703 lines
22 KiB
Python
703 lines
22 KiB
Python
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import copy
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import pickle
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import warnings
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import numpy as np
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import pytest
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from scipy.special import expit
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import sklearn
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from sklearn.datasets import make_regression
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from sklearn.isotonic import (
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IsotonicRegression,
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_make_unique,
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check_increasing,
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isotonic_regression,
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)
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from sklearn.utils import shuffle
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from sklearn.utils._testing import (
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assert_allclose,
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assert_array_almost_equal,
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assert_array_equal,
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)
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from sklearn.utils.validation import check_array
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def test_permutation_invariance():
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# check that fit is permutation invariant.
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# regression test of missing sorting of sample-weights
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ir = IsotonicRegression()
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x = [1, 2, 3, 4, 5, 6, 7]
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y = [1, 41, 51, 1, 2, 5, 24]
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sample_weight = [1, 2, 3, 4, 5, 6, 7]
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x_s, y_s, sample_weight_s = shuffle(x, y, sample_weight, random_state=0)
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y_transformed = ir.fit_transform(x, y, sample_weight=sample_weight)
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y_transformed_s = ir.fit(x_s, y_s, sample_weight=sample_weight_s).transform(x)
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assert_array_equal(y_transformed, y_transformed_s)
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def test_check_increasing_small_number_of_samples():
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x = [0, 1, 2]
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y = [1, 1.1, 1.05]
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with warnings.catch_warnings():
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warnings.simplefilter("error", UserWarning)
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is_increasing = check_increasing(x, y)
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assert is_increasing
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def test_check_increasing_up():
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x = [0, 1, 2, 3, 4, 5]
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y = [0, 1.5, 2.77, 8.99, 8.99, 50]
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# Check that we got increasing=True and no warnings
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with warnings.catch_warnings():
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warnings.simplefilter("error", UserWarning)
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is_increasing = check_increasing(x, y)
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assert is_increasing
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def test_check_increasing_up_extreme():
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x = [0, 1, 2, 3, 4, 5]
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y = [0, 1, 2, 3, 4, 5]
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# Check that we got increasing=True and no warnings
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with warnings.catch_warnings():
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warnings.simplefilter("error", UserWarning)
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is_increasing = check_increasing(x, y)
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assert is_increasing
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def test_check_increasing_down():
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x = [0, 1, 2, 3, 4, 5]
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y = [0, -1.5, -2.77, -8.99, -8.99, -50]
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# Check that we got increasing=False and no warnings
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with warnings.catch_warnings():
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warnings.simplefilter("error", UserWarning)
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is_increasing = check_increasing(x, y)
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assert not is_increasing
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def test_check_increasing_down_extreme():
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x = [0, 1, 2, 3, 4, 5]
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y = [0, -1, -2, -3, -4, -5]
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# Check that we got increasing=False and no warnings
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with warnings.catch_warnings():
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warnings.simplefilter("error", UserWarning)
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is_increasing = check_increasing(x, y)
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assert not is_increasing
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def test_check_ci_warn():
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x = [0, 1, 2, 3, 4, 5]
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y = [0, -1, 2, -3, 4, -5]
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# Check that we got increasing=False and CI interval warning
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msg = "interval"
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with pytest.warns(UserWarning, match=msg):
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is_increasing = check_increasing(x, y)
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assert not is_increasing
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def test_isotonic_regression():
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y = np.array([3, 7, 5, 9, 8, 7, 10])
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y_ = np.array([3, 6, 6, 8, 8, 8, 10])
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assert_array_equal(y_, isotonic_regression(y))
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y = np.array([10, 0, 2])
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y_ = np.array([4, 4, 4])
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assert_array_equal(y_, isotonic_regression(y))
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x = np.arange(len(y))
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ir = IsotonicRegression(y_min=0.0, y_max=1.0)
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ir.fit(x, y)
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assert_array_equal(ir.fit(x, y).transform(x), ir.fit_transform(x, y))
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assert_array_equal(ir.transform(x), ir.predict(x))
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# check that it is immune to permutation
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perm = np.random.permutation(len(y))
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ir = IsotonicRegression(y_min=0.0, y_max=1.0)
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assert_array_equal(ir.fit_transform(x[perm], y[perm]), ir.fit_transform(x, y)[perm])
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assert_array_equal(ir.transform(x[perm]), ir.transform(x)[perm])
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# check we don't crash when all x are equal:
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ir = IsotonicRegression()
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assert_array_equal(ir.fit_transform(np.ones(len(x)), y), np.mean(y))
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def test_isotonic_regression_ties_min():
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# Setup examples with ties on minimum
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x = [1, 1, 2, 3, 4, 5]
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y = [1, 2, 3, 4, 5, 6]
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y_true = [1.5, 1.5, 3, 4, 5, 6]
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# Check that we get identical results for fit/transform and fit_transform
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ir = IsotonicRegression()
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ir.fit(x, y)
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assert_array_equal(ir.fit(x, y).transform(x), ir.fit_transform(x, y))
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assert_array_equal(y_true, ir.fit_transform(x, y))
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def test_isotonic_regression_ties_max():
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# Setup examples with ties on maximum
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x = [1, 2, 3, 4, 5, 5]
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y = [1, 2, 3, 4, 5, 6]
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y_true = [1, 2, 3, 4, 5.5, 5.5]
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# Check that we get identical results for fit/transform and fit_transform
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ir = IsotonicRegression()
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ir.fit(x, y)
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assert_array_equal(ir.fit(x, y).transform(x), ir.fit_transform(x, y))
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assert_array_equal(y_true, ir.fit_transform(x, y))
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def test_isotonic_regression_ties_secondary_():
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"""
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Test isotonic regression fit, transform and fit_transform
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against the "secondary" ties method and "pituitary" data from R
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"isotone" package, as detailed in: J. d. Leeuw, K. Hornik, P. Mair,
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Isotone Optimization in R: Pool-Adjacent-Violators Algorithm
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(PAVA) and Active Set Methods
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Set values based on pituitary example and
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the following R command detailed in the paper above:
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> library("isotone")
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> data("pituitary")
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> res1 <- gpava(pituitary$age, pituitary$size, ties="secondary")
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> res1$x
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`isotone` version: 1.0-2, 2014-09-07
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R version: R version 3.1.1 (2014-07-10)
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"""
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x = [8, 8, 8, 10, 10, 10, 12, 12, 12, 14, 14]
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y = [21, 23.5, 23, 24, 21, 25, 21.5, 22, 19, 23.5, 25]
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y_true = [
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22.22222,
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22.22222,
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22.22222,
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22.22222,
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22.22222,
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22.22222,
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22.22222,
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22.22222,
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22.22222,
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24.25,
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24.25,
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]
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# Check fit, transform and fit_transform
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ir = IsotonicRegression()
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ir.fit(x, y)
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assert_array_almost_equal(ir.transform(x), y_true, 4)
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assert_array_almost_equal(ir.fit_transform(x, y), y_true, 4)
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def test_isotonic_regression_with_ties_in_differently_sized_groups():
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"""
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Non-regression test to handle issue 9432:
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https://github.com/scikit-learn/scikit-learn/issues/9432
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Compare against output in R:
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> library("isotone")
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> x <- c(0, 1, 1, 2, 3, 4)
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> y <- c(0, 0, 1, 0, 0, 1)
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> res1 <- gpava(x, y, ties="secondary")
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> res1$x
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`isotone` version: 1.1-0, 2015-07-24
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R version: R version 3.3.2 (2016-10-31)
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"""
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x = np.array([0, 1, 1, 2, 3, 4])
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y = np.array([0, 0, 1, 0, 0, 1])
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y_true = np.array([0.0, 0.25, 0.25, 0.25, 0.25, 1.0])
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ir = IsotonicRegression()
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ir.fit(x, y)
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assert_array_almost_equal(ir.transform(x), y_true)
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assert_array_almost_equal(ir.fit_transform(x, y), y_true)
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def test_isotonic_regression_reversed():
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y = np.array([10, 9, 10, 7, 6, 6.1, 5])
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y_ = IsotonicRegression(increasing=False).fit_transform(np.arange(len(y)), y)
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assert_array_equal(np.ones(y_[:-1].shape), ((y_[:-1] - y_[1:]) >= 0))
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def test_isotonic_regression_auto_decreasing():
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# Set y and x for decreasing
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y = np.array([10, 9, 10, 7, 6, 6.1, 5])
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x = np.arange(len(y))
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# Create model and fit_transform
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ir = IsotonicRegression(increasing="auto")
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter("always")
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y_ = ir.fit_transform(x, y)
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# work-around for pearson divide warnings in scipy <= 0.17.0
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assert all(["invalid value encountered in " in str(warn.message) for warn in w])
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# Check that relationship decreases
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is_increasing = y_[0] < y_[-1]
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assert not is_increasing
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def test_isotonic_regression_auto_increasing():
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# Set y and x for decreasing
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y = np.array([5, 6.1, 6, 7, 10, 9, 10])
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x = np.arange(len(y))
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# Create model and fit_transform
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ir = IsotonicRegression(increasing="auto")
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter("always")
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y_ = ir.fit_transform(x, y)
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# work-around for pearson divide warnings in scipy <= 0.17.0
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assert all(["invalid value encountered in " in str(warn.message) for warn in w])
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# Check that relationship increases
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is_increasing = y_[0] < y_[-1]
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assert is_increasing
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def test_assert_raises_exceptions():
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ir = IsotonicRegression()
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rng = np.random.RandomState(42)
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msg = "Found input variables with inconsistent numbers of samples"
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with pytest.raises(ValueError, match=msg):
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ir.fit([0, 1, 2], [5, 7, 3], [0.1, 0.6])
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with pytest.raises(ValueError, match=msg):
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ir.fit([0, 1, 2], [5, 7])
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msg = "X should be a 1d array"
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with pytest.raises(ValueError, match=msg):
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ir.fit(rng.randn(3, 10), [0, 1, 2])
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msg = "Isotonic regression input X should be a 1d array"
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with pytest.raises(ValueError, match=msg):
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ir.transform(rng.randn(3, 10))
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def test_isotonic_sample_weight_parameter_default_value():
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# check if default value of sample_weight parameter is one
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ir = IsotonicRegression()
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# random test data
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rng = np.random.RandomState(42)
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n = 100
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x = np.arange(n)
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y = rng.randint(-50, 50, size=(n,)) + 50.0 * np.log(1 + np.arange(n))
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# check if value is correctly used
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weights = np.ones(n)
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y_set_value = ir.fit_transform(x, y, sample_weight=weights)
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y_default_value = ir.fit_transform(x, y)
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assert_array_equal(y_set_value, y_default_value)
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def test_isotonic_min_max_boundaries():
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# check if min value is used correctly
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ir = IsotonicRegression(y_min=2, y_max=4)
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n = 6
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x = np.arange(n)
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y = np.arange(n)
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y_test = [2, 2, 2, 3, 4, 4]
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y_result = np.round(ir.fit_transform(x, y))
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assert_array_equal(y_result, y_test)
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def test_isotonic_sample_weight():
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ir = IsotonicRegression()
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x = [1, 2, 3, 4, 5, 6, 7]
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y = [1, 41, 51, 1, 2, 5, 24]
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sample_weight = [1, 2, 3, 4, 5, 6, 7]
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expected_y = [1, 13.95, 13.95, 13.95, 13.95, 13.95, 24]
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received_y = ir.fit_transform(x, y, sample_weight=sample_weight)
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assert_array_equal(expected_y, received_y)
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def test_isotonic_regression_oob_raise():
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# Set y and x
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y = np.array([3, 7, 5, 9, 8, 7, 10])
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x = np.arange(len(y))
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# Create model and fit
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ir = IsotonicRegression(increasing="auto", out_of_bounds="raise")
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ir.fit(x, y)
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# Check that an exception is thrown
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msg = "in x_new is below the interpolation range"
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with pytest.raises(ValueError, match=msg):
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ir.predict([min(x) - 10, max(x) + 10])
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def test_isotonic_regression_oob_clip():
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# Set y and x
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y = np.array([3, 7, 5, 9, 8, 7, 10])
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x = np.arange(len(y))
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# Create model and fit
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ir = IsotonicRegression(increasing="auto", out_of_bounds="clip")
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ir.fit(x, y)
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# Predict from training and test x and check that min/max match.
|
||
|
|
y1 = ir.predict([min(x) - 10, max(x) + 10])
|
||
|
|
y2 = ir.predict(x)
|
||
|
|
assert max(y1) == max(y2)
|
||
|
|
assert min(y1) == min(y2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_isotonic_regression_oob_nan():
|
||
|
|
# Set y and x
|
||
|
|
y = np.array([3, 7, 5, 9, 8, 7, 10])
|
||
|
|
x = np.arange(len(y))
|
||
|
|
|
||
|
|
# Create model and fit
|
||
|
|
ir = IsotonicRegression(increasing="auto", out_of_bounds="nan")
|
||
|
|
ir.fit(x, y)
|
||
|
|
|
||
|
|
# Predict from training and test x and check that we have two NaNs.
|
||
|
|
y1 = ir.predict([min(x) - 10, max(x) + 10])
|
||
|
|
assert sum(np.isnan(y1)) == 2
|
||
|
|
|
||
|
|
|
||
|
|
def test_isotonic_regression_pickle():
|
||
|
|
y = np.array([3, 7, 5, 9, 8, 7, 10])
|
||
|
|
x = np.arange(len(y))
|
||
|
|
|
||
|
|
# Create model and fit
|
||
|
|
ir = IsotonicRegression(increasing="auto", out_of_bounds="clip")
|
||
|
|
ir.fit(x, y)
|
||
|
|
|
||
|
|
ir_ser = pickle.dumps(ir, pickle.HIGHEST_PROTOCOL)
|
||
|
|
ir2 = pickle.loads(ir_ser)
|
||
|
|
np.testing.assert_array_equal(ir.predict(x), ir2.predict(x))
|
||
|
|
|
||
|
|
|
||
|
|
def test_isotonic_duplicate_min_entry():
|
||
|
|
x = [0, 0, 1]
|
||
|
|
y = [0, 0, 1]
|
||
|
|
|
||
|
|
ir = IsotonicRegression(increasing=True, out_of_bounds="clip")
|
||
|
|
ir.fit(x, y)
|
||
|
|
all_predictions_finite = np.all(np.isfinite(ir.predict(x)))
|
||
|
|
assert all_predictions_finite
|
||
|
|
|
||
|
|
|
||
|
|
def test_isotonic_ymin_ymax():
|
||
|
|
# Test from @NelleV's issue:
|
||
|
|
# https://github.com/scikit-learn/scikit-learn/issues/6921
|
||
|
|
x = np.array(
|
||
|
|
[
|
||
|
|
1.263,
|
||
|
|
1.318,
|
||
|
|
-0.572,
|
||
|
|
0.307,
|
||
|
|
-0.707,
|
||
|
|
-0.176,
|
||
|
|
-1.599,
|
||
|
|
1.059,
|
||
|
|
1.396,
|
||
|
|
1.906,
|
||
|
|
0.210,
|
||
|
|
0.028,
|
||
|
|
-0.081,
|
||
|
|
0.444,
|
||
|
|
0.018,
|
||
|
|
-0.377,
|
||
|
|
-0.896,
|
||
|
|
-0.377,
|
||
|
|
-1.327,
|
||
|
|
0.180,
|
||
|
|
]
|
||
|
|
)
|
||
|
|
y = isotonic_regression(x, y_min=0.0, y_max=0.1)
|
||
|
|
|
||
|
|
assert np.all(y >= 0)
|
||
|
|
assert np.all(y <= 0.1)
|
||
|
|
|
||
|
|
# Also test decreasing case since the logic there is different
|
||
|
|
y = isotonic_regression(x, y_min=0.0, y_max=0.1, increasing=False)
|
||
|
|
|
||
|
|
assert np.all(y >= 0)
|
||
|
|
assert np.all(y <= 0.1)
|
||
|
|
|
||
|
|
# Finally, test with only one bound
|
||
|
|
y = isotonic_regression(x, y_min=0.0, increasing=False)
|
||
|
|
|
||
|
|
assert np.all(y >= 0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_isotonic_zero_weight_loop():
|
||
|
|
# Test from @ogrisel's issue:
|
||
|
|
# https://github.com/scikit-learn/scikit-learn/issues/4297
|
||
|
|
|
||
|
|
# Get deterministic RNG with seed
|
||
|
|
rng = np.random.RandomState(42)
|
||
|
|
|
||
|
|
# Create regression and samples
|
||
|
|
regression = IsotonicRegression()
|
||
|
|
n_samples = 50
|
||
|
|
x = np.linspace(-3, 3, n_samples)
|
||
|
|
y = x + rng.uniform(size=n_samples)
|
||
|
|
|
||
|
|
# Get some random weights and zero out
|
||
|
|
w = rng.uniform(size=n_samples)
|
||
|
|
w[5:8] = 0
|
||
|
|
regression.fit(x, y, sample_weight=w)
|
||
|
|
|
||
|
|
# This will hang in failure case.
|
||
|
|
regression.fit(x, y, sample_weight=w)
|
||
|
|
|
||
|
|
|
||
|
|
def test_fast_predict():
|
||
|
|
# test that the faster prediction change doesn't
|
||
|
|
# affect out-of-sample predictions:
|
||
|
|
# https://github.com/scikit-learn/scikit-learn/pull/6206
|
||
|
|
rng = np.random.RandomState(123)
|
||
|
|
n_samples = 10**3
|
||
|
|
# X values over the -10,10 range
|
||
|
|
X_train = 20.0 * rng.rand(n_samples) - 10
|
||
|
|
y_train = (
|
||
|
|
np.less(rng.rand(n_samples), expit(X_train)).astype("int64").astype("float64")
|
||
|
|
)
|
||
|
|
|
||
|
|
weights = rng.rand(n_samples)
|
||
|
|
# we also want to test that everything still works when some weights are 0
|
||
|
|
weights[rng.rand(n_samples) < 0.1] = 0
|
||
|
|
|
||
|
|
slow_model = IsotonicRegression(y_min=0, y_max=1, out_of_bounds="clip")
|
||
|
|
fast_model = IsotonicRegression(y_min=0, y_max=1, out_of_bounds="clip")
|
||
|
|
|
||
|
|
# Build interpolation function with ALL input data, not just the
|
||
|
|
# non-redundant subset. The following 2 lines are taken from the
|
||
|
|
# .fit() method, without removing unnecessary points
|
||
|
|
X_train_fit, y_train_fit = slow_model._build_y(
|
||
|
|
X_train, y_train, sample_weight=weights, trim_duplicates=False
|
||
|
|
)
|
||
|
|
slow_model._build_f(X_train_fit, y_train_fit)
|
||
|
|
|
||
|
|
# fit with just the necessary data
|
||
|
|
fast_model.fit(X_train, y_train, sample_weight=weights)
|
||
|
|
|
||
|
|
X_test = 20.0 * rng.rand(n_samples) - 10
|
||
|
|
y_pred_slow = slow_model.predict(X_test)
|
||
|
|
y_pred_fast = fast_model.predict(X_test)
|
||
|
|
|
||
|
|
assert_array_equal(y_pred_slow, y_pred_fast)
|
||
|
|
|
||
|
|
|
||
|
|
def test_isotonic_copy_before_fit():
|
||
|
|
# https://github.com/scikit-learn/scikit-learn/issues/6628
|
||
|
|
ir = IsotonicRegression()
|
||
|
|
copy.copy(ir)
|
||
|
|
|
||
|
|
|
||
|
|
def test_isotonic_dtype():
|
||
|
|
y = [2, 1, 4, 3, 5]
|
||
|
|
weights = np.array([0.9, 0.9, 0.9, 0.9, 0.9], dtype=np.float64)
|
||
|
|
reg = IsotonicRegression()
|
||
|
|
|
||
|
|
for dtype in (np.int32, np.int64, np.float32, np.float64):
|
||
|
|
for sample_weight in (None, weights.astype(np.float32), weights):
|
||
|
|
y_np = np.array(y, dtype=dtype)
|
||
|
|
expected_dtype = check_array(
|
||
|
|
y_np, dtype=[np.float64, np.float32], ensure_2d=False
|
||
|
|
).dtype
|
||
|
|
|
||
|
|
res = isotonic_regression(y_np, sample_weight=sample_weight)
|
||
|
|
assert res.dtype == expected_dtype
|
||
|
|
|
||
|
|
X = np.arange(len(y)).astype(dtype)
|
||
|
|
reg.fit(X, y_np, sample_weight=sample_weight)
|
||
|
|
res = reg.predict(X)
|
||
|
|
assert res.dtype == expected_dtype
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("y_dtype", [np.int32, np.int64, np.float32, np.float64])
|
||
|
|
def test_isotonic_mismatched_dtype(y_dtype):
|
||
|
|
# regression test for #15004
|
||
|
|
# check that data are converted when X and y dtype differ
|
||
|
|
reg = IsotonicRegression()
|
||
|
|
y = np.array([2, 1, 4, 3, 5], dtype=y_dtype)
|
||
|
|
X = np.arange(len(y), dtype=np.float32)
|
||
|
|
reg.fit(X, y)
|
||
|
|
assert reg.predict(X).dtype == X.dtype
|
||
|
|
|
||
|
|
|
||
|
|
def test_make_unique_dtype():
|
||
|
|
x_list = [2, 2, 2, 3, 5]
|
||
|
|
for dtype in (np.float32, np.float64):
|
||
|
|
x = np.array(x_list, dtype=dtype)
|
||
|
|
y = x.copy()
|
||
|
|
w = np.ones_like(x)
|
||
|
|
x, y, w = _make_unique(x, y, w)
|
||
|
|
assert_array_equal(x, [2, 3, 5])
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [np.float64, np.float32])
|
||
|
|
def test_make_unique_tolerance(dtype):
|
||
|
|
# Check that equality takes account of np.finfo tolerance
|
||
|
|
x = np.array([0, 1e-16, 1, 1 + 1e-14], dtype=dtype)
|
||
|
|
y = x.copy()
|
||
|
|
w = np.ones_like(x)
|
||
|
|
x, y, w = _make_unique(x, y, w)
|
||
|
|
if dtype == np.float64:
|
||
|
|
x_out = np.array([0, 1, 1 + 1e-14])
|
||
|
|
else:
|
||
|
|
x_out = np.array([0, 1])
|
||
|
|
assert_array_equal(x, x_out)
|
||
|
|
|
||
|
|
|
||
|
|
def test_isotonic_make_unique_tolerance():
|
||
|
|
# Check that averaging of targets for duplicate X is done correctly,
|
||
|
|
# taking into account tolerance
|
||
|
|
X = np.array([0, 1, 1 + 1e-16, 2], dtype=np.float64)
|
||
|
|
y = np.array([0, 1, 2, 3], dtype=np.float64)
|
||
|
|
ireg = IsotonicRegression().fit(X, y)
|
||
|
|
y_pred = ireg.predict([0, 0.5, 1, 1.5, 2])
|
||
|
|
|
||
|
|
assert_array_equal(y_pred, np.array([0, 0.75, 1.5, 2.25, 3]))
|
||
|
|
assert_array_equal(ireg.X_thresholds_, np.array([0.0, 1.0, 2.0]))
|
||
|
|
assert_array_equal(ireg.y_thresholds_, np.array([0.0, 1.5, 3.0]))
|
||
|
|
|
||
|
|
|
||
|
|
def test_isotonic_non_regression_inf_slope():
|
||
|
|
# Non-regression test to ensure that inf values are not returned
|
||
|
|
# see: https://github.com/scikit-learn/scikit-learn/issues/10903
|
||
|
|
X = np.array([0.0, 4.1e-320, 4.4e-314, 1.0])
|
||
|
|
y = np.array([0.42, 0.42, 0.44, 0.44])
|
||
|
|
ireg = IsotonicRegression().fit(X, y)
|
||
|
|
y_pred = ireg.predict(np.array([0, 2.1e-319, 5.4e-316, 1e-10]))
|
||
|
|
assert np.all(np.isfinite(y_pred))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("increasing", [True, False])
|
||
|
|
def test_isotonic_thresholds(increasing):
|
||
|
|
rng = np.random.RandomState(42)
|
||
|
|
n_samples = 30
|
||
|
|
X = rng.normal(size=n_samples)
|
||
|
|
y = rng.normal(size=n_samples)
|
||
|
|
ireg = IsotonicRegression(increasing=increasing).fit(X, y)
|
||
|
|
X_thresholds, y_thresholds = ireg.X_thresholds_, ireg.y_thresholds_
|
||
|
|
assert X_thresholds.shape == y_thresholds.shape
|
||
|
|
|
||
|
|
# Input thresholds are a strict subset of the training set (unless
|
||
|
|
# the data is already strictly monotonic which is not the case with
|
||
|
|
# this random data)
|
||
|
|
assert X_thresholds.shape[0] < X.shape[0]
|
||
|
|
assert np.isin(X_thresholds, X).all()
|
||
|
|
|
||
|
|
# Output thresholds lie in the range of the training set:
|
||
|
|
assert y_thresholds.max() <= y.max()
|
||
|
|
assert y_thresholds.min() >= y.min()
|
||
|
|
|
||
|
|
assert all(np.diff(X_thresholds) > 0)
|
||
|
|
if increasing:
|
||
|
|
assert all(np.diff(y_thresholds) >= 0)
|
||
|
|
else:
|
||
|
|
assert all(np.diff(y_thresholds) <= 0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_input_shape_validation():
|
||
|
|
# Test from #15012
|
||
|
|
# Check that IsotonicRegression can handle 2darray with only 1 feature
|
||
|
|
X = np.arange(10)
|
||
|
|
X_2d = X.reshape(-1, 1)
|
||
|
|
y = np.arange(10)
|
||
|
|
|
||
|
|
iso_reg = IsotonicRegression().fit(X, y)
|
||
|
|
iso_reg_2d = IsotonicRegression().fit(X_2d, y)
|
||
|
|
|
||
|
|
assert iso_reg.X_max_ == iso_reg_2d.X_max_
|
||
|
|
assert iso_reg.X_min_ == iso_reg_2d.X_min_
|
||
|
|
assert iso_reg.y_max == iso_reg_2d.y_max
|
||
|
|
assert iso_reg.y_min == iso_reg_2d.y_min
|
||
|
|
assert_array_equal(iso_reg.X_thresholds_, iso_reg_2d.X_thresholds_)
|
||
|
|
assert_array_equal(iso_reg.y_thresholds_, iso_reg_2d.y_thresholds_)
|
||
|
|
|
||
|
|
y_pred1 = iso_reg.predict(X)
|
||
|
|
y_pred2 = iso_reg_2d.predict(X_2d)
|
||
|
|
assert_allclose(y_pred1, y_pred2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_isotonic_2darray_more_than_1_feature():
|
||
|
|
# Ensure IsotonicRegression raises error if input has more than 1 feature
|
||
|
|
X = np.arange(10)
|
||
|
|
X_2d = np.c_[X, X]
|
||
|
|
y = np.arange(10)
|
||
|
|
|
||
|
|
msg = "should be a 1d array or 2d array with 1 feature"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
IsotonicRegression().fit(X_2d, y)
|
||
|
|
|
||
|
|
iso_reg = IsotonicRegression().fit(X, y)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
iso_reg.predict(X_2d)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
iso_reg.transform(X_2d)
|
||
|
|
|
||
|
|
|
||
|
|
def test_isotonic_regression_sample_weight_not_overwritten():
|
||
|
|
"""Check that calling fitting function of isotonic regression will not
|
||
|
|
overwrite `sample_weight`.
|
||
|
|
Non-regression test for:
|
||
|
|
https://github.com/scikit-learn/scikit-learn/issues/20508
|
||
|
|
"""
|
||
|
|
X, y = make_regression(n_samples=10, n_features=1, random_state=41)
|
||
|
|
sample_weight_original = np.ones_like(y)
|
||
|
|
sample_weight_original[0] = 10
|
||
|
|
sample_weight_fit = sample_weight_original.copy()
|
||
|
|
|
||
|
|
isotonic_regression(y, sample_weight=sample_weight_fit)
|
||
|
|
assert_allclose(sample_weight_fit, sample_weight_original)
|
||
|
|
|
||
|
|
IsotonicRegression().fit(X, y, sample_weight=sample_weight_fit)
|
||
|
|
assert_allclose(sample_weight_fit, sample_weight_original)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("shape", ["1d", "2d"])
|
||
|
|
def test_get_feature_names_out(shape):
|
||
|
|
"""Check `get_feature_names_out` for `IsotonicRegression`."""
|
||
|
|
X = np.arange(10)
|
||
|
|
if shape == "2d":
|
||
|
|
X = X.reshape(-1, 1)
|
||
|
|
y = np.arange(10)
|
||
|
|
|
||
|
|
iso = IsotonicRegression().fit(X, y)
|
||
|
|
names = iso.get_feature_names_out()
|
||
|
|
assert isinstance(names, np.ndarray)
|
||
|
|
assert names.dtype == object
|
||
|
|
assert_array_equal(["isotonicregression0"], names)
|
||
|
|
|
||
|
|
|
||
|
|
def test_isotonic_regression_output_predict():
|
||
|
|
"""Check that `predict` does return the expected output type.
|
||
|
|
|
||
|
|
We need to check that `transform` will output a DataFrame and a NumPy array
|
||
|
|
when we set `transform_output` to `pandas`.
|
||
|
|
|
||
|
|
Non-regression test for:
|
||
|
|
https://github.com/scikit-learn/scikit-learn/issues/25499
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"""
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pd = pytest.importorskip("pandas")
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X, y = make_regression(n_samples=10, n_features=1, random_state=42)
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regressor = IsotonicRegression()
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with sklearn.config_context(transform_output="pandas"):
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regressor.fit(X, y)
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X_trans = regressor.transform(X)
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y_pred = regressor.predict(X)
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assert isinstance(X_trans, pd.DataFrame)
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assert isinstance(y_pred, np.ndarray)
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