lale.lib.sklearn.normalizer module¶
- class lale.lib.sklearn.normalizer.Normalizer(*, norm='l2', copy=True)¶
Bases:
PlannedIndividualOp
Normalizer transformer from scikit-learn.
This documentation is auto-generated from JSON schemas.
- Parameters
norm (‘l1’, ‘l2’, or ‘max’, default ‘l2’) – The norm to use to normalize each non zero sample.
copy (boolean, optional, not for optimizer, default True) – Set to False to perform inplace row normalization and avoid a copy.
- 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) – Features; the outer array is over samples.
y (any type, optional) – Target class labels; the array is over samples.
- 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 data to normalize, row by row. scipy.sparse matrices should be
copy (union type, optional, default None) –
Copy the input X or not.
boolean
or None
- Returns
result – Scale each non zero row of X to unit norm
- Return type
array of items : array of items : float