pytorch - naming refactor - max_iters to n_steps

freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py

@@ -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,
                 },

freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py

@@ -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,
                 },

freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py

@@ -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
                 },

freqtrade/freqai/torch/PyTorchModelTrainer.py

@@ -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:
+        if not self.n_steps and not self.n_epochs:
-            raise Exception("Either `max_iters` or `n_epochs` should be set.")
+            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

tests/freqai/conftest.py

@@ -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,