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
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
|
"""
===========================
Covertype dataset benchmark
===========================
Benchmark stochastic gradient descent (SGD), Liblinear, and Naive Bayes, CART
(decision tree), RandomForest and Extra-Trees on the forest covertype dataset
of Blackard, Jock, and Dean [1]. The dataset comprises 581,012 samples. It is
low dimensional with 54 features and a sparsity of approx. 23%. Here, we
consider the task of predicting class 1 (spruce/fir). The classification
performance of SGD is competitive with Liblinear while being two orders of
magnitude faster to train::
[..]
Classification performance:
===========================
Classifier train-time test-time error-rate
--------------------------------------------
Liblinear 11.8977s 0.0285s 0.2305
GaussianNB 3.5931s 0.6645s 0.3633
SGD 0.2924s 0.0114s 0.2300
CART 39.9829s 0.0345s 0.0476
RandomForest 794.6232s 1.0526s 0.0249
Extra-Trees 1401.7051s 1.1181s 0.0230
The same task has been used in a number of papers including:
* `"SVM Optimization: Inverse Dependence on Training Set Size"
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.2112>`_
S. Shalev-Shwartz, N. Srebro - In Proceedings of ICML '08.
* `"Pegasos: Primal estimated sub-gradient solver for svm"
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.74.8513>`_
S. Shalev-Shwartz, Y. Singer, N. Srebro - In Proceedings of ICML '07.
* `"Training Linear SVMs in Linear Time"
<www.cs.cornell.edu/People/tj/publications/joachims_06a.pdf>`_
T. Joachims - In SIGKDD '06
[1] http://archive.ics.uci.edu/ml/datasets/Covertype
"""
from __future__ import division
print __doc__
# Author: Peter Prettenhoer <peter.prettenhofer@gmail.com>
# License: BSD Style.
# $Id$
from time import time
import os
import numpy as np
from optparse import OptionParser
from sklearn.svm import LinearSVC
from sklearn.linear_model import SGDClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn import metrics
op = OptionParser()
op.add_option("--classifiers",
dest="classifiers", default='liblinear,GaussianNB,SGD,CART',
help="comma-separated list of classifiers to benchmark. "
"default: %default. available: "
"liblinear,GaussianNB,SGD,CART,ExtraTrees,RandomForest")
op.print_help()
(opts, args) = op.parse_args()
if len(args) > 0:
op.error("this script takes no arguments.")
sys.exit(1)
######################################################################
## Download the data, if not already on disk
if not os.path.exists('covtype.data.gz'):
# Download the data
import urllib
print "Downloading data, Please Wait (11MB)..."
opener = urllib.urlopen(
'http://archive.ics.uci.edu/ml/'
'machine-learning-databases/covtype/covtype.data.gz')
open('covtype.data.gz', 'wb').write(opener.read())
######################################################################
## Load dataset
print("Loading dataset...")
import gzip
f = gzip.open('covtype.data.gz')
X = np.fromstring(f.read().replace(",", " "), dtype=np.float64, sep=" ",
count=-1)
X = X.reshape((581012, 55))
f.close()
# class 1 vs. all others.
y = np.ones(X.shape[0]) * -1
y[np.where(X[:, -1] == 1)] = 1
X = X[:, :-1]
######################################################################
## Create train-test split (as [Joachims, 2006])
print("Creating train-test split...")
idx = np.arange(X.shape[0])
np.random.seed(13)
np.random.shuffle(idx)
train_idx = idx[:522911]
test_idx = idx[522911:]
X_train = X[train_idx]
y_train = y[train_idx]
X_test = X[test_idx]
y_test = y[test_idx]
# free memory
del X
del y
######################################################################
## Standardize first 10 features (the numerical ones)
mean = X_train.mean(axis=0)
std = X_train.std(axis=0)
mean[10:] = 0.0
std[10:] = 1.0
X_train = (X_train - mean) / std
X_test = (X_test - mean) / std
######################################################################
## Print dataset statistics
print("")
print("Dataset statistics:")
print("===================")
print("%s %d" % ("number of features:".ljust(25),
X_train.shape[1]))
print("%s %d" % ("number of classes:".ljust(25),
np.unique(y_train).shape[0]))
print("%s %d (%d, %d)" % ("number of train samples:".ljust(25),
X_train.shape[0], np.sum(y_train == 1),
np.sum(y_train == -1)))
print("%s %d (%d, %d)" % ("number of test samples:".ljust(25),
X_test.shape[0], np.sum(y_test == 1),
np.sum(y_test == -1)))
classifiers = dict()
######################################################################
## Benchmark classifiers
def benchmark(clf):
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
err = metrics.zero_one(y_test, pred) / float(pred.shape[0])
return err, train_time, test_time
######################################################################
## Train Liblinear model
liblinear_parameters = {
'loss': 'l2',
'penalty': 'l2',
'C': 1000,
'dual': False,
'tol': 1e-3,
}
classifiers['liblinear'] = LinearSVC(**liblinear_parameters)
######################################################################
## Train GaussianNB model
classifiers['GaussianNB'] = GaussianNB()
######################################################################
## Train SGD model
sgd_parameters = {
'alpha': 0.001,
'n_iter': 2,
}
classifiers['SGD'] = SGDClassifier( **sgd_parameters)
######################################################################
## Train CART model
classifiers['CART'] = DecisionTreeClassifier(min_samples_split=5,
max_depth=None)
######################################################################
## Train RandomForest model
classifiers['RandomForest'] = RandomForestClassifier(n_estimators=20,
min_samples_split=5,
max_features=None,
max_depth=None)
######################################################################
## Train Extra-Trees model
classifiers['ExtraTrees'] = ExtraTreesClassifier(n_estimators=20,
min_samples_split=5,
max_features=None,
max_depth=None)
selected_classifiers = opts.classifiers.split(',')
for name in selected_classifiers:
if name not in classifiers:
op.error('classifier %r unknwon')
sys.exit(1)
print("")
print("Training Classifiers")
print("====================")
print("")
err, train_time, test_time = {}, {}, {}
for name in sorted(selected_classifiers):
print("Training %s ..." % name)
err[name], train_time[name], test_time[name] = benchmark(classifiers[name])
######################################################################
## Print classification performance
print("")
print("Classification performance:")
print("===========================")
print("")
def print_row(clf_type, train_time, test_time, err):
print("%s %s %s %s" % (clf_type.ljust(12),
("%.4fs" % train_time).center(10),
("%.4fs" % test_time).center(10),
("%.4f" % err).center(10)))
print("%s %s %s %s" % ("Classifier ", "train-time", "test-time",
"error-rate"))
print("-" * 44)
for name in sorted(selected_classifiers, key=lambda name: err[name]):
print_row(name, train_time[name], test_time[name], err[name])
print("")
print("")
|
