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# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
"""Returns points that minimizes the maximum distance of any point to a center.
Implements the k-Center-Greedy method in
Ozan Sener and Silvio Savarese. A Geometric Approach to Active Learning for
Convolutional Neural Networks. 2017
Distance metric defaults to l2 distance. Features used to calculate distance
are either raw features or if a model has transform method then uses the output
of model.transform(X).
Can be extended to a robust k centers algorithm that ignores a certain number of
outlier datapoints. Resulting centers are solution to multiple integer program.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from sklearn.metrics import pairwise_distances
from sampling_methods.sampling_def import SamplingMethod
class kCenterGreedy(SamplingMethod):
def __init__(self, X, y, seed, metric='euclidean'):
self.X = X
self.y = y
self.flat_X = self.flatten_X() = 'kcenter'
self.features = self.flat_X
self.metric = metric
self.min_distances = None
self.n_obs = self.X.shape[0]
self.already_selected = []
def update_distances(self, cluster_centers, only_new=True, reset_dist=False):
"""Update min distances given cluster centers.
cluster_centers: indices of cluster centers
only_new: only calculate distance for newly selected points and update
rest_dist: whether to reset min_distances.
if reset_dist:
self.min_distances = None
if only_new:
cluster_centers = [d for d in cluster_centers
if d not in self.already_selected]
if cluster_centers:
# Update min_distances for all examples given new cluster center.
x = self.features[cluster_centers]
dist = pairwise_distances(self.features, x, metric=self.metric)
if self.min_distances is None:
self.min_distances = np.min(dist, axis=1).reshape(-1,1)
self.min_distances = np.minimum(self.min_distances, dist)
def select_batch_(self, model, already_selected, N, **kwargs):
Diversity promoting active learning method that greedily forms a batch
to minimize the maximum distance to a cluster center among all unlabeled
model: model with scikit-like API with decision_function implemented
already_selected: index of datapoints already selected
N: batch size
indices of points selected to minimize distance to cluster centers
# Assumes that the transform function takes in original data and not
# flattened data.
print('Getting transformed features...')
self.features = model.transform(self.X)
print('Calculating distances...')
self.update_distances(already_selected, only_new=False, reset_dist=True)
print('Using flat_X as features.')
self.update_distances(already_selected, only_new=True, reset_dist=False)
new_batch = []
for _ in range(N):
if self.already_selected is None:
# Initialize centers with a randomly selected datapoint
ind = np.random.choice(np.arange(self.n_obs))
ind = np.argmax(self.min_distances)
# New examples should not be in already selected since those points
# should have min_distance of zero to a cluster center.
assert ind not in already_selected
self.update_distances([ind], only_new=True, reset_dist=False)
print('Maximum distance from cluster centers is %0.2f'
% max(self.min_distances))
self.already_selected = already_selected
return new_batch
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