本文整理汇总了Python中sklearn.preprocessing.label.LabelBinarizer.fit方法的典型用法代码示例。如果您正苦于以下问题：Python LabelBinarizer.fit方法的具体用法？Python LabelBinarizer.fit怎么用？Python LabelBinarizer.fit使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.preprocessing.label.LabelBinarizer的用法示例。
在下文中一共展示了LabelBinarizer.fit方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_label_binarizer_multilabel
# 需要导入模块: from sklearn.preprocessing.label import LabelBinarizer [as 别名]
# 或者: from sklearn.preprocessing.label.LabelBinarizer import fit [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])示例2: fit_binarizers
# 需要导入模块: from sklearn.preprocessing.label import LabelBinarizer [as 别名]
# 或者: from sklearn.preprocessing.label.LabelBinarizer import fit [as 别名]
def fit_binarizers(all_values):
binarizers = {}
for f in range(len(all_values[0])):
cur_features = [context[f] for context in all_values]
# only categorical values need to be binarized, ints/floats are left as they are
if type(cur_features[0]) == str or type(cur_features[0]) == unicode:
lb = LabelBinarizer()
lb.fit(cur_features)
binarizers[f] = lb
elif type(cur_features[0]) == list:
mlb = MultiLabelBinarizer()
# default feature for unknown values
cur_features.append(tuple(("__unk__",)))
mlb.fit([tuple(x) for x in cur_features])
binarizers[f] = mlb
return binarizers示例3: test_label_binarize_with_multilabel_indicator
# 需要导入模块: from sklearn.preprocessing.label import LabelBinarizer [as 别名]
# 或者: from sklearn.preprocessing.label.LabelBinarizer import fit [as 别名]
def test_label_binarize_with_multilabel_indicator():
"""Check that passing a binary indicator matrix is not noop"""
classes = np.arange(3)
neg_label = -1
pos_label = 2
y = np.array([[0, 1, 0], [1, 1, 1]])
expected = np.array([[-1, 2, -1], [2, 2, 2]])
# With label binarize
output = label_binarize(y, classes, multilabel=True, neg_label=neg_label,
pos_label=pos_label)
assert_array_equal(output, expected)
# With the transformer
lb = LabelBinarizer(pos_label=pos_label, neg_label=neg_label)
output = lb.fit_transform(y)
assert_array_equal(output, expected)
output = lb.fit(y).transform(y)
assert_array_equal(output, expected)