504 lines
18 KiB
Python
504 lines
18 KiB
Python
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import warnings
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import numpy as np
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import pytest
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import scipy.sparse as sp
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from sklearn import clone
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from sklearn.preprocessing import KBinsDiscretizer, OneHotEncoder
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from sklearn.utils._testing import (
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assert_allclose,
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assert_allclose_dense_sparse,
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assert_array_almost_equal,
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assert_array_equal,
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)
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X = [[-2, 1.5, -4, -1], [-1, 2.5, -3, -0.5], [0, 3.5, -2, 0.5], [1, 4.5, -1, 2]]
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@pytest.mark.parametrize(
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"strategy, expected, sample_weight",
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[
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("uniform", [[0, 0, 0, 0], [1, 1, 1, 0], [2, 2, 2, 1], [2, 2, 2, 2]], None),
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("kmeans", [[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 2, 2]], None),
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("quantile", [[0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 2, 2], [2, 2, 2, 2]], None),
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(
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"quantile",
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[[0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 2, 2], [2, 2, 2, 2]],
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[1, 1, 2, 1],
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),
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(
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"quantile",
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[[0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 2, 2], [2, 2, 2, 2]],
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[1, 1, 1, 1],
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),
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(
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"quantile",
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[[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1]],
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[0, 1, 1, 1],
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),
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(
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"kmeans",
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[[0, 0, 0, 0], [1, 1, 1, 0], [1, 1, 1, 1], [2, 2, 2, 2]],
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[1, 0, 3, 1],
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),
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(
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"kmeans",
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[[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [2, 2, 2, 2]],
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[1, 1, 1, 1],
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),
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],
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)
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# TODO(1.5) remove warning filter when kbd's subsample default is changed
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@pytest.mark.filterwarnings("ignore:In version 1.5 onwards, subsample=200_000")
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def test_fit_transform(strategy, expected, sample_weight):
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est = KBinsDiscretizer(n_bins=3, encode="ordinal", strategy=strategy)
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est.fit(X, sample_weight=sample_weight)
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assert_array_equal(expected, est.transform(X))
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def test_valid_n_bins():
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KBinsDiscretizer(n_bins=2).fit_transform(X)
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KBinsDiscretizer(n_bins=np.array([2])[0]).fit_transform(X)
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assert KBinsDiscretizer(n_bins=2).fit(X).n_bins_.dtype == np.dtype(int)
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@pytest.mark.parametrize("strategy", ["uniform"])
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def test_kbinsdiscretizer_wrong_strategy_with_weights(strategy):
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"""Check that we raise an error when the wrong strategy is used."""
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sample_weight = np.ones(shape=(len(X)))
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est = KBinsDiscretizer(n_bins=3, strategy=strategy)
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err_msg = (
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"`sample_weight` was provided but it cannot be used with strategy='uniform'."
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)
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with pytest.raises(ValueError, match=err_msg):
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est.fit(X, sample_weight=sample_weight)
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def test_invalid_n_bins_array():
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# Bad shape
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n_bins = np.full((2, 4), 2.0)
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est = KBinsDiscretizer(n_bins=n_bins)
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err_msg = r"n_bins must be a scalar or array of shape \(n_features,\)."
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with pytest.raises(ValueError, match=err_msg):
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est.fit_transform(X)
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# Incorrect number of features
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n_bins = [1, 2, 2]
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est = KBinsDiscretizer(n_bins=n_bins)
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err_msg = r"n_bins must be a scalar or array of shape \(n_features,\)."
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with pytest.raises(ValueError, match=err_msg):
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est.fit_transform(X)
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# Bad bin values
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n_bins = [1, 2, 2, 1]
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est = KBinsDiscretizer(n_bins=n_bins)
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err_msg = (
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"KBinsDiscretizer received an invalid number of bins "
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"at indices 0, 3. Number of bins must be at least 2, "
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"and must be an int."
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)
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with pytest.raises(ValueError, match=err_msg):
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est.fit_transform(X)
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# Float bin values
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n_bins = [2.1, 2, 2.1, 2]
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est = KBinsDiscretizer(n_bins=n_bins)
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err_msg = (
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"KBinsDiscretizer received an invalid number of bins "
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"at indices 0, 2. Number of bins must be at least 2, "
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"and must be an int."
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)
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with pytest.raises(ValueError, match=err_msg):
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est.fit_transform(X)
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@pytest.mark.parametrize(
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"strategy, expected, sample_weight",
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[
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("uniform", [[0, 0, 0, 0], [0, 1, 1, 0], [1, 2, 2, 1], [1, 2, 2, 2]], None),
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("kmeans", [[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [1, 2, 2, 2]], None),
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("quantile", [[0, 0, 0, 0], [0, 1, 1, 1], [1, 2, 2, 2], [1, 2, 2, 2]], None),
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(
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"quantile",
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[[0, 0, 0, 0], [0, 1, 1, 1], [1, 2, 2, 2], [1, 2, 2, 2]],
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[1, 1, 3, 1],
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),
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(
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"quantile",
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[[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1]],
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[0, 1, 3, 1],
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),
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# (
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# "quantile",
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# [[0, 0, 0, 0], [0, 1, 1, 1], [1, 2, 2, 2], [1, 2, 2, 2]],
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# [1, 1, 1, 1],
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# ),
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#
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# TODO: This test case above aims to test if the case where an array of
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# ones passed in sample_weight parameter is equal to the case when
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# sample_weight is None.
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# Unfortunately, the behavior of `_weighted_percentile` when
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# `sample_weight = [1, 1, 1, 1]` are currently not equivalent.
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# This problem has been addressed in issue :
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# https://github.com/scikit-learn/scikit-learn/issues/17370
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(
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"kmeans",
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[[0, 0, 0, 0], [0, 1, 1, 0], [1, 1, 1, 1], [1, 2, 2, 2]],
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[1, 0, 3, 1],
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),
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],
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)
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# TODO(1.5) remove warning filter when kbd's subsample default is changed
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@pytest.mark.filterwarnings("ignore:In version 1.5 onwards, subsample=200_000")
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def test_fit_transform_n_bins_array(strategy, expected, sample_weight):
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est = KBinsDiscretizer(
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n_bins=[2, 3, 3, 3], encode="ordinal", strategy=strategy
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).fit(X, sample_weight=sample_weight)
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assert_array_equal(expected, est.transform(X))
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# test the shape of bin_edges_
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n_features = np.array(X).shape[1]
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assert est.bin_edges_.shape == (n_features,)
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for bin_edges, n_bins in zip(est.bin_edges_, est.n_bins_):
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assert bin_edges.shape == (n_bins + 1,)
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@pytest.mark.filterwarnings("ignore: Bins whose width are too small")
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def test_kbinsdiscretizer_effect_sample_weight():
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"""Check the impact of `sample_weight` one computed quantiles."""
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X = np.array([[-2], [-1], [1], [3], [500], [1000]])
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# add a large number of bins such that each sample with a non-null weight
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# will be used as bin edge
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est = KBinsDiscretizer(n_bins=10, encode="ordinal", strategy="quantile")
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est.fit(X, sample_weight=[1, 1, 1, 1, 0, 0])
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assert_allclose(est.bin_edges_[0], [-2, -1, 1, 3])
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assert_allclose(est.transform(X), [[0.0], [1.0], [2.0], [2.0], [2.0], [2.0]])
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# TODO(1.5) remove warning filter when kbd's subsample default is changed
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@pytest.mark.filterwarnings("ignore:In version 1.5 onwards, subsample=200_000")
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@pytest.mark.parametrize("strategy", ["kmeans", "quantile"])
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def test_kbinsdiscretizer_no_mutating_sample_weight(strategy):
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"""Make sure that `sample_weight` is not changed in place."""
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est = KBinsDiscretizer(n_bins=3, encode="ordinal", strategy=strategy)
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sample_weight = np.array([1, 3, 1, 2], dtype=np.float64)
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sample_weight_copy = np.copy(sample_weight)
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est.fit(X, sample_weight=sample_weight)
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assert_allclose(sample_weight, sample_weight_copy)
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@pytest.mark.parametrize("strategy", ["uniform", "kmeans", "quantile"])
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def test_same_min_max(strategy):
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warnings.simplefilter("always")
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X = np.array([[1, -2], [1, -1], [1, 0], [1, 1]])
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est = KBinsDiscretizer(strategy=strategy, n_bins=3, encode="ordinal")
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warning_message = "Feature 0 is constant and will be replaced with 0."
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with pytest.warns(UserWarning, match=warning_message):
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est.fit(X)
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assert est.n_bins_[0] == 1
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# replace the feature with zeros
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Xt = est.transform(X)
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assert_array_equal(Xt[:, 0], np.zeros(X.shape[0]))
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def test_transform_1d_behavior():
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X = np.arange(4)
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est = KBinsDiscretizer(n_bins=2)
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with pytest.raises(ValueError):
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est.fit(X)
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est = KBinsDiscretizer(n_bins=2)
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est.fit(X.reshape(-1, 1))
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with pytest.raises(ValueError):
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est.transform(X)
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@pytest.mark.parametrize("i", range(1, 9))
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def test_numeric_stability(i):
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X_init = np.array([2.0, 4.0, 6.0, 8.0, 10.0]).reshape(-1, 1)
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Xt_expected = np.array([0, 0, 1, 1, 1]).reshape(-1, 1)
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# Test up to discretizing nano units
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X = X_init / 10**i
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Xt = KBinsDiscretizer(n_bins=2, encode="ordinal").fit_transform(X)
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assert_array_equal(Xt_expected, Xt)
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def test_encode_options():
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est = KBinsDiscretizer(n_bins=[2, 3, 3, 3], encode="ordinal").fit(X)
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Xt_1 = est.transform(X)
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est = KBinsDiscretizer(n_bins=[2, 3, 3, 3], encode="onehot-dense").fit(X)
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Xt_2 = est.transform(X)
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assert not sp.issparse(Xt_2)
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assert_array_equal(
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OneHotEncoder(
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categories=[np.arange(i) for i in [2, 3, 3, 3]], sparse_output=False
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).fit_transform(Xt_1),
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Xt_2,
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)
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est = KBinsDiscretizer(n_bins=[2, 3, 3, 3], encode="onehot").fit(X)
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Xt_3 = est.transform(X)
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assert sp.issparse(Xt_3)
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assert_array_equal(
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OneHotEncoder(
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categories=[np.arange(i) for i in [2, 3, 3, 3]], sparse_output=True
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)
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.fit_transform(Xt_1)
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.toarray(),
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Xt_3.toarray(),
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)
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@pytest.mark.parametrize(
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"strategy, expected_2bins, expected_3bins, expected_5bins",
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[
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("uniform", [0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 2, 2], [0, 0, 1, 1, 4, 4]),
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("kmeans", [0, 0, 0, 0, 1, 1], [0, 0, 1, 1, 2, 2], [0, 0, 1, 2, 3, 4]),
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("quantile", [0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 2, 2], [0, 1, 2, 3, 4, 4]),
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],
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)
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# TODO(1.5) remove warning filter when kbd's subsample default is changed
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@pytest.mark.filterwarnings("ignore:In version 1.5 onwards, subsample=200_000")
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def test_nonuniform_strategies(
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strategy, expected_2bins, expected_3bins, expected_5bins
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):
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X = np.array([0, 0.5, 2, 3, 9, 10]).reshape(-1, 1)
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# with 2 bins
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est = KBinsDiscretizer(n_bins=2, strategy=strategy, encode="ordinal")
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Xt = est.fit_transform(X)
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assert_array_equal(expected_2bins, Xt.ravel())
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# with 3 bins
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est = KBinsDiscretizer(n_bins=3, strategy=strategy, encode="ordinal")
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Xt = est.fit_transform(X)
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assert_array_equal(expected_3bins, Xt.ravel())
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# with 5 bins
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est = KBinsDiscretizer(n_bins=5, strategy=strategy, encode="ordinal")
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Xt = est.fit_transform(X)
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assert_array_equal(expected_5bins, Xt.ravel())
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@pytest.mark.parametrize(
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"strategy, expected_inv",
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[
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(
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"uniform",
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[
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[-1.5, 2.0, -3.5, -0.5],
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[-0.5, 3.0, -2.5, -0.5],
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[0.5, 4.0, -1.5, 0.5],
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[0.5, 4.0, -1.5, 1.5],
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],
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),
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(
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"kmeans",
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[
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[-1.375, 2.125, -3.375, -0.5625],
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[-1.375, 2.125, -3.375, -0.5625],
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[-0.125, 3.375, -2.125, 0.5625],
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[0.75, 4.25, -1.25, 1.625],
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],
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),
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(
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"quantile",
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[
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[-1.5, 2.0, -3.5, -0.75],
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[-0.5, 3.0, -2.5, 0.0],
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[0.5, 4.0, -1.5, 1.25],
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[0.5, 4.0, -1.5, 1.25],
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],
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),
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],
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)
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# TODO(1.5) remove warning filter when kbd's subsample default is changed
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@pytest.mark.filterwarnings("ignore:In version 1.5 onwards, subsample=200_000")
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@pytest.mark.parametrize("encode", ["ordinal", "onehot", "onehot-dense"])
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||
|
|
def test_inverse_transform(strategy, encode, expected_inv):
|
||
|
|
kbd = KBinsDiscretizer(n_bins=3, strategy=strategy, encode=encode)
|
||
|
|
Xt = kbd.fit_transform(X)
|
||
|
|
Xinv = kbd.inverse_transform(Xt)
|
||
|
|
assert_array_almost_equal(expected_inv, Xinv)
|
||
|
|
|
||
|
|
|
||
|
|
# TODO(1.5) remove warning filter when kbd's subsample default is changed
|
||
|
|
@pytest.mark.filterwarnings("ignore:In version 1.5 onwards, subsample=200_000")
|
||
|
|
@pytest.mark.parametrize("strategy", ["uniform", "kmeans", "quantile"])
|
||
|
|
def test_transform_outside_fit_range(strategy):
|
||
|
|
X = np.array([0, 1, 2, 3])[:, None]
|
||
|
|
kbd = KBinsDiscretizer(n_bins=4, strategy=strategy, encode="ordinal")
|
||
|
|
kbd.fit(X)
|
||
|
|
|
||
|
|
X2 = np.array([-2, 5])[:, None]
|
||
|
|
X2t = kbd.transform(X2)
|
||
|
|
assert_array_equal(X2t.max(axis=0) + 1, kbd.n_bins_)
|
||
|
|
assert_array_equal(X2t.min(axis=0), [0])
|
||
|
|
|
||
|
|
|
||
|
|
def test_overwrite():
|
||
|
|
X = np.array([0, 1, 2, 3])[:, None]
|
||
|
|
X_before = X.copy()
|
||
|
|
|
||
|
|
est = KBinsDiscretizer(n_bins=3, encode="ordinal")
|
||
|
|
Xt = est.fit_transform(X)
|
||
|
|
assert_array_equal(X, X_before)
|
||
|
|
|
||
|
|
Xt_before = Xt.copy()
|
||
|
|
Xinv = est.inverse_transform(Xt)
|
||
|
|
assert_array_equal(Xt, Xt_before)
|
||
|
|
assert_array_equal(Xinv, np.array([[0.5], [1.5], [2.5], [2.5]]))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"strategy, expected_bin_edges", [("quantile", [0, 1, 3]), ("kmeans", [0, 1.5, 3])]
|
||
|
|
)
|
||
|
|
def test_redundant_bins(strategy, expected_bin_edges):
|
||
|
|
X = [[0], [0], [0], [0], [3], [3]]
|
||
|
|
kbd = KBinsDiscretizer(n_bins=3, strategy=strategy, subsample=None)
|
||
|
|
warning_message = "Consider decreasing the number of bins."
|
||
|
|
with pytest.warns(UserWarning, match=warning_message):
|
||
|
|
kbd.fit(X)
|
||
|
|
assert_array_almost_equal(kbd.bin_edges_[0], expected_bin_edges)
|
||
|
|
|
||
|
|
|
||
|
|
def test_percentile_numeric_stability():
|
||
|
|
X = np.array([0.05, 0.05, 0.95]).reshape(-1, 1)
|
||
|
|
bin_edges = np.array([0.05, 0.23, 0.41, 0.59, 0.77, 0.95])
|
||
|
|
Xt = np.array([0, 0, 4]).reshape(-1, 1)
|
||
|
|
kbd = KBinsDiscretizer(n_bins=10, encode="ordinal", strategy="quantile")
|
||
|
|
warning_message = "Consider decreasing the number of bins."
|
||
|
|
with pytest.warns(UserWarning, match=warning_message):
|
||
|
|
kbd.fit(X)
|
||
|
|
|
||
|
|
assert_array_almost_equal(kbd.bin_edges_[0], bin_edges)
|
||
|
|
assert_array_almost_equal(kbd.transform(X), Xt)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("in_dtype", [np.float16, np.float32, np.float64])
|
||
|
|
@pytest.mark.parametrize("out_dtype", [None, np.float32, np.float64])
|
||
|
|
@pytest.mark.parametrize("encode", ["ordinal", "onehot", "onehot-dense"])
|
||
|
|
def test_consistent_dtype(in_dtype, out_dtype, encode):
|
||
|
|
X_input = np.array(X, dtype=in_dtype)
|
||
|
|
kbd = KBinsDiscretizer(n_bins=3, encode=encode, dtype=out_dtype)
|
||
|
|
kbd.fit(X_input)
|
||
|
|
|
||
|
|
# test output dtype
|
||
|
|
if out_dtype is not None:
|
||
|
|
expected_dtype = out_dtype
|
||
|
|
elif out_dtype is None and X_input.dtype == np.float16:
|
||
|
|
# wrong numeric input dtype are cast in np.float64
|
||
|
|
expected_dtype = np.float64
|
||
|
|
else:
|
||
|
|
expected_dtype = X_input.dtype
|
||
|
|
Xt = kbd.transform(X_input)
|
||
|
|
assert Xt.dtype == expected_dtype
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("input_dtype", [np.float16, np.float32, np.float64])
|
||
|
|
@pytest.mark.parametrize("encode", ["ordinal", "onehot", "onehot-dense"])
|
||
|
|
def test_32_equal_64(input_dtype, encode):
|
||
|
|
# TODO this check is redundant with common checks and can be removed
|
||
|
|
# once #16290 is merged
|
||
|
|
X_input = np.array(X, dtype=input_dtype)
|
||
|
|
|
||
|
|
# 32 bit output
|
||
|
|
kbd_32 = KBinsDiscretizer(n_bins=3, encode=encode, dtype=np.float32)
|
||
|
|
kbd_32.fit(X_input)
|
||
|
|
Xt_32 = kbd_32.transform(X_input)
|
||
|
|
|
||
|
|
# 64 bit output
|
||
|
|
kbd_64 = KBinsDiscretizer(n_bins=3, encode=encode, dtype=np.float64)
|
||
|
|
kbd_64.fit(X_input)
|
||
|
|
Xt_64 = kbd_64.transform(X_input)
|
||
|
|
|
||
|
|
assert_allclose_dense_sparse(Xt_32, Xt_64)
|
||
|
|
|
||
|
|
|
||
|
|
def test_kbinsdiscretizer_subsample_default():
|
||
|
|
# Since the size of X is small (< 2e5), subsampling will not take place.
|
||
|
|
X = np.array([-2, 1.5, -4, -1]).reshape(-1, 1)
|
||
|
|
kbd_default = KBinsDiscretizer(n_bins=10, encode="ordinal", strategy="quantile")
|
||
|
|
kbd_default.fit(X)
|
||
|
|
|
||
|
|
kbd_without_subsampling = clone(kbd_default)
|
||
|
|
kbd_without_subsampling.set_params(subsample=None)
|
||
|
|
kbd_without_subsampling.fit(X)
|
||
|
|
|
||
|
|
for bin_kbd_default, bin_kbd_with_subsampling in zip(
|
||
|
|
kbd_default.bin_edges_[0], kbd_without_subsampling.bin_edges_[0]
|
||
|
|
):
|
||
|
|
np.testing.assert_allclose(bin_kbd_default, bin_kbd_with_subsampling)
|
||
|
|
assert kbd_default.bin_edges_.shape == kbd_without_subsampling.bin_edges_.shape
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"encode, expected_names",
|
||
|
|
[
|
||
|
|
(
|
||
|
|
"onehot",
|
||
|
|
[
|
||
|
|
f"feat{col_id}_{float(bin_id)}"
|
||
|
|
for col_id in range(3)
|
||
|
|
for bin_id in range(4)
|
||
|
|
],
|
||
|
|
),
|
||
|
|
(
|
||
|
|
"onehot-dense",
|
||
|
|
[
|
||
|
|
f"feat{col_id}_{float(bin_id)}"
|
||
|
|
for col_id in range(3)
|
||
|
|
for bin_id in range(4)
|
||
|
|
],
|
||
|
|
),
|
||
|
|
("ordinal", [f"feat{col_id}" for col_id in range(3)]),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_kbinsdiscrtizer_get_feature_names_out(encode, expected_names):
|
||
|
|
"""Check get_feature_names_out for different settings.
|
||
|
|
Non-regression test for #22731
|
||
|
|
"""
|
||
|
|
X = [[-2, 1, -4], [-1, 2, -3], [0, 3, -2], [1, 4, -1]]
|
||
|
|
|
||
|
|
kbd = KBinsDiscretizer(n_bins=4, encode=encode).fit(X)
|
||
|
|
Xt = kbd.transform(X)
|
||
|
|
|
||
|
|
input_features = [f"feat{i}" for i in range(3)]
|
||
|
|
output_names = kbd.get_feature_names_out(input_features)
|
||
|
|
assert Xt.shape[1] == output_names.shape[0]
|
||
|
|
|
||
|
|
assert_array_equal(output_names, expected_names)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("strategy", ["uniform", "kmeans", "quantile"])
|
||
|
|
def test_kbinsdiscretizer_subsample(strategy, global_random_seed):
|
||
|
|
# Check that the bin edges are almost the same when subsampling is used.
|
||
|
|
X = np.random.RandomState(global_random_seed).random_sample((100000, 1)) + 1
|
||
|
|
|
||
|
|
kbd_subsampling = KBinsDiscretizer(
|
||
|
|
strategy=strategy, subsample=50000, random_state=global_random_seed
|
||
|
|
)
|
||
|
|
kbd_subsampling.fit(X)
|
||
|
|
|
||
|
|
kbd_no_subsampling = clone(kbd_subsampling)
|
||
|
|
kbd_no_subsampling.set_params(subsample=None)
|
||
|
|
kbd_no_subsampling.fit(X)
|
||
|
|
|
||
|
|
# We use a large tolerance because we can't expect the bin edges to be exactly the
|
||
|
|
# same when subsampling is used.
|
||
|
|
assert_allclose(
|
||
|
|
kbd_subsampling.bin_edges_[0], kbd_no_subsampling.bin_edges_[0], rtol=1e-2
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
# TODO(1.5) remove this test
|
||
|
|
@pytest.mark.parametrize("strategy", ["uniform", "kmeans"])
|
||
|
|
def test_kbd_subsample_warning(strategy):
|
||
|
|
# Check the future warning for the change of default of subsample
|
||
|
|
X = np.random.RandomState(0).random_sample((100, 1))
|
||
|
|
|
||
|
|
kbd = KBinsDiscretizer(strategy=strategy, random_state=0)
|
||
|
|
with pytest.warns(FutureWarning, match="subsample=200_000 will be used by default"):
|
||
|
|
kbd.fit(X)
|