lale.lib.autogen.label_propagation module

class lale.lib.autogen.label_propagation.LabelPropagation(*, kernel='rbf', gamma=20, n_neighbors=7, max_iter=1000, tol=0.001, n_jobs=1)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

• kernel (union type, default 'rbf') –

  String identifier for kernel function to use or the kernel function itself

  • ’knn’ or ‘rbf’

  • or callable, not for optimizer

• gamma (float, >=0 for optimizer, <=20 for optimizer, uniform distribution, default 20) – Parameter for rbf kernel

• n_neighbors (integer, >=5 for optimizer, <=20 for optimizer, uniform distribution, default 7) – Parameter for knn kernel

• max_iter (integer, >=10 for optimizer, <=1000 for optimizer, uniform distribution, default 1000) – Change maximum number of iterations allowed

• tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 0.001) – Convergence tolerance: threshold to consider the system at steady state

• n_jobs (union type, not for optimizer, default 1) –

  The number of parallel jobs to run

  • integer

  • or None

Notes

constraint-1 : any type

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) – A {n_samples by n_samples} size matrix will be created from this

• y (array of items : float) – n_labeled_samples (unlabeled points are marked as -1) All unlabeled samples will be transductively assigned labels

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 (array of items : array of items : float) –

Returns

result – Predictions for input data

Return type

array of items : float

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 (array of items : array of items : float) –

Returns

result – Normalized probability distributions across class labels

Return type

array of items : array of items : float