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Commit b2a46bd6 authored by Baptiste Bauvin's avatar Baptiste Bauvin
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Simplified ExecClassif

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Code/MonoMutliViewClassifiers/ExecClassif.py
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......@@ -33,146 +33,7 @@ __status__ = "Prototype" # Production, Development, P
testVersions()
parser = argparse.ArgumentParser(
description='This file is used to benchmark the accuracies fo multiple classification algorithm on multiview data.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
groupStandard = parser.add_argument_group('Standard arguments')
groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
groupStandard.add_argument('--name', metavar='STRING', action='store', help='Name of Database (default: %(default)s)',
default='Plausible')
groupStandard.add_argument('--type', metavar='STRING', action='store', help='Type of database : .hdf5 or .csv',
default='.hdf5')
groupStandard.add_argument('--views', metavar='STRING', action='store',help='Name of the views selected for learning',
default='')
groupStandard.add_argument('--pathF', metavar='STRING', action='store',help='Path to the views (default: %(default)s)',
default='/home/bbauvin/Documents/Data/Data_multi_omics/')
groupStandard.add_argument('--nice', metavar='INT', action='store', type=int,
help='Niceness for the process', default=0)
groupClass = parser.add_argument_group('Classification arguments')
groupClass.add_argument('--CL_split', metavar='FLOAT', action='store',
help='Determine the learning rate if > 1.0, number of fold for cross validation', type=float,
default=0.7)
groupClass.add_argument('--CL_nbFolds', metavar='INT', action='store', help='Number of folds in cross validation',
type=int, default=2 )
groupClass.add_argument('--CL_nb_class', metavar='INT', action='store', help='Number of classes, -1 for all', type=int,
default=2)
groupClass.add_argument('--CL_classes', metavar='STRING', action='store',
help='Classes used in the dataset (names of the folders) if not filled, random classes will be '
'selected ex. walrus:mole:leopard', default="jambon:poney")
groupClass.add_argument('--CL_type', metavar='STRING', action='store',
help='Determine whether to use Multiview, Monoview, or Benchmark, separate with : if multiple',
default='Benchmark')
# groupClass.add_argument('--CL_algorithm', metavar='STRING', action='store',
# help='Determine which classifier to use, if empty, considering all', default='')
groupClass.add_argument('--CL_algos_monoview', metavar='STRING', action='store',
help='Determine which monoview classifier to use, separate with : if multiple, if empty, considering all', default='')
groupClass.add_argument('--CL_algos_multiview', metavar='STRING', action='store',
help='Determine which multiview classifier to use, separate with : if multiple, if empty, considering all', default='')
groupClass.add_argument('--CL_cores', metavar='INT', action='store', help='Number of cores, -1 for all', type=int,
default=1)
groupClass.add_argument('--CL_statsiter', metavar='INT', action='store', help='Number of iteration for each algorithm to mean results', type=int,
default=2)
groupClass.add_argument('--CL_metrics', metavar='STRING', action='store', nargs="+",
help='Determine which metrics to use, separate metric and configuration with ":". If multiple, separate with space. If no metric is specified, considering all with accuracy for classification '
'first one will be used for classification', default=[''])
groupClass.add_argument('--CL_GS_iter', metavar='INT', action='store',
help='Determine how many Randomized grid search tests to do', type=int, default=2)
groupClass.add_argument('--CL_GS_type', metavar='STRING', action='store',
help='Determine which hyperparamter search function use', default="randomizedSearch")
groupRF = parser.add_argument_group('Random Forest arguments')
groupRF.add_argument('--CL_RF_trees', metavar='STRING', action='store', help='GridSearch: Determine the trees',
default='25 75 125 175')
groupRF.add_argument('--CL_RF_max_depth', metavar='STRING', action='store', help='GridSearch: Determine the trees',
default='5:10:15')
groupSVMLinear = parser.add_argument_group('Linear SVM arguments')
groupSVMLinear.add_argument('--CL_SVML_C', metavar='STRING', action='store', help='GridSearch : Penalty parameters used',
default='1:10:100:1000')
groupSVMRBF = parser.add_argument_group('SVW-RBF arguments')
groupSVMRBF.add_argument('--CL_SVMR_C', metavar='STRING', action='store', help='GridSearch : Penalty parameters used',
default='1:10:100:1000')
groupSVMPoly = parser.add_argument_group('Poly SVM arguments')
groupSVMPoly.add_argument('--CL_SVMP_C', metavar='STRING', action='store', help='GridSearch : Penalty parameters used',
default='1:10:100:1000')
groupSVMPoly.add_argument('--CL_SVMP_deg', metavar='STRING', action='store', help='GridSearch : Degree parameters used',
default='1:2:5:10')
groupAdaboost = parser.add_argument_group('Adaboost arguments')
groupAdaboost.add_argument('--CL_Ada_n_est', metavar='STRING', action='store', help='GridSearch : Penalty parameters used',
default='1:10:100:1000')
groupAdaboost.add_argument('--CL_Ada_b_est', metavar='STRING', action='store', help='GridSearch : Degree parameters used',
default='DecisionTreeClassifier')
groupRF = parser.add_argument_group('Decision Trees arguments')
groupRF.add_argument('--CL_DT_depth', metavar='STRING', action='store',
help='GridSearch: Determine max depth for Decision Trees', default='1:3:5:7')
groupSGD = parser.add_argument_group('SGD arguments')
groupSGD.add_argument('--CL_SGD_alpha', metavar='STRING', action='store',
help='GridSearch: Determine alpha for SGDClassifier', default='0.1:0.2:0.5:0.9')
groupSGD.add_argument('--CL_SGD_loss', metavar='STRING', action='store',
help='GridSearch: Determine loss for SGDClassifier', default='log')
groupSGD.add_argument('--CL_SGD_penalty', metavar='STRING', action='store',
help='GridSearch: Determine penalty for SGDClassifier', default='l2')
groupSGD = parser.add_argument_group('KNN arguments')
groupSGD.add_argument('--CL_KNN_neigh', metavar='STRING', action='store',
help='GridSearch: Determine number of neighbors for KNN', default='1:5:10:15')
groupSGD = parser.add_argument_group('SCM arguments')
groupSGD.add_argument('--CL_SCM_max_rules', metavar='STRING', action='store',
help='Max number of rules for SCM', default='1')
groupMumbo = parser.add_argument_group('Mumbo arguments')
groupMumbo.add_argument('--MU_types', metavar='STRING', action='store',
help='Determine which monoview classifier to use with Mumbo',default='DecisionTree')
groupMumbo.add_argument('--MU_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the monoview classifier in Mumbo',
default=['3:1.0', '3:1.0', '3:1.0','3:1.0'])
groupMumbo.add_argument('--MU_iter', metavar='INT', action='store', nargs=3,
help='Max number of iteration, min number of iteration, convergence threshold', type=float,
default=[10,1, 0.01])
groupFusion = parser.add_argument_group('Fusion arguments')
groupFusion.add_argument('--FU_types', metavar='STRING', action='store',
help='Determine which type of fusion to use, if multiple separate with :',
default='LateFusion:EarlyFusion')
groupFusion.add_argument('--FU_early_methods', metavar='STRING', action='store',
help='Determine which early fusion method of fusion to use, if multiple separate with :',
default='')
groupFusion.add_argument('--FU_late_methods', metavar='STRING', action='store',
help='Determine which late fusion method of fusion to use, if multiple separate with :',
default='')
groupFusion.add_argument('--FU_method_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the fusion method', default=[''])
groupFusion.add_argument('--FU_cl_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the monoview classifiers used', default=[''])
groupFusion.add_argument('--FU_cl_names', metavar='STRING', action='store',
help='Names of the classifier used for fusion, one per view separated by :', default='')
groupFusion.add_argument('--FU_fixed', action='store_true',
help='Determine if you want fusion for the monoview classifier in the same order as written')
args = parser.parse_args()
os.nice(args.nice)
nbCores = args.CL_cores
statsIter = args.CL_statsiter
start = time.time()
if args.name not in ["MultiOmic", "ModifiedMultiOmic", "Caltech", "Fake", "Plausible", "KMultiOmic"]:
getDatabase = getattr(DB, "getClassicDB" + args.type[1:])
else:
getDatabase = getattr(DB, "get" + args.name + "DB" + args.type[1:])
try:
gridSearch = args.CL_GS_type
except:
gridSearch = "None"
def initLogFile(args):
directory = os.path.dirname(os.path.abspath(__file__)) + "/Results/"
logFileName = time.strftime("%Y%m%d-%H%M%S") + "-CMultiV-" + args.CL_type + "-" + "_".join(args.views.split(":")) + "-" + args.name + \
"-LOG"
......@@ -190,9 +51,7 @@ logging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s', filename=lo
if args.log:
logging.getLogger().addHandler(logging.StreamHandler())
DATASET, LABELS_DICTIONARY = getDatabase(args.views.split(":"), args.pathF, args.name, len(args.CL_classes), args.CL_classes)
datasetLength = DATASET.get("Metadata").attrs["datasetLength"]
def initMultipleDatasets(args, nbCores):
if nbCores>1:
if DB.datasetsAlreadyExist(args.pathF, args.name, nbCores):
logging.debug("Info:\t Enough copies of the dataset are already available")
......@@ -204,28 +63,23 @@ if nbCores>1:
time.sleep(5)
datasetFiles = DB.copyHDF5(args.pathF, args.name, nbCores)
logging.debug("Start:\t Creating datasets for multiprocessing")
return datasetFiles
def initViews(DATASET, args):
NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
if args.views!="":
allowedViews = args.views.split(":")
allViews = [str(DATASET.get("View"+str(viewIndex)).attrs["name"]) for viewIndex in range(NB_VIEW)]
views = [str(DATASET.get("View"+str(viewIndex)).attrs["name"]) for viewIndex in range(NB_VIEW) if str(DATASET.get("View"+str(viewIndex)).attrs["name"]) in allowedViews]
viewsIndices = [viewIndex for viewIndex in range(NB_VIEW) if str(DATASET.get("View"+str(viewIndex)).attrs["name"]) in allowedViews]
return views, viewsIndices, allViews
else:
views = [str(DATASET.get("View"+str(viewIndex)).attrs["name"]) for viewIndex in range(NB_VIEW)]
viewsIndices = np.arange(NB_VIEW)
allViews = views
if not views:
raise ValueError, "Empty views list, modify selected views to match dataset "+args.views
NB_VIEW = 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(['Metrics']) if not isPackage and name!="log_loss"]
metrics = [[metricName, None] for metricName in metricsNames]
return views, viewsIndices, allViews
logging.info("Start:\t Finding all available mono- & multiview algorithms")
def initBenchmark(args):
benchmark = {"Monoview":{}, "Multiview":[]}
if args.CL_type.split(":")==["Benchmark"]:
# if args.CL_algorithm=='':
......@@ -282,28 +136,11 @@ if "Multiview" in args.CL_type.strip(":"):
and (name!="SCM")]
else:
benchmark["Multiview"]["Fusion"]["Classifiers"] = args.CL_algos_monoview.split(":")
if "Monoview" in args.CL_type.strip(":"):
benchmark["Monoview"] = args.CL_algos_monoview.split(":")
return benchmark
fusionMethodConfig = [args.FU_method_config[0].split(":"), "b"]
RandomForestKWARGSInit = {"0":map(int, args.CL_RF_trees.split())[0], "1":map(int, args.CL_RF_max_depth.split(":"))[0]}
SVMLinearKWARGSInit = {"0":map(int, args.CL_SVML_C.split(":"))[0]}
SVMRBFKWARGSInit = {"0":map(int, args.CL_SVMR_C.split(":"))[0]}
SVMPolyKWARGSInit = {"0":map(int, args.CL_SVMP_C.split(":"))[0], '1':map(int, args.CL_SVMP_deg.split(":"))[0]}
DecisionTreeKWARGSInit = {"0":map(int, args.CL_DT_depth.split(":"))[0]}
SGDKWARGSInit = {"2": map(float, args.CL_SGD_alpha.split(":"))[0], "1": args.CL_SGD_penalty.split(":")[0],
"0":args.CL_SGD_loss.split(":")[0]}
KNNKWARGSInit = {"0": map(float, args.CL_KNN_neigh.split(":"))[0]}
AdaboostKWARGSInit = {"0": args.CL_Ada_n_est.split(":")[0], "1": args.CL_Ada_b_est.split(":")[0]}
SCMKWARGSInit = {"0":args.CL_SCM_max_rules.split(":")[0]}
dataBaseTime = time.time()-start
argumentDictionaries = {"Monoview": {}, "Multiview": []}
def initMonoviewArguments(benchmark, argumentDictionaries, views, allViews, DATASET, NB_CLASS, kwargsInit):
try:
if benchmark["Monoview"]:
argumentDictionaries["Monoview"] = []
......@@ -311,44 +148,35 @@ try:
for classifier in benchmark["Monoview"]:
if classifier=="SCM":
if DATASET.get("View"+str(allViews.index(view))).attrs["binary"]:
arguments = {"args":{classifier+"KWARGS": globals()[classifier+"KWARGSInit"], "feat":view,
arguments = {"args":{classifier+"KWARGS": kwargsInit[classifier+"KWARGSInit"], "feat":view,
"CL_type": classifier, "nbClass":NB_CLASS}, "viewIndex":allViews.index(view)}
argumentDictionaries["Monoview"].append(arguments)
else:
pass
else:
arguments = {"args":{classifier+"KWARGS": globals()[classifier+"KWARGSInit"], "feat":view,
arguments = {"args":{classifier+"KWARGS": kwargsInit[classifier+"KWARGSInit"], "feat":view,
"CL_type": classifier, "nbClass":NB_CLASS}, "viewIndex":allViews.index(view)}
argumentDictionaries["Monoview"].append(arguments)
except:
pass
bestClassifiers = []
bestClassifiersConfigs = []
resultsMonoview = []
labelsNames = LABELS_DICTIONARY.values()
if nbCores>1:
nbExperiments = len(argumentDictionaries["Monoview"])
for stepIndex in range(int(math.ceil(float(nbExperiments)/nbCores))):
resultsMonoview+=(Parallel(n_jobs=nbCores)(
delayed(ExecMonoview_multicore)(args.name, labelsNames, args.CL_split, args.CL_nbFolds, coreIndex, args.type, args.pathF, statsIter, gridSearch=gridSearch,
metrics=metrics, nIter=args.CL_GS_iter, **argumentDictionaries["Monoview"][coreIndex + stepIndex * nbCores])
for coreIndex in range(min(nbCores, nbExperiments - stepIndex * nbCores))))
accuracies = [[result[1][1] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
classifiersNames = [[result[1][0] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
classifiersConfigs = [[result[1][1][:-1] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
else:
resultsMonoview+=([ExecMonoview(DATASET.get("View"+str(arguments["viewIndex"])),
DATASET.get("Labels").value, args.name, labelsNames,
args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF, statsIter,
gridSearch=gridSearch, metrics=metrics, nIter=args.CL_GS_iter,
**arguments)
for arguments in argumentDictionaries["Monoview"]])
accuracies = [[result[1][2][metrics[0][0]][2] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in viewsIndices]
classifiersNames = [[result[1][0] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in viewsIndices]
classifiersConfigs = [[result[1][1][:-1] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in viewsIndices]
monoviewTime = time.time()-dataBaseTime-start
if True:
return argumentDictionaries
def initKWARGS(args):
kwargsInit={
"RandomForestKWARGSInit" : {"0":map(int, args.CL_RF_trees.split())[0], "1":map(int, args.CL_RF_max_depth.split(":"))[0]},
"SVMLinearKWARGSInit" : {"0":map(int, args.CL_SVML_C.split(":"))[0]},
"SVMRBFKWARGSInit" : {"0":map(int, args.CL_SVMR_C.split(":"))[0]},
"SVMPolyKWARGSInit" : {"0":map(int, args.CL_SVMP_C.split(":"))[0], '1':map(int, args.CL_SVMP_deg.split(":"))[0]},
"DecisionTreeKWARGSInit" : {"0":map(int, args.CL_DT_depth.split(":"))[0]},
"SGDKWARGSInit" : {"2": map(float, args.CL_SGD_alpha.split(":"))[0], "1": args.CL_SGD_penalty.split(":")[0],
"0":args.CL_SGD_loss.split(":")[0]},
"KNNKWARGSInit" : {"0": map(float, args.CL_KNN_neigh.split(":"))[0]},
"AdaboostKWARGSInit" : {"0": args.CL_Ada_n_est.split(":")[0], "1": args.CL_Ada_b_est.split(":")[0]},
"SCMKWARGSInit" : {"0":args.CL_SCM_max_rules.split(":")[0]},
}
return kwargsInit
def initMultiviewArguments(args, benchmark, views, viewsIndices, accuracies, classifiersConfigs, classifiersNames, fusionMethodConfig, NB_VIEW):
if benchmark["Multiview"]:
try:
if benchmark["Multiview"]["Fusion"]:
......@@ -454,8 +282,211 @@ if True:
argumentDictionaries["Multiview"].append(arguments)
except:
pass
return argumentDictionaries
parser = argparse.ArgumentParser(
description='This file is used to benchmark the accuracies fo multiple classification algorithm on multiview data.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
groupStandard = parser.add_argument_group('Standard arguments')
groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
groupStandard.add_argument('--name', metavar='STRING', action='store', help='Name of Database (default: %(default)s)',
default='Plausible')
groupStandard.add_argument('--type', metavar='STRING', action='store', help='Type of database : .hdf5 or .csv (default: %(default)s)',
default='.hdf5')
groupStandard.add_argument('--views', metavar='STRING', action='store',help='Name of the views selected for learning (default: %(default)s)',
default='')
groupStandard.add_argument('--pathF', metavar='STRING', action='store',help='Path to the views (default: %(default)s)',
default='/home/bbauvin/Documents/Data/Data_multi_omics/')
groupStandard.add_argument('--nice', metavar='INT', action='store', type=int,
help='Niceness for the process', default=0)
groupClass = parser.add_argument_group('Classification arguments')
groupClass.add_argument('--CL_split', metavar='FLOAT', action='store',
help='Determine the learning rate if > 1.0, number of fold for cross validation', type=float,
default=0.7)
groupClass.add_argument('--CL_nbFolds', metavar='INT', action='store', help='Number of folds in cross validation',
type=int, default=2 )
groupClass.add_argument('--CL_nb_class', metavar='INT', action='store', help='Number of classes, -1 for all', type=int,
default=2)
groupClass.add_argument('--CL_classes', metavar='STRING', action='store',
help='Classes used in the dataset (names of the folders) if not filled, random classes will be '
'selected ex. walrus:mole:leopard', default="jambon:poney")
groupClass.add_argument('--CL_type', metavar='STRING', action='store',
help='Determine whether to use Multiview, Monoview, or Benchmark, separate with : if multiple',
default='Benchmark')
# groupClass.add_argument('--CL_algorithm', metavar='STRING', action='store',
# help='Determine which classifier to use, if empty, considering all', default='')
groupClass.add_argument('--CL_algos_monoview', metavar='STRING', action='store',
help='Determine which monoview classifier to use, separate with : if multiple, if empty, considering all', default='')
groupClass.add_argument('--CL_algos_multiview', metavar='STRING', action='store',
help='Determine which multiview classifier to use, separate with : if multiple, if empty, considering all', default='')
groupClass.add_argument('--CL_cores', metavar='INT', action='store', help='Number of cores, -1 for all', type=int,
default=1)
groupClass.add_argument('--CL_statsiter', metavar='INT', action='store', help='Number of iteration for each algorithm to mean results', type=int,
default=2)
groupClass.add_argument('--CL_metrics', metavar='STRING', action='store', nargs="+",
help='Determine which metrics to use, separate metric and configuration with ":". If multiple, separate with space. If no metric is specified, considering all with accuracy for classification '
'first one will be used for classification', default=[''])
groupClass.add_argument('--CL_GS_iter', metavar='INT', action='store',
help='Determine how many Randomized grid search tests to do', type=int, default=2)
groupClass.add_argument('--CL_GS_type', metavar='STRING', action='store',
help='Determine which hyperparamter search function use', default="randomizedSearch")
groupRF = parser.add_argument_group('Random Forest arguments')
groupRF.add_argument('--CL_RF_trees', metavar='STRING', action='store', help='Number max trees',
default='25')
groupRF.add_argument('--CL_RF_max_depth', metavar='STRING', action='store', help='Max depth for the trees',
default='5')
groupSVMLinear = parser.add_argument_group('Linear SVM arguments')
groupSVMLinear.add_argument('--CL_SVML_C', metavar='STRING', action='store', help='Penalty parameter used',
default='1')
groupSVMRBF = parser.add_argument_group('SVW-RBF arguments')
groupSVMRBF.add_argument('--CL_SVMR_C', metavar='STRING', action='store', help='Penalty parameter used',
default='1')
groupSVMPoly = parser.add_argument_group('Poly SVM arguments')
groupSVMPoly.add_argument('--CL_SVMP_C', metavar='STRING', action='store', help='Penalty parameter used',
default='1')
groupSVMPoly.add_argument('--CL_SVMP_deg', metavar='STRING', action='store', help='Degree parameter used',
default='2')
groupAdaboost = parser.add_argument_group('Adaboost arguments')
groupAdaboost.add_argument('--CL_Ada_n_est', metavar='STRING', action='store', help='Number of estimators',
default='2')
groupAdaboost.add_argument('--CL_Ada_b_est', metavar='STRING', action='store', help='Estimators',
default='DecisionTreeClassifier')
groupRF = parser.add_argument_group('Decision Trees arguments')
groupRF.add_argument('--CL_DT_depth', metavar='STRING', action='store',
help='Determine max depth for Decision Trees', default='3')
groupSGD = parser.add_argument_group('SGD arguments')
groupSGD.add_argument('--CL_SGD_alpha', metavar='STRING', action='store',
help='Determine alpha for SGDClassifier', default='0.1')
groupSGD.add_argument('--CL_SGD_loss', metavar='STRING', action='store',
help='Determine loss for SGDClassifier', default='log')
groupSGD.add_argument('--CL_SGD_penalty', metavar='STRING', action='store',
help='Determine penalty for SGDClassifier', default='l2')
groupSGD = parser.add_argument_group('KNN arguments')
groupSGD.add_argument('--CL_KNN_neigh', metavar='STRING', action='store',
help='Determine number of neighbors for KNN', default='1')
groupSGD = parser.add_argument_group('SCM arguments')
groupSGD.add_argument('--CL_SCM_max_rules', metavar='STRING', action='store',
help='Max number of rules for SCM', default='1')
groupMumbo = parser.add_argument_group('Mumbo arguments')
groupMumbo.add_argument('--MU_types', metavar='STRING', action='store',
help='Determine which monoview classifier to use with Mumbo',default='DecisionTree')
groupMumbo.add_argument('--MU_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the monoview classifier in Mumbo',
default=[''])
groupMumbo.add_argument('--MU_iter', metavar='INT', action='store', nargs=3,
help='Max number of iteration, min number of iteration, convergence threshold', type=float,
default=[10,1, 0.01])
groupFusion = parser.add_argument_group('Fusion arguments')
groupFusion.add_argument('--FU_types', metavar='STRING', action='store',
help='Determine which type of fusion to use, if multiple separate with :',
default='LateFusion:EarlyFusion')
groupFusion.add_argument('--FU_early_methods', metavar='STRING', action='store',
help='Determine which early fusion method of fusion to use, if multiple separate with :',
default='')
groupFusion.add_argument('--FU_late_methods', metavar='STRING', action='store',
help='Determine which late fusion method of fusion to use, if multiple separate with :',
default='')
groupFusion.add_argument('--FU_method_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the fusion method', default=[''])
groupFusion.add_argument('--FU_cl_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the monoview classifiers used', default=[''])
groupFusion.add_argument('--FU_cl_names', metavar='STRING', action='store',
help='Names of the classifier used for fusion, one per view separated by :', default='')
groupFusion.add_argument('--FU_fixed', action='store_true',
help='Determine if you want fusion for the monoview classifier in the same order as written')
args = parser.parse_args()
os.nice(args.nice)
nbCores = args.CL_cores
statsIter = args.CL_statsiter
start = time.time()
if args.name not in ["MultiOmic", "ModifiedMultiOmic", "Caltech", "Fake", "Plausible", "KMultiOmic"]:
getDatabase = getattr(DB, "getClassicDB" + args.type[1:])
else:
pass
getDatabase = getattr(DB, "get" + args.name + "DB" + args.type[1:])
try:
gridSearch = args.CL_GS_type
except:
gridSearch = False
initLogFile(args)
DATASET, LABELS_DICTIONARY = getDatabase(args.views.split(":"), args.pathF, args.name, len(args.CL_classes), args.CL_classes)
# datasetLength = DATASET.get("Metadata").attrs["datasetLength"]
datasetFiles = initMultipleDatasets(args, nbCores)
views, viewsIndices, allViews = initViews(DATASET, args)
if not views:
raise ValueError, "Empty views list, modify selected views to match dataset "+args.views
NB_VIEW = 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(['Metrics']) if not isPackage and name!="log_loss"]
metrics = [[metricName, None] for metricName in metricsNames]
logging.info("Start:\t Finding all available mono- & multiview algorithms")
benchmark = initBenchmark(args)
fusionMethodConfig = [args.FU_method_config[0].split(":"), "b"]
initKWARGS = initKWARGS(args)
dataBaseTime = time.time()-start
argumentDictionaries = {"Monoview": {}, "Multiview": []}
argumentDictionaries = initMonoviewArguments(benchmark, argumentDictionaries, views, allViews, DATASET, NB_CLASS, initKWARGS)
bestClassifiers = []
bestClassifiersConfigs = []
resultsMonoview = []
labelsNames = LABELS_DICTIONARY.values()
if nbCores>1:
nbExperiments = len(argumentDictionaries["Monoview"])
for stepIndex in range(int(math.ceil(float(nbExperiments)/nbCores))):
resultsMonoview+=(Parallel(n_jobs=nbCores)(
delayed(ExecMonoview_multicore)(args.name, labelsNames, args.CL_split, args.CL_nbFolds, coreIndex, args.type, args.pathF, statsIter, gridSearch=gridSearch,
metrics=metrics, nIter=args.CL_GS_iter, **argumentDictionaries["Monoview"][coreIndex + stepIndex * nbCores])
for coreIndex in range(min(nbCores, nbExperiments - stepIndex * nbCores))))
accuracies = [[result[1][1] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
classifiersNames = [[result[1][0] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
classifiersConfigs = [[result[1][1][:-1] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
else:
resultsMonoview+=([ExecMonoview(DATASET.get("View"+str(arguments["viewIndex"])),
DATASET.get("Labels").value, args.name, labelsNames,
args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF, statsIter,
gridSearch=gridSearch, metrics=metrics, nIter=args.CL_GS_iter,
**arguments)
for arguments in argumentDictionaries["Monoview"]])
accuracies = [[result[1][2][metrics[0][0]][2] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in viewsIndices]
classifiersNames = [[result[1][0] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in viewsIndices]
classifiersConfigs = [[result[1][1][:-1] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in viewsIndices]
monoviewTime = time.time()-dataBaseTime-start
argumentDictionaries = initMultiviewArguments(args, benchmark, views, viewsIndices, accuracies, classifiersConfigs, classifiersNames, fusionMethodConfig, NB_VIEW)
if nbCores>1:
resultsMultiview = []
nbExperiments = len(argumentDictionaries["Multiview"])
......
Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/DecisionTree.py
+ 1
− 0
View file @ b2a46bd6
......@@ -26,6 +26,7 @@ def DecisionTree(data, labels, arg, weights):
def getConfig(classifierConfig):
print classifierConfig
depth = classifierConfig[0]
subSampling = classifierConfig[1]
return 'with depth ' + str(depth) + ', ' + ' sub-sampled at ' + str(subSampling) + ' '
......
Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
+ 5
− 4
View file @ b2a46bd6
......@@ -89,7 +89,7 @@ def getDBConfig(DATASET, LEARNING_RATE, nbFolds, databaseName, validationIndices
return DBString, viewNames
def getAlgoConfig(initKWARGS, NB_CORES, viewNames, gridSearch, nIter, times):
def getAlgoConfig(classifiersIterations, initKWARGS, NB_CORES, viewNames, gridSearch, nIter, times):
classifierNames = initKWARGS["classifiersNames"]
maxIter = initKWARGS["maxIter"]
minIter = initKWARGS["minIter"]
......@@ -102,13 +102,14 @@ def getAlgoConfig(initKWARGS, NB_CORES, viewNames, gridSearch, nIter, times):
# kFoldPredictionTime = [np.mean(np.array([kFoldPredictionTime[statsIterIndex][foldIdx]
# for statsIterIndex in range(len(kFoldPredictionTime))]))
# for foldIdx in range(len(kFoldPredictionTime[0]))]
weakClassifierConfigs = [getattr(globals()[classifierName], 'getConfig')(classifiersConfig) for classifiersConfig,
classifierName
in zip(classifiersConfig, classifierNames)]
in zip(classifiersIterations[0].classifiersConfigs, classifiersIterations[0].classifiersNames)]
classifierAnalysis = [classifierName + " " + weakClassifierConfig + "on " + feature for classifierName,
weakClassifierConfig,
feature
in zip(classifierNames, weakClassifierConfigs, viewNames)]
in zip(classifiersIterations[0].classifiersNames, weakClassifierConfigs, viewNames)]
gridSearchString = ""
if gridSearch:
gridSearchString += "Configurations found by randomized search with "+str(nIter)+" iterations"
......@@ -423,7 +424,7 @@ def execute(classifiersIterations, trainLabelsIterations,testLabelsIterations, D
CLASS_LABELS = DATASET.get("Labels")[...]
dbConfigurationString, viewNames = getDBConfig(DATASET, LEARNING_RATE, nbFolds, databaseName, validationIndices, LABELS_DICTIONARY)
algoConfigurationString, classifierAnalysis = getAlgoConfig(initKWARGS, NB_CORES, viewNames, gridSearch, nIter, times)
algoConfigurationString, classifierAnalysis = getAlgoConfig(classifiersIterations, initKWARGS, NB_CORES, viewNames, gridSearch, nIter, times)
(totalScoreOnTrain, totalScoreOnTest, meanAverageAccuracies, viewsStats, scoresOnTainByIter,
......
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