249 lines
9.1 KiB
Python
249 lines
9.1 KiB
Python
import numpy as np
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from itertools import product
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from sklearn.externals.six.moves import xrange
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from sklearn.externals.six import iteritems
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_true
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from sklearn.utils.testing import assert_false
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.multiclass import unique_labels
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from sklearn.utils.multiclass import is_label_indicator_matrix
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from sklearn.utils.multiclass import is_multilabel
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from sklearn.utils.multiclass import is_sequence_of_sequences
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from sklearn.utils.multiclass import type_of_target
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EXAMPLES = {
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'multilabel-indicator': [
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np.random.RandomState(42).randint(2, size=(10, 10)),
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np.array([[0, 1], [1, 0]]),
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np.array([[0, 1], [1, 0]], dtype=np.bool),
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np.array([[0, 1], [1, 0]], dtype=np.int8),
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np.array([[0, 1], [1, 0]], dtype=np.uint8),
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np.array([[0, 1], [1, 0]], dtype=np.float),
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np.array([[0, 1], [1, 0]], dtype=np.float32),
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np.array([[0, 0], [0, 0]]),
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np.array([[-1, 1], [1, -1]]),
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np.array([[-3, 3], [3, -3]]),
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np.array([[0, 1]]),
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],
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'multilabel-sequences': [
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[[0, 1]],
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[[0], [1]],
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[[1, 2, 3]],
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[[1, 2, 1]], # duplicate values, why not?
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[[1], [2], [0, 1]],
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[[1], [2]],
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[[]],
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[()],
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np.array([[], [1, 2]], dtype='object'),
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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=np.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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[0, 1, 2],
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['a', 'b', 'c'],
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np.array([u'a', u'b', u'c']),
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],
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'multiclass-multioutput': [
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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=np.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([[u'a', u'b'], [u'c', u'd']]),
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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=np.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=np.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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[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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[u'a', u'b'],
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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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# 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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# not currently supported sequence of sequences
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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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[set([1, 2, 3]), set([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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]
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}
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NON_ARRAY_LIKE_EXAMPLES = [
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set([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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def test_unique_labels():
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# Empty iterable
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assert_raises(ValueError, unique_labels)
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# Multiclass problem
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assert_array_equal(unique_labels(xrange(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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# Multilabels
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assert_array_equal(unique_labels([(0, 1, 2), (0,), tuple(), (2, 1)]),
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np.arange(3))
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assert_array_equal(unique_labels([[0, 1, 2], [0], list(), [2, 1]]),
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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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[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], xrange(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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assert_raises(ValueError, unique_labels, [4, 0, 2], np.ones((5, 5)))
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assert_raises(ValueError, 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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# Some tests with strings input
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assert_array_equal(unique_labels(["a", "b", "c"], ["d"]),
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["a", "b", "c", "d"])
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assert_array_equal(unique_labels([["a", "b"], ["c"]], [["d"]]),
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["a", "b", "c", "d"])
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# Smoke test for all supported format
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for format in ["binary", "multiclass", "multilabel-sequences",
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"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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assert_raises(ValueError, 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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assert_raises(ValueError, unique_labels, example)
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#Mix of multilabel-indicator and multilabel-sequences
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mix_multilabel_format = product(EXAMPLES["multilabel-indicator"],
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EXAMPLES["multilabel-sequences"])
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for y_multilabel, y_multiclass in mix_multilabel_format:
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assert_raises(ValueError, unique_labels, y_multiclass, y_multilabel)
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assert_raises(ValueError, unique_labels, y_multilabel, y_multiclass)
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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["multilabel-sequences"],
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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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assert_raises(ValueError, unique_labels, y_multiclass, y_multilabel)
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assert_raises(ValueError, unique_labels, y_multilabel, y_multiclass)
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# Mix string and number input type
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assert_raises(ValueError, unique_labels, [[1, 2], [3]],
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[["a", "d"]])
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assert_raises(ValueError, unique_labels, ["1", 2])
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assert_raises(ValueError, unique_labels, [["1", 2], [3]])
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assert_raises(ValueError, unique_labels, [["1", "2"], [3]])
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assert_array_equal(unique_labels([(2,), (0, 2,)], [(), ()]), [0, 2])
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assert_array_equal(unique_labels([("2",), ("0", "2",)], [(), ()]),
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["0", "2"])
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def test_is_multilabel():
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for group, group_examples in iteritems(EXAMPLES):
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if group.startswith('multilabel'):
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assert_, exp = assert_true, 'True'
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else:
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assert_, exp = assert_false, 'False'
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for example in group_examples:
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assert_(is_multilabel(example),
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msg='is_multilabel(%r) should be %s' % (example, exp))
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def test_is_label_indicator_matrix():
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for group, group_examples in iteritems(EXAMPLES):
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if group == 'multilabel-indicator':
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assert_, exp = assert_true, 'True'
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else:
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assert_, exp = assert_false, 'False'
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for example in group_examples:
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assert_(is_label_indicator_matrix(example),
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msg='is_label_indicator_matrix(%r) should be %s'
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% (example, exp))
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def test_is_sequence_of_sequences():
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for group, group_examples in iteritems(EXAMPLES):
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if group == 'multilabel-sequences':
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assert_, exp = assert_true, 'True'
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else:
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assert_, exp = assert_false, 'False'
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for example in group_examples:
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assert_(is_sequence_of_sequences(example),
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msg='is_sequence_of_sequences(%r) should be %s'
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% (example, exp))
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def test_type_of_target():
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for group, group_examples in iteritems(EXAMPLES):
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for example in group_examples:
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assert_equal(type_of_target(example), group,
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msg='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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assert_raises(ValueError, type_of_target, example)
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