Upload hansard_speech.py

hansard_speech.py

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+# 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.
+"""
+A dataset containing every speech in the House of Commons from May 1979-July 2020.
+"""
+
+import os
+import pandas as pd
+
+import datasets
+
+_CITATION = """@misc{odell, evan_2021, 
+title={Hansard Speeches 1979-2021: Version 3.1.0}, 
+DOI={10.5281/zenodo.4843485}, 
+abstractNote={<p>Full details are available at <a href="https://evanodell.com/projects/datasets/hansard-data">https://evanodell.com/projects/datasets/hansard-data</a></p> <p><strong>Version 3.1.0 contains the following changes:</strong></p> <p>- Coverage up to the end of April 2021</p>}, 
+note={This release is an update of previously released datasets. See full documentation for details.}, 
+publisher={Zenodo}, 
+author={Odell, Evan}, 
+year={2021}, 
+month={May} }
+"""
+
+_DESCRIPTION = """
+A dataset containing every speech in the House of Commons from May 1979-July 2020.
+"""
+
+_HOMEPAGE = "https://evanodell.com/projects/datasets/hansard-data/"
+
+_LICENSE = "Creative Commons Attribution 4.0 International License"
+
+_URLS = {
+    "csv": "https://zenodo.org/record/4843485/files/hansard-speeches-v310.csv.zip?download=1",
+    "json": "https://zenodo.org/record/4843485/files/parliamentary_posts.json?download=1",
+}
+
+fields = [
+    "id",
+    "speech",
+    "display_as",
+    "party",
+    "constituency",
+    "mnis_id",
+    "date",
+    "time",
+    "colnum",
+    "speech_class",
+    "major_heading",
+    "minor_heading",
+    "oral_heading",
+    "year",
+    "hansard_membership_id",
+    "speakerid",
+    "person_id",
+    "speakername",
+    "url",
+    "parliamentary_posts",
+    "opposition_posts",
+    "government_posts",
+]
+
+logger = datasets.utils.logging.get_logger(__name__)
+
+
+class HansardSpeech(datasets.GeneratorBasedBuilder):
+    """A dataset containing every speech in the House of Commons from May 1979-July 2020."""
+
+    VERSION = datasets.Version("3.1.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "speech": datasets.Value("string"),
+                "display_as": datasets.Value("string"),
+                "party": datasets.Value("string"),
+                "constituency": datasets.Value("string"),
+                "mnis_id": datasets.Value("string"),
+                "date": datasets.Value("string"),
+                "time": datasets.Value("string"),
+                "colnum": datasets.Value("string"),
+                "speech_class": datasets.Value("string"),
+                "major_heading": datasets.Value("string"),
+                "minor_heading": datasets.Value("string"),
+                "oral_heading": datasets.Value("string"),
+                "year": datasets.Value("string"),
+                "hansard_membership_id": datasets.Value("string"),
+                "speakerid": datasets.Value("string"),
+                "person_id": datasets.Value("string"),
+                "speakername": datasets.Value("string"),
+                "url": datasets.Value("string"),
+                "government_posts": datasets.Sequence(datasets.Value("string")),
+                "opposition_posts": datasets.Sequence(datasets.Value("string")),
+                "parliamentary_posts": datasets.Sequence(datasets.Value("string")),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            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
+        temp_dir = dl_manager.download_and_extract(_URLS["csv"])
+        csv_file = os.path.join(temp_dir, "hansard-speeches-v310.csv")
+        json_file = dl_manager.download(_URLS["json"])
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepaths": [csv_file, json_file], "split": "train",},
+            ),
+        ]
+
+    def _generate_examples(self, filepaths, split):
+        logger.warn("\nThis is a large dataset. Please be patient")
+        json_data = pd.read_json(filepaths[1])
+        csv_data_chunks = pd.read_csv(filepaths[0], chunksize=50000)
+        for data_chunk in csv_data_chunks:
+            for _, row in data_chunk.iterrows():
+                data_point = {}
+                for field in fields[:-3]:
+                    data_point[field] = row[field]
+                parl_post = json_data.loc[
+                    (json_data["mnis_id"] == data_point["mnis_id"])
+                    & (json_data["date"] == data_point["date"])
+                ]
+                opp_post = []
+                gov_post = []
+                data_point["government_posts"] = gov_post
+                data_point["opposition_posts"] = opp_post
+                data_point["parliamentary_posts"] = parl_post
+                yield data_point["id"], data_point