lale.lib.lale.observing module

class lale.lib.lale.observing.LoggingObserver[source]

Bases: object

An observer that logs everything. This is also useful for debugging, since you can set breakpoints here

class lale.lib.lale.observing.Observing(*, op, observer)

Bases: PlannedIndividualOp

This should functionally be identical to the identity wrapper, except that it calls methods on the observer (if they exist) before and after calls to the underlying wrapper. This is similar to aspect-oriented programming. See also Tee, which provides a simpler method for observing/logging data.

This documentation is auto-generated from JSON schemas.

Parameters

  • op (operator, optional) –

  • observer (Any, optional, not for optimizer) –

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 (any type) –

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:

Parameters

  • X (any type) –

  • y (any type) –

Returns

result – Output data schema for transformations using identity.

Return type

Any

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 type) –

  • y (any type) –

Returns

result – Output data schema for transformations using identity.

Return type

Any

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 (any type) –

  • y (any type) –

Returns

result – Output data schema for transformations using identity.

Return type

Any

lale.lib.lale.observing.observe(f)[source]