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