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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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
# import csv
import json
import os
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
# _URLS = {
# "home_value_forecasts": "https://files.zillowstatic.com/research/public_csvs/zhvf_growth/Metro_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv",
# # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
# }
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class NewDataset(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'home_value_forecasts')
# data = datasets.load_dataset('my_dataset', 'second_domain')
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="home_value_forecasts",
version=VERSION,
description="This part of my dataset covers a first domain",
),
datasets.BuilderConfig(
name="new_constructions",
version=VERSION,
description="This part of my dataset covers a second domain",
),
datasets.BuilderConfig(
name="for_sale_listings",
version=VERSION,
description="This part of my dataset covers a second domain",
),
datasets.BuilderConfig(
name="rentals",
version=VERSION,
description="This part of my dataset covers a second domain",
),
]
DEFAULT_CONFIG_NAME = "home_value_forecasts" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
if (
self.config.name == "home_value_forecasts"
): # This is the name of the configuration selected in BUILDER_CONFIGS above
features = datasets.Features(
{
"RegionID": datasets.Value(dtype="string", id="RegionID"),
"SizeRank": datasets.Value(dtype="int32", id="SizeRank"),
"RegionName": datasets.Value(dtype="string", id="RegionName"),
"RegionType": datasets.Value(dtype="string", id="RegionType"),
"State": datasets.Value(dtype="string", id="State"),
"City": datasets.Value(dtype="string", id="City"),
"Metro": datasets.Value(dtype="string", id="Metro"),
"County": datasets.Value(dtype="string", id="County"),
"BaseDate": datasets.Value(dtype="string", id="BaseDate"),
"Month Over Month % (Smoothed)": datasets.Value(
dtype="float32", id="Month Over Month % (Smoothed)"
),
"Quarter Over Quarter % (Smoothed)": datasets.Value(
dtype="float32", id="Month Over Month % (Smoothed)"
),
"Year Over Year % (Smoothed)": datasets.Value(
dtype="float32", id="Month Over Month % (Smoothed)"
),
"Month Over Month % (Raw)": datasets.Value(
dtype="float32", id="Month Over Month % (Smoothed)"
),
"Quarter Over Quarter % (Raw)": datasets.Value(
dtype="float32", id="Month Over Month % (Smoothed)"
),
"Year Over Year % (Raw)": datasets.Value(
dtype="float32", id="Month Over Month % (Smoothed)"
),
# These are the features of your dataset like images, labels ...
}
)
elif self.config.name == "new_constructions":
features = datasets.Features(
{
"Region ID": datasets.Value(dtype="string", id="Region ID"),
"Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
"Region": datasets.Value(dtype="string", id="Region"),
"Region Type": datasets.Value(dtype="string", id="Region Type"),
"State": datasets.Value(dtype="string", id="State"),
"Home Type": datasets.Value(dtype="string", id="Home Type"),
"Date": datasets.Value(dtype="string", id="Date"),
"Sale Price": datasets.Value(dtype="float32", id="Sale Price"),
"Sale Price per Sqft": datasets.Value(
dtype="float32", id="Sale Price per Sqft"
),
"Count": datasets.Value(dtype="int32", id="Count"),
# These are the features of your dataset like images, labels ...
}
)
elif self.config.name == "for_sale_listings":
features = datasets.Features(
{
"Region ID": datasets.Value(dtype="string", id="Region ID"),
"Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
"Region": datasets.Value(dtype="string", id="Region"),
"Region Type": datasets.Value(dtype="string", id="Region Type"),
"State": datasets.Value(dtype="string", id="State"),
"Home Type": datasets.Value(dtype="string", id="Home Type"),
"Date": datasets.Value(dtype="string", id="Date"),
"Median Listing Price": datasets.Value(
dtype="float32", id="Median Listing Price"
),
"Median Listing Price (Smoothed)": datasets.Value(
dtype="float32", id="Median Listing Price (Smoothed)"
),
"New Listings": datasets.Value(dtype="int32", id="New Listings"),
"New Listings (Smoothed)": datasets.Value(
dtype="int32", id="New Listings (Smoothed)"
),
"New Pending (Smoothed)": datasets.Value(
dtype="int32", id="New Pending (Smoothed)"
),
"New Pending": datasets.Value(dtype="int32", id="New Pending"),
# These are the features of your dataset like images, labels ...
}
)
elif self.config.name == "rentals":
features = datasets.Features(
{
"Region ID": datasets.Value(dtype="string", id="Region ID"),
"Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
"Region": datasets.Value(dtype="string", id="Region"),
"Region Type": datasets.Value(dtype="string", id="Region Type"),
"State": datasets.Value(dtype="string", id="State"),
"Home Type": datasets.Value(dtype="string", id="Home Type"),
"Date": datasets.Value(dtype="string", id="Date"),
"Rent (Smoothed)": datasets.Value(
dtype="float32", id="Rent (Smoothed)"
),
"Rent (Smoothed) (Seasonally Adjusted)": datasets.Value(
dtype="float32", id="Rent (Smoothed) (Seasonally Adjusted)"
),
# These are the features of your dataset like images, labels ...
}
)
# else: # This is an example to show how to have different features for "home_value_forecasts" and "second_domain"
# features = datasets.Features(
# {
# "sentence": datasets.Value("string"),
# "option2": datasets.Value("string"),
# "second_domain_answer": datasets.Value("string"),
# # These are the features of your dataset like images, labels ...
# }
# )
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
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
# urls = _URLS[self.config.name]
# data_dir = dl_manager.download_and_extract(urls)
# file_train = dl_manager.download(os.path.join('./data/home_value_forecasts', "Metro_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_month.csv"))
file_path = os.path.join("processed", self.config.name, "final.jsonl")
# print('*********************')
# print(file_path)
file_train = dl_manager.download(file_path)
# file_test = dl_manager.download(os.path.join(self.config.name, "test.csv"))
# file_eval = dl_manager.download(os.path.join(self.config.name, "valid.csv"))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": file_train, # os.path.join(data_dir, "train.jsonl"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": file_train, # os.path.join(data_dir, "dev.jsonl"),
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": file_train, # os.path.join(data_dir, "test.jsonl"),
"split": "test",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
if self.config.name == "home_value_forecasts":
# Yields examples as (key, example) tuples
yield key, {
"RegionID": data["RegionID"],
"SizeRank": data["SizeRank"],
"RegionName": data["RegionName"],
"RegionType": data["RegionType"],
"State": data["State"],
"City": data["City"],
"Metro": data["Metro"],
"County": data["County"],
"BaseDate": data["BaseDate"],
"Month Over Month % (Smoothed)": data[
"Month Over Month % (Smoothed)"
],
"Quarter Over Quarter % (Smoothed)": data[
"Quarter Over Quarter % (Smoothed)"
],
"Year Over Year % (Smoothed)": data[
"Year Over Year % (Smoothed)"
],
"Month Over Month % (Raw)": data["Month Over Month % (Raw)"],
"Quarter Over Quarter % (Raw)": data[
"Quarter Over Quarter % (Raw)"
],
"Year Over Year % (Raw)": data["Year Over Year % (Raw)"],
# "answer": "" if split == "test" else data["answer"],
}
elif self.config.name == "new_constructions":
# Yields examples as (key, example) tuples
yield key, {
"Region ID": data["Region ID"],
"Size Rank": data["Size Rank"],
"Region": data["Region"],
"Region Type": data["Region Type"],
"State": data["State"],
"Home Type": data["Home Type"],
"Date": data["Date"],
"Sale Price": data["Sale Price"],
"Sale Price per Sqft": data["Sale Price per Sqft"],
"Count": data["Count"],
# "answer": "" if split == "test" else data["answer"],
}
elif self.config.name == "for_sale_listings":
# Yields examples as (key, example) tuples
yield key, {
"Region ID": data["Region ID"],
"Size Rank": data["Size Rank"],
"Region": data["Region"],
"Region Type": data["Region Type"],
"State": data["State"],
"Home Type": data["Home Type"],
"Date": data["Date"],
"Median Listing Price": data["Median Listing Price"],
"Median Listing Price (Smoothed)": data[
"Median Listing Price (Smoothed)"
],
"New Listings": data["New Listings"],
"New Listings (Smoothed)": data["New Listings (Smoothed)"],
"New Pending (Smoothed)": data["New Pending (Smoothed)"],
"New Pending": data["New Pending"],
# "answer": "" if split == "test" else data["answer"],
}
elif self.config.name == "rentals":
# Yields examples as (key, example) tuples
yield key, {
"Region ID": data["Region ID"],
"Size Rank": data["Size Rank"],
"Region": data["Region"],
"Region Type": data["Region Type"],
"State": data["State"],
"Home Type": data["Home Type"],
"Date": data["Date"],
"Rent (Smoothed)": data["Rent (Smoothed)"],
"Rent (Smoothed) (Seasonally Adjusted)": data[
"Rent (Smoothed) (Seasonally Adjusted)"
],
# "answer": "" if split == "test" else data["answer"],
}
# else:
# yield key, {
# "sentence": data["sentence"],
# "option2": data["option2"],
# "second_domain_answer": (
# "" if split == "test" else data["second_domain_answer"]
# ),
# }
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