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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

from os.path import exists
from os import makedirs, remove
import tarfile

import numpy as np
import logging

from .base import get_data_home
from .base import _fetch_remote
from .base import _pkl_filepath
from .base import RemoteFileMetadata
from ..utils import Bunch
from ..externals import joblib

The original data can be found at:

http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz

ARCHIVE = RemoteFileMetadata(
filename='cal_housing.tgz',
url='https://ndownloader.figshare.com/files/5976036',
checksum=('aaa5c9a6afe2225cc2aed2723682ae40'
'3280c4a3695a2ddda4ffb5d8215ea681'))

Grab the module-level docstring to use as a description of the

dataset

MODULE_DOCS = doc

logger = logging.getLogger(name)

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, 'cal_housing.pkz')
if not exists(filepath):
    if not download_if_missing:
        raise IOError("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

return Bunch(data=data,
             target=target,
             feature_names=feature_names,
             DESCR=MODULE_DOCS)`

Scikit-learn 0.19.1 release #6809

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