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August 25, 2024 18:50
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HiGHS Log Plotter
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| import re | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| def parse_highs_log(log_file_path): | |
| last_full_entry = [] | |
| current_entry = [] | |
| found_solution = False | |
| with open(log_file_path, "r") as f: | |
| for line in f: | |
| if "Running HiGHS" in line: | |
| if found_solution: | |
| last_full_entry = current_entry | |
| current_entry = [line] | |
| found_solution = False | |
| else: | |
| current_entry.append(line) | |
| if "Writing the solution to" in line: | |
| found_solution = True | |
| if not last_full_entry: | |
| last_full_entry = current_entry | |
| if not last_full_entry: | |
| return None, None, None, None, None, None | |
| time_values, best_bound_values, best_sol_values, in_queue_values, expl_values, gap_values = ( | |
| [], | |
| [], | |
| [], | |
| [], | |
| [], | |
| [], | |
| ) | |
| for line in last_full_entry: | |
| match = re.search(r"\dk?\s+\d+\.\ds$", line) | |
| if not match: | |
| continue | |
| tokens = line.split() | |
| if len(tokens) == 13: | |
| tokens = tokens[1:] | |
| assert len(tokens) == 12, f"{line}" | |
| in_queue_values.append(float(tokens[1])) # InQueue | |
| expl_values.append(float(tokens[3].replace("%", ""))) # Expl.% | |
| best_bound_values.append(float(tokens[4].replace("inf", "nan"))) # Best Bound | |
| best_sol_values.append(float(tokens[5].replace("inf", "nan"))) # Best Sol | |
| gap_values.append( | |
| float(tokens[6].replace("%", "").replace("inf", "nan").replace("Large", "nan")) | |
| ) # Gap% | |
| time_values.append(float(tokens[11].replace("s", ""))) # Time | |
| return time_values, best_bound_values, best_sol_values, in_queue_values, expl_values, gap_values | |
| def plot_highs_log( | |
| time_values, best_bound_values, best_sol_values, in_queue_values, expl_values, gap_values | |
| ): | |
| fig, ax1 = plt.subplots(figsize=(10, 6)) | |
| # Plot Objective Bounds | |
| ax1.plot(time_values, best_bound_values, label="Best Bound", color="blue") | |
| ax1.plot(time_values, best_sol_values, label="Best Solution", color="green") | |
| ax1.set_xlabel("Time (seconds)") | |
| ax1.set_ylabel("Objective Bounds", color="blue", labelpad=15) | |
| ax1.tick_params(axis="y", labelcolor="blue") | |
| # Limit y-axis to the range between min and max of the non-NaN values | |
| valid_gap_index = next(i for i, gap in enumerate(gap_values) if not np.isnan(gap)) | |
| min_y = min(best_bound_values[valid_gap_index], best_sol_values[valid_gap_index]) | |
| max_y = max(best_bound_values[valid_gap_index], best_sol_values[valid_gap_index]) | |
| padding = (max_y - min_y) * 0.1 | |
| ax1.set_ylim(min_y - padding, max_y + padding) | |
| # Add second y-axis for InQueue values | |
| ax2 = ax1.twinx() | |
| ax2.plot(time_values, in_queue_values, label="InQueue", color="red") | |
| ax2.set_ylabel("InQueue", color="red", loc="top", labelpad=12) | |
| ax2.yaxis.label.set_rotation(0) | |
| ax2.tick_params(axis="y", labelcolor="red") | |
| # Add third y-axis for Explored % values (scaled) | |
| ax3 = ax1.twinx() | |
| ax3.spines["right"].set_position(("outward", 50)) | |
| ax3.plot(time_values, expl_values, label="Expl.%", color="purple") | |
| ax3.set_ylabel("Expl.%", color="purple", loc="top", labelpad=10) | |
| ax3.yaxis.label.set_rotation(0) | |
| ax3.tick_params(axis="y", labelcolor="purple") | |
| # Add fourth y-axis for Gap % values (scaled) | |
| ax4 = ax1.twinx() | |
| ax4.spines["right"].set_position(("outward", 90)) | |
| ax4.plot(time_values, gap_values, label="Gap.%", color="orange") | |
| ax4.set_ylabel("Gap.%", color="orange", loc="top", labelpad=22) | |
| ax4.yaxis.label.set_rotation(0) | |
| ax4.tick_params(axis="y", labelcolor="orange") | |
| # Plot vertical hash lines where Best Solution changes | |
| for i in range(1, len(best_sol_values)): | |
| if best_sol_values[i] != best_sol_values[i - 1]: # Change detected | |
| ax1.axvline(x=time_values[i], color="grey", linestyle="--", linewidth=0.5) | |
| # Shift plot area left to make room on the right for the three y-axis labels. | |
| fig.subplots_adjust(left=0.08, right=0.85) | |
| # Set up legend | |
| fig.legend(loc="upper center", ncols=5) | |
| # Show plot | |
| plt.title("HiGHS MIP Log Analysis") | |
| plt.show() | |
| log_file_path = "/path/to/your/logfile.log" | |
| time_values, best_bound_values, best_sol_values, in_queue_values, expl_values, gap_values = ( | |
| parse_highs_log(log_file_path) | |
| ) | |
| plot_highs_log( | |
| time_values, best_bound_values, best_sol_values, in_queue_values, expl_values, gap_values | |
| ) |
Author
Thell
commented
Aug 25, 2024
- Added vertical BestSol indicators.
- Handle initial non-valid bounds dominating vertical space.
Update comment lines for gap%
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