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)
