Changed my_metric.py to resemble accuracy.py

my_metric.py

@@ -9,3 +9,88 @@ def _compute(self, list_of_generated_smiles):
     results = metrics.get_all_metrics(preprocessed_smiles, test_set)
 
     return results
+
+
+_DESCRIPTION = """
+Moses and PyTDC metrics
+"""
+
+
+_KWARGS_DESCRIPTION = """
+Args:
+    predictions (`list` of `int`): Predicted labels.
+    references (`list` of `int`): Ground truth labels.
+    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
+    sample_weight (`list` of `float`): Sample weights Defaults to None.
+
+Returns:
+    All moses metrics
+"""
+
+
+_CITATION = """
+@article{DBLP:journals/corr/abs-1811-12823,
+  author       = {Daniil Polykovskiy and
+                  Alexander Zhebrak and
+                  Benjam{\'{\i}}n S{\'{a}}nchez{-}Lengeling and
+                  Sergey Golovanov and
+                  Oktai Tatanov and
+                  Stanislav Belyaev and
+                  Rauf Kurbanov and
+                  Aleksey Artamonov and
+                  Vladimir Aladinskiy and
+                  Mark Veselov and
+                  Artur Kadurin and
+                  Sergey I. Nikolenko and
+                  Al{\'{a}}n Aspuru{-}Guzik and
+                  Alex Zhavoronkov},
+  title        = {Molecular Sets {(MOSES):} {A} Benchmarking Platform for Molecular
+                  Generation Models},
+  journal      = {CoRR},
+  volume       = {abs/1811.12823},
+  year         = {2018},
+  url          = {http://arxiv.org/abs/1811.12823},
+  eprinttype    = {arXiv},
+  eprint       = {1811.12823},
+  timestamp    = {Fri, 26 Nov 2021 15:34:30 +0100},
+  biburl       = {https://dblp.org/rec/journals/corr/abs-1811-12823.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+
+@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class my_metric(evaluate.Metric):
+    def _info(self):
+        return evaluate.MetricInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            inputs_description=_KWARGS_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "list_of_generated_smiles": datasets.Sequence(datasets.Value("int32")),
+                }
+                if self.config_name == "multilabel"
+                else {
+                    "list_of_generated_smiles": datasets.Value("int32"),
+                }
+            ),
+            reference_urls=["https://github.com/molecularsets/moses"],
+        )
+
+    def _compute(self, list_of_generated_smiles):
+        test_set = moses.get_dataset('test')
+        preprocessed_smiles = [smile for smile in list_of_generated_smiles if moses.utils.canonicalize_smiles(smile)]
+
+        results = metrics.get_all_metrics(preprocessed_smiles, test_set)
+
+        return {
+            "results": results
+            }
+
+    # def _compute(self, predictions, references, normalize=True, sample_weight=None):
+    #     return {
+    #         "accuracy": float(
+    #             accuracy_score(references, predictions, normalize=normalize, sample_weight=sample_weight)
+    #         )
+    #     }