UPDATE: example for bo_ei

jungtaekkim · Jun 11, 2018 · 7b167cd · 7b167cd
1 parent 179187c
commit 7b167cd
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Showing 3 changed files with 18 additions and 17 deletions.
diff --git a/bayeso/bo.py b/bayeso/bo.py
@@ -108,7 +108,7 @@ def _get_initial(self, is_random=False, is_grid=False, fun_obj=None, int_seed=No
     def _optimize_objective(self, fun_acq, X_train, Y_train, X_test, cov_X_X, inv_cov_X_X, hyps):
         X_test = np.atleast_2d(X_test)
         pred_mean, pred_std = gp.predict_test_(X_train, Y_train, X_test, cov_X_X, inv_cov_X_X, hyps, self.str_cov, self.prior_mu)
-        result_acq = fun_acq(pred_mean, pred_std, Y_train)
+        result_acq = fun_acq(np.squeeze(pred_mean), np.squeeze(pred_std), Y_train)
         return result_acq
 
     def _optimize(self, fun_obj, is_grid_optimized=True, verbose=False):

diff --git a/bayeso/utils/utils_plot.py b/bayeso/utils/utils_plot.py
@@ -48,17 +48,17 @@ def plot_gp(X_train, Y_train, X_test, mu, sigma,
 
     if Y_test_truth is not None:
         ax.plot(X_test.flatten(), Y_test_truth.flatten(),
-            c=colors[2],
+            c=colors[1],
             linewidth=4,
             marker='None')
     ax.plot(X_test.flatten(), mu.flatten(), 
-        c=colors[1], 
+        c=colors[2], 
         linewidth=4, 
         marker='None')
     ax.fill_between(X_test.flatten(), 
         mu.flatten() - range_shade * sigma.flatten(), 
         mu.flatten() + range_shade * sigma.flatten(), 
-        color=colors[1], 
+        color=colors[2], 
         alpha=0.3)
     ax.plot(X_train.flatten(), Y_train.flatten(), 
         'x', 
@@ -177,7 +177,7 @@ def plot_bo_step(X_train, Y_train, X_test, Y_test, mean_test, std_test,
     ax.fill_between(X_test.flatten(), 
         mean_test.flatten() - range_shade * std_test.flatten(), 
         mean_test.flatten() + range_shade * std_test.flatten(), 
-        color='blue', 
+        color='b', 
         alpha=0.3)
     if num_init is not None:
         if X_train.shape[0] > num_init:
@@ -190,7 +190,7 @@ def plot_bo_step(X_train, Y_train, X_test, Y_test, mean_test, std_test,
         ax.plot(X_train[:X_train.shape[0]-1, :], Y_train[:X_train.shape[0]-1, :], 'rx', ms=14, markeredgewidth=6)
         ax.plot(X_train[X_train.shape[0]-1, :], Y_train[X_train.shape[0]-1, :], c='orange', marker='+', ms=18, markeredgewidth=6)
 
-    ax.set_xlabel('$x$', fontsize=32)
+    ax.set_xlabel('$\mathbf{x}$', fontsize=32)
     ax.set_ylabel('$y$', fontsize=32)
     ax.set_xlim([np.min(X_test), np.max(X_test)])
     ax.tick_params(labelsize=22)
@@ -222,7 +222,7 @@ def plot_bo_step_acq(X_train, Y_train, X_test, Y_test, mean_test, std_test, acq_
     ax1.fill_between(X_test.flatten(), 
         mean_test.flatten() - range_shade * std_test.flatten(), 
         mean_test.flatten() + range_shade * std_test.flatten(), 
-        color='blue', 
+        color='b', 
         alpha=0.3)
     if num_init is not None:
         if X_train.shape[0] > num_init:
@@ -236,8 +236,8 @@ def plot_bo_step_acq(X_train, Y_train, X_test, Y_test, mean_test, std_test, acq_
     ax1.tick_params(labelsize=22)
 
     ax2.plot(X_test, acq_test, 'b', linewidth=4)
-    ax2.set_xlabel('$x$', fontsize=32)
-    ax2.set_ylabel('Acq.', fontsize=32)
+    ax2.set_xlabel('$\mathbf{x}$', fontsize=32)
+    ax2.set_ylabel('acq.', fontsize=32)
     ax2.spines['top'].set_color('none')
     ax2.spines['right'].set_color('none')
     ax2.set_xlim([np.min(X_test), np.max(X_test)])

diff --git a/examples/example_bo_ei.py b/examples/example_bo_ei.py
@@ -1,11 +1,10 @@
 import numpy as np
-import sys
-sys.path.append('../bayeso')
 
-import gp
-import bo
-import acquisition
-import utils
+from bayeso import gp
+from bayeso import bo
+from bayeso import acquisition
+from bayeso.utils import utils_plot
+
 
 def fun_target(X):
     return 4.0 * np.cos(X) + 0.1 * X + 2.0 * np.sin(X) + 0.4 * (X - 0.5)**2
@@ -17,6 +16,7 @@ def main():
         [1],
         [2],
     ])
+    num_init = X_train.shape[0]
     model_bo = bo.BO(np.array([[-6., 6.]]))
     X_test = np.linspace(-6, 6, 400)
     X_test = np.reshape(X_test, (400, 1))
@@ -28,8 +28,9 @@ def main():
         acq_test = np.reshape(acq_test, (acq_test.shape[0], 1))
         X_train = np.vstack((X_train, next_x))
         Y_train = fun_target(X_train)
-        utils.plot_bo_step(X_train, Y_train, X_test, fun_target(X_test), mu_test, sigma_test, '../results/gp/', 'test_cos_' + str(ind_), 4)
-        utils.plot_bo_step_acq(X_train, Y_train, X_test, fun_target(X_test), mu_test, sigma_test, acq_test, '../results/gp/', 'test_cos_' + str(ind_), 4)
+        utils_plot.plot_bo_step(X_train, Y_train, X_test, fun_target(X_test), mu_test, sigma_test, '../results/gp/', 'test_cos_' + str(ind_), num_init)
+        utils_plot.plot_bo_step_acq(X_train, Y_train, X_test, fun_target(X_test), mu_test, sigma_test, acq_test, '../results/gp/', 'test_cos_' + str(ind_), num_init)
+
 
 if __name__ == '__main__':
     main()