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"""California housing dataset.
The original database is available from StatLib
http://lib.stat.cmu.edu/datasets/
The data contains 20,640 observations on 9 variables.
This dataset contains the average house value as target variable
and the following input variables (features): average income,
housing average age, average rooms, average bedrooms, population,
average occupation, latitude, and longitude in that order.
References
----------
Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
Statistics and Probability Letters, 33 (1997) 291-297.
"""
# Authors: Peter Prettenhofer
# License: BSD 3 clause
import logging
import tarfile
from os import PathLike, makedirs, remove
from os.path import exists
import joblib
import numpy as np
from ..utils import Bunch
from ..utils._param_validation import validate_params
from . import get_data_home
from ._base import (
RemoteFileMetadata,
_convert_data_dataframe,
_fetch_remote,
_pkl_filepath,
load_descr,
)
# The original data can be found at:
# https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz
ARCHIVE = RemoteFileMetadata(
filename="cal_housing.tgz",
url="https://ndownloader.figshare.com/files/5976036",
checksum="aaa5c9a6afe2225cc2aed2723682ae403280c4a3695a2ddda4ffb5d8215ea681",
)
logger = logging.getLogger(__name__)
@validate_params(
{
"data_home": [str, PathLike, None],
"download_if_missing": ["boolean"],
"return_X_y": ["boolean"],
"as_frame": ["boolean"],
},
prefer_skip_nested_validation=True,
)
def fetch_california_housing(
*, data_home=None, download_if_missing=True, return_X_y=False, as_frame=False
):
"""Load the California housing dataset (regression).
============== ==============
Samples total 20640
Dimensionality 8
Features real
Target real 0.15 - 5.
============== ==============
Read more in the :ref:`User Guide <california_housing_dataset>`.
Parameters
----------
data_home : str or path-like, default=None
Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
download_if_missing : bool, default=True
If False, raise an OSError if the data is not locally available
instead of trying to download the data from the source site.
return_X_y : bool, default=False
If True, returns ``(data.data, data.target)`` instead of a Bunch
object.
.. versionadded:: 0.20
as_frame : bool, default=False
If True, the data is a pandas DataFrame including columns with
appropriate dtypes (numeric, string or categorical). The target is
a pandas DataFrame or Series depending on the number of target_columns.
.. versionadded:: 0.23
Returns
-------
dataset : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
data : ndarray, shape (20640, 8)
Each row corresponding to the 8 feature values in order.
If ``as_frame`` is True, ``data`` is a pandas object.
target : numpy array of shape (20640,)
Each value corresponds to the average
house value in units of 100,000.
If ``as_frame`` is True, ``target`` is a pandas object.
feature_names : list of length 8
Array of ordered feature names used in the dataset.
DESCR : str
Description of the California housing dataset.
frame : pandas DataFrame
Only present when `as_frame=True`. DataFrame with ``data`` and
``target``.
.. versionadded:: 0.23
(data, target) : tuple if ``return_X_y`` is True
A tuple of two ndarray. The first containing a 2D array of
shape (n_samples, n_features) with each row representing one
sample and each column representing the features. The second
ndarray of shape (n_samples,) containing the target samples.
.. versionadded:: 0.20
Notes
-----
This dataset consists of 20,640 samples and 9 features.
Examples
--------
>>> from sklearn.datasets import fetch_california_housing
>>> housing = fetch_california_housing()
>>> print(housing.data.shape, housing.target.shape)
(20640, 8) (20640,)
>>> print(housing.feature_names[0:6])
['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup']
"""
data_home = get_data_home(data_home=data_home)
if not exists(data_home):
makedirs(data_home)
filepath = _pkl_filepath(data_home, "cal_housing.pkz")
if not exists(filepath):
if not download_if_missing:
raise OSError("Data not found and `download_if_missing` is False")
logger.info(
"Downloading Cal. housing from {} to {}".format(ARCHIVE.url, data_home)
)
archive_path = _fetch_remote(ARCHIVE, dirname=data_home)
with tarfile.open(mode="r:gz", name=archive_path) as f:
cal_housing = np.loadtxt(
f.extractfile("CaliforniaHousing/cal_housing.data"), delimiter=","
)
# Columns are not in the same order compared to the previous
# URL resource on lib.stat.cmu.edu
columns_index = [8, 7, 2, 3, 4, 5, 6, 1, 0]
cal_housing = cal_housing[:, columns_index]
joblib.dump(cal_housing, filepath, compress=6)
remove(archive_path)
else:
cal_housing = joblib.load(filepath)
feature_names = [
"MedInc",
"HouseAge",
"AveRooms",
"AveBedrms",
"Population",
"AveOccup",
"Latitude",
"Longitude",
]
target, data = cal_housing[:, 0], cal_housing[:, 1:]
# avg rooms = total rooms / households
data[:, 2] /= data[:, 5]
# avg bed rooms = total bed rooms / households
data[:, 3] /= data[:, 5]
# avg occupancy = population / households
data[:, 5] = data[:, 4] / data[:, 5]
# target in units of 100,000
target = target / 100000.0
descr = load_descr("california_housing.rst")
X = data
y = target
frame = None
target_names = [
"MedHouseVal",
]
if as_frame:
frame, X, y = _convert_data_dataframe(
"fetch_california_housing", data, target, feature_names, target_names
)
if return_X_y:
return X, y
return Bunch(
data=X,
target=y,
frame=frame,
target_names=target_names,
feature_names=feature_names,
DESCR=descr,
)
|
