本文整理匯總了Python中sklearn.preprocessing.label.LabelEncoder.inverse_transform方法的典型用法代碼示例。如果您正苦於以下問題：Python LabelEncoder.inverse_transform方法的具體用法？Python LabelEncoder.inverse_transform怎麽用？Python LabelEncoder.inverse_transform使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類sklearn.preprocessing.label.LabelEncoder的用法示例。
在下文中一共展示了LabelEncoder.inverse_transform方法的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_label_encoder
# 需要導入模塊: from sklearn.preprocessing.label import LabelEncoder [as 別名]
# 或者: from sklearn.preprocessing.label.LabelEncoder import inverse_transform [as 別名]
def test_label_encoder():
"""Test LabelEncoder's transform and inverse_transform methods"""
le = LabelEncoder()
le.fit([1, 1, 4, 5, -1, 0])
assert_array_equal(le.classes_, [-1, 0, 1, 4, 5])
assert_array_equal(le.transform([0, 1, 4, 4, 5, -1, -1]), [1, 2, 3, 3, 4, 0, 0])
assert_array_equal(le.inverse_transform([1, 2, 3, 3, 4, 0, 0]), [0, 1, 4, 4, 5, -1, -1])
assert_raises(ValueError, le.transform, [0, 6])示例2: test_label_encoder_empty_array
# 需要導入模塊: from sklearn.preprocessing.label import LabelEncoder [as 別名]
# 或者: from sklearn.preprocessing.label.LabelEncoder import inverse_transform [as 別名]
def test_label_encoder_empty_array(values):
le = LabelEncoder()
le.fit(values)
# test empty transform
transformed = le.transform([])
assert_array_equal(np.array([]), transformed)
# test empty inverse transform
inverse_transformed = le.inverse_transform([])
assert_array_equal(np.array([]), inverse_transformed)示例3: test_label_encoder_negative_ints
# 需要導入模塊: from sklearn.preprocessing.label import LabelEncoder [as 別名]
# 或者: from sklearn.preprocessing.label.LabelEncoder import inverse_transform [as 別名]
def test_label_encoder_negative_ints():
le = LabelEncoder()
le.fit([1, 1, 4, 5, -1, 0])
assert_array_equal(le.classes_, [-1, 0, 1, 4, 5])
assert_array_equal(le.transform([0, 1, 4, 4, 5, -1, -1]),
[1, 2, 3, 3, 4, 0, 0])
assert_array_equal(le.inverse_transform([1, 2, 3, 3, 4, 0, 0]),
[0, 1, 4, 4, 5, -1, -1])
assert_raises(ValueError, le.transform, [0, 6])示例4: test_label_encoder_string_labels
# 需要導入模塊: from sklearn.preprocessing.label import LabelEncoder [as 別名]
# 或者: from sklearn.preprocessing.label.LabelEncoder import inverse_transform [as 別名]
def test_label_encoder_string_labels():
"""Test LabelEncoder's transform and inverse_transform methods with
non-numeric labels"""
le = LabelEncoder()
le.fit(["paris", "paris", "tokyo", "amsterdam"])
assert_array_equal(le.classes_, ["amsterdam", "paris", "tokyo"])
assert_array_equal(le.transform(["tokyo", "tokyo", "paris"]),
[2, 2, 1])
assert_array_equal(le.inverse_transform([2, 2, 1]),
["tokyo", "tokyo", "paris"])
assert_raises(ValueError, le.transform, ["london"])示例5: test_label_encoder
# 需要導入模塊: from sklearn.preprocessing.label import LabelEncoder [as 別名]
# 或者: from sklearn.preprocessing.label.LabelEncoder import inverse_transform [as 別名]
def test_label_encoder():
# Test LabelEncoder's transform and inverse_transform methods
le = LabelEncoder()
le.fit([1, 1, 4, 5, -1, 0])
assert_array_equal(le.classes_, [-1, 0, 1, 4, 5])
assert_array_equal(le.transform([0, 1, 4, 4, 5, -1, -1]), [1, 2, 3, 3, 4, 0, 0])
assert_array_equal(le.inverse_transform([1, 2, 3, 3, 4, 0, 0]), [0, 1, 4, 4, 5, -1, -1])
assert_raises(ValueError, le.transform, [0, 6])
le.fit(["apple", "orange"])
msg = "bad input shape"
assert_raise_message(ValueError, msg, le.transform, "apple")示例6: test_label_encoder
# 需要導入模塊: from sklearn.preprocessing.label import LabelEncoder [as 別名]
# 或者: from sklearn.preprocessing.label.LabelEncoder import inverse_transform [as 別名]
def test_label_encoder(values, classes, unknown):
# Test LabelEncoder's transform, fit_transform and
# inverse_transform methods
le = LabelEncoder()
le.fit(values)
assert_array_equal(le.classes_, classes)
assert_array_equal(le.transform(values), [1, 0, 2, 0, 2])
assert_array_equal(le.inverse_transform([1, 0, 2, 0, 2]), values)
le = LabelEncoder()
ret = le.fit_transform(values)
assert_array_equal(ret, [1, 0, 2, 0, 2])
with pytest.raises(ValueError, match="unseen labels"):
le.transform(unknown)