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# License: BSD 3 clause
import inspect
import numpy as np
import pytest
from sklearn.base import is_classifier
from sklearn.datasets import make_low_rank_matrix
from sklearn.linear_model import (
ARDRegression,
BayesianRidge,
ElasticNet,
ElasticNetCV,
Lars,
LarsCV,
Lasso,
LassoCV,
LassoLarsCV,
LassoLarsIC,
LinearRegression,
LogisticRegression,
LogisticRegressionCV,
MultiTaskElasticNet,
MultiTaskElasticNetCV,
MultiTaskLasso,
MultiTaskLassoCV,
OrthogonalMatchingPursuit,
OrthogonalMatchingPursuitCV,
PoissonRegressor,
Ridge,
RidgeCV,
SGDRegressor,
TweedieRegressor,
)
# Note: GammaRegressor() and TweedieRegressor(power != 1) have a non-canonical link.
@pytest.mark.parametrize(
"model",
[
ARDRegression(),
BayesianRidge(),
ElasticNet(),
ElasticNetCV(),
Lars(),
LarsCV(),
Lasso(),
LassoCV(),
LassoLarsCV(),
LassoLarsIC(),
LinearRegression(),
# TODO: FIx SAGA which fails badly with sample_weights.
# This is a known limitation, see:
# https://github.com/scikit-learn/scikit-learn/issues/21305
pytest.param(
LogisticRegression(
penalty="elasticnet", solver="saga", l1_ratio=0.5, tol=1e-15
),
marks=pytest.mark.xfail(reason="Missing importance sampling scheme"),
),
LogisticRegressionCV(tol=1e-6),
MultiTaskElasticNet(),
MultiTaskElasticNetCV(),
MultiTaskLasso(),
MultiTaskLassoCV(),
OrthogonalMatchingPursuit(),
OrthogonalMatchingPursuitCV(),
PoissonRegressor(),
Ridge(),
RidgeCV(),
pytest.param(
SGDRegressor(tol=1e-15),
marks=pytest.mark.xfail(reason="Insufficient precision."),
),
SGDRegressor(penalty="elasticnet", max_iter=10_000),
TweedieRegressor(power=0), # same as Ridge
],
ids=lambda x: x.__class__.__name__,
)
@pytest.mark.parametrize("with_sample_weight", [False, True])
def test_balance_property(model, with_sample_weight, global_random_seed):
# Test that sum(y_predicted) == sum(y_observed) on the training set.
# This must hold for all linear models with deviance of an exponential disperson
# family as loss and the corresponding canonical link if fit_intercept=True.
# Examples:
# - squared error and identity link (most linear models)
# - Poisson deviance with log link
# - log loss with logit link
# This is known as balance property or unconditional calibration/unbiasedness.
# For reference, see Corollary 3.18, 3.20 and Chapter 5.1.5 of
# M.V. Wuthrich and M. Merz, "Statistical Foundations of Actuarial Learning and its
# Applications" (June 3, 2022). http://doi.org/10.2139/ssrn.3822407
if (
with_sample_weight
and "sample_weight" not in inspect.signature(model.fit).parameters.keys()
):
pytest.skip("Estimator does not support sample_weight.")
rel = 2e-4 # test precision
if isinstance(model, SGDRegressor):
rel = 1e-1
elif hasattr(model, "solver") and model.solver == "saga":
rel = 1e-2
rng = np.random.RandomState(global_random_seed)
n_train, n_features, n_targets = 100, 10, None
if isinstance(
model,
(MultiTaskElasticNet, MultiTaskElasticNetCV, MultiTaskLasso, MultiTaskLassoCV),
):
n_targets = 3
X = make_low_rank_matrix(n_samples=n_train, n_features=n_features, random_state=rng)
if n_targets:
coef = (
rng.uniform(low=-2, high=2, size=(n_features, n_targets))
/ np.max(X, axis=0)[:, None]
)
else:
coef = rng.uniform(low=-2, high=2, size=n_features) / np.max(X, axis=0)
expectation = np.exp(X @ coef + 0.5)
y = rng.poisson(lam=expectation) + 1 # strict positive, i.e. y > 0
if is_classifier(model):
y = (y > expectation + 1).astype(np.float64)
if with_sample_weight:
sw = rng.uniform(low=1, high=10, size=y.shape[0])
else:
sw = None
model.set_params(fit_intercept=True) # to be sure
if with_sample_weight:
model.fit(X, y, sample_weight=sw)
else:
model.fit(X, y)
# Assert balance property.
if is_classifier(model):
assert np.average(model.predict_proba(X)[:, 1], weights=sw) == pytest.approx(
np.average(y, weights=sw), rel=rel
)
else:
assert np.average(model.predict(X), weights=sw, axis=0) == pytest.approx(
np.average(y, weights=sw, axis=0), rel=rel
)
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