add initial version of airbnb dataset

README.md

@@ -1,3 +1,157 @@
 ---
 license: cc-by-4.0
 ---
+dataset_info:
+- config_name: weekdays
+  features:
+  - name: _id
+    dtype: string
+  - name: city
+    dtype: string
+  - name: realSum
+    dtype: float64
+  - name: room_type
+    dtype: string
+  - name: room_shared
+    dtype: bool
+  - name: room_private
+    dtype: bool
+  - name: person_capacity
+    dtype: float64
+  - name: host_is_superhost
+    dtype: bool
+  - name: multi
+    dtype: int64
+  - name: biz
+    dtype: int64
+  - name: cleanliness_rating
+    dtype: float64
+  - name: guest_satisfaction_overall
+    dtype: float64
+  - name: bedrooms
+    dtype: int64
+  - name: dist
+    dtype: float64
+  - name: metro_dist
+    dtype: float64
+  - name: attr_index
+    dtype: float64
+  - name: attr_index_norm
+    dtype: float64
+  - name: rest_index
+    dtype: float64
+  - name: rest_index_norm
+    dtype: float64
+  - name: lng
+    dtype: float64
+  - name: lat
+    dtype: float64
+  splits:
+  - name: train
+    num_bytes: 3998764
+    num_examples: 25500
+  download_size: 5303928
+  dataset_size: 3998764
+- config_name: weekends
+  features:
+  - name: _id
+    dtype: string
+  - name: city
+    dtype: string
+  - name: realSum
+    dtype: float64
+  - name: room_type
+    dtype: string
+  - name: room_shared
+    dtype: bool
+  - name: room_private
+    dtype: bool
+  - name: person_capacity
+    dtype: float64
+  - name: host_is_superhost
+    dtype: bool
+  - name: multi
+    dtype: int64
+  - name: biz
+    dtype: int64
+  - name: cleanliness_rating
+    dtype: float64
+  - name: guest_satisfaction_overall
+    dtype: float64
+  - name: bedrooms
+    dtype: int64
+  - name: dist
+    dtype: float64
+  - name: metro_dist
+    dtype: float64
+  - name: attr_index
+    dtype: float64
+  - name: attr_index_norm
+    dtype: float64
+  - name: rest_index
+    dtype: float64
+  - name: rest_index_norm
+    dtype: float64
+  - name: lng
+    dtype: float64
+  - name: lat
+    dtype: float64
+  splits:
+  - name: train
+    num_bytes: 4108612
+    num_examples: 26207
+  download_size: 5451150
+  dataset_size: 4108612
+- config_name: all
+  features:
+  - name: _id
+    dtype: string
+  - name: city
+    dtype: string
+  - name: realSum
+    dtype: float64
+  - name: room_type
+    dtype: string
+  - name: room_shared
+    dtype: bool
+  - name: room_private
+    dtype: bool
+  - name: person_capacity
+    dtype: float64
+  - name: host_is_superhost
+    dtype: bool
+  - name: multi
+    dtype: int64
+  - name: biz
+    dtype: int64
+  - name: cleanliness_rating
+    dtype: float64
+  - name: guest_satisfaction_overall
+    dtype: float64
+  - name: bedrooms
+    dtype: int64
+  - name: dist
+    dtype: float64
+  - name: metro_dist
+    dtype: float64
+  - name: attr_index
+    dtype: float64
+  - name: attr_index_norm
+    dtype: float64
+  - name: rest_index
+    dtype: float64
+  - name: rest_index_norm
+    dtype: float64
+  - name: lng
+    dtype: float64
+  - name: lat
+    dtype: float64
+  - name: day_type
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 8738970
+    num_examples: 51707
+  download_size: 10755078
+  dataset_size: 8738970
+---

airbnb.py

@@ -0,0 +1,174 @@
+import datasets
+from enum import Enum
+from dataclasses import dataclass
+from typing import List
+import pandas as pd
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@dataset{gyodi_kristof_2021_4446043,
+  author       = {Gyódi, Kristóf and
+                  Nawaro, Łukasz},
+  title        = {{Determinants of Airbnb prices in European cities: 
+                   A spatial econometrics approach (Supplementary
+                   Material)}},
+  month        = jan,
+  year         = 2021,
+  note         = {{This research was supported by National Science 
+                   Centre, Poland: Project number 2017/27/N/HS4/00951}},
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.4446043},
+  url          = {https://doi.org/10.5281/zenodo.4446043}
+}"""
+
+_DESCRIPTION = """
+"""
+
+_CITIES = [
+    "Amsterdam",
+    "Athens",
+    "Barcelona",
+    "Berlin",
+    "Budapest",
+    "Lisbon",
+    "London",
+    "Paris",
+    "Rome",
+    "Vienna"
+]
+
+_BASE_URL = "https://zenodo.org/record/4446043/files/"
+_URL_TEMPLATE = _BASE_URL + "{city}_{day_type}.csv"
+
+class DayType(str, Enum):
+    WEEKDAYS = "weekdays"
+    WEEKENDS = "weekends"
+
+
+@dataclass
+class AirbnbFile:
+    """A file from the Airbnb dataset."""
+
+    city: str
+    day_type: DayType
+    @property
+    def url(self) -> str:
+        return _URL_TEMPLATE.format(city=self.city.lower(), day_type=self.day_type.value)
+
+
+
+class AirbnbConfig(datasets.BuilderConfig):
+    """BuilderConfig for Airbnb."""
+
+    def __init__(self, files: List[AirbnbFile], **kwargs):
+        """BuilderConfig for Airbnb.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(AirbnbConfig, self).__init__(**kwargs)
+        self.files = files
+
+_WEEKDAY_FILES = [AirbnbFile(city=city, day_type=DayType.WEEKDAYS) for city in _CITIES]
+_WEEKEND_FILES = [AirbnbFile(city=city, day_type=DayType.WEEKENDS) for city in _CITIES]
+
+class Airbnb(datasets.GeneratorBasedBuilder):
+    """"""
+
+    BUILDER_CONFIGS = [
+        AirbnbConfig(
+            name=DayType.WEEKDAYS.value,
+            files=_WEEKDAY_FILES,
+        ),
+        AirbnbConfig(
+            name=DayType.WEEKENDS.value,
+            files=_WEEKEND_FILES,
+        ),
+        AirbnbConfig(
+            name="all",
+            files=_WEEKDAY_FILES + _WEEKEND_FILES,
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+                {
+                    "_id": datasets.Value("string"),
+                    "city": datasets.Value("string"),
+                    "realSum": datasets.Value(dtype="float64"),
+                    "room_type": datasets.Value(dtype="string"),
+                    "room_shared": datasets.Value(dtype="bool"),
+                    "room_private": datasets.Value(dtype="bool"),
+                    "person_capacity": datasets.Value(dtype="float64"),
+                    "host_is_superhost": datasets.Value(dtype="bool"),
+                    "multi": datasets.Value(dtype="int64"),
+                    "biz": datasets.Value(dtype="int64"),
+                    "cleanliness_rating": datasets.Value(dtype="float64"),
+                    "guest_satisfaction_overall": datasets.Value(dtype="float64"),
+                    "bedrooms": datasets.Value(dtype="int64"),
+                    "dist": datasets.Value(dtype="float64"),
+                    "metro_dist": datasets.Value(dtype="float64"),
+                    "attr_index": datasets.Value(dtype="float64"),
+                    "attr_index_norm": datasets.Value(dtype="float64"),
+                    "rest_index": datasets.Value(dtype="float64"),
+                    "rest_index_norm": datasets.Value(dtype="float64"),
+                    "lng": datasets.Value(dtype="float64"),
+                    "lat": datasets.Value(dtype="float64")
+                })
+        if self.config.name == "all":
+            features["day_type"] = datasets.Value(dtype="string")
+        
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage="https://zenodo.org/record/4446043#.ZEV8d-zMI-R",
+            citation=_CITATION
+        )
+
+    def _split_generators(self, dl_manager):
+        config_files: List[AirbnbFile] = self.config.files
+        urls = [file.url for file in config_files]
+        downloaded_files = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"paths": downloaded_files})
+        ]
+
+    def _generate_examples(self, paths: List[str]):
+        _id = 0
+        config_files: List[AirbnbFile] = self.config.files
+        include_day_type = self.config.name == "all"
+        for file, path in zip(config_files, paths):
+            logger.info("generating examples from = %s", path)
+            df = pd.read_csv(path, index_col=0, header=0)
+            for row in df.itertuples():
+                city = file.city
+                data = {
+                    "_id": _id,
+                    "city": city,
+                    "realSum": row.realSum,
+                    "room_type": row.room_type,
+                    "room_shared": row.room_shared,
+                    "room_private": row.room_private,
+                    "person_capacity": row.person_capacity,
+                    "host_is_superhost": row.host_is_superhost,
+                    "multi": row.multi,
+                    "biz": row.biz,
+                    "cleanliness_rating": row.cleanliness_rating,
+                    "guest_satisfaction_overall": row.guest_satisfaction_overall,
+                    "bedrooms": row.bedrooms,
+                    "dist": row.dist,
+                    "metro_dist": row.metro_dist,
+                    "attr_index": row.attr_index,
+                    "attr_index_norm": row.attr_index_norm,
+                    "rest_index": row.rest_index,
+                    "rest_index_norm": row.rest_index_norm,
+                    "lng": row.lng,
+                    "lat": row.lat
+                }
+                if include_day_type:
+                    data["day_type"] = file.day_type.value
+                yield _id, data
+                _id += 1