def test_val_grad_custom(model, model_custom): criterion = HeldOutLogistic(X_val, y_val, model) algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000) val, grad = criterion.get_val_grad( log_alpha, algo.get_beta_jac_v, tol=tol) criterion = HeldOutLogistic(X_val, y_val, model_custom) algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000) val_custom, grad_custom = criterion.get_val_grad( log_alpha, algo.get_beta_jac_v, tol=tol) assert np.allclose(val, val_custom) assert np.allclose(grad, grad_custom)
def test_grad_search(model, crit): """check that the paths are the same in the line search""" n_outer = 2 criterion = HeldOutLogistic(X_val, y_val, model) monitor1 = Monitor() algo = Forward() grad_search(algo, criterion, log_alpha, monitor1, n_outer=n_outer, tol=tol) criterion = HeldOutLogistic(X_val, y_val, model) monitor2 = Monitor() algo = Implicit() grad_search(algo, criterion, log_alpha, monitor2, n_outer=n_outer, tol=tol) criterion = HeldOutLogistic(X_val, y_val, model) monitor3 = Monitor() algo = ImplicitForward(tol_jac=tol, n_iter_jac=5000) grad_search(algo, criterion, log_alpha, monitor3, n_outer=n_outer, tol=tol) assert np.allclose( np.array(monitor1.log_alphas), np.array(monitor3.log_alphas)) assert np.allclose( np.array(monitor1.grads), np.array(monitor3.grads), atol=1e-4) assert np.allclose( np.array(monitor1.objs), np.array(monitor3.objs)) assert not np.allclose( np.array(monitor1.times), np.array(monitor3.times))
def test_grad_search(model): # criterion = SURE( # X_train, y_train, model, sigma=sigma_star, X_test=X_test, # y_test=y_test) n_outer = 3 criterion = HeldOutSmoothedHinge(X_val, y_val, model, X_test=None, y_test=None) monitor1 = Monitor() algo = Forward() grad_search(algo, criterion, np.log(1e-3), monitor1, n_outer=n_outer, tol=1e-13) # criterion = SURE( # X_train, y_train, model, sigma=sigma_star, X_test=X_test, # y_test=y_test) criterion = HeldOutSmoothedHinge(X_val, y_val, model, X_test=None, y_test=None) monitor2 = Monitor() algo = Implicit() grad_search(algo, criterion, np.log(1e-3), monitor2, n_outer=n_outer, tol=1e-13) # criterion = SURE( # X_train, y_train, model, sigma=sigma_star, X_test=X_test, # y_test=y_test) criterion = HeldOutSmoothedHinge(X_val, y_val, model, X_test=None, y_test=None) monitor3 = Monitor() algo = ImplicitForward(tol_jac=1e-6, n_iter_jac=100) grad_search(algo, criterion, np.log(1e-3), monitor3, n_outer=n_outer, tol=1e-13) assert np.allclose(np.array(monitor1.log_alphas), np.array(monitor3.log_alphas)) assert np.allclose(np.array(monitor1.grads), np.array(monitor3.grads)) assert np.allclose(np.array(monitor1.objs), np.array(monitor3.objs)) # assert np.allclose( # np.array(monitor1.objs_test), np.array(monitor3.objs_test)) assert not np.allclose(np.array(monitor1.times), np.array(monitor3.times))
def test_val_grad(model): ####################################################################### # Not all methods computes the full Jacobian, but all # compute the gradients # check that the gradient returned by all methods are the same criterion = HeldOutLogistic(X_val, y_val, model) algo = Forward() val_fwd, grad_fwd = criterion.get_val_grad(log_C, algo.get_beta_jac_v, tol=tol) criterion = HeldOutLogistic(X_val, y_val, model) algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=100) val_imp_fwd, grad_imp_fwd = criterion.get_val_grad(log_C, algo.get_beta_jac_v, tol=tol) criterion = HeldOutLogistic(X_val, y_val, model) algo = Implicit() val_imp, grad_imp = criterion.get_val_grad(log_C, algo.get_beta_jac_v, tol=tol) assert np.allclose(val_fwd, val_imp_fwd) assert np.allclose(grad_fwd, grad_imp_fwd) assert np.allclose(val_imp_fwd, val_imp) assert np.allclose(grad_imp_fwd, grad_imp, atol=1e-5)
def parallel_function( dataset_name, method, tol=1e-8, n_outer=15): # load data X, y = fetch_libsvm(dataset_name) # subsample the samples and the features n_samples, n_features = dict_subsampling[dataset_name] t_max = dict_t_max[dataset_name] # t_max = 3600 X, y = clean_dataset(X, y, n_samples, n_features) alpha_max, n_classes = get_alpha_max(X, y) log_alpha_max = np.log(alpha_max) # maybe to change alpha max value algo = ImplicitForward(None, n_iter_jac=2000) estimator = LogisticRegression( C=1, fit_intercept=False, warm_start=True, max_iter=30, verbose=False) model = SparseLogreg(estimator=estimator) idx_train, idx_val, idx_test = get_splits(X, y) logit_multiclass = LogisticMulticlass( idx_train, idx_val, algo, idx_test=idx_test) monitor = Monitor() if method == "implicit_forward": log_alpha0 = np.ones(n_classes) * np.log(0.1 * alpha_max) optimizer = LineSearch(n_outer=100) grad_search( algo, logit_multiclass, model, optimizer, X, y, log_alpha0, monitor) elif method.startswith(('random', 'bayesian')): max_evals = dict_max_eval[dataset_name] log_alpha_min = np.log(alpha_max) - 7 hyperopt_wrapper( algo, logit_multiclass, model, X, y, log_alpha_min, log_alpha_max, monitor, max_evals=max_evals, tol=tol, t_max=t_max, method=method, size_space=n_classes) elif method == 'grid_search': n_alphas = 20 p_alphas = np.geomspace(1, 0.001, n_alphas) p_alphas = np.tile(p_alphas, (n_classes, 1)) for i in range(n_alphas): log_alpha_i = np.log(alpha_max * p_alphas[:, i]) logit_multiclass.get_val( model, X, y, log_alpha_i, None, monitor, tol) monitor.times = np.array(monitor.times).copy() monitor.objs = np.array(monitor.objs).copy() monitor.acc_vals = np.array(monitor.acc_vals).copy() monitor.acc_tests = np.array(monitor.acc_tests).copy() monitor.log_alphas = np.array(monitor.log_alphas).copy() return ( dataset_name, method, tol, n_outer, monitor.times, monitor.objs, monitor.acc_vals, monitor.acc_tests, monitor.log_alphas, log_alpha_max, n_samples, n_features, n_classes)
def test_grad_search(model, crit): """check that the paths are the same in the line search""" if crit == 'cv': n_outer = 2 criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) else: n_outer = 2 criterion = SURE(X_train, y_train, model, sigma=sigma_star, X_test=X_test, y_test=y_test) criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) monitor1 = Monitor() algo = Forward() grad_search(algo, criterion, log_alpha, monitor1, n_outer=n_outer, tol=1e-16) criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) monitor2 = Monitor() algo = Implicit() grad_search(algo, criterion, log_alpha, monitor2, n_outer=n_outer, tol=1e-16) criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) monitor3 = Monitor() algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000) grad_search(algo, criterion, log_alpha, monitor3, n_outer=n_outer, tol=1e-16) assert np.allclose(np.array(monitor1.log_alphas), np.array(monitor3.log_alphas)) assert np.allclose(np.array(monitor1.grads), np.array(monitor3.grads)) assert np.allclose(np.array(monitor1.objs), np.array(monitor3.objs)) assert np.allclose(np.array(monitor1.objs_test), np.array(monitor3.objs_test)) assert not np.allclose(np.array(monitor1.times), np.array(monitor3.times))
def test_grad_search_custom(model, model_custom, crit): """check that the paths are the same in the line search""" n_outer = 5 criterion = HeldOutLogistic(X_val, y_val, model) monitor = Monitor() algo = ImplicitForward(tol_jac=tol, n_iter_jac=5000) grad_search(algo, criterion, log_alpha, monitor, n_outer=n_outer, tol=tol) criterion = HeldOutLogistic(X_val, y_val, model_custom) monitor_custom = Monitor() algo = ImplicitForward(tol_jac=tol, n_iter_jac=5000) grad_search(algo, criterion, log_alpha, monitor_custom, n_outer=n_outer, tol=tol) assert np.allclose( np.array(monitor.log_alphas), np.array(monitor_custom.log_alphas)) assert np.allclose( np.array(monitor.grads), np.array(monitor_custom.grads), atol=1e-4) assert np.allclose( np.array(monitor.objs), np.array(monitor_custom.objs)) assert not np.allclose( np.array(monitor.times), np.array(monitor_custom.times))
def test_grad_search(): n_outer = 3 criterion = HeldOutMSE(X_val, y_val, model, X_test=None, y_test=None) monitor1 = Monitor() algo = Forward() grad_search(algo, criterion, np.array([log_alpha1, log_alpha2]), monitor1, n_outer=n_outer, tol=1e-16) criterion = HeldOutMSE(X_val, y_val, model, X_test=None, y_test=None) monitor2 = Monitor() algo = Implicit() grad_search(algo, criterion, np.array([log_alpha1, log_alpha2]), monitor2, n_outer=n_outer, tol=1e-16) criterion = HeldOutMSE(X_val, y_val, model, X_test=None, y_test=None) monitor3 = Monitor() algo = ImplicitForward(tol_jac=1e-3, n_iter_jac=1000) grad_search(algo, criterion, np.array([log_alpha1, log_alpha2]), monitor3, n_outer=n_outer, tol=1e-16) [np.linalg.norm(grad) for grad in monitor1.grads] [np.exp(alpha) for alpha in monitor1.log_alphas] assert np.allclose(np.array(monitor1.log_alphas), np.array(monitor3.log_alphas)) assert np.allclose(np.array(monitor1.grads), np.array(monitor3.grads)) assert np.allclose(np.array(monitor1.objs), np.array(monitor3.objs)) assert not np.allclose(np.array(monitor1.times), np.array(monitor3.times)) assert np.allclose(np.array(monitor1.log_alphas), np.array(monitor2.log_alphas), atol=1e-2) assert np.allclose(np.array(monitor1.grads), np.array(monitor2.grads), atol=1e-2) assert np.allclose(np.array(monitor1.objs), np.array(monitor2.objs), atol=1e-2) assert not np.allclose(np.array(monitor1.times), np.array(monitor2.times))
def test_val_grad(): ####################################################################### # Not all methods computes the full Jacobian, but all # compute the gradients # check that the gradient returned by all methods are the same criterion = HeldOutMSE(X_val, y_val, model) algo = Forward() val_fwd, grad_fwd = criterion.get_val_grad(np.array( [log_alpha1, log_alpha2]), algo.get_beta_jac_v, tol=tol) criterion = HeldOutMSE(X_val, y_val, model) algo = ImplicitForward(tol_jac=1e-16, n_iter_jac=5000) val_imp_fwd, grad_imp_fwd = criterion.get_val_grad(np.array( [log_alpha1, log_alpha2]), algo.get_beta_jac_v, tol=tol) criterion = HeldOutMSE(X_val, y_val, model) algo = ImplicitForward(tol_jac=1e-16, n_iter_jac=5000) val_imp_fwd_custom, grad_imp_fwd_custom = criterion.get_val_grad( np.array([log_alpha1, log_alpha2]), algo.get_beta_jac_v, tol=tol) criterion = HeldOutMSE(X_val, y_val, model) algo = Implicit() val_imp, grad_imp = criterion.get_val_grad(np.array( [log_alpha1, log_alpha2]), algo.get_beta_jac_v, tol=tol) assert np.allclose(val_fwd, val_imp_fwd) assert np.allclose(grad_fwd, grad_imp_fwd) assert np.allclose(val_imp_fwd, val_imp) assert np.allclose(val_imp_fwd, val_imp_fwd_custom) # for the implcit the conjugate grad does not converge # hence the rtol=1e-2 assert np.allclose(grad_imp_fwd, grad_imp, atol=1e-3) assert np.allclose(grad_imp_fwd, grad_imp_fwd_custom)
def test_val_grad(model): criterion = HeldOutLogistic(X_val, y_val, model) algo = Forward() val_fwd, grad_fwd = criterion.get_val_grad( log_alpha, algo.get_beta_jac_v, tol=tol) criterion = HeldOutLogistic(X_val, y_val, model) algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000) val_imp_fwd, grad_imp_fwd = criterion.get_val_grad( log_alpha, algo.get_beta_jac_v, tol=tol) criterion = HeldOutLogistic(X_val, y_val, model) algo = Implicit() val_imp, grad_imp = criterion.get_val_grad( log_alpha, algo.get_beta_jac_v, tol=tol) assert np.allclose(val_fwd, val_imp_fwd, atol=1e-4) assert np.allclose(grad_fwd, grad_imp_fwd, atol=1e-4) assert np.allclose(val_imp_fwd, val_imp, atol=1e-4) # for the implcit the conjugate grad does not converge # hence the rtol=1e-2 assert np.allclose(grad_imp_fwd, grad_imp, rtol=1e-2)
def parallel_function(dataset_name, method, tol=1e-5, n_outer=50, tolerance_decrease='exponential'): # load data X_train, X_val, X_test, y_train, y_val, y_test = get_data(dataset_name) n_samples, n_features = X_train.shape print('n_samples', n_samples) print('n_features', n_features) y_train[y_train == 0.0] = -1.0 y_val[y_val == 0.0] = -1.0 y_test[y_test == 0.0] = -1.0 alpha_max = np.max(np.abs(X_train.T @ y_train)) alpha_max /= X_train.shape[0] log_alpha_max = np.log(alpha_max) alpha_min = alpha_max * 1e-2 # alphas = np.geomspace(alpha_max, alpha_min, 10) # log_alphas = np.log(alphas) log_alpha1_0 = np.log(0.1 * alpha_max) log_alpha2_0 = np.log(0.1 * alpha_max) log_alpha_max = np.log(alpha_max) n_outer = 25 if dataset_name == "rcv1": size_loop = 2 else: size_loop = 2 model = ElasticNet(X_train, y_train, log_alpha1_0, log_alpha2_0, log_alpha_max, max_iter=1000, tol=tol) for i in range(size_loop): monitor = Monitor() if method == "implicit_forward": criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = ImplicitForward(criterion, tol_jac=1e-3, n_iter_jac=100) _, _, _ = grad_search(algo=algo, verbose=False, log_alpha0=np.array( [log_alpha1_0, log_alpha2_0]), tol=tol, n_outer=n_outer, monitor=monitor, t_max=dict_t_max[dataset_name], tolerance_decrease=tolerance_decrease) elif method == "forward": criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = Forward(criterion) _, _, _ = grad_search(algo=algo, log_alpha0=np.array( [log_alpha1_0, log_alpha2_0]), tol=tol, n_outer=n_outer, monitor=monitor, t_max=dict_t_max[dataset_name], tolerance_decrease=tolerance_decrease) elif method == "implicit": criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = Implicit(criterion) _, _, _ = grad_search(algo=algo, log_alpha0=np.array( [log_alpha1_0, log_alpha2_0]), tol=tol, n_outer=n_outer, monitor=monitor, t_max=dict_t_max[dataset_name], tolerance_decrease=tolerance_decrease) elif method == "grid_search": criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = Forward(criterion) log_alpha_min = np.log(alpha_min) log_alpha_opt, min_g_func = grid_search( algo, log_alpha_min, log_alpha_max, monitor, max_evals=10, tol=tol, samp="grid", t_max=dict_t_max[dataset_name], log_alphas=None, nb_hyperparam=2) print(log_alpha_opt) elif method == "random": criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = Forward(criterion) log_alpha_min = np.log(alpha_min) log_alpha_opt, min_g_func = grid_search( algo, log_alpha_min, np.log(alpha_max), monitor, max_evals=10, tol=tol, samp="random", t_max=dict_t_max[dataset_name], nb_hyperparam=2) print(log_alpha_opt) elif method == "lhs": criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = Forward(criterion) log_alpha_min = np.log(alpha_min) log_alpha_opt, min_g_func = grid_search( algo, log_alpha_min, np.log(alpha_max), monitor, max_evals=10, tol=tol, samp="lhs", t_max=dict_t_max[dataset_name]) print(log_alpha_opt) monitor.times = np.array(monitor.times).copy() monitor.objs = np.array(monitor.objs).copy() monitor.objs_test = np.array(monitor.objs_test).copy() monitor.log_alphas = np.array(monitor.log_alphas).copy() return (dataset_name, method, tol, n_outer, tolerance_decrease, monitor.times, monitor.objs, monitor.objs_test, monitor.log_alphas, norm(y_val), norm(y_test), log_alpha_max)
print("Time to compute CV for scikit-learn: %.2f" % t_sk) ############################################################################## # Grad-search # ----------- print('sparse-ho started') t0 = time.time() estimator = LogisticRegression(penalty='l1', fit_intercept=False, solver='saga') model = SparseLogreg(X_train, y_train, max_iter=max_iter, estimator=estimator) criterion = HeldOutLogistic(X_val, y_val, model) monitor_grad = Monitor() algo = ImplicitForward(tol_jac=tol, n_iter_jac=100) grad_search(algo, criterion, np.log(0.1 * alpha_max), monitor_grad, n_outer=10, tol=tol) objs_grad = np.array(monitor_grad.objs) t_grad_search = time.time() - t0 print('sparse-ho finished') print("Time to compute CV for sparse-ho: %.2f" % t_grad_search) p_alphas_grad = np.exp(np.array(monitor_grad.log_alphas)) / alpha_max
def test_val_grad(): ####################################################################### # Not all methods computes the full Jacobian, but all # compute the gradients # check that the gradient returned by all methods are the same for key in models.keys(): # model = Lasso(log_alpha) log_alpha = dict_log_alpha[key] model = models[key] # model = Lasso(log_alpha) criterion = HeldOutMSE(X_val, y_val, model) algo = Forward() val_fwd, grad_fwd = criterion.get_val_grad(log_alpha, algo.get_beta_jac_v, tol=tol) criterion = HeldOutMSE(X_val, y_val, model) algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000) val_imp_fwd, grad_imp_fwd = criterion.get_val_grad(log_alpha, algo.get_beta_jac_v, tol=tol) criterion = HeldOutMSE(X_val, y_val, model) algo = Implicit() val_imp, grad_imp = criterion.get_val_grad(log_alpha, algo.get_beta_jac_v, tol=tol) # import ipdb; ipdb.set_trace() criterion = HeldOutMSE(X_val, y_val, model) algo = Backward() val_bwd, grad_bwd = criterion.get_val_grad(log_alpha, algo.get_beta_jac_v, tol=tol) assert np.allclose(val_fwd, val_imp_fwd) assert np.allclose(grad_fwd, grad_imp_fwd) # assert np.allclose(val_imp_fwd, val_imp) assert np.allclose(val_bwd, val_fwd) assert np.allclose(val_bwd, val_imp_fwd) assert np.allclose(grad_fwd, grad_bwd) assert np.allclose(grad_bwd, grad_imp_fwd) # for the implcit the conjugate grad does not converge # hence the rtol=1e-2 assert np.allclose(grad_imp_fwd, grad_imp, atol=1e-3) for key in models.keys(): # model = Lasso(log_alpha) log_alpha = dict_log_alpha[key] model = models[key] # model = Lasso(log_alpha) criterion = SURE(X_train, y_train, model, sigma_star) algo = Forward() val_fwd, grad_fwd = criterion.get_val_grad(log_alpha, algo.get_beta_jac_v, tol=tol) criterion = SURE(X_train, y_train, model, sigma_star) algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000) val_imp_fwd, grad_imp_fwd = criterion.get_val_grad(log_alpha, algo.get_beta_jac_v, tol=tol) criterion = SURE(X_train, y_train, model, sigma_star) algo = Implicit(criterion) val_imp, grad_imp = criterion.get_val_grad(log_alpha, algo.get_beta_jac_v, tol=tol) criterion = SURE(X_train, y_train, model, sigma_star) algo = Backward() val_bwd, grad_bwd = criterion.get_val_grad(log_alpha, algo.get_beta_jac_v, tol=tol) assert np.allclose(val_fwd, val_imp_fwd) assert np.allclose(grad_fwd, grad_imp_fwd) assert np.allclose(val_imp_fwd, val_imp) assert np.allclose(val_bwd, val_fwd) assert np.allclose(val_bwd, val_imp_fwd) assert np.allclose(grad_fwd, grad_bwd) assert np.allclose(grad_bwd, grad_imp_fwd)
n_jobs=2).fit(X_train_val, y_train_val) # Measure mse on test mse_cv = mean_squared_error(y_test, model_cv.predict(X_test)) print("Vanilla LassoCV: Mean-squared error on test data %f" % mse_cv) ############################################################################## ############################################################################## # Weighted Lasso with sparse-ho. # We use the vanilla lassoCV coefficients as a starting point alpha0 = np.log(model_cv.alpha_) * np.ones(X_train.shape[1]) # Weighted Lasso: Sparse-ho: 1 param per feature estimator = Lasso(fit_intercept=False, max_iter=10, warm_start=True) model = WeightedLasso(X_train, y_train, estimator=estimator) criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = ImplicitForward() monitor = Monitor() grad_search(algo, criterion, alpha0, monitor, n_outer=20, tol=1e-6) ############################################################################## ############################################################################## # MSE on validation set mse_sho_val = mean_squared_error(y_val, estimator.predict(X_val)) # MSE on test set, ie unseen data mse_sho_test = mean_squared_error(y_test, estimator.predict(X_test)) print("Sparse-ho: Mean-squared error on validation data %f" % mse_sho_val) print("Sparse-ho: Mean-squared error on test (unseen) data %f" % mse_sho_test) labels = ['WeightedLasso val', 'WeightedLasso test', 'Lasso CV']
# grad search print("Started grad-search") t_grad_search = -time.time() monitor = Monitor() n_outer = 10 model = ElasticNet(X_train, y_train, log_alphas_1[-1], log_alphas_2[-1], log_alpha_max, max_iter=max_iter, tol=tol) criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = ImplicitForward(criterion, tol_jac=1e-2, n_iter_jac=1000, max_iter=max_iter) _, _, _ = grad_search(algo=algo, verbose=True, log_alpha0=np.array( [np.log(alpha_max / 10), np.log(alpha_max / 10)]), tol=tol, n_outer=n_outer, monitor=monitor, tolerance_decrease='constant') alphas_grad = np.exp(np.array(monitor.log_alphas)) alphas_grad /= alpha_max t_grad_search += time.time()
log_alphas = np.log(alphas) tol = 1e-7 # grid search # model = Lasso(X_train, y_train, np.log(alpha_max/10)) # criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) # algo = Forward(criterion) # monitor_grid_sk = Monitor() # grid_search( # algo, None, None, monitor_grid_sk, log_alphas=log_alphas, # tol=tol) # np.save("p_alphas.npy", p_alphas) # objs = np.array(monitor_grid_sk.objs) # np.save("objs.npy", objs) # grad_search estimator = linear_model.Lasso(fit_intercept=False, warm_start=True) model = Lasso(X_train, y_train, np.log(alpha_max / 10), estimator=estimator) criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = ImplicitForward(criterion) monitor_grad = Monitor() grad_search(algo, np.log(alpha_max / 10), monitor_grad, n_outer=10, tol=tol) p_alphas_grad = np.exp(np.array(monitor_grad.log_alphas)) / alpha_max np.save("p_alphas_grad.npy", p_alphas_grad) objs_grad = np.array(monitor_grad.objs) np.save("objs_grad.npy", objs_grad)
def parallel_function( dataset_name, method, tol=1e-5, n_outer=50, tolerance_decrease='exponential'): # load data X_train, X_val, X_test, y_train, y_val, y_test = get_data(dataset_name, csr=True) n_samples, n_features = X_train.shape print('n_samples', n_samples) print('n_features', n_features) y_train[y_train == 0.0] = -1.0 y_val[y_val == 0.0] = -1.0 y_test[y_test == 0.0] = -1.0 C_max = 100 logC = np.log(1e-2) n_outer = 5 if dataset_name == "rcv1": size_loop = 1 else: size_loop = 1 model = SVM( X_train, y_train, logC, max_iter=10000, tol=tol) for i in range(size_loop): monitor = Monitor() if method == "implicit_forward": criterion = HeldOutSmoothedHinge(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = ImplicitForward(criterion, tol_jac=1e-3, n_iter_jac=100) _, _, _ = grad_search( algo=algo, verbose=False, log_alpha0=logC, tol=tol, n_outer=n_outer, monitor=monitor, t_max=dict_t_max[dataset_name], tolerance_decrease=tolerance_decrease) elif method == "forward": criterion = HeldOutSmoothedHinge(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = Forward(criterion) _, _, _ = grad_search( algo=algo, log_alpha0=logC, tol=tol, n_outer=n_outer, monitor=monitor, t_max=dict_t_max[dataset_name], tolerance_decrease=tolerance_decrease) elif method == "implicit": criterion = HeldOutSmoothedHinge(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = Implicit(criterion) _, _, _ = grad_search( algo=algo, log_alpha0=logC, tol=tol, n_outer=n_outer, monitor=monitor, t_max=dict_t_max[dataset_name], tolerance_decrease=tolerance_decrease) elif method == "grid_search": criterion = HeldOutSmoothedHinge(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = Forward(criterion) log_alpha_min = np.log(1e-2) log_alpha_opt, min_g_func = grid_search( algo, log_alpha_min, np.log(C_max), monitor, max_evals=25, tol=tol, samp="grid") print(log_alpha_opt) elif method == "random": criterion = HeldOutSmoothedHinge(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = Forward(criterion) log_alpha_min = np.log(1e-2) log_alpha_opt, min_g_func = grid_search( algo, log_alpha_min, np.log(C_max), monitor, max_evals=25, tol=tol, samp="random") print(log_alpha_opt) elif method == "lhs": criterion = HeldOutSmoothedHinge(X_val, y_val, model, X_test=X_test, y_test=y_test) algo = Forward(criterion) log_alpha_min = np.log(1e-2) log_alpha_opt, min_g_func = grid_search( algo, log_alpha_min, np.log(C_max), monitor, max_evals=25, tol=tol, samp="lhs") print(log_alpha_opt) monitor.times = np.array(monitor.times) monitor.objs = np.array(monitor.objs) monitor.objs_test = np.array(monitor.objs_test) monitor.log_alphas = np.array(monitor.log_alphas) return (dataset_name, method, tol, n_outer, tolerance_decrease, monitor.times, monitor.objs, monitor.objs_test, monitor.log_alphas, norm(y_val), norm(y_test))