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multiview_platform.tests.test_multiview_classifiers.Test_DoubleFaultFusion.html
test_ExecClassif.py 14.07 KiB
import unittest
import argparse
from MonoMultiViewClassifiers import ExecClassif
class Test_initBenchmark(unittest.TestCase):
def test_benchmark_wanted(self):
# benchmark_output = ExecClassif.initBenchmark(self.args)
self.assertEqual(1,1)
class Test_initKWARGS(unittest.TestCase):
def test_initKWARGSFunc_no_monoview(self):
benchmark = {"Monoview":{}, "Multiview":{}}
args = ExecClassif.initKWARGSFunc({}, benchmark)
self.assertEqual(args, {})
class Test_initMonoviewArguments(unittest.TestCase):
def test_initMonoviewArguments_no_monoview(self):
benchmark = {"Monoview":{}, "Multiview":{}}
arguments = ExecClassif.initMonoviewArguments(benchmark, {}, [], [], None, 0, {})
self.assertEqual(arguments, {})
def test_initMonoviewArguments_empty(self):
benchmark = {"Monoview":{}, "Multiview":{}}
arguments = ExecClassif.initMonoviewArguments(benchmark, {}, [], [], None, 0, {})
class Essai(unittest.TestCase):
def setUp(self):
parser = argparse.ArgumentParser(
description='This file is used to benchmark the scores 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', nargs="+",
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)
groupStandard.add_argument('--randomState', metavar='STRING', action='store',
help="The random state seed to use or a file where we can find it's get_state", default=None)
groupClass = parser.add_argument_group('Classification arguments')
groupClass.add_argument('--CL_split', metavar='FLOAT', action='store',
help='Determine the split between learning and validation sets', type=float,
default=0.2)
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', nargs="+",
help='Classes used in the dataset (names of the folders) if not filled, random classes will be '
'selected ex. walrus mole leopard', default=["yes", "no"])
groupClass.add_argument('--CL_type', metavar='STRING', action='store', nargs="+",
help='Determine whether to use Multiview and/or Monoview, or Benchmark',
default=['Benchmark'])
groupClass.add_argument('--CL_algos_monoview', metavar='STRING', action='store', nargs="+",
help='Determine which monoview classifier to use if empty, considering all',
default=[''])
groupClass.add_argument('--CL_algos_multiview', metavar='STRING', action='store', nargs="+",
help='Determine which multiview classifier to use if empty, considering all',
default=[''])
groupClass.add_argument('--CL_cores', metavar='INT', action='store', help='Number of cores, -1 for all', type=int,
default=2)
groupClass.add_argument('--CL_statsiter', metavar='INT', action='store',
help="Number of iteration for each algorithm to mean results if using multiple cores, it's highly recommended to use statsiter mod(nbCores) = 0",
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 '
, default=[''])
groupClass.add_argument('--CL_metric_princ', metavar='STRING', action='store',
help='Determine which metric to use for randomSearch and optimization', default="f1_score")
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_HPS_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_RandomForest_trees', metavar='INT', type=int, action='store', help='Number max trees',
default=25)
groupRF.add_argument('--CL_RandomForest_max_depth', metavar='INT', type=int, action='store',
help='Max depth for the trees',
default=5)
groupRF.add_argument('--CL_RandomForest_criterion', metavar='STRING', action='store', help='Criterion for the trees',
default="entropy")
groupSVMLinear = parser.add_argument_group('Linear SVM arguments')
groupSVMLinear.add_argument('--CL_SVMLinear_C', metavar='INT', type=int, action='store', help='Penalty parameter used',
default=1)
groupSVMRBF = parser.add_argument_group('SVW-RBF arguments')
groupSVMRBF.add_argument('--CL_SVMRBF_C', metavar='INT', type=int, action='store', help='Penalty parameter used',
default=1)
groupSVMPoly = parser.add_argument_group('Poly SVM arguments')
groupSVMPoly.add_argument('--CL_SVMPoly_C', metavar='INT', type=int, action='store', help='Penalty parameter used',
default=1)
groupSVMPoly.add_argument('--CL_SVMPoly_deg', metavar='INT', type=int, action='store', help='Degree parameter used',
default=2)
groupAdaboost = parser.add_argument_group('Adaboost arguments')
groupAdaboost.add_argument('--CL_Adaboost_n_est', metavar='INT', type=int, action='store', help='Number of estimators',
default=2)
groupAdaboost.add_argument('--CL_Adaboost_b_est', metavar='STRING', action='store', help='Estimators',
default='DecisionTreeClassifier')
groupDT = parser.add_argument_group('Decision Trees arguments')
groupDT.add_argument('--CL_DecisionTree_depth', metavar='INT', type=int, action='store',
help='Determine max depth for Decision Trees', default=3)
groupDT.add_argument('--CL_DecisionTree_criterion', metavar='STRING', action='store',
help='Determine max depth for Decision Trees', default="entropy")
groupDT.add_argument('--CL_DecisionTree_splitter', metavar='STRING', action='store',
help='Determine criterion for Decision Trees', default="random")
groupSGD = parser.add_argument_group('SGD arguments')
groupSGD.add_argument('--CL_SGD_alpha', metavar='FLOAT', type=float, 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')
groupKNN = parser.add_argument_group('KNN arguments')
groupKNN.add_argument('--CL_KNN_neigh', metavar='INT', type=int, action='store',
help='Determine number of neighbors for KNN', default=1)
groupKNN.add_argument('--CL_KNN_weights', metavar='STRING', action='store',
help='Determine number of neighbors for KNN', default="distance")
groupKNN.add_argument('--CL_KNN_algo', metavar='STRING', action='store',
help='Determine number of neighbors for KNN', default="auto")
groupKNN.add_argument('--CL_KNN_p', metavar='INT', type=int, action='store',
help='Determine number of neighbors for KNN', default=1)
groupSCM = parser.add_argument_group('SCM arguments')
groupSCM.add_argument('--CL_SCM_max_rules', metavar='INT', type=int, action='store',
help='Max number of rules for SCM', default=1)
groupSCM.add_argument('--CL_SCM_p', metavar='FLOAT', type=float, action='store',
help='Max number of rules for SCM', default=1.0)
groupSCM.add_argument('--CL_SCM_model_type', metavar='STRING', action='store',
help='Max number of rules for SCM', default="conjunction")
groupMumbo = parser.add_argument_group('Mumbo arguments')
groupMumbo.add_argument('--MU_types', metavar='STRING', action='store', nargs="+",
help='Determine which monoview classifier to use with Mumbo',
default=[''])
groupMumbo.add_argument('--MU_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the monoview classifier in Mumbo separate each classifier with sapce and each argument with:',
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])
groupMumbo.add_argument('--MU_combination', action='store_true',
help='Try all the monoview classifiers combinations for each view',
default=False)
groupFusion = parser.add_argument_group('Fusion arguments')
groupFusion.add_argument('--FU_types', metavar='STRING', action='store', nargs="+",
help='Determine which type of fusion to use',
default=[''])
groupEarlyFusion = parser.add_argument_group('Early Fusion arguments')
groupEarlyFusion.add_argument('--FU_early_methods', metavar='STRING', action='store', nargs="+",
help='Determine which early fusion method of fusion to use',
default=[''])
groupEarlyFusion.add_argument('--FU_E_method_configs', metavar='STRING', action='store', nargs='+',
help='Configuration for the early fusion methods separate '
'method by space and values by :',
default=[''])
groupEarlyFusion.add_argument('--FU_E_cl_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the monoview classifiers used separate classifier by space '
'and configs must be of form argument1_name:value,argument2_name:value',
default=[''])
groupEarlyFusion.add_argument('--FU_E_cl_names', metavar='STRING', action='store', nargs='+',
help='Name of the classifiers used for each early fusion method', default=[''])
groupLateFusion = parser.add_argument_group('Late Early Fusion arguments')
groupLateFusion.add_argument('--FU_late_methods', metavar='STRING', action='store', nargs="+",
help='Determine which late fusion method of fusion to use',
default=[''])
groupLateFusion.add_argument('--FU_L_method_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the fusion method', default=[''])
groupLateFusion.add_argument('--FU_L_cl_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the monoview classifiers used', default=[''])
groupLateFusion.add_argument('--FU_L_cl_names', metavar='STRING', action='store', nargs="+",
help='Names of the classifier used for late fusion', default=[''])
groupLateFusion.add_argument('--FU_L_select_monoview', metavar='STRING', action='store',
help='Determine which method to use to select the monoview classifiers',
default="intersect")
self.args = parser.parse_args([])