# Copyright 2017 Google Inc.
#
# 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.
"""Informative and diverse batch sampler that samples points with small margin
while maintaining same distribution over clusters as entire training data.
Batch is created by sorting datapoints by increasing margin and then growing
the batch greedily. A point is added to the batch if the result batch still
respects the constraint that the cluster distribution of the batch will
match the cluster distribution of the entire training set.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn.cluster import MiniBatchKMeans
import numpy as np
from sampling_methods.sampling_def import SamplingMethod
class InformativeClusterDiverseSampler(SamplingMethod):
"""Selects batch based on informative and diverse criteria.
Returns highest uncertainty lowest margin points while maintaining
same distribution over clusters as entire dataset.
"""
def __init__(self, X, y, seed):
self.name = 'informative_and_diverse'
self.X = X
self.flat_X = self.flatten_X()
# y only used for determining how many clusters there should be
# probably not practical to assume we know # of classes before hand
# should also probably scale with dimensionality of data
self.y = y
self.n_clusters = len(list(set(y)))
self.cluster_model = MiniBatchKMeans(n_clusters=self.n_clusters)
self.cluster_data()
def cluster_data(self):
# Probably okay to always use MiniBatchKMeans
# Should standardize data before clustering
# Can cluster on standardized data but train on raw features if desired
self.cluster_model.fit(self.flat_X)
unique, counts = np.unique(self.cluster_model.labels_, return_counts=True)
self.cluster_prob = counts/sum(counts)
self.cluster_labels = self.cluster_model.labels_
def select_batch_(self, model, already_selected, N, **kwargs):
"""Returns a batch of size N using informative and diverse selection.
Args:
model: scikit learn model with decision_function implemented
already_selected: index of datapoints already selected
N: batch size
Returns:
indices of points selected to add using margin active learner
"""
# TODO(lishal): have MarginSampler and this share margin function
try:
distances = model.decision_function(self.X)
except:
distances = model.predict_proba(self.X)
if len(distances.shape) < 2:
min_margin = abs(distances)
else:
sort_distances = np.sort(distances, 1)[:, -2:]
min_margin = sort_distances[:, 1] - sort_distances[:, 0]
rank_ind = np.argsort(min_margin)
rank_ind = [i for i in rank_ind if i not in already_selected]
new_batch_cluster_counts = [0 for _ in range(self.n_clusters)]
new_batch = []
for i in rank_ind:
if len(new_batch) == N:
break
label = self.cluster_labels[i]
if new_batch_cluster_counts[label] / N < self.cluster_prob[label]:
new_batch.append(i)
new_batch_cluster_counts[label] += 1
n_slot_remaining = N - len(new_batch)
batch_filler = list(set(rank_ind) - set(already_selected) - set(new_batch))
new_batch.extend(batch_filler[0:n_slot_remaining])
return new_batch
def to_dict(self):
output = {}
output['cluster_membership'] = self.cluster_labels
return output
