hansard_speech.py

# 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
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"""
A dataset containing every speech in the House of Commons from May 1979-July 2020.
"""

import json
import os
import time
import pandas as pd
from datetime import datetime
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):
        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, dtype="object")
        for data_chunk in csv_data_chunks:
            data_chunk.fillna("", inplace=True)
            for _, row in data_chunk.iterrows():
                data_point = {}
                for field in fields[:-3]:
                    data_point[field] = str(row[field]) if row[field] else ""
                parl_post_list = []
                if data_point["mnis_id"] and data_point["date"]:
                    speech_dt = data_point["date"] + " 00:00:00"
                    try:
                        parl_posts = json_data[
                            (json_data["mnis_id"] == int(data_point["mnis_id"]))
                            & (json_data["date"] == speech_dt)
                        ]["parliamentary_posts"]
                        if len(parl_posts) > 0:
                            parl_posts = parl_posts.iloc[0]
                            for item in parl_posts:
                                parl_post_list.append(item["parl_post_name"])
                    except Exception as e:
                        logger.warn(
                            f"Data could not be fetched for mnis_id: {data_point['mnis_id']}, date: {data_point['date']}\nError: {repr(e)}"
                        )
                opp_post = []
                gov_post = []
                data_point["government_posts"] = gov_post
                data_point["opposition_posts"] = opp_post
                data_point["parliamentary_posts"] = parl_post_list
                yield data_point["id"], data_point