Update content with metric specific features

peak_signal_to_noise_ratio.py

@@ -1,95 +1,83 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""TODO: Add a description here."""
+"""Accuracy metric."""
 
-import evaluate
 import datasets
+import numpy as np
 
+from skimage.metrics import peak_signal_noise_ratio
+from typing import Dict, Optional
+import evaluate
 
-# TODO: Add BibTeX citation
-_CITATION = """\
-@InProceedings{huggingface:module,
-title = {A great new module},
-authors={huggingface, Inc.},
-year={2020}
-}
-"""
 
-# TODO: Add description of the module here
-_DESCRIPTION = """\
-This new module is designed to solve this great ML task and is crafted with a lot of care.
+_DESCRIPTION = """
+Compute the Peak Signal-to-Noise Ratio (PSNR) for an image.
+
+Please pay attention to the `data_range` parameter with floating-point images.
 """
 
 
-# TODO: Add description of the arguments of the module here
 _KWARGS_DESCRIPTION = """
-Calculates how good are predictions given some references, using certain scores
 Args:
-    predictions: list of predictions to score. Each predictions
-        should be a string with tokens separated by spaces.
-    references: list of reference for each prediction. Each
-        reference should be a string with tokens separated by spaces.
+    predictions (`list` of `np.array`): Predicted labels.
+    references (`list` of `np.array`): Ground truth labels.
+    sample_weight (`list` of `float`): Sample weights Defaults to None.
 Returns:
-    accuracy: description of the first score,
-    another_score: description of the second score,
+    psnr (`float`):Peak Signal-to-Noise Ratio. The SSIM values are positive. Typical
+    values for the PSNR in lossy image and video compression are between 30 and 50 dB,
+    provided the bit depth is 8 bits, where higher is better. 
 Examples:
-    Examples should be written in doctest format, and should illustrate how
-    to use the function.
-
-    >>> my_new_module = evaluate.load("my_new_module")
-    >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
-    >>> print(results)
-    {'accuracy': 1.0}
+    Example 1-A simple example
+        >>> psnr = evaluate.load("jpxkqx/peak_signal_to_noise_ratio")
+        >>> results = psnr.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
+        >>> print(results)
+        {'psnr': 0.5}
+    Example 2-The same as Example 1, except with `sample_weight` set.
+        >>> psnr = evaluate.load("jpxkqx/peak_signal_to_noise_ratio")
+        >>> results = psnr.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
+        >>> print(results)
+        {'psnr': 0.8778625954198473}
 """
 
-# TODO: Define external resources urls if needed
-BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
 
+_CITATION = """
+@article{boulogne2014scikit,
+  title={Scikit-image: Image processing in Python},
+  author={Boulogne, Fran{\c{c}}ois and Warner, Joshua D and Neil Yager, Emmanuelle},
+  journal={J. PeerJ},
+  volume={2},
+  pages={453},
+  year={2014}
+}
+"""
 
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class PeakSignaltoNoiseRatio(evaluate.Metric):
-    """TODO: Short description of my evaluation module."""
 
+@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class StructuralSimilarityIndexMeasure(evaluate.Metric):
     def _info(self):
-        # TODO: Specifies the evaluate.EvaluationModuleInfo object
         return evaluate.MetricInfo(
-            # This is the description that will appear on the modules page.
-            module_type="metric",
             description=_DESCRIPTION,
             citation=_CITATION,
             inputs_description=_KWARGS_DESCRIPTION,
-            # This defines the format of each prediction and reference
             features=datasets.Features({
-                'predictions': datasets.Value('int64'),
-                'references': datasets.Value('int64'),
+                "predictions": datasets.Sequence(datasets.Array2D("float32")),
+                "references": datasets.Sequence(datasets.Array2D("float32")),
             }),
-            # Homepage of the module for documentation
-            homepage="http://module.homepage",
-            # Additional links to the codebase or references
-            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
-            reference_urls=["http://path.to.reference.url/new_module"]
+            reference_urls=["https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio"],
         )
 
-    def _download_and_prepare(self, dl_manager):
-        """Optional: download external resources useful to compute the scores"""
-        # TODO: Download external resources if needed
-        pass
-
-    def _compute(self, predictions, references):
-        """Returns the scores"""
-        # TODO: Compute the different scores of the module
-        accuracy = sum(i == j for i, j in zip(predictions, references)) / len(predictions)
+    def _compute(
+        self, 
+        predictions, 
+        references, 
+        data_range: Optional[float] = None, 
+        sample_weight=None,
+    ) -> Dict[str, float]:
+        samples = zip(predictions, references)
         return {
-            "accuracy": accuracy,
+            "psnr": np.average(
+                list(map(
+                    lambda args: peak_signal_noise_ratio(*args, data_range), 
+                    samples
+                )), 
+                weights=sample_weight
+            )
         }

requirements.txt

@@ -1 +1,2 @@
-git+https://github.com/huggingface/evaluate@main
+git+https://github.com/huggingface/evaluate@main
+scikit-image>=0.19