From 0683bb62f86edb677018f35bd3e0b099dc828ea5 Mon Sep 17 00:00:00 2001
From: bbauvin <baptiste.bauvin@centrale-marseille.fr>
Date: Tue, 17 Oct 2017 11:14:48 -0400
Subject: [PATCH] Refactored Execfile
---
Code/MonoMutliViewClassifiers/ExecClassif.py | 343 +-----------------
.../MonoMutliViewClassifiers/utils/Dataset.py | 48 +++
.../utils/execution.py | 280 ++++++++++++++
3 files changed, 344 insertions(+), 327 deletions(-)
create mode 100644 Code/MonoMutliViewClassifiers/utils/execution.py
diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index f9fb90de..649a5548 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -1,20 +1,16 @@
# Import built-in modules
-import argparse
import pkgutil # for TimeStamp in CSVFile
import os
import time
import sys
-import select
import logging
import errno
-import cPickle
# Import 3rd party modules
from joblib import Parallel, delayed
import numpy as np
import math
import matplotlib
-import sklearn
# Import own modules
import Multiview
@@ -25,6 +21,7 @@ from Monoview.ExecClassifMonoView import ExecMonoview, ExecMonoview_multicore
import Multiview.GetMultiviewDb as DB
from Versions import testVersions
from ResultAnalysis import resultAnalysis, analyzeLabels, analyzeIterResults
+from utils import execution, Dataset
# Author-Info
__author__ = "Baptiste Bauvin"
@@ -33,96 +30,6 @@ __status__ = "Prototype" # Production, Development, Prototype
matplotlib.use('Agg') # Anti-Grain Geometry C++ library to make a raster (pixel) image of the figure
-def initLogFile(args):
- resultDirectory = "../../Results/" + args.name + "/started_" + time.strftime("%Y_%m_%d-%H_%M") + "/"
- logFileName = time.strftime("%Y%m%d-%H%M%S") + "-" + ''.join(args.CL_type) + "-" + "_".join(
- args.views) + "-" + args.name + "-LOG"
- if not os.path.exists(os.path.dirname(resultDirectory + logFileName)):
- try:
- os.makedirs(os.path.dirname(resultDirectory + logFileName))
- except OSError as exc:
- if exc.errno != errno.EEXIST:
- raise
- logFile = resultDirectory + logFileName
- if os.path.isfile(logFile + ".log"):
- for i in range(1, 20):
- testFileName = logFileName + "-" + str(i) + ".log"
- if not (os.path.isfile(resultDirectory + testFileName)):
- logFile = resultDirectory + 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())
-
- return resultDirectory
-
-
-def input_(timeout=15):
- print "You have " + str(timeout) + " seconds to stop the script by typing n"
-
- i, o, e = select.select([sys.stdin], [], [], timeout)
-
- if i:
- return sys.stdin.readline().strip()
- else:
- return "y"
-
-
-def confirm(resp=True, timeout=15):
- ans = input_(timeout)
- if not ans:
- return resp
- if ans not in ['y', 'Y', 'n', 'N']:
- print 'please enter y or n.'
- if ans == 'y' or ans == 'Y':
- return True
- if ans == 'n' or ans == 'N':
- return False
-
-
-def initMultipleDatasets(args, nbCores):
- """Used to create copies of the dataset if multicore computation is used
- Needs arg.pathF and arg.name"""
- 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 /!\ ")
- confirmation = confirm()
- if not confirmation:
- sys.exit(0)
- else:
- datasetFiles = DB.copyHDF5(args.pathF, args.name, nbCores)
- logging.debug("Start:\t Creating datasets for multiprocessing")
- return datasetFiles
-
-
-def initViews(DATASET, args):
- """Used to return the views names that will be used by the algos, their indices and all the views names
- Needs args.views"""
- NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
- if args.views != [""]:
- allowedViews = args.views
- 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
- return views, viewsIndices, allViews
-
-
def initBenchmark(args):
"""Used to create a list of all the algorithm packages names used for the benchmark
Needs args.CL_type, args.CL_algos_multiview, args.MU_types, args.FU_types, args.FU_late_methods,
@@ -241,50 +148,13 @@ def arangeMetrics(metrics, metricPrinc):
return metrics
-def genSplits(statsIter, indices, DATASET, splitRatio, statsIterRandomStates):
- if statsIter > 1:
- splits = []
- for randomState in statsIterRandomStates:
- trainIndices, testIndices, a, b = sklearn.model_selection.train_test_split(indices,
- DATASET.get("Labels").value,
- test_size=splitRatio,
- random_state=randomState)
- splits.append([trainIndices, testIndices])
- return splits
- else:
- trainIndices, testIndices, a, b = sklearn.model_selection.train_test_split(indices, DATASET.get("Labels").value,
- test_size=splitRatio,
- random_state=statsIterRandomStates)
- return trainIndices, testIndices
-
-
-def genKFolds(statsIter, nbFolds, statsIterRandomStates):
- if statsIter > 1:
- foldsList = []
- for randomState in statsIterRandomStates:
- foldsList.append(sklearn.model_selection.KFold(n_splits=nbFolds, random_state=randomState))
- return foldsList
- else:
- return sklearn.model_selection.KFold(n_splits=nbFolds, random_state=statsIterRandomStates)
-
-
-def genDirecortiesNames(directory, statsIter):
- if statsIter > 1:
- directories = []
- for i in range(statsIter):
- directories.append(directory + "iter_" + str(i + 1) + "/")
- return directories
- else:
- return directory
-
-
def classifyOneIter_multicore(LABELS_DICTIONARY, argumentDictionaries, nbCores, directory, args, classificationIndices,
kFolds,
randomState, hyperParamSearch, metrics, coreIndex, viewsIndices, dataBaseTime, start,
benchmark,
views):
resultsMonoview = []
- np.savetxt(directories+"train_indices.csv", classificationIndices[0], delimiter=",")
+ np.savetxt(directories + "train_indices.csv", classificationIndices[0], delimiter=",")
labelsNames = LABELS_DICTIONARY.values()
resultsMonoview += [ExecMonoview_multicore(directory, args.name, labelsNames, classificationIndices, kFolds,
coreIndex, args.type, args.pathF, randomState,
@@ -329,9 +199,10 @@ def classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directory,
randomState, hyperParamSearch, metrics, DATASET, viewsIndices, dataBaseTime, start,
benchmark, views):
print classificationIndices[0]
- import pdb;pdb.set_trace()
+ import pdb;
+ pdb.set_trace()
- np.savetxt(directory+"train_indices.csv", classificationIndices[0], delimiter=",")
+ np.savetxt(directory + "train_indices.csv", classificationIndices[0], delimiter=",")
resultsMonoview = []
labelsNames = LABELS_DICTIONARY.values()
@@ -401,203 +272,21 @@ def classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directory,
return results
-def initRandomState(randomStateArg, directory):
- if randomStateArg is None:
- randomState = np.random.RandomState(randomStateArg)
- else:
- try:
- seed = int(randomStateArg)
- randomState = np.random.RandomState(seed)
- except ValueError:
- fileName = randomStateArg
- with open(fileName, 'rb') as handle:
- randomState = cPickle.load(handle)
- with open(directory + "randomState.pickle", "wb") as handle:
- cPickle.dump(randomState, handle)
- return randomState
-
+# _______________ #
+# __ EXECUTION __ #
+# _______________ #
testVersions()
-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")
-
start = time.time()
-args = parser.parse_args()
+args = execution.parseTheArgs(sys.argv[1:])
os.nice(args.nice)
nbCores = args.CL_cores
statsIter = args.CL_statsiter
hyperParamSearch = args.CL_HPS_type
-directory = initLogFile(args)
-randomState = initRandomState(args.randomState, directory)
+directory = execution.initLogFile(args)
+randomState = execution.initRandomState(args.randomState, directory)
if statsIter > 1:
statsIterRandomStates = [np.random.RandomState(randomState.randint(500)) for _ in range(statsIter)]
else:
@@ -613,13 +302,13 @@ DATASET, LABELS_DICTIONARY = getDatabase(args.views, args.pathF, args.name, args
datasetLength = DATASET.get("Metadata").attrs["datasetLength"]
indices = np.arange(datasetLength)
-classificationIndices = genSplits(statsIter, indices, DATASET, args.CL_split, statsIterRandomStates)
+classificationIndices = execution.genSplits(statsIter, indices, DATASET, args.CL_split, statsIterRandomStates)
-kFolds = genKFolds(statsIter, args.CL_nbFolds, statsIterRandomStates)
+kFolds = execution.genKFolds(statsIter, args.CL_nbFolds, statsIterRandomStates)
-datasetFiles = initMultipleDatasets(args, nbCores)
+datasetFiles = Dataset.initMultipleDatasets(args, nbCores)
-views, viewsIndices, allViews = initViews(DATASET, args)
+views, viewsIndices, allViews = execution.initViews(DATASET, args)
if not views:
raise ValueError, "Empty views list, modify selected views to match dataset " + args.views
@@ -647,7 +336,7 @@ dataBaseTime = time.time() - start
argumentDictionaries = {"Monoview": [], "Multiview": []}
argumentDictionaries = initMonoviewArguments(benchmark, argumentDictionaries, views, allViews, DATASET, NB_CLASS,
initKWARGS)
-directories = genDirecortiesNames(directory, statsIter)
+directories = execution.genDirecortiesNames(directory, statsIter)
if statsIter > 1:
for statIterIndex in range(statsIter):
diff --git a/Code/MonoMutliViewClassifiers/utils/Dataset.py b/Code/MonoMutliViewClassifiers/utils/Dataset.py
index 86c0ba33..0d4d324f 100644
--- a/Code/MonoMutliViewClassifiers/utils/Dataset.py
+++ b/Code/MonoMutliViewClassifiers/utils/Dataset.py
@@ -1,5 +1,10 @@
from scipy import sparse
import numpy as np
+import Multiview.GetMultiviewDb as DB
+import logging
+import os
+import sys
+import select
def getV(DATASET, viewIndex, usedIndices=None):
@@ -59,3 +64,46 @@ def extractSubset(matrix, usedIndices):
return sparse.csr_matrix((newData, newIndices, newIndptr), shape=(len(usedIndices), matrix.shape[1]))
else:
return matrix[usedIndices]
+
+
+def initMultipleDatasets(args, nbCores):
+ """Used to create copies of the dataset if multicore computation is used
+ Needs arg.pathF and arg.name"""
+ 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 /!\ ")
+ confirmation = confirm()
+ if not confirmation:
+ sys.exit(0)
+ else:
+ datasetFiles = DB.copyHDF5(args.pathF, args.name, nbCores)
+ logging.debug("Start:\t Creating datasets for multiprocessing")
+ return datasetFiles
+
+
+def confirm(resp=True, timeout=15):
+ ans = input_(timeout)
+ if not ans:
+ return resp
+ if ans not in ['y', 'Y', 'n', 'N']:
+ print 'please enter y or n.'
+ if ans == 'y' or ans == 'Y':
+ return True
+ if ans == 'n' or ans == 'N':
+ return False
+
+
+def input_(timeout=15):
+ print "You have " + str(timeout) + " seconds to stop the script by typing n"
+ i, o, e = select.select([sys.stdin], [], [], timeout)
+ if i:
+ return sys.stdin.readline().strip()
+ else:
+ return "y"
+
diff --git a/Code/MonoMutliViewClassifiers/utils/execution.py b/Code/MonoMutliViewClassifiers/utils/execution.py
new file mode 100644
index 00000000..ed9459ea
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/utils/execution.py
@@ -0,0 +1,280 @@
+import argparse
+import numpy as np
+import cPickle
+import time
+import os
+import errno
+import logging
+import sklearn
+
+def parseTheArgs(arguments):
+
+
+ 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")
+ args = parser.parse_args(arguments)
+ return args
+
+def initRandomState(randomStateArg, directory):
+ if randomStateArg is None:
+ randomState = np.random.RandomState(randomStateArg)
+ else:
+ try:
+ seed = int(randomStateArg)
+ randomState = np.random.RandomState(seed)
+ except ValueError:
+ fileName = randomStateArg
+ with open(fileName, 'rb') as handle:
+ randomState = cPickle.load(handle)
+ with open(directory + "randomState.pickle", "wb") as handle:
+ cPickle.dump(randomState, handle)
+ return randomState
+
+def initLogFile(args):
+ resultDirectory = "../../Results/" + args.name + "/started_" + time.strftime("%Y_%m_%d-%H_%M") + "/"
+ logFileName = time.strftime("%Y%m%d-%H%M%S") + "-" + ''.join(args.CL_type) + "-" + "_".join(
+ args.views) + "-" + args.name + "-LOG"
+ if not os.path.exists(os.path.dirname(resultDirectory + logFileName)):
+ try:
+ os.makedirs(os.path.dirname(resultDirectory + logFileName))
+ except OSError as exc:
+ if exc.errno != errno.EEXIST:
+ raise
+ logFile = resultDirectory + logFileName
+ if os.path.isfile(logFile + ".log"):
+ for i in range(1, 20):
+ testFileName = logFileName + "-" + str(i) + ".log"
+ if not (os.path.isfile(resultDirectory + testFileName)):
+ logFile = resultDirectory + 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())
+
+ return resultDirectory
+
+
+def genSplits(statsIter, indices, DATASET, splitRatio, statsIterRandomStates):
+ if statsIter > 1:
+ splits = []
+ for randomState in statsIterRandomStates:
+ trainIndices, testIndices, a, b = sklearn.model_selection.train_test_split(indices,
+ DATASET.get("Labels").value,
+ test_size=splitRatio,
+ random_state=randomState)
+ splits.append([trainIndices, testIndices])
+ return splits
+ else:
+ trainIndices, testIndices, a, b = sklearn.model_selection.train_test_split(indices, DATASET.get("Labels").value,
+ test_size=splitRatio,
+ random_state=statsIterRandomStates)
+ return trainIndices, testIndices
+
+
+def genKFolds(statsIter, nbFolds, statsIterRandomStates):
+ if statsIter > 1:
+ foldsList = []
+ for randomState in statsIterRandomStates:
+ foldsList.append(sklearn.model_selection.KFold(n_splits=nbFolds, random_state=randomState))
+ return foldsList
+ else:
+ return sklearn.model_selection.KFold(n_splits=nbFolds, random_state=statsIterRandomStates)
+
+
+def initViews(DATASET, args):
+ """Used to return the views names that will be used by the algos, their indices and all the views names
+ Needs args.views"""
+ NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
+ if args.views != [""]:
+ allowedViews = args.views
+ 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
+ return views, viewsIndices, allViews
+
+
+def genDirecortiesNames(directory, statsIter):
+ if statsIter > 1:
+ directories = []
+ for i in range(statsIter):
+ directories.append(directory + "iter_" + str(i + 1) + "/")
+ return directories
+ else:
+ return directory
--
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