118 lines
5 KiB
Python
118 lines
5 KiB
Python
|
# Copyright 2017 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.
|
||
|
|
# ==============================================================================
|
||
|
|
"""A multivariate TFTS example.
|
||
|
|
|
||
|
|
Fits a multivariate model, exports it, and visualizes the learned correlations
|
||
|
|
by iteratively predicting and sampling from the predictions.
|
||
|
|
"""
|
||
|
|
|
||
|
|
from __future__ import absolute_import
|
||
|
|
from __future__ import division
|
||
|
|
from __future__ import print_function
|
||
|
|
|
||
|
|
from os import path
|
||
|
|
import tempfile
|
||
|
|
|
||
|
|
import numpy
|
||
|
|
import tensorflow as tf
|
||
|
|
|
||
|
|
try:
|
||
|
|
import matplotlib # pylint: disable=g-import-not-at-top
|
||
|
|
matplotlib.use("TkAgg") # Need Tk for interactive plots.
|
||
|
|
from matplotlib import pyplot # pylint: disable=g-import-not-at-top
|
||
|
|
HAS_MATPLOTLIB = True
|
||
|
|
except ImportError:
|
||
|
|
# Plotting requires matplotlib, but the unit test running this code may
|
||
|
|
# execute in an environment without it (i.e. matplotlib is not a build
|
||
|
|
# dependency). We'd still like to test the TensorFlow-dependent parts of this
|
||
|
|
# example, namely train_and_predict.
|
||
|
|
HAS_MATPLOTLIB = False
|
||
|
|
|
||
|
|
_MODULE_PATH = path.dirname(__file__)
|
||
|
|
_DATA_FILE = path.join(_MODULE_PATH, "data/multivariate_level.csv")
|
||
|
|
|
||
|
|
|
||
|
|
def multivariate_train_and_sample(
|
||
|
|
csv_file_name=_DATA_FILE, export_directory=None, training_steps=500):
|
||
|
|
"""Trains, evaluates, and exports a multivariate model."""
|
||
|
|
estimator = tf.contrib.timeseries.StructuralEnsembleRegressor(
|
||
|
|
periodicities=[], num_features=5)
|
||
|
|
reader = tf.contrib.timeseries.CSVReader(
|
||
|
|
csv_file_name,
|
||
|
|
column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES,)
|
||
|
|
+ (tf.contrib.timeseries.TrainEvalFeatures.VALUES,) * 5))
|
||
|
|
train_input_fn = tf.contrib.timeseries.RandomWindowInputFn(
|
||
|
|
# Larger window sizes generally produce a better covariance matrix.
|
||
|
|
reader, batch_size=4, window_size=64)
|
||
|
|
estimator.train(input_fn=train_input_fn, steps=training_steps)
|
||
|
|
evaluation_input_fn = tf.contrib.timeseries.WholeDatasetInputFn(reader)
|
||
|
|
current_state = estimator.evaluate(input_fn=evaluation_input_fn, steps=1)
|
||
|
|
values = [current_state["observed"]]
|
||
|
|
times = [current_state[tf.contrib.timeseries.FilteringResults.TIMES]]
|
||
|
|
# Export the model so we can do iterative prediction and filtering without
|
||
|
|
# reloading model checkpoints.
|
||
|
|
if export_directory is None:
|
||
|
|
export_directory = tempfile.mkdtemp()
|
||
|
|
input_receiver_fn = estimator.build_raw_serving_input_receiver_fn()
|
||
|
|
export_location = estimator.export_savedmodel(
|
||
|
|
export_directory, input_receiver_fn)
|
||
|
|
with tf.Graph().as_default():
|
||
|
|
numpy.random.seed(1) # Make the example a bit more deterministic
|
||
|
|
with tf.Session() as session:
|
||
|
|
signatures = tf.saved_model.loader.load(
|
||
|
|
session, [tf.saved_model.tag_constants.SERVING], export_location)
|
||
|
|
for _ in range(100):
|
||
|
|
current_prediction = (
|
||
|
|
tf.contrib.timeseries.saved_model_utils.predict_continuation(
|
||
|
|
continue_from=current_state, signatures=signatures,
|
||
|
|
session=session, steps=1))
|
||
|
|
next_sample = numpy.random.multivariate_normal(
|
||
|
|
# Squeeze out the batch and series length dimensions (both 1).
|
||
|
|
mean=numpy.squeeze(current_prediction["mean"], axis=[0, 1]),
|
||
|
|
cov=numpy.squeeze(current_prediction["covariance"], axis=[0, 1]))
|
||
|
|
# Update model state so that future predictions are conditional on the
|
||
|
|
# value we just sampled.
|
||
|
|
filtering_features = {
|
||
|
|
tf.contrib.timeseries.TrainEvalFeatures.TIMES: current_prediction[
|
||
|
|
tf.contrib.timeseries.FilteringResults.TIMES],
|
||
|
|
tf.contrib.timeseries.TrainEvalFeatures.VALUES: next_sample[
|
||
|
|
None, None, :]}
|
||
|
|
current_state = (
|
||
|
|
tf.contrib.timeseries.saved_model_utils.filter_continuation(
|
||
|
|
continue_from=current_state,
|
||
|
|
session=session,
|
||
|
|
signatures=signatures,
|
||
|
|
features=filtering_features))
|
||
|
|
values.append(next_sample[None, None, :])
|
||
|
|
times.append(current_state["times"])
|
||
|
|
all_observations = numpy.squeeze(numpy.concatenate(values, axis=1), axis=0)
|
||
|
|
all_times = numpy.squeeze(numpy.concatenate(times, axis=1), axis=0)
|
||
|
|
return all_times, all_observations
|
||
|
|
|
||
|
|
|
||
|
|
def main(unused_argv):
|
||
|
|
if not HAS_MATPLOTLIB:
|
||
|
|
raise ImportError(
|
||
|
|
"Please install matplotlib to generate a plot from this example.")
|
||
|
|
all_times, all_observations = multivariate_train_and_sample()
|
||
|
|
# Show where sampling starts on the plot
|
||
|
|
pyplot.axvline(1000, linestyle="dotted")
|
||
|
|
pyplot.plot(all_times, all_observations)
|
||
|
|
pyplot.show()
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
tf.app.run(main=main)
|