# 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="second_domain", # 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 ... } ) # 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"], } # else: # yield key, { # "sentence": data["sentence"], # "option2": data["option2"], # "second_domain_answer": ( # "" if split == "test" else data["second_domain_answer"] # ), # }