diff --git a/multiview_platform/execute.py b/multiview_platform/execute.py
index 47f485fbdb3e729872d402f92e2b3bc76c85449c..5772286a7c562fed60466478956d673d4bd04145 100644
--- a/multiview_platform/execute.py
+++ b/multiview_platform/execute.py
@@ -1,15 +1,9 @@
"""This is the execution module, used to execute the code"""
-<<<<<<< HEAD
def execute():
import multiview_platform.versions as vs
vs.test_versions()
-=======
-def exec():
- import multiview_platform.versions as versions
- versions.test_versions()
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
import sys
from multiview_platform.mono_multi_view_classifiers import exec_classif
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/execution.py b/multiview_platform/mono_multi_view_classifiers/utils/execution.py
index 7850b6a00dd1615dd3d5ee7ff5b76dcf076b7f1c..105dafa497162c9a9450a338f076989de2bf0c7c 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/execution.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/execution.py
@@ -24,680 +24,6 @@ def parse_the_args(arguments):
help='Path to the hdf5 dataset or database '
'folder (default: %(default)s)',
default='../config_files/config.yml')
-<<<<<<< HEAD
-# groupStandard.add_argument('-log', action='store_true',
-# help='Use option to activate logging to console')
-# groupStandard.add_argument('--name', metavar='STRING', nargs='+', action='store',
-# help='Name of Database (default: %(default)s)',
-# default=['Plausible'])
-# groupStandard.add_argument('--label', metavar='STRING', action='store',
-# help='Labeling the results directory (default: '
-# '%(default)s)',
-# default='')
-# 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 hdf5 dataset or database '
-# 'folder (default: %(default)s)',
-# default='../data/')
-# groupStandard.add_argument('--nice', metavar='INT', action='store',
-# type=int,
-# help='Niceness for the processes', default=0)
-# groupStandard.add_argument('--random_state', metavar='STRING',
-# action='store',
-# help="The random state seed to use or the path "
-# "to a pickle file where it is stored",
-# default=None)
-# groupStandard.add_argument('--nbCores', metavar='INT', action='store',
-# help='Number of cores to use for parallel '
-# 'computing, -1 for all',
-# type=int, default=2)
-# groupStandard.add_argument('--machine', metavar='STRING', action='store',
-# help='Type of machine on which the script runs',
-# default="PC")
-# groupStandard.add_argument('-full', action='store_true',
-# help='Use option to use full dataset and no '
-# 'labels or view filtering')
-# groupStandard.add_argument('-debug', action='store_true',
-# 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', nargs="+", action='store',
-# help='The std of the gaussian noise that will '
-# 'be added to the data.',
-# type=float, default=[0.0])
-# groupStandard.add_argument('--res_dir', metavar='STRING', action='store',
-# help='The path to the result directory',
-# default="../results/")
-#
-# groupClass = parser.add_argument_group('Classification arguments')
-# groupClass.add_argument('--CL_multiclassMethod', metavar='STRING',
-# action='store',
-# help='Determine which multiclass method to use if '
-# 'the dataset is multiclass',
-# default="oneVersusOne")
-# groupClass.add_argument('--CL_split', metavar='FLOAT', action='store',
-# help='Determine the split ratio 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_nbClass', 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', default=["yes", "no"])
-# groupClass.add_argument('--CL_type', metavar='STRING', action='store',
-# nargs="+",
-# help='Determine whether to use multiview and/or '
-# 'monoview, or Benchmark classification',
-# default=['monoview', 'multiview'])
-# 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_statsiter', metavar='INT', action='store',
-# help="Number of iteration for each algorithm to "
-# "mean preds on different random states. "
-# "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'
-# , 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_HPS_iter', metavar='INT', action='store',
-# help='Determine how many hyper parameters '
-# 'optimization 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('--RF_trees', metavar='INT', type=int, action='store',
-# help='Number max trees',nargs="+",
-# default=[25])
-# groupRF.add_argument('--RF_max_depth', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Max depth for the trees',
-# default=[5])
-# groupRF.add_argument('--RF_criterion', metavar='STRING', action='store',
-# help='Criterion for the trees',nargs="+",
-# default=["entropy"])
-#
-# groupSVMLinear = parser.add_argument_group('Linear SVM arguments')
-# groupSVMLinear.add_argument('--SVML_C', metavar='INT', type=int,
-# action='store', nargs="+", help='Penalty parameter used',
-# default=[1])
-#
-# groupSVMRBF = parser.add_argument_group('SVW-RBF arguments')
-# groupSVMRBF.add_argument('--SVMRBF_C', metavar='INT', type=int,
-# action='store', nargs="+", help='Penalty parameter used',
-# default=[1])
-#
-# groupSVMPoly = parser.add_argument_group('Poly SVM arguments')
-# groupSVMPoly.add_argument('--SVMPoly_C', metavar='INT', type=int,
-# action='store', nargs="+", help='Penalty parameter used',
-# default=[1])
-# groupSVMPoly.add_argument('--SVMPoly_deg', nargs="+", metavar='INT', type=int,
-# action='store', help='Degree parameter used',
-# default=[2])
-#
-# groupAdaboost = parser.add_argument_group('Adaboost arguments')
-# groupAdaboost.add_argument('--Ada_n_est', metavar='INT', type=int,
-# action='store', nargs="+", help='Number of estimators',
-# default=[2])
-# groupAdaboost.add_argument('--Ada_b_est', metavar='STRING', action='store',
-# help='Estimators',nargs="+",
-# default=['DecisionTreeClassifier'])
-#
-# groupAdaboostPregen = parser.add_argument_group('AdaboostPregen arguments')
-# groupAdaboostPregen.add_argument('--AdP_n_est', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Number of estimators',
-# default=[100])
-# groupAdaboostPregen.add_argument('--AdP_b_est', metavar='STRING',
-# action='store',nargs="+",
-# help='Estimators',
-# default=['DecisionTreeClassifier'])
-# groupAdaboostPregen.add_argument('--AdP_stumps', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Number of stumps inthe '
-# 'pregenerated dataset',
-# default=[1])
-#
-# groupAdaboostGraalpy = parser.add_argument_group(
-# 'AdaboostGraalpy arguments')
-# groupAdaboostGraalpy.add_argument('--AdG_n_iter', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Number of estimators',
-# default=[100])
-# groupAdaboostGraalpy.add_argument('--AdG_stumps', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Number of stumps inthe '
-# 'pregenerated dataset',
-# default=[1])
-#
-# groupDT = parser.add_argument_group('Decision Trees arguments')
-# groupDT.add_argument('--DT_depth', metavar='INT', type=int, action='store',
-# help='Determine max depth for Decision Trees',nargs="+",
-# default=[3])
-# groupDT.add_argument('--DT_criterion', metavar='STRING', action='store',
-# help='Determine max depth for Decision Trees',nargs="+",
-# default=["entropy"])
-# groupDT.add_argument('--DT_splitter', metavar='STRING', action='store',
-# help='Determine criterion for Decision Trees',nargs="+",
-# default=["random"])
-#
-# groupDTP = parser.add_argument_group('Decision Trees pregen arguments')
-# groupDTP.add_argument('--DTP_depth', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Determine max depth for Decision Trees',
-# default=[3])
-# groupDTP.add_argument('--DTP_criterion', metavar='STRING', action='store',
-# help='Determine max depth for Decision Trees',nargs="+",
-# default=["entropy"])
-# groupDTP.add_argument('--DTP_splitter', metavar='STRING', action='store',
-# help='Determine criterion for Decision Trees',nargs="+",
-# default=["random"])
-# groupDTP.add_argument('--DTP_stumps', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Determine the number of stumps for Decision '
-# 'Trees pregen',
-# default=[1])
-#
-# groupSGD = parser.add_argument_group('SGD arguments')
-# groupSGD.add_argument('--SGD_alpha', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Determine alpha for SGDClassifier', default=[0.1])
-# groupSGD.add_argument('--SGD_loss', metavar='STRING', action='store',
-# help='Determine loss for SGDClassifier',nargs="+",
-# default=['log'])
-# groupSGD.add_argument('--SGD_penalty', metavar='STRING', action='store',
-# help='Determine penalty for SGDClassifier', nargs="+",
-# default=['l2'])
-#
-# groupKNN = parser.add_argument_group('KNN arguments')
-# groupKNN.add_argument('--KNN_neigh', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Determine number of neighbors for KNN',
-# default=[1])
-# groupKNN.add_argument('--KNN_weights', nargs="+",
-# metavar='STRING', action='store',
-# help='Determine number of neighbors for KNN',
-# default=["distance"])
-# groupKNN.add_argument('--KNN_algo', metavar='STRING', action='store',
-# help='Determine number of neighbors for KNN',
-# default=["auto"],nargs="+", )
-# groupKNN.add_argument('--KNN_p', metavar='INT', nargs="+",
-# type=int, action='store',
-# help='Determine number of neighbors for KNN',
-# default=[1])
-#
-# groupSCM = parser.add_argument_group('SCM arguments')
-# groupSCM.add_argument('--SCM_max_rules', metavar='INT', type=int,
-# action='store', nargs="+",
-# help='Max number of rules for SCM', default=[1])
-# groupSCM.add_argument('--SCM_p', metavar='FLOAT', type=float,
-# action='store', nargs="+",
-# help='Max number of rules for SCM', default=[1.0])
-# groupSCM.add_argument('--SCM_model_type', metavar='STRING', action='store',
-# help='Max number of rules for SCM', nargs="+",
-# default=["conjunction"])
-#
-# groupSCMPregen = parser.add_argument_group('SCMPregen arguments')
-# groupSCMPregen.add_argument('--SCP_max_rules', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Max number of rules for SCM', default=[1])
-# groupSCMPregen.add_argument('--SCP_p', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Max number of rules for SCM', default=[1.0])
-# groupSCMPregen.add_argument('--SCP_model_type', metavar='STRING',
-# action='store',nargs="+",
-# help='Max number of rules for SCM',
-# default=["conjunction"])
-# groupSCMPregen.add_argument('--SCP_stumps', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Number of stumps per attribute',
-# default=[1])
-#
-# groupSCMSparsity = parser.add_argument_group('SCMSparsity arguments')
-# groupSCMSparsity.add_argument('--SCS_max_rules', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Max number of rules for SCM', default=[1])
-# groupSCMSparsity.add_argument('--SCS_stumps', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Number of stumps', default=[1])
-# groupSCMSparsity.add_argument('--SCS_p', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Max number of rules for SCM',
-# default=[1.0])
-# groupSCMSparsity.add_argument('--SCS_model_type', metavar='STRING',
-# action='store',nargs="+",
-# help='Max number of rules for SCM',
-# default=["conjunction"])
-#
-# groupCQBoost = parser.add_argument_group('CQBoost arguments')
-# groupCQBoost.add_argument('--CQB_mu', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the mu parameter for CQBoost',
-# default=[0.001])
-# groupCQBoost.add_argument('--CQB_epsilon', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the epsilon parameter for CQBoost',
-# default=[1e-06])
-# groupCQBoost.add_argument('--CQB_stumps', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Set the number of stumps for CQBoost',
-# default=[1])
-# groupCQBoost.add_argument('--CQB_n_iter', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Set the maximum number of iteration in '
-# 'CQBoost',
-# default=[None])
-#
-# groupCQBoostv2 = parser.add_argument_group('CQBoostv2 arguments')
-# groupCQBoostv2.add_argument('--CQB2_mu', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the mu parameter for CQBoostv2',
-# default=[0.002])
-# groupCQBoostv2.add_argument('--CQB2_epsilon', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the epsilon parameter for CQBoostv2',
-# default=[1e-08])
-#
-# groupCQBoostv21 = parser.add_argument_group('CQBoostv21 arguments')
-# groupCQBoostv21.add_argument('--CQB21_mu', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the mu parameter for CQBoostv2',
-# default=[0.001])
-# groupCQBoostv21.add_argument('--CQB21_epsilon', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the epsilon parameter for CQBoostv2',
-# default=[1e-08])
-#
-# groupQarBoost = parser.add_argument_group('QarBoost arguments')
-# groupQarBoost.add_argument('--QarB_mu', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the mu parameter for QarBoost',
-# default=[0.001])
-# groupQarBoost.add_argument('--QarB_epsilon', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the epsilon parameter for QarBoost',
-# default=[1e-08])
-#
-# groupCGreed = parser.add_argument_group('CGreed arguments')
-# groupCGreed.add_argument('--CGR_stumps', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Set the n_stumps_per_attribute parameter '
-# 'for CGreed',
-# default=[1])
-# groupCGreed.add_argument('--CGR_n_iter', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Set the n_max_iterations parameter for '
-# 'CGreed',
-# default=[100])
-#
-# groupCGDesc = parser.add_argument_group('CGDesc arguments')
-# groupCGDesc.add_argument('--CGD_stumps', nargs="+", metavar='INT', type=int,
-# action='store',
-# help='Set the n_stumps_per_attribute parameter '
-# 'for CGreed',
-# default=[1])
-# groupCGDesc.add_argument('--CGD_n_iter', metavar='INT', type=int,
-# action='store', nargs="+",
-# help='Set the n_max_iterations parameter for '
-# 'CGreed',
-# default=[10])
-#
-# groupCBBoost= parser.add_argument_group('CBBoost arguments')
-# groupCBBoost.add_argument('--CBB_stumps', nargs="+", metavar='INT', type=int,
-# action='store',
-# help='Set the n_stumps_per_attribute parameter '
-# 'for CBBoost',
-# default=[1])
-# groupCBBoost.add_argument('--CBB_n_iter', metavar='INT', type=int,
-# action='store', nargs="+",
-# help='Set the n_max_iterations parameter for '
-# 'CBBoost',
-# default=[100])
-#
-# groupCGDescTree = parser.add_argument_group('CGDesc arguments')
-# groupCGDescTree.add_argument('--CGDT_trees', metavar='INT', type=int,
-# action='store', nargs="+",
-# help='Set thenumber of trees for CGreed',
-# default=[100])
-# groupCGDescTree.add_argument('--CGDT_n_iter', metavar='INT', type=int,
-# action='store', nargs="+",
-# help='Set the n_max_iterations parameter for '
-# 'CGreed',
-# default=[100])
-# groupCGDescTree.add_argument('--CGDT_max_depth', metavar='INT', type=int,
-# action='store', nargs="+",
-# help='Set the n_max_iterations parameter for CGreed',
-# default=[2])
-#
-# groupMinCQGraalpyTree = parser.add_argument_group(
-# 'MinCQGraalpyTree arguments')
-# groupMinCQGraalpyTree.add_argument('--MCGT_mu', metavar='FLOAT', type=float,
-# action='store', nargs="+",
-# help='Set the mu_parameter for MinCQGraalpy',
-# default=[0.05])
-# groupMinCQGraalpyTree.add_argument('--MCGT_trees', metavar='INT', type=int,
-# action='store', nargs="+",
-# help='Set the n trees parameter for MinCQGraalpy',
-# default=[100])
-# groupMinCQGraalpyTree.add_argument('--MCGT_max_depth', metavar='INT',
-# type=int,nargs="+",
-# action='store',
-# help='Set the n_stumps_per_attribute parameter for MinCQGraalpy',
-# default=[2])
-#
-# groupCQBoostTree = parser.add_argument_group('CQBoostTree arguments')
-# groupCQBoostTree.add_argument('--CQBT_mu', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the mu parameter for CQBoost',
-# default=[0.001])
-# groupCQBoostTree.add_argument('--CQBT_epsilon', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the epsilon parameter for CQBoost',
-# default=[1e-06])
-# groupCQBoostTree.add_argument('--CQBT_trees', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Set the number of trees for CQBoost',
-# default=[100])
-# groupCQBoostTree.add_argument('--CQBT_max_depth', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Set the number of stumps for CQBoost',
-# default=[2])
-# groupCQBoostTree.add_argument('--CQBT_n_iter', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Set the maximum number of iteration in CQBoostTree',
-# default=[None])
-#
-# groupSCMPregenTree = parser.add_argument_group('SCMPregenTree arguments')
-# groupSCMPregenTree.add_argument('--SCPT_max_rules', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Max number of rules for SCM',
-# default=[1])
-# groupSCMPregenTree.add_argument('--SCPT_p', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Max number of rules for SCM',
-# default=[1.0])
-# groupSCMPregenTree.add_argument('--SCPT_model_type', metavar='STRING',
-# action='store',nargs="+",
-# help='Max number of rules for SCM',
-# default=["conjunction"])
-# groupSCMPregenTree.add_argument('--SCPT_trees', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Number of stumps per attribute',
-# default=[100])
-# groupSCMPregenTree.add_argument('--SCPT_max_depth', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Max_depth of the trees',
-# default=[1])
-#
-# groupSCMSparsityTree = parser.add_argument_group(
-# 'SCMSparsityTree arguments')
-# groupSCMSparsityTree.add_argument('--SCST_max_rules', metavar='INT',
-# type=int,nargs="+",
-# action='store',
-# help='Max number of rules for SCM',
-# default=[1])
-# groupSCMSparsityTree.add_argument('--SCST_p', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Max number of rules for SCM',
-# default=[1.0])
-# groupSCMSparsityTree.add_argument('--SCST_model_type', metavar='STRING',
-# action='store',nargs="+",
-# help='Max number of rules for SCM',
-# default=["conjunction"])
-# groupSCMSparsityTree.add_argument('--SCST_trees', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Number of stumps per attribute',
-# default=[100])
-# groupSCMSparsityTree.add_argument('--SCST_max_depth', metavar='INT',
-# type=int,nargs="+",
-# action='store',
-# help='Max_depth of the trees',
-# default=[1])
-#
-# groupAdaboostPregenTree = parser.add_argument_group(
-# 'AdaboostPregenTrees arguments')
-# groupAdaboostPregenTree.add_argument('--AdPT_n_est', metavar='INT',
-# type=int,nargs="+",
-# action='store',
-# help='Number of estimators',
-# default=[100])
-# groupAdaboostPregenTree.add_argument('--AdPT_b_est', metavar='STRING',
-# action='store',nargs="+",
-# help='Estimators',
-# default=['DecisionTreeClassifier'])
-# groupAdaboostPregenTree.add_argument('--AdPT_trees', metavar='INT',
-# type=int,nargs="+",
-# action='store',
-# help='Number of trees in the pregenerated dataset',
-# default=[100])
-# groupAdaboostPregenTree.add_argument('--AdPT_max_depth', metavar='INT',
-# type=int,nargs="+",
-# action='store',
-# help='Number of stumps inthe pregenerated dataset',
-# default=[3])
-#
-# groupLasso = parser.add_argument_group('Lasso arguments')
-# groupLasso.add_argument('--LA_n_iter', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Set the max_iter parameter for Lasso',
-# default=[1])
-# groupLasso.add_argument('--LA_alpha', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the alpha parameter for Lasso',
-# default=[1.0])
-#
-# groupGradientBoosting = parser.add_argument_group(
-# 'Gradient Boosting arguments')
-# groupGradientBoosting.add_argument('--GB_n_est', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Set the n_estimators_parameter for Gradient Boosting',
-# default=[100])
-#
-# groupMinCQ = parser.add_argument_group('MinCQ arguments')
-# groupMinCQ.add_argument('--MCQ_mu', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the mu_parameter for MinCQ',
-# default=[0.05])
-# groupMinCQ.add_argument('--MCQ_stumps', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Set the n_stumps_per_attribute parameter for MinCQ',
-# default=[1])
-#
-# groupMinCQGraalpy = parser.add_argument_group('MinCQGraalpy arguments')
-# groupMinCQGraalpy.add_argument('--MCG_mu', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the mu_parameter for MinCQGraalpy',
-# default=[0.05])
-# groupMinCQGraalpy.add_argument('--MCG_stumps', metavar='INT', type=int,
-# action='store',nargs="+",
-# help='Set the n_stumps_per_attribute parameter for MinCQGraalpy',
-# default=[1])
-#
-# groupQarBoostv3 = parser.add_argument_group('QarBoostv3 arguments')
-# groupQarBoostv3.add_argument('--QarB3_mu', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the mu parameter for QarBoostv3',
-# default=[0.001])
-# groupQarBoostv3.add_argument('--QarB3_epsilon', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the epsilon parameter for QarBoostv3',
-# default=[1e-08])
-#
-# groupQarBoostNC = parser.add_argument_group('QarBoostNC arguments')
-# groupQarBoostNC.add_argument('--QarBNC_mu', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the mu parameter for QarBoostNC',
-# default=[0.001])
-# groupQarBoostNC.add_argument('--QarBNC_epsilon', metavar='FLOAT',
-# type=float, action='store',nargs="+",
-# help='Set the epsilon parameter for QarBoostNC',
-# default=[1e-08])
-#
-# groupQarBoostNC2 = parser.add_argument_group('QarBoostNC2 arguments')
-# groupQarBoostNC2.add_argument('--QarBNC2_mu', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the mu parameter for QarBoostNC2',
-# default=[0.001])
-# groupQarBoostNC2.add_argument('--QarBNC2_epsilon', metavar='FLOAT',
-# type=float, action='store',nargs="+",
-# help='Set the epsilon parameter for QarBoostNC2',
-# default=[1e-08])
-#
-# groupQarBoostNC3 = parser.add_argument_group('QarBoostNC3 arguments')
-# groupQarBoostNC3.add_argument('--QarBNC3_mu', metavar='FLOAT', type=float,
-# action='store',nargs="+",
-# help='Set the mu parameter for QarBoostNC3',
-# default=[0.001])
-# groupQarBoostNC3.add_argument('--QarBNC3_epsilon', metavar='FLOAT',
-# type=float, action='store',nargs="+",
-# help='Set the epsilon parameter for QarBoostNC3',
-# default=[1e-08])
-#
-# #
-# # multiview args
-# #
-#
-# 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 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")
-#
-# groupFatLateFusion = parser.add_argument_group('Fat Late fusion arguments')
-# groupFatLateFusion.add_argument('--FLF_weights', metavar='FLOAT',
-# action='store', nargs="+",
-# help='Determine the weights of each monoview decision for FLF',
-# type=float,
-# default=[])
-#
-# groupFatSCMLateFusion = parser.add_argument_group(
-# 'Fat SCM Late fusion arguments')
-# groupFatSCMLateFusion.add_argument('--FSCMLF_p', metavar='FLOAT',
-# action='store',
-# help='Determine the p argument of the SCM',
-# type=float,
-# default=0.5)
-# groupFatSCMLateFusion.add_argument('--FSCMLF_max_attributes', metavar='INT',
-# action='store',
-# help='Determine the maximum number of aibutes used by the SCM',
-# type=int,
-# default=4)
-# groupFatSCMLateFusion.add_argument('--FSCMLF_model', metavar='STRING',
-# action='store',
-# help='Determine the model type of the SCM',
-# default="conjunction")
-#
-# groupDisagreeFusion = parser.add_argument_group(
-# 'Disagreement based fusion arguments')
-# groupDisagreeFusion.add_argument('--DGF_weights', metavar='FLOAT',
-# action='store', nargs="+",
-# help='Determine the weights of each monoview decision for DFG',
-# type=float,
-# default=[])
-
-=======
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
args = parser.parse_args(arguments)
return args
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/get_multiview_db.py b/multiview_platform/mono_multi_view_classifiers/utils/get_multiview_db.py
index d91d1b51d5d7ef23dd2a66c656013052ee50068f..02a084a7c229832338458b2a1070deaf954c1e79 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/get_multiview_db.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/get_multiview_db.py
@@ -90,13 +90,8 @@ def makeMeNoisy(viewData, random_state, percentage=5):
return noisyViewData
-<<<<<<< HEAD
-def getPlausibleDBhdf5(features, pathF, name, NB_CLASS=3, LABELS_NAME="",
- random_state=None, full=True, add_noise=False,
-=======
def get_plausible_db_hdf5(features, pathF, name, NB_CLASS=3, LABELS_NAME="",
randomState=None, full=True, add_noise=False,
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
noise_std=0.15, nbView=3,
nbClass=2, datasetLength=100, randomStateInt=42, nbFeatures = 10):
"""Used to generate a plausible dataset to test the algorithms"""
@@ -472,14 +467,9 @@ def add_gaussian_noise(dataset_file, random_state, path_f, dataset_name,
return noisy_dataset, dataset_name + "_noised"
-<<<<<<< HEAD
-def getClassicDBcsv(views, pathF, nameDB, NB_CLASS, askedLabelsNames,
- random_state, full=False, add_noise=False, noise_std=0.15,
-=======
def get_classic_db_csv(views, pathF, nameDB, NB_CLASS, askedLabelsNames,
- randomState, full=False, add_noise=False, noise_std=0.15,
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
- delimiter=","):
+ randomState, full=False, add_noise=False, noise_std=0.15,
+ delimiter=","):
# TODO : Update this one
labels_names = np.genfromtxt(pathF + nameDB + "-labels-names.csv",
dtype='str', delimiter=delimiter)
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
index dee2cd7d8dbab3a000cad4216e8e67156d3110af..114764faf8c6121106f8dcbddebb9866fa5d6b64 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
@@ -108,35 +108,6 @@ def randomized_search_x(X, y, framework, random_state, output_file_name, classif
min_list = np.array(
[min(nb_possible_combination, n_iter) for nb_possible_combination in
nb_possible_combinations])
-<<<<<<< HEAD
- random_search = MultiviewCompatibleRandomizedSearchCV(
- estimator,
- n_iter=int(np.sum(min_list)),
- param_distributions=params_dict,
- refit=True,
- n_jobs=nb_cores, scoring=scorer,
- cv=folds, random_state=random_state,
- learning_indices=learning_indices,
- view_indices=view_indices,
- framework=framework)
- detector = random_search.fit(X, y)
-
- best_params = dict((key, value) for key, value in
- estimator.genBestParams(detector).items() if
- key is not "random_state")
-
- scores_array = detector.cv_results_['mean_test_score']
- params = estimator.genParamsFromDetector(detector)
-
- gen_heat_maps(params, scores_array, output_file_name)
- best_estimator = detector.best_estimator_
- else:
- best_estimator = estimator
- best_params = {}
- test_folds_preds = get_test_folds_preds(X, y, folds, best_estimator,
- framework, learning_indices)
- return best_params, test_folds_preds
-=======
random_search = MultiviewCompatibleRandomizedSearchCV(estimator,
n_iter=int(np.sum(min_list)),
param_distributions=params_dict,
@@ -161,7 +132,6 @@ def randomized_search_x(X, y, framework, random_state, output_file_name, classif
testFoldsPreds = get_test_folds_preds(X, y, folds, best_estimator,
framework, learning_indices)
return best_params, testFoldsPreds
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
from sklearn.base import clone
diff --git a/multiview_platform/tests/test_ExecClassif.py b/multiview_platform/tests/test_ExecClassif.py
index acb10c4f4432c667fb08873f7671483808f52ecb..24d4f0cf6c8eba250c1b0863545cf157ae8d5cc2 100644
--- a/multiview_platform/tests/test_ExecClassif.py
+++ b/multiview_platform/tests/test_ExecClassif.py
@@ -130,11 +130,6 @@ class Test_InitArgumentDictionaries(unittest.TestCase):
]
self.assertEqual(arguments["multiview"][0], expected_output[0])
-<<<<<<< HEAD
-def fakeBenchmarkExec(core_index=-1, a=7, args=1):
- return [core_index, a]
-=======
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
def test_init_argument_dictionaries_multiview_complex(self):
self.multiview_classifier_arg_value = {"fake_value_2":"plif", "plaf":"plouf"}
@@ -213,11 +208,6 @@ def fakeBenchmarkExec(core_index=-1, a=7, args=1):
def fakeBenchmarkExec(core_index=-1, a=7, args=1):
return [core_index, a]
-<<<<<<< HEAD
-def fakeBenchmarkExec_mutlicore(nb_cores=-1, a=6, args=1):
- return [nb_cores, a]
-=======
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
def fakeBenchmarkExec_mutlicore(nb_cores=-1, a=6, args=1):
return [nb_cores, a]
@@ -270,17 +260,10 @@ class Test_execBenchmark(unittest.TestCase):
res = exec_classif.exec_benchmark(2, 1, 2, cls.argument_dictionaries,
[[[1, 2], [3, 4, 5]]], 5, 6, 7, 8, 9,
10, cls.Dataset,
-<<<<<<< HEAD
exec_one_benchmark=fakeBenchmarkExec,
exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
exec_one_benchmark_mono_core=fakeBenchmarkExec_monocore,
get_results=fakegetResults,
-=======
- exec_one_benchmark=fakeBenchmarkExec,
- exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
- exec_one_benchmark_mono_core=fakeBenchmarkExec_monocore,
- get_results=fakegetResults,
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
delete=fakeDelete)
cls.assertEqual(res, 3)
@@ -292,17 +275,10 @@ class Test_execBenchmark(unittest.TestCase):
res = exec_classif.exec_benchmark(2, 2, 2, cls.argument_dictionaries,
[[[1, 2], [3, 4, 5]]], 5, 6, 7, 8, 9,
10, cls.Dataset,
-<<<<<<< HEAD
exec_one_benchmark=fakeBenchmarkExec,
exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
exec_one_benchmark_monoCore=fakeBenchmarkExec_monocore,
get_results=fakegetResults,
-=======
- exec_one_benchmark=fakeBenchmarkExec,
- exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
- exec_one_benchmark_mono_core=fakeBenchmarkExec_monocore,
- get_results=fakegetResults,
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
delete=fakeDelete)
cls.assertEqual(res, 3)
@@ -310,17 +286,10 @@ class Test_execBenchmark(unittest.TestCase):
res = exec_classif.exec_benchmark(2, 1, 1, cls.argument_dictionaries,
[[[1, 2], [3, 4, 5]]], 5, 6, 7, 8, 9,
10, cls.Dataset,
-<<<<<<< HEAD
exec_one_benchmark=fakeBenchmarkExec,
exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
exec_oneBenchmark_mono_core=fakeBenchmarkExec_monocore,
get_results=fakegetResults,
-=======
- exec_one_benchmark=fakeBenchmarkExec,
- exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
- exec_one_benchmark_mono_core=fakeBenchmarkExec_monocore,
- get_results=fakegetResults,
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
delete=fakeDelete)
cls.assertEqual(res, 3)
@@ -333,26 +302,15 @@ class Test_execBenchmark(unittest.TestCase):
os.rmdir(path)
-<<<<<<< HEAD
def fakeExecMono(directory, name, labels_names, classification_indices, k_folds,
coreIndex, type, pathF, random_state, labels,
-=======
-def fakeExecMono(directory, name, labelsNames, classificationIndices, kFolds,
- coreIndex, type, pathF, randomState, labels,
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
hyper_param_search="try", metrics="try", nIter=1, **arguments):
return ["Mono", arguments]
-<<<<<<< HEAD
def fakeExecMulti(directory, coreIndex, name, classification_indices, k_folds,
type, pathF, labels_dictionary,
random_state, labels, hyper_param_search="", metrics=None,
-=======
-def fakeExecMulti(directory, coreIndex, name, classificationIndices, kFolds,
- type, pathF, LABELS_DICTIONARY,
- randomState, labels, hyper_param_search="", metrics=None,
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
nIter=1, **arguments):
return ["Multi", arguments]
@@ -387,7 +345,6 @@ class Test_execOneBenchmark(unittest.TestCase):
def test_simple(cls):
flag, results = exec_classif.exec_one_benchmark(core_index=10,
-<<<<<<< HEAD
labels_dictionary={
0: "a",
1: "b"},
@@ -401,21 +358,6 @@ class Test_execOneBenchmark(unittest.TestCase):
hyper_param_search="try",
metrics="try",
argument_dictionaries={
-=======
- labels_dictionary={
- 0: "a",
- 1: "b"},
- directory="multiview_platform/tests/tmp_tests/",
- classification_indices=(
- [1, 2, 3, 4],
- [0, 5, 6, 7, 8]),
- args=cls.args,
- k_folds=FakeKfold(),
- random_state="try",
- hyper_param_search="try",
- metrics="try",
- argument_dictionaries={
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
"Monoview": [
{
"try": 0},
@@ -427,26 +369,16 @@ class Test_execOneBenchmark(unittest.TestCase):
"try4": 10}]},
benchmark="try",
views="try",
-<<<<<<< HEAD
views_indices="try",
-=======
- views_indices="try",
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
flag=None,
labels=np.array(
[0, 1, 2, 1,
2, 2, 2, 12,
1, 2, 1, 1,
2, 1, 21]),
-<<<<<<< HEAD
exec_monoview_multicore=fakeExecMono,
exec_multiview_multicore=fakeExecMulti,
init_multiview_arguments=fakeInitMulti)
-=======
- exec_monoview_multicore=fakeExecMono,
- exec_multiview_multicore=fakeExecMulti,
- init_multiview_arguments=fakeInitMulti)
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
cls.assertEqual(flag, None)
cls.assertEqual(results ,
@@ -480,11 +412,8 @@ class Test_execOneBenchmark_multicore(unittest.TestCase):
def test_simple(cls):
flag, results = exec_classif.exec_one_benchmark_multicore(
-<<<<<<< HEAD
nbCores=2,
-=======
- nb_cores=2,
->>>>>>> 7b3e918b4fb2938657cae3093d95b1bd6fc461d4
+
labels_dictionary={0: "a", 1: "b"},
directory="multiview_platform/tests/tmp_tests/",
classification_indices=([1, 2, 3, 4], [0, 10, 20, 30, 40]),