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# 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import tempfile
import numpy
import six
from tensorflow.contrib.timeseries.python.timeseries import ar_model
from tensorflow.contrib.timeseries.python.timeseries import estimators
from tensorflow.contrib.timeseries.python.timeseries import feature_keys
from tensorflow.contrib.timeseries.python.timeseries import input_pipeline
from tensorflow.contrib.timeseries.python.timeseries import saved_model_utils
from tensorflow.python.client import session
from tensorflow.python.estimator import estimator_lib
from tensorflow.python.feature_column import feature_column
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.platform import test
from tensorflow.python.saved_model import loader
from tensorflow.python.saved_model import tag_constants
class _SeedRunConfig(estimator_lib.RunConfig):
@property
def tf_random_seed(self):
return 3
class TimeSeriesRegressorTest(test.TestCase):
def _fit_restore_fit_test_template(self, estimator_fn, dtype):
"""Tests restoring previously fit models."""
model_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
exogenous_feature_columns = (
feature_column.numeric_column("exogenous"),
)
first_estimator = estimator_fn(model_dir, exogenous_feature_columns)
times = numpy.arange(20, dtype=numpy.int64)
values = numpy.arange(20, dtype=dtype.as_numpy_dtype)
exogenous = numpy.arange(20, dtype=dtype.as_numpy_dtype)
features = {
feature_keys.TrainEvalFeatures.TIMES: times,
feature_keys.TrainEvalFeatures.VALUES: values,
"exogenous": exogenous
}
train_input_fn = input_pipeline.RandomWindowInputFn(
input_pipeline.NumpyReader(features), shuffle_seed=2, num_threads=1,
batch_size=16, window_size=16)
eval_input_fn = input_pipeline.RandomWindowInputFn(
input_pipeline.NumpyReader(features), shuffle_seed=3, num_threads=1,
batch_size=16, window_size=16)
first_estimator.train(input_fn=train_input_fn, steps=1)
first_evaluation = first_estimator.evaluate(
input_fn=eval_input_fn, steps=1)
first_loss_before_fit = first_evaluation["loss"]
self.assertAllEqual(first_loss_before_fit, first_evaluation["average_loss"])
self.assertAllEqual([], first_loss_before_fit.shape)
first_estimator.train(input_fn=train_input_fn, steps=1)
first_loss_after_fit = first_estimator.evaluate(
input_fn=eval_input_fn, steps=1)["loss"]
self.assertAllEqual([], first_loss_after_fit.shape)
second_estimator = estimator_fn(model_dir, exogenous_feature_columns)
second_estimator.train(input_fn=train_input_fn, steps=1)
whole_dataset_input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(features))
whole_dataset_evaluation = second_estimator.evaluate(
input_fn=whole_dataset_input_fn, steps=1)
exogenous_values_ten_steps = {
"exogenous": numpy.arange(
10, dtype=dtype.as_numpy_dtype)[None, :, None]
}
predict_input_fn = input_pipeline.predict_continuation_input_fn(
evaluation=whole_dataset_evaluation,
exogenous_features=exogenous_values_ten_steps,
steps=10)
# Also tests that limit_epochs in predict_continuation_input_fn prevents
# infinite iteration
(estimator_predictions,
) = list(second_estimator.predict(input_fn=predict_input_fn))
self.assertAllEqual([10, 1], estimator_predictions["mean"].shape)
input_receiver_fn = first_estimator.build_raw_serving_input_receiver_fn()
export_location = first_estimator.export_savedmodel(self.get_temp_dir(),
input_receiver_fn)
with ops.Graph().as_default():
with session.Session() as sess:
signatures = loader.load(sess, [tag_constants.SERVING], export_location)
# Test that prediction and filtering can continue from evaluation output
saved_prediction = saved_model_utils.predict_continuation(
continue_from=whole_dataset_evaluation,
steps=10,
exogenous_features=exogenous_values_ten_steps,
signatures=signatures,
session=sess)
# Saved model predictions should be the same as Estimator predictions
# starting from the same evaluation.
for prediction_key, prediction_value in estimator_predictions.items():
self.assertAllClose(prediction_value,
numpy.squeeze(
saved_prediction[prediction_key], axis=0))
first_filtering = saved_model_utils.filter_continuation(
continue_from=whole_dataset_evaluation,
features={
feature_keys.FilteringFeatures.TIMES: times[None, -1] + 2,
feature_keys.FilteringFeatures.VALUES: values[None, -1] + 2.,
"exogenous": values[None, -1, None] + 12.
},
signatures=signatures,
session=sess)
# Test that prediction and filtering can continue from filtering output
second_saved_prediction = saved_model_utils.predict_continuation(
continue_from=first_filtering,
steps=1,
exogenous_features={
"exogenous": numpy.arange(
1, dtype=dtype.as_numpy_dtype)[None, :, None]
},
signatures=signatures,
session=sess)
self.assertEqual(
times[-1] + 3,
numpy.squeeze(
second_saved_prediction[feature_keys.PredictionResults.TIMES]))
saved_model_utils.filter_continuation(
continue_from=first_filtering,
features={
feature_keys.FilteringFeatures.TIMES: times[-1] + 3,
feature_keys.FilteringFeatures.VALUES: values[-1] + 3.,
"exogenous": values[-1, None] + 13.
},
signatures=signatures,
session=sess)
# Test cold starting
six.assertCountEqual(
self,
[feature_keys.FilteringFeatures.TIMES,
feature_keys.FilteringFeatures.VALUES,
"exogenous"],
signatures.signature_def[
feature_keys.SavedModelLabels.COLD_START_FILTER].inputs.keys())
batch_numpy_times = numpy.tile(
numpy.arange(30, dtype=numpy.int64)[None, :], (10, 1))
batch_numpy_values = numpy.ones([10, 30, 1])
state = saved_model_utils.cold_start_filter(
signatures=signatures,
session=sess,
features={
feature_keys.FilteringFeatures.TIMES: batch_numpy_times,
feature_keys.FilteringFeatures.VALUES: batch_numpy_values,
"exogenous": 10. + batch_numpy_values
}
)
predict_times = numpy.tile(
numpy.arange(30, 45, dtype=numpy.int64)[None, :], (10, 1))
predictions = saved_model_utils.predict_continuation(
continue_from=state,
times=predict_times,
exogenous_features={
"exogenous": numpy.tile(numpy.arange(
15, dtype=dtype.as_numpy_dtype), (10,))[None, :, None]
},
signatures=signatures,
session=sess)
self.assertAllEqual([10, 15, 1], predictions["mean"].shape)
def test_fit_restore_fit_ar_flat(self):
def _estimator_fn(model_dir, exogenous_feature_columns):
return estimators.ARRegressor(
periodicities=10, input_window_size=10, output_window_size=6,
num_features=1, model_dir=model_dir, config=_SeedRunConfig(),
# This test is flaky with normal likelihood loss (could add more
# training iterations instead).
loss=ar_model.ARModel.SQUARED_LOSS,
exogenous_feature_columns=exogenous_feature_columns)
self._fit_restore_fit_test_template(_estimator_fn, dtype=dtypes.float32)
def test_fit_restore_fit_ar_lstm(self):
def _estimator_fn(model_dir, exogenous_feature_columns):
return estimators.TimeSeriesRegressor(
model=ar_model.ARModel(
periodicities=10, input_window_size=10, output_window_size=6,
num_features=1,
exogenous_feature_columns=exogenous_feature_columns,
prediction_model_factory=functools.partial(
ar_model.LSTMPredictionModel,
num_units=10)),
config=_SeedRunConfig(),
model_dir=model_dir)
self._fit_restore_fit_test_template(_estimator_fn, dtype=dtypes.float32)
def test_fit_restore_fit_structural_ensemble_regressor(self):
dtype = dtypes.float32
def _estimator_fn(model_dir, exogenous_feature_columns):
return estimators.StructuralEnsembleRegressor(
num_features=1, periodicities=10, model_dir=model_dir, dtype=dtype,
config=_SeedRunConfig(),
exogenous_feature_columns=exogenous_feature_columns)
self._fit_restore_fit_test_template(_estimator_fn, dtype=dtype)
if __name__ == "__main__":
test.main()
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