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# Copyright 2016 The TensorFlow 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.
# ==============================================================================
"""Implementation of k-means clustering on top of learn (aka skflow) API."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.contrib.factorization.python.ops import clustering_ops
from tensorflow.contrib.learn.python.learn.estimators import estimator
from tensorflow.contrib.learn.python.learn.estimators._sklearn import TransformerMixin
from tensorflow.contrib.learn.python.learn.learn_io import data_feeder
from tensorflow.contrib.learn.python.learn.monitors import BaseMonitor
from tensorflow.contrib.learn.python.learn.utils import checkpoints
from tensorflow.python.ops.control_flow_ops import with_dependencies
SQUARED_EUCLIDEAN_DISTANCE = clustering_ops.SQUARED_EUCLIDEAN_DISTANCE
COSINE_DISTANCE = clustering_ops.COSINE_DISTANCE
RANDOM_INIT = clustering_ops.RANDOM_INIT
KMEANS_PLUS_PLUS_INIT = clustering_ops.KMEANS_PLUS_PLUS_INIT
# TODO(agarwal,ands): support sharded input.
# TODO(agarwal,ands): enable stopping criteria based on improvements to cost.
# TODO(agarwal,ands): support random restarts.
class KMeansClustering(estimator.Estimator,
TransformerMixin):
"""K-Means clustering."""
SCORES = 'scores'
CLUSTER_IDX = 'cluster_idx'
CLUSTERS = 'clusters'
ALL_SCORES = 'all_scores'
def __init__(self,
num_clusters,
model_dir=None,
initial_clusters=clustering_ops.RANDOM_INIT,
distance_metric=clustering_ops.SQUARED_EUCLIDEAN_DISTANCE,
random_seed=0,
use_mini_batch=True,
kmeans_plus_plus_num_retries=2,
config=None):
"""Creates a model for running KMeans training and inference.
Args:
num_clusters: number of clusters to train.
model_dir: the directory to save the model results and log files.
initial_clusters: specifies how to initialize the clusters for training.
See clustering_ops.kmeans for the possible values.
distance_metric: the distance metric used for clustering.
See clustering_ops.kmeans for the possible values.
random_seed: Python integer. Seed for PRNG used to initialize centers.
use_mini_batch: If true, use the mini-batch k-means algorithm. Else assume
full batch.
kmeans_plus_plus_num_retries: For each point that is sampled during
kmeans++ initialization, this parameter specifies the number of
additional points to draw from the current distribution before selecting
the best. If a negative value is specified, a heuristic is used to
sample O(log(num_to_sample)) additional points.
config: See Estimator
"""
super(KMeansClustering, self).__init__(
model_dir=model_dir,
config=config)
self.kmeans_plus_plus_num_retries = kmeans_plus_plus_num_retries
self._num_clusters = num_clusters
self._training_initial_clusters = initial_clusters
self._training_graph = None
self._distance_metric = distance_metric
self._use_mini_batch = use_mini_batch
self._random_seed = random_seed
self._initialized = False
# pylint: disable=protected-access
class _StopWhenConverged(BaseMonitor):
"""Stops when the change in loss goes below a tolerance."""
def __init__(self, tolerance):
"""Initializes a '_StopWhenConverged' monitor.
Args:
tolerance: A relative tolerance of change between iterations.
"""
super(KMeansClustering._StopWhenConverged, self).__init__()
self._tolerance = tolerance
def begin(self, max_steps):
super(KMeansClustering._StopWhenConverged, self).begin(max_steps)
self._prev_loss = None
def step_begin(self, step):
super(KMeansClustering._StopWhenConverged, self).step_begin(step)
return [self._estimator._loss]
def step_end(self, step, output):
super(KMeansClustering._StopWhenConverged, self).step_end(step, output)
loss = output[self._estimator._loss]
if self._prev_loss is None:
self._prev_loss = loss
return False
relative_change = (abs(loss - self._prev_loss)
/ (1 + abs(self._prev_loss)))
self._prev_loss = loss
return relative_change < self._tolerance
# pylint: enable=protected-access
def fit(self, x, y=None, monitors=None, logdir=None, steps=None, batch_size=128,
relative_tolerance=None):
"""Trains a k-means clustering on x.
Note: See Estimator for logic for continuous training and graph
construction across multiple calls to fit.
Args:
x: training input matrix of shape [n_samples, n_features].
y: labels. Should be None.
monitors: Monitor object to print training progress and invoke early
stopping
logdir: the directory to save the log file that can be used for optional
visualization.
steps: number of training steps. If not None, overrides the value passed
in constructor.
batch_size: mini-batch size to use. Requires `use_mini_batch=True`.
relative_tolerance: A relative tolerance of change in the loss between
iterations. Stops learning if the loss changes less than this amount.
Note that this may not work correctly if use_mini_batch=True.
Returns:
Returns self.
"""
assert y is None
if logdir is not None:
self._model_dir = logdir
self._data_feeder = data_feeder.setup_train_data_feeder(
x, None, self._num_clusters, batch_size if self._use_mini_batch else None)
if relative_tolerance is not None:
if monitors is not None:
monitors += [self._StopWhenConverged(relative_tolerance)]
else:
monitors = [self._StopWhenConverged(relative_tolerance)]
# Make sure that we will eventually terminate.
assert ((monitors is not None and len(monitors)) or (steps is not None)
or (self.steps is not None))
self._train_model(input_fn=self._data_feeder.input_builder,
feed_fn=self._data_feeder.get_feed_dict_fn(),
steps=steps,
monitors=monitors,
init_feed_fn=self._data_feeder.get_feed_dict_fn())
return self
def predict(self, x, batch_size=None):
"""Predict cluster id for each element in x.
Args:
x: 2-D matrix or iterator.
batch_size: size to use for batching up x for querying the model.
Returns:
Array with same number of rows as x, containing cluster ids.
"""
return super(KMeansClustering, self).predict(
x=x, batch_size=batch_size)[KMeansClustering.CLUSTER_IDX]
def score(self, x, batch_size=None):
"""Predict total sum of distances to nearest clusters.
Note that this function is different from the corresponding one in sklearn
which returns the negative of the sum of distances.
Args:
x: 2-D matrix or iterator.
batch_size: size to use for batching up x for querying the model.
Returns:
Total sum of distances to nearest clusters.
"""
return np.sum(
self.evaluate(x=x, batch_size=batch_size)[KMeansClustering.SCORES])
def transform(self, x, batch_size=None):
"""Transforms each element in x to distances to cluster centers.
Note that this function is different from the corresponding one in sklearn.
For SQUARED_EUCLIDEAN distance metric, sklearn transform returns the
EUCLIDEAN distance, while this function returns the SQUARED_EUCLIDEAN
distance.
Args:
x: 2-D matrix or iterator.
batch_size: size to use for batching up x for querying the model.
Returns:
Array with same number of rows as x, and num_clusters columns, containing
distances to the cluster centers.
"""
return super(KMeansClustering, self).predict(
x=x, batch_size=batch_size)[KMeansClustering.ALL_SCORES]
def clusters(self):
"""Returns cluster centers."""
return checkpoints.load_variable(self.model_dir, self.CLUSTERS)
def _get_train_ops(self, features, _):
(_,
_,
losses,
training_op) = clustering_ops.KMeans(
features,
self._num_clusters,
self._training_initial_clusters,
self._distance_metric,
self._use_mini_batch,
random_seed=self._random_seed,
kmeans_plus_plus_num_retries=self.kmeans_plus_plus_num_retries
).training_graph()
incr_step = tf.assign_add(tf.contrib.framework.get_global_step(), 1)
self._loss = tf.reduce_sum(losses)
training_op = with_dependencies([training_op, incr_step], self._loss)
return training_op, self._loss
def _get_predict_ops(self, features):
(all_scores,
model_predictions,
_,
_) = clustering_ops.KMeans(
features,
self._num_clusters,
self._training_initial_clusters,
self._distance_metric,
self._use_mini_batch,
random_seed=self._random_seed,
kmeans_plus_plus_num_retries=self.kmeans_plus_plus_num_retries
).training_graph()
return {
KMeansClustering.ALL_SCORES: all_scores[0],
KMeansClustering.CLUSTER_IDX: model_predictions[0]
}
def _get_eval_ops(self, features, _, unused_metrics):
(_,
_,
losses,
_) = clustering_ops.KMeans(
features,
self._num_clusters,
self._training_initial_clusters,
self._distance_metric,
self._use_mini_batch,
random_seed=self._random_seed,
kmeans_plus_plus_num_retries=self.kmeans_plus_plus_num_retries
).training_graph()
return {
KMeansClustering.SCORES: tf.reduce_sum(losses),
}
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