def run(optimizer, objectivefunc, NumOfRuns, params, export_flags):
"""
It serves as the main interface of the framework for running the experiments.
Parameters
----------
optimizer : list
The list of optimizers names
objectivefunc : list
The list of benchmark functions
NumOfRuns : int
The number of independent runs
params : set
The set of parameters which are:
1. Size of population (PopulationSize)
2. The number of iterations (Iterations)
export_flags : set
The set of Boolean flags which are:
1. Export (Exporting the results in a file)
2. Export_details (Exporting the detailed results in files)
3. Export_convergence (Exporting the covergence plots)
4. Export_boxplot (Exporting the box plots)
Returns
-----------
N/A
"""
# Select general parameters for all optimizers (population size, number of iterations) ....
PopulationSize = params['PopulationSize']
Iterations = params['Iterations']
#Export results ?
Export = export_flags['Export_avg']
Export_details = export_flags['Export_details']
Export_convergence = export_flags['Export_convergence']
Export_boxplot = export_flags['Export_boxplot']
Flag = False
Flag_details = False
# CSV Header for for the cinvergence
CnvgHeader = []
results_directory = time.strftime("%Y-%m-%d-%H-%M-%S") + '/'
Path(results_directory).mkdir(parents=True, exist_ok=True)
for l in range(0, Iterations):
CnvgHeader.append("Iter" + str(l + 1))
for i in range(0, len(optimizer)):
for j in range(0, len(objectivefunc)):
convergence = [0] * NumOfRuns
executionTime = [0] * NumOfRuns
for k in range(0, NumOfRuns):
func_details = benchmarks.getFunctionDetails(objectivefunc[j])
x = selector(optimizer[i], func_details, PopulationSize,
Iterations)
convergence[k] = x.convergence
optimizerName = x.optimizer
objfname = x.objfname
if (Export_details == True):
ExportToFile = results_directory + "experiment_details.csv"
with open(ExportToFile, 'a', newline='\n') as out:
writer = csv.writer(out, delimiter=',')
if (
Flag_details == False
): # just one time to write the header of the CSV file
header = numpy.concatenate(
[["Optimizer", "objfname", "ExecutionTime"],
CnvgHeader])
writer.writerow(header)
Flag_details = True # at least one experiment
executionTime[k] = x.executionTime
a = numpy.concatenate(
[[x.optimizer, x.objfname, x.executionTime],
x.convergence])
writer.writerow(a)
out.close()
if (Export == True):
ExportToFile = results_directory + "experiment.csv"
with open(ExportToFile, 'a', newline='\n') as out:
writer = csv.writer(out, delimiter=',')
if (Flag == False
): # just one time to write the header of the CSV file
header = numpy.concatenate(
[["Optimizer", "objfname", "ExecutionTime"],
CnvgHeader])
writer.writerow(header)
Flag = True
avgExecutionTime = float("%0.2f" %
(sum(executionTime) / NumOfRuns))
avgConvergence = numpy.around(numpy.mean(
convergence, axis=0, dtype=numpy.float64),
decimals=2).tolist()
a = numpy.concatenate(
[[optimizerName, objfname, avgExecutionTime],
avgConvergence])
writer.writerow(a)
out.close()
if Export_convergence == True:
conv_plot.run(results_directory, optimizer, objectivefunc, Iterations)
if Export_boxplot == True:
box_plot.run(results_directory, optimizer, objectivefunc, Iterations)
if (Flag == False): # Faild to run at least one experiment
print(
"No Optomizer or Cost function is selected. Check lists of available optimizers and cost functions"
)
print("Execution completed")
Flag = False
# CSV Header for for the cinvergence
CnvgHeader = []
for l in range(0, Iterations):
CnvgHeader.append("Iter" + str(l + 1))
for i in range(0, len(optimizer)):
for j in range(0, len(benchmarkfunc)):
if (
(optimizer[i] == True) and (benchmarkfunc[j] == True)
): # start experiment if an optimizer and an objective function is selected
for k in range(0, NumOfRuns):
func_details = benchmarks.getFunctionDetails(j)
x = selector(i, func_details, PopulationSize, Iterations)
if (Export == True):
with open(ExportToFile, 'a', newline='\n') as out:
writer = csv.writer(out, delimiter=',')
if (
Flag == False
): # just one time to write the header of the CSV file
header = numpy.concatenate([[
"Optimizer", "objfname", "startTime",
"EndTime", "ExecutionTime"
], CnvgHeader])
writer.writerow(header)
a = numpy.concatenate([[
x.optimizer, x.objfname, x.startTime, x.endTime,
x.executionTime
def run(optimizers, objectiveFuncs, NumOfRuns, params, exportFlags):
"""
It serves as the main interface of the framework for running the experiments.
Parameters
----------
optimizers : list
The list of optimizers names
objectiveFuncs : list
The list of benchmark functions
NumOfRuns : int
The number of independent runs
params : set
The set of parameters which are:
1. Size of population (populationSize)
2. The number of iterations (iterations)
exportFlags : set
The set of Boolean flags which are:
1. export (Exporting the results in a file)
2. exportDetails (Exporting the detailed results in files)
3. exportConvergence (Exporting the covergence plots)
4. exportBoxplot (Exporting the box plots)
Returns
-----------
N/A
"""
# Select general parameters for all optimizers (population size, number of iterations) ....
populationSize = params["populationSize"]
iterations = params["iterations"]
# export results ?
export = exportFlags["exportAvg"]
exportDetails = exportFlags["exportDetails"]
exportConvergence = exportFlags["exportConvergence"]
exportBoxplot = exportFlags["exportBoxplot"]
flag = False
flagDetails = False
# CSV Header for for the convergence
cnvgHeader = []
resultsDirectory = time.strftime("%Y-%m-%d-%H-%M-%S") + "/"
Path(resultsDirectory).mkdir(parents=True, exist_ok=True)
for l in range(0, iterations):
cnvgHeader.append("Iter" + str(l + 1))
for _, optimizer in enumerate(optimizers):
for _, objectiveFunc in enumerate(objectiveFuncs):
convergence = [0] * NumOfRuns
executionTime = [0] * NumOfRuns
for k in range(0, NumOfRuns):
func_details = benchmarks.getFunctionDetails(objectiveFunc)
x = selector(optimizer, func_details, populationSize,
iterations)
convergence[k] = x.convergence
optimizerName = x.optimizer
objfname = x.objfname
if exportDetails == True:
exportToFile = resultsDirectory + "experiment_details.csv"
with open(exportToFile, "a", newline="\n") as out:
writer = csv.writer(out, delimiter=",")
if (
flagDetails == False
): # just one time to write the header of the CSV file
header = numpy.concatenate(
[["Optimizer", "objfname", "ExecutionTime"],
cnvgHeader])
writer.writerow(header)
flagDetails = True # at least one experiment
executionTime[k] = x.executionTime
a = numpy.concatenate(
[[x.optimizer, x.objfname, x.executionTime],
x.convergence])
writer.writerow(a)
out.close()
if export == True:
exportToFile = resultsDirectory + "experiment.csv"
with open(exportToFile, "a", newline="\n") as out:
writer = csv.writer(out, delimiter=",")
if (flag == False
): # just one time to write the header of the CSV file
header = numpy.concatenate(
[["Optimizer", "objfname", "ExecutionTime"],
cnvgHeader])
writer.writerow(header)
flag = True
avgExecutionTime = float("%0.2f" %
(sum(executionTime) / NumOfRuns))
avgConvergence = numpy.around(numpy.mean(
convergence, axis=0, dtype=numpy.float64),
decimals=2).tolist()
a = numpy.concatenate(
[[optimizerName, objfname, avgExecutionTime],
avgConvergence])
writer.writerow(a)
out.close()
if exportConvergence == True:
conv_plot.run(resultsDirectory, optimizers, objectiveFuncs, iterations)
if exportBoxplot == True:
box_plot.run(resultsDirectory, optimizers, objectiveFuncs, iterations)
if flag == False: # Faild to run at least one experiment
print(
"No Optomizer or Cost function is selected. Check lists of available optimizers and cost functions"
)
print("Execution completed")
# Check if it works at least once
Flag=False
# CSV Header for for the cinvergence
CnvgHeader=[]
for l in range(0,Iterations):
CnvgHeader.append("Iter"+str(l+1))
for i in range (0, len(optimizer)):
for j in range (0, len(benchmarkfunc)):
if((optimizer[i]==True) and (benchmarkfunc[j]==True)): # start experiment if an optimizer and an objective function is selected
for k in range (0,NumOfRuns):
func_details=benchmarks.getFunctionDetails(j)
x=selector(i,func_details,PopulationSize,Iterations)
if(Export==True):
with open(ExportToFile, 'a',newline='\n') as out:
writer = csv.writer(out,delimiter=',')
if (Flag==False): # just one time to write the header of the CSV file
header= numpy.concatenate([["Optimizer","objfname","startTime","EndTime","ExecutionTime"],CnvgHeader])
writer.writerow(header)
a=numpy.concatenate([[x.optimizer,x.objfname,x.startTime,x.endTime,x.executionTime],x.convergence])
writer.writerow(a)
out.close()
Flag=True # at least one experiment
if (Flag==False): # Faild to run at least one experiment
print("No Optomizer or Cost function is selected. Check lists of available optimizers and cost functions")
def run(optimizer, objectivefunc, NumOfRuns, params, export_flags):
PopulationSize = params["PopulationSize"]
Iterations = params["Iterations"]
Export = export_flags["Export_avg"]
Export_details = export_flags["Export_details"]
Export_convergence = export_flags["Export_convergence"]
Export_boxplot = export_flags["Export_boxplot"]
Flag = False
Flag_details = False
CnvgHeader = []
results_directory = time.strftime("%Y-%m-%d |%H:%M:%S") + "/"
Path(results_directory).mkdir(parents=True, exist_ok=True)
for l in range(0, Iterations):
CnvgHeader.append("Iter" + str(l + 1))
for i in range(0, len(optimizer)):
for j in range(0, len(objectivefunc)):
convergence = [0] * NumOfRuns
executionTime = [0] * NumOfRuns
for k in range(0, NumOfRuns):
func_details = benchmarks.getFunctionDetails(objectivefunc[j])
x = selector(optimizer[i], func_details, PopulationSize,
Iterations)
convergence[k] = x.convergence
optimizerName = x.optimizer
objfname = x.objfname
if Export_details == True:
ExportToFile = results_directory + "experiment_details.csv"
with open(ExportToFile, "a", newline="\n") as out:
writer = csv.writer(out, delimiter=",")
if (Flag_details == False):
header = numpy.concatenate(
[["Optimizer", "objfname", "ExecutionTime"],
CnvgHeader])
writer.writerow(header)
Flag_details = True
executionTime[k] = x.executionTime
a = numpy.concatenate(
[[x.optimizer, x.objfname, x.executionTime],
x.convergence])
writer.writerow(a)
out.close()
if Export == True:
ExportToFile = results_directory + "experiment.csv"
with open(ExportToFile, "a", newline="\n") as out:
writer = csv.writer(out, delimiter=",")
if (Flag == False):
header = numpy.concatenate(
[["Optimizer", "objfname", "ExecutionTime"],
CnvgHeader])
writer.writerow(header)
Flag = True
avgExecutionTime = float("%0.2f" %
(sum(executionTime) / NumOfRuns))
avgConvergence = numpy.around(numpy.mean(
convergence, axis=0, dtype=numpy.float64),
decimals=2).tolist()
a = numpy.concatenate(
[[optimizerName, objfname, avgExecutionTime],
avgConvergence])
writer.writerow(a)
out.close()
if Export_convergence == True:
conv_plot.run(results_directory, optimizer, objectivefunc, Iterations)
if Export_boxplot == True:
box_plot.run(results_directory, optimizer, objectivefunc, Iterations)
if Flag == False:
print(
"Selectionez un optimisateur ou une une fonction a fin de demarrer l'aoptimisation"
)
print("Execution terminée")
