[Scipy-svn] r2143 - in trunk/Lib/sandbox/svm: . tests
scipy-svn at scipy.org
scipy-svn at scipy.org
Thu Aug 3 10:39:04 EDT 2006
Author: fullung
Date: 2006-08-03 09:38:13 -0500 (Thu, 03 Aug 2006)
New Revision: 2143
Modified:
trunk/Lib/sandbox/svm/classification.py
trunk/Lib/sandbox/svm/oneclass.py
trunk/Lib/sandbox/svm/predict.py
trunk/Lib/sandbox/svm/tests/test_classification.py
trunk/Lib/sandbox/svm/tests/test_oneclass.py
trunk/Lib/sandbox/svm/tests/test_regression.py
Log:
Model compaction.
Modified: trunk/Lib/sandbox/svm/classification.py
===================================================================
--- trunk/Lib/sandbox/svm/classification.py 2006-07-28 00:10:27 UTC (rev 2142)
+++ trunk/Lib/sandbox/svm/classification.py 2006-08-03 14:38:13 UTC (rev 2143)
@@ -42,7 +42,7 @@
For training data with nr_class classes, this function returns
nr_class*(nr_class-1)/2 decision values in a dictionary for
each item in the test dataset. The keys of the dictionary are
- 2-tuples, one for each combination of two class labels.
+ 2-tuples, one for each permutation of two class labels.
"""
n = self.nr_class * (self.nr_class - 1) / 2
def p(v):
Modified: trunk/Lib/sandbox/svm/oneclass.py
===================================================================
--- trunk/Lib/sandbox/svm/oneclass.py 2006-07-28 00:10:27 UTC (rev 2142)
+++ trunk/Lib/sandbox/svm/oneclass.py 2006-08-03 14:38:13 UTC (rev 2143)
@@ -33,7 +33,7 @@
distribution, while a non-positive value indicates that is is
not.
"""
- return [self.predictor.predict_values(x, 1)[0] for x in dataset]
+ return [self.predictor.predict_values(x, 1) for x in dataset]
def compact(self):
self.predictor.compact()
Modified: trunk/Lib/sandbox/svm/predict.py
===================================================================
--- trunk/Lib/sandbox/svm/predict.py 2006-07-28 00:10:27 UTC (rev 2142)
+++ trunk/Lib/sandbox/svm/predict.py 2006-08-03 14:38:13 UTC (rev 2143)
@@ -1,4 +1,5 @@
-from ctypes import POINTER, c_double, addressof
+from ctypes import POINTER, c_double, addressof, byref
+from itertools import izip
import numpy as N
from dataset import svm_node_dot
@@ -44,7 +45,10 @@
v = N.empty((n,), dtype=N.float64)
vptr = v.ctypes.data_as(POINTER(c_double))
libsvm.svm_predict_values(self.model, xptr, vptr)
- return v
+ if n == 1:
+ return v[0]
+ else:
+ return v
def predict_probability(self, x, n):
if not self.model.contents.param.probability:
@@ -88,6 +92,7 @@
ids = [int(modelc.SV[i][0].value) for i in range(modelc.l)]
support_vectors = [dataset[id] for id in ids]
self.support_vectors = support_vectors
+ self.is_compact = False
libsvm.svm_destroy_model(model)
def predict(self, x):
@@ -107,7 +112,7 @@
else:
return self.predict_values(x, 1)
- def predict_values(self, x, n):
+ def _predict_values_sparse(self, x, n):
if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]:
kvalue = N.empty((len(self.support_vectors),))
for i, sv in enumerate(self.support_vectors):
@@ -121,12 +126,12 @@
ci, cj = self.nSV[i], self.nSV[j]
coef1 = self.sv_coef[j - 1]
coef2 = self.sv_coef[i]
- sum = -self.rho[p]
+ sum = 0.
for k in range(ci):
sum += coef1[si + k] * kvalue[si + k]
for k in range(cj):
sum += coef2[sj + k] * kvalue[sj + k]
- dec_values[p] = sum
+ dec_values[p] = sum - self.rho[p]
p += 1
return dec_values
else:
@@ -135,8 +140,52 @@
z += sv_coef * self.kernel(x, sv, svm_node_dot)
return z
+ def _predict_values_compact(self, x, n):
+ if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]:
+ for i, sv in enumerate(self.support_vectors):
+ kvalue = N.empty((len(self.support_vectors),))
+ kvalue[i] = self.kernel(x, sv, svm_node_dot)
+ return kvalue - self.rho
+ else:
+ sv = self.support_vectors[0]
+ return self.kernel(x, sv, svm_node_dot) - self.rho
+
+ def predict_values(self, x, n):
+ if self.is_compact:
+ return self._predict_values_compact(x, n)
+ else:
+ return self._predict_values_sparse(x, n)
+
def predict_probability(self, x, n):
raise NotImplementedError
+ def _compact_svs(self, svs, coefs):
+ maxlen = 0
+ for sv in svs:
+ maxlen = N.maximum(maxlen, sv['index'].max())
+ csv = N.zeros((maxlen + 1,), libsvm.svm_node_dtype)
+ csv['index'][:-1] = N.arange(1, maxlen + 1)
+ csv['index'][-1] = -1
+ for coef, sv in izip(coefs, svs):
+ idx = sv['index'][:-1] - 1
+ csv['value'][idx] += coef*sv['value'][:-1]
+ return csv
+
def compact(self):
- raise NotImplementedError
+ if self.svm_type in [libsvm.C_SVC, libsvm.NU_SVC]:
+ compact_support_vectors = []
+ for i in range(self.nr_class):
+ for j in range(i + 1, self.nr_class):
+ si, sj = self.start[i], self.start[j]
+ ci, cj = self.nSV[i], self.nSV[j]
+ svi = self.support_vectors[si:si + ci]
+ svj = self.support_vectors[sj:sj + cj]
+ coef1 = self.sv_coef[j - 1][si:si + ci]
+ coef2 = self.sv_coef[i][sj:sj + cj]
+ csv = self._compact_svs(svi + svj, coef1 + coef2)
+ compact_support_vectors.append(csv)
+ self.support_vectors = compact_support_vectors
+ else:
+ csv = self._compact_svs(self.support_vectors, self.sv_coef)
+ self.support_vectors = [csv]
+ self.is_compact = True
Modified: trunk/Lib/sandbox/svm/tests/test_classification.py
===================================================================
--- trunk/Lib/sandbox/svm/tests/test_classification.py 2006-07-28 00:10:27 UTC (rev 2142)
+++ trunk/Lib/sandbox/svm/tests/test_classification.py 2006-08-03 14:38:13 UTC (rev 2143)
@@ -139,7 +139,7 @@
refx = N.vstack([x1, x2])
trndata = LibSvmClassificationDataSet(zip(reflabels, refx))
testdata = LibSvmTestDataSet(refx)
- return trndata, trndata1, trndata2, testdata
+ return trndata, testdata, trndata1, trndata2
def _make_kernels(self):
def kernelf(x, y, dot):
@@ -158,7 +158,7 @@
return kernels
def check_all(self):
- trndata, trndata1, trndata2, testdata = self._make_datasets()
+ trndata, testdata, trndata1, trndata2 = self._make_datasets()
kernels = self._make_kernels()
weights = [(0, 2.0), (1, 5.0), (2, 3.0)]
for kernel in kernels:
@@ -226,5 +226,20 @@
p = results.predict(testdata)
assert_array_equal(p, refp)
+ def check_compact(self):
+ traindata, testdata = self._make_basic_datasets()
+ kernel = LinearKernel()
+ cost = 10.0
+ weights = [(1, 10.0)]
+ model = LibSvmCClassificationModel(kernel, cost, weights)
+ results = model.fit(traindata, LibSvmPythonPredictor)
+ refvs = results.predict_values(testdata)
+ results.compact()
+ vs = results.predict_values(testdata)
+ print vs
+ for refv, v in zip(refvs, vs):
+ for key, value in refv.iteritems():
+ self.assertEqual(value, v[key])
+
if __name__ == '__main__':
NumpyTest().run()
Modified: trunk/Lib/sandbox/svm/tests/test_oneclass.py
===================================================================
--- trunk/Lib/sandbox/svm/tests/test_oneclass.py 2006-07-28 00:10:27 UTC (rev 2142)
+++ trunk/Lib/sandbox/svm/tests/test_oneclass.py 2006-08-03 14:38:13 UTC (rev 2143)
@@ -5,6 +5,7 @@
from svm.dataset import LibSvmOneClassDataSet, LibSvmTestDataSet
from svm.kernel import *
from svm.oneclass import *
+from svm.predict import *
restore_path()
class test_oneclass(NumpyTestCase):
@@ -24,9 +25,8 @@
return traindata, testdata
def check_train(self):
- ModelType = LibSvmOneClassModel
traindata, testdata = self._make_basic_datasets()
- model = ModelType(LinearKernel())
+ model = LibSvmOneClassModel(LinearKernel())
results = model.fit(traindata)
p = results.predict(testdata)
assert_array_equal(p, [False, False, False, True])
@@ -56,5 +56,14 @@
for p, v in zip(pred, values):
self.assertEqual(v > 0, p)
+ def check_compact(self):
+ traindata, testdata = self._make_basic_datasets()
+ model = LibSvmOneClassModel(LinearKernel())
+ results = model.fit(traindata, LibSvmPythonPredictor)
+ refv = results.predict_values(testdata)
+ results.compact()
+ v = results.predict_values(testdata)
+ assert_array_equal(refv, v)
+
if __name__ == '__main__':
NumpyTest().run()
Modified: trunk/Lib/sandbox/svm/tests/test_regression.py
===================================================================
--- trunk/Lib/sandbox/svm/tests/test_regression.py 2006-07-28 00:10:27 UTC (rev 2142)
+++ trunk/Lib/sandbox/svm/tests/test_regression.py 2006-08-03 14:38:13 UTC (rev 2143)
@@ -163,5 +163,15 @@
p = results.predict(testdata)
assert_array_almost_equal(refp, p)
+ def check_compact(self):
+ traindata, testdata = self._make_basic_datasets()
+ kernel = LinearKernel()
+ model = LibSvmEpsilonRegressionModel(LinearKernel())
+ results = model.fit(traindata, LibSvmPythonPredictor)
+ refp = results.predict(testdata)
+ results.compact()
+ p = results.predict(testdata)
+ assert_array_equal(refp, p)
+
if __name__ == '__main__':
NumpyTest().run()
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