dataset script

sagan-mc.py

@@ -0,0 +1,107 @@
+import csv
+import json
+import os
+import datasets
+
+_CITATION = """
+@inproceedings{gebhard2022inferring,
+  title={Inferring molecular complexity from mass spectrometry data using machine learning},
+  author={Gebhard, Timothy D and Bell, Aaron C and Gong, Jian and Hastings, Jaden J. A. and Fricke, G. Matthew and Cabrol, Nathalie and Sandford, Scott and Phillips, Michael and Warren-Rhodes, Kimberley and Baydin, Atilim Gunes},
+  booktitle={NeurIPS Workshop on Machine Learning and the Physical Sciences},
+  year={2022}
+}
+"""
+
+_DESCRIPTION = """
+SaganMC is a molecular dataset designed to support machine learning research in molecular complexity inference. It includes over 400,000 molecules with computed structural, physico-chemical, and complexity descriptors, and a subset of ~16k molecules that additionally include experimental mass spectra.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/oxai4science/sagan-mc"
+_LICENSE = "CC-BY-4.0"
+
+_URLS = {
+    "sagan-mc-400k": "https://huggingface.co/datasets/oxai4science/sagan-mc/resolve/main/sagan-mc-400k.csv",
+    "sagan-mc-spectra-16k": "https://huggingface.co/datasets/oxai4science/sagan-mc/resolve/main/sagan-mc-spectra-16k.csv",
+}
+
+class SaganMC(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="sagan-mc-400k", version=VERSION, description="Full dataset with ~400k molecules"),
+        datasets.BuilderConfig(name="sagan-mc-spectra-16k", version=VERSION, description="Subset with mass spectra (~16k molecules)"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "sagan-mc-400k"
+
+    def _info(self):
+        features = datasets.Features({
+            "inchi": datasets.Value("string"),
+            "inchikey": datasets.Value("string"),
+            "selfies": datasets.Value("string"),
+            "smiles": datasets.Value("string"),
+            "smiles_scaffold": datasets.Value("string"),
+            "formula": datasets.Value("string"),
+            "fingerprint_morgan": datasets.Value("string"),
+            "num_atoms": datasets.Value("int32"),
+            "num_atoms_all": datasets.Value("int32"),
+            "num_bonds": datasets.Value("int32"),
+            "num_bonds_all": datasets.Value("int32"),
+            "num_rings": datasets.Value("int32"),
+            "num_aromatic_rings": datasets.Value("int32"),
+            "physchem_mol_weight": datasets.Value("float"),
+            "physchem_logp": datasets.Value("float"),
+            "physchem_tpsa": datasets.Value("float"),
+            "physchem_qed": datasets.Value("float"),
+            "physchem_h_acceptors": datasets.Value("int32"),
+            "physchem_h_donors": datasets.Value("int32"),
+            "physchem_rotatable_bonds": datasets.Value("int32"),
+            "physchem_fraction_csp3": datasets.Value("float"),
+            "mass_spectrum_nist": datasets.Value("string"),
+            "complex_ma_score": datasets.Value("int32"),
+            "complex_ma_runtime": datasets.Value("float"),
+            "complex_bertz_score": datasets.Value("float"),
+            "complex_bertz_runtime": datasets.Value("float"),
+            "complex_boettcher_score": datasets.Value("float"),
+            "complex_boettcher_runtime": datasets.Value("float"),
+            "synth_sa_score": datasets.Value("float"),
+            "meta_cas_number": datasets.Value("string"),
+            "meta_names": datasets.Value("string"),
+            "meta_iupac_name": datasets.Value("string"),
+            "meta_comment": datasets.Value("string"),
+            "meta_origin": datasets.Value("string"),
+            "meta_reference": datasets.Value("string"),
+            "split": datasets.ClassLabel(names=["train", "val", "test"])
+        })
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        url = _URLS[self.config.name]
+        data_path = dl_manager.download_and_extract(url)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_path, "split_name": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_path, "split_name": "val"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_path, "split_name": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split_name):
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for idx, row in enumerate(reader):
+                if row["split"] == split_name:
+                    yield idx, row