init

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,50 @@
+---
+title: cohen_weighted_kappa
+datasets:
+-  
+tags:
+- evaluate
+- metric
+description: "TODO: add a description here"
+sdk: gradio
+sdk_version: 3.19.1
+app_file: app.py
+pinned: false
+---
+
+# Metric Card for cohen_weighted_kappa
+
+***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*
+
+## Metric Description
+*Give a brief overview of this metric, including what task(s) it is usually used for, if any.*
+
+## How to Use
+*Give general statement of how to use the metric*
+
+*Provide simplest possible example for using the metric*
+
+### Inputs
+*List all input arguments in the format below*
+- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
+
+### Output Values
+
+*Explain what this metric outputs and provide an example of what the metric output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}*
+
+*State the range of possible values that the metric's output can take, as well as what in that range is considered good. For example: "This metric can take on any value between 0 and 100, inclusive. Higher scores are better."*
+
+#### Values from Popular Papers
+*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.*
+
+### Examples
+*Give code examples of the metric being used. Try to include examples that clear up any potential ambiguity left from the metric description above. If possible, provide a range of examples that show both typical and atypical results, as well as examples where a variety of input parameters are passed.*
+
+## Limitations and Bias
+*Note any known limitations or biases that the metric has, with links and references if possible.*
+
+## Citation
+*Cite the source where this metric was introduced.*
+
+## Further References
+*Add any useful further references.*

.ipynb_checkpoints/app-checkpoint.py

@@ -0,0 +1,6 @@
+import evaluate
+from evaluate.utils import launch_gradio_widget
+
+
+module = evaluate.load("langdonholmes/cohen_weighted_kappa")
+launch_gradio_widget(module)

.ipynb_checkpoints/cohen_weighted_kappa-checkpoint.py

@@ -0,0 +1,97 @@
+# 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."""
+
+import evaluate
+import datasets
+from sklearn.metrics import cohen_kappa_score
+
+
+# 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.
+"""
+
+
+# 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.
+Returns:
+    accuracy: description of the first score,
+    another_score: description of the second score,
+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}
+"""
+
+# TODO: Define external resources urls if needed
+BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
+
+
+@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class cohen_weighted_kappa(evaluate.Metric):
+    """TODO: Short description of my evaluation module."""
+
+    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('float'),
+                'references': datasets.Value('float'),
+            }),
+            # 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"]
+        )
+
+    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"""
+
+        agreement = cohen_kappa_score(references, np.round(predictions), weights='quadratic')
+
+        return {
+            "cohen_weighted_kappa": agreement,
+        }

.ipynb_checkpoints/requirements-checkpoint.txt

@@ -0,0 +1,2 @@
+git+https://github.com/huggingface/evaluate@main
+scikit-learn

cohen_weighted_kappa.py

@@ -15,6 +15,7 @@
 
 import evaluate
 import datasets
+from sklearn.metrics import cohen_kappa_score
 
 
 # TODO: Add BibTeX citation
@@ -71,8 +72,8 @@ class cohen_weighted_kappa(evaluate.Metric):
             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.Value('float'),
+                'references': datasets.Value('float'),
             }),
             # Homepage of the module for documentation
             homepage="http://module.homepage",
@@ -88,8 +89,9 @@ class cohen_weighted_kappa(evaluate.Metric):
 
     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)
+
+        agreement = cohen_kappa_score(references, np.round(predictions), weights='quadratic')
+
         return {
-            "accuracy": accuracy,
+            "cohen_weighted_kappa": agreement,
         }

requirements.txt

@@ -1 +1,2 @@
-git+https://github.com/huggingface/evaluate@main
+git+https://github.com/huggingface/evaluate@main
+scikit-learn