62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
"""
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Testing for the gradient boosting loss functions and initial estimators.
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"""
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import numpy as np
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from numpy.testing import assert_array_equal
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from numpy.testing import assert_almost_equal
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from numpy.testing import assert_equal
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from nose.tools import assert_raises
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from sklearn.ensemble.gradient_boosting import BinomialDeviance
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from sklearn.ensemble.gradient_boosting import LogOddsEstimator
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def test_binomial_deviance():
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"""Check binomial deviance loss.
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Check against alternative definitions in ESLII.
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"""
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bd = BinomialDeviance(2)
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# pred has the same BD for y in {0, 1}
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assert_equal(bd(np.array([0.0]), np.array([0.0])),
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bd(np.array([1.0]), np.array([0.0])))
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assert_almost_equal(bd(np.array([1.0, 1.0, 1.0]),
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np.array([100.0, 100.0, 100.0])),
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0.0)
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assert_almost_equal(bd(np.array([1.0, 0.0, 0.0]),
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np.array([100.0, -100.0, -100.0])), 0)
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# check if same results as alternative definition of deviance (from ESLII)
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alt_dev = lambda y, pred: np.mean(np.logaddexp(0.0, -2.0 *
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(2.0 * y - 1) * pred))
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test_data = [(np.array([1.0, 1.0, 1.0]), np.array([100.0, 100.0, 100.0])),
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(np.array([0.0, 0.0, 0.0]), np.array([100.0, 100.0, 100.0])),
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(np.array([0.0, 0.0, 0.0]),
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np.array([-100.0, -100.0, -100.0])),
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(np.array([1.0, 1.0, 1.0]),
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np.array([-100.0, -100.0, -100.0]))]
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for datum in test_data:
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assert_almost_equal(bd(*datum), alt_dev(*datum))
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# check the gradient against the
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alt_ng = lambda y, pred: (2 * y - 1) / (1 + np.exp(2 * (2 * y - 1) * pred))
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for datum in test_data:
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assert_almost_equal(bd.negative_gradient(*datum), alt_ng(*datum))
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def test_log_odds_estimator():
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"""Check log odds estimator. """
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est = LogOddsEstimator()
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assert_raises(ValueError, est.fit, None, np.array([1]))
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est.fit(None, np.array([1.0, 0.0]))
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assert_equal(est.prior, 0.0)
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assert_array_equal(est.predict(np.array([[1.0], [1.0]])),
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np.array([[0.0], [0.0]]))
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