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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 Gaussian mixture model (GMM) clustering.
This goes on top of skflow API.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import numpy as np
from tensorflow.contrib import framework
from tensorflow.contrib.factorization.python.ops import gmm_ops
from tensorflow.contrib.framework.python.framework import checkpoint_utils
from tensorflow.contrib.framework.python.ops import variables
from tensorflow.contrib.learn.python.learn import graph_actions
from tensorflow.contrib.learn.python.learn import monitors as monitor_lib
from tensorflow.contrib.learn.python.learn.estimators import estimator as estimator_lib
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
from tensorflow.contrib.learn.python.learn.estimators._sklearn import TransformerMixin
from tensorflow.contrib.learn.python.learn.learn_io import data_feeder
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.framework import random_seed as random_seed_lib
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops.control_flow_ops import with_dependencies
from tensorflow.python.platform import tf_logging as logging
def _streaming_sum(scalar_tensor):
"""Create a sum metric and update op."""
sum_metric = framework.local_variable(constant_op.constant(0.0))
sum_update = sum_metric.assign_add(scalar_tensor)
return sum_metric, sum_update
class GMM(estimator_lib.Estimator, TransformerMixin):
"""GMM clustering."""
SCORES = 'scores'
ASSIGNMENTS = 'assignments'
ALL_SCORES = 'all_scores'
def __init__(self,
num_clusters,
model_dir=None,
random_seed=0,
params='wmc',
initial_clusters='random',
covariance_type='full',
batch_size=128,
steps=10,
continue_training=False,
config=None,
verbose=1):
"""Creates a model for running GMM training and inference.
Args:
num_clusters: number of clusters to train.
model_dir: the directory to save the model results and log files.
random_seed: Python integer. Seed for PRNG used to initialize centers.
params: Controls which parameters are updated in the training process.
Can contain any combination of "w" for weights, "m" for means,
and "c" for covars.
initial_clusters: specifies how to initialize the clusters for training.
See gmm_ops.gmm for the possible values.
covariance_type: one of "full", "diag".
batch_size: See Estimator
steps: See Estimator
continue_training: See Estimator
config: See Estimator
verbose: See Estimator
"""
super(GMM, self).__init__(model_dir=model_dir, config=config)
self.batch_size = batch_size
self.steps = steps
self.continue_training = continue_training
self.verbose = verbose
self._num_clusters = num_clusters
self._params = params
self._training_initial_clusters = initial_clusters
self._covariance_type = covariance_type
self._training_graph = None
self._random_seed = random_seed
def fit(self, x, y=None, monitors=None, logdir=None, steps=None):
"""Trains a GMM 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: List of `Monitor` objects 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.
Returns:
Returns self.
"""
if logdir is not None:
self._model_dir = logdir
self._data_feeder = data_feeder.setup_train_data_feeder(x, None,
self._num_clusters,
self.batch_size)
_legacy_train_model( # pylint: disable=protected-access
self,
input_fn=self._data_feeder.input_builder,
feed_fn=self._data_feeder.get_feed_dict_fn(),
steps=steps or self.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 np.array([
prediction[GMM.ASSIGNMENTS]
for prediction in super(GMM, self).predict(
x=x, batch_size=batch_size, as_iterable=True)
])
def score(self, x, batch_size=None):
"""Predict total sum of distances to nearest clusters.
Args:
x: 2-D matrix or iterator.
batch_size: size to use for batching up x for querying the model.
Returns:
Total score.
"""
return np.sum(self.evaluate(x=x, batch_size=batch_size)[GMM.SCORES])
def transform(self, x, batch_size=None):
"""Transforms each element in x to distances to cluster centers.
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 np.array([
prediction[GMM.ALL_SCORES]
for prediction in super(GMM, self).predict(
x=x, batch_size=batch_size, as_iterable=True)
])
def clusters(self):
"""Returns cluster centers."""
clusters = checkpoint_utils.load_variable(
self.model_dir, gmm_ops.GmmAlgorithm.CLUSTERS_VARIABLE)
return np.squeeze(clusters, 1)
def covariances(self):
"""Returns the covariances."""
return checkpoint_utils.load_variable(
self.model_dir, gmm_ops.GmmAlgorithm.CLUSTERS_COVS_VARIABLE)
def _parse_tensor_or_dict(self, features):
if isinstance(features, dict):
return array_ops.concat([features[k] for k in sorted(features.keys())], 1)
return features
def _get_train_ops(self, features, _):
(_, _, losses, training_op) = gmm_ops.gmm(
self._parse_tensor_or_dict(features), self._training_initial_clusters,
self._num_clusters, self._random_seed, self._covariance_type,
self._params)
incr_step = state_ops.assign_add(variables.get_global_step(), 1)
loss = math_ops.reduce_sum(losses)
training_op = with_dependencies([training_op, incr_step], loss)
return training_op, loss
def _get_predict_ops(self, features):
(all_scores, model_predictions, _, _) = gmm_ops.gmm(
self._parse_tensor_or_dict(features), self._training_initial_clusters,
self._num_clusters, self._random_seed, self._covariance_type,
self._params)
return {
GMM.ALL_SCORES: all_scores[0],
GMM.ASSIGNMENTS: model_predictions[0][0],
}
def _get_eval_ops(self, features, _, unused_metrics):
(_,
_,
losses,
_) = gmm_ops.gmm(
self._parse_tensor_or_dict(features),
self._training_initial_clusters,
self._num_clusters,
self._random_seed,
self._covariance_type,
self._params)
return {GMM.SCORES: _streaming_sum(math_ops.reduce_sum(losses))}
# TODO(xavigonzalvo): delete this after implementing model-fn based Estimator.
def _legacy_train_model(estimator,
input_fn,
steps,
feed_fn=None,
init_op=None,
init_feed_fn=None,
init_fn=None,
device_fn=None,
monitors=None,
log_every_steps=100,
fail_on_nan_loss=True,
max_steps=None):
"""Legacy train function of Estimator."""
if hasattr(estimator.config, 'execution_mode'):
if estimator.config.execution_mode not in ('all', 'train'):
return
# Stagger startup of worker sessions based on task id.
sleep_secs = min(
estimator.config.training_worker_max_startup_secs,
estimator.config.task_id *
estimator.config.training_worker_session_startup_stagger_secs)
if sleep_secs:
logging.info('Waiting %d secs before starting task %d.', sleep_secs,
estimator.config.task_id)
time.sleep(sleep_secs)
# Device allocation
device_fn = device_fn or estimator._device_fn # pylint: disable=protected-access
with ops.Graph().as_default() as g, g.device(device_fn):
random_seed_lib.set_random_seed(estimator.config.tf_random_seed)
global_step = framework.create_global_step(g)
features, labels = input_fn()
estimator._check_inputs(features, labels) # pylint: disable=protected-access
# The default return type of _get_train_ops is ModelFnOps. But there are
# some subclasses of tf.contrib.learn.Estimator which override this
# method and use the legacy signature, namely _get_train_ops returns a
# (train_op, loss) tuple. The following else-statement code covers these
# cases, but will soon be deleted after the subclasses are updated.
# TODO(b/32664904): Update subclasses and delete the else-statement.
train_ops = estimator._get_train_ops(features, labels) # pylint: disable=protected-access
if isinstance(train_ops, model_fn_lib.ModelFnOps): # Default signature
train_op = train_ops.train_op
loss_op = train_ops.loss
if estimator.config.is_chief:
hooks = train_ops.training_chief_hooks + train_ops.training_hooks
else:
hooks = train_ops.training_hooks
else: # Legacy signature
if len(train_ops) != 2:
raise ValueError('Expected a tuple of train_op and loss, got {}'.format(
train_ops))
train_op = train_ops[0]
loss_op = train_ops[1]
hooks = []
hooks += monitor_lib.replace_monitors_with_hooks(monitors, estimator)
ops.add_to_collection(ops.GraphKeys.LOSSES, loss_op)
return graph_actions._monitored_train( # pylint: disable=protected-access
graph=g,
output_dir=estimator.model_dir,
train_op=train_op,
loss_op=loss_op,
global_step_tensor=global_step,
init_op=init_op,
init_feed_dict=init_feed_fn() if init_feed_fn is not None else None,
init_fn=init_fn,
log_every_steps=log_every_steps,
supervisor_is_chief=estimator.config.is_chief,
supervisor_master=estimator.config.master,
supervisor_save_model_secs=estimator.config.save_checkpoints_secs,
supervisor_save_model_steps=estimator.config.save_checkpoints_steps,
supervisor_save_summaries_steps=estimator.config.save_summary_steps,
keep_checkpoint_max=estimator.config.keep_checkpoint_max,
keep_checkpoint_every_n_hours=(
estimator.config.keep_checkpoint_every_n_hours),
feed_fn=feed_fn,
steps=steps,
fail_on_nan_loss=fail_on_nan_loss,
hooks=hooks,
max_steps=max_steps)