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convert single quotes to double quotes

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This commit is contained in:
Yinon Polak 2023-03-09 13:29:11 +02:00
parent 2ef11faba7
commit e88a0d5248
3 changed files with 16 additions and 15 deletions
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6
freqtrade/freqai/base_models/BasePyTorchModel.py
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@ -19,9 +19,9 @@ class BasePyTorchModel(IFreqaiModel):
"""
def __init__(self, **kwargs):
super().__init__(config=kwargs['config'])
self.dd.model_type = 'pytorch'
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
super().__init__(config=kwargs["config"])
self.dd.model_type = "pytorch"
self.device = "cuda" if torch.cuda.is_available() else "cpu"
def train(
self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs

23
freqtrade/freqai/base_models/PyTorchModelTrainer.py
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@ -61,7 +61,7 @@ class PyTorchModelTrainer:
"""
data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary)
epochs = self.calc_n_epochs(
n_obs=len(data_dictionary['train_features']),
n_obs=len(data_dictionary["train_features"]),
batch_size=self.batch_size,
n_iters=self.max_iters
)
@ -73,7 +73,7 @@ class PyTorchModelTrainer:
f" train loss {losses['train']:.4f} ; test loss {losses['test']:.4f}"
)
# training
for batch_data in data_loaders_dictionary['train']:
for batch_data in data_loaders_dictionary["train"]:
xb, yb = batch_data
xb = xb.to(self.device)
yb = yb.to(self.device)
@ -93,12 +93,12 @@ class PyTorchModelTrainer:
self.model.eval()
epochs = self.calc_n_epochs(
n_obs=len(data_dictionary['test_features']),
n_obs=len(data_dictionary["test_features"]),
batch_size=self.batch_size,
n_iters=self.eval_iters
)
loss_dictionary = {}
for split in ['train', 'test']:
for split in ["train", "test"]:
losses = torch.zeros(epochs)
for i, batch in enumerate(data_loader_dictionary[split]):
xb, yb = batch
@ -121,12 +121,12 @@ class PyTorchModelTrainer:
Converts the input data to PyTorch tensors using a data loader.
"""
data_loader_dictionary = {}
for split in ['train', 'test']:
labels_shape = data_dictionary[f'{split}_labels'].shape
for split in ["train", "test"]:
labels_shape = data_dictionary[f"{split}_labels"].shape
labels_view = labels_shape[0] if labels_shape[1] == 1 else labels_shape
dataset = TensorDataset(
torch.from_numpy(data_dictionary[f'{split}_features'].values).float(),
torch.from_numpy(data_dictionary[f'{split}_labels'].astype(float).values)
torch.from_numpy(data_dictionary[f"{split}_features"].values).float(),
torch.from_numpy(data_dictionary[f"{split}_labels"].astype(float).values)
.long()
.view(labels_view)
)
@ -148,6 +148,7 @@ class PyTorchModelTrainer:
Calculates the number of epochs required to reach the maximum number
of iterations specified in the model training parameters.
"""
n_batches = n_obs // batch_size
epochs = n_iters // n_batches
return epochs
@ -160,9 +161,9 @@ class PyTorchModelTrainer:
"""
torch.save({
'model_state_dict': self.model.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'model_meta_data': self.model_meta_data,
"model_state_dict": self.model.state_dict(),
"optimizer_state_dict": self.optimizer.state_dict(),
"model_meta_data": self.model_meta_data,
}, path)
def load_from_file(self, path: Path):

2
freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py
View File

@ -59,7 +59,7 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
self.init_class_names_to_index_mapping(self.class_names)
self.encode_classes_name(data_dictionary, dk)
n_features = data_dictionary['train_features'].shape[-1]
n_features = data_dictionary["train_features"].shape[-1]
model = PyTorchMLPModel(
input_dim=n_features,
hidden_dim=self.n_hidden,