import numpy as np from rlscore.learner.greedy_rls import GreedyRLS from rlscore.reader import read_sparse from rlscore.reader import read_sparse from rlscore.measure import auc train_labels = np.loadtxt("./examples/data/class_train.labels") test_labels = np.loadtxt("./examples/data/class_test.labels") train_features = read_sparse("./examples/data/class_train.features") test_features = read_sparse("./examples/data/class_test.features") kwargs = {} kwargs["Y"] = train_labels kwargs["X"] = train_features kwargs["test_labels"] = test_labels kwargs["test_features"] = test_features kwargs["use_default_callback"] = True kwargs["regparam"] = 1 kwargs["bias"] = 1 kwargs["test_measure"] = "auc" kwargs["subsetsize"] = 10 learner = GreedyRLS(**kwargs) 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.greedy_rls import GreedyRLS 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") kwargs = {} kwargs["Y"] = train_labels kwargs["X"] = train_features kwargs["test_labels"] = test_labels kwargs["test_features"] = test_features kwargs["use_default_callback"] = True kwargs["regparam"] = 1 kwargs["bias"] = 1 kwargs["test_measure"] = "auc" kwargs["subsetsize"] = 10 learner = GreedyRLS(**kwargs) 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.greedy_rls import GreedyRLS from rlscore.reader import read_sparse from rlscore.reader import read_sparse from rlscore.measure import auc train_labels = np.loadtxt("./examples/data/class_train.labels") test_labels = np.loadtxt("./examples/data/class_test.labels") train_features = read_sparse("./examples/data/class_train.features") test_features = read_sparse("./examples/data/class_test.features") kwargs = {} kwargs["train_labels"] = train_labels kwargs["test_labels"] = test_labels kwargs["train_features"] = train_features kwargs["test_features"] = test_features kwargs["use_default_callback"] = True kwargs["regparam"] = 1 kwargs["bias"] = 1 kwargs["test_measure"] = "auc" kwargs["subsetsize"] = 10 learner = GreedyRLS.createLearner(**kwargs) learner.train() model = learner.getModel() P = model.predict(test_features) test_perf = auc(test_labels, P) print "test set performance: %f" % test_perf
import numpy as np from rlscore.learner.greedy_rls import GreedyRLS from rlscore.reader import read_sparse from rlscore.reader import read_sparse from rlscore.measure import auc train_labels = np.loadtxt("./examples/data/class_train.labels") test_labels = np.loadtxt("./examples/data/class_test.labels") train_features = read_sparse("./examples/data/class_train.features") test_features = read_sparse("./examples/data/class_test.features") kwargs = {} kwargs["train_labels"] = train_labels kwargs["test_labels"] = test_labels kwargs["train_features"] = train_features kwargs["test_features"] = test_features kwargs["use_default_callback"] = True kwargs["regparam"] = 1 kwargs["bias"] = 1 kwargs["test_measure"] = "auc" kwargs["subsetsize"] = 10 learner = GreedyRLS.createLearner(**kwargs) learner.train() model = learner.getModel() P = model.predict(test_features) test_perf = auc(test_labels, P) print "test set performance: %f" %test_perf