zillow.py

# 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"]
                #         ),
                #     }