# 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"] # ), # }