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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
import logging
from os.path import dirname, exists, join
from os import remove
import numpy as np
from .base import get_data_home
from .base import _fetch_remote
from .base import RemoteFileMetadata
from ..utils import Bunch
from .base import _pkl_filepath
from ..utils.fixes import makedirs
from ..utils import _joblib
from ..utils import check_random_state
# The original data can be found in:
# http://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.data.gz
ARCHIVE = RemoteFileMetadata(
filename='covtype.data.gz',
url='https://ndownloader.figshare.com/files/5976039',
checksum=('614360d0257557dd1792834a85a1cdeb'
'fadc3c4f30b011d56afee7ffb5b15771'))
logger = logging.getLogger(__name__)
def fetch_covtype(data_home=None, download_if_missing=True,
random_state=None, shuffle=False, return_X_y=False):
"""Load the covertype dataset (classification).
Download it if necessary.
================= ============
Classes 7
Samples total 581012
Dimensionality 54
Features int
================= ============
Read more in the :ref:`User Guide <covtype_dataset>`.
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 (default)
Determines random number generation for dataset shuffling. Pass an int
for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
shuffle : bool, default=False
Whether to shuffle dataset.
return_X_y : boolean, default=False.
If True, returns ``(data.data, data.target)`` instead of a Bunch
object.
.. versionadded:: 0.20
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, target) : tuple if ``return_X_y`` is True
.. versionadded:: 0.20
"""
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:
if not exists(covtype_dir):
makedirs(covtype_dir)
logger.info("Downloading %s" % ARCHIVE.url)
archive_path = _fetch_remote(ARCHIVE, dirname=covtype_dir)
Xy = np.genfromtxt(GzipFile(filename=archive_path), delimiter=',')
# delete archive
remove(archive_path)
X = Xy[:, :-1]
y = Xy[:, -1].astype(np.int32)
_joblib.dump(X, samples_path, compress=9)
_joblib.dump(y, targets_path, compress=9)
elif not available and not download_if_missing:
raise IOError("Data not found and `download_if_missing` is False")
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]
module_path = dirname(__file__)
with open(join(module_path, 'descr', 'covtype.rst')) as rst_file:
fdescr = rst_file.read()
if return_X_y:
return X, y
return Bunch(data=X, target=y, DESCR=fdescr)
|
