File size: 24,981 Bytes
51f8985 e10c179 51f8985 e10c179 51f8985 a1911c7 51f8985 416395d a1911c7 416395d 6c39add 3355ad5 ebc591b 51f8985 e10c179 51f8985 a1911c7 51f8985 e10c179 416395d e10c179 416395d a1911c7 416395d 2be48a5 e5d0972 dea1e23 2be48a5 51f8985 6c39add 3355ad5 ebc591b f372b60 51f8985 4aa9533 01b2f0e e42b122 51f8985 ebc591b 51f8985 ebc591b 51f8985 a1911c7 51f8985 e10c179 f2c7521 416395d e10c179 416395d a1911c7 416395d 2be48a5 51f8985 6c39add 3355ad5 ebc591b f372b60 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 |
# 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 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.
"""
_HOMEPAGE = "https://huggingface.co/datasets/misikoff/zillow"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
class Zillow(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="home_values_forecasts",
version=VERSION,
description="This part of my dataset covers a first domain",
),
datasets.BuilderConfig(
name="new_construction",
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",
),
datasets.BuilderConfig(
name="sales",
version=VERSION,
description="This part of my dataset covers a second domain",
),
datasets.BuilderConfig(
name="home_values",
version=VERSION,
description="This part of my dataset covers a second domain",
),
datasets.BuilderConfig(
name="days_on_market",
version=VERSION,
description="This part of my dataset covers a second domain",
),
]
DEFAULT_CONFIG_NAME = ""
def _info(self):
if self.config.name == "home_values_forecasts":
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"),
"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"),
"Date": datasets.Value(dtype="string", id="Date"),
"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)"
),
}
)
elif self.config.name == "new_construction":
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="Median Sale Price"
),
"Median Sale Price per Sqft": datasets.Value(
dtype="float32", id="Sale Price per Sqft"
),
"Sales Count": datasets.Value(dtype="int32", id="Sales Count"),
}
)
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"),
}
)
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)"
),
}
)
elif self.config.name == "sales":
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"),
"Mean Sale to List Ratio (Smoothed)": datasets.Value(
dtype="float32", id="Mean Sale to List Ratio (Smoothed)"
),
"Median Sale to List Ratio": datasets.Value(
dtype="float32", id="Median Sale to List Ratio"
),
"Median Sale Price": datasets.Value(
dtype="float32", id="Median Sale Price"
),
"% Sold Below List (Smoothed)": datasets.Value(
dtype="float32", id="% Sold Below List (Smoothed)"
),
"Median Sale Price (Smoothed) (Seasonally Adjusted)": datasets.Value(
dtype="float32",
id="Median Sale Price (Smoothed) (Seasonally Adjusted)",
),
"% Sold Below List": datasets.Value(
dtype="float32", id="% Sold Below List"
),
"Median Sale Price (Smoothed)": datasets.Value(
dtype="float32", id="Median Sale Price (Smoothed)"
),
"Median Sale to List Ratio (Smoothed)": datasets.Value(
dtype="float32", id="Median Sale to List Ratio (Smoothed)"
),
"% Sold Above List": datasets.Value(
dtype="float32", id="% Sold Above List"
),
"Nowcast": datasets.Value(dtype="float32", id="Nowcast"),
"Mean Sale to List Ratio": datasets.Value(
dtype="float32", id="Mean Sale to List Ratio"
),
"% Sold Above List (Smoothed)": datasets.Value(
dtype="float32", id="% Sold Above List (Smoothed)"
),
}
)
elif self.config.name == "home_values":
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"),
"Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value(
dtype="float32",
id="Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)",
),
"Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value(
dtype="float32",
id="Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)",
),
"Top Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value(
dtype="float32",
id="Top Tier ZHVI (Smoothed) (Seasonally Adjusted)",
),
"ZHVI": datasets.Value(dtype="float32", id="ZHVI"),
"Mid Tier ZHVI": datasets.Value(
dtype="float32", id="Mid Tier ZHVI"
),
}
)
elif self.config.name == "days_on_market":
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"),
"Mean Listings Price Cut Amount (Smoothed)": datasets.Value(
dtype="float32", id="Mean Listings Price Cut Amount (Smoothed)"
),
"Percent Listings Price Cut": datasets.Value(
dtype="float32", id="Percent Listings Price Cut"
),
"Mean Listings Price Cut Amount": datasets.Value(
dtype="float32", id="Mean Listings Price Cut Amount"
),
"Percent Listings Price Cut (Smoothed)": datasets.Value(
dtype="float32", id="Percent Listings Price Cut (Smoothed)"
),
"Median Days on Pending (Smoothed)": datasets.Value(
dtype="float32", id="Median Days on Pending (Smoothed)"
),
"Median Days on Pending": datasets.Value(
dtype="float32", id="Median Days on Pending"
),
}
)
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):
file_path = os.path.join("processed", self.config.name, "final1.jsonl")
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,
"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):
# 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_values_forecasts":
yield key, {
"Region ID": data["Region ID"],
"Size Rank": data["Size Rank"],
"Region": data["Region"],
"RegionType": data["RegionType"],
"State": data["State"],
"City": data["City"],
"Metro": data["Metro"],
"County": data["County"],
"Date": data["Date"],
"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)"],
}
elif self.config.name == "new_construction":
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["Median Sale Price"],
"Median Sale Price per Sqft": data[
"Median Sale Price per Sqft"
],
"Sales Count": data["Sales Count"],
}
elif self.config.name == "for_sale_listings":
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"],
}
elif self.config.name == "rentals":
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)"
],
}
elif self.config.name == "sales":
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"],
"Mean Sale to List Ratio (Smoothed)": data[
"Mean Sale to List Ratio (Smoothed)"
],
"Median Sale to List Ratio": data["Median Sale to List Ratio"],
"Median Sale Price": data["Median Sale Price"],
"% Sold Below List (Smoothed)": data[
"% Sold Below List (Smoothed)"
],
"Median Sale Price (Smoothed) (Seasonally Adjusted)": data[
"Median Sale Price (Smoothed) (Seasonally Adjusted)"
],
"% Sold Below List": data["% Sold Below List"],
"Median Sale Price (Smoothed)": data[
"Median Sale Price (Smoothed)"
],
"Median Sale to List Ratio (Smoothed)": data[
"Median Sale to List Ratio (Smoothed)"
],
"% Sold Above List": data["% Sold Above List"],
"Nowcast": data["Nowcast"],
"Mean Sale to List Ratio": data["Mean Sale to List Ratio"],
"% Sold Above List (Smoothed)": data[
"% Sold Above List (Smoothed)"
],
}
elif self.config.name == "home_values":
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"],
"Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[
"Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)"
],
"Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[
"Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)"
],
"Top Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[
"Top Tier ZHVI (Smoothed) (Seasonally Adjusted)"
],
"ZHVI": data["ZHVI"],
"Mid Tier ZHVI": data["Mid Tier ZHVI"],
}
elif self.config.name == "days_on_market":
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"],
"Mean Listings Price Cut Amount (Smoothed)": data[
"Mean Listings Price Cut Amount (Smoothed)"
],
"Percent Listings Price Cut": data[
"Percent Listings Price Cut"
],
"Mean Listings Price Cut Amount": data[
"Mean Listings Price Cut Amount"
],
"Percent Listings Price Cut (Smoothed)": data[
"Percent Listings Price Cut (Smoothed)"
],
"Median Days on Pending (Smoothed)": data[
"Median Days on Pending (Smoothed)"
],
"Median Days on Pending": data["Median Days on Pending"],
}
|