def run_LR(self, rundir=None):
""" Demo script that evaluates the supervised prediction
performance of the p(t|d) (stored in theta.npy) of
an the LDA model in `rundir`.
"""
if not rundir:
rundir = self.rundir
( (trainX,trainY,train_ids), (testX, testY,test_ids) ) = load_data(rundir, dataset=NIPSDIR)
allX = np.vstack((trainX,testX))
allY = np.concatenate((trainY,testY))
if not self.LRparams:
self.grid_search(allX,allY)
lr = logistic_regression.train_lrpipe(trainX, trainY, self.LRparams)
allLabels = load_labels(NIPSDIR)
allTitles = load_titles(NIPSDIR)
evaluate(lr, testX, testY, testTitles=allTitles[test_ids], testLabels=allLabels[test_ids])
def run_SVM(self, rundir=None):
""" Demo script that evaluates the supervised prediction
performance of the p(t|d) (stored in theta.npy) of
an the LDA model in `rundir`.
"""
if not rundir:
rundir = self.rundir
( (trainX,trainY,train_ids), (testX, testY,test_ids) ) = load_data(rundir, dataset=NIPSDIR)
allX = np.vstack((trainX,testX))
allY = np.concatenate((trainY,testY))
if not self.SVMparams:
self.grid_search(allX,allY)
sv = support_vector_machines.train_svpipe(trainX, trainY, self.SVMparams)
allLabels = load_labels(NIPSDIR)
allTitles = load_titles(NIPSDIR)
evaluate(sv, testX, testY, testTitles=allTitles[test_ids], testLabels=allLabels[test_ids])
