figure added

src/utils.py

@@ -1,4 +1,22 @@
-def plot_results(df):
+import pandas as pd
+from botorch.utils.multi_objective.box_decompositions import DominatedPartitioning
+from botorch.utils.multi_objective.hypervolume import Hypervolume
+
+data = {
+    "iteration": list(range(1, 11)),
+    "target_1": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
+    "target_2": [10, 9, 8, 7, 6, 5, 4, 3, 2, 1], "target_1_qeHVI": [0.11, 0.22, 0.33, 0.44, 0.55, 0.66, 0.77, 0.88, 0.99, 1.01],
+    "target_2_qeHVI": [10.1, 9.2, 8.3, 7.4, 6.5, 5.6, 4.7, 3.8, 2.9, 1.1],
+    "target_1_qNEHVI": [0.12, 0.23, 0.34, 0.45, 0.56, 0.67, 0.78, 0.89, 1.02, 1.11],
+    "target_2_qNEHVI": [10.2, 9.3, 8.4, 7.5, 6.6, 5.7, 4.8, 3.9, 2.8, 1.2],
+    "target_1_random": [0.09, 0.18, 0.29, 0.43, 0.54, 0.61, 0.71, 0.81, 0.92, 1.03],
+    "target_2_random": [9.8, 8.7, 7.6, 6.9, 6.0, 5.2, 4.4, 3.3, 2.4, 1.5]
+}
+
+df=pd.DataFrame.from_dict(data)
+
+# Create
+def plot_results(df,max_iterations):
     """
     Plots the results for the MOBOQM9 model.
     
@@ -8,7 +26,39 @@ def plot_results(df):
     returns:
         matplotlib.pyplot.figure: Figure for the MOBOQM9 model.
     """
-    #iteration | target_1 | target_2 | target_1_qeHVI | target_2_qeHVI | target_1_qNEHVI | target_2_qNEHVI | target_1_random | target_2_random
+    fig,ax=plt.subplots(1,2,figsize=(20,4))
+    ref_point=[1.0,1.0]
+    hv = Hypervolume(ref_point)
+    global_hv=hv.compute(df.values[:,1:3])
+    hv_iterations=[]
+
+    for iter_no in range(1, max_iterations + 1):
+        current_data=df.iloc[:i,1:3].values
+        current_hv=hv.compute(current_data)
+        hv_iterations.append(current_hv)
+        ax[0].plot(range(1, max_iterations + 1), iteration_hvs, marker='o')
+        ax[0].set_xlabel('Iteration')
+        ax[0].set_ylabel('Hypervolume')
+        ax[0].set_title('Hypervolume per Iteration')
+
+    last_iteration_data = df.iloc[max_iterations - 1, 1:3].values
+    last_iteration_hv = hv.compute(last_iteration_data)
+    last_iteration_pareto_front = DominatedPartitioning(ref_point=ref_point, Y=last_iteration_data)
+
+    all_data_pareto_front = DominatedPartitioning(ref_point=ref_point, Y=df.values[:, 1:3])
+
+    ax[1].scatter(df['target_1'], df['target_2'], alpha=0.5, label='All Data')
+
+    global_pareto_front = all_data_pareto_front[0]
+
+    ax[1].plot(global_pareto_front[:, 0], global_pareto_front[:, 1], color='b', label='Global Pareto Front')
+
+    ax[1].scatter(last_iteration_pareto_front[:, 0], last_iteration_pareto_front[:, 1], color='r', label='Last Iteration Pareto Front')
+
+    ax[1].set_xlabel('Target 1')
+    ax[1].set_ylabel('Target 2')
+    ax[1].legend()
+
     # compute the global hv
     # compute the hv for each iteration up until that point
     # plot iteration vs hv
@@ -18,4 +68,4 @@ def plot_results(df):
     # scatter plot all the data with alpha = 0.5
     # global pareto front with blue
     # last iteration pareto front with red *
-    pass
+    return fig