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import datasets |
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from enum import Enum |
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from dataclasses import dataclass |
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from typing import List |
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import pandas as pd |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@dataset{gyodi_kristof_2021_4446043, |
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author = {Gyódi, Kristóf and |
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Nawaro, Łukasz}, |
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title = {{Determinants of Airbnb prices in European cities: |
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A spatial econometrics approach (Supplementary |
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Material)}}, |
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month = jan, |
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year = 2021, |
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note = {{This research was supported by National Science |
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Centre, Poland: Project number 2017/27/N/HS4/00951}}, |
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publisher = {Zenodo}, |
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doi = {10.5281/zenodo.4446043}, |
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url = {https://doi.org/10.5281/zenodo.4446043} |
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}""" |
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_DESCRIPTION = """\ |
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This dataset contains accommodation offers from the AirBnb platform from 10 European cities. |
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It has been copied from https://zenodo.org/record/4446043#.ZEV8d-zMI-R to make it available as a Huggingface Dataset. |
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It was originally published as supplementary material for the article: Determinants of Airbnb prices in European cities: A spatial econometrics approach |
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(DOI: https://doi.org/10.1016/j.tourman.2021.104319)""" |
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_CITIES = [ |
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"Amsterdam", |
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"Athens", |
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"Barcelona", |
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"Berlin", |
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"Budapest", |
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"Lisbon", |
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"London", |
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"Paris", |
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"Rome", |
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"Vienna" |
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] |
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_BASE_URL = "data/" |
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_URL_TEMPLATE = _BASE_URL + "{city}_{day_type}.csv" |
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class DayType(str, Enum): |
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WEEKDAYS = "weekdays" |
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WEEKENDS = "weekends" |
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@dataclass |
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class AirbnbFile: |
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"""A file from the Airbnb dataset.""" |
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city: str |
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day_type: DayType |
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@property |
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def url(self) -> str: |
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return _URL_TEMPLATE.format(city=self.city.lower(), day_type=self.day_type.value) |
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class AirbnbConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Airbnb.""" |
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def __init__(self, files: List[AirbnbFile], **kwargs): |
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"""BuilderConfig for Airbnb. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(AirbnbConfig, self).__init__(**kwargs) |
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self.files = files |
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_WEEKDAY_FILES = [AirbnbFile(city=city, day_type=DayType.WEEKDAYS) for city in _CITIES] |
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_WEEKEND_FILES = [AirbnbFile(city=city, day_type=DayType.WEEKENDS) for city in _CITIES] |
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_DATASET_VERSION = "2.0.0" |
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class Airbnb(datasets.GeneratorBasedBuilder): |
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"""""" |
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BUILDER_CONFIGS = [ |
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AirbnbConfig( |
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name=DayType.WEEKDAYS.value, |
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files=_WEEKDAY_FILES, |
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version=datasets.Version(_DATASET_VERSION), |
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), |
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AirbnbConfig( |
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name=DayType.WEEKENDS.value, |
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files=_WEEKEND_FILES, |
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version=datasets.Version(_DATASET_VERSION), |
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), |
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AirbnbConfig( |
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name="all", |
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files=_WEEKDAY_FILES + _WEEKEND_FILES, |
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version=datasets.Version(_DATASET_VERSION), |
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), |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"_id": datasets.Value("string"), |
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"city": datasets.Value("string"), |
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"realSum": datasets.Value(dtype="float64"), |
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"room_type": datasets.Value(dtype="string"), |
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"room_shared": datasets.Value(dtype="bool"), |
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"room_private": datasets.Value(dtype="bool"), |
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"person_capacity": datasets.Value(dtype="float64"), |
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"host_is_superhost": datasets.Value(dtype="bool"), |
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"multi": datasets.Value(dtype="int64"), |
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"biz": datasets.Value(dtype="int64"), |
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"cleanliness_rating": datasets.Value(dtype="float64"), |
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"guest_satisfaction_overall": datasets.Value(dtype="float64"), |
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"bedrooms": datasets.Value(dtype="int64"), |
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"dist": datasets.Value(dtype="float64"), |
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"metro_dist": datasets.Value(dtype="float64"), |
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"attr_index": datasets.Value(dtype="float64"), |
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"attr_index_norm": datasets.Value(dtype="float64"), |
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"rest_index": datasets.Value(dtype="float64"), |
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"rest_index_norm": datasets.Value(dtype="float64"), |
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"lng": datasets.Value(dtype="float64"), |
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"lat": datasets.Value(dtype="float64") |
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}) |
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if self.config.name == "all": |
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features["day_type"] = datasets.Value(dtype="string") |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage="https://zenodo.org/record/4446043#.ZEV8d-zMI-R", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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config_files: List[AirbnbFile] = self.config.files |
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urls = [file.url for file in config_files] |
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downloaded_files = dl_manager.download_and_extract(urls) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"paths": downloaded_files}) |
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] |
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def _generate_examples(self, paths: List[str]): |
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_id = 0 |
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config_files: List[AirbnbFile] = self.config.files |
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include_day_type = self.config.name == "all" |
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for file, path in zip(config_files, paths): |
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logger.info("generating examples from = %s", path) |
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df = pd.read_csv(path, index_col=0, header=0) |
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for row in df.itertuples(): |
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city = file.city |
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data = { |
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"_id": _id, |
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"city": city, |
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"realSum": row.realSum, |
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"room_type": row.room_type, |
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"room_shared": row.room_shared, |
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"room_private": row.room_private, |
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"person_capacity": row.person_capacity, |
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"host_is_superhost": row.host_is_superhost, |
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"multi": row.multi, |
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"biz": row.biz, |
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"cleanliness_rating": row.cleanliness_rating, |
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"guest_satisfaction_overall": row.guest_satisfaction_overall, |
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"bedrooms": row.bedrooms, |
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"dist": row.dist, |
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"metro_dist": row.metro_dist, |
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"attr_index": row.attr_index, |
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"attr_index_norm": row.attr_index_norm, |
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"rest_index": row.rest_index, |
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"rest_index_norm": row.rest_index_norm, |
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"lng": row.lng, |
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"lat": row.lat |
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} |
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if include_day_type: |
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data["day_type"] = file.day_type.value |
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yield _id, data |
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_id += 1 |
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