lale.lib.sklearn.pipeline module¶

class lale.lib.sklearn.pipeline.Pipeline(*, steps, memory=None, verbose=False)¶

Pipeline from scikit-learn creates a sequential list of operators.

This documentation is auto-generated from JSON schemas.

Parameters

steps (array, not for optimizer) –
List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an estimator.
- items : tuple, >=2 items, <=2 items
  Tuple of (name, transform).
  - item 0 : string
    
    Name.
  - item 1 : union type
    
    operator
    
    Transform.
    
    or None or ‘passthrough’
    
    NoOp
memory (union type, optional, not for optimizer, default None) –
Used to cache the fitted transformers of the pipeline.
- string
  
  Path to the caching directory.
- or dict, not for optimizer
  
  Object with the joblib.Memory interface
- or None
  
  No caching.
verbose (boolean, optional, not for optimizer, default False) – If True, the time elapsed while fitting each step will be printed as it is completed.

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

predict(X, **predict_params)¶

Make predictions.

Note: The predict method is not available until this operator is trained.

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

predict_proba(X)¶

Probability estimates for all classes.

Note: The predict_proba method is not available until this operator is trained.

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

Parameters: X (Any) – Features.
Returns: result – Probability of the sample for each class in the model.
Return type: array of items : array of items : float

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: