update name

alignment_and_uniformity.py

@@ -1,90 +0,0 @@
-import datasets
-import evaluate
-from typing import List
-import torch
-
-
-_DESCRIPTION = """
-Quantifying encoder feature distribution properties, Alignment and Uniformity on the Hypersphere.
-(https://github.com/ssnl/align_uniform)
-"""
-
-_KWARGS_DESCRIPTION = """
-Args:
-    xs (`list` of a list of `int`): a group of embeddings
-    ys (`list` of `int`): the other group of embeddings paired with the ys
-    
-Returns:
-    "align_loss": float(align_loss_val),
-    "x_unif_loss": float(x_unif_loss_v),
-    "y_unif_loss": float(y_unif_loss_v),
-    "unif_loss": float(unif_loss)
-            
-Examples:
-
-    Example 1-A simple example
-        >>> metrics = evaluate.load("ahnyeonchan/Alignment-and-Uniformity")
-        >>> results = metrics.compute(xs=[[1.0, 1.0], [0.0, 1.0]], ys=[[1.0, 1.0], [0.0, 1.0]])
-        >>> print(results)
-        {'align_loss': 0.0, 'x_unif_loss': -2.0, 'y_unif_loss': -2.0, 'unif_loss': -2.0}
-"""
-
-_CITATION = """"""
-
-
-def align_loss(x, y, alpha=2):
-    return (x - y).norm(p=2, dim=1).pow(alpha).mean()
-
-
-def uniform_loss(x, t=2):
-    return torch.pdist(x, p=2).pow(2).mul(-t).exp().mean().log()
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class AlignUniform(evaluate.Metric):
-    def __init__(self, align_alpha: float = 2.0, unif_t: float = 2.0, *args, **kwargs):
-        super(AlignUniform, self).__init__(*args, **kwargs)
-        self.align_alpha = align_alpha
-        self.unif_t = unif_t
-        
-    def _info(self):
-        return evaluate.MetricInfo(
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            inputs_description=_KWARGS_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "xs": datasets.Sequence(datasets.Value("float32")),
-                    "ys": datasets.Sequence(datasets.Value("float32")),
-                }
-            ),
-            reference_urls=[],
-        )
-
-    def _compute(self, xs: List[List], ys: List[List]):
-        
-        if isinstance(xs, torch.Tensor):
-            xs = torch.Tensor(xs)
-        elif isinstance(ys, list):
-            xs = torch.Tensor(xs)
-        else:
-            raise NotImplementedError()
-        
-        if isinstance(ys, torch.Tensor):
-            ys = torch.Tensor(ys)
-        elif isinstance(ys, list):
-            ys = torch.Tensor(ys)
-        else:
-            raise NotImplementedError()
-        
-        align_loss_val = align_loss(xs, ys, self.align_alpha)
-        x_unif_loss_v = uniform_loss(xs, t=self.unif_t)
-        y_unif_loss_v = uniform_loss(ys, t=self.unif_t)
-        unif_loss = (x_unif_loss_v + y_unif_loss_v) / 2
-                
-        return {
-            "align_loss": float(align_loss_val),
-            "x_unif_loss": float(x_unif_loss_v),
-            "y_unif_loss": float(y_unif_loss_v),
-            "unif_loss": float(unif_loss)
-        }