440 lines
16 KiB
Python
440 lines
16 KiB
Python
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import numpy as np
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import scipy.sparse as sp
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from itertools import product
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import pytest
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from scipy.sparse import issparse
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from scipy.sparse import csc_matrix
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from scipy.sparse import csr_matrix
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from scipy.sparse import coo_matrix
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from scipy.sparse import dok_matrix
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from scipy.sparse import lil_matrix
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils._testing import assert_array_almost_equal
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from sklearn.utils._testing import assert_allclose
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from sklearn.utils.estimator_checks import _NotAnArray
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from sklearn.utils.fixes import parse_version
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from sklearn.utils.multiclass import unique_labels
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from sklearn.utils.multiclass import is_multilabel
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from sklearn.utils.multiclass import type_of_target
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from sklearn.utils.multiclass import class_distribution
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from sklearn.utils.multiclass import check_classification_targets
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from sklearn.utils.multiclass import _ovr_decision_function
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from sklearn.utils.metaestimators import _safe_split
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from sklearn.model_selection import ShuffleSplit
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from sklearn.svm import SVC
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from sklearn import datasets
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EXAMPLES = {
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'multilabel-indicator': [
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# valid when the data is formatted as sparse or dense, identified
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# by CSR format when the testing takes place
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csr_matrix(np.random.RandomState(42).randint(2, size=(10, 10))),
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[[0, 1], [1, 0]],
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[[0, 1]],
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csr_matrix(np.array([[0, 1], [1, 0]])),
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csr_matrix(np.array([[0, 1], [1, 0]], dtype=bool)),
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csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.int8)),
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csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.uint8)),
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csr_matrix(np.array([[0, 1], [1, 0]], dtype=float)),
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csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.float32)),
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csr_matrix(np.array([[0, 0], [0, 0]])),
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csr_matrix(np.array([[0, 1]])),
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# Only valid when data is dense
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[[-1, 1], [1, -1]],
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np.array([[-1, 1], [1, -1]]),
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np.array([[-3, 3], [3, -3]]),
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_NotAnArray(np.array([[-3, 3], [3, -3]])),
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],
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'multiclass': [
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[1, 0, 2, 2, 1, 4, 2, 4, 4, 4],
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np.array([1, 0, 2]),
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np.array([1, 0, 2], dtype=np.int8),
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np.array([1, 0, 2], dtype=np.uint8),
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np.array([1, 0, 2], dtype=float),
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np.array([1, 0, 2], dtype=np.float32),
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np.array([[1], [0], [2]]),
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_NotAnArray(np.array([1, 0, 2])),
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[0, 1, 2],
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['a', 'b', 'c'],
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np.array(['a', 'b', 'c']),
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np.array(['a', 'b', 'c'], dtype=object),
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np.array(['a', 'b', 'c'], dtype=object),
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],
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'multiclass-multioutput': [
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[[1, 0, 2, 2], [1, 4, 2, 4]],
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[['a', 'b'], ['c', 'd']],
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np.array([[1, 0, 2, 2], [1, 4, 2, 4]]),
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np.array([[1, 0, 2, 2], [1, 4, 2, 4]], dtype=np.int8),
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np.array([[1, 0, 2, 2], [1, 4, 2, 4]], dtype=np.uint8),
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np.array([[1, 0, 2, 2], [1, 4, 2, 4]], dtype=float),
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np.array([[1, 0, 2, 2], [1, 4, 2, 4]], dtype=np.float32),
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np.array([['a', 'b'], ['c', 'd']]),
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np.array([['a', 'b'], ['c', 'd']]),
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np.array([['a', 'b'], ['c', 'd']], dtype=object),
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np.array([[1, 0, 2]]),
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_NotAnArray(np.array([[1, 0, 2]])),
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],
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'binary': [
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[0, 1],
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[1, 1],
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[],
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[0],
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np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1]),
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np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=bool),
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np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=np.int8),
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np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=np.uint8),
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np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=float),
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np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=np.float32),
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np.array([[0], [1]]),
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_NotAnArray(np.array([[0], [1]])),
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[1, -1],
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[3, 5],
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['a'],
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['a', 'b'],
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['abc', 'def'],
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np.array(['abc', 'def']),
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['a', 'b'],
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np.array(['abc', 'def'], dtype=object),
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],
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'continuous': [
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[1e-5],
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[0, .5],
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np.array([[0], [.5]]),
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np.array([[0], [.5]], dtype=np.float32),
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],
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'continuous-multioutput': [
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np.array([[0, .5], [.5, 0]]),
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np.array([[0, .5], [.5, 0]], dtype=np.float32),
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np.array([[0, .5]]),
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],
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'unknown': [
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[[]],
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[()],
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# sequence of sequences that weren't supported even before deprecation
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np.array([np.array([]), np.array([1, 2, 3])], dtype=object),
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[np.array([]), np.array([1, 2, 3])],
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[{1, 2, 3}, {1, 2}],
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[frozenset([1, 2, 3]), frozenset([1, 2])],
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# and also confusable as sequences of sequences
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[{0: 'a', 1: 'b'}, {0: 'a'}],
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# empty second dimension
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np.array([[], []]),
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# 3d
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np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]),
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]
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}
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NON_ARRAY_LIKE_EXAMPLES = [
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{1, 2, 3},
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{0: 'a', 1: 'b'},
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{0: [5], 1: [5]},
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'abc',
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frozenset([1, 2, 3]),
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None,
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]
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MULTILABEL_SEQUENCES = [
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[[1], [2], [0, 1]],
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[(), (2), (0, 1)],
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np.array([[], [1, 2]], dtype='object'),
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_NotAnArray(np.array([[], [1, 2]], dtype='object'))
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]
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def test_unique_labels():
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# Empty iterable
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with pytest.raises(ValueError):
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unique_labels()
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# Multiclass problem
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assert_array_equal(unique_labels(range(10)), np.arange(10))
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assert_array_equal(unique_labels(np.arange(10)), np.arange(10))
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assert_array_equal(unique_labels([4, 0, 2]), np.array([0, 2, 4]))
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# Multilabel indicator
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assert_array_equal(unique_labels(np.array([[0, 0, 1],
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[1, 0, 1],
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[0, 0, 0]])),
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np.arange(3))
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assert_array_equal(unique_labels(np.array([[0, 0, 1],
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[0, 0, 0]])),
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np.arange(3))
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# Several arrays passed
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assert_array_equal(unique_labels([4, 0, 2], range(5)),
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np.arange(5))
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assert_array_equal(unique_labels((0, 1, 2), (0,), (2, 1)),
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np.arange(3))
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# Border line case with binary indicator matrix
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with pytest.raises(ValueError):
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unique_labels([4, 0, 2], np.ones((5, 5)))
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with pytest.raises(ValueError):
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unique_labels(np.ones((5, 4)), np.ones((5, 5)))
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assert_array_equal(unique_labels(np.ones((4, 5)), np.ones((5, 5))),
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np.arange(5))
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def test_unique_labels_non_specific():
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# Test unique_labels with a variety of collected examples
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# Smoke test for all supported format
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for format in ["binary", "multiclass", "multilabel-indicator"]:
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for y in EXAMPLES[format]:
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unique_labels(y)
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# We don't support those format at the moment
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for example in NON_ARRAY_LIKE_EXAMPLES:
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with pytest.raises(ValueError):
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unique_labels(example)
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for y_type in ["unknown", "continuous", 'continuous-multioutput',
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'multiclass-multioutput']:
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for example in EXAMPLES[y_type]:
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with pytest.raises(ValueError):
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unique_labels(example)
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def test_unique_labels_mixed_types():
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# Mix with binary or multiclass and multilabel
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mix_clf_format = product(EXAMPLES["multilabel-indicator"],
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EXAMPLES["multiclass"] +
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EXAMPLES["binary"])
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for y_multilabel, y_multiclass in mix_clf_format:
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with pytest.raises(ValueError):
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unique_labels(y_multiclass, y_multilabel)
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with pytest.raises(ValueError):
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unique_labels(y_multilabel, y_multiclass)
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with pytest.raises(ValueError):
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unique_labels([[1, 2]], [["a", "d"]])
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with pytest.raises(ValueError):
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unique_labels(["1", 2])
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with pytest.raises(ValueError):
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unique_labels([["1", 2], [1, 3]])
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with pytest.raises(ValueError):
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unique_labels([["1", "2"], [2, 3]])
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def test_is_multilabel():
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for group, group_examples in EXAMPLES.items():
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if group in ['multilabel-indicator']:
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dense_exp = True
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else:
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dense_exp = False
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for example in group_examples:
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# Only mark explicitly defined sparse examples as valid sparse
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# multilabel-indicators
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if group == 'multilabel-indicator' and issparse(example):
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sparse_exp = True
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else:
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sparse_exp = False
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if (issparse(example) or
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(hasattr(example, '__array__') and
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np.asarray(example).ndim == 2 and
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np.asarray(example).dtype.kind in 'biuf' and
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np.asarray(example).shape[1] > 0)):
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examples_sparse = [sparse_matrix(example)
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for sparse_matrix in [coo_matrix,
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csc_matrix,
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csr_matrix,
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dok_matrix,
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lil_matrix]]
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for exmpl_sparse in examples_sparse:
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assert sparse_exp == is_multilabel(exmpl_sparse), (
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'is_multilabel(%r) should be %s'
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% (exmpl_sparse, sparse_exp))
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# Densify sparse examples before testing
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if issparse(example):
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example = example.toarray()
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assert dense_exp == is_multilabel(example), (
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'is_multilabel(%r) should be %s'
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% (example, dense_exp))
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def test_check_classification_targets():
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for y_type in EXAMPLES.keys():
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if y_type in ["unknown", "continuous", 'continuous-multioutput']:
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for example in EXAMPLES[y_type]:
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msg = 'Unknown label type: '
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with pytest.raises(ValueError, match=msg):
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check_classification_targets(example)
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else:
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for example in EXAMPLES[y_type]:
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check_classification_targets(example)
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# @ignore_warnings
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def test_type_of_target():
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for group, group_examples in EXAMPLES.items():
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for example in group_examples:
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assert type_of_target(example) == group, (
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'type_of_target(%r) should be %r, got %r'
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% (example, group, type_of_target(example)))
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for example in NON_ARRAY_LIKE_EXAMPLES:
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msg_regex = r'Expected array-like \(array or non-string sequence\).*'
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with pytest.raises(ValueError, match=msg_regex):
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type_of_target(example)
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for example in MULTILABEL_SEQUENCES:
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msg = ('You appear to be using a legacy multi-label data '
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'representation. Sequence of sequences are no longer supported;'
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' use a binary array or sparse matrix instead.')
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with pytest.raises(ValueError, match=msg):
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type_of_target(example)
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def test_type_of_target_pandas_sparse():
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pd = pytest.importorskip("pandas")
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if parse_version(pd.__version__) >= parse_version('0.25'):
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pd_sparse_array = pd.arrays.SparseArray
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else:
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pd_sparse_array = pd.SparseArray
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y = pd_sparse_array([1, np.nan, np.nan, 1, np.nan])
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msg = "y cannot be class 'SparseSeries' or 'SparseArray'"
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with pytest.raises(ValueError, match=msg):
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type_of_target(y)
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def test_class_distribution():
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y = np.array([[1, 0, 0, 1],
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[2, 2, 0, 1],
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[1, 3, 0, 1],
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[4, 2, 0, 1],
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[2, 0, 0, 1],
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[1, 3, 0, 1]])
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# Define the sparse matrix with a mix of implicit and explicit zeros
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data = np.array([1, 2, 1, 4, 2, 1, 0, 2, 3, 2, 3, 1, 1, 1, 1, 1, 1])
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indices = np.array([0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 5, 0, 1, 2, 3, 4, 5])
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indptr = np.array([0, 6, 11, 11, 17])
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y_sp = sp.csc_matrix((data, indices, indptr), shape=(6, 4))
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classes, n_classes, class_prior = class_distribution(y)
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classes_sp, n_classes_sp, class_prior_sp = class_distribution(y_sp)
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classes_expected = [[1, 2, 4],
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[0, 2, 3],
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|
|
[0],
|
||
|
|
[1]]
|
||
|
|
n_classes_expected = [3, 3, 1, 1]
|
||
|
|
class_prior_expected = [[3/6, 2/6, 1/6],
|
||
|
|
[1/3, 1/3, 1/3],
|
||
|
|
[1.0],
|
||
|
|
[1.0]]
|
||
|
|
|
||
|
|
for k in range(y.shape[1]):
|
||
|
|
assert_array_almost_equal(classes[k], classes_expected[k])
|
||
|
|
assert_array_almost_equal(n_classes[k], n_classes_expected[k])
|
||
|
|
assert_array_almost_equal(class_prior[k], class_prior_expected[k])
|
||
|
|
|
||
|
|
assert_array_almost_equal(classes_sp[k], classes_expected[k])
|
||
|
|
assert_array_almost_equal(n_classes_sp[k], n_classes_expected[k])
|
||
|
|
assert_array_almost_equal(class_prior_sp[k], class_prior_expected[k])
|
||
|
|
|
||
|
|
# Test again with explicit sample weights
|
||
|
|
(classes,
|
||
|
|
n_classes,
|
||
|
|
class_prior) = class_distribution(y, [1.0, 2.0, 1.0, 2.0, 1.0, 2.0])
|
||
|
|
(classes_sp,
|
||
|
|
n_classes_sp,
|
||
|
|
class_prior_sp) = class_distribution(y, [1.0, 2.0, 1.0, 2.0, 1.0, 2.0])
|
||
|
|
class_prior_expected = [[4/9, 3/9, 2/9],
|
||
|
|
[2/9, 4/9, 3/9],
|
||
|
|
[1.0],
|
||
|
|
[1.0]]
|
||
|
|
|
||
|
|
for k in range(y.shape[1]):
|
||
|
|
assert_array_almost_equal(classes[k], classes_expected[k])
|
||
|
|
assert_array_almost_equal(n_classes[k], n_classes_expected[k])
|
||
|
|
assert_array_almost_equal(class_prior[k], class_prior_expected[k])
|
||
|
|
|
||
|
|
assert_array_almost_equal(classes_sp[k], classes_expected[k])
|
||
|
|
assert_array_almost_equal(n_classes_sp[k], n_classes_expected[k])
|
||
|
|
assert_array_almost_equal(class_prior_sp[k], class_prior_expected[k])
|
||
|
|
|
||
|
|
|
||
|
|
def test_safe_split_with_precomputed_kernel():
|
||
|
|
clf = SVC()
|
||
|
|
clfp = SVC(kernel="precomputed")
|
||
|
|
|
||
|
|
iris = datasets.load_iris()
|
||
|
|
X, y = iris.data, iris.target
|
||
|
|
K = np.dot(X, X.T)
|
||
|
|
|
||
|
|
cv = ShuffleSplit(test_size=0.25, random_state=0)
|
||
|
|
train, test = list(cv.split(X))[0]
|
||
|
|
|
||
|
|
X_train, y_train = _safe_split(clf, X, y, train)
|
||
|
|
K_train, y_train2 = _safe_split(clfp, K, y, train)
|
||
|
|
assert_array_almost_equal(K_train, np.dot(X_train, X_train.T))
|
||
|
|
assert_array_almost_equal(y_train, y_train2)
|
||
|
|
|
||
|
|
X_test, y_test = _safe_split(clf, X, y, test, train)
|
||
|
|
K_test, y_test2 = _safe_split(clfp, K, y, test, train)
|
||
|
|
assert_array_almost_equal(K_test, np.dot(X_test, X_train.T))
|
||
|
|
assert_array_almost_equal(y_test, y_test2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ovr_decision_function():
|
||
|
|
# test properties for ovr decision function
|
||
|
|
|
||
|
|
predictions = np.array([[0, 1, 1],
|
||
|
|
[0, 1, 0],
|
||
|
|
[0, 1, 1],
|
||
|
|
[0, 1, 1]])
|
||
|
|
|
||
|
|
confidences = np.array([[-1e16, 0, -1e16],
|
||
|
|
[1., 2., -3.],
|
||
|
|
[-5., 2., 5.],
|
||
|
|
[-0.5, 0.2, 0.5]])
|
||
|
|
|
||
|
|
n_classes = 3
|
||
|
|
|
||
|
|
dec_values = _ovr_decision_function(predictions, confidences, n_classes)
|
||
|
|
|
||
|
|
# check that the decision values are within 0.5 range of the votes
|
||
|
|
votes = np.array([[1, 0, 2],
|
||
|
|
[1, 1, 1],
|
||
|
|
[1, 0, 2],
|
||
|
|
[1, 0, 2]])
|
||
|
|
|
||
|
|
assert_allclose(votes, dec_values, atol=0.5)
|
||
|
|
|
||
|
|
# check that the prediction are what we expect
|
||
|
|
# highest vote or highest confidence if there is a tie.
|
||
|
|
# for the second sample we have a tie (should be won by 1)
|
||
|
|
expected_prediction = np.array([2, 1, 2, 2])
|
||
|
|
assert_array_equal(np.argmax(dec_values, axis=1), expected_prediction)
|
||
|
|
|
||
|
|
# third and fourth sample have the same vote but third sample
|
||
|
|
# has higher confidence, this should reflect on the decision values
|
||
|
|
assert (dec_values[2, 2] > dec_values[3, 2])
|
||
|
|
|
||
|
|
# assert subset invariance.
|
||
|
|
dec_values_one = [_ovr_decision_function(np.array([predictions[i]]),
|
||
|
|
np.array([confidences[i]]),
|
||
|
|
n_classes)[0] for i in range(4)]
|
||
|
|
|
||
|
|
assert_allclose(dec_values, dec_values_one, atol=1e-6)
|