139 lines
4.2 KiB
Python
139 lines
4.2 KiB
Python
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"""California housing dataset.
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The original database is available from StatLib
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http://lib.stat.cmu.edu/datasets/
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The data contains 20,640 observations on 9 variables.
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This dataset contains the average house value as target variable
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and the following input variables (features): average income,
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housing average age, average rooms, average bedrooms, population,
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average occupation, latitude, and longitude in that order.
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References
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----------
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Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
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Statistics and Probability Letters, 33 (1997) 291-297.
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"""
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# Authors: Peter Prettenhofer
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# License: BSD 3 clause
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from os.path import exists
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from os import makedirs, remove
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import tarfile
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import numpy as np
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import logging
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from .base import get_data_home
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from .base import _fetch_remote
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from .base import _pkl_filepath
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from .base import RemoteFileMetadata
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from ..utils import Bunch
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from ..externals import joblib
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# The original data can be found at:
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# http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz
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ARCHIVE = RemoteFileMetadata(
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filename='cal_housing.tgz',
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url='https://ndownloader.figshare.com/files/5976036',
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checksum=('aaa5c9a6afe2225cc2aed2723682ae40'
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'3280c4a3695a2ddda4ffb5d8215ea681'))
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# Grab the module-level docstring to use as a description of the
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# dataset
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MODULE_DOCS = __doc__
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logger = logging.getLogger(__name__)
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def fetch_california_housing(data_home=None, download_if_missing=True):
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"""Loader for the California housing dataset from StatLib.
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Read more in the :ref:`User Guide <datasets>`.
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Parameters
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----------
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data_home : optional, default: None
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Specify another download and cache folder for the datasets. By default
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all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
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download_if_missing : optional, True by default
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If False, raise a IOError if the data is not locally available
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instead of trying to download the data from the source site.
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Returns
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-------
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dataset : dict-like object with the following attributes:
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dataset.data : ndarray, shape [20640, 8]
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Each row corresponding to the 8 feature values in order.
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dataset.target : numpy array of shape (20640,)
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Each value corresponds to the average house value in units of 100,000.
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dataset.feature_names : array of length 8
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Array of ordered feature names used in the dataset.
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dataset.DESCR : string
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Description of the California housing dataset.
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Notes
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------
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This dataset consists of 20,640 samples and 9 features.
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"""
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data_home = get_data_home(data_home=data_home)
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if not exists(data_home):
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makedirs(data_home)
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filepath = _pkl_filepath(data_home, 'cal_housing.pkz')
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if not exists(filepath):
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if not download_if_missing:
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raise IOError("Data not found and `download_if_missing` is False")
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logger.info('Downloading Cal. housing from {} to {}'.format(
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ARCHIVE.url, data_home))
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archive_path = _fetch_remote(ARCHIVE, dirname=data_home)
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with tarfile.open(mode="r:gz", name=archive_path) as f:
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cal_housing = np.loadtxt(
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f.extractfile('CaliforniaHousing/cal_housing.data'),
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delimiter=',')
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# Columns are not in the same order compared to the previous
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# URL resource on lib.stat.cmu.edu
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columns_index = [8, 7, 2, 3, 4, 5, 6, 1, 0]
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cal_housing = cal_housing[:, columns_index]
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joblib.dump(cal_housing, filepath, compress=6)
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remove(archive_path)
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else:
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cal_housing = joblib.load(filepath)
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feature_names = ["MedInc", "HouseAge", "AveRooms", "AveBedrms",
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"Population", "AveOccup", "Latitude", "Longitude"]
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target, data = cal_housing[:, 0], cal_housing[:, 1:]
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# avg rooms = total rooms / households
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data[:, 2] /= data[:, 5]
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# avg bed rooms = total bed rooms / households
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data[:, 3] /= data[:, 5]
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# avg occupancy = population / households
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data[:, 5] = data[:, 4] / data[:, 5]
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# target in units of 100,000
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target = target / 100000.0
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return Bunch(data=data,
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target=target,
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feature_names=feature_names,
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DESCR=MODULE_DOCS)
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