Merge remote-tracking branch 'origin/feat/add-pytorch-model-support' into feat/add-pytorch-model-support
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commit
1c91b4427b
@ -446,7 +446,7 @@ class FreqaiDataDrawer:
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dump(model, save_path / f"{dk.model_filename}_model.joblib")
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dump(model, save_path / f"{dk.model_filename}_model.joblib")
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elif self.model_type == 'keras':
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elif self.model_type == 'keras':
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model.save(save_path / f"{dk.model_filename}_model.h5")
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model.save(save_path / f"{dk.model_filename}_model.h5")
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elif self.model_type in ["stable_baselines", "sb3_contrib", "pytorch"]:
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elif self.model_type in ["stable_baselines3", "sb3_contrib", "pytorch"]:
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model.save(save_path / f"{dk.model_filename}_model.zip")
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model.save(save_path / f"{dk.model_filename}_model.zip")
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if dk.svm_model is not None:
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if dk.svm_model is not None:
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@ -496,7 +496,7 @@ class FreqaiDataDrawer:
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dk.training_features_list = dk.data["training_features_list"]
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dk.training_features_list = dk.data["training_features_list"]
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dk.label_list = dk.data["label_list"]
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dk.label_list = dk.data["label_list"]
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def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any:
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def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any: # noqa: C901
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"""
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"""
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loads all data required to make a prediction on a sub-train time range
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loads all data required to make a prediction on a sub-train time range
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:returns:
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:returns:
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@ -563,7 +563,7 @@ class IFreqaiModel(ABC):
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file_type = ".joblib"
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file_type = ".joblib"
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elif self.dd.model_type == 'keras':
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elif self.dd.model_type == 'keras':
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file_type = ".h5"
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file_type = ".h5"
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elif self.dd.model_type in ["stable_baselines", "sb3_contrib", "pytorch"]:
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elif self.dd.model_type in ["stable_baselines3", "sb3_contrib", "pytorch"]:
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file_type = ".zip"
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file_type = ".zip"
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path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model{file_type}")
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path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model{file_type}")
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@ -41,7 +41,7 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
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self.max_n_eval_batches: Optional[int] = model_training_params.get(
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self.max_n_eval_batches: Optional[int] = model_training_params.get(
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"max_n_eval_batches", None
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"max_n_eval_batches", None
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)
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)
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self.model_kwargs: Dict = model_training_params.get("model_kwargs", {})
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self.model_kwargs: Dict[str, any] = model_training_params.get("model_kwargs", {})
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self.class_name_to_index = None
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self.class_name_to_index = None
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self.index_to_class_name = None
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self.index_to_class_name = None
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