from typing import Optional
from typing import Union
import torch
[docs]def norm_ft(X: torch.Tensor, ord: Optional[Union[int, float]] = None) -> torch.Tensor:
r"""Normalize the input feature matrix with specified ``ord`` refer to pytorch's `torch.linalg.norm <https://pytorch.org/docs/stable/generated/torch.linalg.norm.html#torch.linalg.norm>`_ function.
.. note::
The input feature matrix is expected to be a 1D vector or a 2D tensor with shape (num_samples, num_features).
Args:
``X`` (``torch.Tensor``): The input feature.
``ord`` (``Union[int, float]``, optional): The order of the norm can be either an ``int``, ``float``. If ``ord`` is ``None``, the norm is computed with the 2-norm. Defaults to ``None``.
Examples:
>>> import easygraph.datapipe as dd
>>> import torch
>>> X = torch.tensor([
[0.1, 0.2, 0.5],
[0.5, 0.2, 0.3],
[0.3, 0.2, 0]
])
>>> dd.norm_ft(X)
tensor([[0.1826, 0.3651, 0.9129],
[0.8111, 0.3244, 0.4867],
[0.8321, 0.5547, 0.0000]])
"""
if X.dim() == 1:
X_norm = 1 / torch.linalg.norm(X, ord=ord)
X_norm[torch.isinf(X_norm)] = 0
return X * X_norm
elif X.dim() == 2:
X_norm = 1 / torch.linalg.norm(X, ord=ord, dim=1, keepdim=True)
X_norm[torch.isinf(X_norm)] = 0
return X * X_norm
else:
raise ValueError(
"The input feature matrix is expected to be a 1D verter or a 2D tensor with"
" shape (num_samples, num_features)."
)
[docs]def min_max_scaler(X: torch.Tensor, ft_min: float, ft_max: float) -> torch.Tensor:
r"""Normalize the input feature matrix with min-max scaling.
Args:
``X`` (``torch.Tensor``): The input feature.
``ft_min`` (``float``): The minimum value of the output feature.
``ft_max`` (``float``): The maximum value of the output feature.
Examples:
>>> import easygraph.datapipe as dd
>>> import torch
>>> X = torch.tensor([
[0.1, 0.2, 0.5],
[0.5, 0.2, 0.3],
[0.3, 0.2, 0.0]
])
>>> dd.min_max_scaler(X, -1, 1)
tensor([[-0.6000, -0.2000, 1.0000],
[ 1.0000, -0.2000, 0.2000],
[ 0.2000, -0.2000, -1.0000]])
"""
assert (
ft_min < ft_max
), "The minimum value of the feature should be less than the maximum value."
X_min, X_max = X.min().item(), X.max().item()
X_range = X_max - X_min
scale_ = (ft_max - ft_min) / X_range
min_ = ft_min - X_min * scale_
X = X * scale_ + min_
return X