Changes to code

molgenevalmetric.py

@@ -0,0 +1,134 @@
+import evaluate
+import datasets
+import moses
+from moses import metrics
+import pandas as pd
+from tdc import Evaluator
+from tdc import Oracle
+
+
+_DESCRIPTION = """
+
+Comprehensive suite of metrics designed to assess the performance of molecular generation models, for understanding how well a model can produce novel, chemically valid molecules that are relevant to specific research objectives.
+
+"""
+
+
+_KWARGS_DESCRIPTION = """
+Args:
+    generated_smiles (`list` of `string`): A collection of SMILES (Simplified Molecular Input Line Entry System) strings generated by the model, ideally encompassing more than 30,000 samples.
+    train_smiles (`list` of `string`): The dataset of SMILES strings used to train the model, serving as a reference to evaluate the novelty and diversity of the generated molecules.
+
+Returns:
+    Dectionary item containing various metrics to evaluate model performance
+"""
+
+
+_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 molgenevalmetric(evaluate.Metric):
+    def _info(self):
+        return evaluate.MetricInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            inputs_description=_KWARGS_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "generated_smiles": datasets.Sequence(datasets.Value("string")),
+                    "train_smiles": datasets.Sequence(datasets.Value("string")),
+                }
+                if self.config_name == "multilabel"
+                else {
+                    "generated_smiles": datasets.Value("string"),
+                    "train_smiles": datasets.Value("string"),
+                }
+            ),
+                
+            reference_urls=["https://github.com/molecularsets/moses", "https://tdcommons.ai/functions/oracles/"],
+        )
+
+    def _compute(self, generated_smiles, train_smiles = None):
+        
+        Results = metrics.get_all_metrics(gen = generated_smiles, train= train_smiles)
+        
+        # evaluator = Evaluator(name = 'Diversity')
+        # Diversity = evaluator(generated_smiles)
+        
+        evaluator = Evaluator(name = 'KL_Divergence')
+        KL_Divergence = evaluator(generated_smiles, train_smiles)
+        
+        # evaluator = Evaluator(name = 'FCD_Distance')
+        # FCD_Distance = evaluator(generated_smiles, train_smiles)
+        
+        # evaluator = Evaluator(name = 'Novelty')
+        # Novelty = evaluator(generated_smiles, train_smiles)
+        
+        # evaluator = Evaluator(name = 'Validity')
+        # Validity = evaluator(generated_smiles)
+
+                
+        Results.update({
+            # "PyTDC_Diversity": Diversity,
+            "KL_Divergence": KL_Divergence,
+            # "PyTDC_Validity": Validity,FCD_Distance": FCD_Distance,
+            # "PyTDC_Novelty": Novelty,
+            # "PyTDC_
+        })
+
+
+        oracle_list = [
+        'QED', 'SA', 'MPO', 'GSK3B', 'JNK3',
+        'DRD2', 'LogP', 'Rediscovery', 'Similarity',
+        'Median', 'Isomers', 'Valsartan_SMARTS', 'Hop'
+        ]
+    
+        # Iterate through each oracle and compute its score
+        for oracle_name in oracle_list:
+            oracle = Oracle(name=oracle_name)
+            if oracle_name in ['Rediscovery', 'MPO', 'Similarity', 'Median', 'Isomers', 'Hop']:
+                # Assuming these oracles return a dictionary where values are lists of scores
+                score = oracle(generated_smiles)
+                if isinstance(score, dict):
+                    # Convert lists of scores to average score for these specific metrics
+                    score = {key: sum(values)/len(values) for key, values in score.items()}
+            else:
+                # Assuming other oracles return a list of scores
+                score = oracle(generated_smiles)
+                if isinstance(score, list):
+                    # Convert list of scores to average score
+                    score = sum(score) / len(score)
+            
+            Results.update({f"PyTDC_{oracle_name}": score})
+    
+
+        return {"results": Results}