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comparison external/wrap_libsvm.py @ 517:716c04512dbe

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author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 12 Nov 2008 10:54:38 -0500
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516:2b0e10ac6929 517:716c04512dbe
1 """Run an experiment using libsvm.
2 """
3 import numpy
4 from ..datasets import dataset_from_descr
5
6 # libsvm currently has no python installation instructions/convention.
7 #
8 # This module uses a specific convention for libsvm's installation.
9 # I base this on installing libsvm-2.88.
10 # To install libsvm's python module, do three things:
11 # 1. Build libsvm (run make in both the root dir and the python subdir).
12 # 2. touch a '__init__.py' file in the python subdir
13 # 3. add a symbolic link to a PYTHONPATH location that looks like this:
14 # libsvm -> <your root path>/libsvm-2.88/python/
15 #
16 # That is the sort of thing that this module expects from 'import libsvm'
17
18 import libsvm
19
20 def score_01(x, y, model):
21 assert len(x) == len(y)
22 size = len(x)
23 errors = 0
24 for i in range(size):
25 prediction = model.predict(x[i])
26 #probability = model.predict_probability
27 if (y[i] != prediction):
28 errors = errors + 1
29 return float(errors)/size
30
31 #this is the dbdict experiment interface... if you happen to use dbdict
32 class State(object):
33 #TODO: parametrize to get all the kernel types, not hardcode for RBF
34 dataset = 'MNIST_1k'
35 C = 10.0
36 kernel = 'RBF'
37 # rel_gamma is related to the procedure Jerome used. He mentioned why in
38 # quadratic_neurons/neuropaper/draft3.pdf.
39 rel_gamma = 1.0
40
41 def __init__(self, **kwargs):
42 for k, v in kwargs:
43 setattr(self, k, type(getattr(self, k))(v))
44
45
46 def dbdict_run_svm_experiment(state, channel=lambda *args, **kwargs:None):
47 """Parameters are described in state, and returned in state.
48
49 :param state: object instance to store parameters and return values
50 :param channel: not used
51
52 :returns: None
53
54 This is the kind of function that dbdict-run can use.
55
56 """
57 ((train_x, train_y), (valid_x, valid_y), (test_x, test_y)) = dataset_from_descr(state.dataset)
58
59 #libsvm needs stuff in int32 on a 32bit machine
60 #TODO: test this on a 64bit machine
61 train_y = numpy.asarray(train_y, dtype='int32')
62 valid_y = numpy.asarray(valid_y, dtype='int32')
63 test_y = numpy.asarray(test_y, dtype='int32')
64 problem = svm.svm_problem(train_y, train_x);
65
66 gamma0 = 0.5 / numpy.sum(numpy.var(train_x, axis=0))
67
68 param = svm.svm_parameter(C=state.C,
69 kernel_type=getattr(svm, state.kernel),
70 gamma=state.rel_gamma * gamma0)
71
72 model = svm.svm_model(problem, param) #this is the expensive part
73
74 state.train_01 = score_01(train_x, train_y, model)
75 state.valid_01 = score_01(valid_x, valid_y, model)
76 state.test_01 = score_01(test_x, test_y, model)
77
78 state.n_train = len(train_y)
79 state.n_valid = len(valid_y)
80 state.n_test = len(test_y)
81
82 def run_svm_experiment(**kwargs):
83 """Python-friendly interface to dbdict_run_svm_experiment
84
85 Parameters are used to construct a `State` instance, which is returned after running
86 `dbdict_run_svm_experiment` on it.
87
88 .. code-block:: python
89 results = run_svm_experiment(dataset='MNIST_1k', C=100.0, rel_gamma=0.01)
90 print results.n_train
91 # 1000
92 print results.valid_01, results.test_01
93 # 0.14, 0.10 #.. or something...
94
95 """
96 state = State(**kwargs)
97 state_run_svm_experiment(state)
98 return state
99