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