def __init__(self, model, state_manager=None, optimizer=None, model_dir=None,
config=None, head_type=ts_head_lib.TimeSeriesRegressionHead):
"""Initialize the Estimator.
Args:
model: The time series model to wrap (inheriting from TimeSeriesModel).
state_manager: The state manager to use, or (by default)
PassthroughStateManager if none is needed.
optimizer: The optimization algorithm to use when training, inheriting
from tf.train.Optimizer. Defaults to Adam with step size 0.02.
model_dir: See `Estimator`.
config: See `Estimator`.
head_type: The kind of head to use for the model (inheriting from
`TimeSeriesRegressionHead`).
"""
input_statistics_generator = math_utils.InputStatisticsFromMiniBatch(
dtype=model.dtype, num_features=model.num_features)
if state_manager is None:
if isinstance(model, ar_model.ARModel):
state_manager = state_management.FilteringOnlyStateManager()
else:
state_manager = state_management.PassthroughStateManager()
if optimizer is None:
optimizer = train.AdamOptimizer(0.02)
self._model = model
ts_regression_head = head_type(
model=model, state_manager=state_manager, optimizer=optimizer,
input_statistics_generator=input_statistics_generator)
model_fn = ts_regression_head.create_estimator_spec
super(TimeSeriesRegressor, self).__init__(
model_fn=model_fn,
model_dir=model_dir,
config=config)
def test_queue(self):
for dtype in [tf.dtypes.float32, tf.dtypes.float64]:
for num_features in [1, 2, 3]:
self._input_statistics_test_template(
math_utils.InputStatisticsFromMiniBatch(
num_features=num_features, dtype=dtype),
num_features=num_features,
dtype=dtype,
warmup_iterations=1000,
rtol=0.1)