# 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ["ClusterQuery"]
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, asreview.utils.SeededRandomState
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().__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