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)
+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},
+                author = {Lowe,  Daniel Mark},
+                keywords = {Name to structure,  OPSIN,  Chemical text mining,  Text mining,  Patent reaction extraction,  Reaction mining,  Patents},
+                language = {en},
+                title = {Extraction of chemical structures and reactions from the literature},
+                publisher = {Apollo - University of Cambridge Repository},
+                year = {2012},
+                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)",
+                ],
+            },
+        ],
+        "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},
+              author = {Lowe,  Daniel Mark},
+              keywords = {Name to structure,  OPSIN,  Chemical text mining,  Text mining,  Patent reaction extraction,  Reaction mining,  Patents},
+              language = {en},
+              title = {Extraction of chemical structures and reactions from the literature},
+              publisher = {Apollo - University of Cambridge Repository},
+              year = {2012},
+              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()