diff --git a/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py b/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
index 06e66549a8a04707e7874ee979f967bbdf63daae..2f92d2c5a590491cebfd907f4533a7d736d76461 100644
--- a/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
+++ b/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
@@ -573,7 +573,7 @@ def execBenchmark(nbCores, statsIter, nbMulticlass,
multiclassGroundTruth, metrics, classificationIndices,
directories, directory, labelsDictionary, nbExamples, nbLabels)
logging.debug("Done:\t Analyzing predictions")
-
+ DATASET.close()
return results
@@ -594,76 +594,80 @@ def execClassif(arguments):
multiviewAlgos = args.CL_algos_multiview
dataset_list = execution.find_dataset_names(args.pathF, args.type, args.name)
- for name in dataset_list:
-
- directory = execution.initLogFile(name, args.views, args.CL_type,
- args.log, args.debug, args.label,
- args.res_dir, args.add_noise, args.noise_std)
- randomState = execution.initRandomState(args.randomState, directory)
- statsIterRandomStates = execution.initStatsIterRandomStates(statsIter,
- randomState)
-
- getDatabase = execution.getDatabaseFunction(name, args.type)
-
- DATASET, LABELS_DICTIONARY, datasetname = getDatabase(args.views,
- args.pathF, name,
- args.CL_nbClass,
- args.CL_classes,
- randomState,
- args.full,
- args.add_noise,
- args.noise_std)
- args.name = datasetname
-
- splits = execution.genSplits(DATASET.get("Labels").value, args.CL_split,
- statsIterRandomStates)
-
- multiclassLabels, labelsCombinations, indicesMulticlass = Multiclass.genMulticlassLabels(
- DATASET.get("Labels").value, multiclassMethod, splits)
+ if not args.add_noise:
+ args.noise_std=[0.0]
- kFolds = execution.genKFolds(statsIter, args.CL_nbFolds,
- statsIterRandomStates)
-
- datasetFiles = Dataset.initMultipleDatasets(args.pathF, args.name, nbCores)
-
- # if not views:
- # raise ValueError("Empty views list, modify selected views to match dataset " + args.views)
-
- views, viewsIndices, allViews = execution.initViews(DATASET, args.views)
- viewsDictionary = genViewsDictionnary(DATASET, views)
- nbViews = len(views)
- NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
-
- metrics = [metric.split(":") for metric in args.CL_metrics]
- if metrics == [[""]]:
- metricsNames = [name for _, name, isPackage
- in pkgutil.iter_modules(
- ['./MonoMultiViewClassifiers/Metrics']) if
- not isPackage and name not in ["framework", "log_loss",
- "matthews_corrcoef",
- "roc_auc_score"]]
- metrics = [[metricName] for metricName in metricsNames]
- metrics = arangeMetrics(metrics, args.CL_metric_princ)
- for metricIndex, metric in enumerate(metrics):
- if len(metric) == 1:
- metrics[metricIndex] = [metric[0], None]
-
- benchmark = initBenchmark(CL_type, monoviewAlgos, multiviewAlgos, args)
- initKWARGS = initKWARGSFunc(args, benchmark)
- dataBaseTime = time.time() - start
- argumentDictionaries = initMonoviewExps(benchmark, viewsDictionary,
- NB_CLASS, initKWARGS)
- directories = execution.genDirecortiesNames(directory, statsIter)
- benchmarkArgumentDictionaries = execution.genArgumentDictionaries(
- LABELS_DICTIONARY, directories, multiclassLabels,
- labelsCombinations, indicesMulticlass,
- hyperParamSearch, args, kFolds,
- statsIterRandomStates, metrics,
- argumentDictionaries, benchmark, nbViews,
- views, viewsIndices)
- nbMulticlass = len(labelsCombinations)
-
- execBenchmark(nbCores, statsIter, nbMulticlass,
- benchmarkArgumentDictionaries, splits, directories,
- directory, multiclassLabels, metrics, LABELS_DICTIONARY,
- NB_CLASS, DATASET)
+ for name in dataset_list:
+ for noise_std in args.noise_std:
+
+ directory = execution.initLogFile(name, args.views, args.CL_type,
+ args.log, args.debug, args.label,
+ args.res_dir, args.add_noise, noise_std)
+ randomState = execution.initRandomState(args.randomState, directory)
+ statsIterRandomStates = execution.initStatsIterRandomStates(statsIter,
+ randomState)
+
+ getDatabase = execution.getDatabaseFunction(name, args.type)
+
+ DATASET, LABELS_DICTIONARY, datasetname = getDatabase(args.views,
+ args.pathF, name,
+ args.CL_nbClass,
+ args.CL_classes,
+ randomState,
+ args.full,
+ args.add_noise,
+ noise_std)
+ args.name = datasetname
+
+ splits = execution.genSplits(DATASET.get("Labels").value, args.CL_split,
+ statsIterRandomStates)
+
+ multiclassLabels, labelsCombinations, indicesMulticlass = Multiclass.genMulticlassLabels(
+ DATASET.get("Labels").value, multiclassMethod, splits)
+
+ kFolds = execution.genKFolds(statsIter, args.CL_nbFolds,
+ statsIterRandomStates)
+
+ datasetFiles = Dataset.initMultipleDatasets(args.pathF, args.name, nbCores)
+
+ # if not views:
+ # raise ValueError("Empty views list, modify selected views to match dataset " + args.views)
+
+ views, viewsIndices, allViews = execution.initViews(DATASET, args.views)
+ viewsDictionary = genViewsDictionnary(DATASET, views)
+ nbViews = len(views)
+ NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
+
+ metrics = [metric.split(":") for metric in args.CL_metrics]
+ if metrics == [[""]]:
+ metricsNames = [name for _, name, isPackage
+ in pkgutil.iter_modules(
+ ['./MonoMultiViewClassifiers/Metrics']) if
+ not isPackage and name not in ["framework", "log_loss",
+ "matthews_corrcoef",
+ "roc_auc_score"]]
+ metrics = [[metricName] for metricName in metricsNames]
+ metrics = arangeMetrics(metrics, args.CL_metric_princ)
+ for metricIndex, metric in enumerate(metrics):
+ if len(metric) == 1:
+ metrics[metricIndex] = [metric[0], None]
+
+ benchmark = initBenchmark(CL_type, monoviewAlgos, multiviewAlgos, args)
+ initKWARGS = initKWARGSFunc(args, benchmark)
+ dataBaseTime = time.time() - start
+ argumentDictionaries = initMonoviewExps(benchmark, viewsDictionary,
+ NB_CLASS, initKWARGS)
+ directories = execution.genDirecortiesNames(directory, statsIter)
+ benchmarkArgumentDictionaries = execution.genArgumentDictionaries(
+ LABELS_DICTIONARY, directories, multiclassLabels,
+ labelsCombinations, indicesMulticlass,
+ hyperParamSearch, args, kFolds,
+ statsIterRandomStates, metrics,
+ argumentDictionaries, benchmark, nbViews,
+ views, viewsIndices)
+ nbMulticlass = len(labelsCombinations)
+
+ execBenchmark(nbCores, statsIter, nbMulticlass,
+ benchmarkArgumentDictionaries, splits, directories,
+ directory, multiclassLabels, metrics, LABELS_DICTIONARY,
+ NB_CLASS, DATASET)
diff --git a/multiview_platform/MonoMultiViewClassifiers/utils/execution.py b/multiview_platform/MonoMultiViewClassifiers/utils/execution.py
index 3c0b8fee52f1b68c0912d9f07db6f12be69f7d0e..dc8507bbc4f305c693d4bd213811aa9058927002 100644
--- a/multiview_platform/MonoMultiViewClassifiers/utils/execution.py
+++ b/multiview_platform/MonoMultiViewClassifiers/utils/execution.py
@@ -64,10 +64,10 @@ def parseTheArgs(arguments):
help='Use option to bebug implemented algorithms')
groupStandard.add_argument('-add_noise', action='store_true',
help='Use option to add noise to the data')
- groupStandard.add_argument('--noise_std', metavar='FLOAT', action='store',
+ groupStandard.add_argument('--noise_std', metavar='FLOAT', nargs="+", action='store',
help='The std of the gaussian noise that will '
'be added to the data.',
- type=float, default=0.15)
+ type=float, default=[0.0])
groupStandard.add_argument('--res_dir', metavar='STRING', action='store',
help='The path to the result directory',
default="../Results/")
@@ -800,10 +800,7 @@ def initLogFile(name, views, CL_type, log, debug, label, result_directory, add_n
resultsDirectory : string
Reference to the main results directory for the benchmark.
"""
- if add_noise:
- noise_string = "_n_"+str(int(noise_std*100))
- else:
- noise_string = ""
+ noise_string = "/n_"+str(int(noise_std*100))
if debug:
resultDirectory = result_directory + name + noise_string +"/debug_started_" + time.strftime(
"%Y_%m_%d-%H_%M_%S") + "_" + label + "/"