diff --git a/Code/MonoMultiViewClassifiers/ExecClassif.py b/Code/MonoMultiViewClassifiers/ExecClassif.py
index b5360978e6060743ff25ca7362db294e919daf9d..2f31424de9b68aed992473be56f46bf45288d451 100644
--- a/Code/MonoMultiViewClassifiers/ExecClassif.py
+++ b/Code/MonoMultiViewClassifiers/ExecClassif.py
@@ -18,7 +18,7 @@ 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 resultAnalysis, analyzeLabels, analyzeIterResults, analyzeIterLabels, genNamesFromRes, getResults
+from .ResultAnalysis import getResults #resultAnalysis, analyzeLabels, analyzeIterResults, analyzeIterLabels, genNamesFromRes,
from .utils import execution, Dataset, Multiclass
from . import Metrics
diff --git a/Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py
index 7da23e3e1063b1ccd6e4d85f6138ffaad4c8e31a..2cbce0a85eda5ccf0b27c2b50c57696bf0aaeb0c 100644
--- a/Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -52,7 +52,10 @@ def initTrainTest(X, Y, classificationIndices):
trainIndices, testIndices, testIndicesMulticlass = classificationIndices
X_train = extractSubset(X, trainIndices)
X_test = extractSubset(X, testIndices)
- X_test_multiclass = extractSubset(X, testIndicesMulticlass)
+ if testIndicesMulticlass != []:
+ X_test_multiclass = extractSubset(X, testIndicesMulticlass)
+ else:
+ X_test_multiclass = []
y_train = Y[trainIndices]
y_test = Y[testIndices]
return X_train, y_train, X_test, y_test, X_test_multiclass
diff --git a/Code/MonoMultiViewClassifiers/ResultAnalysis.py b/Code/MonoMultiViewClassifiers/ResultAnalysis.py
index c8a451db5edb05770d4053e0710e8d77981d3661..45d935f9cde8948e140cfb7d60d6035c1d9b8182 100644
--- a/Code/MonoMultiViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMultiViewClassifiers/ResultAnalysis.py
@@ -459,7 +459,7 @@ def publishIterBiclassMetricsScores(iterResults, directory, labelsDictionary, cl
def iterCmap(statsIter):
cmapList = ["red", "0.0"]
for i in range(statsIter):
- cmapList.append(str((i+1)/statsIter))
+ cmapList.append(str(float((i+1))/statsIter))
cmap = mpl.colors.ListedColormap(cmapList)
bounds = [-100*statsIter-0.5, -0.5]
for i in range(statsIter):
@@ -658,14 +658,14 @@ def getResults(results, statsIter, nbMulticlass, benchmarkArgumentDictionaries,
analyzeIterMulticlass(multiclassResults, directory, statsIter, metrics, dataBaseName, nbExamples)
else:
biclassResults = analyzeBiclass(results, benchmarkArgumentDictionaries, statsIter, metrics)
- analyzebiclassIter(biclassResults, metrics, statsIter, directory, labelsDictionary, dataBaseName)
+ analyzebiclassIter(biclassResults, metrics, statsIter, directory, labelsDictionary, dataBaseName, nbExamples)
else:
if nbMulticlass>1:
biclassResults = analyzeBiclass(results, benchmarkArgumentDictionaries, statsIter, metrics)
multiclassResults = analyzeMulticlass(results, statsIter, benchmarkArgumentDictionaries, nbExamples, nbLabels, multiclassLabels,
metrics, classificationIndices, directories)
else:
- analyzeBiclass(results)
+ biclassResults = analyzeBiclass(results, benchmarkArgumentDictionaries, statsIter, metrics)
diff --git a/Code/MonoMultiViewClassifiers/utils/GetMultiviewDb.py b/Code/MonoMultiViewClassifiers/utils/GetMultiviewDb.py
index d51185e882167577d25bbcb1ca0f706232d995be..871f9bb3f56b58e5c50f06df86a8afbb9a2f8b5d 100644
--- a/Code/MonoMultiViewClassifiers/utils/GetMultiviewDb.py
+++ b/Code/MonoMultiViewClassifiers/utils/GetMultiviewDb.py
@@ -52,7 +52,7 @@ def makeMeNoisy(viewData, randomState, percentage=15):
return noisyViewData
-def getPlausibleDBhdf5(features, pathF, name, NB_CLASS, LABELS_NAME, nbView=3,
+def getPlausibleDBhdf5(features, pathF, name, NB_CLASS=3, LABELS_NAME="", nbView=3,
nbClass=2, datasetLength=347, randomStateInt=42):
"""Used to generate a plausible dataset to test the algorithms"""
randomState = np.random.RandomState(randomStateInt)
@@ -64,38 +64,65 @@ def getPlausibleDBhdf5(features, pathF, name, NB_CLASS, LABELS_NAME, nbView=3,
if exc.errno != errno.EEXIST:
raise
datasetFile = h5py.File(pathF + "/Plausible.hdf5", "w")
- firstBound = int(datasetLength / 3)
- rest = datasetLength - 2*int(datasetLength / 3)
- scndBound = 2*int(datasetLength / 3)
- thrdBound = datasetLength
- CLASS_LABELS = np.array([0 for _ in range(firstBound)] + [1 for _ in range(firstBound)] + [2 for _ in range(rest)])
- for viewIndex in range(nbView):
- viewData = np.array([np.zeros(nbFeatures) for _ in range(firstBound)] +
- [np.ones(nbFeatures)for _ in range(firstBound)] +
- [np.ones(nbFeatures)+1 for _ in range(rest)])
- fakeOneIndices = randomState.randint(0, firstBound, int(datasetLength / 12))
- fakeTwoIndices = randomState.randint(firstBound, scndBound, int(datasetLength / 12))
- fakeZeroIndices = randomState.randint(scndBound, thrdBound, int(datasetLength / 12))
+ if NB_CLASS == 2:
+ CLASS_LABELS = np.array([0 for _ in range(datasetLength/2)] + [1 for _ in range(datasetLength-datasetLength/2)])
+ for viewIndex in range(nbView):
+ viewData = np.array([np.zeros(nbFeatures) for _ in range(datasetLength/2)] +
+ [np.ones(nbFeatures)for _ in range(datasetLength-datasetLength/2)])
+ fakeOneIndices = randomState.randint(0, datasetLength/2, int(datasetLength / 12))
+ fakeZeroIndices = randomState.randint(datasetLength/2, datasetLength-datasetLength/2, int(datasetLength / 12))
- viewData[fakeOneIndices] = np.ones((len(fakeOneIndices), nbFeatures))
- viewData[fakeZeroIndices] = np.zeros((len(fakeZeroIndices), nbFeatures))
- viewData[fakeTwoIndices] = np.ones((len(fakeTwoIndices), nbFeatures))+1
- viewData = makeMeNoisy(viewData, randomState)
- viewDset = datasetFile.create_dataset("View" + str(viewIndex), viewData.shape, data=viewData.astype(np.uint8))
- viewDset.attrs["name"] = "View" + str(viewIndex)
- viewDset.attrs["sparse"] = False
- labelsDset = datasetFile.create_dataset("Labels", CLASS_LABELS.shape)
- labelsDset[...] = CLASS_LABELS
- labelsDset.attrs["name"] = "Labels"
- labelsDset.attrs["names"] = ["No".encode(), "Yes".encode(), "Maybe".encode()]
- metaDataGrp = datasetFile.create_group("Metadata")
- metaDataGrp.attrs["nbView"] = nbView
- metaDataGrp.attrs["nbClass"] = 3
- metaDataGrp.attrs["datasetLength"] = len(CLASS_LABELS)
- datasetFile.close()
- datasetFile = h5py.File(pathF + "Plausible.hdf5", "r")
- LABELS_DICTIONARY = {0: "No", 1: "Yes", 2:"Maybe"}
- return datasetFile, LABELS_DICTIONARY
+ viewData[fakeOneIndices] = np.ones((len(fakeOneIndices), nbFeatures))
+ viewData[fakeZeroIndices] = np.zeros((len(fakeZeroIndices), nbFeatures))
+ viewData = makeMeNoisy(viewData, randomState)
+ viewDset = datasetFile.create_dataset("View" + str(viewIndex), viewData.shape, data=viewData.astype(np.uint8))
+ viewDset.attrs["name"] = "View" + str(viewIndex)
+ viewDset.attrs["sparse"] = False
+ labelsDset = datasetFile.create_dataset("Labels", CLASS_LABELS.shape)
+ labelsDset[...] = CLASS_LABELS
+ labelsDset.attrs["name"] = "Labels"
+ labelsDset.attrs["names"] = ["No".encode(), "Yes".encode()]
+ metaDataGrp = datasetFile.create_group("Metadata")
+ metaDataGrp.attrs["nbView"] = nbView
+ metaDataGrp.attrs["nbClass"] = 2
+ metaDataGrp.attrs["datasetLength"] = len(CLASS_LABELS)
+ datasetFile.close()
+ datasetFile = h5py.File(pathF + "Plausible.hdf5", "r")
+ LABELS_DICTIONARY = {0: "No", 1: "Yes"}
+ return datasetFile, LABELS_DICTIONARY
+ elif NB_CLASS >= 3:
+ firstBound = int(datasetLength / 3)
+ rest = datasetLength - 2*int(datasetLength / 3)
+ scndBound = 2*int(datasetLength / 3)
+ thrdBound = datasetLength
+ CLASS_LABELS = np.array([0 for _ in range(firstBound)] + [1 for _ in range(firstBound)] + [2 for _ in range(rest)])
+ for viewIndex in range(nbView):
+ viewData = np.array([np.zeros(nbFeatures) for _ in range(firstBound)] +
+ [np.ones(nbFeatures)for _ in range(firstBound)] +
+ [np.ones(nbFeatures)+1 for _ in range(rest)])
+ fakeOneIndices = randomState.randint(0, firstBound, int(datasetLength / 12))
+ fakeTwoIndices = randomState.randint(firstBound, scndBound, int(datasetLength / 12))
+ fakeZeroIndices = randomState.randint(scndBound, thrdBound, int(datasetLength / 12))
+
+ viewData[fakeOneIndices] = np.ones((len(fakeOneIndices), nbFeatures))
+ viewData[fakeZeroIndices] = np.zeros((len(fakeZeroIndices), nbFeatures))
+ viewData[fakeTwoIndices] = np.ones((len(fakeTwoIndices), nbFeatures))+1
+ viewData = makeMeNoisy(viewData, randomState)
+ viewDset = datasetFile.create_dataset("View" + str(viewIndex), viewData.shape, data=viewData.astype(np.uint8))
+ viewDset.attrs["name"] = "View" + str(viewIndex)
+ viewDset.attrs["sparse"] = False
+ labelsDset = datasetFile.create_dataset("Labels", CLASS_LABELS.shape)
+ labelsDset[...] = CLASS_LABELS
+ labelsDset.attrs["name"] = "Labels"
+ labelsDset.attrs["names"] = ["No".encode(), "Yes".encode(), "Maybe".encode()]
+ metaDataGrp = datasetFile.create_group("Metadata")
+ metaDataGrp.attrs["nbView"] = nbView
+ metaDataGrp.attrs["nbClass"] = 3
+ metaDataGrp.attrs["datasetLength"] = len(CLASS_LABELS)
+ datasetFile.close()
+ datasetFile = h5py.File(pathF + "Plausible.hdf5", "r")
+ LABELS_DICTIONARY = {0: "No", 1: "Yes", 2:"Maybe"}
+ return datasetFile, LABELS_DICTIONARY
# def getFakeDBhdf5(features, pathF, name, NB_CLASS, LABELS_NAME, randomState):
diff --git a/Code/MonoMultiViewClassifiers/utils/Multiclass.py b/Code/MonoMultiViewClassifiers/utils/Multiclass.py
index 89fd7bd1f038442067fed97f0b405f3abe43a0ee..1132dd2141363e8dabe35048c9d24d88d7dae986 100644
--- a/Code/MonoMultiViewClassifiers/utils/Multiclass.py
+++ b/Code/MonoMultiViewClassifiers/utils/Multiclass.py
@@ -6,7 +6,9 @@ def genMulticlassLabels(labels, multiclassMethod, classificationIndices):
if multiclassMethod == "oneVersusOne":
nbLabels = len(set(list(labels)))
if nbLabels == 2:
- classificationIndices = [[trainIndices, testIndices, []] for trainIndices, testIndices in classificationIndices]
+ classificationIndices = [[trainIndices for trainIndices, _ in classificationIndices],
+ [testIndices for _, testIndices in classificationIndices],
+ [[] for _ in classificationIndices]]
return [labels], [(0,1)], [classificationIndices]
else:
combinations = itertools.combinations(np.arange(nbLabels), 2)
diff --git a/Code/MonoMultiViewClassifiers/utils/execution.py b/Code/MonoMultiViewClassifiers/utils/execution.py
index 10906637ba20983c1548c6a4bec1aa9c172c3d10..206f2eea3c1cc366d3e27885d9231e74998ec031 100644
--- a/Code/MonoMultiViewClassifiers/utils/execution.py
+++ b/Code/MonoMultiViewClassifiers/utils/execution.py
@@ -264,7 +264,7 @@ def genKFolds(statsIter, nbFolds, statsIterRandomStates):
foldsList.append(sklearn.model_selection.StratifiedKFold(n_splits=nbFolds, random_state=randomState))
return foldsList
else:
- return sklearn.model_selection.StratifiedKFold(n_splits=nbFolds, random_state=statsIterRandomStates)
+ return [sklearn.model_selection.StratifiedKFold(n_splits=nbFolds, random_state=statsIterRandomStates)]
def initViews(DATASET, args):