from rlscore.measure import auc
from rlscore.learner.rls import LOOCV
from rlscore.utilities.grid_search import grid_search
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["train_features"] = train_features
kwargs["regparam"] = 1
kwargs["coef0"] = 1
kwargs["degree"] = 3
kwargs["gamma"] = 2
kwargs["kernel"] = "PolynomialKernel"
learner = RLS.createLearner(**kwargs)
learner.train()
kwargs = {}
kwargs["learner"] = learner
kwargs["measure"] = auc
crossvalidator = LOOCV(**kwargs)
grid = [2**i for i in range(-10, 11)]
learner, perfs = grid_search(crossvalidator, grid)
for i in range(len(grid)):
print "parameter %f cv_performance %f" % (grid[i], perfs[i])
model = learner.getModel()
P = model.predict(test_features)
test_perf = auc(test_labels, P)
print "test set performance: %f" % test_perf
from rlscore.reader import read_sparse
from rlscore.reader import read_sparse
from rlscore.measure import auc
from rlscore.learner.rls import NfoldCV
from rlscore.utilities.grid_search import grid_search
train_labels = np.loadtxt("./examples/data/class_train.labels")
test_labels = np.loadtxt("./examples/data/class_test.labels")
folds = read_folds("./examples/data/folds.txt")
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["train_features"] = train_features
kwargs["regparam"] = 1
learner = RLS.createLearner(**kwargs)
learner.train()
kwargs = {}
kwargs["learner"] = learner
kwargs["folds"] = folds
kwargs["measure"] = auc
crossvalidator = NfoldCV(**kwargs)
grid = [2 ** i for i in range(-10, 11)]
learner, perfs = grid_search(crossvalidator, grid)
for i in range(len(grid)):
print "parameter %f cv_performance %f" % (grid[i], perfs[i])
model = learner.getModel()
P = model.predict(test_features)
test_perf = auc(test_labels, P)
print "test set performance: %f" % test_perf