data loader script

mlb_data_to_text.py

@@ -0,0 +1,146 @@
+
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{puduppully-etal-2019-data,
+    title = "Data-to-text Generation with Entity Modeling",
+    author = "Puduppully, Ratish  and
+      Dong, Li  and
+      Lapata, Mirella",
+    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2019",
+    address = "Florence, Italy",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/P19-1195",
+    doi = "10.18653/v1/P19-1195",
+    pages = "2023--2035",
+}
+"""
+
+_DESCRIPTION = """\
+The MLB dataset for data to text generation contains Major League Baseball games statistics and 
+their human-written summaries. 
+"""
+
+_HOMEPAGE = "https://github.com/ratishsp/mlb-data-scripts"
+
+_LICENSE = ""
+
+_URLs = {
+    "train": "train.jsonl",
+    "validation": "validation.jsonl",
+    "test": "test.jsonl"
+}
+
+
+class MlbDataToText(datasets.GeneratorBasedBuilder):
+    """MLB dataset for data to text generation"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "home_name": datasets.Value("string"),
+                "box_score": dict,
+                "home_city": datasets.Value("string"),
+                "vis_name": datasets.Value("string"),
+                "play_by_play": dict,
+                "vis_line": dict,
+                "vis_city": datasets.Value("string"),
+                "day": datasets.Value("string"),
+                "home_line": dict,
+                "summary": list,
+                "gem_id": datasets.Value("string")
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # 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
+        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": data_dir["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["validation"],
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ):
+        """ Yields examples as (key, example) tuples. """
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                yield id_, {
+                    "home_name": data["home_name"],
+                    "box_score": data["box_score"],
+                    "home_city": data["home_city"],
+                    "vis_name": data["vis_name"],
+                    "play_by_play": data["play_by_play"],
+                    "vis_line": data["vis_line"],
+                    "vis_city": data["vis_city"],
+                    "day": data["day"],
+                    "home_line": data["home_line"],
+                    "summary": data["summary"],
+                    "gem_id": data["gem_id"]
+                }