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])