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ExecClassif.py 30.75 KiB
# Import built-in modules
import argparse
import pkgutil # for TimeStamp in CSVFile
import os
import time # for time calculations
import operator
import itertools
# Import 3rd party modules
from joblib import Parallel, delayed
import numpy as np
import logging
import matplotlib
matplotlib.use('Agg')
import math
import time
# Import own modules
import Multiview
from Multiview.ExecMultiview import ExecMultiview, ExecMultiview_multicore
from Monoview.ExecClassifMonoView import ExecMonoview, ExecMonoview_multicore
import Multiview.GetMultiviewDb as DB
import Monoview
from ResultAnalysis import resultAnalysis
from Versions import testVersions
import MonoviewClassifiers
# Author-Info
__author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
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='MultiOmic')
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('--fileCL', metavar='STRING', action='store',
help='Name of classLabels CSV-file (default: %(default)s)', default='classLabels.csv')
groupStandard.add_argument('--fileCLD', metavar='STRING', action='store',
help='Name of classLabels-Description CSV-file (default: %(default)s)',
default='classLabels-Description.csv')
groupStandard.add_argument('--fileFeat', metavar='STRING', action='store',
help='Name of feature CSV-file (default: %(default)s)', default='feature.csv')
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=5 )
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=1)
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=30)
groupClass.add_argument('--CL_NoGS', action='store_false',
help='Determine how many Randomized grid search tests to do')
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_ealy_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_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_NoGS
except:
gridSearch = True
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"
logFile = directory + logFileName
if os.path.isfile(logFile + ".log"):
for i in range(1, 20):
testFileName = logFileName + "-" + str(i) + ".log"
if not (os.path.isfile(directory + testFileName)):
logfile = directory + testFileName
break
else:
logFile += ".log"
logging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s', filename=logFile, level=logging.DEBUG,
filemode='w')
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"]
if nbCores>1:
if DB.datasetsAlreadyExist(args.pathF, args.name, nbCores):
logging.debug("Info:\t Enough copies of the dataset are already available")
pass
else:
logging.debug("Start:\t Creating "+str(nbCores)+" temporary datasets for multiprocessing")
logging.warning(" WARNING : /!\ This may use a lot of HDD storage space : "+
str(os.path.getsize(args.pathF+args.name+".hdf5")*nbCores/float(1024)/1000/1000)+" Gbytes /!\ ")
time.sleep(5)
datasetFiles = DB.copyHDF5(args.pathF, args.name, nbCores)
logging.debug("Start:\t Creating datasets for multiprocessing")
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]
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]
logging.info("Start:\t Finding all available mono- & multiview algorithms")
benchmark = {"Monoview":{}, "Multiview":[]}
if args.CL_type.split(":")==["Benchmark"]:
# if args.CL_algorithm=='':
fusionModulesNames = [name for _, name, isPackage
in pkgutil.iter_modules(['Multiview/Fusion/Methods']) if not isPackage]
fusionModules = [getattr(Multiview.Fusion.Methods, fusionModulesName)
for fusionModulesName in fusionModulesNames]
fusionClasses = [getattr(fusionModule, fusionModulesName+"Classifier")
for fusionModulesName, fusionModule in zip(fusionModulesNames, fusionModules)]
fusionMethods = dict((fusionModulesName, [name for _, name, isPackage in
pkgutil.iter_modules(["Multiview/Fusion/Methods/"+fusionModulesName+"Package"])
if not isPackage])
for fusionModulesName, fusionClasse in zip(fusionModulesNames, fusionClasses))
allMonoviewAlgos = [name for _, name, isPackage in
pkgutil.iter_modules(['MonoviewClassifiers'])
if (not isPackage) and (name!="SGD") and (name[:3]!="SVM") and (name!="SCM")]
fusionMonoviewClassifiers = allMonoviewAlgos
allFusionAlgos = {"Methods": fusionMethods, "Classifiers": fusionMonoviewClassifiers}
allMumboAlgos = [name for _, name, isPackage in
pkgutil.iter_modules(['Multiview/Mumbo/Classifiers'])
if not isPackage and not name in ["SubSampling", "ModifiedMulticlass", "Kover"]]
allMultiviewAlgos = {"Fusion": allFusionAlgos, "Mumbo": allMumboAlgos}
benchmark = {"Monoview": allMonoviewAlgos, "Multiview": allMultiviewAlgos}
if "Multiview" in args.CL_type.strip(":"):
benchmark["Multiview"] = {}
if args.CL_algos_multiview.split(":") == [""]:
algosMutliview = ["Mumbo", "Fusion"]
else:
algosMutliview = args.CL_algos_multiview.split(":")
if "Mumbo" in algosMutliview:
benchmark["Multiview"]["Mumbo"] = args.MU_types.split(":")
if "Fusion" in algosMutliview:
benchmark["Multiview"]["Fusion"]= {}
benchmark["Multiview"]["Fusion"]["Methods"] = dict((fusionType, []) for fusionType in args.FU_types.split(":"))
if "LateFusion" in args.FU_types.split(":"):
if args.FU_late_methods.split(":") == [""]:
benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"] = [name for _, name, isPackage in
pkgutil.iter_modules(["Multiview/Fusion/Methods/LateFusionPackage"])
if not isPackage]
else:
benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"] = args.FU_late_methods.split(":")
if "EarlyFusion" in args.FU_types.split(":"):
if args.FU_late_methods.split(":") == [""]:
benchmark["Multiview"]["Fusion"]["Methods"]["EarlyFusion"] = [name for _, name, isPackage in
pkgutil.iter_modules(["Multiview/Fusion/Methods/EarlyFusionPackage"])
if not isPackage]
else:
benchmark["Multiview"]["Fusion"]["Methods"]["EarlyFusion"] = args.FU_early_methods.split(":")
if args.CL_algos_monoview.split(":")==['']:
benchmark["Multiview"]["Fusion"]["Classifiers"] = [name for _, name, isPackage in
pkgutil.iter_modules(['MonoviewClassifiers'])
if (not isPackage) and (name!="SGD") and (name[:3]!="SVM")
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(":")
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": []}
try:
if benchmark["Monoview"]:
argumentDictionaries["Monoview"] = []
for view in views:
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, "fileFeat": args.fileFeat,
"fileCL": args.fileCL, "fileCLD": args.fileCLD, "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, "fileFeat": args.fileFeat,
"fileCL": args.fileCL, "fileCLD": args.fileCLD, "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][2] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
# for viewIndex, view in enumerate(views):
# bestClassifiers.append(classifiersNames[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
# bestClassifiersConfigs.append(classifiersConfigs[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
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:
if benchmark["Multiview"]:
try:
if benchmark["Multiview"]["Mumbo"]:
for combination in itertools.combinations_with_replacement(range(len(benchmark["Multiview"]["Mumbo"])), NB_VIEW):
mumboClassifiersNames = [benchmark["Multiview"]["Mumbo"][index] for index in combination]
arguments = {"CL_type": "Mumbo",
"views": views,
"NB_VIEW": len(views),
"viewsIndices": viewsIndices,
"NB_CLASS": len(args.CL_classes.split(":")),
"LABELS_NAMES": args.CL_classes.split(":"),
"MumboKWARGS": {"classifiersNames": mumboClassifiersNames,
"maxIter":int(args.MU_iter[0]), "minIter":int(args.MU_iter[1]),
"threshold":args.MU_iter[2],
"classifiersConfigs": [argument.split(":") for argument in args.MU_config]}}
argumentDictionaries["Multiview"].append(arguments)
except:
pass
if True:
if benchmark["Multiview"]["Fusion"]:
if args.CL_algos_monoview !=['']:
monoClassifiers = args.CL_algos_monoview.split(":")
monoClassifiersConfigs = [classifier+"KWARGS" for classifier in monoClassifiers]
if args.FU_method_config != [""]:
fusionMethodConfigs = [map(float,config.split(":")) for config in args.FU_method_config]
elif not gridSearch:
raise ValueError("No config for fusion method given and no gridearch wanted")
else:
fusionMethodConfigs = ["" for method in benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"]]
for methodIndex, method in enumerate(benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"]):
if args.FU_fixed:
arguments = {"CL_type": "Fusion",
"views": views,
"NB_VIEW": len(views),
"viewsIndices": viewsIndices,
"NB_CLASS": len(args.CL_classes.split(":")),
"LABELS_NAMES": args.CL_classes.split(":"),
"FusionKWARGS": {"fusionType":"LateFusion", "fusionMethod":method,
"classifiersNames": monoClassifiers,
"classifiersConfigs": monoClassifiersConfigs,
'fusionMethodConfig': fusionMethodConfigs[methodIndex]}}
argumentDictionaries["Multiview"].append(arguments)
else:
for combination in itertools.combinations_with_replacement(range(len(monoClassifiers)), NB_VIEW):
monoClassifiersNamesComb = [monoClassifiers[index] for index in combination]
monoClassifiersConfigsComb = [monoClassifiersConfigs[index] for index in combination]
arguments = {"CL_type": "Fusion",
"views": views,
"NB_VIEW": len(views),
"viewsIndices": viewsIndices,
"NB_CLASS": len(args.CL_classes.split(":")),
"LABELS_NAMES": args.CL_classes.split(":"),
"FusionKWARGS": {"fusionType":"LateFusion", "fusionMethod":method,
"classifiersNames": monoClassifiersNamesComb,
"classifiersConfigs": monoClassifiersConfigsComb,
'fusionMethodConfig': fusionMethodConfigs[methodIndex]}}
argumentDictionaries["Multiview"].append(arguments)
else:
try:
if benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"] and benchmark["Multiview"]["Fusion"]["Classifiers"]:
bestClassifiers = []
bestClassifiersConfigs = []
if argumentDictionaries["Monoview"] != []:
for viewIndex, view in enumerate(views):
bestClassifiers.append(classifiersNames[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
bestClassifiersConfigs.append(classifiersConfigs[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
for method in benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"]:
arguments = {"CL_type": "Fusion",
"views": views,
"NB_VIEW": len(views),
"viewsIndices": viewsIndices,
"NB_CLASS": len(args.CL_classes.split(":")),
"LABELS_NAMES": args.CL_classes.split(":"),
"FusionKWARGS": {"fusionType":"LateFusion", "fusionMethod":method,
"classifiersNames": bestClassifiers,
"classifiersConfigs": bestClassifiersConfigs,
'fusionMethodConfig': fusionMethodConfig}}
argumentDictionaries["Multiview"].append(arguments)
except:
pass
try:
if benchmark["Multiview"]["Fusion"]["Methods"]["EarlyFusion"] and benchmark["Multiview"]["Fusion"]["Classifiers"]:
for method in benchmark["Multiview"]["Fusion"]["Methods"]["EarlyFusion"]:
for classifier in benchmark["Multiview"]["Fusion"]["Classifiers"]:
arguments = {"CL_type": "Fusion",
"views": views,
"NB_VIEW": len(views),
"viewsIndices": viewsIndices,
"NB_CLASS": len(args.CL_classes.split(":")),
"LABELS_NAMES": args.CL_classes.split(":"),
"FusionKWARGS": {"fusionType":"EarlyFusion", "fusionMethod":method,
"classifiersNames": [classifier],
"classifiersConfigs": [globals()[classifier+"KWARGSInit"]],
'fusionMethodConfig': fusionMethodConfig}}
argumentDictionaries["Multiview"].append(arguments)
except:
pass
else:
pass
else:
pass
# resultsMultiview = []
if nbCores>1:
resultsMultiview = []
nbExperiments = len(argumentDictionaries["Multiview"])
for stepIndex in range(int(math.ceil(float(nbExperiments)/nbCores))):
resultsMultiview += Parallel(n_jobs=nbCores)(
delayed(ExecMultiview_multicore)(coreIndex, args.name, args.CL_split, args.CL_nbFolds, args.type, args.pathF,
LABELS_DICTIONARY, statsIter, gridSearch=gridSearch,
metrics=metrics, nIter=args.CL_GS_iter, **argumentDictionaries["Multiview"][stepIndex*nbCores+coreIndex])
for coreIndex in range(min(nbCores, nbExperiments - stepIndex * nbCores)))
else:
resultsMultiview = [ExecMultiview(DATASET, args.name, args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF,
LABELS_DICTIONARY, statsIter, gridSearch=gridSearch,
metrics=metrics, nIter=args.CL_GS_iter, **arguments) for arguments in argumentDictionaries["Multiview"]]
multiviewTime = time.time()-monoviewTime-dataBaseTime-start
if nbCores>1:
logging.debug("Start:\t Deleting "+str(nbCores)+" temporary datasets for multiprocessing")
datasetFiles = DB.deleteHDF5(args.pathF, args.name, nbCores)
logging.debug("Start:\t Deleting datasets for multiprocessing")
times = [dataBaseTime, monoviewTime, multiviewTime]
# times=[]
results = (resultsMonoview, resultsMultiview)
logging.debug("Start:\t Analyze Global Results")
resultAnalysis(benchmark, results, args.name, times, metrics)
logging.debug("Done:\t Analyze Global Results")