freqtrade_origin/freqtrade/freqai/torch/PyTorchDataConvertor.py
2023-04-03 19:02:07 +03:00

68 lines
2.0 KiB
Python

from abc import ABC, abstractmethod
from typing import List, Optional
import pandas as pd
import torch
class PyTorchDataConvertor(ABC):
"""
This class is responsible for converting `*_features` & `*_labels` pandas dataframes
to pytorch tensors.
"""
@abstractmethod
def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> List[torch.Tensor]:
"""
:param df: "*_features" dataframe.
:param device: The device to use for training (e.g. 'cpu', 'cuda').
"""
@abstractmethod
def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> List[torch.Tensor]:
"""
:param df: "*_labels" dataframe.
:param device: The device to use for training (e.g. 'cpu', 'cuda').
"""
class DefaultPyTorchDataConvertor(PyTorchDataConvertor):
"""
A default conversion that keeps features dataframe shapes.
"""
def __init__(
self,
target_tensor_type: Optional[torch.dtype] = None,
squeeze_target_tensor: bool = False
):
"""
:param target_tensor_type: type of target tensor, for classification use
torch.long, for regressor use torch.float or torch.double.
:param squeeze_target_tensor: controls the target shape, used for loss functions
that requires 0D or 1D.
"""
self._target_tensor_type = target_tensor_type
self._squeeze_target_tensor = squeeze_target_tensor
def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> List[torch.Tensor]:
x = torch.from_numpy(df.values).float()
if device:
x = x.to(device)
return [x]
def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> List[torch.Tensor]:
y = torch.from_numpy(df.values)
if self._target_tensor_type:
y = y.to(self._target_tensor_type)
if self._squeeze_target_tensor:
y = y.squeeze()
if device:
y = y.to(device)
return [y]