2019 lines
66 KiB
Python
2019 lines
66 KiB
Python
import numpy as np
|
|
import scipy.sparse as sp
|
|
from scipy import linalg
|
|
from itertools import product
|
|
|
|
import pytest
|
|
import warnings
|
|
|
|
from sklearn.utils import _IS_32BIT
|
|
from sklearn.utils._testing import assert_almost_equal
|
|
from sklearn.utils._testing import assert_allclose
|
|
from sklearn.utils._testing import assert_array_almost_equal
|
|
from sklearn.utils._testing import assert_array_equal
|
|
from sklearn.utils._testing import ignore_warnings
|
|
from sklearn.utils.estimator_checks import check_sample_weights_invariance
|
|
|
|
from sklearn.exceptions import ConvergenceWarning
|
|
|
|
from sklearn import datasets
|
|
from sklearn.metrics import mean_squared_error
|
|
from sklearn.metrics import make_scorer
|
|
from sklearn.metrics import get_scorer
|
|
|
|
from sklearn.linear_model import LinearRegression
|
|
from sklearn.linear_model import ridge_regression
|
|
from sklearn.linear_model import Ridge
|
|
from sklearn.linear_model._ridge import _RidgeGCV
|
|
from sklearn.linear_model import RidgeCV
|
|
from sklearn.linear_model import RidgeClassifier
|
|
from sklearn.linear_model import RidgeClassifierCV
|
|
from sklearn.linear_model._ridge import _solve_cholesky
|
|
from sklearn.linear_model._ridge import _solve_cholesky_kernel
|
|
from sklearn.linear_model._ridge import _solve_svd
|
|
from sklearn.linear_model._ridge import _solve_lbfgs
|
|
from sklearn.linear_model._ridge import _check_gcv_mode
|
|
from sklearn.linear_model._ridge import _X_CenterStackOp
|
|
from sklearn.datasets import make_low_rank_matrix
|
|
from sklearn.datasets import make_regression
|
|
from sklearn.datasets import make_classification
|
|
from sklearn.datasets import make_multilabel_classification
|
|
|
|
from sklearn.model_selection import GridSearchCV
|
|
from sklearn.model_selection import KFold
|
|
from sklearn.model_selection import GroupKFold
|
|
from sklearn.model_selection import cross_val_predict
|
|
from sklearn.model_selection import LeaveOneOut
|
|
|
|
from sklearn.preprocessing import minmax_scale
|
|
from sklearn.utils import check_random_state
|
|
|
|
|
|
SOLVERS = ("svd", "sparse_cg", "cholesky", "lsqr", "sag", "saga")
|
|
SPARSE_SOLVERS_WITH_INTERCEPT = ("sparse_cg", "sag")
|
|
SPARSE_SOLVERS_WITHOUT_INTERCEPT = ("sparse_cg", "cholesky", "lsqr", "sag", "saga")
|
|
|
|
diabetes = datasets.load_diabetes()
|
|
X_diabetes, y_diabetes = diabetes.data, diabetes.target
|
|
ind = np.arange(X_diabetes.shape[0])
|
|
rng = np.random.RandomState(0)
|
|
rng.shuffle(ind)
|
|
ind = ind[:200]
|
|
X_diabetes, y_diabetes = X_diabetes[ind], y_diabetes[ind]
|
|
|
|
iris = datasets.load_iris()
|
|
|
|
X_iris = sp.csr_matrix(iris.data)
|
|
y_iris = iris.target
|
|
|
|
|
|
def DENSE_FILTER(X):
|
|
return X
|
|
|
|
|
|
def SPARSE_FILTER(X):
|
|
return sp.csr_matrix(X)
|
|
|
|
|
|
def _accuracy_callable(y_test, y_pred):
|
|
return np.mean(y_test == y_pred)
|
|
|
|
|
|
def _mean_squared_error_callable(y_test, y_pred):
|
|
return ((y_test - y_pred) ** 2).mean()
|
|
|
|
|
|
@pytest.fixture(params=["long", "wide"])
|
|
def ols_ridge_dataset(global_random_seed, request):
|
|
"""Dataset with OLS and Ridge solutions, well conditioned X.
|
|
|
|
The construction is based on the SVD decomposition of X = U S V'.
|
|
|
|
Parameters
|
|
----------
|
|
type : {"long", "wide"}
|
|
If "long", then n_samples > n_features.
|
|
If "wide", then n_features > n_samples.
|
|
|
|
For "wide", we return the minimum norm solution w = X' (XX')^-1 y:
|
|
|
|
min ||w||_2 subject to X w = y
|
|
|
|
Returns
|
|
-------
|
|
X : ndarray
|
|
Last column of 1, i.e. intercept.
|
|
y : ndarray
|
|
coef_ols : ndarray of shape
|
|
Minimum norm OLS solutions, i.e. min ||X w - y||_2_2 (with mininum ||w||_2 in
|
|
case of ambiguity)
|
|
Last coefficient is intercept.
|
|
coef_ridge : ndarray of shape (5,)
|
|
Ridge solution with alpha=1, i.e. min ||X w - y||_2_2 + ||w||_2^2.
|
|
Last coefficient is intercept.
|
|
"""
|
|
# Make larger dim more than double as big as the smaller one.
|
|
# This helps when constructing singular matrices like (X, X).
|
|
if request.param == "long":
|
|
n_samples, n_features = 12, 4
|
|
else:
|
|
n_samples, n_features = 4, 12
|
|
k = min(n_samples, n_features)
|
|
rng = np.random.RandomState(global_random_seed)
|
|
X = make_low_rank_matrix(
|
|
n_samples=n_samples, n_features=n_features, effective_rank=k, random_state=rng
|
|
)
|
|
X[:, -1] = 1 # last columns acts as intercept
|
|
U, s, Vt = linalg.svd(X)
|
|
assert np.all(s > 1e-3) # to be sure
|
|
U1, U2 = U[:, :k], U[:, k:]
|
|
Vt1, _ = Vt[:k, :], Vt[k:, :]
|
|
|
|
if request.param == "long":
|
|
# Add a term that vanishes in the product X'y
|
|
coef_ols = rng.uniform(low=-10, high=10, size=n_features)
|
|
y = X @ coef_ols
|
|
y += U2 @ rng.normal(size=n_samples - n_features) ** 2
|
|
else:
|
|
y = rng.uniform(low=-10, high=10, size=n_samples)
|
|
# w = X'(XX')^-1 y = V s^-1 U' y
|
|
coef_ols = Vt1.T @ np.diag(1 / s) @ U1.T @ y
|
|
|
|
# Add penalty alpha * ||coef||_2^2 for alpha=1 and solve via normal equations.
|
|
# Note that the problem is well conditioned such that we get accurate results.
|
|
alpha = 1
|
|
d = alpha * np.identity(n_features)
|
|
d[-1, -1] = 0 # intercept gets no penalty
|
|
coef_ridge = linalg.solve(X.T @ X + d, X.T @ y)
|
|
|
|
# To be sure
|
|
R_OLS = y - X @ coef_ols
|
|
R_Ridge = y - X @ coef_ridge
|
|
assert np.linalg.norm(R_OLS) < np.linalg.norm(R_Ridge)
|
|
|
|
return X, y, coef_ols, coef_ridge
|
|
|
|
|
|
@pytest.mark.parametrize("solver", SOLVERS)
|
|
@pytest.mark.parametrize("fit_intercept", [True, False])
|
|
def test_ridge_regression(solver, fit_intercept, ols_ridge_dataset, global_random_seed):
|
|
"""Test that Ridge converges for all solvers to correct solution.
|
|
|
|
We work with a simple constructed data set with known solution.
|
|
"""
|
|
X, y, _, coef = ols_ridge_dataset
|
|
alpha = 1.0 # because ols_ridge_dataset uses this.
|
|
params = dict(
|
|
alpha=alpha,
|
|
fit_intercept=True,
|
|
solver=solver,
|
|
tol=1e-15 if solver in ("sag", "saga") else 1e-10,
|
|
random_state=global_random_seed,
|
|
)
|
|
|
|
# Calculate residuals and R2.
|
|
res_null = y - np.mean(y)
|
|
res_Ridge = y - X @ coef
|
|
R2_Ridge = 1 - np.sum(res_Ridge**2) / np.sum(res_null**2)
|
|
|
|
model = Ridge(**params)
|
|
X = X[:, :-1] # remove intercept
|
|
if fit_intercept:
|
|
intercept = coef[-1]
|
|
else:
|
|
X = X - X.mean(axis=0)
|
|
y = y - y.mean()
|
|
intercept = 0
|
|
model.fit(X, y)
|
|
coef = coef[:-1]
|
|
|
|
assert model.intercept_ == pytest.approx(intercept)
|
|
assert_allclose(model.coef_, coef)
|
|
assert model.score(X, y) == pytest.approx(R2_Ridge)
|
|
|
|
# Same with sample_weight.
|
|
model = Ridge(**params).fit(X, y, sample_weight=np.ones(X.shape[0]))
|
|
assert model.intercept_ == pytest.approx(intercept)
|
|
assert_allclose(model.coef_, coef)
|
|
assert model.score(X, y) == pytest.approx(R2_Ridge)
|
|
|
|
|
|
@pytest.mark.parametrize("solver", SOLVERS)
|
|
@pytest.mark.parametrize("fit_intercept", [True, False])
|
|
def test_ridge_regression_hstacked_X(
|
|
solver, fit_intercept, ols_ridge_dataset, global_random_seed
|
|
):
|
|
"""Test that Ridge converges for all solvers to correct solution on hstacked data.
|
|
|
|
We work with a simple constructed data set with known solution.
|
|
Fit on [X] with alpha is the same as fit on [X, X]/2 with alpha/2.
|
|
For long X, [X, X] is a singular matrix.
|
|
"""
|
|
X, y, _, coef = ols_ridge_dataset
|
|
n_samples, n_features = X.shape
|
|
alpha = 1.0 # because ols_ridge_dataset uses this.
|
|
|
|
model = Ridge(
|
|
alpha=alpha / 2,
|
|
fit_intercept=fit_intercept,
|
|
solver=solver,
|
|
tol=1e-15 if solver in ("sag", "saga") else 1e-10,
|
|
random_state=global_random_seed,
|
|
)
|
|
X = X[:, :-1] # remove intercept
|
|
X = 0.5 * np.concatenate((X, X), axis=1)
|
|
assert np.linalg.matrix_rank(X) <= min(n_samples, n_features - 1)
|
|
if fit_intercept:
|
|
intercept = coef[-1]
|
|
else:
|
|
X = X - X.mean(axis=0)
|
|
y = y - y.mean()
|
|
intercept = 0
|
|
model.fit(X, y)
|
|
coef = coef[:-1]
|
|
|
|
assert model.intercept_ == pytest.approx(intercept)
|
|
# coefficients are not all on the same magnitude, adding a small atol to
|
|
# make this test less brittle
|
|
assert_allclose(model.coef_, np.r_[coef, coef], atol=1e-8)
|
|
|
|
|
|
@pytest.mark.parametrize("solver", SOLVERS)
|
|
@pytest.mark.parametrize("fit_intercept", [True, False])
|
|
def test_ridge_regression_vstacked_X(
|
|
solver, fit_intercept, ols_ridge_dataset, global_random_seed
|
|
):
|
|
"""Test that Ridge converges for all solvers to correct solution on vstacked data.
|
|
|
|
We work with a simple constructed data set with known solution.
|
|
Fit on [X] with alpha is the same as fit on [X], [y]
|
|
[X], [y] with 2 * alpha.
|
|
For wide X, [X', X'] is a singular matrix.
|
|
"""
|
|
X, y, _, coef = ols_ridge_dataset
|
|
n_samples, n_features = X.shape
|
|
alpha = 1.0 # because ols_ridge_dataset uses this.
|
|
|
|
model = Ridge(
|
|
alpha=2 * alpha,
|
|
fit_intercept=fit_intercept,
|
|
solver=solver,
|
|
tol=1e-15 if solver in ("sag", "saga") else 1e-10,
|
|
random_state=global_random_seed,
|
|
)
|
|
X = X[:, :-1] # remove intercept
|
|
X = np.concatenate((X, X), axis=0)
|
|
assert np.linalg.matrix_rank(X) <= min(n_samples, n_features)
|
|
y = np.r_[y, y]
|
|
if fit_intercept:
|
|
intercept = coef[-1]
|
|
else:
|
|
X = X - X.mean(axis=0)
|
|
y = y - y.mean()
|
|
intercept = 0
|
|
model.fit(X, y)
|
|
coef = coef[:-1]
|
|
|
|
assert model.intercept_ == pytest.approx(intercept)
|
|
# coefficients are not all on the same magnitude, adding a small atol to
|
|
# make this test less brittle
|
|
assert_allclose(model.coef_, coef, atol=1e-8)
|
|
|
|
|
|
@pytest.mark.parametrize("solver", SOLVERS)
|
|
@pytest.mark.parametrize("fit_intercept", [True, False])
|
|
def test_ridge_regression_unpenalized(
|
|
solver, fit_intercept, ols_ridge_dataset, global_random_seed
|
|
):
|
|
"""Test that unpenalized Ridge = OLS converges for all solvers to correct solution.
|
|
|
|
We work with a simple constructed data set with known solution.
|
|
Note: This checks the minimum norm solution for wide X, i.e.
|
|
n_samples < n_features:
|
|
min ||w||_2 subject to X w = y
|
|
"""
|
|
X, y, coef, _ = ols_ridge_dataset
|
|
n_samples, n_features = X.shape
|
|
alpha = 0 # OLS
|
|
params = dict(
|
|
alpha=alpha,
|
|
fit_intercept=fit_intercept,
|
|
solver=solver,
|
|
tol=1e-15 if solver in ("sag", "saga") else 1e-10,
|
|
random_state=global_random_seed,
|
|
)
|
|
|
|
model = Ridge(**params)
|
|
# Note that cholesky might give a warning: "Singular matrix in solving dual
|
|
# problem. Using least-squares solution instead."
|
|
if fit_intercept:
|
|
X = X[:, :-1] # remove intercept
|
|
intercept = coef[-1]
|
|
coef = coef[:-1]
|
|
else:
|
|
intercept = 0
|
|
model.fit(X, y)
|
|
|
|
# FIXME: `assert_allclose(model.coef_, coef)` should work for all cases but fails
|
|
# for the wide/fat case with n_features > n_samples. The current Ridge solvers do
|
|
# NOT return the minimum norm solution with fit_intercept=True.
|
|
if n_samples > n_features or not fit_intercept:
|
|
assert model.intercept_ == pytest.approx(intercept)
|
|
assert_allclose(model.coef_, coef)
|
|
else:
|
|
# As it is an underdetermined problem, residuals = 0. This shows that we get
|
|
# a solution to X w = y ....
|
|
assert_allclose(model.predict(X), y)
|
|
assert_allclose(X @ coef + intercept, y)
|
|
# But it is not the minimum norm solution. (This should be equal.)
|
|
assert np.linalg.norm(np.r_[model.intercept_, model.coef_]) > np.linalg.norm(
|
|
np.r_[intercept, coef]
|
|
)
|
|
|
|
pytest.xfail(reason="Ridge does not provide the minimum norm solution.")
|
|
assert model.intercept_ == pytest.approx(intercept)
|
|
assert_allclose(model.coef_, coef)
|
|
|
|
|
|
@pytest.mark.parametrize("solver", SOLVERS)
|
|
@pytest.mark.parametrize("fit_intercept", [True, False])
|
|
def test_ridge_regression_unpenalized_hstacked_X(
|
|
solver, fit_intercept, ols_ridge_dataset, global_random_seed
|
|
):
|
|
"""Test that unpenalized Ridge = OLS converges for all solvers to correct solution.
|
|
|
|
We work with a simple constructed data set with known solution.
|
|
OLS fit on [X] is the same as fit on [X, X]/2.
|
|
For long X, [X, X] is a singular matrix and we check against the minimum norm
|
|
solution:
|
|
min ||w||_2 subject to min ||X w - y||_2
|
|
"""
|
|
X, y, coef, _ = ols_ridge_dataset
|
|
n_samples, n_features = X.shape
|
|
alpha = 0 # OLS
|
|
|
|
model = Ridge(
|
|
alpha=alpha,
|
|
fit_intercept=fit_intercept,
|
|
solver=solver,
|
|
tol=1e-15 if solver in ("sag", "saga") else 1e-10,
|
|
random_state=global_random_seed,
|
|
)
|
|
if fit_intercept:
|
|
X = X[:, :-1] # remove intercept
|
|
intercept = coef[-1]
|
|
coef = coef[:-1]
|
|
else:
|
|
intercept = 0
|
|
X = 0.5 * np.concatenate((X, X), axis=1)
|
|
assert np.linalg.matrix_rank(X) <= min(n_samples, n_features)
|
|
model.fit(X, y)
|
|
|
|
if n_samples > n_features or not fit_intercept:
|
|
assert model.intercept_ == pytest.approx(intercept)
|
|
if solver == "cholesky":
|
|
# Cholesky is a bad choice for singular X.
|
|
pytest.skip()
|
|
assert_allclose(model.coef_, np.r_[coef, coef])
|
|
else:
|
|
# FIXME: Same as in test_ridge_regression_unpenalized.
|
|
# As it is an underdetermined problem, residuals = 0. This shows that we get
|
|
# a solution to X w = y ....
|
|
assert_allclose(model.predict(X), y)
|
|
# But it is not the minimum norm solution. (This should be equal.)
|
|
assert np.linalg.norm(np.r_[model.intercept_, model.coef_]) > np.linalg.norm(
|
|
np.r_[intercept, coef, coef]
|
|
)
|
|
|
|
pytest.xfail(reason="Ridge does not provide the minimum norm solution.")
|
|
assert model.intercept_ == pytest.approx(intercept)
|
|
assert_allclose(model.coef_, np.r_[coef, coef])
|
|
|
|
|
|
@pytest.mark.parametrize("solver", SOLVERS)
|
|
@pytest.mark.parametrize("fit_intercept", [True, False])
|
|
def test_ridge_regression_unpenalized_vstacked_X(
|
|
solver, fit_intercept, ols_ridge_dataset, global_random_seed
|
|
):
|
|
"""Test that unpenalized Ridge = OLS converges for all solvers to correct solution.
|
|
|
|
We work with a simple constructed data set with known solution.
|
|
OLS fit on [X] is the same as fit on [X], [y]
|
|
[X], [y].
|
|
For wide X, [X', X'] is a singular matrix and we check against the minimum norm
|
|
solution:
|
|
min ||w||_2 subject to X w = y
|
|
"""
|
|
X, y, coef, _ = ols_ridge_dataset
|
|
n_samples, n_features = X.shape
|
|
alpha = 0 # OLS
|
|
|
|
model = Ridge(
|
|
alpha=alpha,
|
|
fit_intercept=fit_intercept,
|
|
solver=solver,
|
|
tol=1e-15 if solver in ("sag", "saga") else 1e-10,
|
|
random_state=global_random_seed,
|
|
)
|
|
|
|
if fit_intercept:
|
|
X = X[:, :-1] # remove intercept
|
|
intercept = coef[-1]
|
|
coef = coef[:-1]
|
|
else:
|
|
intercept = 0
|
|
X = np.concatenate((X, X), axis=0)
|
|
assert np.linalg.matrix_rank(X) <= min(n_samples, n_features)
|
|
y = np.r_[y, y]
|
|
model.fit(X, y)
|
|
|
|
if n_samples > n_features or not fit_intercept:
|
|
assert model.intercept_ == pytest.approx(intercept)
|
|
assert_allclose(model.coef_, coef)
|
|
else:
|
|
# FIXME: Same as in test_ridge_regression_unpenalized.
|
|
# As it is an underdetermined problem, residuals = 0. This shows that we get
|
|
# a solution to X w = y ....
|
|
assert_allclose(model.predict(X), y)
|
|
# But it is not the minimum norm solution. (This should be equal.)
|
|
assert np.linalg.norm(np.r_[model.intercept_, model.coef_]) > np.linalg.norm(
|
|
np.r_[intercept, coef]
|
|
)
|
|
|
|
pytest.xfail(reason="Ridge does not provide the minimum norm solution.")
|
|
assert model.intercept_ == pytest.approx(intercept)
|
|
assert_allclose(model.coef_, coef)
|
|
|
|
|
|
@pytest.mark.parametrize("solver", SOLVERS)
|
|
@pytest.mark.parametrize("fit_intercept", [True, False])
|
|
@pytest.mark.parametrize("sparseX", [True, False])
|
|
@pytest.mark.parametrize("alpha", [1.0, 1e-2])
|
|
def test_ridge_regression_sample_weights(
|
|
solver, fit_intercept, sparseX, alpha, ols_ridge_dataset, global_random_seed
|
|
):
|
|
"""Test that Ridge with sample weights gives correct results.
|
|
|
|
We use the following trick:
|
|
||y - Xw||_2 = (z - Aw)' W (z - Aw)
|
|
for z=[y, y], A' = [X', X'] (vstacked), and W[:n/2] + W[n/2:] = 1, W=diag(W)
|
|
"""
|
|
if sparseX:
|
|
if fit_intercept and solver not in SPARSE_SOLVERS_WITH_INTERCEPT:
|
|
pytest.skip()
|
|
elif not fit_intercept and solver not in SPARSE_SOLVERS_WITHOUT_INTERCEPT:
|
|
pytest.skip()
|
|
X, y, _, coef = ols_ridge_dataset
|
|
n_samples, n_features = X.shape
|
|
sw = rng.uniform(low=0, high=1, size=n_samples)
|
|
|
|
model = Ridge(
|
|
alpha=alpha,
|
|
fit_intercept=fit_intercept,
|
|
solver=solver,
|
|
tol=1e-15 if solver in ["sag", "saga"] else 1e-10,
|
|
max_iter=100_000,
|
|
random_state=global_random_seed,
|
|
)
|
|
X = X[:, :-1] # remove intercept
|
|
X = np.concatenate((X, X), axis=0)
|
|
y = np.r_[y, y]
|
|
sw = np.r_[sw, 1 - sw] * alpha
|
|
if fit_intercept:
|
|
intercept = coef[-1]
|
|
else:
|
|
X = X - X.mean(axis=0)
|
|
y = y - y.mean()
|
|
intercept = 0
|
|
if sparseX:
|
|
X = sp.csr_matrix(X)
|
|
model.fit(X, y, sample_weight=sw)
|
|
coef = coef[:-1]
|
|
|
|
assert model.intercept_ == pytest.approx(intercept)
|
|
assert_allclose(model.coef_, coef)
|
|
|
|
|
|
def test_primal_dual_relationship():
|
|
y = y_diabetes.reshape(-1, 1)
|
|
coef = _solve_cholesky(X_diabetes, y, alpha=[1e-2])
|
|
K = np.dot(X_diabetes, X_diabetes.T)
|
|
dual_coef = _solve_cholesky_kernel(K, y, alpha=[1e-2])
|
|
coef2 = np.dot(X_diabetes.T, dual_coef).T
|
|
assert_array_almost_equal(coef, coef2)
|
|
|
|
|
|
def test_ridge_regression_convergence_fail():
|
|
rng = np.random.RandomState(0)
|
|
y = rng.randn(5)
|
|
X = rng.randn(5, 10)
|
|
warning_message = r"sparse_cg did not converge after" r" [0-9]+ iterations."
|
|
with pytest.warns(ConvergenceWarning, match=warning_message):
|
|
ridge_regression(
|
|
X, y, alpha=1.0, solver="sparse_cg", tol=0.0, max_iter=None, verbose=1
|
|
)
|
|
|
|
|
|
def test_ridge_shapes_type():
|
|
# Test shape of coef_ and intercept_
|
|
rng = np.random.RandomState(0)
|
|
n_samples, n_features = 5, 10
|
|
X = rng.randn(n_samples, n_features)
|
|
y = rng.randn(n_samples)
|
|
Y1 = y[:, np.newaxis]
|
|
Y = np.c_[y, 1 + y]
|
|
|
|
ridge = Ridge()
|
|
|
|
ridge.fit(X, y)
|
|
assert ridge.coef_.shape == (n_features,)
|
|
assert ridge.intercept_.shape == ()
|
|
assert isinstance(ridge.coef_, np.ndarray)
|
|
assert isinstance(ridge.intercept_, float)
|
|
|
|
ridge.fit(X, Y1)
|
|
assert ridge.coef_.shape == (1, n_features)
|
|
assert ridge.intercept_.shape == (1,)
|
|
assert isinstance(ridge.coef_, np.ndarray)
|
|
assert isinstance(ridge.intercept_, np.ndarray)
|
|
|
|
ridge.fit(X, Y)
|
|
assert ridge.coef_.shape == (2, n_features)
|
|
assert ridge.intercept_.shape == (2,)
|
|
assert isinstance(ridge.coef_, np.ndarray)
|
|
assert isinstance(ridge.intercept_, np.ndarray)
|
|
|
|
|
|
def test_ridge_intercept():
|
|
# Test intercept with multiple targets GH issue #708
|
|
rng = np.random.RandomState(0)
|
|
n_samples, n_features = 5, 10
|
|
X = rng.randn(n_samples, n_features)
|
|
y = rng.randn(n_samples)
|
|
Y = np.c_[y, 1.0 + y]
|
|
|
|
ridge = Ridge()
|
|
|
|
ridge.fit(X, y)
|
|
intercept = ridge.intercept_
|
|
|
|
ridge.fit(X, Y)
|
|
assert_almost_equal(ridge.intercept_[0], intercept)
|
|
assert_almost_equal(ridge.intercept_[1], intercept + 1.0)
|
|
|
|
|
|
def test_ridge_vs_lstsq():
|
|
# On alpha=0., Ridge and OLS yield the same solution.
|
|
|
|
rng = np.random.RandomState(0)
|
|
# we need more samples than features
|
|
n_samples, n_features = 5, 4
|
|
y = rng.randn(n_samples)
|
|
X = rng.randn(n_samples, n_features)
|
|
|
|
ridge = Ridge(alpha=0.0, fit_intercept=False)
|
|
ols = LinearRegression(fit_intercept=False)
|
|
|
|
ridge.fit(X, y)
|
|
ols.fit(X, y)
|
|
assert_almost_equal(ridge.coef_, ols.coef_)
|
|
|
|
ridge.fit(X, y)
|
|
ols.fit(X, y)
|
|
assert_almost_equal(ridge.coef_, ols.coef_)
|
|
|
|
|
|
def test_ridge_individual_penalties():
|
|
# Tests the ridge object using individual penalties
|
|
|
|
rng = np.random.RandomState(42)
|
|
|
|
n_samples, n_features, n_targets = 20, 10, 5
|
|
X = rng.randn(n_samples, n_features)
|
|
y = rng.randn(n_samples, n_targets)
|
|
|
|
penalties = np.arange(n_targets)
|
|
|
|
coef_cholesky = np.array(
|
|
[
|
|
Ridge(alpha=alpha, solver="cholesky").fit(X, target).coef_
|
|
for alpha, target in zip(penalties, y.T)
|
|
]
|
|
)
|
|
|
|
coefs_indiv_pen = [
|
|
Ridge(alpha=penalties, solver=solver, tol=1e-12).fit(X, y).coef_
|
|
for solver in ["svd", "sparse_cg", "lsqr", "cholesky", "sag", "saga"]
|
|
]
|
|
for coef_indiv_pen in coefs_indiv_pen:
|
|
assert_array_almost_equal(coef_cholesky, coef_indiv_pen)
|
|
|
|
# Test error is raised when number of targets and penalties do not match.
|
|
ridge = Ridge(alpha=penalties[:-1])
|
|
err_msg = "Number of targets and number of penalties do not correspond: 4 != 5"
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
ridge.fit(X, y)
|
|
|
|
|
|
@pytest.mark.parametrize("n_col", [(), (1,), (3,)])
|
|
def test_X_CenterStackOp(n_col):
|
|
rng = np.random.RandomState(0)
|
|
X = rng.randn(11, 8)
|
|
X_m = rng.randn(8)
|
|
sqrt_sw = rng.randn(len(X))
|
|
Y = rng.randn(11, *n_col)
|
|
A = rng.randn(9, *n_col)
|
|
operator = _X_CenterStackOp(sp.csr_matrix(X), X_m, sqrt_sw)
|
|
reference_operator = np.hstack([X - sqrt_sw[:, None] * X_m, sqrt_sw[:, None]])
|
|
assert_allclose(reference_operator.dot(A), operator.dot(A))
|
|
assert_allclose(reference_operator.T.dot(Y), operator.T.dot(Y))
|
|
|
|
|
|
@pytest.mark.parametrize("shape", [(10, 1), (13, 9), (3, 7), (2, 2), (20, 20)])
|
|
@pytest.mark.parametrize("uniform_weights", [True, False])
|
|
def test_compute_gram(shape, uniform_weights):
|
|
rng = np.random.RandomState(0)
|
|
X = rng.randn(*shape)
|
|
if uniform_weights:
|
|
sw = np.ones(X.shape[0])
|
|
else:
|
|
sw = rng.chisquare(1, shape[0])
|
|
sqrt_sw = np.sqrt(sw)
|
|
X_mean = np.average(X, axis=0, weights=sw)
|
|
X_centered = (X - X_mean) * sqrt_sw[:, None]
|
|
true_gram = X_centered.dot(X_centered.T)
|
|
X_sparse = sp.csr_matrix(X * sqrt_sw[:, None])
|
|
gcv = _RidgeGCV(fit_intercept=True)
|
|
computed_gram, computed_mean = gcv._compute_gram(X_sparse, sqrt_sw)
|
|
assert_allclose(X_mean, computed_mean)
|
|
assert_allclose(true_gram, computed_gram)
|
|
|
|
|
|
@pytest.mark.parametrize("shape", [(10, 1), (13, 9), (3, 7), (2, 2), (20, 20)])
|
|
@pytest.mark.parametrize("uniform_weights", [True, False])
|
|
def test_compute_covariance(shape, uniform_weights):
|
|
rng = np.random.RandomState(0)
|
|
X = rng.randn(*shape)
|
|
if uniform_weights:
|
|
sw = np.ones(X.shape[0])
|
|
else:
|
|
sw = rng.chisquare(1, shape[0])
|
|
sqrt_sw = np.sqrt(sw)
|
|
X_mean = np.average(X, axis=0, weights=sw)
|
|
X_centered = (X - X_mean) * sqrt_sw[:, None]
|
|
true_covariance = X_centered.T.dot(X_centered)
|
|
X_sparse = sp.csr_matrix(X * sqrt_sw[:, None])
|
|
gcv = _RidgeGCV(fit_intercept=True)
|
|
computed_cov, computed_mean = gcv._compute_covariance(X_sparse, sqrt_sw)
|
|
assert_allclose(X_mean, computed_mean)
|
|
assert_allclose(true_covariance, computed_cov)
|
|
|
|
|
|
def _make_sparse_offset_regression(
|
|
n_samples=100,
|
|
n_features=100,
|
|
proportion_nonzero=0.5,
|
|
n_informative=10,
|
|
n_targets=1,
|
|
bias=13.0,
|
|
X_offset=30.0,
|
|
noise=30.0,
|
|
shuffle=True,
|
|
coef=False,
|
|
positive=False,
|
|
random_state=None,
|
|
):
|
|
X, y, c = make_regression(
|
|
n_samples=n_samples,
|
|
n_features=n_features,
|
|
n_informative=n_informative,
|
|
n_targets=n_targets,
|
|
bias=bias,
|
|
noise=noise,
|
|
shuffle=shuffle,
|
|
coef=True,
|
|
random_state=random_state,
|
|
)
|
|
if n_features == 1:
|
|
c = np.asarray([c])
|
|
X += X_offset
|
|
mask = (
|
|
np.random.RandomState(random_state).binomial(1, proportion_nonzero, X.shape) > 0
|
|
)
|
|
removed_X = X.copy()
|
|
X[~mask] = 0.0
|
|
removed_X[mask] = 0.0
|
|
y -= removed_X.dot(c)
|
|
if positive:
|
|
y += X.dot(np.abs(c) + 1 - c)
|
|
c = np.abs(c) + 1
|
|
if n_features == 1:
|
|
c = c[0]
|
|
if coef:
|
|
return X, y, c
|
|
return X, y
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"solver, sparse_X",
|
|
(
|
|
(solver, sparse_X)
|
|
for (solver, sparse_X) in product(
|
|
["cholesky", "sag", "sparse_cg", "lsqr", "saga", "ridgecv"],
|
|
[False, True],
|
|
)
|
|
if not (sparse_X and solver not in ["sparse_cg", "ridgecv"])
|
|
),
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"n_samples,dtype,proportion_nonzero",
|
|
[(20, "float32", 0.1), (40, "float32", 1.0), (20, "float64", 0.2)],
|
|
)
|
|
@pytest.mark.parametrize("seed", np.arange(3))
|
|
def test_solver_consistency(
|
|
solver, proportion_nonzero, n_samples, dtype, sparse_X, seed
|
|
):
|
|
alpha = 1.0
|
|
noise = 50.0 if proportion_nonzero > 0.9 else 500.0
|
|
X, y = _make_sparse_offset_regression(
|
|
bias=10,
|
|
n_features=30,
|
|
proportion_nonzero=proportion_nonzero,
|
|
noise=noise,
|
|
random_state=seed,
|
|
n_samples=n_samples,
|
|
)
|
|
|
|
# Manually scale the data to avoid pathological cases. We use
|
|
# minmax_scale to deal with the sparse case without breaking
|
|
# the sparsity pattern.
|
|
X = minmax_scale(X)
|
|
|
|
svd_ridge = Ridge(solver="svd", alpha=alpha).fit(X, y)
|
|
X = X.astype(dtype, copy=False)
|
|
y = y.astype(dtype, copy=False)
|
|
if sparse_X:
|
|
X = sp.csr_matrix(X)
|
|
if solver == "ridgecv":
|
|
ridge = RidgeCV(alphas=[alpha])
|
|
else:
|
|
ridge = Ridge(solver=solver, tol=1e-10, alpha=alpha)
|
|
ridge.fit(X, y)
|
|
assert_allclose(ridge.coef_, svd_ridge.coef_, atol=1e-3, rtol=1e-3)
|
|
assert_allclose(ridge.intercept_, svd_ridge.intercept_, atol=1e-3, rtol=1e-3)
|
|
|
|
|
|
@pytest.mark.parametrize("gcv_mode", ["svd", "eigen"])
|
|
@pytest.mark.parametrize("X_constructor", [np.asarray, sp.csr_matrix])
|
|
@pytest.mark.parametrize("X_shape", [(11, 8), (11, 20)])
|
|
@pytest.mark.parametrize("fit_intercept", [True, False])
|
|
@pytest.mark.parametrize(
|
|
"y_shape, noise",
|
|
[
|
|
((11,), 1.0),
|
|
((11, 1), 30.0),
|
|
((11, 3), 150.0),
|
|
],
|
|
)
|
|
def test_ridge_gcv_vs_ridge_loo_cv(
|
|
gcv_mode, X_constructor, X_shape, y_shape, fit_intercept, noise
|
|
):
|
|
n_samples, n_features = X_shape
|
|
n_targets = y_shape[-1] if len(y_shape) == 2 else 1
|
|
X, y = _make_sparse_offset_regression(
|
|
n_samples=n_samples,
|
|
n_features=n_features,
|
|
n_targets=n_targets,
|
|
random_state=0,
|
|
shuffle=False,
|
|
noise=noise,
|
|
n_informative=5,
|
|
)
|
|
y = y.reshape(y_shape)
|
|
|
|
alphas = [1e-3, 0.1, 1.0, 10.0, 1e3]
|
|
loo_ridge = RidgeCV(
|
|
cv=n_samples,
|
|
fit_intercept=fit_intercept,
|
|
alphas=alphas,
|
|
scoring="neg_mean_squared_error",
|
|
)
|
|
gcv_ridge = RidgeCV(
|
|
gcv_mode=gcv_mode,
|
|
fit_intercept=fit_intercept,
|
|
alphas=alphas,
|
|
)
|
|
|
|
loo_ridge.fit(X, y)
|
|
|
|
X_gcv = X_constructor(X)
|
|
gcv_ridge.fit(X_gcv, y)
|
|
|
|
assert gcv_ridge.alpha_ == pytest.approx(loo_ridge.alpha_)
|
|
assert_allclose(gcv_ridge.coef_, loo_ridge.coef_, rtol=1e-3)
|
|
assert_allclose(gcv_ridge.intercept_, loo_ridge.intercept_, rtol=1e-3)
|
|
|
|
|
|
def test_ridge_loo_cv_asym_scoring():
|
|
# checking on asymmetric scoring
|
|
scoring = "explained_variance"
|
|
n_samples, n_features = 10, 5
|
|
n_targets = 1
|
|
X, y = _make_sparse_offset_regression(
|
|
n_samples=n_samples,
|
|
n_features=n_features,
|
|
n_targets=n_targets,
|
|
random_state=0,
|
|
shuffle=False,
|
|
noise=1,
|
|
n_informative=5,
|
|
)
|
|
|
|
alphas = [1e-3, 0.1, 1.0, 10.0, 1e3]
|
|
loo_ridge = RidgeCV(
|
|
cv=n_samples, fit_intercept=True, alphas=alphas, scoring=scoring
|
|
)
|
|
|
|
gcv_ridge = RidgeCV(fit_intercept=True, alphas=alphas, scoring=scoring)
|
|
|
|
loo_ridge.fit(X, y)
|
|
gcv_ridge.fit(X, y)
|
|
|
|
assert gcv_ridge.alpha_ == pytest.approx(loo_ridge.alpha_)
|
|
assert_allclose(gcv_ridge.coef_, loo_ridge.coef_, rtol=1e-3)
|
|
assert_allclose(gcv_ridge.intercept_, loo_ridge.intercept_, rtol=1e-3)
|
|
|
|
|
|
@pytest.mark.parametrize("gcv_mode", ["svd", "eigen"])
|
|
@pytest.mark.parametrize("X_constructor", [np.asarray, sp.csr_matrix])
|
|
@pytest.mark.parametrize("n_features", [8, 20])
|
|
@pytest.mark.parametrize(
|
|
"y_shape, fit_intercept, noise",
|
|
[
|
|
((11,), True, 1.0),
|
|
((11, 1), True, 20.0),
|
|
((11, 3), True, 150.0),
|
|
((11, 3), False, 30.0),
|
|
],
|
|
)
|
|
def test_ridge_gcv_sample_weights(
|
|
gcv_mode, X_constructor, fit_intercept, n_features, y_shape, noise
|
|
):
|
|
alphas = [1e-3, 0.1, 1.0, 10.0, 1e3]
|
|
rng = np.random.RandomState(0)
|
|
n_targets = y_shape[-1] if len(y_shape) == 2 else 1
|
|
X, y = _make_sparse_offset_regression(
|
|
n_samples=11,
|
|
n_features=n_features,
|
|
n_targets=n_targets,
|
|
random_state=0,
|
|
shuffle=False,
|
|
noise=noise,
|
|
)
|
|
y = y.reshape(y_shape)
|
|
|
|
sample_weight = 3 * rng.randn(len(X))
|
|
sample_weight = (sample_weight - sample_weight.min() + 1).astype(int)
|
|
indices = np.repeat(np.arange(X.shape[0]), sample_weight)
|
|
sample_weight = sample_weight.astype(float)
|
|
X_tiled, y_tiled = X[indices], y[indices]
|
|
|
|
cv = GroupKFold(n_splits=X.shape[0])
|
|
splits = cv.split(X_tiled, y_tiled, groups=indices)
|
|
kfold = RidgeCV(
|
|
alphas=alphas,
|
|
cv=splits,
|
|
scoring="neg_mean_squared_error",
|
|
fit_intercept=fit_intercept,
|
|
)
|
|
kfold.fit(X_tiled, y_tiled)
|
|
|
|
ridge_reg = Ridge(alpha=kfold.alpha_, fit_intercept=fit_intercept)
|
|
splits = cv.split(X_tiled, y_tiled, groups=indices)
|
|
predictions = cross_val_predict(ridge_reg, X_tiled, y_tiled, cv=splits)
|
|
kfold_errors = (y_tiled - predictions) ** 2
|
|
kfold_errors = [
|
|
np.sum(kfold_errors[indices == i], axis=0) for i in np.arange(X.shape[0])
|
|
]
|
|
kfold_errors = np.asarray(kfold_errors)
|
|
|
|
X_gcv = X_constructor(X)
|
|
gcv_ridge = RidgeCV(
|
|
alphas=alphas,
|
|
store_cv_values=True,
|
|
gcv_mode=gcv_mode,
|
|
fit_intercept=fit_intercept,
|
|
)
|
|
gcv_ridge.fit(X_gcv, y, sample_weight=sample_weight)
|
|
if len(y_shape) == 2:
|
|
gcv_errors = gcv_ridge.cv_values_[:, :, alphas.index(kfold.alpha_)]
|
|
else:
|
|
gcv_errors = gcv_ridge.cv_values_[:, alphas.index(kfold.alpha_)]
|
|
|
|
assert kfold.alpha_ == pytest.approx(gcv_ridge.alpha_)
|
|
assert_allclose(gcv_errors, kfold_errors, rtol=1e-3)
|
|
assert_allclose(gcv_ridge.coef_, kfold.coef_, rtol=1e-3)
|
|
assert_allclose(gcv_ridge.intercept_, kfold.intercept_, rtol=1e-3)
|
|
|
|
|
|
@pytest.mark.parametrize("sparse", [True, False])
|
|
@pytest.mark.parametrize(
|
|
"mode, mode_n_greater_than_p, mode_p_greater_than_n",
|
|
[
|
|
(None, "svd", "eigen"),
|
|
("auto", "svd", "eigen"),
|
|
("eigen", "eigen", "eigen"),
|
|
("svd", "svd", "svd"),
|
|
],
|
|
)
|
|
def test_check_gcv_mode_choice(
|
|
sparse, mode, mode_n_greater_than_p, mode_p_greater_than_n
|
|
):
|
|
X, _ = make_regression(n_samples=5, n_features=2)
|
|
if sparse:
|
|
X = sp.csr_matrix(X)
|
|
assert _check_gcv_mode(X, mode) == mode_n_greater_than_p
|
|
assert _check_gcv_mode(X.T, mode) == mode_p_greater_than_n
|
|
|
|
|
|
def _test_ridge_loo(filter_):
|
|
# test that can work with both dense or sparse matrices
|
|
n_samples = X_diabetes.shape[0]
|
|
|
|
ret = []
|
|
|
|
fit_intercept = filter_ == DENSE_FILTER
|
|
ridge_gcv = _RidgeGCV(fit_intercept=fit_intercept)
|
|
|
|
# check best alpha
|
|
ridge_gcv.fit(filter_(X_diabetes), y_diabetes)
|
|
alpha_ = ridge_gcv.alpha_
|
|
ret.append(alpha_)
|
|
|
|
# check that we get same best alpha with custom loss_func
|
|
f = ignore_warnings
|
|
scoring = make_scorer(mean_squared_error, greater_is_better=False)
|
|
ridge_gcv2 = RidgeCV(fit_intercept=False, scoring=scoring)
|
|
f(ridge_gcv2.fit)(filter_(X_diabetes), y_diabetes)
|
|
assert ridge_gcv2.alpha_ == pytest.approx(alpha_)
|
|
|
|
# check that we get same best alpha with custom score_func
|
|
def func(x, y):
|
|
return -mean_squared_error(x, y)
|
|
|
|
scoring = make_scorer(func)
|
|
ridge_gcv3 = RidgeCV(fit_intercept=False, scoring=scoring)
|
|
f(ridge_gcv3.fit)(filter_(X_diabetes), y_diabetes)
|
|
assert ridge_gcv3.alpha_ == pytest.approx(alpha_)
|
|
|
|
# check that we get same best alpha with a scorer
|
|
scorer = get_scorer("neg_mean_squared_error")
|
|
ridge_gcv4 = RidgeCV(fit_intercept=False, scoring=scorer)
|
|
ridge_gcv4.fit(filter_(X_diabetes), y_diabetes)
|
|
assert ridge_gcv4.alpha_ == pytest.approx(alpha_)
|
|
|
|
# check that we get same best alpha with sample weights
|
|
if filter_ == DENSE_FILTER:
|
|
ridge_gcv.fit(filter_(X_diabetes), y_diabetes, sample_weight=np.ones(n_samples))
|
|
assert ridge_gcv.alpha_ == pytest.approx(alpha_)
|
|
|
|
# simulate several responses
|
|
Y = np.vstack((y_diabetes, y_diabetes)).T
|
|
|
|
ridge_gcv.fit(filter_(X_diabetes), Y)
|
|
Y_pred = ridge_gcv.predict(filter_(X_diabetes))
|
|
ridge_gcv.fit(filter_(X_diabetes), y_diabetes)
|
|
y_pred = ridge_gcv.predict(filter_(X_diabetes))
|
|
|
|
assert_allclose(np.vstack((y_pred, y_pred)).T, Y_pred, rtol=1e-5)
|
|
|
|
return ret
|
|
|
|
|
|
def _test_ridge_cv(filter_):
|
|
ridge_cv = RidgeCV()
|
|
ridge_cv.fit(filter_(X_diabetes), y_diabetes)
|
|
ridge_cv.predict(filter_(X_diabetes))
|
|
|
|
assert len(ridge_cv.coef_.shape) == 1
|
|
assert type(ridge_cv.intercept_) == np.float64
|
|
|
|
cv = KFold(5)
|
|
ridge_cv.set_params(cv=cv)
|
|
ridge_cv.fit(filter_(X_diabetes), y_diabetes)
|
|
ridge_cv.predict(filter_(X_diabetes))
|
|
|
|
assert len(ridge_cv.coef_.shape) == 1
|
|
assert type(ridge_cv.intercept_) == np.float64
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"ridge, make_dataset",
|
|
[
|
|
(RidgeCV(store_cv_values=False), make_regression),
|
|
(RidgeClassifierCV(store_cv_values=False), make_classification),
|
|
],
|
|
)
|
|
def test_ridge_gcv_cv_values_not_stored(ridge, make_dataset):
|
|
# Check that `cv_values_` is not stored when store_cv_values is False
|
|
X, y = make_dataset(n_samples=6, random_state=42)
|
|
ridge.fit(X, y)
|
|
assert not hasattr(ridge, "cv_values_")
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"ridge, make_dataset",
|
|
[(RidgeCV(), make_regression), (RidgeClassifierCV(), make_classification)],
|
|
)
|
|
@pytest.mark.parametrize("cv", [None, 3])
|
|
def test_ridge_best_score(ridge, make_dataset, cv):
|
|
# check that the best_score_ is store
|
|
X, y = make_dataset(n_samples=6, random_state=42)
|
|
ridge.set_params(store_cv_values=False, cv=cv)
|
|
ridge.fit(X, y)
|
|
assert hasattr(ridge, "best_score_")
|
|
assert isinstance(ridge.best_score_, float)
|
|
|
|
|
|
def test_ridge_cv_individual_penalties():
|
|
# Tests the ridge_cv object optimizing individual penalties for each target
|
|
|
|
rng = np.random.RandomState(42)
|
|
|
|
# Create random dataset with multiple targets. Each target should have
|
|
# a different optimal alpha.
|
|
n_samples, n_features, n_targets = 20, 5, 3
|
|
y = rng.randn(n_samples, n_targets)
|
|
X = (
|
|
np.dot(y[:, [0]], np.ones((1, n_features)))
|
|
+ np.dot(y[:, [1]], 0.05 * np.ones((1, n_features)))
|
|
+ np.dot(y[:, [2]], 0.001 * np.ones((1, n_features)))
|
|
+ rng.randn(n_samples, n_features)
|
|
)
|
|
|
|
alphas = (1, 100, 1000)
|
|
|
|
# Find optimal alpha for each target
|
|
optimal_alphas = [RidgeCV(alphas=alphas).fit(X, target).alpha_ for target in y.T]
|
|
|
|
# Find optimal alphas for all targets simultaneously
|
|
ridge_cv = RidgeCV(alphas=alphas, alpha_per_target=True).fit(X, y)
|
|
assert_array_equal(optimal_alphas, ridge_cv.alpha_)
|
|
|
|
# The resulting regression weights should incorporate the different
|
|
# alpha values.
|
|
assert_array_almost_equal(
|
|
Ridge(alpha=ridge_cv.alpha_).fit(X, y).coef_, ridge_cv.coef_
|
|
)
|
|
|
|
# Test shape of alpha_ and cv_values_
|
|
ridge_cv = RidgeCV(alphas=alphas, alpha_per_target=True, store_cv_values=True).fit(
|
|
X, y
|
|
)
|
|
assert ridge_cv.alpha_.shape == (n_targets,)
|
|
assert ridge_cv.best_score_.shape == (n_targets,)
|
|
assert ridge_cv.cv_values_.shape == (n_samples, len(alphas), n_targets)
|
|
|
|
# Test edge case of there being only one alpha value
|
|
ridge_cv = RidgeCV(alphas=1, alpha_per_target=True, store_cv_values=True).fit(X, y)
|
|
assert ridge_cv.alpha_.shape == (n_targets,)
|
|
assert ridge_cv.best_score_.shape == (n_targets,)
|
|
assert ridge_cv.cv_values_.shape == (n_samples, n_targets, 1)
|
|
|
|
# Test edge case of there being only one target
|
|
ridge_cv = RidgeCV(alphas=alphas, alpha_per_target=True, store_cv_values=True).fit(
|
|
X, y[:, 0]
|
|
)
|
|
assert np.isscalar(ridge_cv.alpha_)
|
|
assert np.isscalar(ridge_cv.best_score_)
|
|
assert ridge_cv.cv_values_.shape == (n_samples, len(alphas))
|
|
|
|
# Try with a custom scoring function
|
|
ridge_cv = RidgeCV(alphas=alphas, alpha_per_target=True, scoring="r2").fit(X, y)
|
|
assert_array_equal(optimal_alphas, ridge_cv.alpha_)
|
|
assert_array_almost_equal(
|
|
Ridge(alpha=ridge_cv.alpha_).fit(X, y).coef_, ridge_cv.coef_
|
|
)
|
|
|
|
# Using a custom CV object should throw an error in combination with
|
|
# alpha_per_target=True
|
|
ridge_cv = RidgeCV(alphas=alphas, cv=LeaveOneOut(), alpha_per_target=True)
|
|
msg = "cv!=None and alpha_per_target=True are incompatible"
|
|
with pytest.raises(ValueError, match=msg):
|
|
ridge_cv.fit(X, y)
|
|
ridge_cv = RidgeCV(alphas=alphas, cv=6, alpha_per_target=True)
|
|
with pytest.raises(ValueError, match=msg):
|
|
ridge_cv.fit(X, y)
|
|
|
|
|
|
def _test_ridge_diabetes(filter_):
|
|
ridge = Ridge(fit_intercept=False)
|
|
ridge.fit(filter_(X_diabetes), y_diabetes)
|
|
return np.round(ridge.score(filter_(X_diabetes), y_diabetes), 5)
|
|
|
|
|
|
def _test_multi_ridge_diabetes(filter_):
|
|
# simulate several responses
|
|
Y = np.vstack((y_diabetes, y_diabetes)).T
|
|
n_features = X_diabetes.shape[1]
|
|
|
|
ridge = Ridge(fit_intercept=False)
|
|
ridge.fit(filter_(X_diabetes), Y)
|
|
assert ridge.coef_.shape == (2, n_features)
|
|
Y_pred = ridge.predict(filter_(X_diabetes))
|
|
ridge.fit(filter_(X_diabetes), y_diabetes)
|
|
y_pred = ridge.predict(filter_(X_diabetes))
|
|
assert_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3)
|
|
|
|
|
|
def _test_ridge_classifiers(filter_):
|
|
n_classes = np.unique(y_iris).shape[0]
|
|
n_features = X_iris.shape[1]
|
|
for reg in (RidgeClassifier(), RidgeClassifierCV()):
|
|
reg.fit(filter_(X_iris), y_iris)
|
|
assert reg.coef_.shape == (n_classes, n_features)
|
|
y_pred = reg.predict(filter_(X_iris))
|
|
assert np.mean(y_iris == y_pred) > 0.79
|
|
|
|
cv = KFold(5)
|
|
reg = RidgeClassifierCV(cv=cv)
|
|
reg.fit(filter_(X_iris), y_iris)
|
|
y_pred = reg.predict(filter_(X_iris))
|
|
assert np.mean(y_iris == y_pred) >= 0.8
|
|
|
|
|
|
@pytest.mark.parametrize("scoring", [None, "accuracy", _accuracy_callable])
|
|
@pytest.mark.parametrize("cv", [None, KFold(5)])
|
|
@pytest.mark.parametrize("filter_", [DENSE_FILTER, SPARSE_FILTER])
|
|
def test_ridge_classifier_with_scoring(filter_, scoring, cv):
|
|
# non-regression test for #14672
|
|
# check that RidgeClassifierCV works with all sort of scoring and
|
|
# cross-validation
|
|
scoring_ = make_scorer(scoring) if callable(scoring) else scoring
|
|
clf = RidgeClassifierCV(scoring=scoring_, cv=cv)
|
|
# Smoke test to check that fit/predict does not raise error
|
|
clf.fit(filter_(X_iris), y_iris).predict(filter_(X_iris))
|
|
|
|
|
|
@pytest.mark.parametrize("cv", [None, KFold(5)])
|
|
@pytest.mark.parametrize("filter_", [DENSE_FILTER, SPARSE_FILTER])
|
|
def test_ridge_regression_custom_scoring(filter_, cv):
|
|
# check that custom scoring is working as expected
|
|
# check the tie breaking strategy (keep the first alpha tried)
|
|
|
|
def _dummy_score(y_test, y_pred):
|
|
return 0.42
|
|
|
|
alphas = np.logspace(-2, 2, num=5)
|
|
clf = RidgeClassifierCV(alphas=alphas, scoring=make_scorer(_dummy_score), cv=cv)
|
|
clf.fit(filter_(X_iris), y_iris)
|
|
assert clf.best_score_ == pytest.approx(0.42)
|
|
# In case of tie score, the first alphas will be kept
|
|
assert clf.alpha_ == pytest.approx(alphas[0])
|
|
|
|
|
|
def _test_tolerance(filter_):
|
|
ridge = Ridge(tol=1e-5, fit_intercept=False)
|
|
ridge.fit(filter_(X_diabetes), y_diabetes)
|
|
score = ridge.score(filter_(X_diabetes), y_diabetes)
|
|
|
|
ridge2 = Ridge(tol=1e-3, fit_intercept=False)
|
|
ridge2.fit(filter_(X_diabetes), y_diabetes)
|
|
score2 = ridge2.score(filter_(X_diabetes), y_diabetes)
|
|
|
|
assert score >= score2
|
|
|
|
|
|
def check_dense_sparse(test_func):
|
|
# test dense matrix
|
|
ret_dense = test_func(DENSE_FILTER)
|
|
# test sparse matrix
|
|
ret_sparse = test_func(SPARSE_FILTER)
|
|
# test that the outputs are the same
|
|
if ret_dense is not None and ret_sparse is not None:
|
|
assert_array_almost_equal(ret_dense, ret_sparse, decimal=3)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"test_func",
|
|
(
|
|
_test_ridge_loo,
|
|
_test_ridge_cv,
|
|
_test_ridge_diabetes,
|
|
_test_multi_ridge_diabetes,
|
|
_test_ridge_classifiers,
|
|
_test_tolerance,
|
|
),
|
|
)
|
|
def test_dense_sparse(test_func):
|
|
check_dense_sparse(test_func)
|
|
|
|
|
|
def test_class_weights():
|
|
# Test class weights.
|
|
X = np.array([[-1.0, -1.0], [-1.0, 0], [-0.8, -1.0], [1.0, 1.0], [1.0, 0.0]])
|
|
y = [1, 1, 1, -1, -1]
|
|
|
|
reg = RidgeClassifier(class_weight=None)
|
|
reg.fit(X, y)
|
|
assert_array_equal(reg.predict([[0.2, -1.0]]), np.array([1]))
|
|
|
|
# we give a small weights to class 1
|
|
reg = RidgeClassifier(class_weight={1: 0.001})
|
|
reg.fit(X, y)
|
|
|
|
# now the hyperplane should rotate clock-wise and
|
|
# the prediction on this point should shift
|
|
assert_array_equal(reg.predict([[0.2, -1.0]]), np.array([-1]))
|
|
|
|
# check if class_weight = 'balanced' can handle negative labels.
|
|
reg = RidgeClassifier(class_weight="balanced")
|
|
reg.fit(X, y)
|
|
assert_array_equal(reg.predict([[0.2, -1.0]]), np.array([1]))
|
|
|
|
# class_weight = 'balanced', and class_weight = None should return
|
|
# same values when y has equal number of all labels
|
|
X = np.array([[-1.0, -1.0], [-1.0, 0], [-0.8, -1.0], [1.0, 1.0]])
|
|
y = [1, 1, -1, -1]
|
|
reg = RidgeClassifier(class_weight=None)
|
|
reg.fit(X, y)
|
|
rega = RidgeClassifier(class_weight="balanced")
|
|
rega.fit(X, y)
|
|
assert len(rega.classes_) == 2
|
|
assert_array_almost_equal(reg.coef_, rega.coef_)
|
|
assert_array_almost_equal(reg.intercept_, rega.intercept_)
|
|
|
|
|
|
@pytest.mark.parametrize("reg", (RidgeClassifier, RidgeClassifierCV))
|
|
def test_class_weight_vs_sample_weight(reg):
|
|
"""Check class_weights resemble sample_weights behavior."""
|
|
|
|
# Iris is balanced, so no effect expected for using 'balanced' weights
|
|
reg1 = reg()
|
|
reg1.fit(iris.data, iris.target)
|
|
reg2 = reg(class_weight="balanced")
|
|
reg2.fit(iris.data, iris.target)
|
|
assert_almost_equal(reg1.coef_, reg2.coef_)
|
|
|
|
# Inflate importance of class 1, check against user-defined weights
|
|
sample_weight = np.ones(iris.target.shape)
|
|
sample_weight[iris.target == 1] *= 100
|
|
class_weight = {0: 1.0, 1: 100.0, 2: 1.0}
|
|
reg1 = reg()
|
|
reg1.fit(iris.data, iris.target, sample_weight)
|
|
reg2 = reg(class_weight=class_weight)
|
|
reg2.fit(iris.data, iris.target)
|
|
assert_almost_equal(reg1.coef_, reg2.coef_)
|
|
|
|
# Check that sample_weight and class_weight are multiplicative
|
|
reg1 = reg()
|
|
reg1.fit(iris.data, iris.target, sample_weight**2)
|
|
reg2 = reg(class_weight=class_weight)
|
|
reg2.fit(iris.data, iris.target, sample_weight)
|
|
assert_almost_equal(reg1.coef_, reg2.coef_)
|
|
|
|
|
|
def test_class_weights_cv():
|
|
# Test class weights for cross validated ridge classifier.
|
|
X = np.array([[-1.0, -1.0], [-1.0, 0], [-0.8, -1.0], [1.0, 1.0], [1.0, 0.0]])
|
|
y = [1, 1, 1, -1, -1]
|
|
|
|
reg = RidgeClassifierCV(class_weight=None, alphas=[0.01, 0.1, 1])
|
|
reg.fit(X, y)
|
|
|
|
# we give a small weights to class 1
|
|
reg = RidgeClassifierCV(class_weight={1: 0.001}, alphas=[0.01, 0.1, 1, 10])
|
|
reg.fit(X, y)
|
|
|
|
assert_array_equal(reg.predict([[-0.2, 2]]), np.array([-1]))
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"scoring", [None, "neg_mean_squared_error", _mean_squared_error_callable]
|
|
)
|
|
def test_ridgecv_store_cv_values(scoring):
|
|
rng = np.random.RandomState(42)
|
|
|
|
n_samples = 8
|
|
n_features = 5
|
|
x = rng.randn(n_samples, n_features)
|
|
alphas = [1e-1, 1e0, 1e1]
|
|
n_alphas = len(alphas)
|
|
|
|
scoring_ = make_scorer(scoring) if callable(scoring) else scoring
|
|
|
|
r = RidgeCV(alphas=alphas, cv=None, store_cv_values=True, scoring=scoring_)
|
|
|
|
# with len(y.shape) == 1
|
|
y = rng.randn(n_samples)
|
|
r.fit(x, y)
|
|
assert r.cv_values_.shape == (n_samples, n_alphas)
|
|
|
|
# with len(y.shape) == 2
|
|
n_targets = 3
|
|
y = rng.randn(n_samples, n_targets)
|
|
r.fit(x, y)
|
|
assert r.cv_values_.shape == (n_samples, n_targets, n_alphas)
|
|
|
|
r = RidgeCV(cv=3, store_cv_values=True, scoring=scoring)
|
|
with pytest.raises(ValueError, match="cv!=None and store_cv_values"):
|
|
r.fit(x, y)
|
|
|
|
|
|
@pytest.mark.parametrize("scoring", [None, "accuracy", _accuracy_callable])
|
|
def test_ridge_classifier_cv_store_cv_values(scoring):
|
|
x = np.array([[-1.0, -1.0], [-1.0, 0], [-0.8, -1.0], [1.0, 1.0], [1.0, 0.0]])
|
|
y = np.array([1, 1, 1, -1, -1])
|
|
|
|
n_samples = x.shape[0]
|
|
alphas = [1e-1, 1e0, 1e1]
|
|
n_alphas = len(alphas)
|
|
|
|
scoring_ = make_scorer(scoring) if callable(scoring) else scoring
|
|
|
|
r = RidgeClassifierCV(
|
|
alphas=alphas, cv=None, store_cv_values=True, scoring=scoring_
|
|
)
|
|
|
|
# with len(y.shape) == 1
|
|
n_targets = 1
|
|
r.fit(x, y)
|
|
assert r.cv_values_.shape == (n_samples, n_targets, n_alphas)
|
|
|
|
# with len(y.shape) == 2
|
|
y = np.array(
|
|
[[1, 1, 1, -1, -1], [1, -1, 1, -1, 1], [-1, -1, 1, -1, -1]]
|
|
).transpose()
|
|
n_targets = y.shape[1]
|
|
r.fit(x, y)
|
|
assert r.cv_values_.shape == (n_samples, n_targets, n_alphas)
|
|
|
|
|
|
@pytest.mark.parametrize("Estimator", [RidgeCV, RidgeClassifierCV])
|
|
def test_ridgecv_alphas_conversion(Estimator):
|
|
rng = np.random.RandomState(0)
|
|
alphas = (0.1, 1.0, 10.0)
|
|
|
|
n_samples, n_features = 5, 5
|
|
if Estimator is RidgeCV:
|
|
y = rng.randn(n_samples)
|
|
else:
|
|
y = rng.randint(0, 2, n_samples)
|
|
X = rng.randn(n_samples, n_features)
|
|
|
|
ridge_est = Estimator(alphas=alphas)
|
|
assert (
|
|
ridge_est.alphas is alphas
|
|
), f"`alphas` was mutated in `{Estimator.__name__}.__init__`"
|
|
|
|
ridge_est.fit(X, y)
|
|
assert_array_equal(ridge_est.alphas, np.asarray(alphas))
|
|
|
|
|
|
def test_ridgecv_sample_weight():
|
|
rng = np.random.RandomState(0)
|
|
alphas = (0.1, 1.0, 10.0)
|
|
|
|
# There are different algorithms for n_samples > n_features
|
|
# and the opposite, so test them both.
|
|
for n_samples, n_features in ((6, 5), (5, 10)):
|
|
y = rng.randn(n_samples)
|
|
X = rng.randn(n_samples, n_features)
|
|
sample_weight = 1.0 + rng.rand(n_samples)
|
|
|
|
cv = KFold(5)
|
|
ridgecv = RidgeCV(alphas=alphas, cv=cv)
|
|
ridgecv.fit(X, y, sample_weight=sample_weight)
|
|
|
|
# Check using GridSearchCV directly
|
|
parameters = {"alpha": alphas}
|
|
gs = GridSearchCV(Ridge(), parameters, cv=cv)
|
|
gs.fit(X, y, sample_weight=sample_weight)
|
|
|
|
assert ridgecv.alpha_ == gs.best_estimator_.alpha
|
|
assert_array_almost_equal(ridgecv.coef_, gs.best_estimator_.coef_)
|
|
|
|
|
|
def test_raises_value_error_if_sample_weights_greater_than_1d():
|
|
# Sample weights must be either scalar or 1D
|
|
|
|
n_sampless = [2, 3]
|
|
n_featuress = [3, 2]
|
|
|
|
rng = np.random.RandomState(42)
|
|
|
|
for n_samples, n_features in zip(n_sampless, n_featuress):
|
|
X = rng.randn(n_samples, n_features)
|
|
y = rng.randn(n_samples)
|
|
sample_weights_OK = rng.randn(n_samples) ** 2 + 1
|
|
sample_weights_OK_1 = 1.0
|
|
sample_weights_OK_2 = 2.0
|
|
sample_weights_not_OK = sample_weights_OK[:, np.newaxis]
|
|
sample_weights_not_OK_2 = sample_weights_OK[np.newaxis, :]
|
|
|
|
ridge = Ridge(alpha=1)
|
|
|
|
# make sure the "OK" sample weights actually work
|
|
ridge.fit(X, y, sample_weights_OK)
|
|
ridge.fit(X, y, sample_weights_OK_1)
|
|
ridge.fit(X, y, sample_weights_OK_2)
|
|
|
|
def fit_ridge_not_ok():
|
|
ridge.fit(X, y, sample_weights_not_OK)
|
|
|
|
def fit_ridge_not_ok_2():
|
|
ridge.fit(X, y, sample_weights_not_OK_2)
|
|
|
|
err_msg = "Sample weights must be 1D array or scalar"
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
fit_ridge_not_ok()
|
|
|
|
err_msg = "Sample weights must be 1D array or scalar"
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
fit_ridge_not_ok_2()
|
|
|
|
|
|
def test_sparse_design_with_sample_weights():
|
|
# Sample weights must work with sparse matrices
|
|
|
|
n_sampless = [2, 3]
|
|
n_featuress = [3, 2]
|
|
|
|
rng = np.random.RandomState(42)
|
|
|
|
sparse_matrix_converters = [
|
|
sp.coo_matrix,
|
|
sp.csr_matrix,
|
|
sp.csc_matrix,
|
|
sp.lil_matrix,
|
|
sp.dok_matrix,
|
|
]
|
|
|
|
sparse_ridge = Ridge(alpha=1.0, fit_intercept=False)
|
|
dense_ridge = Ridge(alpha=1.0, fit_intercept=False)
|
|
|
|
for n_samples, n_features in zip(n_sampless, n_featuress):
|
|
X = rng.randn(n_samples, n_features)
|
|
y = rng.randn(n_samples)
|
|
sample_weights = rng.randn(n_samples) ** 2 + 1
|
|
for sparse_converter in sparse_matrix_converters:
|
|
X_sparse = sparse_converter(X)
|
|
sparse_ridge.fit(X_sparse, y, sample_weight=sample_weights)
|
|
dense_ridge.fit(X, y, sample_weight=sample_weights)
|
|
|
|
assert_array_almost_equal(sparse_ridge.coef_, dense_ridge.coef_, decimal=6)
|
|
|
|
|
|
def test_ridgecv_int_alphas():
|
|
X = np.array([[-1.0, -1.0], [-1.0, 0], [-0.8, -1.0], [1.0, 1.0], [1.0, 0.0]])
|
|
y = [1, 1, 1, -1, -1]
|
|
|
|
# Integers
|
|
ridge = RidgeCV(alphas=(1, 10, 100))
|
|
ridge.fit(X, y)
|
|
|
|
|
|
@pytest.mark.parametrize("Estimator", [RidgeCV, RidgeClassifierCV])
|
|
@pytest.mark.parametrize(
|
|
"params, err_type, err_msg",
|
|
[
|
|
({"alphas": (1, -1, -100)}, ValueError, r"alphas\[1\] == -1, must be > 0.0"),
|
|
(
|
|
{"alphas": (-0.1, -1.0, -10.0)},
|
|
ValueError,
|
|
r"alphas\[0\] == -0.1, must be > 0.0",
|
|
),
|
|
(
|
|
{"alphas": (1, 1.0, "1")},
|
|
TypeError,
|
|
r"alphas\[2\] must be an instance of float, not str",
|
|
),
|
|
],
|
|
)
|
|
def test_ridgecv_alphas_validation(Estimator, params, err_type, err_msg):
|
|
"""Check the `alphas` validation in RidgeCV and RidgeClassifierCV."""
|
|
|
|
n_samples, n_features = 5, 5
|
|
X = rng.randn(n_samples, n_features)
|
|
y = rng.randint(0, 2, n_samples)
|
|
|
|
with pytest.raises(err_type, match=err_msg):
|
|
Estimator(**params).fit(X, y)
|
|
|
|
|
|
@pytest.mark.parametrize("Estimator", [RidgeCV, RidgeClassifierCV])
|
|
def test_ridgecv_alphas_scalar(Estimator):
|
|
"""Check the case when `alphas` is a scalar.
|
|
This case was supported in the past when `alphas` where converted
|
|
into array in `__init__`.
|
|
We add this test to ensure backward compatibility.
|
|
"""
|
|
|
|
n_samples, n_features = 5, 5
|
|
X = rng.randn(n_samples, n_features)
|
|
if Estimator is RidgeCV:
|
|
y = rng.randn(n_samples)
|
|
else:
|
|
y = rng.randint(0, 2, n_samples)
|
|
|
|
Estimator(alphas=1).fit(X, y)
|
|
|
|
|
|
def test_raises_value_error_if_solver_not_supported():
|
|
# Tests whether a ValueError is raised if a non-identified solver
|
|
# is passed to ridge_regression
|
|
|
|
wrong_solver = "This is not a solver (MagritteSolveCV QuantumBitcoin)"
|
|
|
|
exception = ValueError
|
|
message = (
|
|
"Known solvers are 'sparse_cg', 'cholesky', 'svd'"
|
|
" 'lsqr', 'sag' or 'saga'. Got %s." % wrong_solver
|
|
)
|
|
|
|
def func():
|
|
X = np.eye(3)
|
|
y = np.ones(3)
|
|
ridge_regression(X, y, alpha=1.0, solver=wrong_solver)
|
|
|
|
with pytest.raises(exception, match=message):
|
|
func()
|
|
|
|
|
|
def test_sparse_cg_max_iter():
|
|
reg = Ridge(solver="sparse_cg", max_iter=1)
|
|
reg.fit(X_diabetes, y_diabetes)
|
|
assert reg.coef_.shape[0] == X_diabetes.shape[1]
|
|
|
|
|
|
@ignore_warnings
|
|
def test_n_iter():
|
|
# Test that self.n_iter_ is correct.
|
|
n_targets = 2
|
|
X, y = X_diabetes, y_diabetes
|
|
y_n = np.tile(y, (n_targets, 1)).T
|
|
|
|
for max_iter in range(1, 4):
|
|
for solver in ("sag", "saga", "lsqr"):
|
|
reg = Ridge(solver=solver, max_iter=max_iter, tol=1e-12)
|
|
reg.fit(X, y_n)
|
|
assert_array_equal(reg.n_iter_, np.tile(max_iter, n_targets))
|
|
|
|
for solver in ("sparse_cg", "svd", "cholesky"):
|
|
reg = Ridge(solver=solver, max_iter=1, tol=1e-1)
|
|
reg.fit(X, y_n)
|
|
assert reg.n_iter_ is None
|
|
|
|
|
|
@pytest.mark.parametrize("solver", ["lsqr", "sparse_cg", "lbfgs", "auto"])
|
|
@pytest.mark.parametrize("with_sample_weight", [True, False])
|
|
def test_ridge_fit_intercept_sparse(solver, with_sample_weight, global_random_seed):
|
|
"""Check that ridge finds the same coefs and intercept on dense and sparse input
|
|
in the presence of sample weights.
|
|
|
|
For now only sparse_cg and lbfgs can correctly fit an intercept
|
|
with sparse X with default tol and max_iter.
|
|
'sag' is tested separately in test_ridge_fit_intercept_sparse_sag because it
|
|
requires more iterations and should raise a warning if default max_iter is used.
|
|
Other solvers raise an exception, as checked in
|
|
test_ridge_fit_intercept_sparse_error
|
|
"""
|
|
positive = solver == "lbfgs"
|
|
X, y = _make_sparse_offset_regression(
|
|
n_features=20, random_state=global_random_seed, positive=positive
|
|
)
|
|
|
|
sample_weight = None
|
|
if with_sample_weight:
|
|
rng = np.random.RandomState(global_random_seed)
|
|
sample_weight = 1.0 + rng.uniform(size=X.shape[0])
|
|
|
|
# "auto" should switch to "sparse_cg" when X is sparse
|
|
# so the reference we use for both ("auto" and "sparse_cg") is
|
|
# Ridge(solver="sparse_cg"), fitted using the dense representation (note
|
|
# that "sparse_cg" can fit sparse or dense data)
|
|
dense_solver = "sparse_cg" if solver == "auto" else solver
|
|
dense_ridge = Ridge(solver=dense_solver, tol=1e-12, positive=positive)
|
|
sparse_ridge = Ridge(solver=solver, tol=1e-12, positive=positive)
|
|
|
|
dense_ridge.fit(X, y, sample_weight=sample_weight)
|
|
sparse_ridge.fit(sp.csr_matrix(X), y, sample_weight=sample_weight)
|
|
|
|
assert_allclose(dense_ridge.intercept_, sparse_ridge.intercept_)
|
|
assert_allclose(dense_ridge.coef_, sparse_ridge.coef_, rtol=5e-7)
|
|
|
|
|
|
@pytest.mark.parametrize("solver", ["saga", "svd", "cholesky"])
|
|
def test_ridge_fit_intercept_sparse_error(solver):
|
|
X, y = _make_sparse_offset_regression(n_features=20, random_state=0)
|
|
X_csr = sp.csr_matrix(X)
|
|
sparse_ridge = Ridge(solver=solver)
|
|
err_msg = "solver='{}' does not support".format(solver)
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
sparse_ridge.fit(X_csr, y)
|
|
|
|
|
|
@pytest.mark.parametrize("with_sample_weight", [True, False])
|
|
def test_ridge_fit_intercept_sparse_sag(with_sample_weight, global_random_seed):
|
|
X, y = _make_sparse_offset_regression(
|
|
n_features=5, n_samples=20, random_state=global_random_seed, X_offset=5.0
|
|
)
|
|
if with_sample_weight:
|
|
rng = np.random.RandomState(global_random_seed)
|
|
sample_weight = 1.0 + rng.uniform(size=X.shape[0])
|
|
else:
|
|
sample_weight = None
|
|
X_csr = sp.csr_matrix(X)
|
|
|
|
params = dict(
|
|
alpha=1.0, solver="sag", fit_intercept=True, tol=1e-10, max_iter=100000
|
|
)
|
|
dense_ridge = Ridge(**params)
|
|
sparse_ridge = Ridge(**params)
|
|
dense_ridge.fit(X, y, sample_weight=sample_weight)
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter("error", UserWarning)
|
|
sparse_ridge.fit(X_csr, y, sample_weight=sample_weight)
|
|
assert_allclose(dense_ridge.intercept_, sparse_ridge.intercept_, rtol=1e-4)
|
|
assert_allclose(dense_ridge.coef_, sparse_ridge.coef_, rtol=1e-4)
|
|
with pytest.warns(UserWarning, match='"sag" solver requires.*'):
|
|
Ridge(solver="sag", fit_intercept=True, tol=1e-3, max_iter=None).fit(X_csr, y)
|
|
|
|
|
|
@pytest.mark.parametrize("return_intercept", [False, True])
|
|
@pytest.mark.parametrize("sample_weight", [None, np.ones(1000)])
|
|
@pytest.mark.parametrize("arr_type", [np.array, sp.csr_matrix])
|
|
@pytest.mark.parametrize(
|
|
"solver", ["auto", "sparse_cg", "cholesky", "lsqr", "sag", "saga", "lbfgs"]
|
|
)
|
|
def test_ridge_regression_check_arguments_validity(
|
|
return_intercept, sample_weight, arr_type, solver
|
|
):
|
|
"""check if all combinations of arguments give valid estimations"""
|
|
|
|
# test excludes 'svd' solver because it raises exception for sparse inputs
|
|
|
|
rng = check_random_state(42)
|
|
X = rng.rand(1000, 3)
|
|
true_coefs = [1, 2, 0.1]
|
|
y = np.dot(X, true_coefs)
|
|
true_intercept = 0.0
|
|
if return_intercept:
|
|
true_intercept = 10000.0
|
|
y += true_intercept
|
|
X_testing = arr_type(X)
|
|
|
|
alpha, tol = 1e-3, 1e-6
|
|
atol = 1e-3 if _IS_32BIT else 1e-4
|
|
|
|
positive = solver == "lbfgs"
|
|
|
|
if solver not in ["sag", "auto"] and return_intercept:
|
|
with pytest.raises(ValueError, match="In Ridge, only 'sag' solver"):
|
|
ridge_regression(
|
|
X_testing,
|
|
y,
|
|
alpha=alpha,
|
|
solver=solver,
|
|
sample_weight=sample_weight,
|
|
return_intercept=return_intercept,
|
|
positive=positive,
|
|
tol=tol,
|
|
)
|
|
return
|
|
|
|
out = ridge_regression(
|
|
X_testing,
|
|
y,
|
|
alpha=alpha,
|
|
solver=solver,
|
|
sample_weight=sample_weight,
|
|
positive=positive,
|
|
return_intercept=return_intercept,
|
|
tol=tol,
|
|
)
|
|
|
|
if return_intercept:
|
|
coef, intercept = out
|
|
assert_allclose(coef, true_coefs, rtol=0, atol=atol)
|
|
assert_allclose(intercept, true_intercept, rtol=0, atol=atol)
|
|
else:
|
|
assert_allclose(out, true_coefs, rtol=0, atol=atol)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"solver", ["svd", "sparse_cg", "cholesky", "lsqr", "sag", "saga", "lbfgs"]
|
|
)
|
|
def test_dtype_match(solver):
|
|
rng = np.random.RandomState(0)
|
|
alpha = 1.0
|
|
positive = solver == "lbfgs"
|
|
|
|
n_samples, n_features = 6, 5
|
|
X_64 = rng.randn(n_samples, n_features)
|
|
y_64 = rng.randn(n_samples)
|
|
X_32 = X_64.astype(np.float32)
|
|
y_32 = y_64.astype(np.float32)
|
|
|
|
tol = 2 * np.finfo(np.float32).resolution
|
|
# Check type consistency 32bits
|
|
ridge_32 = Ridge(
|
|
alpha=alpha, solver=solver, max_iter=500, tol=tol, positive=positive
|
|
)
|
|
ridge_32.fit(X_32, y_32)
|
|
coef_32 = ridge_32.coef_
|
|
|
|
# Check type consistency 64 bits
|
|
ridge_64 = Ridge(
|
|
alpha=alpha, solver=solver, max_iter=500, tol=tol, positive=positive
|
|
)
|
|
ridge_64.fit(X_64, y_64)
|
|
coef_64 = ridge_64.coef_
|
|
|
|
# Do the actual checks at once for easier debug
|
|
assert coef_32.dtype == X_32.dtype
|
|
assert coef_64.dtype == X_64.dtype
|
|
assert ridge_32.predict(X_32).dtype == X_32.dtype
|
|
assert ridge_64.predict(X_64).dtype == X_64.dtype
|
|
assert_allclose(ridge_32.coef_, ridge_64.coef_, rtol=1e-4, atol=5e-4)
|
|
|
|
|
|
def test_dtype_match_cholesky():
|
|
# Test different alphas in cholesky solver to ensure full coverage.
|
|
# This test is separated from test_dtype_match for clarity.
|
|
rng = np.random.RandomState(0)
|
|
alpha = np.array([1.0, 0.5])
|
|
|
|
n_samples, n_features, n_target = 6, 7, 2
|
|
X_64 = rng.randn(n_samples, n_features)
|
|
y_64 = rng.randn(n_samples, n_target)
|
|
X_32 = X_64.astype(np.float32)
|
|
y_32 = y_64.astype(np.float32)
|
|
|
|
# Check type consistency 32bits
|
|
ridge_32 = Ridge(alpha=alpha, solver="cholesky")
|
|
ridge_32.fit(X_32, y_32)
|
|
coef_32 = ridge_32.coef_
|
|
|
|
# Check type consistency 64 bits
|
|
ridge_64 = Ridge(alpha=alpha, solver="cholesky")
|
|
ridge_64.fit(X_64, y_64)
|
|
coef_64 = ridge_64.coef_
|
|
|
|
# Do all the checks at once, like this is easier to debug
|
|
assert coef_32.dtype == X_32.dtype
|
|
assert coef_64.dtype == X_64.dtype
|
|
assert ridge_32.predict(X_32).dtype == X_32.dtype
|
|
assert ridge_64.predict(X_64).dtype == X_64.dtype
|
|
assert_almost_equal(ridge_32.coef_, ridge_64.coef_, decimal=5)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"solver", ["svd", "cholesky", "lsqr", "sparse_cg", "sag", "saga", "lbfgs"]
|
|
)
|
|
@pytest.mark.parametrize("seed", range(1))
|
|
def test_ridge_regression_dtype_stability(solver, seed):
|
|
random_state = np.random.RandomState(seed)
|
|
n_samples, n_features = 6, 5
|
|
X = random_state.randn(n_samples, n_features)
|
|
coef = random_state.randn(n_features)
|
|
y = np.dot(X, coef) + 0.01 * random_state.randn(n_samples)
|
|
alpha = 1.0
|
|
positive = solver == "lbfgs"
|
|
results = dict()
|
|
# XXX: Sparse CG seems to be far less numerically stable than the
|
|
# others, maybe we should not enable float32 for this one.
|
|
atol = 1e-3 if solver == "sparse_cg" else 1e-5
|
|
for current_dtype in (np.float32, np.float64):
|
|
results[current_dtype] = ridge_regression(
|
|
X.astype(current_dtype),
|
|
y.astype(current_dtype),
|
|
alpha=alpha,
|
|
solver=solver,
|
|
random_state=random_state,
|
|
sample_weight=None,
|
|
positive=positive,
|
|
max_iter=500,
|
|
tol=1e-10,
|
|
return_n_iter=False,
|
|
return_intercept=False,
|
|
)
|
|
|
|
assert results[np.float32].dtype == np.float32
|
|
assert results[np.float64].dtype == np.float64
|
|
assert_allclose(results[np.float32], results[np.float64], atol=atol)
|
|
|
|
|
|
def test_ridge_sag_with_X_fortran():
|
|
# check that Fortran array are converted when using SAG solver
|
|
X, y = make_regression(random_state=42)
|
|
# for the order of X and y to not be C-ordered arrays
|
|
X = np.asfortranarray(X)
|
|
X = X[::2, :]
|
|
y = y[::2]
|
|
Ridge(solver="sag").fit(X, y)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"Classifier, params",
|
|
[
|
|
(RidgeClassifier, {}),
|
|
(RidgeClassifierCV, {"cv": None}),
|
|
(RidgeClassifierCV, {"cv": 3}),
|
|
],
|
|
)
|
|
def test_ridgeclassifier_multilabel(Classifier, params):
|
|
"""Check that multilabel classification is supported and give meaningful
|
|
results."""
|
|
X, y = make_multilabel_classification(n_classes=1, random_state=0)
|
|
y = y.reshape(-1, 1)
|
|
Y = np.concatenate([y, y], axis=1)
|
|
clf = Classifier(**params).fit(X, Y)
|
|
Y_pred = clf.predict(X)
|
|
|
|
assert Y_pred.shape == Y.shape
|
|
assert_array_equal(Y_pred[:, 0], Y_pred[:, 1])
|
|
Ridge(solver="sag").fit(X, y)
|
|
|
|
|
|
@pytest.mark.parametrize("solver", ["auto", "lbfgs"])
|
|
@pytest.mark.parametrize("fit_intercept", [True, False])
|
|
@pytest.mark.parametrize("alpha", [1e-3, 1e-2, 0.1, 1.0])
|
|
def test_ridge_positive_regression_test(solver, fit_intercept, alpha):
|
|
"""Test that positive Ridge finds true positive coefficients."""
|
|
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
|
|
coef = np.array([1, -10])
|
|
if fit_intercept:
|
|
intercept = 20
|
|
y = X.dot(coef) + intercept
|
|
else:
|
|
y = X.dot(coef)
|
|
|
|
model = Ridge(
|
|
alpha=alpha, positive=True, solver=solver, fit_intercept=fit_intercept
|
|
)
|
|
model.fit(X, y)
|
|
assert np.all(model.coef_ >= 0)
|
|
|
|
|
|
@pytest.mark.parametrize("fit_intercept", [True, False])
|
|
@pytest.mark.parametrize("alpha", [1e-3, 1e-2, 0.1, 1.0])
|
|
def test_ridge_ground_truth_positive_test(fit_intercept, alpha):
|
|
"""Test that Ridge w/wo positive converges to the same solution.
|
|
|
|
Ridge with positive=True and positive=False must give the same
|
|
when the ground truth coefs are all positive.
|
|
"""
|
|
rng = np.random.RandomState(42)
|
|
X = rng.randn(300, 100)
|
|
coef = rng.uniform(0.1, 1.0, size=X.shape[1])
|
|
if fit_intercept:
|
|
intercept = 1
|
|
y = X @ coef + intercept
|
|
else:
|
|
y = X @ coef
|
|
y += rng.normal(size=X.shape[0]) * 0.01
|
|
|
|
results = []
|
|
for positive in [True, False]:
|
|
model = Ridge(
|
|
alpha=alpha, positive=positive, fit_intercept=fit_intercept, tol=1e-10
|
|
)
|
|
results.append(model.fit(X, y).coef_)
|
|
assert_allclose(*results, atol=1e-6, rtol=0)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"solver", ["svd", "cholesky", "lsqr", "sparse_cg", "sag", "saga"]
|
|
)
|
|
def test_ridge_positive_error_test(solver):
|
|
"""Test input validation for positive argument in Ridge."""
|
|
alpha = 0.1
|
|
X = np.array([[1, 2], [3, 4]])
|
|
coef = np.array([1, -1])
|
|
y = X @ coef
|
|
|
|
model = Ridge(alpha=alpha, positive=True, solver=solver, fit_intercept=False)
|
|
with pytest.raises(ValueError, match="does not support positive"):
|
|
model.fit(X, y)
|
|
|
|
with pytest.raises(ValueError, match="only 'lbfgs' solver can be used"):
|
|
_, _ = ridge_regression(
|
|
X, y, alpha, positive=True, solver=solver, return_intercept=False
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("alpha", [1e-3, 1e-2, 0.1, 1.0])
|
|
def test_positive_ridge_loss(alpha):
|
|
"""Check ridge loss consistency when positive argument is enabled."""
|
|
X, y = make_regression(n_samples=300, n_features=300, random_state=42)
|
|
alpha = 0.10
|
|
n_checks = 100
|
|
|
|
def ridge_loss(model, random_state=None, noise_scale=1e-8):
|
|
intercept = model.intercept_
|
|
if random_state is not None:
|
|
rng = np.random.RandomState(random_state)
|
|
coef = model.coef_ + rng.uniform(0, noise_scale, size=model.coef_.shape)
|
|
else:
|
|
coef = model.coef_
|
|
|
|
return 0.5 * np.sum((y - X @ coef - intercept) ** 2) + 0.5 * alpha * np.sum(
|
|
coef**2
|
|
)
|
|
|
|
model = Ridge(alpha=alpha).fit(X, y)
|
|
model_positive = Ridge(alpha=alpha, positive=True).fit(X, y)
|
|
|
|
# Check 1:
|
|
# Loss for solution found by Ridge(positive=False)
|
|
# is lower than that for solution found by Ridge(positive=True)
|
|
loss = ridge_loss(model)
|
|
loss_positive = ridge_loss(model_positive)
|
|
assert loss <= loss_positive
|
|
|
|
# Check 2:
|
|
# Loss for solution found by Ridge(positive=True)
|
|
# is lower than that for small random positive perturbation
|
|
# of the positive solution.
|
|
for random_state in range(n_checks):
|
|
loss_perturbed = ridge_loss(model_positive, random_state=random_state)
|
|
assert loss_positive <= loss_perturbed
|
|
|
|
|
|
@pytest.mark.parametrize("alpha", [1e-3, 1e-2, 0.1, 1.0])
|
|
def test_lbfgs_solver_consistency(alpha):
|
|
"""Test that LBGFS gets almost the same coef of svd when positive=False."""
|
|
X, y = make_regression(n_samples=300, n_features=300, random_state=42)
|
|
y = np.expand_dims(y, 1)
|
|
alpha = np.asarray([alpha])
|
|
config = {
|
|
"positive": False,
|
|
"tol": 1e-16,
|
|
"max_iter": 500000,
|
|
}
|
|
|
|
coef_lbfgs = _solve_lbfgs(X, y, alpha, **config)
|
|
coef_cholesky = _solve_svd(X, y, alpha)
|
|
assert_allclose(coef_lbfgs, coef_cholesky, atol=1e-4, rtol=0)
|
|
|
|
|
|
def test_lbfgs_solver_error():
|
|
"""Test that LBFGS solver raises ConvergenceWarning."""
|
|
X = np.array([[1, -1], [1, 1]])
|
|
y = np.array([-1e10, 1e10])
|
|
|
|
model = Ridge(
|
|
alpha=0.01,
|
|
solver="lbfgs",
|
|
fit_intercept=False,
|
|
tol=1e-12,
|
|
positive=True,
|
|
max_iter=1,
|
|
)
|
|
with pytest.warns(ConvergenceWarning, match="lbfgs solver did not converge"):
|
|
model.fit(X, y)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"solver", ["cholesky", "lsqr", "sparse_cg", "svd", "sag", "saga", "lbfgs"]
|
|
)
|
|
def test_ridge_sample_weight_invariance(solver):
|
|
"""Test that Ridge fulfils sample weight invariance.
|
|
|
|
Note that this test is stricter than the common test
|
|
check_sample_weights_invariance alone.
|
|
"""
|
|
params = dict(
|
|
alpha=1.0,
|
|
solver=solver,
|
|
tol=1e-12,
|
|
positive=(solver == "lbfgs"),
|
|
)
|
|
reg = Ridge(**params)
|
|
name = reg.__class__.__name__
|
|
check_sample_weights_invariance(name, reg, kind="ones")
|
|
check_sample_weights_invariance(name, reg, kind="zeros")
|
|
|
|
# Check that duplicating the training dataset is equivalent to multiplying
|
|
# the weights by 2:
|
|
|
|
rng = np.random.RandomState(42)
|
|
X, y = make_regression(
|
|
n_samples=100,
|
|
n_features=300,
|
|
effective_rank=10,
|
|
n_informative=50,
|
|
random_state=rng,
|
|
)
|
|
sw = rng.uniform(low=0.01, high=2, size=X.shape[0])
|
|
X_dup = np.concatenate([X, X], axis=0)
|
|
y_dup = np.concatenate([y, y], axis=0)
|
|
sw_dup = np.concatenate([sw, sw], axis=0)
|
|
|
|
ridge_2sw = Ridge(**params).fit(X, y, sample_weight=2 * sw)
|
|
ridge_dup = Ridge(**params).fit(X_dup, y_dup, sample_weight=sw_dup)
|
|
|
|
assert_allclose(ridge_2sw.coef_, ridge_dup.coef_)
|
|
assert_allclose(ridge_2sw.intercept_, ridge_dup.intercept_)
|