search

Functions:

construct_knn(n_neighbors, metric, **knn_kwargs)

Constructs a k-nearest neighbors search object.

cleanlab.internal.neighbor.search.construct_knn(n_neighbors, metric, **knn_kwargs)

Constructs a k-nearest neighbors search object. You can implement a similar method to run cleanlab with your own approximate-KNN library.

Parameters:

• n_neighbors (int) – The number of nearest neighbors to consider.

• metric (Union[str, Callable]) – The distance metric to use for computing distances between points. See metric for more information.

• **knn_kwargs – Additional keyword arguments to be passed to the search index constructor. See https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html for more details on the available options.

Return type:

NearestNeighbors

Returns:

knn – A k-nearest neighbors search object compatible with the scikit-learn NearestNeighbors class interface.

Implements:

• fit method: Accepts a feature array X to fit the model.

  This enables subsequent neighbor searches on the data.

• kneighbors method: Finds the K-neighbors of a point, returning distances and indices of the k-nearest neighbors. Handles two scenarios:

  1. When a query array features: np.ndarray is provided, it returns the distances and indices for each point in the query array.

  2. When no query array is provided (features = None), it returns neighbors for each indexed point without considering the query point as its own neighbor. Optionally, allows re-specification of the number of neighbors for each query point, defaulting to the constructor’s value if not specified.

Attributes:

• n_neighbors: Number of neighbors to consider.

• metric: Distance metric used to compute distances between points.

• metric_params: Additional parameters for the distance metric function.

Optional:

• kneighbors_graph method: Not required but can be implemented for convenience.

Responsibility shifted to construct_knn_graph_from_index.

Fitted Attributes:

• n_features_in_: Number of features observed during fit.

• effective_metric_params_: Metric parameters used in distance computation.

• effective_metric_: Metric used for computing distances to neighbors.

• n_samples_fit_: Number of samples in the fitted data.

Additional:

• __sklearn_is_fitted__: Method returning a boolean indicating if the object is fitted,

  useful for conducting an is_fitted validation, which verifies the presence of fitted attributes (typically ending with a trailing underscore).

The above specifications ensure compatibility and provide a clear directive for developers needing to integrate alternative k-nearest neighbors implementations or modify existing functionalities.

Note

The metric argument should be a callable that takes two arguments (the two points) and returns the distance between them. The additional keyword arguments (**knn_kwargs) are passed directly to the underlying k-nearest neighbors search algorithm.