freqtrade_origin/freqtrade/freqai/torch/PyTorchDataConvertor.py

from abc import ABC, abstractmethod

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: str) -> 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: str) -> 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: torch.dtype = torch.float32,
        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: str) -> torch.Tensor:
        numpy_arrays = df.values
        x = torch.tensor(numpy_arrays, device=device, dtype=torch.float32)
        return x

    def convert_y(self, df: pd.DataFrame, device: str) -> torch.Tensor:
        numpy_arrays = df.values
        y = torch.tensor(numpy_arrays, device=device, dtype=self._target_tensor_type)
        if self._squeeze_target_tensor:
            y = y.squeeze()
        return y