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