317 lines
12 KiB
Python
317 lines
12 KiB
Python
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import numpy as np
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import pytest
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from numpy.testing import assert_allclose
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from sklearn.datasets import make_blobs
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from sklearn.linear_model import LogisticRegression
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal
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from sklearn.utils.class_weight import compute_class_weight, compute_sample_weight
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from sklearn.utils.fixes import CSC_CONTAINERS
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def test_compute_class_weight():
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# Test (and demo) compute_class_weight.
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y = np.asarray([2, 2, 2, 3, 3, 4])
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classes = np.unique(y)
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cw = compute_class_weight("balanced", classes=classes, y=y)
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# total effect of samples is preserved
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class_counts = np.bincount(y)[2:]
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assert_almost_equal(np.dot(cw, class_counts), y.shape[0])
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assert cw[0] < cw[1] < cw[2]
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@pytest.mark.parametrize(
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"y_type, class_weight, classes, err_msg",
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[
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(
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"numeric",
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"balanced",
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np.arange(4),
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"classes should have valid labels that are in y",
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),
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# Non-regression for https://github.com/scikit-learn/scikit-learn/issues/8312
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(
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"numeric",
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{"label_not_present": 1.0},
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np.arange(4),
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r"The classes, \[0, 1, 2, 3\], are not in class_weight",
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),
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(
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"numeric",
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"balanced",
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np.arange(2),
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"classes should include all valid labels",
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),
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(
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"numeric",
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{0: 1.0, 1: 2.0},
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np.arange(2),
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"classes should include all valid labels",
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),
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(
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"string",
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{"dogs": 3, "cat": 2},
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np.array(["dog", "cat"]),
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r"The classes, \['dog'\], are not in class_weight",
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),
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],
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)
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def test_compute_class_weight_not_present(y_type, class_weight, classes, err_msg):
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# Raise error when y does not contain all class labels
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y = (
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np.asarray([0, 0, 0, 1, 1, 2])
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if y_type == "numeric"
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else np.asarray(["dog", "cat", "dog"])
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)
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print(y)
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with pytest.raises(ValueError, match=err_msg):
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compute_class_weight(class_weight, classes=classes, y=y)
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def test_compute_class_weight_dict():
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classes = np.arange(3)
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class_weights = {0: 1.0, 1: 2.0, 2: 3.0}
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y = np.asarray([0, 0, 1, 2])
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cw = compute_class_weight(class_weights, classes=classes, y=y)
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# When the user specifies class weights, compute_class_weights should just
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# return them.
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assert_array_almost_equal(np.asarray([1.0, 2.0, 3.0]), cw)
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# When a class weight is specified that isn't in classes, the weight is ignored
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class_weights = {0: 1.0, 1: 2.0, 2: 3.0, 4: 1.5}
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cw = compute_class_weight(class_weights, classes=classes, y=y)
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assert_allclose([1.0, 2.0, 3.0], cw)
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class_weights = {-1: 5.0, 0: 4.0, 1: 2.0, 2: 3.0}
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cw = compute_class_weight(class_weights, classes=classes, y=y)
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assert_allclose([4.0, 2.0, 3.0], cw)
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def test_compute_class_weight_invariance():
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# Test that results with class_weight="balanced" is invariant wrt
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# class imbalance if the number of samples is identical.
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# The test uses a balanced two class dataset with 100 datapoints.
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# It creates three versions, one where class 1 is duplicated
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# resulting in 150 points of class 1 and 50 of class 0,
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# one where there are 50 points in class 1 and 150 in class 0,
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# and one where there are 100 points of each class (this one is balanced
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# again).
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# With balancing class weights, all three should give the same model.
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X, y = make_blobs(centers=2, random_state=0)
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# create dataset where class 1 is duplicated twice
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X_1 = np.vstack([X] + [X[y == 1]] * 2)
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y_1 = np.hstack([y] + [y[y == 1]] * 2)
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# create dataset where class 0 is duplicated twice
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X_0 = np.vstack([X] + [X[y == 0]] * 2)
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y_0 = np.hstack([y] + [y[y == 0]] * 2)
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# duplicate everything
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X_ = np.vstack([X] * 2)
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y_ = np.hstack([y] * 2)
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# results should be identical
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logreg1 = LogisticRegression(class_weight="balanced").fit(X_1, y_1)
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logreg0 = LogisticRegression(class_weight="balanced").fit(X_0, y_0)
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logreg = LogisticRegression(class_weight="balanced").fit(X_, y_)
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assert_array_almost_equal(logreg1.coef_, logreg0.coef_)
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assert_array_almost_equal(logreg.coef_, logreg0.coef_)
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def test_compute_class_weight_balanced_negative():
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# Test compute_class_weight when labels are negative
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# Test with balanced class labels.
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classes = np.array([-2, -1, 0])
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y = np.asarray([-1, -1, 0, 0, -2, -2])
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cw = compute_class_weight("balanced", classes=classes, y=y)
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assert len(cw) == len(classes)
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assert_array_almost_equal(cw, np.array([1.0, 1.0, 1.0]))
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# Test with unbalanced class labels.
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y = np.asarray([-1, 0, 0, -2, -2, -2])
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cw = compute_class_weight("balanced", classes=classes, y=y)
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assert len(cw) == len(classes)
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class_counts = np.bincount(y + 2)
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assert_almost_equal(np.dot(cw, class_counts), y.shape[0])
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assert_array_almost_equal(cw, [2.0 / 3, 2.0, 1.0])
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def test_compute_class_weight_balanced_unordered():
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# Test compute_class_weight when classes are unordered
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classes = np.array([1, 0, 3])
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y = np.asarray([1, 0, 0, 3, 3, 3])
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cw = compute_class_weight("balanced", classes=classes, y=y)
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class_counts = np.bincount(y)[classes]
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assert_almost_equal(np.dot(cw, class_counts), y.shape[0])
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assert_array_almost_equal(cw, [2.0, 1.0, 2.0 / 3])
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def test_compute_class_weight_default():
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# Test for the case where no weight is given for a present class.
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# Current behaviour is to assign the unweighted classes a weight of 1.
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y = np.asarray([2, 2, 2, 3, 3, 4])
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classes = np.unique(y)
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classes_len = len(classes)
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# Test for non specified weights
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cw = compute_class_weight(None, classes=classes, y=y)
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assert len(cw) == classes_len
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assert_array_almost_equal(cw, np.ones(3))
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# Tests for partly specified weights
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cw = compute_class_weight({2: 1.5}, classes=classes, y=y)
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assert len(cw) == classes_len
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assert_array_almost_equal(cw, [1.5, 1.0, 1.0])
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cw = compute_class_weight({2: 1.5, 4: 0.5}, classes=classes, y=y)
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assert len(cw) == classes_len
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assert_array_almost_equal(cw, [1.5, 1.0, 0.5])
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def test_compute_sample_weight():
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# Test (and demo) compute_sample_weight.
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# Test with balanced classes
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y = np.asarray([1, 1, 1, 2, 2, 2])
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sample_weight = compute_sample_weight("balanced", y)
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assert_array_almost_equal(sample_weight, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
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# Test with user-defined weights
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sample_weight = compute_sample_weight({1: 2, 2: 1}, y)
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assert_array_almost_equal(sample_weight, [2.0, 2.0, 2.0, 1.0, 1.0, 1.0])
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# Test with column vector of balanced classes
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y = np.asarray([[1], [1], [1], [2], [2], [2]])
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sample_weight = compute_sample_weight("balanced", y)
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assert_array_almost_equal(sample_weight, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
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# Test with unbalanced classes
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y = np.asarray([1, 1, 1, 2, 2, 2, 3])
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sample_weight = compute_sample_weight("balanced", y)
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expected_balanced = np.array(
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[0.7777, 0.7777, 0.7777, 0.7777, 0.7777, 0.7777, 2.3333]
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)
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assert_array_almost_equal(sample_weight, expected_balanced, decimal=4)
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# Test with `None` weights
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sample_weight = compute_sample_weight(None, y)
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assert_array_almost_equal(sample_weight, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
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# Test with multi-output of balanced classes
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y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]])
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sample_weight = compute_sample_weight("balanced", y)
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assert_array_almost_equal(sample_weight, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
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# Test with multi-output with user-defined weights
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y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]])
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sample_weight = compute_sample_weight([{1: 2, 2: 1}, {0: 1, 1: 2}], y)
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assert_array_almost_equal(sample_weight, [2.0, 2.0, 2.0, 2.0, 2.0, 2.0])
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# Test with multi-output of unbalanced classes
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y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1], [3, -1]])
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sample_weight = compute_sample_weight("balanced", y)
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assert_array_almost_equal(sample_weight, expected_balanced**2, decimal=3)
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def test_compute_sample_weight_with_subsample():
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# Test compute_sample_weight with subsamples specified.
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# Test with balanced classes and all samples present
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y = np.asarray([1, 1, 1, 2, 2, 2])
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sample_weight = compute_sample_weight("balanced", y, indices=range(6))
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assert_array_almost_equal(sample_weight, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
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# Test with column vector of balanced classes and all samples present
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y = np.asarray([[1], [1], [1], [2], [2], [2]])
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sample_weight = compute_sample_weight("balanced", y, indices=range(6))
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assert_array_almost_equal(sample_weight, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
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# Test with a subsample
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y = np.asarray([1, 1, 1, 2, 2, 2])
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sample_weight = compute_sample_weight("balanced", y, indices=range(4))
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assert_array_almost_equal(sample_weight, [2.0 / 3, 2.0 / 3, 2.0 / 3, 2.0, 2.0, 2.0])
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# Test with a bootstrap subsample
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y = np.asarray([1, 1, 1, 2, 2, 2])
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sample_weight = compute_sample_weight("balanced", y, indices=[0, 1, 1, 2, 2, 3])
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expected_balanced = np.asarray([0.6, 0.6, 0.6, 3.0, 3.0, 3.0])
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assert_array_almost_equal(sample_weight, expected_balanced)
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# Test with a bootstrap subsample for multi-output
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y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]])
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sample_weight = compute_sample_weight("balanced", y, indices=[0, 1, 1, 2, 2, 3])
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assert_array_almost_equal(sample_weight, expected_balanced**2)
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# Test with a missing class
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y = np.asarray([1, 1, 1, 2, 2, 2, 3])
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sample_weight = compute_sample_weight("balanced", y, indices=range(6))
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assert_array_almost_equal(sample_weight, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0])
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# Test with a missing class for multi-output
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y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1], [2, 2]])
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sample_weight = compute_sample_weight("balanced", y, indices=range(6))
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assert_array_almost_equal(sample_weight, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0])
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@pytest.mark.parametrize(
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"y_type, class_weight, indices, err_msg",
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[
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(
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"single-output",
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{1: 2, 2: 1},
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range(4),
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"The only valid class_weight for subsampling is 'balanced'.",
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),
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(
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"multi-output",
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{1: 2, 2: 1},
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None,
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"For multi-output, class_weight should be a list of dicts, or the string",
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),
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(
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"multi-output",
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[{1: 2, 2: 1}],
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None,
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r"Got 1 element\(s\) while having 2 outputs",
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),
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],
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)
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def test_compute_sample_weight_errors(y_type, class_weight, indices, err_msg):
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# Test compute_sample_weight raises errors expected.
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# Invalid preset string
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y_single_output = np.asarray([1, 1, 1, 2, 2, 2])
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y_multi_output = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]])
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y = y_single_output if y_type == "single-output" else y_multi_output
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with pytest.raises(ValueError, match=err_msg):
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compute_sample_weight(class_weight, y, indices=indices)
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def test_compute_sample_weight_more_than_32():
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# Non-regression smoke test for #12146
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y = np.arange(50) # more than 32 distinct classes
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indices = np.arange(50) # use subsampling
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weight = compute_sample_weight("balanced", y, indices=indices)
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assert_array_almost_equal(weight, np.ones(y.shape[0]))
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def test_class_weight_does_not_contains_more_classes():
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"""Check that class_weight can contain more labels than in y.
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Non-regression test for #22413
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"""
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tree = DecisionTreeClassifier(class_weight={0: 1, 1: 10, 2: 20})
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# Does not raise
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tree.fit([[0, 0, 1], [1, 0, 1], [1, 2, 0]], [0, 0, 1])
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@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
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def test_compute_sample_weight_sparse(csc_container):
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"""Check that we can compute weight for sparse `y`."""
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y = csc_container(np.asarray([[0], [1], [1]]))
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sample_weight = compute_sample_weight("balanced", y)
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assert_allclose(sample_weight, [1.5, 0.75, 0.75])
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