Reference similarity

inference.classifier

KnnClassifier(database_labels, k=1, return_scores=False)

Predict query label as k labels of nearest matches in the database. If there is a tie at a given k, the prediction with the best score is used.

Parameters:

Name	Type	Description	Default
database_labels	ndarray	Array containing the labels of the database.	required
k	int	The number of nearest neighbors to consider.	1
return_scores	bool	Indicates whether to return scores along with predictions.	False

__call__(similarity)

Predicts the label for each query based on the k nearest matches in the database.

Parameters:

Name	Type	Description	Default
similarity	ndarray	A 2D similarity matrix with n_query x n_database shape.	required

Returns:

Type	Description
ndarray | Tuple[ndarray, ndarray]	If return_scores is False: preds (np.ndarray): Prediction for each query.
ndarray | Tuple[ndarray, ndarray]	If return_scores is True, tuple of two arrays: preds (np.ndarray): Prediction for each query. scores (np.ndarray): The similarity scores corresponding to the predictions (mean for k > 1).

TopkClassifier(database_labels, k=10, return_all=False)

Predict top k query labels given nearest matches in the database.

Parameters:

Name	Type	Description	Default
database_labels	ndarray	Array containing the labels of the database.	required
k	int	The number of top predictions to return.	10
return_all	bool	Indicates whether to return scores along with predictions.	False

__call__(similarity)

Predicts the top k labels for each query based on the similarity matrix.

Parameters:

Name	Type	Description	Default
similarity	ndarray	A 2D similarity matrix with n_query x n_database shape	required

Returns:

Type	Description
ndarray | Tuple[ndarray, ndarray, ndarray]	If return_all is False, single 2D array of shape n_query x k preds (np.ndarray): The top k predicted labels for each query.
ndarray | Tuple[ndarray, ndarray, ndarray]	If return_all is True, tuple of three 2D arrays of shape n_query x k: preds (np.ndarray): The top k predicted labels for each query. scores (np.ndarray): The similarity scores corresponding to the top k predictions. idx (np.ndarray): The indices of the database entries corresponding to the top k predictions.