class ClusterQuery(ProbaQueryStrategy):
"Query strategy using clustering algorithms."
name = "cluster"
def __init__(self, cluster_size=350, update_interval=200, **kwargs):
"""Initialize the clustering strategy.
Arguments
---------
texts: list
List of sequences to create feature matrix.
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_cluster: int
Update the clustering every x instances.
**kwargs: dict
Keyword arguments for the doc2vec feature model.
"""
super(ClusterQuery, self).__init__()
self.cluster_size = cluster_size
self.update_interval = update_interval
self.last_update = None
self.fallback_model = MaxQuery()
def _query(self, X, pool_idx, n_instances, proba):
n_samples = X.shape[0]
if pool_idx is None:
pool_idx = np.arange(n_samples)
last_update = self.last_update
if (last_update is None or self.update_interval is None
or last_update - len(pool_idx) >= self.update_interval):
n_clusters = round(len(pool_idx) / self.cluster_size)
if n_clusters <= 1:
return self.fallback_model._query(X,
pool_idx=pool_idx,
n_instances=n_instances,
proba=proba)
model = KMeans(n_clusters=n_clusters, n_init=1)
self.clusters = model.fit_predict(X)
self.last_update = len(pool_idx)
clusters = {}
for idx in pool_idx:
cluster_id = self.clusters[idx]
if cluster_id in clusters:
clusters[cluster_id].append((idx, proba[idx, 1]))
else:
clusters[cluster_id] = [(idx, proba[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 = np.random.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, X[clust_idx]
def full_hyper_space(self):
from hyperopt import hp
parameter_space = {
"qry_cluster_size": hp.quniform('qry_cluster_size', 50, 1000, 1),
"qry_update_cluster": hp.quniform('qry_update_cluster', 100, 300,
1),
"qry_max_frac": hp.uniform('qry_max_frac', 0, 1),
}
return parameter_space, {}
class ClusterQuery(ProbaQueryStrategy):
"""Query strategy using clustering algorithms.
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"
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, X, pool_idx, n_instances, proba):
n_samples = X.shape[0]
if pool_idx is None:
pool_idx = np.arange(n_samples)
last_update = self.last_update
if (last_update is None or self.update_interval is None
or last_update - len(pool_idx) >= self.update_interval):
n_clusters = round(len(pool_idx) / self.cluster_size)
if n_clusters <= 1:
return self.fallback_model._query(X,
pool_idx=pool_idx,
n_instances=n_instances,
proba=proba)
model = KMeans(n_clusters=n_clusters,
n_init=1,
random_state=self._random_state)
self.clusters = model.fit_predict(X)
self.last_update = len(pool_idx)
clusters = {}
for idx in pool_idx:
cluster_id = self.clusters[idx]
if cluster_id in clusters:
clusters[cluster_id].append((idx, proba[idx, 1]))
else:
clusters[cluster_id] = [(idx, proba[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, X[clust_idx]
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, {}