def _test_pass_to_next(self, read_offset, step, correct_offset):
stub_model = StubTimeSeriesModel(correct_offset=correct_offset)
data = self._make_test_data(
length=100 + read_offset, cut_start=None, cut_end=None, offset=100.,
step=step)
init_input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(
{k: v[:-read_offset] for k, v in data.items()}))
result_input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(
{k: v[read_offset:] for k, v in data.items()}))
chainer = state_management.ChainingStateManager(
state_saving_interval=1)
stub_model.initialize_graph()
chainer.initialize_graph(model=stub_model)
init_model_outputs = chainer.define_loss(
model=stub_model, features=init_input_fn()[0],
mode=estimator_lib.ModeKeys.TRAIN)
result_model_outputs = chainer.define_loss(
model=stub_model, features=result_input_fn()[0],
mode=estimator_lib.ModeKeys.TRAIN)
with self.test_session() as session:
variables.global_variables_initializer().run()
coordinator = coordinator_lib.Coordinator()
queue_runner_impl.start_queue_runners(session, coord=coordinator)
init_model_outputs.loss.eval()
returned_loss = result_model_outputs.loss.eval()
coordinator.request_stop()
coordinator.join()
return returned_loss
def _gap_test_template(self, times, values):
random_model = RandomStateSpaceModel(
state_dimension=1,
state_noise_dimension=1,
configuration=state_space_model.StateSpaceModelConfiguration(
num_features=1))
random_model.initialize_graph()
input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader({
feature_keys.TrainEvalFeatures.TIMES:
times,
feature_keys.TrainEvalFeatures.VALUES:
values
}))
features, _ = input_fn()
times = features[feature_keys.TrainEvalFeatures.TIMES]
values = features[feature_keys.TrainEvalFeatures.VALUES]
model_outputs = random_model.get_batch_loss(
features={
feature_keys.TrainEvalFeatures.TIMES: times,
feature_keys.TrainEvalFeatures.VALUES: values
},
mode=None,
state=math_utils.replicate_state(
start_state=random_model.get_start_state(),
batch_size=array_ops.shape(times)[0]))
with self.test_session() as session:
variables.global_variables_initializer().run()
coordinator = coordinator_lib.Coordinator()
queue_runner_impl.start_queue_runners(session, coord=coordinator)
model_outputs.loss.eval()
coordinator.request_stop()
coordinator.join()
def test_exogenous_input(self):
"""Test that no errors are raised when using exogenous features."""
dtype = dtypes.float64
times = [1, 2, 3, 4, 5, 6]
values = [[0.01], [5.10], [5.21], [0.30], [5.41], [0.50]]
feature_a = [["off"], ["on"], ["on"], ["off"], ["on"], ["off"]]
sparse_column_a = feature_column.sparse_column_with_keys(
column_name="feature_a", keys=["on", "off"])
one_hot_a = layers.one_hot_column(sparse_id_column=sparse_column_a)
regressor = estimators.StructuralEnsembleRegressor(
periodicities=[],
num_features=1,
moving_average_order=0,
exogenous_feature_columns=[one_hot_a],
dtype=dtype)
features = {TrainEvalFeatures.TIMES: times,
TrainEvalFeatures.VALUES: values,
"feature_a": feature_a}
train_input_fn = input_pipeline.RandomWindowInputFn(
input_pipeline.NumpyReader(features),
window_size=6, batch_size=1)
regressor.train(input_fn=train_input_fn, steps=1)
eval_input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(features))
evaluation = regressor.evaluate(input_fn=eval_input_fn, steps=1)
predict_input_fn = input_pipeline.predict_continuation_input_fn(
evaluation, times=[[7, 8, 9]],
exogenous_features={"feature_a": [[["on"], ["off"], ["on"]]]})
regressor.predict(input_fn=predict_input_fn)
def dry_run_train_helper(
self, sample_every, period, num_samples, model_type, model_args,
num_features=1):
numpy.random.seed(1)
dtype = dtypes.float32
features = self.simple_data(
sample_every, dtype=dtype, period=period, num_samples=num_samples,
num_features=num_features)
model = model_type(
configuration=(
state_space_model.StateSpaceModelConfiguration(
num_features=num_features,
dtype=dtype,
covariance_prior_fn=lambda _: 0.)),
**model_args)
class _RunConfig(estimator_lib.RunConfig):
@property
def tf_random_seed(self):
return 4
estimator = estimators.StateSpaceRegressor(model, config=_RunConfig())
train_input_fn = input_pipeline.RandomWindowInputFn(
input_pipeline.NumpyReader(features), num_threads=1, shuffle_seed=1,
batch_size=16, window_size=16)
eval_input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(features))
estimator.train(input_fn=train_input_fn, max_steps=1)
first_evaluation = estimator.evaluate(input_fn=eval_input_fn, steps=1)
estimator.train(input_fn=train_input_fn, max_steps=3)
second_evaluation = estimator.evaluate(input_fn=eval_input_fn, steps=1)
self.assertLess(second_evaluation["loss"], first_evaluation["loss"])
def _equivalent_to_single_model_test_template(self, model_generator):
with self.test_session() as session:
random_model = RandomStateSpaceModel(
state_dimension=5,
state_noise_dimension=4,
configuration=state_space_model.StateSpaceModelConfiguration(
dtype=dtypes.float64, num_features=1))
random_model.initialize_graph()
series_length = 10
model_data = random_model.generate(
number_of_series=1,
series_length=series_length,
model_parameters=random_model.random_model_parameters())
input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(model_data))
features, _ = input_fn()
model_outputs = random_model.get_batch_loss(
features=features,
mode=None,
state=math_utils.replicate_state(
start_state=random_model.get_start_state(),
batch_size=array_ops.shape(
features[feature_keys.TrainEvalFeatures.TIMES])[0]))
variables.global_variables_initializer().run()
compare_outputs_evaled_fn = model_generator(
random_model, model_data)
coordinator = coordinator_lib.Coordinator()
queue_runner_impl.start_queue_runners(session, coord=coordinator)
compare_outputs_evaled = compare_outputs_evaled_fn(session)
model_outputs_evaled = session.run(
(model_outputs.end_state, model_outputs.predictions))
coordinator.request_stop()
coordinator.join()
model_posteriors, model_predictions = model_outputs_evaled
(_, compare_posteriors,
compare_predictions) = compare_outputs_evaled
(model_posterior_mean, model_posterior_var,
model_from_time) = model_posteriors
(compare_posterior_mean, compare_posterior_var,
compare_from_time) = compare_posteriors
self.assertAllClose(model_posterior_mean,
compare_posterior_mean[0])
self.assertAllClose(model_posterior_var, compare_posterior_var[0])
self.assertAllClose(model_from_time, compare_from_time)
self.assertEqual(sorted(model_predictions.keys()),
sorted(compare_predictions.keys()))
for prediction_name in model_predictions:
if prediction_name == "loss":
# Chunking means that losses will be different; skip testing them.
continue
# Compare the last chunk to their corresponding un-chunked model
# predictions
last_prediction_chunk = compare_predictions[prediction_name][
-1]
comparison_values = last_prediction_chunk.shape[0]
model_prediction = (
model_predictions[prediction_name][0, -comparison_values:])
self.assertAllClose(model_prediction, last_prediction_chunk)
def test_structural_ensemble_numpy_input(self):
numpy_data = {"times": numpy.arange(50),
"values": numpy.random.normal(size=[50])}
estimators.StructuralEnsembleRegressor(
num_features=1, periodicities=[], model_dir=self.get_temp_dir(),
config=_SeedRunConfig()).train(
input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(numpy_data)),
steps=1)
def _time_dependency_test_template(self, model_type):
"""Test that a time-dependent observation model influences predictions."""
model = model_type()
estimator = estimators.StateSpaceRegressor(
model=model, optimizer=gradient_descent.GradientDescentOptimizer(0.1))
values = numpy.reshape([1., 2., 3., 4.],
newshape=[1, 4, 1])
input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader({
feature_keys.TrainEvalFeatures.TIMES: [[0, 1, 2, 3]],
feature_keys.TrainEvalFeatures.VALUES: values
}))
estimator.train(input_fn=input_fn, max_steps=1)
predicted_values = estimator.evaluate(input_fn=input_fn, steps=1)["mean"]
# Throw out the first value so we don't test the prior
self.assertAllEqual(values[1:], predicted_values[1:])
def _whole_dataset_input_fn_test_template(
self, time_series_reader, num_features, num_samples):
result, _ = input_pipeline.WholeDatasetInputFn(time_series_reader)()
with self.test_session() as session:
session.run(variables.local_variables_initializer())
coordinator = coordinator_lib.Coordinator()
queue_runner_impl.start_queue_runners(session, coord=coordinator)
features = session.run(result)
coordinator.request_stop()
coordinator.join()
self.assertEqual("int64", features[TrainEvalFeatures.TIMES].dtype)
self.assertAllEqual(numpy.arange(num_samples, dtype=numpy.int64)[None, :],
features[TrainEvalFeatures.TIMES])
for feature_number in range(num_features):
self.assertAllEqual(
features[TrainEvalFeatures.TIMES] * 2. + feature_number,
features[TrainEvalFeatures.VALUES][:, :, feature_number])
def test_loop_unrolling(self):
"""Tests running/restoring from a checkpoint with static unrolling."""
model = TimeDependentStateSpaceModel(
# Unroll during training, but not evaluation
static_unrolling_window_size_threshold=2)
estimator = estimators.StateSpaceRegressor(model=model)
times = numpy.arange(100)
values = numpy.arange(100)
dataset = {
feature_keys.TrainEvalFeatures.TIMES: times,
feature_keys.TrainEvalFeatures.VALUES: values
}
train_input_fn = input_pipeline.RandomWindowInputFn(
input_pipeline.NumpyReader(dataset), batch_size=16, window_size=2)
eval_input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(dataset))
estimator.train(input_fn=train_input_fn, max_steps=1)
estimator.evaluate(input_fn=eval_input_fn, steps=1)
def test_exact_posterior_recovery_no_transition_noise(self):
with self.test_session() as session:
stub_model, data, true_params = self._get_single_model()
input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(data))
features, _ = input_fn()
model_outputs = stub_model.get_batch_loss(
features=features,
mode=None,
state=math_utils.replicate_state(
start_state=stub_model.get_start_state(),
batch_size=array_ops.shape(
features[feature_keys.TrainEvalFeatures.TIMES])[0]))
variables.global_variables_initializer().run()
coordinator = coordinator_lib.Coordinator()
queue_runner_impl.start_queue_runners(session, coord=coordinator)
posterior_mean, posterior_var, posterior_times = session.run(
# Feed the true model parameters so that this test doesn't depend on
# the generated parameters being close to the variable initializations
# (an alternative would be training steps to fit the noise values,
# which would be slow).
model_outputs.end_state,
feed_dict=true_params)
coordinator.request_stop()
coordinator.join()
self.assertAllClose(numpy.zeros([1, 4, 4]),
posterior_var,
atol=1e-2)
self.assertAllClose(numpy.dot(
numpy.linalg.matrix_power(
stub_model.transition,
data[feature_keys.TrainEvalFeatures.TIMES].shape[1]),
true_params[stub_model.prior_state_mean]),
posterior_mean[0],
rtol=1e-1)
self.assertAllClose(
math_utils.batch_end_time(
features[feature_keys.TrainEvalFeatures.TIMES]).eval(),
posterior_times)
def test_state_override(self):
test_start_state = (numpy.array([[2, 3, 4]]), (numpy.array([2]),
numpy.array([[3., 5.]])))
data = {
feature_keys.FilteringFeatures.TIMES: numpy.arange(5),
feature_keys.FilteringFeatures.VALUES: numpy.zeros(shape=[5, 3])
}
features, _ = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(data))()
features[feature_keys.FilteringFeatures.STATE_TUPLE] = test_start_state
stub_model = _StateOverrideModel()
chainer = state_management.ChainingStateManager()
stub_model.initialize_graph()
chainer.initialize_graph(model=stub_model)
model_outputs = chainer.define_loss(
model=stub_model, features=features, mode=estimator_lib.ModeKeys.EVAL)
with train.MonitoredSession() as session:
end_state = session.run(model_outputs.end_state)
nest.assert_same_structure(test_start_state, end_state)
for expected, received in zip(
nest.flatten(test_start_state), nest.flatten(end_state)):
self.assertAllEqual(expected, received)
def test_no_periodicity(self):
"""Test that no errors are raised when periodicites is None."""
dtype = dtypes.float64
times = [1, 2, 3, 4, 5, 6]
values = [[0.01], [5.10], [5.21], [0.30], [5.41], [0.50]]
regressor = estimators.StructuralEnsembleRegressor(
periodicities=None,
num_features=1,
moving_average_order=0,
dtype=dtype)
features = {TrainEvalFeatures.TIMES: times,
TrainEvalFeatures.VALUES: values}
train_input_fn = input_pipeline.RandomWindowInputFn(
input_pipeline.NumpyReader(features),
window_size=6, batch_size=1)
regressor.train(input_fn=train_input_fn, steps=1)
eval_input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(features))
evaluation = regressor.evaluate(input_fn=eval_input_fn, steps=1)
predict_input_fn = input_pipeline.predict_continuation_input_fn(
evaluation, times=[[7, 8, 9]])
regressor.predict(input_fn=predict_input_fn)
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=5)
first_loss_before_fit = first_estimator.evaluate(
input_fn=eval_input_fn, steps=1)["loss"]
first_estimator.train(input_fn=train_input_fn, steps=50)
first_loss_after_fit = first_estimator.evaluate(input_fn=eval_input_fn,
steps=1)["loss"]
self.assertLess(first_loss_after_fit, first_loss_before_fit)
second_estimator = estimator_fn(model_dir, exogenous_feature_columns)
second_estimator.train(input_fn=train_input_fn, steps=2)
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 _train_on_generated_data(
generate_fn, generative_model, train_iterations, seed,
learning_rate=0.1, ignore_params_fn=lambda _: (),
derived_param_test_fn=lambda _: (),
train_input_fn_type=input_pipeline.WholeDatasetInputFn,
train_state_manager=state_management.PassthroughStateManager()):
"""The training portion of parameter recovery tests."""
random_seed.set_random_seed(seed)
generate_graph = ops.Graph()
with generate_graph.as_default():
with session.Session(graph=generate_graph):
generative_model.initialize_graph()
time_series_reader, true_parameters = generate_fn(generative_model)
true_parameters = {
tensor.name: value for tensor, value in true_parameters.items()}
eval_input_fn = input_pipeline.WholeDatasetInputFn(time_series_reader)
eval_state_manager = state_management.PassthroughStateManager()
true_parameter_eval_graph = ops.Graph()
with true_parameter_eval_graph.as_default():
generative_model.initialize_graph()
ignore_params = ignore_params_fn(generative_model)
feature_dict, _ = eval_input_fn()
eval_state_manager.initialize_graph(generative_model)
feature_dict[TrainEvalFeatures.VALUES] = math_ops.cast(
feature_dict[TrainEvalFeatures.VALUES], generative_model.dtype)
model_outputs = eval_state_manager.define_loss(
model=generative_model,
features=feature_dict,
mode=estimator_lib.ModeKeys.EVAL)
with session.Session(graph=true_parameter_eval_graph) as sess:
variables.global_variables_initializer().run()
coordinator = coordinator_lib.Coordinator()
queue_runner_impl.start_queue_runners(sess, coord=coordinator)
true_param_loss = model_outputs.loss.eval(feed_dict=true_parameters)
true_transformed_params = {
param: param.eval(feed_dict=true_parameters)
for param in derived_param_test_fn(generative_model)}
coordinator.request_stop()
coordinator.join()
saving_hook = _SavingTensorHook(
tensors=true_parameters.keys(),
every_n_iter=train_iterations - 1)
class _RunConfig(estimator_lib.RunConfig):
@property
def tf_random_seed(self):
return seed
estimator = estimators.TimeSeriesRegressor(
model=generative_model,
config=_RunConfig(),
state_manager=train_state_manager,
optimizer=adam.AdamOptimizer(learning_rate))
train_input_fn = train_input_fn_type(time_series_reader=time_series_reader)
trained_loss = (estimator.train(
input_fn=train_input_fn,
max_steps=train_iterations,
hooks=[saving_hook]).evaluate(
input_fn=eval_input_fn, steps=1))["loss"]
logging.info("Final trained loss: %f", trained_loss)
logging.info("True parameter loss: %f", true_param_loss)
return (ignore_params, true_parameters, true_transformed_params,
trained_loss, true_param_loss, saving_hook,
true_parameter_eval_graph)
def test_savedmodel_state_override(self):
random_model = RandomStateSpaceModel(
state_dimension=5,
state_noise_dimension=4,
configuration=state_space_model.StateSpaceModelConfiguration(
exogenous_feature_columns=[
layers.real_valued_column("exogenous")
],
dtype=dtypes.float64,
num_features=1))
estimator = estimators.StateSpaceRegressor(
model=random_model,
optimizer=gradient_descent.GradientDescentOptimizer(0.1))
combined_input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader({
feature_keys.FilteringFeatures.TIMES: [1, 2, 3, 4],
feature_keys.FilteringFeatures.VALUES: [1., 2., 3., 4.],
"exogenous": [-1., -2., -3., -4.]
}))
estimator.train(combined_input_fn, steps=1)
export_location = estimator.export_savedmodel(
self.get_temp_dir(),
estimator.build_raw_serving_input_receiver_fn(
exogenous_features={
"exogenous": numpy.zeros((0, 0), dtype=numpy.float32)
}))
with ops.Graph().as_default() as graph:
random_model.initialize_graph()
with self.test_session(graph=graph) as session:
variables.global_variables_initializer().run()
evaled_start_state = session.run(
random_model.get_start_state())
evaled_start_state = [
state_element[None, ...] for state_element in evaled_start_state
]
with ops.Graph().as_default() as graph:
with self.test_session(graph=graph) as session:
signatures = loader.load(session, [tag_constants.SERVING],
export_location)
first_split_filtering = saved_model_utils.filter_continuation(
continue_from={
feature_keys.FilteringResults.STATE_TUPLE:
evaled_start_state
},
signatures=signatures,
session=session,
features={
feature_keys.FilteringFeatures.TIMES: [1, 2],
feature_keys.FilteringFeatures.VALUES: [1., 2.],
"exogenous": [-1., -2.]
})
second_split_filtering = saved_model_utils.filter_continuation(
continue_from=first_split_filtering,
signatures=signatures,
session=session,
features={
feature_keys.FilteringFeatures.TIMES: [3, 4],
feature_keys.FilteringFeatures.VALUES: [3., 4.],
"exogenous": [-3., -4.]
})
combined_filtering = saved_model_utils.filter_continuation(
continue_from={
feature_keys.FilteringResults.STATE_TUPLE:
evaled_start_state
},
signatures=signatures,
session=session,
features={
feature_keys.FilteringFeatures.TIMES: [1, 2, 3, 4],
feature_keys.FilteringFeatures.VALUES:
[1., 2., 3., 4.],
"exogenous": [-1., -2., -3., -4.]
})
split_predict = saved_model_utils.predict_continuation(
continue_from=second_split_filtering,
signatures=signatures,
session=session,
steps=1,
exogenous_features={"exogenous": [[-5.]]})
combined_predict = saved_model_utils.predict_continuation(
continue_from=combined_filtering,
signatures=signatures,
session=session,
steps=1,
exogenous_features={"exogenous": [[-5.]]})
for state_key, combined_state_value in combined_filtering.items():
if state_key == feature_keys.FilteringResults.TIMES:
continue
self.assertAllClose(combined_state_value,
second_split_filtering[state_key])
for prediction_key, combined_value in combined_predict.items():
self.assertAllClose(combined_value, split_predict[prediction_key])
def _fit_restore_fit_test_template(self, estimator_fn, dtype):
"""Tests restoring previously fit models."""
model_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
first_estimator = estimator_fn(model_dir)
times = numpy.arange(20, dtype=numpy.int64)
values = numpy.arange(20, dtype=dtype.as_numpy_dtype)
features = {
feature_keys.TrainEvalFeatures.TIMES: times,
feature_keys.TrainEvalFeatures.VALUES: values
}
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=5)
first_loss_before_fit = first_estimator.evaluate(
input_fn=eval_input_fn, steps=1)["loss"]
first_estimator.train(input_fn=train_input_fn, steps=50)
first_loss_after_fit = first_estimator.evaluate(
input_fn=eval_input_fn, steps=1)["loss"]
self.assertLess(first_loss_after_fit, first_loss_before_fit)
second_estimator = estimator_fn(model_dir)
second_estimator.train(input_fn=train_input_fn, steps=2)
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)
predict_input_fn = input_pipeline.predict_continuation_input_fn(
evaluation=whole_dataset_evaluation,
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,
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.
},
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,
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.
},
signatures=signatures,
session=sess)
