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law_area_prediction.py

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+import csv
+import json
+import lzma
+import os
+
+import datasets
+try:
+    import lzma as xz
+except ImportError:
+    import pylzma as xz
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+This dataset contains court decision for law area prediction task.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "main": "https://huggingface.co/datasets/rcds/law_area_prediction/resolve/main/data/areas/huggingface",
+    "sub": "https://huggingface.co/datasets/rcds/law_area_prediction/resolve/main/data/sub_areas/huggingface"
+}
+
+def get_url(config_name):
+    if config_name == "main":
+        return _URLS["main"]
+    if config_name == "public sub" or config_name == "civil sub" or config_name == "criminal sub":
+        return _URLS["sub"]
+
+
+class CourtViewGeneration(datasets.GeneratorBasedBuilder):
+    """This dataset contains court decision for law area prediction task."""
+
+    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="main", version=VERSION, description="This part of my dataset covers the whole dataset"),
+        datasets.BuilderConfig(name="public sub", version=VERSION, description="This dataset is for predicting the sub law areas of the public law"),
+        datasets.BuilderConfig(name="civil sub", version=VERSION, description="This dataset is for predicting the sub law areas of the civil law"),
+        datasets.BuilderConfig(name="criminal sub", version=VERSION, description="This dataset is for predicting the sub law areas of the criminal law"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "main"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        if self.config.name == "main":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "decision_id": datasets.Value("string"),
+                    "facts": datasets.Value("string"),
+                    "considerations": datasets.Value("string"),
+                    "label": datasets.Value("string"),
+                    "law_sub_area": datasets.Value("string"),
+                    "language": datasets.Value("string"),
+                    "year": datasets.Value("int32"),
+                    "court": datasets.Value("string"),
+                    "chamber": datasets.Value("string"),
+                    "canton": datasets.Value("string"),
+                    "region": datasets.Value("string")
+
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        if self.config.name != "main":  # for law sub area prediction
+            features = datasets.Features(
+                {
+                    "decision_id": datasets.Value("string"),
+                    "facts": datasets.Value("string"),
+                    "considerations": datasets.Value("string"),
+                    "law_area": datasets.Value("string"),
+                    "label": datasets.Value("string"),
+                    "language": datasets.Value("string"),
+                    "year": datasets.Value("int32"),
+                    "court": datasets.Value("string"),
+                    "chamber": datasets.Value("string"),
+                    "canton": datasets.Value("string"),
+                    "region": 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,
+        )
+
+    def _split_generators(self, dl_manager):
+        # 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 = get_url(self.config.name)
+        filepath_train = dl_manager.download(os.path.join(urls, "train.jsonl.xz"))
+        filepath_validation = dl_manager.download(os.path.join(urls, "validation.jsonl.xz"))
+        filepath_test = dl_manager.download(os.path.join(urls, "test.jsonl.xz"))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": filepath_train,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": filepath_validation,
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": filepath_test,
+                    "split": "test"
+                },
+            )
+        ]
+
+    def belongs_to_law_area(self, law_sub_area):
+        area_map = {
+            "public sub": ['Tax', 'Urban Planning and Environmental', 'Expropriation', 'Public Administration', 'Other Fiscal'],
+            "civil sub": ['Rental and Lease', 'Employment Contract', 'Bankruptcy', 'Family', 'Competition and Antitrust', 'Intellectual Property'],
+            'criminal sub': ['Substantive Criminal', 'Criminal Procedure']
+        }
+        if law_sub_area in area_map[self.config.name]:
+            return True
+        # raise Error if law_sub_area not found in any area
+        for area in area_map:
+            if law_sub_area in area_map[area]:
+                return False
+        raise ValueError("law_sub_area not found in any area")
+
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        line_counter = 0
+        try:
+            with xz.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
+                for id, line in enumerate(f):
+                    line_counter += 1
+                    if line:
+                        data = json.loads(line)
+                        if self.config.name == "main":
+                            yield id,  {
+                                "decision_id": data["decision_id"],
+                                "facts": data["facts"],
+                                "considerations": data["considerations"],
+                                "label": data["label"],
+                                "law_sub_area": data["law_sub_area"],
+                                "language": data["language"],
+                                "year": data["year"],
+                                "court": data["court"],
+                                "chamber": data["chamber"],
+                                "canton": data["canton"],
+                                "region": data["region"]
+                            }
+                        if self.config.name == "public sub" or self.config.name == "civil sub" or self.config.name == "criminal sub":
+                            if self.belongs_to_law_area(data["law_sub_area"]):
+                                yield id, {
+                                    "decision_id": data["decision_id"],
+                                    "facts": data["facts"],
+                                    "considerations": data["considerations"],
+                                    "law_area": data["law_area"],
+                                    "label": data["label"],
+                                    "language": data["language"],
+                                    "year": data["year"],
+                                    "court": data["court"],
+                                    "chamber": data["chamber"],
+                                    "canton": data["canton"],
+                                    "region": data["region"]
+                                }
+        except lzma.LZMAError as e:
+            print(split, e)
+            if line_counter == 0:
+                raise e