def retrain_in_f_with_grid(name, label_p, label_n, oracle, n_features, ftype,
test_x, test_y, benchmark):
print '--------------- retrain in F with grid -----------------'
for n_pts in xrange(50, 601, 50):
online = OnlineBase(name,
label_p,
label_n,
oracle,
n_features,
ftype,
error=.1)
online.collect_pts(n_pts, -1)
ex = RBFKernelRetraining(
name,
online.get_QSV(),
online.get_QSV_labels(), # training data
online.get_QSV(),
online.get_QSV_labels(), # validation data
test_x,
test_y, # test data
n_features)
print 'nQSV=%d, Q=%d, dim=100,' % (
n_pts, online.get_n_query()), ex.grid_retrain_in_f(100)
# assume gamma=1, c = 0
n = len(x)
r = []
r.extend([x[i] * x[i] for i in range(n - 1, -1, -1)])
for i in range(n - 1, -1, -1):
for j in range(i - 1, -1, -1):
r.append(sqrt(2) * x[i] * x[j])
return r
print 'solve in F'
online = OnlineBase(train_data, p, n, poly_svc.predict, n_features, f_type,
1e-5)
online.collect_pts(-1, budget=5000)
ex = PolySolver(online.get_QSV(), online.get_QSV_labels(), Xt, Yt,
polynomial_map, n_features)
ex.solve_in_f()
print 'TRAIN SCORE : %f' % ex.solve_score
print 'TEST SCORE : %f' % ex.calc_test_score()
# print 'retrain in F'
# ex = RBFKernelRetraining(train_data,
# poly_svc.predict, Xt, Yt,
# n_features, OfflineMethods.RT_in_F, error=1,
# kernel='poly', fmap=polynomial_map)
# ex.train_SGD_for_poly_in_F()
# ex.benchmark()
# ex.print_perf()
# feature map for polynomial kernel (gamma* u`v + c)^2
# assume gamma=1, c = 0
n = len(x)
r = []
r.extend([x[i]*x[i] for i in range(n-1, -1, -1)])
for i in range(n-1, -1, -1):
for j in range(i-1, -1, -1):
r.append(sqrt(2)*x[i]*x[j])
return r
print 'solve in F'
online = OnlineBase(train_data, p, n, poly_svc.predict, n_features, f_type, 1e-5)
online.collect_pts(-1, budget=5000)
ex = PolySolver(online.get_QSV(), online.get_QSV_labels(), Xt, Yt, polynomial_map, n_features)
ex.solve_in_f()
print 'TRAIN SCORE : %f' % ex.solve_score
print 'TEST SCORE : %f' % ex.calc_test_score()
# print 'retrain in F'
# ex = RBFKernelRetraining(train_data,
# poly_svc.predict, Xt, Yt,
# n_features, OfflineMethods.RT_in_F, error=1,
# kernel='poly', fmap=polynomial_map)
# ex.train_SGD_for_poly_in_F()
# ex.benchmark()
# ex.print_perf()
# run('./data/mushrooms.aa', './data/mushrooms.ab', (1,2), 112, 'binary')