110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
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import numpy as np
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from numpy.testing import assert_allclose, assert_array_equal
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import pytest
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from sklearn._loss.link import (
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_LINKS,
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_inclusive_low_high,
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MultinomialLogit,
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Interval,
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)
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LINK_FUNCTIONS = list(_LINKS.values())
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def test_interval_raises():
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"""Test that interval with low > high raises ValueError."""
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with pytest.raises(
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ValueError, match="One must have low <= high; got low=1, high=0."
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):
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Interval(1, 0, False, False)
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@pytest.mark.parametrize(
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"interval",
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[
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Interval(0, 1, False, False),
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Interval(0, 1, False, True),
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Interval(0, 1, True, False),
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Interval(0, 1, True, True),
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Interval(-np.inf, np.inf, False, False),
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Interval(-np.inf, np.inf, False, True),
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Interval(-np.inf, np.inf, True, False),
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Interval(-np.inf, np.inf, True, True),
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Interval(-10, -1, False, False),
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Interval(-10, -1, False, True),
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Interval(-10, -1, True, False),
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Interval(-10, -1, True, True),
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],
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)
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def test_is_in_range(interval):
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# make sure low and high are always within the interval, used for linspace
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low, high = _inclusive_low_high(interval)
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x = np.linspace(low, high, num=10)
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assert interval.includes(x)
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# x contains lower bound
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assert interval.includes(np.r_[x, interval.low]) == interval.low_inclusive
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# x contains upper bound
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assert interval.includes(np.r_[x, interval.high]) == interval.high_inclusive
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# x contains upper and lower bound
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assert interval.includes(np.r_[x, interval.low, interval.high]) == (
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interval.low_inclusive and interval.high_inclusive
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)
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@pytest.mark.parametrize("link", LINK_FUNCTIONS)
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def test_link_inverse_identity(link, global_random_seed):
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# Test that link of inverse gives identity.
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rng = np.random.RandomState(global_random_seed)
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link = link()
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n_samples, n_classes = 100, None
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# The values for `raw_prediction` are limited from -20 to 20 because in the
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# class `LogitLink` the term `expit(x)` comes very close to 1 for large
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# positive x and therefore loses precision.
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if link.is_multiclass:
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n_classes = 10
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raw_prediction = rng.uniform(low=-20, high=20, size=(n_samples, n_classes))
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if isinstance(link, MultinomialLogit):
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raw_prediction = link.symmetrize_raw_prediction(raw_prediction)
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else:
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raw_prediction = rng.uniform(low=-20, high=20, size=(n_samples))
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assert_allclose(link.link(link.inverse(raw_prediction)), raw_prediction)
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y_pred = link.inverse(raw_prediction)
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assert_allclose(link.inverse(link.link(y_pred)), y_pred)
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@pytest.mark.parametrize("link", LINK_FUNCTIONS)
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def test_link_out_argument(link):
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# Test that out argument gets assigned the result.
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rng = np.random.RandomState(42)
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link = link()
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n_samples, n_classes = 100, None
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if link.is_multiclass:
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n_classes = 10
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raw_prediction = rng.normal(loc=0, scale=10, size=(n_samples, n_classes))
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if isinstance(link, MultinomialLogit):
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raw_prediction = link.symmetrize_raw_prediction(raw_prediction)
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else:
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# So far, the valid interval of raw_prediction is (-inf, inf) and
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# we do not need to distinguish.
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raw_prediction = rng.normal(loc=0, scale=10, size=(n_samples))
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y_pred = link.inverse(raw_prediction, out=None)
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out = np.empty_like(raw_prediction)
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y_pred_2 = link.inverse(raw_prediction, out=out)
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assert_allclose(y_pred, out)
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assert_array_equal(out, y_pred_2)
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assert np.shares_memory(out, y_pred_2)
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out = np.empty_like(y_pred)
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raw_prediction_2 = link.link(y_pred, out=out)
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assert_allclose(raw_prediction, out)
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assert_array_equal(out, raw_prediction_2)
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assert np.shares_memory(out, raw_prediction_2)
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