def RunLinearRegressionShogun(q): totalTimer = Timer() # Load input dataset. # If the dataset contains two files then the second file is the responses # file. try: Log.Info("Loading dataset", self.verbose) if len(self.dataset) == 2: X = np.genfromtxt(self.dataset[0], delimiter=',') y = np.genfromtxt(self.dataset[1], delimiter=',') else: X = np.genfromtxt(self.dataset, delimiter=',') y = X[:, (X.shape[1] - 1)] X = X[:,:-1] with totalTimer: # Perform linear regression. model = LeastSquaresRegression(RealFeatures(X.T), RegressionLabels(y)) model.train() b = model.get_w() except Exception as e: q.put(-1) return -1 time = totalTimer.ElapsedTime() q.put(time) return time
def RunLinearRegressionShogun(q): totalTimer = Timer() # Load input dataset. # If the dataset contains two files then the second file is the responses # file. try: Log.Info("Loading dataset", self.verbose) if len(self.dataset) == 2: X = np.genfromtxt(self.dataset[0], delimiter=',') y = np.genfromtxt(self.dataset[1], delimiter=',') else: X = np.genfromtxt(self.dataset, delimiter=',') y = X[:, (X.shape[1] - 1)] X = X[:, :-1] with totalTimer: # Perform linear regression. model = LeastSquaresRegression(RealFeatures(X.T), RegressionLabels(y)) model.train() b = model.get_w() except Exception as e: q.put(-1) return -1 time = totalTimer.ElapsedTime() q.put(time) return time
def regression_least_squares_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,tau=1e-6): from shogun.Features import RegressionLabels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import LeastSquaresRegression ls=LeastSquaresRegression(RealFeatures(traindat), RegressionLabels(label_train)) ls.train() out = ls.apply(RealFeatures(fm_test)).get_labels() return out,ls
def regression_least_squares_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,tau=1e-6): from shogun.Features import Labels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import LeastSquaresRegression ls=LeastSquaresRegression(RealFeatures(traindat), Labels(label_train)) ls.train() out = ls.apply(RealFeatures(fm_test)).get_labels() return out,ls
for i in xrange(p): X[:, i] -= np.mean(X[:, i]) X[:, i] /= np.linalg.norm(X[:, i]) y -= np.mean(y) # train LASSO LeastAngleRegression = LeastAngleRegression() LeastAngleRegression.set_labels(RegressionLabels(y)) LeastAngleRegression.train(RealFeatures(X.T)) # train ordinary LSR if use_ridge: lsr = LinearRidgeRegression(0.01, RealFeatures(X.T), Labels(y)) lsr.train() else: lsr = LeastSquaresRegression() lsr.set_labels(RegressionLabels(y)) lsr.train(RealFeatures(X.T)) # gather LASSO path path = np.zeros((p, LeastAngleRegression.get_path_size())) for i in xrange(path.shape[1]): path[:, i] = LeastAngleRegression.get_w(i) # apply on training data mse_train = np.zeros(LeastAngleRegression.get_path_size()) for i in xrange(mse_train.shape[0]): LeastAngleRegression.switch_w(i) ypred = LeastAngleRegression.apply(RealFeatures(X.T)).get_labels() mse_train[i] = np.dot(ypred - y, ypred - y) / y.shape[0] ypred = lsr.apply(RealFeatures(X.T)).get_labels()