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Commit 08a88f7b authored by Dominique Benielli's avatar Dominique Benielli
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refactoring names

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multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/DifficultyFusion/__init__.py deleted 100644 → 0
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− 1
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from . import DifficultyFusionModule, analyzeResults
\ No newline at end of file
multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/FatLateFusion/analyzeResults.py deleted 100644 → 0
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− 21
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from ...Multiview import analyzeResults
# Author-Info
__author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
def execute(classifier, trainLabels,
testLabels, DATASET,
classificationKWARGS, classificationIndices,
LABELS_DICTIONARY, views, nbCores, times,
name, KFolds,
hyperParamSearch, nIter, metrics,
viewsIndices, randomState, labels, classifierModule):
return analyzeResults.execute(classifier, trainLabels,
testLabels, DATASET,
classificationKWARGS, classificationIndices,
LABELS_DICTIONARY, views, nbCores, times,
name, KFolds,
hyperParamSearch, nIter, metrics,
viewsIndices, randomState, labels, classifierModule)
\ No newline at end of file
multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/FatSCMLateFusion/analyzeResults.py deleted 100644 → 0
+ 0
− 21
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from ...Multiview import analyzeResults
# Author-Info
__author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
def execute(classifier, trainLabels,
testLabels, DATASET,
classificationKWARGS, classificationIndices,
LABELS_DICTIONARY, views, nbCores, times,
name, KFolds,
hyperParamSearch, nIter, metrics,
viewsIndices, randomState, labels, classifierModule):
return analyzeResults.execute(classifier, trainLabels,
testLabels, DATASET,
classificationKWARGS, classificationIndices,
LABELS_DICTIONARY, views, nbCores, times,
name, KFolds,
hyperParamSearch, nIter, metrics,
viewsIndices, randomState, labels, classifierModule)
\ No newline at end of file
multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/PseudoCQFusion/analyzeResults.py deleted 100644 → 0
+ 0
− 21
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from ...Multiview import analyzeResults
# Author-Info
__author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
def execute(classifier, trainLabels,
testLabels, DATASET,
classificationKWARGS, classificationIndices,
LABELS_DICTIONARY, views, nbCores, times,
name, KFolds,
hyperParamSearch, nIter, metrics,
viewsIndices, randomState, labels, classifierModule):
return analyzeResults.execute(classifier, trainLabels,
testLabels, DATASET,
classificationKWARGS, classificationIndices,
LABELS_DICTIONARY, views, nbCores, times,
name, KFolds,
hyperParamSearch, nIter, metrics,
viewsIndices, randomState, labels, classifierModule)
\ No newline at end of file
multiview_platform/MonoMultiViewClassifiers/utils/__init__.py deleted 100644 → 0
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− 1
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from . import Dataset, execution, HyperParameterSearch, Transformations
multiview_platform/__init__.py
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− 1
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......@@ -2,4 +2,4 @@
__version__ = "0.0.0.0"
from . import MonoMultiViewClassifiers, Tests, Exec, Versions
from . import mono_multi_view_classifiers, tests, execute, versions
multiview_platform/Exec.py → multiview_platform/execute.py
+ 0
− 0
View file @ 08a88f7b
File moved
multiview_platform/MonoMultiViewClassifiers/__init__.py → multiview_platform/mono_multi_view_classifiers/__init__.py
+ 4
− 0
View file @ 08a88f7b
from . import ExecClassif, ResultAnalysis, Metrics, MonoviewClassifiers, \
Monoview, Multiview, utils, MultiviewClassifiers
from . import exec_classif, result_analysis, metrics, monoview_classifiers, \
monoview, multiview, utils, multiview_classifiers
__all__ = ['Metrics', 'Monoview', 'MonoviewClassifiers', 'Multiview', 'utils']
multiview_platform/MonoMultiViewClassifiers/ExecClassif.py → multiview_platform/mono_multi_view_classifiers/exec_classif.py
+ 68
− 68
View file @ 08a88f7b
......@@ -15,14 +15,14 @@ matplotlib.use(
'Agg') # Anti-Grain Geometry C++ library to make a raster (pixel) image of the figure
# Import own modules
from . import MonoviewClassifiers
from . import MultiviewClassifiers
from .Multiview.ExecMultiview import ExecMultiview, ExecMultiview_multicore
from .Monoview.ExecClassifMonoView import ExecMonoview, ExecMonoview_multicore
from .utils import GetMultiviewDb as DB
from .ResultAnalysis import \
from . import monoview_classifiers
from . import multiview_classifiers
from .multiview.exec_multiview import ExecMultiview, ExecMultiview_multicore
from .monoview.exec_classif_mono_view import ExecMonoview, ExecMonoview_multicore
from .utils import get_multiview_db as DB
from .result_analysis import \
getResults, plot_results_noise # resultAnalysis, analyzeLabels, analyzeIterResults, analyzeIterLabels, genNamesFromRes,
from .utils import execution, Dataset, Multiclass
from .utils import execution, dataset, multiclass
# Author-Info
__author__ = "Baptiste Bauvin"
......@@ -53,30 +53,30 @@ def initBenchmark(CL_type, monoviewAlgos, multiviewAlgos, args):
benchmark : Dictionary of dictionaries
Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.
"""
benchmark = {"Monoview": {}, "Multiview": {}}
benchmark = {"monoview": {}, "multiview": {}}
allMultiviewPackages = [name for _, name, isPackage
in pkgutil.iter_modules(
['./MonoMultiViewClassifiers/MultiviewClassifiers/']) if isPackage]
['./mono_multi_view_classifiers/multiview_classifiers/']) if isPackage]
if "Monoview" in CL_type:
if "monoview" in CL_type:
if monoviewAlgos == ['']:
benchmark["Monoview"] = [name for _, name, isPackage in
benchmark["monoview"] = [name for _, name, isPackage in
pkgutil.iter_modules([
"./MonoMultiViewClassifiers/MonoviewClassifiers"])
"./mono_multi_view_classifiers/monoview_classifiers"])
if not isPackage]
else:
benchmark["Monoview"] = monoviewAlgos
benchmark["monoview"] = monoviewAlgos
if "Multiview" in CL_type:
benchmark["Multiview"] = {}
if "multiview" in CL_type:
benchmark["multiview"] = {}
if multiviewAlgos == [""]:
algosMutliview = allMultiviewPackages
else:
algosMutliview = multiviewAlgos
for multiviewPackageName in allMultiviewPackages:
if multiviewPackageName in algosMutliview:
multiviewPackage = getattr(MultiviewClassifiers,
multiviewPackage = getattr(multiview_classifiers,
multiviewPackageName)
multiviewModule = getattr(multiviewPackage,
multiviewPackageName + "Module")
......@@ -85,14 +85,14 @@ def initBenchmark(CL_type, monoviewAlgos, multiviewAlgos, args):
if CL_type == ["Benchmark"]:
allMonoviewAlgos = [name for _, name, isPackage in
pkgutil.iter_modules([
'./MonoMultiViewClassifiers/MonoviewClassifiers'])
'./mono_multi_view_classifiers/monoview_classifiers'])
if (not isPackage) and name not in ["framework"]]
benchmark["Monoview"] = allMonoviewAlgos
benchmark["Multiview"] = dict(
benchmark["monoview"] = allMonoviewAlgos
benchmark["multiview"] = dict(
(multiviewPackageName, "_") for multiviewPackageName in
allMultiviewPackages)
for multiviewPackageName in allMultiviewPackages:
multiviewPackage = getattr(MultiviewClassifiers,
multiviewPackage = getattr(multiview_classifiers,
multiviewPackageName)
multiviewModule = getattr(multiviewPackage,
multiviewPackageName + "Module")
......@@ -153,20 +153,20 @@ def initMonoviewExps(benchmark, viewsDictionary, nbClass, kwargsInit):
benchmark : Dictionary of dictionaries
Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.
"""
argumentDictionaries = {"Monoview": [], "Multiview": []}
if benchmark["Monoview"]:
argumentDictionaries["Monoview"] = []
argumentDictionaries = {"monoview": [], "multiview": []}
if benchmark["monoview"]:
argumentDictionaries["monoview"] = []
for viewName, viewIndex in viewsDictionary.items():
for classifier in benchmark["Monoview"]:
for classifier in benchmark["monoview"]:
if multiple_args(classifier, kwargsInit):
argumentDictionaries["Monoview"] += gen_multiple_args_dictionnaries(nbClass, kwargsInit, classifier, viewName, viewIndex)
argumentDictionaries["monoview"] += gen_multiple_args_dictionnaries(nbClass, kwargsInit, classifier, viewName, viewIndex)
else:
arguments = {
"args": {classifier + "KWARGS": dict((key, value[0]) for key, value in kwargsInit[
classifier + "KWARGSInit"].items()), "feat": viewName,
"CL_type": classifier, "nbClass": nbClass},
"viewIndex": viewIndex}
argumentDictionaries["Monoview"].append(arguments)
argumentDictionaries["monoview"].append(arguments)
return argumentDictionaries
def multiple_args(classifier, kwargsInit):
......@@ -228,24 +228,24 @@ def initMonoviewKWARGS(args, classifiersNames):
For example, for Adaboost, the KWARGS will be `{"n_estimators":<value>, "base_estimator":<value>}`"""
logging.debug("Start:\t Initializing Monoview classifiers arguments")
logging.debug("Start:\t Initializing monoview classifiers arguments")
monoviewKWARGS = {}
for classifiersName in classifiersNames:
try:
classifierModule = getattr(MonoviewClassifiers, classifiersName)
classifierModule = getattr(monoview_classifiers, classifiersName)
except AttributeError:
raise AttributeError(
classifiersName + " is not implemented in MonoviewClassifiers, "
"please specify the name of the file in MonoviewClassifiers")
classifiersName + " is not implemented in monoview_classifiers, "
"please specify the name of the file in monoview_classifiers")
monoviewKWARGS[
classifiersName + "KWARGSInit"] = classifierModule.formatCmdArgs(
args)
logging.debug("Done:\t Initializing Monoview classifiers arguments")
logging.debug("Done:\t Initializing monoview classifiers arguments")
return monoviewKWARGS
def initKWARGSFunc(args, benchmark):
monoviewKWARGS = initMonoviewKWARGS(args, benchmark["Monoview"])
monoviewKWARGS = initMonoviewKWARGS(args, benchmark["monoview"])
return monoviewKWARGS
......@@ -253,11 +253,11 @@ def initMultiviewArguments(args, benchmark, views, viewsIndices,
argumentDictionaries, randomState, directory,
resultsMonoview, classificationIndices):
"""Used to add each monoview exeperience args to the list of monoview experiences args"""
logging.debug("Start:\t Initializing Multiview classifiers arguments")
logging.debug("Start:\t Initializing multiview classifiers arguments")
multiviewArguments = []
if "Multiview" in benchmark:
for multiviewAlgoName in benchmark["Multiview"]:
multiviewPackage = getattr(MultiviewClassifiers, multiviewAlgoName)
if "multiview" in benchmark:
for multiviewAlgoName in benchmark["multiview"]:
multiviewPackage = getattr(multiview_classifiers, multiviewAlgoName)
mutliviewModule = getattr(multiviewPackage,
multiviewAlgoName + "Module")
......@@ -267,8 +267,8 @@ def initMultiviewArguments(args, benchmark, views, viewsIndices,
directory,
resultsMonoview,
classificationIndices)
argumentDictionaries["Multiview"] = multiviewArguments
logging.debug("Start:\t Initializing Multiview classifiers arguments")
argumentDictionaries["multiview"] = multiviewArguments
logging.debug("Start:\t Initializing multiview classifiers arguments")
return argumentDictionaries
......@@ -334,7 +334,7 @@ def execOneBenchmark(coreIndex=-1, LABELS_DICTIONARY=None, directory=None,
classificationIndices, labels,
LABELS_DICTIONARY, kFolds)
logging.debug("Start:\t Monoview benchmark")
logging.debug("Start:\t monoview benchmark")
resultsMonoview += [
ExecMonoview_multicore(directory, args.name, labelsNames,
classificationIndices, kFolds,
......@@ -343,19 +343,19 @@ def execOneBenchmark(coreIndex=-1, LABELS_DICTIONARY=None, directory=None,
hyperParamSearch=hyperParamSearch,
metrics=metrics,
nIter=args.CL_HPS_iter, **argument)
for argument in argumentDictionaries["Monoview"]]
logging.debug("Done:\t Monoview benchmark")
for argument in argumentDictionaries["monoview"]]
logging.debug("Done:\t monoview benchmark")
logging.debug("Start:\t Multiview arguments initialization")
logging.debug("Start:\t multiview arguments initialization")
argumentDictionaries = initMultiviewArguments(args, benchmark, views,
viewsIndices,
argumentDictionaries,
randomState, directory,
resultsMonoview,
classificationIndices)
logging.debug("Done:\t Multiview arguments initialization")
logging.debug("Done:\t multiview arguments initialization")
logging.debug("Start:\t Multiview benchmark")
logging.debug("Start:\t multiview benchmark")
resultsMultiview = [
ExecMultiview_multicore(directory, coreIndex, args.name,
classificationIndices, kFolds, args.type,
......@@ -363,8 +363,8 @@ def execOneBenchmark(coreIndex=-1, LABELS_DICTIONARY=None, directory=None,
labels, hyperParamSearch=hyperParamSearch,
metrics=metrics, nIter=args.CL_HPS_iter,
**arguments)
for arguments in argumentDictionaries["Multiview"]]
logging.debug("Done:\t Multiview benchmark")
for arguments in argumentDictionaries["multiview"]]
logging.debug("Done:\t multiview benchmark")
return [flag, resultsMonoview + resultsMultiview]
......@@ -387,8 +387,8 @@ def execOneBenchmark_multicore(nbCores=-1, LABELS_DICTIONARY=None,
classificationIndices, labels,
LABELS_DICTIONARY, kFolds)
logging.debug("Start:\t Monoview benchmark")
nbExperiments = len(argumentDictionaries["Monoview"])
logging.debug("Start:\t monoview benchmark")
nbExperiments = len(argumentDictionaries["monoview"])
nbMulticoreToDo = int(math.ceil(float(nbExperiments) / nbCores))
for stepIndex in range(nbMulticoreToDo):
resultsMonoview += (Parallel(n_jobs=nbCores)(
......@@ -399,24 +399,24 @@ def execOneBenchmark_multicore(nbCores=-1, LABELS_DICTIONARY=None,
hyperParamSearch=hyperParamSearch,
metrics=metrics,
nIter=args.CL_HPS_iter,
**argumentDictionaries["Monoview"][
**argumentDictionaries["monoview"][
coreIndex + stepIndex * nbCores])
for coreIndex in
range(min(nbCores, nbExperiments - stepIndex * nbCores))))
logging.debug("Done:\t Monoview benchmark")
logging.debug("Done:\t monoview benchmark")
logging.debug("Start:\t Multiview arguments initialization")
logging.debug("Start:\t multiview arguments initialization")
argumentDictionaries = initMultiviewArguments(args, benchmark, views,
viewsIndices,
argumentDictionaries,
randomState, directory,
resultsMonoview,
classificationIndices)
logging.debug("Done:\t Multiview arguments initialization")
logging.debug("Done:\t multiview arguments initialization")
logging.debug("Start:\t Multiview benchmark")
logging.debug("Start:\t multiview benchmark")
resultsMultiview = []
nbExperiments = len(argumentDictionaries["Multiview"])
nbExperiments = len(argumentDictionaries["multiview"])
nbMulticoreToDo = int(math.ceil(float(nbExperiments) / nbCores))
for stepIndex in range(nbMulticoreToDo):
resultsMultiview += Parallel(n_jobs=nbCores)(
......@@ -429,11 +429,11 @@ def execOneBenchmark_multicore(nbCores=-1, LABELS_DICTIONARY=None,
metrics=metrics,
nIter=args.CL_HPS_iter,
**
argumentDictionaries["Multiview"][
argumentDictionaries["multiview"][
stepIndex * nbCores + coreIndex])
for coreIndex in
range(min(nbCores, nbExperiments - stepIndex * nbCores)))
logging.debug("Done:\t Multiview benchmark")
logging.debug("Done:\t multiview benchmark")
return [flag, resultsMonoview + resultsMultiview]
......@@ -452,8 +452,8 @@ def execOneBenchmarkMonoCore(DATASET=None, LABELS_DICTIONARY=None,
resultsMonoview, labelsNames = benchmarkInit(directory,
classificationIndices, labels,
LABELS_DICTIONARY, kFolds)
logging.debug("Start:\t Monoview benchmark")
for arguments in argumentDictionaries["Monoview"]:
logging.debug("Start:\t monoview benchmark")
for arguments in argumentDictionaries["monoview"]:
X = DATASET.get("View" + str(arguments["viewIndex"]))
Y = labels
resultsMonoview += [
......@@ -462,9 +462,9 @@ def execOneBenchmarkMonoCore(DATASET=None, LABELS_DICTIONARY=None,
1, args.type, args.pathF, randomState,
hyperParamSearch=hyperParamSearch, metrics=metrics,
nIter=args.CL_HPS_iter, **arguments)]
logging.debug("Done:\t Monoview benchmark")
logging.debug("Done:\t monoview benchmark")
logging.debug("Start:\t Multiview arguments initialization")
logging.debug("Start:\t multiview arguments initialization")
argumentDictionaries = initMultiviewArguments(args, benchmark, views,
viewsIndices,
......@@ -472,18 +472,18 @@ def execOneBenchmarkMonoCore(DATASET=None, LABELS_DICTIONARY=None,
randomState, directory,
resultsMonoview,
classificationIndices)
logging.debug("Done:\t Multiview arguments initialization")
logging.debug("Done:\t multiview arguments initialization")
logging.debug("Start:\t Multiview benchmark")
logging.debug("Start:\t multiview benchmark")
resultsMultiview = []
for arguments in argumentDictionaries["Multiview"]:
for arguments in argumentDictionaries["multiview"]:
resultsMultiview += [
ExecMultiview(directory, DATASET, args.name, classificationIndices,
kFolds, 1, args.type,
args.pathF, LABELS_DICTIONARY, randomState, labels,
hyperParamSearch=hyperParamSearch,
metrics=metrics, nIter=args.CL_HPS_iter, **arguments)]
logging.debug("Done:\t Multiview benchmark")
logging.debug("Done:\t multiview benchmark")
return [flag, resultsMonoview + resultsMultiview]
......@@ -519,7 +519,7 @@ def execBenchmark(nbCores, statsIter, nbMulticlass,
indices for the biclass training set, the ones for the biclass testing set and the ones for the
multiclass testing set.
metrics : list of lists
Metrics that will be used to evaluate the algorithms performance.
metrics that will be used to evaluate the algorithms performance.
labelsDictionary : dictionary
Dictionary mapping labels indices to labels names.
nbLabels : int
......@@ -618,13 +618,13 @@ def execClassif(arguments):
splits = execution.genSplits(DATASET.get("Labels").value, args.CL_split,
statsIterRandomStates)
multiclassLabels, labelsCombinations, indicesMulticlass = Multiclass.genMulticlassLabels(
multiclassLabels, labelsCombinations, indicesMulticlass = multiclass.genMulticlassLabels(
DATASET.get("Labels").value, multiclassMethod, splits)
kFolds = execution.genKFolds(statsIter, args.CL_nbFolds,
statsIterRandomStates)
datasetFiles = Dataset.initMultipleDatasets(args.pathF, args.name, nbCores)
datasetFiles = dataset.initMultipleDatasets(args.pathF, args.name, nbCores)
# if not views:
# raise ValueError("Empty views list, modify selected views to match dataset " + args.views)
......@@ -638,7 +638,7 @@ def execClassif(arguments):
if metrics == [[""]]:
metricsNames = [name for _, name, isPackage
in pkgutil.iter_modules(
['./MonoMultiViewClassifiers/Metrics']) if
['./mono_multi_view_classifiers/metrics']) if
not isPackage and name not in ["framework", "log_loss",
"matthews_corrcoef",
"roc_auc_score"]]
......
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