chore: import upstream snapshot with attribution

2026-07-13 12:36:30 +08:00
commit 55ab4e4a73
473 changed files with 72932 additions and 0 deletions
@@ -0,0 +1,74 @@
+from typing import Optional
+from typing import Union
+
+import torch
+
+
+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)."
+        )
+
+
+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