-
-
Notifications
You must be signed in to change notification settings - Fork 25.3k
/
covtype.py
146 lines (109 loc) · 4.44 KB
/
covtype.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
"""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 ..externals 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)