117 lines
3.4 KiB
Python
117 lines
3.4 KiB
Python
"""Forest covertype dataset.
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A classic dataset for classification benchmarks, featuring categorical and
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real-valued features.
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The dataset page is available from UCI Machine Learning Repository
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http://archive.ics.uci.edu/ml/datasets/Covertype
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Courtesy of Jock A. Blackard and Colorado State University.
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"""
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# Author: Lars Buitinck
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# Peter Prettenhofer <peter.prettenhofer@gmail.com>
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# License: BSD 3 clause
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from gzip import GzipFile
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from io import BytesIO
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import logging
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from os.path import exists, join
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try:
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from urllib2 import urlopen
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except ImportError:
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from urllib.request import urlopen
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import numpy as np
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from .base import get_data_home
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from .base import Bunch
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from .base import _pkl_filepath
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from ..utils.fixes import makedirs
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from ..externals import joblib
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from ..utils import check_random_state
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URL = ('http://archive.ics.uci.edu/ml/'
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'machine-learning-databases/covtype/covtype.data.gz')
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logger = logging.getLogger()
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def fetch_covtype(data_home=None, download_if_missing=True,
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random_state=None, shuffle=False):
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"""Load the covertype dataset, downloading it if necessary.
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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 : string, optional
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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 : boolean, default=True
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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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random_state : int, RandomState instance or None, optional (default=None)
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Random state for shuffling the dataset.
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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shuffle : bool, default=False
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Whether to shuffle dataset.
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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 : numpy array of shape (581012, 54)
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Each row corresponds to the 54 features in the dataset.
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dataset.target : numpy array of shape (581012,)
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Each value corresponds to one of the 7 forest covertypes with values
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ranging between 1 to 7.
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dataset.DESCR : string
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Description of the forest covertype dataset.
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"""
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data_home = get_data_home(data_home=data_home)
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covtype_dir = join(data_home, "covertype")
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samples_path = _pkl_filepath(covtype_dir, "samples")
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targets_path = _pkl_filepath(covtype_dir, "targets")
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available = exists(samples_path)
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if download_if_missing and not available:
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makedirs(covtype_dir, exist_ok=True)
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logger.warning("Downloading %s" % URL)
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f = BytesIO(urlopen(URL).read())
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Xy = np.genfromtxt(GzipFile(fileobj=f), delimiter=',')
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X = Xy[:, :-1]
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y = Xy[:, -1].astype(np.int32)
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joblib.dump(X, samples_path, compress=9)
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joblib.dump(y, targets_path, compress=9)
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try:
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X, y
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except NameError:
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X = joblib.load(samples_path)
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y = joblib.load(targets_path)
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if shuffle:
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ind = np.arange(X.shape[0])
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rng = check_random_state(random_state)
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rng.shuffle(ind)
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X = X[ind]
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y = y[ind]
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return Bunch(data=X, target=y, DESCR=__doc__)
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