type hints fixes
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@ -13,8 +13,7 @@ class PyTorchMLPClassifier(PyTorchClassifier):
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This class implements the fit method of IFreqaiModel.
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in the fit method we initialize the model and trainer objects.
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the only requirement from the model is to be aligned to PyTorchClassifier
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predict method that expects the model to predict tensor of type long.
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the trainer defines the training loop.
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predict method that expects the model to predict a tensor of type long.
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parameters are passed via `model_training_parameters` under the freqai
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section in the config file. e.g:
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@ -37,8 +36,6 @@ class PyTorchMLPClassifier(PyTorchClassifier):
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}
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}
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}
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"""
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def __init__(
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@ -51,15 +48,15 @@ class PyTorchMLPClassifier(PyTorchClassifier):
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super().__init__(**kwargs)
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config = self.freqai_info.get("model_training_parameters", {})
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self.learning_rate: float = config.get("learning_rate", learning_rate)
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self.model_kwargs: Dict[str, any] = config.get("model_kwargs", model_kwargs)
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self.trainer_kwargs: Dict[str, any] = config.get("trainer_kwargs", trainer_kwargs)
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self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", model_kwargs)
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self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", trainer_kwargs)
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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:raises ValueError: If self.class_names is not defined in the parent class.
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:raises ValueError: If self.class_names is empty.
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"""
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class_names = self.get_class_names()
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@ -49,8 +49,8 @@ class PyTorchMLPRegressor(PyTorchRegressor):
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super().__init__(**kwargs)
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config = self.freqai_info.get("model_training_parameters", {})
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self.learning_rate: float = config.get("learning_rate", learning_rate)
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self.model_kwargs: Dict[str, any] = config.get("model_kwargs", model_kwargs)
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self.trainer_kwargs: Dict[str, any] = config.get("trainer_kwargs", trainer_kwargs)
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self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", model_kwargs)
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self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", trainer_kwargs)
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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