reformat code
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@ -69,7 +69,7 @@ class PyTorchModelTrainer:
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self.model.eval()
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epochs = self.calc_n_epochs(
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n_obs=len(data_dictionary[f'test_features']),
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n_obs=len(data_dictionary['test_features']),
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batch_size=self.batch_size,
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n_iters=self.eval_iters
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)
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@ -101,8 +101,11 @@ class PyTorchModelTrainer:
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torch.from_numpy(data_dictionary[f'{split}_features'].values).float(),
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torch.from_numpy(data_dictionary[f'{split}_labels'].astype(float).values)
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.long()
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.view(labels_view) # todo currently assuming class labels are strings ['0.0', '1.0' .. n_classes]. need to resolve it per ClassifierModel
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.view(labels_view)
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)
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# todo currently assuming class labels are strings ['0.0', '1.0' .. n_classes].
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# need to resolve it per ClassifierModel
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data_loader = DataLoader(
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dataset,
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batch_size=self.batch_size,
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@ -24,7 +24,6 @@ from freqtrade.exceptions import OperationalException
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.strategy.interface import IStrategy
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logger = logging.getLogger(__name__)
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@ -446,9 +445,9 @@ class FreqaiDataDrawer:
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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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model.save(save_path / f"{dk.model_filename}_model.h5")
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elif 'stable_baselines' in self.model_type or\
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'sb3_contrib' == self.model_type or\
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'pytorch' == self.model_type:
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elif ('stable_baselines' in self.model_type or
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'sb3_contrib' == self.model_type or
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'pytorch' == self.model_type):
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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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