import numpy as np from rlscore.learner.rls import LeaveOneOutRLS from rlscore.utilities.reader import read_sparse from rlscore.measure import auc train_labels = np.loadtxt("./legacy_tests/data/class_train.labels") test_labels = np.loadtxt("./legacy_tests/data/class_test.labels") basis_vectors = np.loadtxt("./legacy_tests/data/bvectors.indices") train_features = read_sparse("./legacy_tests/data/class_train.features") test_features = read_sparse("./legacy_tests/data/class_test.features") kwargs = {} kwargs['measure']=auc kwargs['regparams'] = [2**i for i in range(-10,11)] kwargs["Y"] = train_labels kwargs["X"] = train_features kwargs["basis_vectors"] = train_features[basis_vectors] learner = LeaveOneOutRLS(**kwargs) grid = kwargs['regparams'] perfs = learner.cv_performances for i in range(len(grid)): print "parameter %f cv_performance %f" %(grid[i], perfs[i]) P = learner.predict(test_features) test_perf = auc(test_labels, P) print "test set performance: %f" %test_perf
import numpy as np from rlscore.learner.rls import LeaveOneOutRLS from rlscore.utilities.reader import read_sparse from rlscore.measure import auc train_labels = np.loadtxt("./legacy_tests/data/class_train.labels") test_labels = np.loadtxt("./legacy_tests/data/class_test.labels") train_features = read_sparse("./legacy_tests/data/class_train.features") test_features = read_sparse("./legacy_tests/data/class_test.features") basis_vectors = np.loadtxt("./legacy_tests/data/bvectors.indices") K_r = train_features.dot(train_features[basis_vectors,:].T).todense() K_rr = train_features[basis_vectors,:].dot(train_features[basis_vectors,:].T).todense() kwargs = {} kwargs['measure']=auc kwargs['regparams'] = [2**i for i in range(-10,11)] kwargs["Y"] = train_labels kwargs["X"] = K_r +1 kwargs["basis_vectors"] = K_rr +1 kwargs["kernel"] = "PrecomputedKernel" learner = LeaveOneOutRLS(**kwargs) grid = kwargs['regparams'] perfs = learner.cv_performances for i in range(len(grid)): print("parameter %f cv_performance %f" %(grid[i], perfs[i])) K_test = test_features.dot(train_features[basis_vectors,:].T).todense() +1 P = learner.predict(K_test) test_perf = auc(test_labels, P) print("test set performance: %f" %test_perf)
import numpy as np from rlscore.learner.rls import LeaveOneOutRLS from rlscore.utilities.reader import read_sparse from rlscore.measure import auc train_labels = np.loadtxt("./legacy_tests/data/class_train.labels") test_labels = np.loadtxt("./legacy_tests/data/class_test.labels") basis_vectors = np.loadtxt("./legacy_tests/data/bvectors.indices") train_features = read_sparse("./legacy_tests/data/class_train.features") test_features = read_sparse("./legacy_tests/data/class_test.features") kwargs = {} kwargs['measure'] = auc kwargs['regparams'] = [2**i for i in range(-10, 11)] kwargs["Y"] = train_labels kwargs["X"] = train_features kwargs["basis_vectors"] = train_features[basis_vectors] kwargs["kernel"] = "PolynomialKernel" kwargs["gamma"] = 0.01 learner = LeaveOneOutRLS(**kwargs) grid = kwargs['regparams'] perfs = learner.cv_performances for i in range(len(grid)): print("parameter %f cv_performance %f" % (grid[i], perfs[i])) P = learner.predict(test_features) test_perf = auc(test_labels, P) print("test set performance: %f" % test_perf)
import numpy as np from rlscore.learner.rls import LeaveOneOutRLS from rlscore.utilities.reader import read_sparse from rlscore.measure import auc train_labels = np.loadtxt("./legacy_tests/data/class_train.labels") test_labels = np.loadtxt("./legacy_tests/data/class_test.labels") train_features = read_sparse("./legacy_tests/data/class_train.features") test_features = read_sparse("./legacy_tests/data/class_test.features") K = train_features.dot(train_features.T).todense() kwargs = {} kwargs['measure']=auc kwargs['regparams'] = [2**i for i in range(-10,11)] kwargs["Y"] = train_labels kwargs["X"] = K + 1 kwargs["kernel"] = "PrecomputedKernel" learner = LeaveOneOutRLS(**kwargs) grid = kwargs['regparams'] perfs = learner.cv_performances for i in range(len(grid)): print("parameter %f cv_performance %f" %(grid[i], perfs[i])) K_test = test_features.dot(train_features.T).todense()+1 P = learner.predict(K_test) test_perf = auc(test_labels, P) print("test set performance: %f" %test_perf)