# 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", | |
), | |
] | |
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"), | |
"Median Sale Price": datasets.Value( | |
dtype="float32", id="Size Rank" | |
), | |
"Median Sale Price per Sqft": datasets.Value( | |
dtype="float32", id="Size Rank" | |
), | |
"Sales Count": datasets.Value(dtype="int32", id="Size Rank"), | |
# 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"], | |
"Median Sale Price": data["Size Rank"], | |
"Median Sale Price per Sqft": data["Size Rank"], | |
"Sales Count": data["Size Rank"], | |
# "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"] | |
# ), | |
# } | |