def run(optimizer, objectivefunc, dataset_List, 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 boolean preference of objective functions
dataset_List : list
The list of the names of the data sets files
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_details_labels (Exporting the labels detailed results in files)
4. Export_convergence (Exporting the covergence plots)
5. 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_details_labels = export_flags['Export_details_labels']
Export_convergence = export_flags['Export_convergence']
Export_boxplot = export_flags['Export_boxplot']
#Automaticly generated name by date and time
# Check if it works at least once
Flag = False
Flag_details = False
Flag_details_Labels = False
# CSV Header for for the cinvergence
CnvgHeader = []
datasets_directory = "datasets/" # the directory where the dataset is stored
results_directory = time.strftime("%Y-%m-%d-%H-%M-%S") + '/'
Path(results_directory).mkdir(parents=True, exist_ok=True)
dataset_len = len(dataset_List)
k = [-1] * dataset_len
f = [-1] * dataset_len
points = [0] * dataset_len
labelsTrue = [0] * dataset_len
for l in range(0, Iterations):
CnvgHeader.append("Iter" + str(l + 1))
#read all datasets
for h in range(dataset_len):
dataset_filename = dataset_List[h] + '.csv'
# Read the dataset file and generate the points list and true values
rawData = open(
os.path.join(os.path.abspath(os.path.dirname(__file__)),
datasets_directory + dataset_filename), 'rt')
data = numpy.loadtxt(rawData, delimiter=",")
nPoints, nValues = data.shape #Number of points and Number of values for each point
f[h] = nValues - 1 #Dimension value
k[h] = len(numpy.unique(data[:, -1])) #k: Number of clusters
points[h] = data[:, :-1].tolist() #list of points
labelsTrue[h] = data[:, -1].tolist(
) #List of actual cluster of each points (last field)
points[h] = preprocessing.normalize(points[h], norm='max', axis=0)
for i in range(0, len(optimizer)):
for j in range(0, len(objectivefunc)):
for h in range(len(dataset_List)):
HS = [0] * NumOfRuns
CS = [0] * NumOfRuns
VM = [0] * NumOfRuns
AMI = [0] * NumOfRuns
ARI = [0] * NumOfRuns
Fmeasure = [0] * NumOfRuns
SC = [0] * NumOfRuns
accuracy = [0] * NumOfRuns
DI = [0] * NumOfRuns
DB = [0] * NumOfRuns
stdev = [0] * NumOfRuns
exSSE = [0] * NumOfRuns
exTWCV = [0] * NumOfRuns
purity = [0] * NumOfRuns
entropy = [0] * NumOfRuns
convergence = [0] * NumOfRuns
executionTime = [0] * NumOfRuns
#Agg = [0]*NumOfRuns
for z in range(0, NumOfRuns):
print("Dataset: " + dataset_List[h])
print("Run no.: " + str(z))
print("Population Size: " + str(PopulationSize))
print("Iterations: " + str(Iterations))
objective_name = objectivefunc[j]
x = selector(optimizer[i], objective_name, k[h], f[h],
PopulationSize, Iterations, points[h])
HS[z] = measures.HS(labelsTrue[h], x.labelsPred)
CS[z] = measures.CS(labelsTrue[h], x.labelsPred)
VM[z] = measures.VM(labelsTrue[h], x.labelsPred)
AMI[z] = measures.AMI(labelsTrue[h], x.labelsPred)
ARI[z] = measures.ARI(labelsTrue[h], x.labelsPred)
Fmeasure[z] = measures.Fmeasure(labelsTrue[h],
x.labelsPred)
SC[z] = measures.SC(points[h], x.labelsPred)
accuracy[z] = measures.accuracy(labelsTrue[h],
x.labelsPred)
DI[z] = measures.DI(points[h], x.labelsPred)
DB[z] = measures.DB(points[h], x.labelsPred)
stdev[z] = measures.stdev(x.bestIndividual, x.labelsPred,
k[h], points[h])
exSSE[z] = measures.SSE(x.bestIndividual, x.labelsPred,
k[h], points[h])
exTWCV[z] = measures.TWCV(x.bestIndividual, x.labelsPred,
k[h], points[h])
purity[z] = measures.purity(labelsTrue[h], x.labelsPred)
entropy[z] = measures.entropy(labelsTrue[h], x.labelsPred)
#Agg[z] = float("%0.2f"%(float("%0.2f"%(HS[z] + CS[z] + VM[z] + AMI[z] + ARI[z])) / 5))
executionTime[z] = x.executionTime
convergence[z] = x.convergence
optimizerName = x.optimizer
objfname = x.objfname
if (Export_details_labels == True):
ExportToFileDetailsLabels = results_directory + "experiment_details_Labels.csv"
with open(ExportToFileDetailsLabels, 'a',
newline='\n') as out_details_labels:
writer_details = csv.writer(out_details_labels,
delimiter=',')
if (
Flag_details_Labels == False
): # just one time to write the header of the CSV file
header_details = numpy.concatenate(
[["Dataset", "Optimizer", "objfname"]])
writer_details.writerow(header_details)
Flag_details_Labels = True
a = numpy.concatenate(
[[dataset_List[h], optimizerName, objfname],
x.labelsPred])
writer_details.writerow(a)
out_details_labels.close()
if (Export_details == True):
ExportToFileDetails = results_directory + "experiment_details.csv"
with open(ExportToFileDetails, 'a',
newline='\n') as out_details:
writer_details = csv.writer(out_details,
delimiter=',')
if (
Flag_details == False
): # just one time to write the header of the CSV file
header_details = numpy.concatenate([[
"Dataset", "Optimizer", "objfname",
"ExecutionTime", "SSE", "Purity",
"Entropy", "HS", "CS", "VM", "AMI", "ARI",
"Fmeasure", "TWCV", "SC", "Accuracy", "DI",
"DB", "STDev"
], CnvgHeader])
writer_details.writerow(header_details)
Flag_details = True
a = numpy.concatenate([[
dataset_List[h], optimizerName, objfname,
float("%0.2f" % (executionTime[z])),
float("%0.2f" % (exSSE[z])),
float("%0.2f" % (purity[z])),
float("%0.2f" % (entropy[z])),
float("%0.2f" % (HS[z])),
float("%0.2f" % (CS[z])),
float("%0.2f" % (VM[z])),
float("%0.2f" % (AMI[z])),
float("%0.2f" % (ARI[z])),
float("%0.2f" % (Fmeasure[z])),
float("%0.2f" % (exTWCV[z])),
float("%0.2f" % (SC[z])),
float("%0.2f" % (accuracy[z])),
float("%0.2f" % (DI[z])),
float("%0.2f" % (DB[z])),
float("%0.2f" % (stdev[z]))
],
numpy.around(convergence[z],
decimals=2)])
writer_details.writerow(a)
out_details.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([[
"Dataset", "Optimizer", "objfname",
"ExecutionTime", "SSE", "Purity", "Entropy",
"HS", "CS", "VM", "AMI", "ARI", "Fmeasure",
"TWCV", "SC", "Accuracy", "DI", "DB", "STDev"
], CnvgHeader])
writer.writerow(header)
avgSSE = str(float("%0.2f" % (sum(exSSE) / NumOfRuns)))
avgTWCV = str(
float("%0.2f" % (sum(exTWCV) / NumOfRuns)))
avgPurity = str(
float("%0.2f" % (sum(purity) / NumOfRuns)))
avgEntropy = str(
float("%0.2f" % (sum(entropy) / NumOfRuns)))
avgHomo = str(float("%0.2f" % (sum(HS) / NumOfRuns)))
avgComp = str(float("%0.2f" % (sum(CS) / NumOfRuns)))
avgVmeas = str(float("%0.2f" % (sum(VM) / NumOfRuns)))
avgAMI = str(float("%0.2f" % (sum(AMI) / NumOfRuns)))
avgARI = str(float("%0.2f" % (sum(ARI) / NumOfRuns)))
avgFmeasure = str(
float("%0.2f" % (sum(Fmeasure) / NumOfRuns)))
avgSC = str(float("%0.2f" % (sum(SC) / NumOfRuns)))
avgAccuracy = str(
float("%0.2f" % (sum(accuracy) / NumOfRuns)))
avgDI = str(float("%0.2f" % (sum(DI) / NumOfRuns)))
avgDB = str(float("%0.2f" % (sum(DB) / NumOfRuns)))
avgStdev = str(
float("%0.2f" % (sum(stdev) / NumOfRuns)))
#avgAgg = str(float("%0.2f"%(sum(Agg) / NumOfRuns)))
avgExecutionTime = float(
"%0.2f" % (sum(executionTime) / NumOfRuns))
avgConvergence = numpy.around(numpy.mean(
convergence, axis=0, dtype=numpy.float64),
decimals=2).tolist()
a = numpy.concatenate([[
dataset_List[h], optimizerName, objfname,
avgExecutionTime, avgSSE, avgPurity, avgEntropy,
avgHomo, avgComp, avgVmeas, avgAMI, avgARI,
avgFmeasure, avgTWCV, avgSC, avgAccuracy, avgDI,
avgDB, avgStdev
], avgConvergence])
writer.writerow(a)
out.close()
Flag = True # at least one experiment
if Export_convergence == True:
conv_plot.run(results_directory, optimizer, objectivefunc,
dataset_List, Iterations)
if Export_boxplot == True:
ev_measures = [
'SSE', 'Purity', 'Entropy', 'HS', 'CS', 'VM', 'AMI', 'ARI',
'Fmeasure', 'TWCV', 'SC', 'Accuracy', 'DI', 'DB', 'STDev'
]
box_plot.run(results_directory, optimizer, objectivefunc, dataset_List,
ev_measures, 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")
def run(optimizer,
objectivefunc,
dataset_List,
NumOfRuns,
params,
export_flags,
auto_cluster=True,
n_clusters='supervised',
labels_exist=True,
metric='euclidean'):
"""
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 objective functions
dataset_List : list
The list of the names of the data sets files
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_details_labels (Exporting the labels detailed results in files)
4. Export_convergence (Exporting the covergence plots)
5. Export_boxplot (Exporting the box plots)
auto_cluster : boolean, default = True
Choose whether the number of clusters is detected automatically.
If True, select one of the following: 'supervised', 'CH', 'silhouette', 'elbow', 'gap', 'min', 'max', 'median' for n_clusters.
If False, specify a list of integers for n_clusters.
n_clusters : string, or list, default = 'supervised'
A list of the number of clusters for the datasets in dataset_List
Other values can be considered instead of specifying the real value, which are as follows:
- supervised: The number of clusters is derived from the true labels of the datasets
- elbow: The number of clusters is automatically detected by elbow method
- gap: The number of clusters is automatically detected by gap analysis methos
- silhouette: The number of clusters is automatically detected by silhouette coefficient method
- CH: The number of clusters is automatically detected by Calinski-Harabasz index
- DB: The number of clusters is automatically detected by Davies Bouldin index
- BIC: The number of clusters is automatically detected by Bayesian Information Criterion score
- min: The number of clusters is automatically detected by the minimum value of the number of clusters detected by all detection techniques
- max: The number of clusters is automatically detected by the maximum value of the number of clusters detected by all detection techniques
- median: The number of clusters is automatically detected by the median value of the number of clusters detected by all detection techniques
- majority: The number of clusters is automatically detected by the majority vote of the number of clusters detected by all detection techniques
labels_exist : boolean, default = True
Specify if labels exist as the last column of the csv file of the datasets in dataset_List
if the value is False, the following hold:
- supervised value for n_clusters is not allowed
- experiments, and experiments_details files contain only the evaluation measures for
"SSE","TWCV","SC","DB","DI","STDev"
- Export_boxplot is set for "SSE","TWCV","SC","DB","DI","STDev"
metric : string, default = 'euclidean'
The metric to use when calculating the distance between points if applicable for the objective function selected.
It must be one of the options allowed by scipy.spatial.distance.pdist for its metric parameter
Returns
-----------
N/A
"""
if not labels_exist and n_clusters == 'supervised':
print(
'Syupervised value for n_clusters is not allowed when labels_exist value is false'
)
sys.exit()
if isinstance(n_clusters, list):
if len(n_clusters) != len(dataset_List):
print(
'Length of n_clusters list should equal the length of dataset_List list'
)
sys.exit()
if min(n_clusters) < 2:
print('n_clusters value should be larger than 2')
sys.exit()
if auto_cluster == True:
print('n_clusters should be string if auto_cluster is true')
sys.exit()
else:
if auto_cluster == False:
print(
'n_clusters should be a list of integers if auto_cluster is false'
)
sys.exit()
# 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_details_labels = export_flags['Export_details_labels']
Export_convergence = export_flags['Export_convergence']
Export_boxplot = export_flags['Export_boxplot']
# Check if it works at least once
Flag = False
Flag_details = False
Flag_details_Labels = False
# CSV Header for for the cinvergence
CnvgHeader = []
if labels_exist:
datasets_directory = "datasets/" # the directory where the dataset is stored
else:
datasets_directory = "datasets/unsupervised/" # the directory where the dataset is stored
results_directory = time.strftime("%Y-%m-%d-%H-%M-%S") + '/'
Path(results_directory).mkdir(parents=True, exist_ok=True)
dataset_len = len(dataset_List)
k = [-1] * dataset_len
f = [-1] * dataset_len
points = [0] * dataset_len
labelsTrue = [0] * dataset_len
for l in range(0, Iterations):
CnvgHeader.append("Iter" + str(l + 1))
#read all datasets
for h in range(dataset_len):
dataset_filename = dataset_List[h] + '.csv'
# Read the dataset file and generate the points list and true values
rawData = open(
os.path.join(os.path.abspath(os.path.dirname(__file__)),
datasets_directory + dataset_filename), 'rt')
data = numpy.loadtxt(rawData, delimiter=",")
nPoints, nValues = data.shape #Number of points and Number of values for each point
if labels_exist:
f[h] = nValues - 1 #Dimension value
points[h] = data[:, :-1].tolist() #list of points
labelsTrue[h] = data[:, -1].tolist(
) #List of actual cluster of each points (last field)
else:
f[h] = nValues #Dimension value
points[h] = data.copy().tolist() #list of points
labelsTrue[
h] = None #List of actual cluster of each points (last field)
points[h] = preprocessing.normalize(points[h], norm='max', axis=0)
if n_clusters == 'supervised':
k[h] = len(numpy.unique(data[:, -1])) #k: Number of clusters
elif n_clusters == 'elbow':
k[h] = clus_det.ELBOW(points[h]) #k: Number of clusters
elif n_clusters == 'gap':
k[h] = clus_det.GAP_STATISTICS(points[h]) #k: Number of clusters
elif n_clusters == 'silhouette':
k[h] = clus_det.SC(points[h]) #k: Number of clusters
elif n_clusters == 'DB':
k[h] = clus_det.DB(points[h]) #k: Number of clusters
elif n_clusters == 'CH':
k[h] = clus_det.CH(points[h]) #k: Number of clusters
elif n_clusters == 'DB':
k[h] = clus_det.DB(points[h]) #k: Number of clusters
elif n_clusters == 'BIC':
k[h] = clus_det.BIC(points[h]) #k: Number of clusters
elif n_clusters == 'min':
k[h] = clus_det.min_clusters(points[h]) #k: Number of clusters
elif n_clusters == 'max':
k[h] = clus_det.max_clusters(points[h]) #k: Number of clusters
elif n_clusters == 'median':
k[h] = clus_det.median_clusters(points[h]) #k: Number of clusters
elif n_clusters == 'majority':
k[h] = clus_det.majority_clusters(
points[h]) #k: Number of clusters
else:
k[h] = n_clusters[h] #k: Number of clusters
for i in range(0, len(optimizer)):
for j in range(0, len(objectivefunc)):
for h in range(len(dataset_List)):
HS = [0] * NumOfRuns
CS = [0] * NumOfRuns
VM = [0] * NumOfRuns
AMI = [0] * NumOfRuns
ARI = [0] * NumOfRuns
Fmeasure = [0] * NumOfRuns
SC = [0] * NumOfRuns
accuracy = [0] * NumOfRuns
DI = [0] * NumOfRuns
DB = [0] * NumOfRuns
stdev = [0] * NumOfRuns
exSSE = [0] * NumOfRuns
exTWCV = [0] * NumOfRuns
purity = [0] * NumOfRuns
entropy = [0] * NumOfRuns
convergence = [0] * NumOfRuns
executionTime = [0] * NumOfRuns
#Agg = [0]*NumOfRuns
for z in range(0, NumOfRuns):
print("Dataset: " + dataset_List[h])
print("k: " + str(k[h]))
print("Run no.: " + str(z))
print("Population Size: " + str(PopulationSize))
print("Iterations: " + str(Iterations))
objective_name = objectivefunc[j]
x = selector(optimizer[i], objective_name, k[h], f[h],
PopulationSize, Iterations, points[h], metric)
if labels_exist:
HS[z] = measures.HS(labelsTrue[h], x.labelsPred)
CS[z] = measures.CS(labelsTrue[h], x.labelsPred)
VM[z] = measures.VM(labelsTrue[h], x.labelsPred)
AMI[z] = measures.AMI(labelsTrue[h], x.labelsPred)
ARI[z] = measures.ARI(labelsTrue[h], x.labelsPred)
Fmeasure[z] = measures.Fmeasure(
labelsTrue[h], x.labelsPred)
accuracy[z] = measures.accuracy(
labelsTrue[h], x.labelsPred)
purity[z] = measures.purity(labelsTrue[h],
x.labelsPred)
entropy[z] = measures.entropy(labelsTrue[h],
x.labelsPred)
#Agg[z] = float("%0.2f"%(float("%0.2f"%(HS[z] + CS[z] + VM[z] + AMI[z] + ARI[z])) / 5))
SC[z] = measures.SC(points[h], x.labelsPred)
DI[z] = measures.DI(points[h], x.labelsPred)
DB[z] = measures.DB(points[h], x.labelsPred)
stdev[z] = measures.stdev(x.bestIndividual, x.labelsPred,
k[h], points[h])
exSSE[z] = measures.SSE(x.bestIndividual, x.labelsPred,
k[h], points[h])
exTWCV[z] = measures.TWCV(x.bestIndividual, x.labelsPred,
k[h], points[h])
executionTime[z] = x.executionTime
convergence[z] = x.convergence
optimizerName = x.optimizer
objfname = x.objfname
if (Export_details_labels == True):
ExportToFileDetailsLabels = results_directory + "experiment_details_Labels.csv"
with open(ExportToFileDetailsLabels, 'a',
newline='\n') as out_details_labels:
writer_details = csv.writer(out_details_labels,
delimiter=',')
if (
Flag_details_Labels == False
): # just one time to write the header of the CSV file
header_details = numpy.concatenate(
[["Dataset", "Optimizer", "objfname",
"k"]])
writer_details.writerow(header_details)
Flag_details_Labels = True
a = numpy.concatenate([[
dataset_List[h], optimizerName, objfname, k[h]
], x.labelsPred])
writer_details.writerow(a)
out_details_labels.close()
if (Export_details == True):
ExportToFileDetails = results_directory + "experiment_details.csv"
with open(ExportToFileDetails, 'a',
newline='\n') as out_details:
writer_details = csv.writer(out_details,
delimiter=',')
if (
Flag_details == False
): # just one time to write the header of the CSV file
if labels_exist:
header_details = numpy.concatenate([[
"Dataset", "Optimizer", "objfname",
"k", "ExecutionTime", "SSE", "Purity",
"Entropy", "HS", "CS", "VM", "AMI",
"ARI", "Fmeasure", "TWCV", "SC",
"Accuracy", "DI", "DB", "STDev"
], CnvgHeader])
else:
header_details = numpy.concatenate([[
"Dataset", "Optimizer", "objfname",
"k", "ExecutionTime", "SSE", "TWCV",
"SC", "DI", "DB", "STDev"
], CnvgHeader])
writer_details.writerow(header_details)
Flag_details = True
if labels_exist:
a = numpy.concatenate([[
dataset_List[h], optimizerName, objfname,
k[h],
float("%0.2f" % (executionTime[z])),
float("%0.2f" % (exSSE[z])),
float("%0.2f" % (purity[z])),
float("%0.2f" % (entropy[z])),
float("%0.2f" % (HS[z])),
float("%0.2f" % (CS[z])),
float("%0.2f" % (VM[z])),
float("%0.2f" % (AMI[z])),
float("%0.2f" % (ARI[z])),
float("%0.2f" % (Fmeasure[z])),
float("%0.2f" % (exTWCV[z])),
float("%0.2f" % (SC[z])),
float("%0.2f" % (accuracy[z])),
float("%0.2f" % (DI[z])),
float("%0.2f" % (DB[z])),
float("%0.2f" % (stdev[z]))
],
numpy.around(
convergence[z],
decimals=2)])
else:
a = numpy.concatenate([[
dataset_List[h], optimizerName, objfname,
k[h],
float("%0.2f" % (executionTime[z])),
float("%0.2f" % (exSSE[z])),
float("%0.2f" % (exTWCV[z])),
float("%0.2f" % (SC[z])),
float("%0.2f" % (DI[z])),
float("%0.2f" % (DB[z])),
float("%0.2f" % (stdev[z]))
],
numpy.around(
convergence[z],
decimals=2)])
writer_details.writerow(a)
out_details.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
if labels_exist:
header = numpy.concatenate([[
"Dataset", "Optimizer", "objfname", "k",
"ExecutionTime", "SSE", "Purity",
"Entropy", "HS", "CS", "VM", "AMI", "ARI",
"Fmeasure", "TWCV", "SC", "Accuracy", "DI",
"DB", "STDev"
], CnvgHeader])
else:
header = numpy.concatenate([[
"Dataset", "Optimizer", "objfname", "k",
"ExecutionTime", "SSE", "TWCV", "SC", "DI",
"DB", "STDev"
], CnvgHeader])
writer.writerow(header)
Flag = True # at least one experiment
avgSSE = str(float("%0.2f" % (sum(exSSE) / NumOfRuns)))
avgTWCV = str(
float("%0.2f" % (sum(exTWCV) / NumOfRuns)))
avgPurity = str(
float("%0.2f" % (sum(purity) / NumOfRuns)))
avgEntropy = str(
float("%0.2f" % (sum(entropy) / NumOfRuns)))
avgHomo = str(float("%0.2f" % (sum(HS) / NumOfRuns)))
avgComp = str(float("%0.2f" % (sum(CS) / NumOfRuns)))
avgVmeas = str(float("%0.2f" % (sum(VM) / NumOfRuns)))
avgAMI = str(float("%0.2f" % (sum(AMI) / NumOfRuns)))
avgARI = str(float("%0.2f" % (sum(ARI) / NumOfRuns)))
avgFmeasure = str(
float("%0.2f" % (sum(Fmeasure) / NumOfRuns)))
avgSC = str(float("%0.2f" % (sum(SC) / NumOfRuns)))
avgAccuracy = str(
float("%0.2f" % (sum(accuracy) / NumOfRuns)))
avgDI = str(float("%0.2f" % (sum(DI) / NumOfRuns)))
avgDB = str(float("%0.2f" % (sum(DB) / NumOfRuns)))
avgStdev = str(
float("%0.2f" % (sum(stdev) / NumOfRuns)))
#avgAgg = str(float("%0.2f"%(sum(Agg) / NumOfRuns)))
avgExecutionTime = float(
"%0.2f" % (sum(executionTime) / NumOfRuns))
avgConvergence = numpy.around(numpy.mean(
convergence, axis=0, dtype=numpy.float64),
decimals=2).tolist()
if labels_exist:
a = numpy.concatenate([[
dataset_List[h], optimizerName, objfname, k[h],
avgExecutionTime, avgSSE, avgPurity,
avgEntropy, avgHomo, avgComp, avgVmeas, avgAMI,
avgARI, avgFmeasure, avgTWCV, avgSC,
avgAccuracy, avgDI, avgDB, avgStdev
], avgConvergence])
else:
a = numpy.concatenate([[
dataset_List[h], optimizerName, objfname, k[h],
avgExecutionTime, avgSSE, avgTWCV, avgSC,
avgDI, avgDB, avgStdev
], avgConvergence])
writer.writerow(a)
out.close()
if Export_convergence == True:
conv_plot.run(results_directory, optimizer, objectivefunc,
dataset_List, Iterations)
if Export_boxplot == True:
if labels_exist:
ev_measures = [
'SSE', 'Purity', 'Entropy', 'HS', 'CS', 'VM', 'AMI', 'ARI',
'Fmeasure', 'TWCV', 'SC', 'Accuracy', 'DI', 'DB', 'STDev'
]
else:
ev_measures = ['SSE', 'TWCV', 'SC', 'DI', 'DB', 'STDev']
box_plot.run(results_directory, optimizer, objectivefunc, dataset_List,
ev_measures, Iterations)
print("Execution completed")
for z in range(0, NumOfRuns):
print("Run no.: " + str(z))
func_details = objectives.getFunctionDetails(j)
x = selector(i, func_details, k[h], f[h], PopulationSize,
Iterations, points[h])
HS[z] = measures.HS(labelsTrue[h], x.labelsPred)
CS[z] = measures.CS(labelsTrue[h], x.labelsPred)
VM[z] = measures.VM(labelsTrue[h], x.labelsPred)
AMI[z] = measures.AMI(labelsTrue[h], x.labelsPred)
ARI[z] = measures.ARI(labelsTrue[h], x.labelsPred)
Fmeasure[z] = measures.Fmeasure(labelsTrue[h],
x.labelsPred)
SC[z] = measures.SC(points[h], x.labelsPred)
accuracy[z] = measures.accuracy(labelsTrue[h],
x.labelsPred)
DI[z] = measures.DI(points[h], x.labelsPred)
DB[z] = measures.DB(points[h], x.labelsPred)
stdev[z] = measures.stdev(x.bestIndividual, x.labelsPred,
k[h], points[h])
exSSE[z] = measures.SSE(x.bestIndividual, x.labelsPred,
k[h], points[h])
exTWCV[z] = measures.TWCV(x.bestIndividual, x.labelsPred,
k[h], points[h])
purity[z] = measures.purity(labelsTrue[h], x.labelsPred)
entropy[z] = measures.entropy(labelsTrue[h], x.labelsPred)
#Agg[z] = float("%0.2f"%(float("%0.2f"%(HS[z] + CS[z] + VM[z] + AMI[z] + ARI[z])) / 5))
executionTime[z] = x.executionTime