stable/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
2023-03-20 18:41:04 +02:00

83 lines
3.1 KiB
Python

from typing import Any, Dict
import torch
from freqtrade.freqai.base_models.PyTorchModelTrainer import PyTorchModelTrainer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.PyTorchClassifier import PyTorchClassifier
from freqtrade.freqai.prediction_models.PyTorchMLPModel import PyTorchMLPModel
class PyTorchMLPClassifier(PyTorchClassifier):
"""
This class implements the fit method of IFreqaiModel.
in the fit method we initialize the model and trainer objects.
the only requirement from the model is to be aligned to PyTorchClassifier
predict method that expects the model to predict tensor of type long.
the trainer defines the training loop.
parameters are passed via `model_training_parameters` under the freqai
section in the config file. e.g:
{
...
"freqai": {
...
"model_training_parameters" : {
"learning_rate": 3e-4,
"trainer_kwargs": {
"max_iters": 5000,
"batch_size": 64,
"max_n_eval_batches": None,
},
"model_kwargs": {
"hidden_dim": 512,
"dropout_percent": 0.2,
"n_layer": 1,
},
}
}
}
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
model_training_params = self.freqai_info.get("model_training_parameters", {})
self.learning_rate: float = model_training_params.get("learning_rate", 3e-4)
self.model_kwargs: Dict[str, any] = model_training_params.get("model_kwargs", {})
self.trainer_kwargs: Dict[str, any] = model_training_params.get("trainer_kwargs", {})
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:raises ValueError: If self.class_names is not defined in the parent class.
"""
class_names = self.get_class_names()
self.convert_label_column_to_int(data_dictionary, dk, class_names)
n_features = data_dictionary["train_features"].shape[-1]
model = PyTorchMLPModel(
input_dim=n_features,
output_dim=len(class_names),
**self.model_kwargs
)
model.to(self.device)
optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
criterion = torch.nn.CrossEntropyLoss()
init_model = self.get_init_model(dk.pair)
trainer = PyTorchModelTrainer(
model=model,
optimizer=optimizer,
criterion=criterion,
model_meta_data={"class_names": class_names},
device=self.device,
init_model=init_model,
target_tensor_type=torch.long,
**self.trainer_kwargs,
)
trainer.fit(data_dictionary)
return trainer