From 5734358d91399e5e4caac8c9722bf8a23165d863 Mon Sep 17 00:00:00 2001
From: Yinon Polak <inon51@gmail.com>
Date: Thu, 13 Jul 2023 20:59:33 +0300
Subject: [PATCH] pytorch - trainer - add assertion that either n_epochs or
 max_iters is been set.

---
 freqtrade/freqai/torch/PyTorchModelTrainer.py | 29 ++++++++++---------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/freqtrade/freqai/torch/PyTorchModelTrainer.py b/freqtrade/freqai/torch/PyTorchModelTrainer.py
index a34d673b4..efdf3ed5a 100644
--- a/freqtrade/freqai/torch/PyTorchModelTrainer.py
+++ b/freqtrade/freqai/torch/PyTorchModelTrainer.py
@@ -39,9 +39,9 @@ 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: The number of training iterations to run.
-            iteration here refers to the number of times optimizer.step() is called,
-            used to calculate n_epochs. ignored if n_epochs is set.
+        :param max_iters: 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.
         :param batch_size: The size of the batches to use during training.
         """
@@ -52,6 +52,9 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         self.device = device
         self.max_iters: int = kwargs.get("max_iters", 100)
         self.n_epochs: Optional[int] = kwargs.get("n_epochs", None)
+        if not self.max_iters and not self.n_epochs:
+            raise Exception("Either `max_iters` or `n_epochs` should be set.")
+
         self.batch_size: int = kwargs.get("batch_size", 64)
         self.data_convertor = data_convertor
         self.window_size: int = window_size
@@ -75,8 +78,8 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
 
         data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary, splits)
         n_obs = len(data_dictionary["train_features"])
-        epochs = self.n_epochs or self.calc_n_epochs(n_obs=n_obs, batch_size=self.batch_size, n_iters=self.max_iters)
-        for epoch in range(1, epochs + 1):
+        n_epochs = self.n_epochs or self.calc_n_epochs(n_obs=n_obs, batch_size=self.batch_size, n_iters=self.max_iters)
+        for epoch in range(1, n_epochs + 1):
             for i, batch_data in enumerate(data_loaders_dictionary["train"]):
                 xb, yb = batch_data
                 xb = xb.to(self.device)
@@ -146,14 +149,14 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         """
 
         n_batches = n_obs // batch_size
-        epochs = n_iters // n_batches
-        if epochs <= 10:
-            logger.warning("User set `max_iters` in such a way that the trainer will only perform "
-                           f" {epochs} epochs. Please consider increasing this value accordingly")
-            if epochs <= 1:
-                logger.warning("Epochs set to 1. Please review your `max_iters` value")
-                epochs = 1
-        return epochs
+        n_epochs = min(n_iters // n_batches, 1)
+        if n_epochs <= 10:
+            logger.warning(
+                f"Setting low n_epochs. {n_epochs} = n_epochs = n_iters // n_batches = {n_iters} // {n_batches}. "
+                f"Please consider increasing `max_iters` hyper-parameter."
+            )
+
+        return n_epochs
 
     def save(self, path: Path):
         """