lale.lib.autogen.nearest_centroid module¶

class lale.lib.autogen.nearest_centroid.NearestCentroid(*, metric='euclidean', shrink_threshold=None)¶

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

metric (union type, default 'euclidean') –
The metric to use when calculating distance between instances in a feature array
- callable, not for optimizer
- or ‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’, ‘manhattan’, ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, or ‘yule’
shrink_threshold (union type, default None) –
Threshold for shrinking centroids to remove features.
- float, >=0.0 for optimizer, <=1.0 for optimizer, uniform distribution
- or None
See also constraint-1.

Notes

constraint-1 : union type

threshold shrinking not supported for sparse input

negated type of ‘X/isSparse’

or shrink_threshold : 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 of items : array of items : float) – Training vector, where n_samples is the number of samples and n_features is the number of features
y (array of items : float) – Target values (integers)

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: