621 lines
22 KiB
Python
621 lines
22 KiB
Python
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import numpy as np
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import pytest
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from scipy.sparse import issparse
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from scipy.sparse import coo_matrix
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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 dok_matrix
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from scipy.sparse import lil_matrix
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from sklearn.utils.multiclass import type_of_target
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils._testing import assert_warns_message
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from sklearn.utils._testing import ignore_warnings
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from sklearn.utils import _to_object_array
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from sklearn.preprocessing._label import LabelBinarizer
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from sklearn.preprocessing._label import MultiLabelBinarizer
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from sklearn.preprocessing._label import LabelEncoder
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from sklearn.preprocessing._label import label_binarize
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from sklearn.preprocessing._label import _inverse_binarize_thresholding
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from sklearn.preprocessing._label import _inverse_binarize_multiclass
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from sklearn import datasets
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iris = datasets.load_iris()
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def toarray(a):
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if hasattr(a, "toarray"):
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a = a.toarray()
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return a
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def test_label_binarizer():
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# one-class case defaults to negative label
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# For dense case:
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inp = ["pos", "pos", "pos", "pos"]
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lb = LabelBinarizer(sparse_output=False)
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expected = np.array([[0, 0, 0, 0]]).T
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got = lb.fit_transform(inp)
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assert_array_equal(lb.classes_, ["pos"])
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assert_array_equal(expected, got)
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assert_array_equal(lb.inverse_transform(got), inp)
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# For sparse case:
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lb = LabelBinarizer(sparse_output=True)
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got = lb.fit_transform(inp)
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assert issparse(got)
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assert_array_equal(lb.classes_, ["pos"])
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assert_array_equal(expected, got.toarray())
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assert_array_equal(lb.inverse_transform(got.toarray()), inp)
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lb = LabelBinarizer(sparse_output=False)
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# two-class case
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inp = ["neg", "pos", "pos", "neg"]
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expected = np.array([[0, 1, 1, 0]]).T
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got = lb.fit_transform(inp)
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assert_array_equal(lb.classes_, ["neg", "pos"])
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assert_array_equal(expected, got)
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to_invert = np.array([[1, 0],
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[0, 1],
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[0, 1],
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[1, 0]])
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assert_array_equal(lb.inverse_transform(to_invert), inp)
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# multi-class case
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inp = ["spam", "ham", "eggs", "ham", "0"]
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expected = np.array([[0, 0, 0, 1],
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[0, 0, 1, 0],
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[0, 1, 0, 0],
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[0, 0, 1, 0],
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[1, 0, 0, 0]])
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got = lb.fit_transform(inp)
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assert_array_equal(lb.classes_, ['0', 'eggs', 'ham', 'spam'])
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assert_array_equal(expected, got)
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assert_array_equal(lb.inverse_transform(got), inp)
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def test_label_binarizer_unseen_labels():
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lb = LabelBinarizer()
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expected = np.array([[1, 0, 0],
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[0, 1, 0],
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[0, 0, 1]])
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got = lb.fit_transform(['b', 'd', 'e'])
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assert_array_equal(expected, got)
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expected = np.array([[0, 0, 0],
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[1, 0, 0],
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[0, 0, 0],
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[0, 1, 0],
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[0, 0, 1],
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[0, 0, 0]])
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got = lb.transform(['a', 'b', 'c', 'd', 'e', 'f'])
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assert_array_equal(expected, got)
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def test_label_binarizer_set_label_encoding():
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lb = LabelBinarizer(neg_label=-2, pos_label=0)
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# two-class case with pos_label=0
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inp = np.array([0, 1, 1, 0])
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expected = np.array([[-2, 0, 0, -2]]).T
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got = lb.fit_transform(inp)
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assert_array_equal(expected, got)
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assert_array_equal(lb.inverse_transform(got), inp)
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lb = LabelBinarizer(neg_label=-2, pos_label=2)
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# multi-class case
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inp = np.array([3, 2, 1, 2, 0])
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expected = np.array([[-2, -2, -2, +2],
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[-2, -2, +2, -2],
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[-2, +2, -2, -2],
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[-2, -2, +2, -2],
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[+2, -2, -2, -2]])
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got = lb.fit_transform(inp)
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assert_array_equal(expected, got)
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assert_array_equal(lb.inverse_transform(got), inp)
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@ignore_warnings
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def test_label_binarizer_errors():
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# Check that invalid arguments yield ValueError
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one_class = np.array([0, 0, 0, 0])
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lb = LabelBinarizer().fit(one_class)
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multi_label = [(2, 3), (0,), (0, 2)]
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with pytest.raises(ValueError):
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lb.transform(multi_label)
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lb = LabelBinarizer()
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with pytest.raises(ValueError):
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lb.transform([])
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with pytest.raises(ValueError):
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lb.inverse_transform([])
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with pytest.raises(ValueError):
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LabelBinarizer(neg_label=2, pos_label=1)
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with pytest.raises(ValueError):
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LabelBinarizer(neg_label=2, pos_label=2)
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with pytest.raises(ValueError):
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LabelBinarizer(neg_label=1, pos_label=2, sparse_output=True)
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# Fail on y_type
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with pytest.raises(ValueError):
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_inverse_binarize_thresholding(y=csr_matrix([[1, 2], [2, 1]]),
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output_type="foo", classes=[1, 2],
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threshold=0)
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# Sequence of seq type should raise ValueError
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y_seq_of_seqs = [[], [1, 2], [3], [0, 1, 3], [2]]
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with pytest.raises(ValueError):
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LabelBinarizer().fit_transform(y_seq_of_seqs)
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# Fail on the number of classes
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with pytest.raises(ValueError):
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_inverse_binarize_thresholding(y=csr_matrix([[1, 2], [2, 1]]),
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output_type="foo",
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classes=[1, 2, 3],
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threshold=0)
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# Fail on the dimension of 'binary'
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with pytest.raises(ValueError):
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_inverse_binarize_thresholding(y=np.array([[1, 2, 3], [2, 1, 3]]),
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output_type="binary",
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classes=[1, 2, 3],
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threshold=0)
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# Fail on multioutput data
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with pytest.raises(ValueError):
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LabelBinarizer().fit(np.array([[1, 3], [2, 1]]))
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with pytest.raises(ValueError):
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label_binarize(np.array([[1, 3], [2, 1]]), classes=[1, 2, 3])
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@pytest.mark.parametrize(
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"values, classes, unknown",
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[(np.array([2, 1, 3, 1, 3], dtype='int64'),
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np.array([1, 2, 3], dtype='int64'), np.array([4], dtype='int64')),
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(np.array(['b', 'a', 'c', 'a', 'c'], dtype=object),
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np.array(['a', 'b', 'c'], dtype=object),
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np.array(['d'], dtype=object)),
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(np.array(['b', 'a', 'c', 'a', 'c']),
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np.array(['a', 'b', 'c']), np.array(['d']))],
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ids=['int64', 'object', 'str'])
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def test_label_encoder(values, classes, unknown):
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# Test LabelEncoder's transform, fit_transform and
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# inverse_transform methods
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le = LabelEncoder()
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le.fit(values)
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assert_array_equal(le.classes_, classes)
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assert_array_equal(le.transform(values), [1, 0, 2, 0, 2])
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assert_array_equal(le.inverse_transform([1, 0, 2, 0, 2]), values)
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le = LabelEncoder()
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ret = le.fit_transform(values)
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assert_array_equal(ret, [1, 0, 2, 0, 2])
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with pytest.raises(ValueError, match="unseen labels"):
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le.transform(unknown)
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def test_label_encoder_negative_ints():
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le = LabelEncoder()
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le.fit([1, 1, 4, 5, -1, 0])
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assert_array_equal(le.classes_, [-1, 0, 1, 4, 5])
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assert_array_equal(le.transform([0, 1, 4, 4, 5, -1, -1]),
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[1, 2, 3, 3, 4, 0, 0])
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assert_array_equal(le.inverse_transform([1, 2, 3, 3, 4, 0, 0]),
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[0, 1, 4, 4, 5, -1, -1])
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with pytest.raises(ValueError):
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le.transform([0, 6])
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@pytest.mark.parametrize("dtype", ['str', 'object'])
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def test_label_encoder_str_bad_shape(dtype):
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le = LabelEncoder()
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le.fit(np.array(["apple", "orange"], dtype=dtype))
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msg = "should be a 1d array"
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with pytest.raises(ValueError, match=msg):
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le.transform("apple")
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def test_label_encoder_errors():
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# Check that invalid arguments yield ValueError
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le = LabelEncoder()
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with pytest.raises(ValueError):
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le.transform([])
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with pytest.raises(ValueError):
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le.inverse_transform([])
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# Fail on unseen labels
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le = LabelEncoder()
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le.fit([1, 2, 3, -1, 1])
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msg = "contains previously unseen labels"
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with pytest.raises(ValueError, match=msg):
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le.inverse_transform([-2])
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with pytest.raises(ValueError, match=msg):
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le.inverse_transform([-2, -3, -4])
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# Fail on inverse_transform("")
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msg = r"should be a 1d array.+shape \(\)"
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with pytest.raises(ValueError, match=msg):
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le.inverse_transform("")
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@pytest.mark.parametrize(
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"values",
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[np.array([2, 1, 3, 1, 3], dtype='int64'),
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np.array(['b', 'a', 'c', 'a', 'c'], dtype=object),
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np.array(['b', 'a', 'c', 'a', 'c'])],
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ids=['int64', 'object', 'str'])
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def test_label_encoder_empty_array(values):
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le = LabelEncoder()
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le.fit(values)
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# test empty transform
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transformed = le.transform([])
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assert_array_equal(np.array([]), transformed)
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# test empty inverse transform
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inverse_transformed = le.inverse_transform([])
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assert_array_equal(np.array([]), inverse_transformed)
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def test_sparse_output_multilabel_binarizer():
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# test input as iterable of iterables
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inputs = [
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lambda: [(2, 3), (1,), (1, 2)],
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lambda: ({2, 3}, {1}, {1, 2}),
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lambda: iter([iter((2, 3)), iter((1,)), {1, 2}]),
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]
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indicator_mat = np.array([[0, 1, 1],
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[1, 0, 0],
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[1, 1, 0]])
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inverse = inputs[0]()
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for sparse_output in [True, False]:
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for inp in inputs:
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# With fit_transform
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mlb = MultiLabelBinarizer(sparse_output=sparse_output)
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got = mlb.fit_transform(inp())
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assert issparse(got) == sparse_output
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if sparse_output:
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# verify CSR assumption that indices and indptr have same dtype
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assert got.indices.dtype == got.indptr.dtype
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got = got.toarray()
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assert_array_equal(indicator_mat, got)
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assert_array_equal([1, 2, 3], mlb.classes_)
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assert mlb.inverse_transform(got) == inverse
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# With fit
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mlb = MultiLabelBinarizer(sparse_output=sparse_output)
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got = mlb.fit(inp()).transform(inp())
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assert issparse(got) == sparse_output
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if sparse_output:
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# verify CSR assumption that indices and indptr have same dtype
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assert got.indices.dtype == got.indptr.dtype
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got = got.toarray()
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assert_array_equal(indicator_mat, got)
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assert_array_equal([1, 2, 3], mlb.classes_)
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assert mlb.inverse_transform(got) == inverse
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with pytest.raises(ValueError):
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mlb.inverse_transform(csr_matrix(np.array([[0, 1, 1],
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[2, 0, 0],
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[1, 1, 0]])))
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def test_multilabel_binarizer():
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# test input as iterable of iterables
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inputs = [
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lambda: [(2, 3), (1,), (1, 2)],
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lambda: ({2, 3}, {1}, {1, 2}),
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lambda: iter([iter((2, 3)), iter((1,)), {1, 2}]),
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]
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indicator_mat = np.array([[0, 1, 1],
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[1, 0, 0],
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[1, 1, 0]])
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inverse = inputs[0]()
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for inp in inputs:
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# With fit_transform
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mlb = MultiLabelBinarizer()
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got = mlb.fit_transform(inp())
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assert_array_equal(indicator_mat, got)
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assert_array_equal([1, 2, 3], mlb.classes_)
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assert mlb.inverse_transform(got) == inverse
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# With fit
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mlb = MultiLabelBinarizer()
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got = mlb.fit(inp()).transform(inp())
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assert_array_equal(indicator_mat, got)
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assert_array_equal([1, 2, 3], mlb.classes_)
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assert mlb.inverse_transform(got) == inverse
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def test_multilabel_binarizer_empty_sample():
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mlb = MultiLabelBinarizer()
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y = [[1, 2], [1], []]
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Y = np.array([[1, 1],
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[1, 0],
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[0, 0]])
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assert_array_equal(mlb.fit_transform(y), Y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multilabel_binarizer_unknown_class():
|
||
|
|
mlb = MultiLabelBinarizer()
|
||
|
|
y = [[1, 2]]
|
||
|
|
Y = np.array([[1, 0], [0, 1]])
|
||
|
|
w = 'unknown class(es) [0, 4] will be ignored'
|
||
|
|
matrix = assert_warns_message(UserWarning, w,
|
||
|
|
mlb.fit(y).transform, [[4, 1], [2, 0]])
|
||
|
|
assert_array_equal(matrix, Y)
|
||
|
|
|
||
|
|
Y = np.array([[1, 0, 0], [0, 1, 0]])
|
||
|
|
mlb = MultiLabelBinarizer(classes=[1, 2, 3])
|
||
|
|
matrix = assert_warns_message(UserWarning, w,
|
||
|
|
mlb.fit(y).transform, [[4, 1], [2, 0]])
|
||
|
|
assert_array_equal(matrix, Y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multilabel_binarizer_given_classes():
|
||
|
|
inp = [(2, 3), (1,), (1, 2)]
|
||
|
|
indicator_mat = np.array([[0, 1, 1],
|
||
|
|
[1, 0, 0],
|
||
|
|
[1, 0, 1]])
|
||
|
|
# fit_transform()
|
||
|
|
mlb = MultiLabelBinarizer(classes=[1, 3, 2])
|
||
|
|
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
|
||
|
|
assert_array_equal(mlb.classes_, [1, 3, 2])
|
||
|
|
|
||
|
|
# fit().transform()
|
||
|
|
mlb = MultiLabelBinarizer(classes=[1, 3, 2])
|
||
|
|
assert_array_equal(mlb.fit(inp).transform(inp), indicator_mat)
|
||
|
|
assert_array_equal(mlb.classes_, [1, 3, 2])
|
||
|
|
|
||
|
|
# ensure works with extra class
|
||
|
|
mlb = MultiLabelBinarizer(classes=[4, 1, 3, 2])
|
||
|
|
assert_array_equal(mlb.fit_transform(inp),
|
||
|
|
np.hstack(([[0], [0], [0]], indicator_mat)))
|
||
|
|
assert_array_equal(mlb.classes_, [4, 1, 3, 2])
|
||
|
|
|
||
|
|
# ensure fit is no-op as iterable is not consumed
|
||
|
|
inp = iter(inp)
|
||
|
|
mlb = MultiLabelBinarizer(classes=[1, 3, 2])
|
||
|
|
assert_array_equal(mlb.fit(inp).transform(inp), indicator_mat)
|
||
|
|
|
||
|
|
# ensure a ValueError is thrown if given duplicate classes
|
||
|
|
err_msg = "The classes argument contains duplicate classes. Remove " \
|
||
|
|
"these duplicates before passing them to MultiLabelBinarizer."
|
||
|
|
mlb = MultiLabelBinarizer(classes=[1, 3, 2, 3])
|
||
|
|
with pytest.raises(ValueError, match=err_msg):
|
||
|
|
mlb.fit(inp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multilabel_binarizer_multiple_calls():
|
||
|
|
inp = [(2, 3), (1,), (1, 2)]
|
||
|
|
indicator_mat = np.array([[0, 1, 1],
|
||
|
|
[1, 0, 0],
|
||
|
|
[1, 0, 1]])
|
||
|
|
|
||
|
|
indicator_mat2 = np.array([[0, 1, 1],
|
||
|
|
[1, 0, 0],
|
||
|
|
[1, 1, 0]])
|
||
|
|
|
||
|
|
# first call
|
||
|
|
mlb = MultiLabelBinarizer(classes=[1, 3, 2])
|
||
|
|
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
|
||
|
|
# second call change class
|
||
|
|
mlb.classes = [1, 2, 3]
|
||
|
|
assert_array_equal(mlb.fit_transform(inp), indicator_mat2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multilabel_binarizer_same_length_sequence():
|
||
|
|
# Ensure sequences of the same length are not interpreted as a 2-d array
|
||
|
|
inp = [[1], [0], [2]]
|
||
|
|
indicator_mat = np.array([[0, 1, 0],
|
||
|
|
[1, 0, 0],
|
||
|
|
[0, 0, 1]])
|
||
|
|
# fit_transform()
|
||
|
|
mlb = MultiLabelBinarizer()
|
||
|
|
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
|
||
|
|
assert_array_equal(mlb.inverse_transform(indicator_mat), inp)
|
||
|
|
|
||
|
|
# fit().transform()
|
||
|
|
mlb = MultiLabelBinarizer()
|
||
|
|
assert_array_equal(mlb.fit(inp).transform(inp), indicator_mat)
|
||
|
|
assert_array_equal(mlb.inverse_transform(indicator_mat), inp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multilabel_binarizer_non_integer_labels():
|
||
|
|
tuple_classes = _to_object_array([(1,), (2,), (3,)])
|
||
|
|
inputs = [
|
||
|
|
([('2', '3'), ('1',), ('1', '2')], ['1', '2', '3']),
|
||
|
|
([('b', 'c'), ('a',), ('a', 'b')], ['a', 'b', 'c']),
|
||
|
|
([((2,), (3,)), ((1,),), ((1,), (2,))], tuple_classes),
|
||
|
|
]
|
||
|
|
indicator_mat = np.array([[0, 1, 1],
|
||
|
|
[1, 0, 0],
|
||
|
|
[1, 1, 0]])
|
||
|
|
for inp, classes in inputs:
|
||
|
|
# fit_transform()
|
||
|
|
mlb = MultiLabelBinarizer()
|
||
|
|
inp = np.array(inp, dtype=object)
|
||
|
|
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
|
||
|
|
assert_array_equal(mlb.classes_, classes)
|
||
|
|
indicator_mat_inv = np.array(mlb.inverse_transform(indicator_mat),
|
||
|
|
dtype=object)
|
||
|
|
assert_array_equal(indicator_mat_inv, inp)
|
||
|
|
|
||
|
|
# fit().transform()
|
||
|
|
mlb = MultiLabelBinarizer()
|
||
|
|
assert_array_equal(mlb.fit(inp).transform(inp), indicator_mat)
|
||
|
|
assert_array_equal(mlb.classes_, classes)
|
||
|
|
indicator_mat_inv = np.array(mlb.inverse_transform(indicator_mat),
|
||
|
|
dtype=object)
|
||
|
|
assert_array_equal(indicator_mat_inv, inp)
|
||
|
|
|
||
|
|
mlb = MultiLabelBinarizer()
|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
mlb.fit_transform([({}), ({}, {'a': 'b'})])
|
||
|
|
|
||
|
|
|
||
|
|
def test_multilabel_binarizer_non_unique():
|
||
|
|
inp = [(1, 1, 1, 0)]
|
||
|
|
indicator_mat = np.array([[1, 1]])
|
||
|
|
mlb = MultiLabelBinarizer()
|
||
|
|
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multilabel_binarizer_inverse_validation():
|
||
|
|
inp = [(1, 1, 1, 0)]
|
||
|
|
mlb = MultiLabelBinarizer()
|
||
|
|
mlb.fit_transform(inp)
|
||
|
|
# Not binary
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlb.inverse_transform(np.array([[1, 3]]))
|
||
|
|
# The following binary cases are fine, however
|
||
|
|
mlb.inverse_transform(np.array([[0, 0]]))
|
||
|
|
mlb.inverse_transform(np.array([[1, 1]]))
|
||
|
|
mlb.inverse_transform(np.array([[1, 0]]))
|
||
|
|
|
||
|
|
# Wrong shape
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlb.inverse_transform(np.array([[1]]))
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlb.inverse_transform(np.array([[1, 1, 1]]))
|
||
|
|
|
||
|
|
|
||
|
|
def test_label_binarize_with_class_order():
|
||
|
|
out = label_binarize([1, 6], classes=[1, 2, 4, 6])
|
||
|
|
expected = np.array([[1, 0, 0, 0], [0, 0, 0, 1]])
|
||
|
|
assert_array_equal(out, expected)
|
||
|
|
|
||
|
|
# Modified class order
|
||
|
|
out = label_binarize([1, 6], classes=[1, 6, 4, 2])
|
||
|
|
expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0]])
|
||
|
|
assert_array_equal(out, expected)
|
||
|
|
|
||
|
|
out = label_binarize([0, 1, 2, 3], classes=[3, 2, 0, 1])
|
||
|
|
expected = np.array([[0, 0, 1, 0],
|
||
|
|
[0, 0, 0, 1],
|
||
|
|
[0, 1, 0, 0],
|
||
|
|
[1, 0, 0, 0]])
|
||
|
|
assert_array_equal(out, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def check_binarized_results(y, classes, pos_label, neg_label, expected):
|
||
|
|
for sparse_output in [True, False]:
|
||
|
|
if ((pos_label == 0 or neg_label != 0) and sparse_output):
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
label_binarize(y, classes=classes, neg_label=neg_label,
|
||
|
|
pos_label=pos_label,
|
||
|
|
sparse_output=sparse_output)
|
||
|
|
continue
|
||
|
|
|
||
|
|
# check label_binarize
|
||
|
|
binarized = label_binarize(y, classes=classes, neg_label=neg_label,
|
||
|
|
pos_label=pos_label,
|
||
|
|
sparse_output=sparse_output)
|
||
|
|
assert_array_equal(toarray(binarized), expected)
|
||
|
|
assert issparse(binarized) == sparse_output
|
||
|
|
|
||
|
|
# check inverse
|
||
|
|
y_type = type_of_target(y)
|
||
|
|
if y_type == "multiclass":
|
||
|
|
inversed = _inverse_binarize_multiclass(binarized, classes=classes)
|
||
|
|
|
||
|
|
else:
|
||
|
|
inversed = _inverse_binarize_thresholding(binarized,
|
||
|
|
output_type=y_type,
|
||
|
|
classes=classes,
|
||
|
|
threshold=((neg_label +
|
||
|
|
pos_label) /
|
||
|
|
2.))
|
||
|
|
|
||
|
|
assert_array_equal(toarray(inversed), toarray(y))
|
||
|
|
|
||
|
|
# Check label binarizer
|
||
|
|
lb = LabelBinarizer(neg_label=neg_label, pos_label=pos_label,
|
||
|
|
sparse_output=sparse_output)
|
||
|
|
binarized = lb.fit_transform(y)
|
||
|
|
assert_array_equal(toarray(binarized), expected)
|
||
|
|
assert issparse(binarized) == sparse_output
|
||
|
|
inverse_output = lb.inverse_transform(binarized)
|
||
|
|
assert_array_equal(toarray(inverse_output), toarray(y))
|
||
|
|
assert issparse(inverse_output) == issparse(y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_label_binarize_binary():
|
||
|
|
y = [0, 1, 0]
|
||
|
|
classes = [0, 1]
|
||
|
|
pos_label = 2
|
||
|
|
neg_label = -1
|
||
|
|
expected = np.array([[2, -1], [-1, 2], [2, -1]])[:, 1].reshape((-1, 1))
|
||
|
|
|
||
|
|
check_binarized_results(y, classes, pos_label, neg_label, expected)
|
||
|
|
|
||
|
|
# Binary case where sparse_output = True will not result in a ValueError
|
||
|
|
y = [0, 1, 0]
|
||
|
|
classes = [0, 1]
|
||
|
|
pos_label = 3
|
||
|
|
neg_label = 0
|
||
|
|
expected = np.array([[3, 0], [0, 3], [3, 0]])[:, 1].reshape((-1, 1))
|
||
|
|
|
||
|
|
check_binarized_results(y, classes, pos_label, neg_label, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_label_binarize_multiclass():
|
||
|
|
y = [0, 1, 2]
|
||
|
|
classes = [0, 1, 2]
|
||
|
|
pos_label = 2
|
||
|
|
neg_label = 0
|
||
|
|
expected = 2 * np.eye(3)
|
||
|
|
|
||
|
|
check_binarized_results(y, classes, pos_label, neg_label, expected)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
label_binarize(y, classes=classes, neg_label=-1, pos_label=pos_label,
|
||
|
|
sparse_output=True)
|
||
|
|
|
||
|
|
|
||
|
|
def test_label_binarize_multilabel():
|
||
|
|
y_ind = np.array([[0, 1, 0], [1, 1, 1], [0, 0, 0]])
|
||
|
|
classes = [0, 1, 2]
|
||
|
|
pos_label = 2
|
||
|
|
neg_label = 0
|
||
|
|
expected = pos_label * y_ind
|
||
|
|
y_sparse = [sparse_matrix(y_ind)
|
||
|
|
for sparse_matrix in [coo_matrix, csc_matrix, csr_matrix,
|
||
|
|
dok_matrix, lil_matrix]]
|
||
|
|
|
||
|
|
for y in [y_ind] + y_sparse:
|
||
|
|
check_binarized_results(y, classes, pos_label, neg_label,
|
||
|
|
expected)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
label_binarize(y, classes=classes, neg_label=-1, pos_label=pos_label,
|
||
|
|
sparse_output=True)
|
||
|
|
|
||
|
|
|
||
|
|
def test_invalid_input_label_binarize():
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
label_binarize([0, 2], classes=[0, 2], pos_label=0, neg_label=1)
|
||
|
|
with pytest.raises(ValueError, match="continuous target data is not "):
|
||
|
|
label_binarize([1.2, 2.7], classes=[0, 1])
|
||
|
|
with pytest.raises(ValueError, match="mismatch with the labels"):
|
||
|
|
label_binarize([[1, 3]], classes=[1, 2, 3])
|
||
|
|
|
||
|
|
|
||
|
|
def test_inverse_binarize_multiclass():
|
||
|
|
got = _inverse_binarize_multiclass(csr_matrix([[0, 1, 0],
|
||
|
|
[-1, 0, -1],
|
||
|
|
[0, 0, 0]]),
|
||
|
|
np.arange(3))
|
||
|
|
assert_array_equal(got, np.array([1, 1, 0]))
|