FAISS Wrapper¶
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class
deslib.util.faiss_knn_wrapper.FaissKNNClassifier(n_neighbors=5, n_jobs=None, algorithm=None)[source]¶ Faiss KNN wrapper.
Parameters: - n_neighbors : int (Default = 5)
Number of neighbors used in the nearest neighbor search.
- n_jobs : int (Default = None)
- The number of jobs to run in parallel for both fit and predict.
If -1, then the number of jobs is set to the number of cores.
- algorithm : str (Default = None)
Algorithm used for nearest
References
Johnson Jeff, Matthijs Douze, and Hervé Jégou. “Billion-scale similarity search with gpus.” arXiv preprint arXiv:1702.08734 (2017).
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fit(X, y)[source]¶ Fit the model according to the given training data.
Parameters: - X : array of shape = [n_samples, n_features]
Data used to fit the model.
- y : array of shape = [n_samples]
class labels of each example in X.
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kneighbors(X, n_neighbors=None, return_distance=True)[source]¶ Finds the K-neighbors of a point.
Parameters: - X : array of shape = [n_samples, n_features]
The input data.
- n_neighbors : int
Number of neighbors to get (default is the value passed to the constructor).
- return_distance : boolean, optional. Defaults to True.
If False, distances will not be returned
Returns: - dists : list of shape = [n_samples, k]
The distances between the query and each sample in the region of competence. The vector is ordered in an ascending fashion.
- idx : list of shape = [n_samples, k]
Indices of the instances belonging to the region of competence of the given query sample.