Convert dataset to Parquet (#1)

- Convert dataset to Parquet (c4b92606af20e1d38de9d1567cffa6b76caaeb02)
- Add 'sagan-mc-spectra-16k' config data files (9a363f7616cdf7ef94571bc5603db5dccbbb2223)
- Delete data file (aed399c7b73276521d73c959c8d855c0486b0f2b)
- Delete loading script (6bab11eb0e91ae7423cb15d8dd413a3ca9339933)
- Delete data file (c63257415ecffec0097abfcd9ae60287a4caa4e0)

README.md

@@ -10,6 +10,207 @@ tags:
 pretty_name: SaganMC
 size_categories:
 - 100K<n<1M
+dataset_info:
+- config_name: sagan-mc-400k
+  features:
+  - name: inchi
+    dtype: string
+  - name: inchikey
+    dtype: string
+  - name: selfies
+    dtype: string
+  - name: smiles
+    dtype: string
+  - name: smiles_scaffold
+    dtype: string
+  - name: formula
+    dtype: string
+  - name: fingerprint_morgan
+    dtype: string
+  - name: num_atoms
+    dtype: int32
+  - name: num_atoms_all
+    dtype: int32
+  - name: num_bonds
+    dtype: int32
+  - name: num_bonds_all
+    dtype: int32
+  - name: num_rings
+    dtype: int32
+  - name: num_aromatic_rings
+    dtype: int32
+  - name: physchem_mol_weight
+    dtype: float32
+  - name: physchem_logp
+    dtype: float32
+  - name: physchem_tpsa
+    dtype: float32
+  - name: physchem_qed
+    dtype: float32
+  - name: physchem_h_acceptors
+    dtype: int32
+  - name: physchem_h_donors
+    dtype: int32
+  - name: physchem_rotatable_bonds
+    dtype: int32
+  - name: physchem_fraction_csp3
+    dtype: float32
+  - name: mass_spectrum_nist
+    dtype: string
+  - name: complex_ma_score
+    dtype: int32
+  - name: complex_ma_runtime
+    dtype: float32
+  - name: complex_bertz_score
+    dtype: float32
+  - name: complex_bertz_runtime
+    dtype: float32
+  - name: complex_boettcher_score
+    dtype: float32
+  - name: complex_boettcher_runtime
+    dtype: float32
+  - name: synth_sa_score
+    dtype: float32
+  - name: meta_cas_number
+    dtype: string
+  - name: meta_names
+    dtype: string
+  - name: meta_iupac_name
+    dtype: string
+  - name: meta_comment
+    dtype: string
+  - name: meta_origin
+    dtype: string
+  - name: meta_reference
+    dtype: string
+  - name: split
+    dtype:
+      class_label:
+        names:
+          '0': train
+          '1': val
+          '2': test
+  splits:
+  - name: train
+    num_bytes: 262218794
+    num_examples: 325392
+  - name: validation
+    num_bytes: 32619164
+    num_examples: 40521
+  - name: test
+    num_bytes: 32805389
+    num_examples: 40533
+  download_size: 121350317
+  dataset_size: 327643347
+- config_name: sagan-mc-spectra-16k
+  features:
+  - name: inchi
+    dtype: string
+  - name: inchikey
+    dtype: string
+  - name: selfies
+    dtype: string
+  - name: smiles
+    dtype: string
+  - name: smiles_scaffold
+    dtype: string
+  - name: formula
+    dtype: string
+  - name: fingerprint_morgan
+    dtype: string
+  - name: num_atoms
+    dtype: int32
+  - name: num_atoms_all
+    dtype: int32
+  - name: num_bonds
+    dtype: int32
+  - name: num_bonds_all
+    dtype: int32
+  - name: num_rings
+    dtype: int32
+  - name: num_aromatic_rings
+    dtype: int32
+  - name: physchem_mol_weight
+    dtype: float32
+  - name: physchem_logp
+    dtype: float32
+  - name: physchem_tpsa
+    dtype: float32
+  - name: physchem_qed
+    dtype: float32
+  - name: physchem_h_acceptors
+    dtype: int32
+  - name: physchem_h_donors
+    dtype: int32
+  - name: physchem_rotatable_bonds
+    dtype: int32
+  - name: physchem_fraction_csp3
+    dtype: float32
+  - name: mass_spectrum_nist
+    dtype: string
+  - name: complex_ma_score
+    dtype: int32
+  - name: complex_ma_runtime
+    dtype: float32
+  - name: complex_bertz_score
+    dtype: float32
+  - name: complex_bertz_runtime
+    dtype: float32
+  - name: complex_boettcher_score
+    dtype: float32
+  - name: complex_boettcher_runtime
+    dtype: float32
+  - name: synth_sa_score
+    dtype: float32
+  - name: meta_cas_number
+    dtype: string
+  - name: meta_names
+    dtype: string
+  - name: meta_iupac_name
+    dtype: string
+  - name: meta_comment
+    dtype: string
+  - name: meta_origin
+    dtype: string
+  - name: meta_reference
+    dtype: string
+  - name: split
+    dtype:
+      class_label:
+        names:
+          '0': train
+          '1': val
+          '2': test
+  splits:
+  - name: train
+    num_bytes: 31175057
+    num_examples: 13297
+  - name: validation
+    num_bytes: 3829030
+    num_examples: 1634
+  - name: test
+    num_bytes: 4067843
+    num_examples: 1722
+  download_size: 17763908
+  dataset_size: 39071930
+configs:
+- config_name: sagan-mc-400k
+  data_files:
+  - split: train
+    path: sagan-mc-400k/train-*
+  - split: validation
+    path: sagan-mc-400k/validation-*
+  - split: test
+    path: sagan-mc-400k/test-*
+  default: true
+- config_name: sagan-mc-spectra-16k
+  data_files:
+  - split: train
+    path: sagan-mc-spectra-16k/train-*
+  - split: validation
+    path: sagan-mc-spectra-16k/validation-*
+  - split: test
+    path: sagan-mc-spectra-16k/test-*
 ---
 # SaganMC: A Molecular Complexity Dataset with Mass Spectra
 

sagan-mc-spectra-16k.csv → sagan-mc-400k/test-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-size 40237369
+oid sha256:1dcb5598cdcf1a7628941f36197be5323bf6d559840f7f28f05954a074cb9f8f
+size 12187096

sagan-mc-400k.csv → sagan-mc-400k/train-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-size 356803750
+oid sha256:d915240d6646a5307b81e71c0c9a0beb2a1a18af7979068bd8ff343dc74bd263
+size 97073154

sagan-mc-400k/validation-00000-of-00001.parquet

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+oid sha256:f2c6c711949e4d7ec8608029c7bcf2ccc1b5f4e7379f92c085d2acfb489b78d9
+size 12090067

sagan-mc-spectra-16k/test-00000-of-00001.parquet

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+oid sha256:5f37d79d7c857941db7e40cfe17d2a9db65890555f52308281e2be1986443e77
+size 1865086

sagan-mc-spectra-16k/train-00000-of-00001.parquet

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+oid sha256:4a34bca85f946ba882c9e73eb756bd9c3a36e1fd6294d88f3c023a719ae86dec
+size 14140579

sagan-mc-spectra-16k/validation-00000-of-00001.parquet

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+size 1758243

sagan-mc.py

@@ -1,117 +0,0 @@
-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):
-        numeric_fields = [
-            "num_atoms", "num_atoms_all", "num_bonds", "num_bonds_all", "num_rings", "num_aromatic_rings",
-            "physchem_mol_weight", "physchem_logp", "physchem_tpsa", "physchem_qed",
-            "physchem_h_acceptors", "physchem_h_donors", "physchem_rotatable_bonds", "physchem_fraction_csp3",
-            "complex_ma_score", "complex_ma_runtime", "complex_bertz_score", "complex_bertz_runtime",
-            "complex_boettcher_score", "complex_boettcher_runtime", "synth_sa_score"
-        ]
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.DictReader(f)
-            for idx, row in enumerate(reader):
-                if row["split"] == split_name:
-                    for field in numeric_fields:
-                        if field in row and row[field] == "":
-                            row[field] = None
-                    yield idx, row