lale.lib.sklearn.k_neighbors_classifier module

class lale.lib.sklearn.k_neighbors_classifier.KNeighborsClassifier(*, n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)

Bases: PlannedIndividualOp

K nearest neighbors classifier from scikit-learn.

This documentation is auto-generated from JSON schemas.

Parameters

• n_neighbors (integer, >=1, <='X/maxItems', <=100 for optimizer, uniform distribution, default 5) – Number of neighbors to use by default for kneighbors queries.

• weights (‘uniform’ or ‘distance’, default ‘uniform’) – Weight function used in prediction.

• algorithm (‘ball_tree’, ‘kd_tree’, ‘brute’, or ‘auto’, default ‘auto’) – Algorithm used to compute the nearest neighbors.

• leaf_size (integer, >=1, <=100 for optimizer, uniform distribution, not for optimizer, default 30) – Leaf size passed to BallTree or KDTree.

• p (integer, >=1, <=3 for optimizer, uniform distribution, default 2) – Power parameter for the Minkowski metric.

• metric (union type, default 'minkowski') –

  The distance metric to use for the tree.

  • ’euclidean’, ‘manhattan’, or ‘minkowski’

  • or string, not for optimizer

  • or callable, not for optimizer

    Takes two arrays representing 1D vectors as inputs and must return one value indicating the distance between those vectors.

• metric_params (union type, not for optimizer, default None) –

  Additional keyword arguments for the metric function.

  • None

  • or dict

• n_jobs (union type, not for optimizer, default None) –

  Number of parallel jobs to run for the neighbor search.

  • None

    1 unless in joblib.parallel_backend context.

  • or -1

    Use all processors.

  • or integer, >=1

    Number of CPU cores.

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) – Features; the outer array is over samples.

• y (union type) –

  Target class labels; the array is over samples.

  • array of items : float

  • or array of items : array of items : float

  • or array of items : string

  • or array of items : boolean

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 (union type) –

Features; the outer array is over samples.

• array of items : array of items : float

• or None

  Predictions for all training set points are returned, and points are not included into their own neighbors

Returns

result – Predicted class label per sample.

• array of items : float

• or array of items : array of items : float

• or array of items : string

• or array of items : boolean

Return type

union type

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 (union type) –

Features; the outer array is over samples.

• array of items : array of items : float

• or None

  Predictions for all training set points are returned, and points are not included into their own neighbors

Returns

result – Probability of the sample for each class in the model.

Return type

array of items : array of items : float