Python LabelBinarizer.inverse_transform方法代码示例

# 需要导入模块: from sklearn.preprocessing.label import LabelBinarizer [as 别名]
# 或者: from sklearn.preprocessing.label.LabelBinarizer import inverse_transform [as 别名]
def test_label_binarizer():
    lb = LabelBinarizer()

    # one-class case defaults to negative label
    inp = ["pos", "pos", "pos", "pos"]
    expected = np.array([[0, 0, 0, 0]]).T
    got = lb.fit_transform(inp)
    assert_array_equal(lb.classes_, ["pos"])
    assert_array_equal(expected, got)
    assert_array_equal(lb.inverse_transform(got), inp)

    # two-class case
    inp = ["neg", "pos", "pos", "neg"]
    expected = np.array([[0, 1, 1, 0]]).T
    got = lb.fit_transform(inp)
    assert_array_equal(lb.classes_, ["neg", "pos"])
    assert_array_equal(expected, got)

    to_invert = np.array([[1, 0],
                          [0, 1],
                          [0, 1],
                          [1, 0]])
    assert_array_equal(lb.inverse_transform(to_invert), inp)

    # multi-class case
    inp = ["spam", "ham", "eggs", "ham", "0"]
    expected = np.array([[0, 0, 0, 1],
                         [0, 0, 1, 0],
                         [0, 1, 0, 0],
                         [0, 0, 1, 0],
                         [1, 0, 0, 0]])
    got = lb.fit_transform(inp)
    assert_array_equal(lb.classes_, ['0', 'eggs', 'ham', 'spam'])
    assert_array_equal(expected, got)
    assert_array_equal(lb.inverse_transform(got), inp)

# 需要导入模块: from sklearn.preprocessing.label import LabelBinarizer [as 别名]
# 或者: from sklearn.preprocessing.label.LabelBinarizer import inverse_transform [as 别名]
def test_label_binarizer_multilabel():
    lb = LabelBinarizer()

    # test input as lists of tuples
    inp = [(2, 3), (1,), (1, 2)]
    indicator_mat = np.array([[0, 1, 1],
                              [1, 0, 0],
                              [1, 1, 0]])
    got = lb.fit_transform(inp)
    assert_true(lb.multilabel_)
    assert_array_equal(indicator_mat, got)
    assert_equal(lb.inverse_transform(got), inp)

    # test input as label indicator matrix
    lb.fit(indicator_mat)
    assert_array_equal(indicator_mat,
                       lb.inverse_transform(indicator_mat))

    # regression test for the two-class multilabel case
    lb = LabelBinarizer()
    inp = [[1, 0], [0], [1], [0, 1]]
    expected = np.array([[1, 1],
                         [1, 0],
                         [0, 1],
                         [1, 1]])
    got = lb.fit_transform(inp)
    assert_true(lb.multilabel_)
    assert_array_equal(expected, got)
    assert_equal([set(x) for x in lb.inverse_transform(got)],
                 [set(x) for x in inp])

# 需要导入模块: from sklearn.preprocessing.label import LabelBinarizer [as 别名]
# 或者: from sklearn.preprocessing.label.LabelBinarizer import inverse_transform [as 别名]
def test_label_binarizer():
    lb = LabelBinarizer()

    # one-class case defaults to negative label
    inp = ["pos", "pos", "pos", "pos"]
    expected = np.array([[0, 0, 0, 0]]).T
    got = lb.fit_transform(inp)
    assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_"))
    assert_array_equal(lb.classes_, ["pos"])
    assert_array_equal(expected, got)
    assert_array_equal(lb.inverse_transform(got), inp)

    # two-class case
    inp = ["neg", "pos", "pos", "neg"]
    expected = np.array([[0, 1, 1, 0]]).T
    got = lb.fit_transform(inp)
    assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_"))
    assert_array_equal(lb.classes_, ["neg", "pos"])
    assert_array_equal(expected, got)

    to_invert = np.array([[1, 0], [0, 1], [0, 1], [1, 0]])
    assert_array_equal(lb.inverse_transform(to_invert), inp)

    # multi-class case
    inp = ["spam", "ham", "eggs", "ham", "0"]
    expected = np.array([[0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]])
    got = lb.fit_transform(inp)
    assert_array_equal(lb.classes_, ["0", "eggs", "ham", "spam"])
    assert_false(assert_warns(DeprecationWarning, getattr, lb, "multilabel_"))
    assert_array_equal(expected, got)
    assert_array_equal(lb.inverse_transform(got), inp)

# 需要导入模块: from sklearn.preprocessing.label import LabelBinarizer [as 别名]
# 或者: from sklearn.preprocessing.label.LabelBinarizer import inverse_transform [as 别名]
def test_label_binarizer_set_label_encoding():
    lb = LabelBinarizer(neg_label=-2, pos_label=0)

    # two-class case with pos_label=0
    inp = np.array([0, 1, 1, 0])
    expected = np.array([[-2, 0, 0, -2]]).T
    got = lb.fit_transform(inp)
    assert_array_equal(expected, got)
    assert_array_equal(lb.inverse_transform(got), inp)

    lb = LabelBinarizer(neg_label=-2, pos_label=2)

    # multi-class case
    inp = np.array([3, 2, 1, 2, 0])
    expected = np.array([[-2, -2, -2, +2], [-2, -2, +2, -2], [-2, +2, -2, -2], [-2, -2, +2, -2], [+2, -2, -2, -2]])
    got = lb.fit_transform(inp)
    assert_array_equal(expected, got)
    assert_array_equal(lb.inverse_transform(got), inp)

# 需要导入模块: from sklearn.preprocessing.label import LabelBinarizer [as 别名]
# 或者: from sklearn.preprocessing.label.LabelBinarizer import inverse_transform [as 别名]
def test_label_binarizer():
    # one-class case defaults to negative label
    # For dense case:
    inp = ["pos", "pos", "pos", "pos"]
    lb = LabelBinarizer(sparse_output=False)
    expected = np.array([[0, 0, 0, 0]]).T
    got = lb.fit_transform(inp)
    assert_array_equal(lb.classes_, ["pos"])
    assert_array_equal(expected, got)
    assert_array_equal(lb.inverse_transform(got), inp)

    # For sparse case:
    lb = LabelBinarizer(sparse_output=True)
    got = lb.fit_transform(inp)
    assert issparse(got)
    assert_array_equal(lb.classes_, ["pos"])
    assert_array_equal(expected, got.toarray())
    assert_array_equal(lb.inverse_transform(got.toarray()), inp)

    lb = LabelBinarizer(sparse_output=False)
    # two-class case
    inp = ["neg", "pos", "pos", "neg"]
    expected = np.array([[0, 1, 1, 0]]).T
    got = lb.fit_transform(inp)
    assert_array_equal(lb.classes_, ["neg", "pos"])
    assert_array_equal(expected, got)

    to_invert = np.array([[1, 0],
                          [0, 1],
                          [0, 1],
                          [1, 0]])
    assert_array_equal(lb.inverse_transform(to_invert), inp)

    # multi-class case
    inp = ["spam", "ham", "eggs", "ham", "0"]
    expected = np.array([[0, 0, 0, 1],
                         [0, 0, 1, 0],
                         [0, 1, 0, 0],
                         [0, 0, 1, 0],
                         [1, 0, 0, 0]])
    got = lb.fit_transform(inp)
    assert_array_equal(lb.classes_, ['0', 'eggs', 'ham', 'spam'])
    assert_array_equal(expected, got)
    assert_array_equal(lb.inverse_transform(got), inp)

# 需要导入模块: from sklearn.preprocessing.label import LabelBinarizer [as 别名]
# 或者: from sklearn.preprocessing.label.LabelBinarizer import inverse_transform [as 别名]
def test_label_binarizer_iris():
    lb = LabelBinarizer()
    Y = lb.fit_transform(iris.target)
    clfs = [SGDClassifier().fit(iris.data, Y[:, k])
            for k in range(len(lb.classes_))]
    Y_pred = np.array([clf.decision_function(iris.data) for clf in clfs]).T
    y_pred = lb.inverse_transform(Y_pred)
    accuracy = np.mean(iris.target == y_pred)
    y_pred2 = SGDClassifier().fit(iris.data, iris.target).predict(iris.data)
    accuracy2 = np.mean(iris.target == y_pred2)
    assert_almost_equal(accuracy, accuracy2)

# 需要导入模块: from sklearn.preprocessing.label import LabelBinarizer [as 别名]
# 或者: from sklearn.preprocessing.label.LabelBinarizer import inverse_transform [as 别名]
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):
            assert_raises(ValueError, label_binarize, y, classes,
                          neg_label=neg_label, pos_label=pos_label,
                          sparse_output=sparse_output)
            continue

        # check label_binarize
        binarized = label_binarize(y, classes, neg_label=neg_label,
                                   pos_label=pos_label,
                                   sparse_output=sparse_output)
        assert_array_equal(toarray(binarized), expected)
        assert_equal(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_equal(issparse(binarized), sparse_output)
        inverse_output = lb.inverse_transform(binarized)
        assert_array_equal(toarray(inverse_output), toarray(y))
        assert_equal(issparse(inverse_output), issparse(y))