477 lines
15 KiB
Python
477 lines
15 KiB
Python
# Authors: Christian Lorentzen <lorentzen.ch@gmail.com>
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#
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# License: BSD 3 clause
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import re
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import numpy as np
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from numpy.testing import assert_allclose
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import pytest
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import warnings
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from sklearn.base import clone
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from sklearn._loss.glm_distribution import TweedieDistribution
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from sklearn._loss.link import IdentityLink, LogLink
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from sklearn.datasets import make_regression
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from sklearn.linear_model._glm import _GeneralizedLinearRegressor
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from sklearn.linear_model import TweedieRegressor, PoissonRegressor, GammaRegressor
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from sklearn.linear_model import Ridge
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.metrics import d2_tweedie_score
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from sklearn.model_selection import train_test_split
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@pytest.fixture(scope="module")
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def regression_data():
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X, y = make_regression(
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n_samples=107, n_features=10, n_informative=80, noise=0.5, random_state=2
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)
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return X, y
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def test_sample_weights_validation():
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"""Test the raised errors in the validation of sample_weight."""
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# scalar value but not positive
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X = [[1]]
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y = [1]
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weights = 0
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glm = _GeneralizedLinearRegressor()
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# Positive weights are accepted
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glm.fit(X, y, sample_weight=1)
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# 2d array
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weights = [[0]]
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with pytest.raises(ValueError, match="must be 1D array or scalar"):
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glm.fit(X, y, weights)
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# 1d but wrong length
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weights = [1, 0]
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msg = r"sample_weight.shape == \(2,\), expected \(1,\)!"
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with pytest.raises(ValueError, match=msg):
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glm.fit(X, y, weights)
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@pytest.mark.parametrize("fit_intercept", ["not bool", 1, 0, [True]])
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def test_glm_fit_intercept_argument(fit_intercept):
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"""Test GLM for invalid fit_intercept argument."""
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y = np.array([1, 2])
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X = np.array([[1], [1]])
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glm = _GeneralizedLinearRegressor(fit_intercept=fit_intercept)
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with pytest.raises(ValueError, match="fit_intercept must be bool"):
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glm.fit(X, y)
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@pytest.mark.parametrize("solver", ["not a solver", 1, [1]])
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def test_glm_solver_argument(solver):
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"""Test GLM for invalid solver argument."""
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y = np.array([1, 2])
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X = np.array([[1], [2]])
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glm = _GeneralizedLinearRegressor(solver=solver)
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with pytest.raises(ValueError):
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glm.fit(X, y)
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@pytest.mark.parametrize(
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"Estimator",
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[_GeneralizedLinearRegressor, PoissonRegressor, GammaRegressor, TweedieRegressor],
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)
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@pytest.mark.parametrize(
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"params, err_type, err_msg",
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[
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({"max_iter": 0}, ValueError, "max_iter == 0, must be >= 1"),
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({"max_iter": -1}, ValueError, "max_iter == -1, must be >= 1"),
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(
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{"max_iter": "not a number"},
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TypeError,
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"max_iter must be an instance of int, not str",
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),
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(
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{"max_iter": [1]},
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TypeError,
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"max_iter must be an instance of int, not list",
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),
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(
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{"max_iter": 5.5},
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TypeError,
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"max_iter must be an instance of int, not float",
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),
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({"alpha": -1}, ValueError, "alpha == -1, must be >= 0.0"),
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(
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{"alpha": "1"},
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TypeError,
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"alpha must be an instance of float, not str",
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),
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({"tol": -1.0}, ValueError, "tol == -1.0, must be > 0."),
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({"tol": 0.0}, ValueError, "tol == 0.0, must be > 0.0"),
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({"tol": 0}, ValueError, "tol == 0, must be > 0.0"),
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(
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{"tol": "1"},
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TypeError,
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"tol must be an instance of float, not str",
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),
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(
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{"tol": [1e-3]},
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TypeError,
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"tol must be an instance of float, not list",
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),
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({"verbose": -1}, ValueError, "verbose == -1, must be >= 0."),
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(
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{"verbose": "1"},
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TypeError,
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"verbose must be an instance of int, not str",
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),
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(
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{"verbose": 1.0},
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TypeError,
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"verbose must be an instance of int, not float",
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),
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],
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)
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def test_glm_scalar_argument(Estimator, params, err_type, err_msg):
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"""Test GLM for invalid parameter arguments."""
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y = np.array([1, 2])
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X = np.array([[1], [2]])
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glm = Estimator(**params)
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with pytest.raises(err_type, match=err_msg):
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glm.fit(X, y)
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@pytest.mark.parametrize("warm_start", ["not bool", 1, 0, [True]])
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def test_glm_warm_start_argument(warm_start):
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"""Test GLM for invalid warm_start argument."""
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y = np.array([1, 2])
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X = np.array([[1], [1]])
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glm = _GeneralizedLinearRegressor(warm_start=warm_start)
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with pytest.raises(ValueError, match="warm_start must be bool"):
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glm.fit(X, y)
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@pytest.mark.parametrize(
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"glm",
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[
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TweedieRegressor(power=3),
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PoissonRegressor(),
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GammaRegressor(),
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TweedieRegressor(power=1.5),
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],
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)
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def test_glm_wrong_y_range(glm):
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y = np.array([-1, 2])
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X = np.array([[1], [1]])
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msg = r"Some value\(s\) of y are out of the valid range of the loss"
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with pytest.raises(ValueError, match=msg):
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glm.fit(X, y)
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@pytest.mark.parametrize("fit_intercept", [False, True])
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def test_glm_identity_regression(fit_intercept):
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"""Test GLM regression with identity link on a simple dataset."""
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coef = [1.0, 2.0]
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X = np.array([[1, 1, 1, 1, 1], [0, 1, 2, 3, 4]]).T
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y = np.dot(X, coef)
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glm = _GeneralizedLinearRegressor(
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alpha=0,
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fit_intercept=fit_intercept,
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tol=1e-12,
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)
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if fit_intercept:
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glm.fit(X[:, 1:], y)
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assert_allclose(glm.coef_, coef[1:], rtol=1e-10)
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assert_allclose(glm.intercept_, coef[0], rtol=1e-10)
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else:
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glm.fit(X, y)
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assert_allclose(glm.coef_, coef, rtol=1e-12)
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@pytest.mark.parametrize("fit_intercept", [False, True])
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@pytest.mark.parametrize("alpha", [0.0, 1.0])
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@pytest.mark.parametrize(
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"GLMEstimator", [_GeneralizedLinearRegressor, PoissonRegressor, GammaRegressor]
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)
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def test_glm_sample_weight_consistency(fit_intercept, alpha, GLMEstimator):
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"""Test that the impact of sample_weight is consistent"""
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rng = np.random.RandomState(0)
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n_samples, n_features = 10, 5
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X = rng.rand(n_samples, n_features)
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y = rng.rand(n_samples)
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glm_params = dict(alpha=alpha, fit_intercept=fit_intercept)
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glm = GLMEstimator(**glm_params).fit(X, y)
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coef = glm.coef_.copy()
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# sample_weight=np.ones(..) should be equivalent to sample_weight=None
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sample_weight = np.ones(y.shape)
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glm.fit(X, y, sample_weight=sample_weight)
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assert_allclose(glm.coef_, coef, rtol=1e-12)
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# sample_weight are normalized to 1 so, scaling them has no effect
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sample_weight = 2 * np.ones(y.shape)
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glm.fit(X, y, sample_weight=sample_weight)
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assert_allclose(glm.coef_, coef, rtol=1e-12)
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# setting one element of sample_weight to 0 is equivalent to removing
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# the corresponding sample
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sample_weight = np.ones(y.shape)
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sample_weight[-1] = 0
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glm.fit(X, y, sample_weight=sample_weight)
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coef1 = glm.coef_.copy()
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glm.fit(X[:-1], y[:-1])
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assert_allclose(glm.coef_, coef1, rtol=1e-12)
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# check that multiplying sample_weight by 2 is equivalent
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# to repeating corresponding samples twice
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X2 = np.concatenate([X, X[: n_samples // 2]], axis=0)
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y2 = np.concatenate([y, y[: n_samples // 2]])
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sample_weight_1 = np.ones(len(y))
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sample_weight_1[: n_samples // 2] = 2
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glm1 = GLMEstimator(**glm_params).fit(X, y, sample_weight=sample_weight_1)
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glm2 = GLMEstimator(**glm_params).fit(X2, y2, sample_weight=None)
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assert_allclose(glm1.coef_, glm2.coef_)
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@pytest.mark.parametrize("fit_intercept", [True, False])
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@pytest.mark.parametrize(
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"estimator",
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[
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PoissonRegressor(),
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GammaRegressor(),
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TweedieRegressor(power=3.0),
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TweedieRegressor(power=0, link="log"),
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TweedieRegressor(power=1.5),
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TweedieRegressor(power=4.5),
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],
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)
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def test_glm_log_regression(fit_intercept, estimator):
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"""Test GLM regression with log link on a simple dataset."""
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coef = [0.2, -0.1]
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X = np.array([[0, 1, 2, 3, 4], [1, 1, 1, 1, 1]]).T
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y = np.exp(np.dot(X, coef))
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glm = clone(estimator).set_params(
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alpha=0,
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fit_intercept=fit_intercept,
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tol=1e-8,
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)
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if fit_intercept:
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res = glm.fit(X[:, :-1], y)
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assert_allclose(res.coef_, coef[:-1], rtol=1e-6)
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assert_allclose(res.intercept_, coef[-1], rtol=1e-6)
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else:
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res = glm.fit(X, y)
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assert_allclose(res.coef_, coef, rtol=2e-6)
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@pytest.mark.parametrize("fit_intercept", [True, False])
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def test_warm_start(fit_intercept):
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n_samples, n_features = 110, 10
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X, y = make_regression(
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n_samples=n_samples,
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n_features=n_features,
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n_informative=n_features - 2,
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noise=0.5,
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random_state=42,
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)
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glm1 = _GeneralizedLinearRegressor(
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warm_start=False, fit_intercept=fit_intercept, max_iter=1000
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)
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glm1.fit(X, y)
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glm2 = _GeneralizedLinearRegressor(
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warm_start=True, fit_intercept=fit_intercept, max_iter=1
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)
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# As we intentionally set max_iter=1, L-BFGS-B will issue a
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# ConvergenceWarning which we here simply ignore.
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", category=ConvergenceWarning)
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glm2.fit(X, y)
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assert glm1.score(X, y) > glm2.score(X, y)
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glm2.set_params(max_iter=1000)
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glm2.fit(X, y)
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# The two model are not exactly identical since the lbfgs solver
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# computes the approximate hessian from previous iterations, which
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# will not be strictly identical in the case of a warm start.
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assert_allclose(glm1.coef_, glm2.coef_, rtol=1e-5)
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assert_allclose(glm1.score(X, y), glm2.score(X, y), rtol=1e-4)
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# FIXME: 'normalize' to be removed in 1.2 in LinearRegression
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@pytest.mark.filterwarnings("ignore:'normalize' was deprecated")
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@pytest.mark.parametrize("n_samples, n_features", [(100, 10), (10, 100)])
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@pytest.mark.parametrize("fit_intercept", [True, False])
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@pytest.mark.parametrize("sample_weight", [None, True])
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def test_normal_ridge_comparison(
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n_samples, n_features, fit_intercept, sample_weight, request
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):
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"""Compare with Ridge regression for Normal distributions."""
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test_size = 10
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X, y = make_regression(
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n_samples=n_samples + test_size,
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n_features=n_features,
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n_informative=n_features - 2,
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noise=0.5,
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random_state=42,
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)
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if n_samples > n_features:
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ridge_params = {"solver": "svd"}
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else:
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ridge_params = {"solver": "saga", "max_iter": 1000000, "tol": 1e-7}
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(
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X_train,
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X_test,
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y_train,
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y_test,
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) = train_test_split(X, y, test_size=test_size, random_state=0)
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alpha = 1.0
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if sample_weight is None:
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sw_train = None
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alpha_ridge = alpha * n_samples
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else:
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sw_train = np.random.RandomState(0).rand(len(y_train))
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alpha_ridge = alpha * sw_train.sum()
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# GLM has 1/(2*n) * Loss + 1/2*L2, Ridge has Loss + L2
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ridge = Ridge(
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alpha=alpha_ridge,
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normalize=False,
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random_state=42,
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fit_intercept=fit_intercept,
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**ridge_params,
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)
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ridge.fit(X_train, y_train, sample_weight=sw_train)
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glm = _GeneralizedLinearRegressor(
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alpha=alpha,
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fit_intercept=fit_intercept,
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max_iter=300,
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tol=1e-5,
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)
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glm.fit(X_train, y_train, sample_weight=sw_train)
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assert glm.coef_.shape == (X.shape[1],)
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assert_allclose(glm.coef_, ridge.coef_, atol=5e-5)
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assert_allclose(glm.intercept_, ridge.intercept_, rtol=1e-5)
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assert_allclose(glm.predict(X_train), ridge.predict(X_train), rtol=2e-4)
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assert_allclose(glm.predict(X_test), ridge.predict(X_test), rtol=2e-4)
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def test_poisson_glmnet():
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"""Compare Poisson regression with L2 regularization and LogLink to glmnet"""
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# library("glmnet")
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# options(digits=10)
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# df <- data.frame(a=c(-2,-1,1,2), b=c(0,0,1,1), y=c(0,1,1,2))
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# x <- data.matrix(df[,c("a", "b")])
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# y <- df$y
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# fit <- glmnet(x=x, y=y, alpha=0, intercept=T, family="poisson",
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# standardize=F, thresh=1e-10, nlambda=10000)
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# coef(fit, s=1)
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# (Intercept) -0.12889386979
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# a 0.29019207995
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# b 0.03741173122
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X = np.array([[-2, -1, 1, 2], [0, 0, 1, 1]]).T
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y = np.array([0, 1, 1, 2])
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glm = PoissonRegressor(
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alpha=1,
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fit_intercept=True,
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tol=1e-7,
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max_iter=300,
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)
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glm.fit(X, y)
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assert_allclose(glm.intercept_, -0.12889386979, rtol=1e-5)
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assert_allclose(glm.coef_, [0.29019207995, 0.03741173122], rtol=1e-5)
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def test_convergence_warning(regression_data):
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X, y = regression_data
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est = _GeneralizedLinearRegressor(max_iter=1, tol=1e-20)
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with pytest.warns(ConvergenceWarning):
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est.fit(X, y)
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@pytest.mark.parametrize(
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"name, link_class", [("identity", IdentityLink), ("log", LogLink)]
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)
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def test_tweedie_link_argument(name, link_class):
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"""Test GLM link argument set as string."""
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y = np.array([0.1, 0.5]) # in range of all distributions
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X = np.array([[1], [2]])
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glm = TweedieRegressor(power=1, link=name).fit(X, y)
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assert isinstance(glm._linear_loss.base_loss.link, link_class)
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glm = TweedieRegressor(power=1, link="not a link")
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with pytest.raises(
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ValueError,
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match=re.escape("The link must be an element of ['auto', 'identity', 'log']"),
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):
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glm.fit(X, y)
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@pytest.mark.parametrize(
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"power, expected_link_class",
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[
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(0, IdentityLink), # normal
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(1, LogLink), # poisson
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(2, LogLink), # gamma
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(3, LogLink), # inverse-gaussian
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],
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)
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def test_tweedie_link_auto(power, expected_link_class):
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"""Test that link='auto' delivers the expected link function"""
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y = np.array([0.1, 0.5]) # in range of all distributions
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X = np.array([[1], [2]])
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glm = TweedieRegressor(link="auto", power=power).fit(X, y)
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assert isinstance(glm._linear_loss.base_loss.link, expected_link_class)
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@pytest.mark.parametrize("power", [0, 1, 1.5, 2, 3])
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@pytest.mark.parametrize("link", ["log", "identity"])
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def test_tweedie_score(regression_data, power, link):
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"""Test that GLM score equals d2_tweedie_score for Tweedie losses."""
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X, y = regression_data
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# make y positive
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y = np.abs(y) + 1.0
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glm = TweedieRegressor(power=power, link=link).fit(X, y)
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assert glm.score(X, y) == pytest.approx(
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d2_tweedie_score(y, glm.predict(X), power=power)
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)
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@pytest.mark.parametrize(
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"estimator, value",
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[
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(PoissonRegressor(), True),
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(GammaRegressor(), True),
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(TweedieRegressor(power=1.5), True),
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(TweedieRegressor(power=0), False),
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],
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)
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def test_tags(estimator, value):
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assert estimator._get_tags()["requires_positive_y"] is value
|
|
|
|
|
|
# TODO(1.3): remove
|
|
@pytest.mark.parametrize(
|
|
"est, family",
|
|
[
|
|
(PoissonRegressor(), "poisson"),
|
|
(GammaRegressor(), "gamma"),
|
|
(TweedieRegressor(), TweedieDistribution()),
|
|
(TweedieRegressor(power=2), TweedieDistribution(power=2)),
|
|
(TweedieRegressor(power=3), TweedieDistribution(power=3)),
|
|
],
|
|
)
|
|
def test_family_deprecation(est, family):
|
|
"""Test backward compatibility of the family property."""
|
|
with pytest.warns(FutureWarning, match="`family` was deprecated"):
|
|
if isinstance(family, str):
|
|
assert est.family == family
|
|
else:
|
|
assert est.family.__class__ == family.__class__
|
|
assert est.family.power == family.power
|