add file basics

README.md

@@ -1,8 +1,8 @@
 ---
 title: FixedF1
 emoji: 📈
-colorFrom: blue
-colorTo: blue
+colorFrom: red
+colorTo: purple
 sdk: gradio
 sdk_version: 5.5.0
 app_file: app.py

fixed_f1.py

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+import datasets
+import evaluate
+from evaluate import evaluator, Metric
+# from evaluate.metrics.f1 import F1
+from sklearn.metrics import f1_score
+
+
+_CITATION = """
+@online{MarioBarbeque@HuggingFace,
+  author = {John Graham Reynolds aka @MarioBarbeque},
+  title = {{Fixed F1 Hugging Face Metric},
+  year = 2024,
+  url = {https://huggingface.co/spaces/MarioBarbeque/FixedF1},
+  urldate = {0000-00-00}
+}
+"""
+
+# could in principle subclass F1, but ideally we can work the fix into the HF main F1 class to maintain SOLID code
+class FixedF1(evaluate.Metric):
+
+    def __init__(self, average="binary"):
+        super().__init__()
+        self.average = average
+        # additional values passed to compute() could and probably should (?) all be passed here so that the final computation is configured immediately at Metric instantiation
+
+    def _info(self):
+        return evaluate.MetricInfo(
+            description="Custom built F1 metric for true *multilabel* classification - the 'multilabel' config_name var in the evaluate.EvaluationModules class appears to better address multi-class classification, where features can fall under a plethora of labels. This class is implemented with the intention of enabling the evaluation of multiple multilabel classification metrics at the same time using the evaluate.CombinedEvaluations.combine method.",
+            citation="",
+            inputs_description="'average': This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Options include: {‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’} or None.",
+            features=datasets.Features(
+                {
+                    "predictions": datasets.Sequence(datasets.Value("int32")),
+                    "references": datasets.Sequence(datasets.Value("int32")),
+                }
+                if self.config_name == "multilabel"
+                else {
+                    "predictions": datasets.Value("int32"),
+                    "references": datasets.Value("int32"),
+                }
+            ),
+            reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"],
+        )
+    
+    # could remove specific kwargs like average, sample_weight from _compute() method of F1
+
+    def _compute(self, predictions, references, labels=None, pos_label=1, average="binary", sample_weight=None):
+        score = f1_score(
+            references, predictions, labels=labels, pos_label=pos_label, average=self.average, sample_weight=sample_weight
+        )
+        return {"f1": float(score) if score.size == 1 else score}

requirements.txt

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+scikit-learn
+evaluate
+datasets