lale.lib.sklearn.missing_indicator module¶
- class lale.lib.sklearn.missing_indicator.MissingIndicator(*, missing_values=nan, features='missing-only', sparse='auto', error_on_new=True)¶
Bases:
PlannedIndividualOp
Missing values indicator transformer from scikit-learn.
This documentation is auto-generated from JSON schemas.
- Parameters
missing_values (union type, not for optimizer, default nan) –
The placeholder for the missing values.
float
or string
or nan
or None
See also constraint-2.
features (‘missing-only’ or ‘all’, not for optimizer, default ‘missing-only’) –
Whether the imputer mask should represent all or a subset of features.
See also constraint-1.
sparse (union type, not for optimizer, default 'auto') –
Whether the imputer mask format should be sparse or dense.
boolean
or ‘auto’
error_on_new (boolean, not for optimizer, default True) –
If True (default), transform will raise an error when there are
See also constraint-1.
Notes
constraint-1 : union type
error_on_new, only when features=”missing-only”
error_on_new : True
or features : ‘missing-only’
constraint-2 : union type
Sparse input with missing_values=0 is not supported. Provide a dense array instead.
negated type of ‘X/isSparse’
or missing_values : negated type of 0
- fit(X, y=None, **fit_params)¶
Train the operator.
Note: The fit method is not available until this operator is trainable.
Once this method is available, it will have the following signature:
- Parameters
X (array of items : array of items : float) – Input data, where
n_samples
is the number of samples and
- transform(X, y=None)¶
Transform the data.
Note: The transform method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (array of items : array of items : float) – The input data to complete.
- Returns
result – The missing indicator for input data.
- Return type
array of items : array of items : boolean