wehub-resource-sync
76 lines
3.5 KiB
Python
76 lines
3.5 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING
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from sklearn.neighbors import NearestNeighbors
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if TYPE_CHECKING:
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from cleanlab.typing import Metric
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def construct_knn(n_neighbors: int, metric: Metric, **knn_kwargs) -> NearestNeighbors:
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"""
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Constructs a k-nearest neighbors search object. You can implement a similar method to run cleanlab with your own approximate-KNN library.
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Parameters
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----------
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n_neighbors :
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The number of nearest neighbors to consider.
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metric :
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The distance metric to use for computing distances between points.
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See :py:mod:`~cleanlab.internal.neighbor.metric` for more information.
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**knn_kwargs:
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Additional keyword arguments to be passed to the search index constructor.
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See https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html for more details on the available options.
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Returns
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-------
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knn :
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A k-nearest neighbors search object compatible with the scikit-learn NearestNeighbors class interface.
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Implements:
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- `fit` method: Accepts a feature array `X` to fit the model.
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This enables subsequent neighbor searches on the data.
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- `kneighbors` method: Finds the K-neighbors of a point, returning distances and indices of the k-nearest neighbors. Handles two scenarios:
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1. When a query array `features: np.ndarray` is provided, it returns the distances and indices for each point in the query array.
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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.
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Optionally, allows re-specification of the number of neighbors for each query point, defaulting to the constructor's value if not specified.
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Attributes:
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- `n_neighbors`: Number of neighbors to consider.
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- `metric`: Distance metric used to compute distances between points.
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- `metric_params`: Additional parameters for the distance metric function.
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Optional:
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- `kneighbors_graph` method: Not required but can be implemented for convenience.
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Responsibility shifted to :py:ref:`construct_knn_graph_from_index <cleanlab.internal.neighbor.neighbor.construct_knn_graph_from_index>`.
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Fitted Attributes:
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- `n_features_in_`: Number of features observed during fit.
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- `effective_metric_params_`: Metric parameters used in distance computation.
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- `effective_metric_`: Metric used for computing distances to neighbors.
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- `n_samples_fit_`: Number of samples in the fitted data.
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Additional:
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- `__sklearn_is_fitted__`: Method returning a boolean indicating if the object is fitted,
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useful for conducting an is_fitted validation, which verifies the presence of fitted attributes (typically ending with a trailing underscore).
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The above specifications ensure compatibility and provide a clear directive for developers needing to integrate alternative k-nearest neighbors implementations or modify existing functionalities.
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Note
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----
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The `metric` argument should be a callable that takes two arguments (the two points) and returns the distance between them.
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The additional keyword arguments (`**knn_kwargs`) are passed directly to the underlying k-nearest neighbors search algorithm.
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"""
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sklearn_knn = NearestNeighbors(n_neighbors=n_neighbors, metric=metric, **knn_kwargs)
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return sklearn_knn
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