Add APOSMM opt run plot scripts (#1461)

Author: shuds13

scripts/aposmm_min_loc.py

@@ -0,0 +1,84 @@
+#!/usr/bin/env python3
+
+"""
+Plots the location of minima for each optimization run in 3D input space for
+the N best runs.
+
+To be run with both the history file (*.npy) and the persis_info file (*.pickle)
+from a libEnsemble/APOSMM run present in the current directory. The most recent
+of each file type present will be used for the plot.
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import glob
+import os
+import pickle
+import sys
+
+N = 6  # number of opt runs to show.
+
+x_name = 'x0'
+y_name = 'x1'
+z_name = 'x2'
+
+full_bounds = False  # For entire input space enter bounds below
+
+if full_bounds:
+    # Define parameter bounds
+    mcr = 1e-2
+    x0_min, x0_max = mcr, 1.0 - mcr
+    x1_min, x1_max = -20.0, 20.0
+    x2_min, x2_max = 1.0, 20.0
+
+# Find the most recent .npy and pickle files
+try:
+    H_file = max(glob.glob("*.npy"), key=os.path.getmtime)
+    persis_info_file = max(glob.iglob('*.pickle'), key=os.path.getctime)
+except Exception:
+    sys.exit("Need a *.npy and a *.pickle files in run dir. Exiting...")
+
+H = np.load(H_file)
+
+with open(persis_info_file, "rb") as f:
+    index_sets = pickle.load(f)["run_order"]
+
+# Filter best N opt runs for clearer graph
+trimmed_index_sets = {key: indices[:-1] for key, indices in index_sets.items()}
+min_f_per_set = [(key, indices, H['f'][indices].min()) for key, indices in trimmed_index_sets.items() if len(indices) > 0]
+min_f_per_set_sorted = sorted(min_f_per_set, key=lambda x: x[2])[:N]
+
+# Plotting
+fig = plt.figure(figsize=(6, 6))
+ax = fig.add_subplot(111, projection='3d')
+
+for key, indices, _ in min_f_per_set_sorted:
+    min_f_index = indices[np.argmin(H['f'][indices])]
+
+    # Extract the corresponding 3D x position from H
+    try:
+        x, y, z = H['x'][min_f_index]
+    except ValueError:
+        x = H[x_name][min_f_index]
+        y = H[y_name][min_f_index]
+        z = H[z_name][min_f_index]
+
+    # Plot the 3D point
+    ax.scatter(x, y, z, marker='o', s=50, label=f'Opt run {key}')
+
+    # Draw a line from the point to the XY plane (z=0)
+    ax.plot([x, x], [y, y], [0, z], color='grey', linestyle='--')
+
+if full_bounds:
+    ax.set_xlim(x0_min, x0_max)
+    ax.set_ylim(x1_min, x1_max)
+    ax.set_zlim(x2_min, x2_max)
+
+# Label the plot
+ax.set_xlabel(x_name)
+ax.set_ylabel(y_name)
+ax.set_zlabel(z_name)
+ax.set_title('Locations of best points from each optimization run')
+ax.legend(bbox_to_anchor=(-0.1, 0.9), loc='upper left', borderaxespad=0)
+plt.savefig(f"location_min_best{N}.png")

scripts/aposmm_plot_local_runs.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+
+"""
+Plot f by optimization run, for N best runs. Other points are shown in grey
+
+To be run with both the history file (*.npy) and the persis_info file (*.pickle)
+from a libEnsemble/APOSMM run present in the current directory. The most recent
+of each file type present will be used for the plot.
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import glob
+import os
+import pickle
+import sys
+
+N = 6  # number of opt runs to show.
+
+# Find the most recent .npy and pickle files
+try:
+    H_file = max(glob.glob("*.npy"), key=os.path.getmtime)
+    persis_info_file = max(glob.iglob('*.pickle'), key=os.path.getctime)
+except Exception:
+    sys.exit("Need a *.npy and a *.pickle files in run dir. Exiting...")
+
+H = np.load(H_file)
+
+with open(persis_info_file, "rb") as f:
+    index_sets = pickle.load(f)["run_order"]
+
+# Filter best N opt runs for clearer graph
+trimmed_index_sets = {key: indices[:-1] for key, indices in index_sets.items()}
+min_f_per_set = [(key, indices, H['f'][indices].min()) for key, indices in trimmed_index_sets.items() if len(indices) > 0]
+min_f_per_set_sorted = sorted(min_f_per_set, key=lambda x: x[2])[:N]
+
+# Plotting
+plt.figure(figsize=(10, 6))
+plt.scatter(range(len(H['f'])), H['f'], color='lightgrey', label='Other points', zorder=1)
+
+num_runs = len(index_sets)
+# for key, indices in index_sets.items():
+for key, indices, _ in min_f_per_set_sorted:
+    # Extract the 'f' values for each index set
+    f_values = H['f'][indices]
+
+    ratio = (f_values[-1] - f_values[-2])/ f_values[-2]
+    # print(f"Tolerance of the last two values for {key}: {ratio}")
+
+    plt.plot(indices, f_values, marker='o', label=f'Opt run {key}', zorder=2)
+
+    # Identify the index and value of the minimum f
+    min_index = indices[np.argmin(f_values)]
+    min_f_value = np.min(f_values)
+
+    # Mark the minimum f value with a distinct marker
+    plt.scatter(min_index, min_f_value, color='red', edgecolor='black', s=50, zorder=3)
+
+# Add a dummy point to the legend for "minima of opt run"
+plt.scatter([], [], color='red', edgecolor='black', s=50, label='Best value of opt run')
+
+plt.xlabel('Simulation ID')
+plt.ylabel('Objective value')
+plt.title(f'Objective values by Optimization runs. Best {N} runs from {num_runs}')
+plt.legend()
+plt.grid(True)
+plt.savefig(f"opt_runs_best{N}.png")