Source code for asreview.models.query.cluster

# Copyright 2019-2022 The ASReview Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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import numpy as np
from sklearn.cluster import KMeans

from asreview.models.query.base import ProbaQueryStrategy
from asreview.models.query.max import MaxQuery
from asreview.utils import get_random_state

[docs]class ClusterQuery(ProbaQueryStrategy): """Clustering query strategy (``cluster``). Use clustering after feature extraction on the dataset. Then the highest probabilities within random clusters are sampled. Arguments --------- cluster_size: int Size of the clusters to be made. If the size of the clusters is smaller than the size of the pool, fall back to max sampling. update_interval: int Update the clustering every x instances. random_state: int, RandomState State/seed of the RNG. """ name = "cluster" label = "Clustering" def __init__(self, cluster_size=350, update_interval=200, random_state=None): """Initialize the clustering strategy. """ super(ClusterQuery, self).__init__() self.cluster_size = cluster_size self.update_interval = update_interval self.last_update = None self.fallback_model = MaxQuery() self._random_state = get_random_state(random_state) def _query(self, predictions, n_instances, X): n_samples = X.shape[0] last_update = self.last_update if (last_update is None or self.update_interval is None or last_update - n_samples >= self.update_interval): n_clusters = round(n_samples / self.cluster_size) if n_clusters <= 1: return self.fallback_model._query( predictions, n_instances, X) model = KMeans( n_clusters=n_clusters, n_init=1, random_state=self._random_state) self.clusters = model.fit_predict(X) self.last_update = n_samples clusters = {} for idx in np.arange(n_samples): cluster_id = self.clusters[idx] if cluster_id in clusters: clusters[cluster_id].append((idx, predictions[idx, 1])) else: clusters[cluster_id] = [(idx, predictions[idx, 1])] for cluster_id in clusters: try: clusters[cluster_id] = sorted( clusters[cluster_id], key=lambda x: x[1]) except ValueError: raise clust_idx = [] cluster_ids = list(clusters) for _ in range(n_instances): cluster_id = self._random_state.choice(cluster_ids, 1)[0] clust_idx.append(clusters[cluster_id].pop()[0]) if len(clusters[cluster_id]) == 0: del clusters[cluster_id] cluster_ids = list(clusters) clust_idx = np.array(clust_idx, dtype=int) return clust_idx
[docs] def full_hyper_space(self): from hyperopt import hp parameter_space = { "qry_cluster_size": hp.quniform('qry_cluster_size', 50, 1000, 1), "qry_update_interval": hp.quniform('qry_update_interval', 100, 300, 1), } return parameter_space, {}