Upload PAIR.py

PAIR.py

@@ -49,19 +49,24 @@ _URLS = {
 
 
 # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-
+annotation2type = {"names": datasets.Value("string"),
+                    "EC": datasets.Sequence(datasets.Value("string")),
+   }
 
 class PAIRDataset(datasets.GeneratorBasedBuilder):
     """PAIRDataset."""
-
+    def __init__(self, *args, annotation_type=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.annotation_type = annotation_type  # Save the custom argument for later use
+        print(self.annotation_type)
+        exit()
     def _info(self):
         """_info."""
         return datasets.DatasetInfo(
             description="My custom dataset.",
             features=datasets.Features(
                 {
-                    "EC": datasets.Sequence(datasets.Value("string")),
-                    "names": datasets.Value("string"),
+                    self.annotation_type: annotation2type[self.annotation_type],
                     "sequence": datasets.Value("string"),
                     "pid": datasets.Value("string"),
                 }
@@ -109,12 +114,13 @@ class PAIRDataset(datasets.GeneratorBasedBuilder):
             data = json.load(f)
             counter = 0
             for idx, annotation_type in enumerate(data.keys()):
+                if annotation_type != self.annotation_type: continue
                 # Parse your line into the appropriate fields
                 samples = data[annotation_type]
                 for idx_2, elem in enumerate(samples):
                     # example = parse_line_to_example(line)
                     if elem["content"] != [None]:
-                        unique_id = f"{elem['pid']}_{idx_2}"
+                        unique_id = f"{elem['pid']}_{idx}"
                         content = elem["content"][0]
                         # print(literal_eval(content), "done")
                         yield unique_id, {
@@ -126,97 +132,4 @@ class PAIRDataset(datasets.GeneratorBasedBuilder):
 
 
                             #"annotation_type": annotation_type,
-                            #"annotation": content,
-# class PAIRDataset(datasets.GeneratorBasedBuilder):
-#    """TODO: Short description of my dataset."""
-#
-#    VERSION = datasets.Version("1.1.0")
-#
-#    # This is an example of a dataset with multiple configurations.
-#    # If you don't want/need to define several sub-sets in your dataset,
-#    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-#
-#    # If you need to make complex sub-parts in the datasets with configurable options
-#    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-#    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-#
-#    # You will be able to load one or the other configurations in the following list with
-#    # data = datasets.load_dataset('my_dataset', 'first_domain')
-#    # data = datasets.load_dataset('my_dataset', 'second_domain')
-#    BUILDER_CONFIGS = [
-#        datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
-#    ]
-#
-#    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-#
-#    def _info(self):
-#        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-#        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-#            features = datasets.Features(
-#                {
-#                    "sentence": datasets.Value("string"),
-#                    "option1": datasets.Value("string"),
-#                    "answer": datasets.Value("string")
-#                    # These are the features of your dataset like images, labels ...
-#                }
-#            )
-#
-#        return datasets.DatasetInfo(
-#            # This is the description that will appear on the datasets page.
-#            description=_DESCRIPTION,
-#            # This defines the different columns of the dataset and their types
-#            features=features,  # Here we define them above because they are different between the two configurations
-#            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-#            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-#            # supervised_keys=("sentence", "label"),
-#            # Homepage of the dataset for documentation
-#            homepage=_HOMEPAGE,
-#            # License for the dataset if available
-#            license=_LICENSE,
-#            # Citation for the dataset
-#            citation=_CITATION,
-#        )
-#
-#    def _split_generators(self, dl_manager):
-#        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-#        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-#
-#        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-#        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-#        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-#       # urls = _URLS[self.config.name]
-#        #data_dir = dl_manager.download_and_extract(urls)
-#        return [
-#          #  datasets.SplitGenerator(
-#          #      name=datasets.Split.TRAIN,
-#          #      # These kwargs will be passed to _generate_examples
-#          ###      gen_kwargs={
-#              #      "filepath": os.path.join(data_dir, "train.json"),
-#             #       "split": "train",
-#               # },
-#           # ),
-#            datasets.SplitGenerator(
-#                name=datasets.Split.TEST,
-#                # These kwargs will be passed to _generate_examples
-#                gen_kwargs={
-#                    "filepath": "test.json", #os.path.join(data_dir, "test.json"),
-#                    "split": "test"
-#                },
-#            ),
-#        ]
-#
-#    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-#    def _generate_examples(self, filepath, split):
-#        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-#        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-#        with open(filepath, encoding="utf-8") as f:
-#            for key, row in enumerate(f):
-#                data = json.loads(row)
-#                if data['content'] != [None]:
-#                    yield key, {
-#                        "sequence": data["seq"],
-#                        "pid": data["pid"],
-#                        #"content": "" if split == "test" else data["answer"],
-#                        "content": data['content'][0],
-#                    }