import numpy as np from rlscore.learner.cg_rankrls import CGRankRLS from rlscore.utilities.reader import read_qids from rlscore.utilities.reader import read_sparse from rlscore.measure import cindex train_labels = np.loadtxt("./legacy_tests/data/rank_train.labels") test_labels = np.loadtxt("./legacy_tests/data/rank_test.labels") train_qids = read_qids("./legacy_tests/data/rank_train.qids") test_features = read_sparse("./legacy_tests/data/rank_test.features") train_features = read_sparse("./legacy_tests/data/rank_train.features") test_qids = read_qids("./legacy_tests/data/rank_test.qids") kwargs = {} kwargs["Y"] = train_labels kwargs["X"] = train_features kwargs["qids"] = train_qids kwargs["regparam"] = 1 learner = CGRankRLS(**kwargs) P = learner.predict(test_features) from rlscore.measure.measure_utilities import UndefinedPerformance from rlscore.measure.measure_utilities import qids_to_splits test_qids = qids_to_splits(test_qids) perfs = [] for query in test_qids: try: perf = cindex(test_labels[query], P[query]) perfs.append(perf) except UndefinedPerformance: pass test_perf = np.mean(perfs) print "test set performance: %f" %test_perf
from rlscore.utilities.reader import read_qids train_labels = np.loadtxt("./legacy_tests/data/rank_train.labels") test_labels = np.loadtxt("./legacy_tests/data/rank_test.labels") train_features = read_sparse("./legacy_tests/data/rank_train.features") test_features = read_sparse("./legacy_tests/data/rank_test.features") train_qids = read_qids("./legacy_tests/data/rank_train.qids") test_qids = read_qids("./legacy_tests/data/rank_test.qids") kwargs = {} kwargs["Y"] = train_labels kwargs["X"] = train_features kwargs["qids"] = train_qids kwargs["regparam"] = 1 kwargs["callbackfun"] = EarlyStopCB(test_features, test_labels, test_qids, measure=cindex) learner = CGRankRLS(**kwargs) P = learner.predict(test_features) test_perf = cindex(test_labels, P) from rlscore.measure.measure_utilities import UndefinedPerformance from rlscore.measure.measure_utilities import qids_to_splits test_qids = qids_to_splits(test_qids) perfs = [] for query in test_qids: try: perf = cindex(test_labels[query], P[query]) perfs.append(perf) except UndefinedPerformance: pass test_perf = np.mean(perfs) print("test set performance: %f" % test_perf)
import numpy as np from rlscore.learner.cg_rankrls import CGRankRLS from rlscore.reader import read_sparse from rlscore.reader import read_sparse from rlscore.measure import cindex train_labels = np.loadtxt("./examples/data/rank_train.labels") test_labels = np.loadtxt("./examples/data/rank_test.labels") train_features = read_sparse("./examples/data/rank_train.features") test_features = read_sparse("./examples/data/rank_test.features") kwargs = {} kwargs["train_labels"] = train_labels kwargs["train_features"] = train_features kwargs["regparam"] = 1 learner = CGRankRLS.createLearner(**kwargs) learner.train() model = learner.getModel() P = model.predict(test_features) test_perf = cindex(test_labels, P) print "test set performance: %f" % test_perf