lale.lib.autogen.birch module

class lale.lib.autogen.birch.Birch(*, threshold=0.5, branching_factor=50, n_clusters=3, compute_labels=True, copy=True)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • threshold (float, not for optimizer, default 0.5) – The radius of the subcluster obtained by merging a new sample and the closest subcluster should be lesser than the threshold

  • branching_factor (integer, >=50 for optimizer, <=51 for optimizer, uniform distribution, default 50) – Maximum number of CF subclusters in each node

  • n_clusters (integer, >=2 for optimizer, <=8 for optimizer, uniform distribution, default 3) – Number of clusters after the final clustering step, which treats the subclusters from the leaves as new samples

  • compute_labels (boolean, default True) – Whether or not to compute labels for each fit.

  • copy (boolean, default True) – Whether or not to make a copy of the given data

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) – Input data.

  • y (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 (array of items : array of items : float) – Input data.

Returns

result – Labelled data.

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 (array of items : array of items : float) – Input data.

Returns

result – Transformed data.

Return type

array of items : array of items : float