lale.lib.rasl.ordinal_encoder module¶
- class lale.lib.rasl.ordinal_encoder.OrdinalEncoder(*, categories='auto', dtype='float64', handle_unknown='use_encoded_value', unknown_value)¶
Bases:
PlannedIndividualOp
Relational algebra reimplementation of scikit-learn’s OrdinalEncoder transformer that encodes categorical features as numbers.
This documentation is auto-generated from JSON schemas.
Works on both pandas and Spark dataframes by using Aggregate for fit and Map for transform, which in turn use the appropriate backend.
- Parameters
categories (union type, not for optimizer, default 'auto') –
‘auto’ or None
Determine categories automatically from training data.
or array
The ith list element holds the categories expected in the ith column.
items : union type
array of items : string
or array of items : float
Should be sorted.
dtype ('float64', not for optimizer, default 'float64') – This implementation only supports dtype=’float64’.
handle_unknown ('use_encoded_value', optional, not for optimizer, default 'use_encoded_value') – This implementation only supports handle_unknown=’use_encoded_value’.
unknown_value (union type, optional, not for optimizer) –
The encoded value of unknown categories to use when handle_unknown=’use_encoded_value’. It has to be distinct from the values used to encode any of the categories in fit. If set to np.nan, the dtype hyperparameter must be a float dtype.
integer
or nan or None
- 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) –
Features; the outer array is over samples.
items : union type
array of items : float
or array of items : string
y (any type, optional) – Target class labels; the array is over samples.
- 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) –
Features; the outer array is over samples.
items : union type
array of items : float
or array of items : string
- Returns
result – Ordinal codes.
- Return type
array of items : array of items : float