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Code Issues Actions 2 Packages Projects Releases Wiki Activity

pytorch - naming refactor - max_iters to n_steps

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This commit is contained in:
yinon 2023-08-04 13:45:21 +00:00
parent d17bf6350d
commit a3c6904fbc
5 changed files with 12 additions and 12 deletions
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2
freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
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@ -26,7 +26,7 @@ class PyTorchMLPClassifier(BasePyTorchClassifier):
"model_training_parameters" : {
"learning_rate": 3e-4,
"trainer_kwargs": {
"max_iters": 5000,
"n_steps": 5000,
"batch_size": 64,
"n_epochs": null,
},

2
freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
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@ -27,7 +27,7 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
"model_training_parameters" : {
"learning_rate": 3e-4,
"trainer_kwargs": {
"max_iters": 5000,
"n_steps": 5000,
"batch_size": 64,
"n_epochs": null,
},

2
freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
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@ -30,7 +30,7 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
"model_training_parameters" : {
"learning_rate": 3e-4,
"trainer_kwargs": {
"max_iters": 5000,
"n_steps": 5000,
"batch_size": 64,
"n_epochs": null
},

14
freqtrade/freqai/torch/PyTorchModelTrainer.py
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@ -39,7 +39,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
state_dict and model_meta_data saved by self.save() method.
:param model_meta_data: Additional metadata about the model (optional).
:param data_convertor: convertor from pd.DataFrame to torch.tensor.
:param max_iters: used to calculate n_epochs. The number of training iterations to run.
:param n_steps: used to calculate n_epochs. The number of training iterations to run.
iteration here refers to the number of times optimizer.step() is called.
ignored if n_epochs is set.
:param n_epochs: The maximum number batches to use for evaluation.
@ -50,10 +50,10 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
self.criterion = criterion
self.model_meta_data = model_meta_data
self.device = device
self.max_iters: int = kwargs.get("max_iters", None)
self.n_steps: int = kwargs.get("n_steps", None)
self.n_epochs: Optional[int] = kwargs.get("n_epochs", 10)
if not self.max_iters and not self.n_epochs:
raise Exception("Either `max_iters` or `n_epochs` should be set.")
if not self.n_steps and not self.n_epochs:
raise Exception("Either `n_steps` or `n_epochs` should be set.")
self.batch_size: int = kwargs.get("batch_size", 64)
self.data_convertor = data_convertor
@ -82,7 +82,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
n_epochs = self.n_epochs or self.calc_n_epochs(
n_obs=n_obs,
batch_size=self.batch_size,
n_iters=self.max_iters,
n_iters=self.n_steps,
)
batch_counter = 0
@ -153,7 +153,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
Calculates the number of epochs required to reach the maximum number
of iterations specified in the model training parameters.
the motivation here is that `max_iters` is easier to optimize and keep stable,
the motivation here is that `n_steps` is easier to optimize and keep stable,
across different n_obs - the number of data points.
"""
@ -162,7 +162,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
if n_epochs <= 10:
logger.warning(
f"Setting low n_epochs: {n_epochs}. "
f"Please consider increasing `max_iters` hyper-parameter."
f"Please consider increasing `n_steps` hyper-parameter."
)
return n_epochs

4
tests/freqai/conftest.py
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@ -97,9 +97,9 @@ def mock_pytorch_mlp_model_training_parameters() -> Dict[str, Any]:
return {
"learning_rate": 3e-4,
"trainer_kwargs": {
"max_iters": 1,
"n_steps": None,
"batch_size": 64,
"n_epochs": None,
"n_epochs": 1,
},
"model_kwargs": {
"hidden_dim": 32,