add module default template

README.md

@@ -1,12 +1,50 @@
 ---
-title: Regression Evaluate
-emoji: 📉
-colorFrom: green
-colorTo: indigo
+title: regression_evaluate
+datasets:
+-  GeoBenchmark
+tags:
+- evaluate
+- metric
+description: "TODO: add a description here"
 sdk: gradio
-sdk_version: 6.5.1
+sdk_version: 3.19.1
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Metric Card for regression_evaluate
+
+***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.*

app.py

@@ -0,0 +1,6 @@
+import evaluate
+from evaluate.utils import launch_gradio_widget
+
+
+module = evaluate.load("rfr2003/regression_evaluate")
+launch_gradio_widget(module)

regression_evaluate.py

@@ -0,0 +1,130 @@
+# 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
+import re
+from statistics import median
+import numpy as np
+
+# 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 metric aims to evaluate regression tasks done by LMs.
+"""
+
+
+# TODO: Add description of the arguments of the module here
+_KWARGS_DESCRIPTION = """
+Calculates Accuracy and Blue-1 between generations and gold answers in a MCQ context.
+Args:
+    generations: list of predictions to score. Each predictions
+        should be a string generated by a LM model.
+    golds: list of reference for each prediction. Each
+        reference should be a list of floats.
+Returns:
+    precision:  ,
+    recall:
+Examples:
+    Here is an exemple on how to use the metric:
+
+    >>> metric = evaluate.load("rfr2003/regression_evaluate")
+    >>> results = metric.compute(generations=['[150, 0]'], golds=[183, 177, 146, 85, 70, 78, 55, 17, 0, -1, -1])
+    >>> print(results)
+    {'precision': 4.0, 'recall': 345.0, 'macro-mean': 174.5, 'median macro-mean': 174.5}
+"""
+
+
+@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class regression_evaluate(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({
+                'generations': datasets.Value('string'),
+                'golds': datasets.Sequence(datasets.Value('float32')),
+            }),
+        )
+
+    def _download_and_prepare(self, dl_manager):
+        """Optional: download external resources useful to compute the scores"""
+        pass
+
+    def _calculate_pre_rec(self, gens, golds, d):
+        dists = []
+    
+        for gold in golds:
+            g_dist = []
+            for gen in gens:
+                g_dist.append(d(gold, gen))
+            if len(g_dist) == 0:
+                g_dist.append(100) #penalty if the model doesnt generate anything
+            dists.append(g_dist)
+    
+        dists = np.array(dists)
+        precision = np.min(dists, axis=0).sum()
+    
+        recall = np.min(dists, axis=1).sum()
+    
+        return precision, recall
+
+    def _compute(self, generations, golds):
+        assert len(generations) == len(golds)
+        assert isinstance(golds, list)
+    
+        precisions, recalls, means_pre_rec = [], [], []
+    
+        for gen, gold in zip(generations, golds):
+            f_gold = list(set([float(g) for g in gold]))
+     
+            f_ans = re.findall(r'\d+(?:\.\d+)?', gen)
+                
+            f_ans = list(set([float(a) for a in f_ans])) #get rid of duples values
+    
+            precision, recall = self._calculate_pre_rec(f_ans, f_gold, lambda x,y: abs(x-y))
+    
+            precisions.append(precision)
+            recalls.append(recall)
+            means_pre_rec.append((precision+recall)/2)
+            
+    
+        macro_prec = np.mean(precisions).item()
+        macro_rec = np.mean(recalls).item()
+    
+        metrics = {}
+        metrics.update({ 
+            'precision': np.mean(precisions).item(),
+            'recall': np.mean(recalls).item(),
+            'macro-mean': np.mean(means_pre_rec).item(),
+            'median macro-mean': median(means_pre_rec)
+        })
+    
+        return metrics

requirements.txt

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

tests.py

@@ -0,0 +1,7 @@
+test_cases = [
+    {
+        'generations': ['[150, 0]'],
+        'golds': [183, 177, 146, 85, 70, 78, 55, 17, 0, -1, -1],
+        "result": {'precision': 4.0, 'recall': 345.0, 'macro-mean': 174.5, 'median macro-mean': 174.5}
+    }
+]