Add uspto data from drfp

data/uspto_500k/meta.yaml

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+---
+name: uspto_500k
+description: United States Patent and Trademark Office reaction dataset with yields.
+targets:
+    - id: yield
+      description: Reaction yields analyzed by UPLC
+      units: '%'
+      type: continuous
+      names:
+          - Reaction yield
+          - yield
+      uris:
+          - https://bioportal.bioontology.org/ontologies/AFO?p=classes&conceptid=http%3A%2F%2Fpurl.allotrope.org%2Fontologies%2Fquality%23AFQ_0000227
+          - https://en.wikipedia.org/wiki/Yield_(chemistry)
+benchmarks:
+    - name: TDC
+      link: https://tdcommons.ai/
+      split_column: split
+identifiers:
+    - id: reaction_SMILES
+      type: SMILES
+      description: reaction SMILES
+license: CC0
+links:
+    - url: https://doi.org/10.17863/CAM.16293
+      description: corresponding publication
+    - url: https://github.com/reymond-group/drfp/blob/main/data/uspto_yields_below.csv
+      description: data source
+    - url: https://github.com/reymond-group/drfp/blob/main/data/uspto_yields_above.csv
+      description: data source
+    - url: https://tdcommons.ai/single_pred_tasks/yields/#uspto
+      description: other source
+num_points: 498721
+bibtex:
+    - |-
+      @article{https://doi.org/10.17863/cam.16293,
+      doi = {10.17863/CAM.16293},
+      url = {https://www.repository.cam.ac.uk/handle/1810/244727},
+      year = {2012},
+      publisher = {Apollo - University of Cambridge Repository},
+      keywords = {Name to structure,  OPSIN,  Chemical text mining,  Text mining,
+      Patent reaction extraction,  Reaction mining,  Patents},
+      language = {en},
+      author = {Lowe,  Daniel Mark},
+      title = {Extraction of chemical structures and reactions from the literature},
+      copyright = {All Rights Reserved}

data/uspto_500k/transform.py

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+import pandas as pd
+import yaml
+from tdc.single_pred import Tox
+
+
+def get_and_transform_data():
+    # get raw data
+    df1 = pd.read_csv(
+        "https://github.com/reymond-group/drfp/raw/main/data/uspto_yields_above.csv"
+    )
+    df2 = pd.read_csv(
+        "https://github.com/reymond-group/drfp/raw/main/data/uspto_yields_below.csv"
+    )
+    data = pd.concat([df1, df2])
+    data = data[["rxn", "yield"]]
+    data = data.drop_duplicates(subset="rxn")
+    fn_data_original = "uptso.csv"
+    data.to_csv(fn_data_original, index=False)
+
+    # create dataframe
+    df = pd.read_csv(
+        fn_data_original, delimiter=","
+    )  # not necessary but ensure we can load the saved data
+
+    # check if fields are the same
+    fields_orig = df.columns.tolist()
+    assert fields_orig == ["rxn", "yield"]
+    fields_clean = ["reaction_SMILES", "yield"]
+
+    # overwrite column names = fields
+    df.columns = fields_clean
+    assert fields_orig != fields_clean
+
+    # remove leading and trailing white space characters
+    assert not df.duplicated().sum()
+
+    # save to csv
+    fn_data_csv = "data_clean.csv"
+    df.to_csv(fn_data_csv, index=False)
+
+    # create meta yaml
+    meta = {
+        "name": "uspto_500k",  # unique identifier, we will also use this for directory names
+        "description": """United States Patent and Trademark Office reaction dataset with yields.""",
+        "targets": [
+            {
+                "id": "yield",  # name of the column in a tabular dataset
+                "description": "Reaction yields analyzed by UPLC",  # description of what this column means
+                "units": "%",  # units of the values in this column (leave empty if unitless)
+                "type": "continuous",  # can be "categorical", "ordinal", "continuous"
+                "names": [  # names for the property (to sample from for building the prompts)
+                    "Reaction yield",
+                    "yield",
+                ],
+                "uris": [
+                    "https://bioportal.bioontology.org/ontologies/AFO?p=classes&conceptid=http%3A%2F%2Fpurl.allotrope.org%2Fontologies%2Fquality%23AFQ_0000227",
+                    "https://en.wikipedia.org/wiki/Yield_(chemistry)",
+                ],
+            },
+        ],
+        "benchmarks": [
+            {
+                "name": "TDC",  # unique benchmark name
+                "link": "https://tdcommons.ai/",  # benchmark URL
+                "split_column": "split",  # name of the column that contains the split information
+            },
+        ],
+        "identifiers": [
+            {
+                "id": "reaction_SMILES",  # column name
+                "type": "SMILES",  # can be "SMILES", "SELFIES", "IUPAC", "Other"
+                "description": "reaction SMILES",  # description (optional, except for "Other")
+            },
+        ],
+        "license": "CC0",  # license under which the original dataset was published
+        "links": [  # list of relevant links (original dataset, other uses, etc.)
+            {
+                "url": "https://doi.org/10.17863/CAM.16293",
+                "description": "corresponding publication",
+            },
+            {
+                "url": "https://github.com/reymond-group/drfp/blob/main/data/uspto_yields_below.csv",
+                "description": "data source",
+            },
+            {
+                "url": "https://github.com/reymond-group/drfp/blob/main/data/uspto_yields_above.csv",
+                "description": "data source",
+            },
+            {
+                "url": "https://tdcommons.ai/single_pred_tasks/yields/#uspto",
+                "description": "other source",
+            },
+        ],
+        "num_points": len(df),  # number of datapoints in this dataset
+        "bibtex": [
+            """@article{https://doi.org/10.17863/cam.16293,
+doi = {10.17863/CAM.16293},
+url = {https://www.repository.cam.ac.uk/handle/1810/244727},
+year = {2012},
+publisher = {Apollo - University of Cambridge Repository},
+keywords = {Name to structure,  OPSIN,  Chemical text mining,  Text mining,
+Patent reaction extraction,  Reaction mining,  Patents},
+language = {en},
+author = {Lowe,  Daniel Mark},
+title = {Extraction of chemical structures and reactions from the literature},
+copyright = {All Rights Reserved}""",
+        ],
+    }
+
+    def str_presenter(dumper, data):
+        """configures yaml for dumping multiline strings
+        Ref: https://stackoverflow.com/questions/8640959/how-can-i-control-what-scalar-form-pyyaml-uses-for-my-data
+        """
+        if data.count("\n") > 0:  # check for multiline string
+            return dumper.represent_scalar("tag:yaml.org,2002:str", data, style="|")
+        return dumper.represent_scalar("tag:yaml.org,2002:str", data)
+
+    yaml.add_representer(str, str_presenter)
+    yaml.representer.SafeRepresenter.add_representer(
+        str, str_presenter
+    )  # to use with safe_dum
+    fn_meta = "meta.yaml"
+    with open(fn_meta, "w") as f:
+        yaml.dump(meta, f, sort_keys=False)
+
+    print(f"Finished processing {meta['name']} dataset!")
+
+
+if __name__ == "__main__":
+    get_and_transform_data()