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Respect num_eval_samples in predict method #103

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Jun 13, 2019
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Respect num_eval_samples in predict method #103

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27 changes: 24 additions & 3 deletions src/gluonts/model/predictor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@
from gluonts.core.serde import dump_json, fqname_for, load_json
from gluonts.dataset.common import DataEntry, Dataset, ListDataset
from gluonts.dataset.loader import DataBatch, InferenceDataLoader
from gluonts.model.forecast import Forecast
from gluonts.model.forecast import Forecast, SampleForecast
from gluonts.support.util import (
export_repr_block,
export_symb_block,
Expand All @@ -42,8 +42,9 @@
)
from gluonts.transform import Transformation


if TYPE_CHECKING: # avoid circular import
from gluonts.model.estimator import Estimator
from gluonts.model.estimator import Estimator # noqa


class Predictor:
Expand Down Expand Up @@ -261,7 +262,9 @@ def as_symbol_block_predictor(
"""
raise NotImplementedError

def predict(self, dataset: Dataset, **kwargs) -> Iterator[Forecast]:
def predict(
self, dataset: Dataset, num_eval_samples: Optional[int] = None
) -> Iterator[Forecast]:
inference_data_loader = InferenceDataLoader(
dataset,
self.input_transform,
Expand All @@ -274,6 +277,24 @@ def predict(self, dataset: Dataset, **kwargs) -> Iterator[Forecast]:
outputs = self.prediction_net(*inputs).asnumpy()
if self.output_transform is not None:
outputs = self.output_transform(batch, outputs)
if num_eval_samples and not self._forecast_cls == SampleForecast:
logging.info(
'Forecast is not sample based. Ignoring parameter `num_eval_samples` from predict method.'
)
if num_eval_samples and self._forecast_cls == SampleForecast:
num_collected_samples = outputs[0].shape[0]
collected_samples = [outputs]
while num_collected_samples < num_eval_samples:
outputs = self.prediction_net(*inputs).asnumpy()
if self.output_transform is not None:
outputs = self.output_transform(batch, outputs)
collected_samples.append(outputs)
num_collected_samples += outputs[0].shape[0]
outputs = [
np.concatenate(s)[:num_eval_samples]
for s in zip(*collected_samples)
]
assert len(outputs[0]) == num_eval_samples
assert len(batch['forecast_start']) == len(outputs)
for i, output in enumerate(outputs):
yield self._forecast_cls(
Expand Down