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"""California housing dataset.
The original database is available from StatLib
http://lib.stat.cmu.edu/
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
from io import BytesIO
from os.path import exists
from os import makedirs
import tarfile
try:
# Python 2
from urllib2 import urlopen
except ImportError:
# Python 3+
from urllib.request import urlopen
import numpy as np
from .base import get_data_home, Bunch
from .base import _pkl_filepath
from ..externals import joblib
DATA_URL = "http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz"
TARGET_FILENAME = "cal_housing.pkz"
# Grab the module-level docstring to use as a description of the
# dataset
MODULE_DOCS = __doc__
def fetch_california_housing(data_home=None, download_if_missing=True):
"""Loader for the California housing dataset from StatLib.
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
data_home : optional, 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: optional, True by default
If False, raise a IOError if the data is not locally available
instead of trying to download the data from the source site.
Returns
-------
dataset : dict-like object with the following attributes:
dataset.data : ndarray, shape [20640, 8]
Each row corresponding to the 8 feature values in order.
dataset.target : numpy array of shape (20640,)
Each value corresponds to the average house value in units of 100,000.
dataset.feature_names : array of length 8
Array of ordered feature names used in the dataset.
dataset.DESCR : string
Description of the California housing dataset.
Notes
------
This dataset consists of 20,640 samples and 9 features.
"""
data_home = get_data_home(data_home=data_home)
if not exists(data_home):
makedirs(data_home)
filepath = _pkl_filepath(data_home, TARGET_FILENAME)
if not exists(filepath):
print('downloading Cal. housing from %s to %s' % (DATA_URL, data_home))
archive_fileobj = BytesIO(urlopen(DATA_URL).read())
fileobj = tarfile.open(
mode="r:gz",
fileobj=archive_fileobj).extractfile(
'CaliforniaHousing/cal_housing.data')
cal_housing = np.loadtxt(fileobj, 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)
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
return Bunch(data=data,
target=target,
feature_names=feature_names,
DESCR=MODULE_DOCS)
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