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#
# 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.
import csv
import json
import os
from typing import List
import datasets
import logging
import pandas as pd
_CITATION = """
@InProceedings{huggingface:dataset,
title = {Seattle Housing Permits Dataset},
author={Xinyan(Hathaway) Liu
},
year={2024}
}
"""
_DESCRIPTION = """
This typical dataset contains all the building permits issued or in progress
within the city of Seattle starting from 1990 to recent, and this dataset is
still updating as time flows. Information includes permit records urls,
detailed address, and building costs etc.
"""
_HOMEPAGE = "https://data.seattle.gov/Permitting/Building-Permits/76t5-zqzr/about_data"
_LICENSE = " http://www.seattle.gov/sdci"
_URL = "https://data.seattle.gov/Permitting/Building-Permits/76t5-zqzr/about_data"
_URLS = {
"train": "https://github.com/HathawayLiu/Housing_dataset/raw/main/housing_train_dataset.csv",
"test": "https://github.com/HathawayLiu/Housing_dataset/raw/main/housing_test_dataset.csv",
}
class HousingDataset(datasets.GeneratorBasedBuilder):
"""This dataset contains all building permits issued or in progress within
the city of Seattle. It includes the original columns in the datasets, with
new added columns for corresponding neighborhood district and parking lot
near by each housing."""
_URLS = _URLS
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
# columns from original dataset
"PermitNum": datasets.Value("string"),
"PermitClass": datasets.Value("string"),
"PermitClassMapped": datasets.Value("string"),
"PermitTypeMapped": datasets.Value("string"),
"PermitTypeDesc": datasets.Value("string"),
"Description": datasets.Value("string"),
"HousingUnits": datasets.Value("int64"),
"HousingUnitsRemoved": datasets.Value("int64"),
"HousingUnitsAdded": datasets.Value("int64"),
"EstProjectCost": datasets.Value("float32"),
"AppliedDate": datasets.Value("string"),
"IssuedDate": datasets.Value("string"),
"ExpiresDate": datasets.Value("string"),
"CompletedDate": datasets.Value("string"),
"StatusCurrent": datasets.Value("string"),
"RelatedMup": datasets.Value("string"),
"OriginalAddress1": datasets.Value("string"),
"OriginalCity": datasets.Value("string"),
"OriginalState": datasets.Value("string"),
"OriginalZip": datasets.Value("int64"),
"ContractorCompanyName": datasets.Value("string"),
"Link": datasets.Value("string"),
"Latitude": datasets.Value("float32"),
"Longitude": datasets.Value("float32"),
"Location1": datasets.Value("string"),
# new added columns below
"NeighborDistrict": datasets.Value("string")
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
urls = self._URLS
downloaded_files = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logging.info("generating examples from = %s", filepath)
with open(filepath) as f:
housing_df = pd.read_csv(f)
housing_df['EstProjectCost'] = housing_df["EstProjectCost"].replace('NA', 0)
housing_df.dropna(subset = ['Latitude'], inplace = True)
housing_df.dropna(subset = ['OriginalZip'], inplace = True)
housing_df['Latitude'] = housing_df['Latitude'].astype(float)
housing_df['Longitude'] = housing_df['Longitude'].astype(float)
# Iterating through each row to generate examples
for index, row in housing_df.iterrows():
yield index, {
"PermitNum": row.get("PermitNum", ""),
"PermitClass": row.get("PermitClass", ""),
"PermitClassMapped": row.get("PermitClassMapped", ""),
"PermitTypeMapped": row.get("PermitTypeMapped", ""),
"PermitTypeDesc": row.get("PermitTypeDesc", ""),
"Description": row.get("Description", ""),
"HousingUnits": int(row.get("HousingUnits", "")),
"HousingUnitsRemoved": int(row.get("HousingUnitsRemoved", "")),
"HousingUnitsAdded": int(row.get("HousingUnitsAdded", "")),
"EstProjectCost": float(row.get("EstProjectCost", "")),
"AppliedDate": str(row.get("AppliedDate", "")),
"IssuedDate": str(row.get("IssuedDate", "")),
"ExpiresDate": str(row.get("ExpiresDate", "")),
"CompletedDate": str(row.get("CompletedDate", "")),
"StatusCurrent": row.get("StatusCurrent", ""),
"RelatedMup": row.get("RelatedMup", ""),
"OriginalAddress1": row.get("OriginalAddress1", ""),
"OriginalCity": row.get("OriginalCity", ""),
"OriginalState": row.get("OriginalState", ""),
"OriginalZip": int(row.get("OriginalZip", "")),
"ContractorCompanyName": row.get("ContractorCompanyName", ""),
"Link": row.get("Link", ""),
"Latitude": row["Latitude"],
"Longitude": row["Longitude"],
"Location1": str(row["Latitude"]) + ", " + str(row["Longitude"]),
"NeighborDistrict": row.get("NeighborDistrict", "")
} |