lale.lib.sklearn.min_max_scaler module¶

class lale.lib.sklearn.min_max_scaler.MinMaxScaler(*, feature_range='(0, 1)', copy=True, clip=False)¶

Min-max scaler transformer from scikit-learn.

This documentation is auto-generated from JSON schemas.

Parameters

feature_range (tuple, >=2 items, <=2 items, not for optimizer, default (0, 1)) –
Desired range of transformed data.
- item 0 : float, >=-1 for optimizer, <=0 for optimizer
- item 1 : float, >=0.001 for optimizer, <=1 for optimizer
copy (boolean, not for optimizer, default True) – Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array).
clip (boolean, optional, not for optimizer, default False) – Set to True to clip transformed values of held-out data to provided feature range.

Notes

constraint-1 : negated type of ‘X/isSparse’

MinMaxScaler does not support sparse input. Consider using MaxAbsScaler instead.

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) –

partial_fit(X, y=None, **fit_params)¶

Incremental fit to train train the operator on a batch of samples.

Note: The partial_fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

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) – Features; the outer array is over samples.
Returns: result – Output data schema for transformed data.
Return type: array of items : array of items : float