pytorch - ruff fixes
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@@ -78,7 +78,11 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
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data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary, splits)
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data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary, splits)
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n_obs = len(data_dictionary["train_features"])
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n_obs = len(data_dictionary["train_features"])
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n_epochs = self.n_epochs or self.calc_n_epochs(n_obs=n_obs, batch_size=self.batch_size, n_iters=self.max_iters)
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n_epochs = self.n_epochs or self.calc_n_epochs(
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n_obs=n_obs,
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batch_size=self.batch_size,
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n_iters=self.max_iters,
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)
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for epoch in range(1, n_epochs + 1):
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for epoch in range(1, n_epochs + 1):
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for i, batch_data in enumerate(data_loaders_dictionary["train"]):
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for i, batch_data in enumerate(data_loaders_dictionary["train"]):
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xb, yb = batch_data
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xb, yb = batch_data
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@@ -152,7 +156,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
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n_epochs = min(n_iters // n_batches, 1)
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n_epochs = min(n_iters // n_batches, 1)
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if n_epochs <= 10:
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if n_epochs <= 10:
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logger.warning(
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logger.warning(
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f"Setting low n_epochs. {n_epochs} = n_epochs = n_iters // n_batches = {n_iters} // {n_batches}. "
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f"Setting low n_epochs: {n_epochs}. "
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f"Please consider increasing `max_iters` hyper-parameter."
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f"Please consider increasing `max_iters` hyper-parameter."
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)
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)
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