Upload PAIR.py

PAIR.py

@@ -13,13 +13,10 @@
 # limitations under the License.
 
 
-import csv
 import json
-import os
 
 import datasets
 
-
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
@@ -52,95 +49,168 @@ _URLS = {
 
 
 # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-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.
+class PAIRDataset(datasets.GeneratorBasedBuilder):
+    """PAIRDataset."""
 
     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(
+        """_info."""
+        return datasets.DatasetInfo(
+            description="My custom dataset.",
+            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 ...
+                    "annotation_type": datasets.Value("string"),
+                    "sequence": datasets.Value("string"),
+                    "pid": datasets.Value("string"),
+                    "annotation": datasets.Value("string"),
                 }
-            )
-
-        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,
+            ),
+            supervised_keys=None,
         )
 
     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)
+        """_split_generators.
+
+        Parameters
+        ----------
+        dl_manager :
+            dl_manager
+        """
+        # Implement logic to download and extract data files
+        # For simplicity, assume data_files is a dict with paths to your data
+        data_files = {
+            "train": "train.json",
+            "test": "test.json",
+        }
         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.TRAIN,
+            #    gen_kwargs={'filepath': data_files['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"
-                },
+                gen_kwargs={"filepath": data_files["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.
+    def _generate_examples(self, filepath):
+        """_generate_examples.
+
+        Parameters
+        ----------
+        filepath :
+            filepath
+        """
+        # Implement your data reading logic here
         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],
-                    }
+            data = json.load(f)
+            counter = 0
+            for idx, annotation_type in enumerate(data.keys()):
+                # Parse your line into the appropriate fields
+                samples = data[annotation_type]
+                for idx_2, elem in enumerate(samples):
+                    # example = parse_line_to_example(line)
+                    print(elem["content"], type(elem["content"]))
+                    if elem["content"] != [None]:
+                        yield annotation_type, {
+                            "annotation_type": annotation_type,
+                            "sequence": elem["seq"],
+                            "pid": elem["pid"],
+                            "annotation": elem["content"][0],
+                        }
+                        counter += 1
+
+
+# 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],
+#                    }