Added working set of experiments

experiments/.gitignore

@@ -1 +1,3 @@
-results/
+results/
+
+**.rew

experiments/GP_visualization.py

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+# GP_visualization.py
+# Written Ian Rankin December 2021
+#
+# A set of functions to visualize the learned GPs.
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pdb
+
+import lop
+
+def record_gp_state(model, fake_f, bounds=[(0,0),(1.5,1.5)], folder='./', \
+                    file_header='', visualize=False):
+    # Generate test grid
+    num_side = 25
+    x = np.linspace(bounds[0][0], bounds[0][1], num_side)
+    y = np.linspace(bounds[1][0], bounds[1][1], num_side)
+
+    X, Y = np.meshgrid(x,y)
+    pts = np.vstack([X.ravel(), Y.ravel()]).transpose()
+
+
+    fake_ut = fake_f(pts)
+    max_fake = np.linalg.norm(fake_ut, ord=np.inf)
+    fake_ut = fake_ut / max_fake
+
+    pred_ut, pred_sigma = model.predict_large(pts)
+
+
+    # save useful data
+    # save entire pickle file
+    gp_filename = folder+file_header+'_gp.p'
+    print(gp_filename)
+    #pickle.dump(gp, open(gp_filename, "wb"))
+    # Save useful visualization data
+    viz_filename = folder+file_header+'_viz'
+    print(viz_filename)
+    np.savez(viz_filename, \
+            pts=pts, \
+            fake_ut=fake_ut, \
+            pred_ut=pred_ut, \
+            pred_sigma=pred_sigma, \
+            GP_pts=model.X_train, \
+            GP_pref=model.y_train, \
+            GP_prior_idx=model.prior_idx)
+
+
+    if visualize == True:
+        visualize_data(X, Y, num_side, fake_ut, pred_ut, pred_sigma,
+            model.X_train, model.prior_idx, \
+            folder, file_header, \
+            also_display=False)
+
+
+def visualize_data(X,Y, num_side, fake_ut, pred_ut, pred_sigma, \
+                    GP_pts, GP_prior_idx, \
+                    folder='./', file_header='', also_display=False):
+
+    Z_pred = np.reshape(pred_ut, (num_side, num_side))
+    Z_fake = np.reshape(fake_ut, (num_side, num_side))
+    Z_sigma = np.reshape(pred_sigma, (num_side, num_side))
+    Z_std = np.sqrt(Z_sigma)
+    Z_ucb = Z_pred + Z_std
+
+    plt.figure()
+
+    plt.pcolor(X, Y, Z_pred)
+    plt.contour(X, Y, Z_pred)
+    if GP_prior_idx is None:
+        GP_prior_idx = [0,0]
+    if GP_pts is not None:
+        plt.scatter(GP_pts[GP_prior_idx[1]:,0],GP_pts[GP_prior_idx[1]:,1])
+    else:
+        plt.scatter([],[])
+    plt.title(file_header+'_active samples')
+    plt.xlabel('Migratory fish reward')
+    plt.ylabel('Sea floor fish reward')
+
+    plt.savefig(folder+file_header+'_active_samples.jpg')
+
+    plt.figure()
+
+    plt.pcolor(X, Y, Z_pred)
+    plt.contour(X, Y, Z_pred)
+    if GP_pts is not None:
+        plt.scatter(GP_pts[:,0],GP_pts[:,1])
+    else:
+        plt.scatter([],[])
+    plt.title(file_header+'_with prior points')
+    plt.xlabel('Migratory fish reward')
+    plt.ylabel('Sea floor fish reward')
+    plt.savefig(folder+file_header+'_with_prior.jpg')
+
+    if also_display:
+        plt.show()

experiments/config.yaml

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+######### for active learning selectors
+default_to_pareto: false
+always_select_best: true
+
+# alpha
+UCB_scalar: 1.0
+
+
+
+######## For model
+pareto_pairs: false
+
+rbf_sigma: 0.3
+rbf_lengthscale: 0.25
+
+hyperparameter_optimization: false
+
+# does this need to be updated for depending on the fake function being used?
+normalize_gp: true
+normalize_postive: false
+
+
+add_model_prior: false
+prior_pts: 25
+prior_bounds:
+  -
+    - 0.0
+    - 1.5
+  -
+    - 0.0
+    - 1.5
+