本文整理汇总了Python中sklearn.impute.MissingIndicator方法的典型用法代码示例。如果您正苦于以下问题:Python impute.MissingIndicator方法的具体用法?Python impute.MissingIndicator怎么用?Python impute.MissingIndicator使用的例子?那么, 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.impute
的用法示例。
在下文中一共展示了impute.MissingIndicator方法的11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_missing_indicator_raise_on_sparse_with_missing_0
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import MissingIndicator [as 别名]
def test_missing_indicator_raise_on_sparse_with_missing_0(arr_type):
# test for sparse input and missing_value == 0
missing_values = 0
X_fit = np.array([[missing_values, missing_values, 1],
[4, missing_values, 2]])
X_trans = np.array([[missing_values, missing_values, 1],
[4, 12, 10]])
# convert the input to the right array format
X_fit_sparse = arr_type(X_fit)
X_trans_sparse = arr_type(X_trans)
indicator = MissingIndicator(missing_values=missing_values)
with pytest.raises(ValueError, match="Sparse input with missing_values=0"):
indicator.fit_transform(X_fit_sparse)
indicator.fit_transform(X_fit)
with pytest.raises(ValueError, match="Sparse input with missing_values=0"):
indicator.transform(X_trans_sparse)
示例2: fit
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import MissingIndicator [as 别名]
def fit(self, X, y=None):
"""Fit the transformer on X.
Parameters
----------
X : {array-like}, shape (n_samples, n_features)
Input data, where ``n_samples`` is the number of samples and
``n_features`` is the number of features.
Returns
-------
self : RobustMissingIndicator
"""
X = self._validate_input(X)
self.vectorized_mask_function_ = self.mask_function or is_finite_numeric
X = _apply_mask(X, _get_mask(X, self.vectorized_mask_function_))
self.missing_indicator_ = MissingIndicator(features=self.features, error_on_new=self.error_on_new)
self.missing_indicator_.fit(X)
return self
示例3: test_missing_indicator_error
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import MissingIndicator [as 别名]
def test_missing_indicator_error(X_fit, X_trans, params, msg_err):
indicator = MissingIndicator(missing_values=-1)
indicator.set_params(**params)
with pytest.raises(ValueError, match=msg_err):
indicator.fit(X_fit).transform(X_trans)
示例4: test_missing_indicator_sparse_param
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import MissingIndicator [as 别名]
def test_missing_indicator_sparse_param(arr_type, missing_values,
param_sparse):
# check the format of the output with different sparse parameter
X_fit = np.array([[missing_values, missing_values, 1],
[4, missing_values, 2]])
X_trans = np.array([[missing_values, missing_values, 1],
[4, 12, 10]])
X_fit = arr_type(X_fit).astype(np.float64)
X_trans = arr_type(X_trans).astype(np.float64)
indicator = MissingIndicator(missing_values=missing_values,
sparse=param_sparse)
X_fit_mask = indicator.fit_transform(X_fit)
X_trans_mask = indicator.transform(X_trans)
if param_sparse is True:
assert X_fit_mask.format == 'csc'
assert X_trans_mask.format == 'csc'
elif param_sparse == 'auto' and missing_values == 0:
assert isinstance(X_fit_mask, np.ndarray)
assert isinstance(X_trans_mask, np.ndarray)
elif param_sparse is False:
assert isinstance(X_fit_mask, np.ndarray)
assert isinstance(X_trans_mask, np.ndarray)
else:
if sparse.issparse(X_fit):
assert X_fit_mask.format == 'csc'
assert X_trans_mask.format == 'csc'
else:
assert isinstance(X_fit_mask, np.ndarray)
assert isinstance(X_trans_mask, np.ndarray)
示例5: test_missing_indicator_string
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import MissingIndicator [as 别名]
def test_missing_indicator_string():
X = np.array([['a', 'b', 'c'], ['b', 'c', 'a']], dtype=object)
indicator = MissingIndicator(missing_values='a', features='all')
X_trans = indicator.fit_transform(X)
assert_array_equal(X_trans, np.array([[True, False, False],
[False, False, True]]))
示例6: test_missing_indicator_no_missing
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import MissingIndicator [as 别名]
def test_missing_indicator_no_missing():
# check that all features are dropped if there are no missing values when
# features='missing-only' (#13491)
X = np.array([[1, 1],
[1, 1]])
mi = MissingIndicator(features='missing-only', missing_values=-1)
Xt = mi.fit_transform(X)
assert Xt.shape[1] == 0
示例7: test_missing_indicator_sparse_no_explicit_zeros
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import MissingIndicator [as 别名]
def test_missing_indicator_sparse_no_explicit_zeros():
# Check that non missing values don't become explicit zeros in the mask
# generated by missing indicator when X is sparse. (#13491)
X = sparse.csr_matrix([[0, 1, 2],
[1, 2, 0],
[2, 0, 1]])
mi = MissingIndicator(features='all', missing_values=1)
Xt = mi.fit_transform(X)
assert Xt.getnnz() == Xt.sum()
示例8: fit
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import MissingIndicator [as 别名]
def fit(self, X, y=None):
self._wrapped_model = SKLModel(**self._hyperparams)
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
示例9: __init__
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import MissingIndicator [as 别名]
def __init__(self, missing_values='nan', features='missing-only', sparse='auto', error_on_new=True):
self._hyperparams = {
'missing_values': missing_values,
'features': features,
'sparse': sparse,
'error_on_new': error_on_new}
self._wrapped_model = Op(**self._hyperparams)
示例10: transform
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import MissingIndicator [as 别名]
def transform(self, X):
"""Perform imputation using interpolation.
Parameters
----------
X : array-like, shape = (n_samples, n_timestamps)
Data with missing values.
Returns
-------
X_new : array-like, shape = (n_samples, n_timestamps)
Data without missing values.
"""
missing_values, force_all_finite = self._check_params()
X = check_array(X, dtype='float64', force_all_finite=force_all_finite)
n_samples, n_timestamps = X.shape
indicator = MissingIndicator(
missing_values=missing_values, features='all', sparse=False,
)
non_missing_idx = ~(indicator.fit_transform(X))
x_new = np.arange(n_timestamps)
X_imputed = np.asarray(
[self._impute_one_sample(X[i], non_missing_idx[i], x_new)
for i in range(n_samples)]
)
return X_imputed
示例11: test_missing_indicator_new
# 需要导入模块: from sklearn import impute [as 别名]
# 或者: from sklearn.impute import MissingIndicator [as 别名]
def test_missing_indicator_new(missing_values, arr_type, dtype, param_features,
n_features, features_indices):
X_fit = np.array([[missing_values, missing_values, 1],
[4, 2, missing_values]])
X_trans = np.array([[missing_values, missing_values, 1],
[4, 12, 10]])
X_fit_expected = np.array([[1, 1, 0], [0, 0, 1]])
X_trans_expected = np.array([[1, 1, 0], [0, 0, 0]])
# convert the input to the right array format and right dtype
X_fit = arr_type(X_fit).astype(dtype)
X_trans = arr_type(X_trans).astype(dtype)
X_fit_expected = X_fit_expected.astype(dtype)
X_trans_expected = X_trans_expected.astype(dtype)
indicator = MissingIndicator(missing_values=missing_values,
features=param_features,
sparse=False)
X_fit_mask = indicator.fit_transform(X_fit)
X_trans_mask = indicator.transform(X_trans)
assert X_fit_mask.shape[1] == n_features
assert X_trans_mask.shape[1] == n_features
assert_array_equal(indicator.features_, features_indices)
assert_allclose(X_fit_mask, X_fit_expected[:, features_indices])
assert_allclose(X_trans_mask, X_trans_expected[:, features_indices])
assert X_fit_mask.dtype == bool
assert X_trans_mask.dtype == bool
assert isinstance(X_fit_mask, np.ndarray)
assert isinstance(X_trans_mask, np.ndarray)
indicator.set_params(sparse=True)
X_fit_mask_sparse = indicator.fit_transform(X_fit)
X_trans_mask_sparse = indicator.transform(X_trans)
assert X_fit_mask_sparse.dtype == bool
assert X_trans_mask_sparse.dtype == bool
assert X_fit_mask_sparse.format == 'csc'
assert X_trans_mask_sparse.format == 'csc'
assert_allclose(X_fit_mask_sparse.toarray(), X_fit_mask)
assert_allclose(X_trans_mask_sparse.toarray(), X_trans_mask)