def __init__(self):
super(LinearSvmFGs, self).__init__()
self.linearSVM = LinearSVM()
class LinearSvmFGs(AbstractFunctionalPredictor):
def __init__(self):
super(LinearSvmFGs, self).__init__()
self.linearSVM = LinearSVM()
def learnModel(self, X, y, folds=3):
"""
Train using the given examples and labels, however first conduct grid
search in conjunction with cross validation to find the best parameters.
We also conduct filtering with a variety of values.
"""
#Hard coding this is bad
Cs = 2**numpy.arange(-2, 7, dtype=numpy.float)
#Cs = numpy.array([0.1, 2.0])
if self.waveletInds == None:
self.waveletInds = numpy.arange(X.shape[1])
nonWaveletInds = numpy.setdiff1d(numpy.arange(X.shape[1]), self.waveletInds)
Xw = X[:, self.waveletInds]
Xo = X[:, nonWaveletInds]
featureInds = numpy.flipud(numpy.argsort(numpy.sum(Xw**2, 0)))
meanAUCs = numpy.zeros((Cs.shape[0], self.candidatesN.shape[0]))
stdAUCs = numpy.zeros((Cs.shape[0], self.candidatesN.shape[0]))
#Standardise the data
Xw = Standardiser().standardiseArray(Xw)
Xo = Standardiser().standardiseArray(Xo)
for i in range(Cs.shape[0]):
for j in range(self.candidatesN.shape[0]):
self.linearSVM.setC(Cs[i])
newX = numpy.c_[Xw[:, featureInds[0:self.candidatesN[j]]], Xo]
meanAUCs[i, j], stdAUCs[i, j] = self.linearSVM.evaluateStratifiedCv(newX, y, folds, metricMethod=Evaluator.auc)
(bestI, bestJ) = numpy.unravel_index(numpy.argmax(meanAUCs), meanAUCs.shape)
self.linearSVM.setC(Cs[bestI])
self.featureInds = numpy.r_[self.waveletInds[featureInds[0:self.candidatesN[bestJ]]], nonWaveletInds]
logging.debug("Best learner found: " + str(self.linearSVM) + " N:" + str(self.candidatesN[bestJ]))
self.standardiser = Standardiser()
newX = self.standardiser.standardiseArray(X[:, self.featureInds])
self.linearSVM.learnModel(newX, y)
def predict(self, X):
newX = self.standardiser.standardiseArray(X[:, self.featureInds])
return self.linearSVM.predict(newX)
@staticmethod
def generate(waveletInds=None):
"""
Generate a classifier which does a grid search.
"""
def generatorFunc():
linearSvm = LinearSvmFGs()
linearSvm.setWaveletInds(waveletInds)
return linearSvm
return generatorFunc
def setWeight(self, weight):
self.linearSVM.setWeight(weight)
