本文整理匯總了Python中sklearn.preprocessing.FunctionTransformer.inverse_transform方法的典型用法代碼示例。如果您正苦於以下問題：Python FunctionTransformer.inverse_transform方法的具體用法？Python FunctionTransformer.inverse_transform怎麽用？Python FunctionTransformer.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.FunctionTransformer的用法示例。
在下文中一共展示了FunctionTransformer.inverse_transform方法的2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_inverse_transform
# 需要導入模塊: from sklearn.preprocessing import FunctionTransformer [as 別名]
# 或者: from sklearn.preprocessing.FunctionTransformer import inverse_transform [as 別名]
def test_inverse_transform():
X = np.array([1, 4, 9, 16]).reshape((2, 2))
# Test that inverse_transform works correctly
F = FunctionTransformer(
func=np.sqrt,
inverse_func=np.around, inv_kw_args=dict(decimals=3))
testing.assert_array_equal(
F.inverse_transform(F.transform(X)),
np.around(np.sqrt(X), decimals=3))示例2: test_check_inverse
# 需要導入模塊: from sklearn.preprocessing import FunctionTransformer [as 別名]
# 或者: from sklearn.preprocessing.FunctionTransformer import inverse_transform [as 別名]
def test_check_inverse():
X_dense = np.array([1, 4, 9, 16], dtype=np.float64).reshape((2, 2))
X_list = [X_dense,
sparse.csr_matrix(X_dense),
sparse.csc_matrix(X_dense)]
for X in X_list:
if sparse.issparse(X):
accept_sparse = True
else:
accept_sparse = False
trans = FunctionTransformer(func=np.sqrt,
inverse_func=np.around,
accept_sparse=accept_sparse,
check_inverse=True,
validate=True)
assert_warns_message(UserWarning,
"The provided functions are not strictly"
" inverse of each other. If you are sure you"
" want to proceed regardless, set"
" 'check_inverse=False'.",
trans.fit, X)
trans = FunctionTransformer(func=np.expm1,
inverse_func=np.log1p,
accept_sparse=accept_sparse,
check_inverse=True,
validate=True)
Xt = assert_no_warnings(trans.fit_transform, X)
assert_allclose_dense_sparse(X, trans.inverse_transform(Xt))
# check that we don't check inverse when one of the func or inverse is not
# provided.
trans = FunctionTransformer(func=np.expm1, inverse_func=None,
check_inverse=True, validate=True)
assert_no_warnings(trans.fit, X_dense)
trans = FunctionTransformer(func=None, inverse_func=np.expm1,
check_inverse=True, validate=True)
assert_no_warnings(trans.fit, X_dense)