Adding all project files

venv/Lib/site-packages/sklearn/linear_model/tests/test_common.py

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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(),
+        MultiTaskElasticNet(),
+        MultiTaskElasticNetCV(),
+        MultiTaskLasso(),
+        MultiTaskLassoCV(),
+        OrthogonalMatchingPursuit(),
+        OrthogonalMatchingPursuitCV(),
+        PoissonRegressor(),
+        Ridge(),
+        RidgeCV(),
+        pytest.param(
+            SGDRegressor(tol=1e-15),
+            marks=pytest.mark.xfail(reason="Unsufficient 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
+        )