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squall.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""SQUALL: Lexical-level Supervised Table Question Answering Dataset."""
+
+
+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{Shi:Zhao:Boyd-Graber:Daume-III:Lee-2020,
+	Title = {On the Potential of Lexico-logical Alignments for Semantic Parsing to {SQL} Queries},
+	Author = {Tianze Shi and Chen Zhao and Jordan Boyd-Graber and Hal {Daum\'{e} III} and Lillian Lee},
+	Booktitle = {Findings of EMNLP},
+	Year = {2020},
+}
+"""
+
+_DESCRIPTION = """\
+To explore the utility of fine-grained, lexical-level supervision, authors \
+introduce SQUALL, a dataset that enriches 11,276 WikiTableQuestions \ 
+English-language questions with manually created SQL equivalents plus \ 
+alignments between SQL and question fragments.
+"""
+
+_URL = "https://github.com/tzshi/squall/tree/main/data/"
+_URLS = {
+    "squall": _URL + "squall.json",
+    "twtq-test": _URL + "wtq-test.json",
+    "dev-0": _URL +  "dev-0.ids",
+    "dev-1": _URL +  "dev-1.ids",
+    "dev-2": _URL +  "dev-2.ids",
+    "dev-3": _URL +  "dev-3.ids",
+    "dev-4": _URL +  "dev-4.ids",
+}
+
+
+class SquallConfig(datasets.BuilderConfig):
+    """BuilderConfig for Squall."""
+
+    def __init__(self, fold_num, **kwargs):
+        """BuilderConfig for Squall.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SquallConfig, self).__init__(**kwargs)
+        self.fold_num = fold_num
+
+
+class Squall(datasets.GeneratorBasedBuilder):
+    """SQUALL: Lexical-level Supervised Table Question Answering Dataset."""
+
+    BUILDER_CONFIGS = [
+        SquallConfig(
+            fold_num=0,
+        ),
+    ]
+    
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "nt": datasets.Value("string"),
+                    "tbl": datasets.Value("string"),
+                    "columns":
+                        {
+                            "raw_header": datasets.Value("string"),
+                            "tokenized_header": datasets.features.Sequence(datasets.Value("string")),
+                            "column_suffixes": datasets.features.Sequence(datasets.Value("string")),
+                            "column_dtype": datasets.Value("string"),
+                            "example": datasets.Value("string")
+                        },
+                    "nl": datasets.features.Sequence(datasets.Value("string")),
+                    "nl_pos": datasets.features.Sequence(datasets.Value("string")),
+                    "nl_ner": datasets.features.Sequence(datasets.Value("string")),
+                    "nl_incolumns": datasets.features.Sequence(datasets.Value("bool_")),
+                    "nl_incells": datasets.features.Sequence(datasets.Value("bool_")),
+                    "columns_innl": datasets.features.Sequence(datasets.Value("bool_")),
+                    "tgt": datasets.Value("string"),
+                    "sql": datasets.features.Sequence(datasets.Value("string"))
+                    # "align" is not implemented
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://github.com/tzshi/squall/tree/main",
+            citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="nl", context_column="columns", answers_column="tgt"
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, 
+                gen_kwargs={"split_key": "train", "filepath": downloaded_files}),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, 
+                gen_kwargs={"split_key": "dev", "filepath": downloaded_files}),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, 
+                gen_kwargs={"split_key": "test", "filepath": downloaded_files}),
+        ]
+
+    def _generate_examples(self, split_key, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+
+        squall_full = filepath["squall"] + '/squall.json'
+        dev_ids = filepath[f"dev-{self.fold_num}"] + f"/dev-{self.fold_num}.ids"
+        test = filepath["twtq-test"] + "/twtq-test.json"
+
+        if split_key != 'test':
+            with open(squall_full, encoding="utf-8") as f:
+                squall_full_data = json.load(f)
+            with open(dev_ids) as f:
+                dev_ids = set(json.load(f))
+            if split_key == "train":
+                set = [x for x in squall_full_data if x["tbl"] not in dev_ids]
+            else:
+                set = [x for x in squall_full_data if x["tbl"] in dev_ids]
+            idx = 0
+            for sample in set:
+                cols = {}
+                keys = ["raw_header", "tokenized_header", "column_suffixes", "column_dtype", "example"]
+                for k in range(5):
+                    cols.update({keys[k]: sample["columns"][k]})
+                sql = [x[1] for x in sample["sql"]]
+                yield idx, {
+                    "nt": sample["nt"],
+                    "tbl": sample["tbl"],
+                    "columns": cols,
+                    "nl": sample["nl"],
+                    "nl_pos": sample["nl_pos"],
+                    "nl_ner": sample["nl_ner"],
+                    # "nl_ralign": sample["nl_ralign"],
+                    "nl_incolumns": sample["nl_incolumns"],
+                    "nl_incells": sample["nl_incells"],
+                    "columns_innl": sample["columns_innl"],
+                    "tgt": sample["tgt"],
+                    "sql": sql,
+                    # "align": sample["align"]
+                }
+                idx += 1
+        else:
+            with open(test, encoding="utf-8") as f:
+                test_data = json.load(f)
+            idx = 0
+            for sample in test_data:
+                cols = {}
+                keys = ["raw_header", "tokenized_header", "column_suffixes", "column_dtype", "example"]
+                for k in range(5):
+                    cols.update({keys[k]: sample["columns"][k]})
+                sql = [x[1] for x in sample["sql"]]
+                yield idx, {
+                    "nt": sample["nt"],
+                    "tbl": sample["tbl"],
+                    "columns": cols,
+                    "nl": sample["nl"],
+                    "nl_pos": sample["nl_pos"],
+                    "nl_ner": sample["nl_ner"],
+                    # "nl_ralign": sample["nl_ralign"],
+                    "nl_incolumns": sample["nl_incolumns"],
+                    "nl_incells": sample["nl_incells"],
+                    "columns_innl": sample["columns_innl"],
+                    "tgt": '',
+                    "sql": [],
+                    # "align": sample["align"]
+                }
+                idx += 1