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* Do not call kcandidates if no hr is used * Adapted changelog * Early return for no hubness reduction * Avoid NotFittedError * Fixed index order * Improve no hubness * Remove float transformation * Started refactoring * Fixed some inconsistencies * More detailled analysis * Fix import * Set only fit target flat * Major refactor and simplification * Fixed some doc struff
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,41 +1,97 @@ | ||
| # -*- coding: utf-8 -*- | ||
| # adapted from skhubness | ||
| # SPDX-License-Identifier: BSD-3-Clause | ||
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| import warnings | ||
| from abc import ABC, abstractmethod | ||
| from typing import Optional, Tuple | ||
|
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| import numpy as np | ||
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| from ..neighbors import NNAlgorithm | ||
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| class HubnessReduction(ABC): | ||
| """Base class for hubness reduction.""" | ||
|
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| @abstractmethod | ||
| def __init__(self, **kwargs): | ||
| pass | ||
| def __init__(self, nn_algo: NNAlgorithm, verbose: int = 0, **kwargs): | ||
| self.nn_algo = nn_algo | ||
| self.verbose = verbose | ||
| if nn_algo.n_candidates == 1: | ||
| raise ValueError( | ||
| "Cannot perform hubness reduction with a single candidate per query!" | ||
| ) | ||
|
|
||
| @abstractmethod | ||
| def fit( | ||
| self, neigh_dist, neigh_ind, source, target, assume_sorted, *args, **kwargs | ||
| ): | ||
| def _fit(self, neigh_dist, neigh_ind, source, target): | ||
| pass # pragma: no cover | ||
|
|
||
| def fit(self, source, target=None): | ||
| self.nn_algo.fit(source, target) | ||
| if target is None: | ||
| target = source | ||
| neigh_dist_t_to_s, neigh_ind_t_to_s = self.nn_algo.kneighbors( | ||
| k=self.nn_algo.n_candidates, | ||
| query=target, | ||
| s_to_t=False, | ||
| return_distance=True, | ||
| ) | ||
| self._fit( | ||
| neigh_dist_t_to_s, | ||
| neigh_ind_t_to_s, | ||
| source, | ||
| target, | ||
| ) | ||
|
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||
| @abstractmethod | ||
| def transform(self, neigh_dist, neigh_ind, query, assume_sorted, *args, **kwargs): | ||
| def transform(self, neigh_dist, neigh_ind, query) -> Tuple[np.ndarray, np.ndarray]: | ||
| pass # pragma: no cover | ||
|
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| def _set_k_if_needed(self, k: Optional[int] = None) -> int: | ||
| if k is None: | ||
| warnings.warn( | ||
| f"No k supplied, setting to n_candidates = {self.nn_algo.n_candidates}" | ||
| ) | ||
| return self.nn_algo.n_candidates | ||
| if k > self.nn_algo.n_candidates: | ||
| warnings.warn( | ||
| "k > n_candidates supplied! Setting to n_candidates =" | ||
| f" {self.nn_algo.n_candidates}" | ||
| ) | ||
| return self.nn_algo.n_candidates | ||
| return k | ||
|
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||
| class NoHubnessReduction(HubnessReduction): | ||
| """Compatibility class for neighbor search without hubness reduction.""" | ||
| def kneighbors(self, k: Optional[int] = None) -> Tuple[np.ndarray, np.ndarray]: | ||
| n_neighbors = self._set_k_if_needed(k) | ||
| # First obtain candidate neighbors | ||
| query_dist, query_ind = self.nn_algo.kneighbors( | ||
| query=None, k=self.nn_algo.n_candidates, return_distance=True | ||
| ) | ||
|
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||
| def __init__(self, **kwargs): | ||
| super().__init__(**kwargs) | ||
| # Second, reduce hubness | ||
| hubness_reduced_query_dist, query_ind = self.transform( | ||
| query_dist, | ||
| query_ind, | ||
| self.nn_algo.source_, | ||
| ) | ||
| # Third, sort hubness reduced candidate neighbors to get the final k neighbors | ||
| kth = np.arange(n_neighbors) | ||
| mask = np.argpartition(hubness_reduced_query_dist, kth=kth)[:, :n_neighbors] | ||
| hubness_reduced_query_dist = np.take_along_axis( | ||
| hubness_reduced_query_dist, mask, axis=1 | ||
| ) | ||
| query_ind = np.take_along_axis(query_ind, mask, axis=1) | ||
| return hubness_reduced_query_dist, query_ind | ||
|
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| def fit(self, *args, **kwargs): | ||
|
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| class NoHubnessReduction(HubnessReduction): | ||
| """Base class for hubness reduction.""" | ||
|
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| def _fit(self, neigh_dist, neigh_ind, source, target): | ||
| pass # pragma: no cover | ||
|
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| def __repr__(self): | ||
| return "NoHubnessReduction" | ||
| def fit(self, source, target=None): | ||
| self.nn_algo.fit(source, target, only_fit_target=True) | ||
|
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||
| def transform( | ||
| self, neigh_dist, neigh_ind, query, assume_sorted=True, *args, **kwargs | ||
| ): | ||
| def transform(self, neigh_dist, neigh_ind, query) -> Tuple[np.ndarray, np.ndarray]: | ||
| return neigh_dist, neigh_ind | ||
|
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| def kneighbors(self, k: Optional[int] = None) -> Tuple[np.ndarray, np.ndarray]: | ||
| n_neighbors = self._set_k_if_needed(k) | ||
| return self.nn_algo.kneighbors(query=None, k=n_neighbors, return_distance=True) |
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