = ("RANSAC could not find a valid consensus set")
assert_raises_regexp(ValueError, msg, ransac_estimator.fit, X, y)
assert ransac_estimator.n_skips_no_inliers_ == 0
assert ransac_estimator.n_skips_invalid_data_ == 0
assert ransac_estimator.n_skips_invalid_model_ == 5
def test_ransac_predict():
X = np.arange(100)[:, None]
y = np.zeros((100, ))
y[0] = 1
y[1] = 100
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=0.5,
random_state=0)
ransac_estimator.fit(X, y)
assert_array_equal(ransac_estimator.predict(X), np.zeros(100))
def test_ransac_is_model_valid():
def is_model_valid(estimator, X, y):
assert X.shape[0] == 2
assert y.shape[0] == 2
return False
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
is_model_valid=is_model_valid,
random_state=0)
assert_raises(ValueError, ransac_estimator.fit, X, y)
def test_ransac_resid_thresh_no_inliers():
# When residual_threshold=0.0 there are no inliers and a
# ValueError with a message should be raised
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=0.0,
random_state=0,
max_trials=5)
msg = ("RANSAC could not find a valid consensus set")
assert_raises_regexp(ValueError, msg, ransac_estimator.fit, X, y)
assert ransac_estimator.n_skips_no_inliers_ == 5
assert ransac_estimator.n_skips_invalid_data_ == 0
assert ransac_estimator.n_skips_invalid_model_ == 0
def test_less_samples_than_features():
random_state = np.random.RandomState(0)
n_samples, n_features = 10, 20
X = random_state.normal(size=(n_samples, n_features))
y = random_state.normal(size=n_samples)
# Check that Theil-Sen falls back to Least Squares if fit_intercept=False
theil_sen = TheilSenRegressor(fit_intercept=False,
random_state=0).fit(X, y)
lstq = LinearRegression(fit_intercept=False).fit(X, y)
assert_array_almost_equal(theil_sen.coef_, lstq.coef_, 12)
# Check fit_intercept=True case. This will not be equal to the Least
# Squares solution since the intercept is calculated differently.
theil_sen = TheilSenRegressor(fit_intercept=True, random_state=0).fit(X, y)
y_pred = theil_sen.predict(X)
assert_array_almost_equal(y_pred, y, 12)
def test_plot_partial_dependence_multioutput(pyplot):
# Test partial dependence plot function on multi-output input.
(X, y), _ = multioutput_regression_data
clf = LinearRegression()
clf.fit(X, y)
grid_resolution = 25
plot_partial_dependence(clf,
X, [0, 1],
target=0,
grid_resolution=grid_resolution)
fig = pyplot.gcf()
axs = fig.get_axes()
assert len(axs) == 2
assert all(ax.has_data for ax in axs)
plot_partial_dependence(clf,
X, [0, 1],
target=1,
grid_resolution=grid_resolution)
fig = pyplot.gcf()
axs = fig.get_axes()
assert len(axs) == 2
assert all(ax.has_data for ax in axs)
def test_ransac_score():
X = np.arange(100)[:, None]
y = np.zeros((100, ))
y[0] = 1
y[1] = 100
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=0.5,
random_state=0)
ransac_estimator.fit(X, y)
assert ransac_estimator.score(X[2:], y[2:]) == 1
assert ransac_estimator.score(X[:2], y[:2]) < 1
def test_ransac_default_residual_threshold():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
random_state=0)
# Estimate parameters of corrupted data
ransac_estimator.fit(X, y)
# Ground truth / reference inlier mask
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_).astype(
np.bool_)
ref_inlier_mask[outliers] = False
assert_array_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def test_ransac_none_estimator():
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0)
ransac_none_estimator = RANSACRegressor(None, 2, 5, random_state=0)
ransac_estimator.fit(X, y)
ransac_none_estimator.fit(X, y)
assert_array_almost_equal(ransac_estimator.predict(X),
ransac_none_estimator.predict(X))
def test_ransac_sparse_csc():
X_sparse = sparse.csc_matrix(X)
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0)
ransac_estimator.fit(X_sparse, y)
ref_inlier_mask = np.ones_like(ransac_estimator.inlier_mask_).astype(
np.bool_)
ref_inlier_mask[outliers] = False
assert_array_equal(ransac_estimator.inlier_mask_, ref_inlier_mask)
def test_ransac_exceed_max_skips():
def is_data_valid(X, y):
return False
base_estimator = LinearRegression()
ransac_estimator = RANSACRegressor(base_estimator,
is_data_valid=is_data_valid,
max_trials=5,
max_skips=3)
msg = ("RANSAC skipped more iterations than `max_skips`")
assert_raises_regexp(ValueError, msg, ransac_estimator.fit, X, y)
assert ransac_estimator.n_skips_no_inliers_ == 0
assert ransac_estimator.n_skips_invalid_data_ == 4
assert ransac_estimator.n_skips_invalid_model_ == 0
def test_ransac_residual_loss():
loss_multi1 = lambda y_true, y_pred: np.sum(np.abs(y_true - y_pred),
axis=1)
loss_multi2 = lambda y_true, y_pred: np.sum((y_true - y_pred)**2, axis=1)
loss_mono = lambda y_true, y_pred: np.abs(y_true - y_pred)
yyy = np.column_stack([y, y, y])
base_estimator = LinearRegression()
ransac_estimator0 = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0)
ransac_estimator1 = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0,
loss=loss_multi1)
ransac_estimator2 = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0,
loss=loss_multi2)
# multi-dimensional
ransac_estimator0.fit(X, yyy)
ransac_estimator1.fit(X, yyy)
ransac_estimator2.fit(X, yyy)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator1.predict(X))
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
# one-dimensional
ransac_estimator0.fit(X, y)
ransac_estimator2.loss = loss_mono
ransac_estimator2.fit(X, y)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
ransac_estimator3 = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0,
loss="squared_loss")
ransac_estimator3.fit(X, y)
assert_array_almost_equal(ransac_estimator0.predict(X),
ransac_estimator2.predict(X))
def test_ransac_min_n_samples():
base_estimator = LinearRegression()
ransac_estimator1 = RANSACRegressor(base_estimator,
min_samples=2,
residual_threshold=5,
random_state=0)
ransac_estimator2 = RANSACRegressor(base_estimator,
min_samples=2. / X.shape[0],
residual_threshold=5,
random_state=0)
ransac_estimator3 = RANSACRegressor(base_estimator,
min_samples=-1,
residual_threshold=5,
random_state=0)
ransac_estimator4 = RANSACRegressor(base_estimator,
min_samples=5.2,
residual_threshold=5,
random_state=0)
ransac_estimator5 = RANSACRegressor(base_estimator,
min_samples=2.0,
residual_threshold=5,
random_state=0)
ransac_estimator6 = RANSACRegressor(base_estimator,
residual_threshold=5,
random_state=0)
ransac_estimator7 = RANSACRegressor(base_estimator,
min_samples=X.shape[0] + 1,
residual_threshold=5,
random_state=0)
ransac_estimator1.fit(X, y)
ransac_estimator2.fit(X, y)
ransac_estimator5.fit(X, y)
ransac_estimator6.fit(X, y)
assert_array_almost_equal(ransac_estimator1.predict(X),
ransac_estimator2.predict(X))
assert_array_almost_equal(ransac_estimator1.predict(X),
ransac_estimator5.predict(X))
assert_array_almost_equal(ransac_estimator1.predict(X),
ransac_estimator6.predict(X))
assert_raises(ValueError, ransac_estimator3.fit, X, y)
assert_raises(ValueError, ransac_estimator4.fit, X, y)
assert_raises(ValueError, ransac_estimator7.fit, X, y)