# Run each experiment
for exp in experiments:
print("\n" + exp[0]) # prints the algorithm
# The CSV file to which the statistics should be written
output = exp[0] + ".csv"
outputs += [output]
# Run the experiment
frodo2.run(java, javaParamsNew, generator, exp[1], 1, exp[2], timeout, output, saveProblems = False)
# Plot the results for each experiment
for j in range(0, len(experiments)):
exp = experiments[j]
# The CSV file to which the statistics should have been written
output = outputs[j]
# Plot curves with x = problem size and y = performance of each algorithm
# frodo2.plot(output, xCol = 9, yCol = 15, block = False) # 15 = message size
# frodo2.plot(output, xCol = 9, yCol = 13, block = (j == len(experiments)-1)) # 13 = runtime
# Scatter plot with one data point per instance, x = old algorithm and y = new algorithm
xAlgo = exp[2][0][0]
yAlgo = exp[2][1][0]
frodo2.plotScatter(output, xAlgo, yAlgo, metricsCol = 19, timeouts = False, block = False, loglog = False) # 19 = solution quality
frodo2.plotScatter(output, xAlgo, yAlgo, metricsCol = 15, timeouts = True, block = False) # 15 = message size
frodo2.plotScatter(output, xAlgo, yAlgo, metricsCol = 15, timeouts = False, block = False) # 15 = message size
frodo2.plotScatter(output, xAlgo, yAlgo, metricsCol = 13, timeouts = True, block = False) # 13 = runtime
frodo2.plotScatter(output, xAlgo, yAlgo, metricsCol = 13, timeouts = False, block = (j == len(experiments)-1)) # 13 = runtime
# Run each experiment
for exp in experiments:
print("\n" + exp[0]) # prints the algorithm
# The CSV file to which the statistics should be written
output = exp[0] + ".csv"
outputs += [output]
# Run the experiment
frodo2.run(java, javaParamsNew, generator, exp[1], 1, exp[2], timeout, output, saveProblems = False)
# Plot the results for each experiment
for j in range(0, len(experiments)):
exp = experiments[j]
# The CSV file to which the statistics should have been written
output = outputs[j]
# Plot curves with x = problem size and y = performance of each algorithm
# frodo2.plot(output, xCol = 7, yCol = 16, block = False) # 16 = message size
# frodo2.plot(output, xCol = 7, yCol = 14, block = (j == len(experiments)-1)) # 14 = runtime
# Scatter plot with one data point per instance, x = old algorithm and y = new algorithm
xAlgo = exp[2][0][0]
yAlgo = exp[2][1][0]
frodo2.plotScatter(output, xAlgo, yAlgo, metricsCol = 20, timeouts = False, block = False, loglog = False) # 20 = solution quality
frodo2.plotScatter(output, xAlgo, yAlgo, metricsCol = 16, timeouts = True, block = False) # 16 = message size
frodo2.plotScatter(output, xAlgo, yAlgo, metricsCol = 16, timeouts = False, block = False) # 16 = message size
frodo2.plotScatter(output, xAlgo, yAlgo, metricsCol = 14, timeouts = True, block = False) # 14 = runtime
frodo2.plotScatter(output, xAlgo, yAlgo, metricsCol = 14, timeouts = False, block = (j == len(experiments)-1)) # 14 = runtime