Upload odinsynth_dataset.py

odinsynth_dataset.py

@@ -0,0 +1,153 @@
+import csv
+import json
+import os
+from collections import defaultdict
+
+import datasets
+from tqdm import tqdm
+
+_DESCRIPTION = """\
+Supervised training data for odinsynth
+"""
+
+
+
+class OdinsynthDatasetBuilder(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    # 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"),
+    #     datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second 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
+
+        features = datasets.Features(
+            {
+                "rule_id": datasets.Value("int32"),
+                "source": datasets.Value("string"),
+                "destination": datasets.Value("string"),
+                "spec": datasets.Sequence(datasets.Value("string")),
+                "step": datasets.Value("int8"),
+                "length": datasets.Value("int8")
+                # 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 _build_specs(self, path:str):
+        id_to_rule = {}
+        specs = defaultdict(set)
+        with open(path) as f:
+            for l in tqdm(f, desc="Pre-computing specs"):
+                try:
+                    instance = json.loads(l)
+                    rule_id = int(instance['id'])
+                    rule = instance['question']
+                    sent  = instance['context']
+                    specs[rule].add(sent)
+                    id_to_rule[rule_id] = rule
+                except:
+                    # TODO log
+                    pass
+
+        return {rule_id:specs[rule] for rule_id, rule in id_to_rule.items()}
+
+
+
+    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
+
+        JSON_PATH = os.path.join('merged_train_split_train.jsonl')
+        archive_iter = dl_manager.iter_archive('data.tar.bz2')
+        specs = self._build_specs(JSON_PATH)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "archive_iter": dl_manager.iter_archive('data.tar.bz2'),
+                    "specs": specs,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "archive_iter": dl_manager.iter_archive('data.tar.bz2'),
+                    "specs": specs,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "archive_iter": dl_manager.iter_archive('data.tar.bz2'),
+                    "specs": specs,
+                    "split": "val",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, archive_iter, specs, split):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+
+        key = 0
+        for tsv_path, file in archive_iter:
+            if tsv_path.startswith(split) and tsv_path.endswith(".tsv"):
+                # Read the lines
+                reader = csv.reader((l.decode() for l in  file), delimiter='\t')
+                for row in reader:
+                    rule_id = int(row[0])
+                    if rule_id in specs:
+                        yield key, {
+                            "rule_id": rule_id,
+                            "source": row[1],
+                            "destination": row[2],
+                            "spec": specs[rule_id],
+                            "step": int(row[3]),
+                            "length": int(row[4]),
+                        }
+                        # Increase the key after yielding the instacne
+                        key += 1
+
+
+if __name__  == "__main__":
+    ds = OdinsynthDatasetBuilder()
+    ds.download_and_prepare()
+    print(ds.cache_dir)