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Solved a problem similar to Exception: Reached maximum number of idle transformation calls #130

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Solved a problem similar to Exception: Reached maximum number of idle transformation calls #130

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210cf75
fixed Exception: Reached maximum number of idle transformation calls
LemonCANDY42 Mar 18, 2023
683d130
fixed module 'numpy' has no attribute 'long'
LemonCANDY42 Mar 18, 2023
6a2b1fc
Fixed TypeError: PyTorchPredictor .__init__() got an unexpected keywo…
LemonCANDY42 Mar 18, 2023
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527 changes: 29 additions & 498 deletions examples/Time-Grad-Electricity.ipynb
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2 changes: 1 addition & 1 deletion pts/model/deepar/deepar_estimator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -135,7 +135,7 @@ def create_transformation(self) -> Transformation:
AsNumpyArray(
field=FieldName.FEAT_STATIC_CAT,
expected_ndim=1,
dtype=np.long,
dtype=np.int_,
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Suggested change
dtype=np.int_,
dtype=int,

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@stathius ok let me check... do we need to change the notebook?

The notebook seems to run fine.

),
AsNumpyArray(
field=FieldName.FEAT_STATIC_REAL,
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6 changes: 4 additions & 2 deletions pts/model/estimator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,10 +31,12 @@ class PyTorchEstimator(Estimator):
@validated()
def __init__(
self, trainer: Trainer, lead_time: int = 0, dtype: np.dtype = np.float32
,**kwargs,
) -> None:
super().__init__(lead_time=lead_time)
self.trainer = trainer
self.dtype = dtype
self.max_idle_transforms = kwargs["max_idle_transforms"] if "max_idle_transforms" in kwargs else None
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@stathius stathius Mar 30, 2023 •
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This is by no means wrong but it seems to me that newer versions of gluonts seem to handle this using the env variable. If so might be better to stick with it for better compatibility. @kashif

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ok yes if you can peek into the 0.7.0 branch, you can also see i have merged the implementation of deepAR and deepVAR as they differ in the output side and the vanilla transformer also works for both univariate and multivariate...

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Thanks a lot for pointing me to the 0.7.0 branch, really good to know you're activelty working on this. Will have a more thorough look. I realize you're now using the pytorch-lightning trainer (was entertaining doing that).


def create_transformation(self) -> Transformation:
"""
Expand Down Expand Up @@ -104,7 +106,7 @@ def train_model(

input_names = get_module_forward_input_names(trained_net)

with env._let(max_idle_transforms=maybe_len(training_data) or 0):
with env._let(max_idle_transforms=self.max_idle_transforms or maybe_len(training_data) or 0):
training_instance_splitter = self.create_instance_splitter("training")
training_iter_dataset = TransformedIterableDataset(
dataset=training_data,
Expand All @@ -128,7 +130,7 @@ def train_model(

validation_data_loader = None
if validation_data is not None:
with env._let(max_idle_transforms=maybe_len(validation_data) or 0):
with env._let(max_idle_transforms=self.max_idle_transforms or maybe_len(validation_data) or 0):
validation_instance_splitter = self.create_instance_splitter("validation")
validation_iter_dataset = TransformedIterableDataset(
dataset=validation_data,
Expand Down
1 change: 0 additions & 1 deletion pts/model/time_grad/time_grad_estimator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -251,7 +251,6 @@ def create_predictor(
input_names=input_names,
prediction_net=prediction_network,
batch_size=self.trainer.batch_size,
freq=self.freq,
prediction_length=self.prediction_length,
device=device,
)