diff --git a/docs/source/monomulti/multiview_classifiers/diversity_fusion.rst b/docs/source/monomulti/multiview_classifiers/diversity_fusion.rst
new file mode 100644
index 0000000000000000000000000000000000000000..cab3a2bc569bd3190d643b2dfed3b0675793cbcc
--- /dev/null
+++ b/docs/source/monomulti/multiview_classifiers/diversity_fusion.rst
@@ -0,0 +1,6 @@
+Metrics framework
+=================
+
+.. automodule:: multiview_platform.MonoMultiViewClassifiers.MultiviewClassifiers.diversity_utils
+ :members:
+ :inherited-members:
\ No newline at end of file
diff --git a/docs/source/monomultidoc.rst b/docs/source/monomultidoc.rst
index 12b23c9b43fd3ed18e69dbd6a6fd4b86065b4575..0bff743d48380198ca8f287c71189084df27e0a8 100644
--- a/docs/source/monomultidoc.rst
+++ b/docs/source/monomultidoc.rst
@@ -10,3 +10,4 @@ Mono and mutliview classification
monomulti/multiview_classifier
monomulti/exec_classif
monomulti/result_analysis
+ monomulti/multiview_classifiers/diversity_fusion
diff --git a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/diversity_utils.py b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/diversity_utils.py
index 136cbf2744490d8f70e2afd4c1f0e4cb879be197..a64c8c5eb8f8a50caa0e7ed86a646709163fe550 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/diversity_utils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MultiviewClassifiers/diversity_utils.py
@@ -7,6 +7,13 @@ from ..utils.Multiclass import isBiclass, genMulticlassMonoviewDecision
def getClassifiersDecisions(allClassifersNames, viewsIndices, resultsMonoview):
+ """
+ This function gets the monoview classifiers decisions from resultsMonoview.
+ The classifiersDecisions variable is ordered as :
+ classifiersDecisions[viewIndex, classifierIndex, foldIndex, exampleIndex]
+ And the classifiersNames variable is ordered as :
+ classifiersNames[viewIndex][classifierIndex]
+ """
nbViews = len(viewsIndices)
nbClassifiers = len(allClassifersNames)
nbFolds = len(resultsMonoview[0][1][6])
@@ -25,12 +32,16 @@ def getClassifiersDecisions(allClassifersNames, viewsIndices, resultsMonoview):
def couple_div_measure(allClassifersNames, viewsIndices, resultsMonoview, measurement, foldsGroudTruth):
+ """
+ This function is used to get the max of a couple diversity measurement,passed as an argument
+ It generates all possible combinations and all the couples to estimate the diversity on a combination
+ The best combination is the one that maximize the measurement.
+ """
classifiersDecisions, classifiersNames = getClassifiersDecisions(allClassifersNames,
viewsIndices,
resultsMonoview)
- foldsLen = len(resultsMonoview[0][1][6][0])
nbViews = len(viewsIndices)
nbClassifiers = len(allClassifersNames)
combinations = itertools.combinations_with_replacement(range(nbClassifiers), nbViews)
@@ -43,14 +54,14 @@ def couple_div_measure(allClassifersNames, viewsIndices, resultsMonoview, measur
combiWithView = [(viewIndex,combiIndex) for viewIndex, combiIndex in enumerate(combination)]
binomes = itertools.combinations(combiWithView, 2)
nbBinomes = int(math.factorial(nbViews) / 2 / math.factorial(nbViews-2))
- disagreement = np.zeros(nbBinomes)
+ couple_diversities = np.zeros(nbBinomes)
for binomeIndex, binome in enumerate(binomes):
(viewIndex1, classifierIndex1), (viewIndex2, classifierIndex2) = binome
- nbDisagree = np.sum(measurement(classifiersDecisions[viewIndex1, classifierIndex1],
+ folds_couple_diversity = np.mean(measurement(classifiersDecisions[viewIndex1, classifierIndex1],
classifiersDecisions[viewIndex2, classifierIndex2], foldsGroudTruth)
- , axis=1)/float(foldsLen)
- disagreement[binomeIndex] = np.mean(nbDisagree)
- div_measure[combinationsIndex] = np.mean(disagreement)
+ , axis=1)
+ couple_diversities[binomeIndex] = np.mean(folds_couple_diversity)
+ div_measure[combinationsIndex] = np.mean(couple_diversities)
bestCombiIndex = np.argmax(div_measure)
bestCombination = combis[bestCombiIndex]
@@ -58,6 +69,11 @@ def couple_div_measure(allClassifersNames, viewsIndices, resultsMonoview, measur
def global_div_measure(allClassifersNames, viewsIndices, resultsMonoview, measurement, foldsGroudTruth):
+ """
+ This function is used to get the max of a diversity measurement,passed as an argument
+ It generates all possible combinations to estimate the diversity on a combination
+ The best combination is the one that maximize the measurement.
+ """
classifiersDecisions, classifiersNames = getClassifiersDecisions(allClassifersNames,
viewsIndices,
resultsMonoview)
@@ -81,6 +97,10 @@ def global_div_measure(allClassifersNames, viewsIndices, resultsMonoview, measur
def CQ_div_measure(allClassifersNames, viewsIndices, resultsMonoview, measurement, foldsGroudTruth):
+ """
+ This function is used to measure a pseudo-CQ measurement based on the minCq algorithm.
+ It's a mix between couple_div_measure and global_div_measure that uses multiple measurements.
+ """
classifiersDecisions, classifiersNames = getClassifiersDecisions(allClassifersNames,
viewsIndices,
resultsMonoview)
@@ -115,6 +135,9 @@ def CQ_div_measure(allClassifersNames, viewsIndices, resultsMonoview, measuremen
def getFoldsGroundTruth(directory):
+ """This function is used to get the labels of each fold example used in the measurements
+ foldsGroundTruth is formatted as
+ foldsGroundTruth[foldIndex, exampleIndex]"""
foldsFilesNames = os.listdir(directory+"folds/")
foldLen = len(np.genfromtxt(directory+"folds/"+foldsFilesNames[0], delimiter=','))
foldsGroudTruth = np.zeros((len(foldsFilesNames), foldLen), dtype=int)
@@ -126,6 +149,7 @@ def getFoldsGroundTruth(directory):
def getArgs(args, benchmark, views, viewsIndices, randomState,
directory, resultsMonoview, classificationIndices, measurement, name):
+ """This function is a general function to get the args for all the measurements used"""
foldsGroundTruth = getFoldsGroundTruth(directory)
monoviewClassifierModulesNames = benchmark["Monoview"]
if name in ['DisagreeFusion', 'DoubleFaultFusion']:
@@ -157,8 +181,8 @@ def getArgs(args, benchmark, views, viewsIndices, randomState,
"weights": args.DGF_weights,
"classifiersNames": classifiersNames,
"monoviewDecisions": monoviewDecisions,
- "nbCLass":len(args.CL_classes),
- "div_measure":div_measure
+ "nbCLass": len(args.CL_classes),
+ "div_measure": div_measure
}
}
argumentsList.append(arguments)
@@ -170,9 +194,13 @@ def genParamsSets(classificationKWARGS, randomState, nIter=1):
nomralizedWeights = [[weightVector/np.sum(weightVector)] for weightVector in weights]
return nomralizedWeights
+
class DiversityFusionClass:
+ """This is a parent class for all the diversity fusion based classifiers."""
+
def __init__(self, randomState, NB_CORES=1, **kwargs):
+ """Used to init the instances"""
if kwargs["weights"] == []:
self.weights = [1.0/len(kwargs["classifiersNames"]) for _ in range(len(kwargs["classifiersNames"]))]
else:
@@ -183,12 +211,15 @@ class DiversityFusionClass:
self.div_measure = kwargs["div_measure"]
def setParams(self, paramsSet):
+ """ Used to set the weights"""
self.weights = paramsSet[0]
def fit_hdf5(self, DATASET, labels, trainIndices=None, viewsIndices=None, metric=["f1_score", None]):
+ """No need to fit as the monoview classifiers are already fitted"""
pass
def predict_hdf5(self, DATASET, usedIndices=None, viewsIndices=None):
+ """Just a weighted majority vote"""
if usedIndices is None:
usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
votes = np.zeros((len(usedIndices), self.nbClass), dtype=float)