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import re
import matplotlib.pyplot as plt
import numpy as np
import math as math
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))
best_bound_colour = "blue"
best_solution_colour = "green"
in_queue_colour = "red"
explored_colour = "purple"
gap_colour = "orange"
# Plot Objective Bounds
ax1.plot(time_values, best_bound_values, label="Best Bound", color=best_bound_colour)
ax1.plot(time_values, best_sol_values, label="Best Solution", color=best_solution_colour)
ax1.set_xlabel("Time (seconds)")
ax1.set_ylabel("Objective Bounds", color=best_bound_colour, labelpad=15)
ax1.tick_params(axis="y", labelcolor=best_bound_colour)
# 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=in_queue_colour)
# ax2.set_ylabel("InQueue", color=in_queue_colour, loc="top", labelpad=12)
ax2.yaxis.label.set_rotation(0)
ax2.tick_params(axis="y", labelcolor=in_queue_colour)
# 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="Explored %", color=explored_colour)
# ax3.set_ylabel("Expl.%", color=explored_colour, loc="top", labelpad=10)
ax3.yaxis.label.set_rotation(0)
ax3.tick_params(axis="y", labelcolor=explored_colour)
# 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=gap_colour, linestyle="--", linewidth=1)
# ax4.set_ylabel("Gap.%", color=gap_colour, loc="top", labelpad=22)
ax4.yaxis.label.set_rotation(0)
ax4.tick_params(axis="y", labelcolor=gap_colour)
# Plot vertical hash lines where Best Solution changes
for i in range(1, len(best_sol_values)):
# Print if change detected and not NaN
if (best_sol_values[i] != best_sol_values[i - 1]) and not(math.isnan(best_sol_values[i])):
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="lower center", ncols=5)
# Show plot
plt.title("HiGHS MIP Log Analysis", pad=20)
plt.show()
#log_file_path = "/path/to/your/logfile.log"
log_file_path = "HiGHS.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
)
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