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