Using full dataset

Code/MonoMultiViewClassifiers/ExecClassif.py

@@ -356,7 +356,7 @@ def execClassif(arguments):
         getDatabase = getattr(DB, "get" + args.name + "DB" + args.type[1:])
 
     DATASET, LABELS_DICTIONARY = getDatabase(args.views, args.pathF, args.name, args.CL_nbClass,
-                                             args.CL_classes, randomState)
+                                             args.CL_classes, randomState, args.full)
 
     classificationIndices = execution.genSplits(DATASET.get("Labels").value, args.CL_split, statsIterRandomStates)
 

Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py

@@ -29,12 +29,14 @@ def initConstants(args, X, classificationIndices, labelsNames, name, directory):
     except KeyError:
         kwargs = args
     t_start = time.time()
+    if type(X.attrs["name"]) == bytes:
+        feat = X.attrs["name"].decode("utf-8")
+    else:
         feat = X.attrs["name"]
     CL_type = kwargs["CL_type"]
     X = getValue(X)
     learningRate = float(len(classificationIndices[0])) / (len(classificationIndices[0]) + len(classificationIndices[1]))
     labelsString = "-".join(labelsNames)
-    timestr = time.strftime("%Y%m%d-%H%M%S")
     CL_type_string = CL_type
 
     outputFileName = directory + CL_type_string + "/" + feat + "/" + "Results-" + CL_type_string + "-" + labelsString + \

Code/MonoMultiViewClassifiers/utils/GetMultiviewDb.py

@@ -52,7 +52,7 @@ def makeMeNoisy(viewData, randomState, percentage=15):
     return noisyViewData
 
 
-def getPlausibleDBhdf5(features, pathF, name, NB_CLASS=3, LABELS_NAME="", nbView=3,
+def getPlausibleDBhdf5(features, pathF, name, NB_CLASS=3, LABELS_NAME="", randomState=None, full=True, nbView=3,
                        nbClass=2, datasetLength=347, randomStateInt=None):
     """Used to generate a plausible dataset to test the algorithms"""
     randomStateInt = 42
@@ -257,6 +257,9 @@ def filterLabels(labelsSet, askedLabelsNamesSet, fullLabels, availableLabelsName
 
 def filterViews(datasetFile, temp_dataset, views, usedIndices):
     newViewIndex = 0
+    if views == [""]:
+        for viewIndex in range(datasetFile.get("Metadata").attrs["nbView"]):
+            copyhdf5Dataset(datasetFile, temp_dataset, "View" + str(viewIndex), "View" + str(viewIndex), usedIndices)
     for askedViewName in views:
         for viewIndex in range(datasetFile.get("Metadata").attrs["nbView"]):
             viewName = datasetFile.get("View" + str(viewIndex)).attrs["name"]
@@ -286,8 +289,14 @@ def copyhdf5Dataset(sourceDataFile, destinationDataFile, sourceDatasetName, dest
             newDset.attrs[key] = value
 
 
-def getClassicDBhdf5(views, pathF, nameDB, NB_CLASS, askedLabelsNames, randomState):
+def getClassicDBhdf5(views, pathF, nameDB, NB_CLASS, askedLabelsNames, randomState, full=False):
     """Used to load a hdf5 database"""
+    if full:
+        datasetFile = h5py.File(pathF + nameDB + ".hdf5", "r")
+        labelsDictionary = dict((labelIndex, labelName.decode("utf-8")) for labelIndex, labelName in
+                                enumerate(datasetFile.get("Labels").attrs["names"]))
+        return datasetFile, labelsDictionary
+    else:
         askedLabelsNames = [askedLabelName.encode("utf8") for askedLabelName in askedLabelsNames]
         datasetFile = h5py.File(pathF + nameDB + ".hdf5", "r")
         fullLabels = datasetFile.get("Labels").value
@@ -311,7 +320,7 @@ def getClassicDBhdf5(views, pathF, nameDB, NB_CLASS, askedLabelsNames, randomSta
         return temp_dataset, labelsDictionary
 
 
-def getClassicDBcsv(views, pathF, nameDB, NB_CLASS, askedLabelsNames, randomState, delimiter=","):
+def getClassicDBcsv(views, pathF, nameDB, NB_CLASS, askedLabelsNames, randomState, full=False, delimiter=","):
     # TODO : Update this one
     labelsNames = np.genfromtxt(pathF + nameDB + "-labels-names.csv", dtype='str', delimiter=delimiter)
     datasetFile = h5py.File(pathF + nameDB + ".hdf5", "w")
@@ -333,7 +342,7 @@ def getClassicDBcsv(views, pathF, nameDB, NB_CLASS, askedLabelsNames, randomStat
     metaDataGrp.attrs["nbClass"] = len(labelsNames)
     metaDataGrp.attrs["datasetLength"] = len(labels)
     datasetFile.close()
-    datasetFile, labelsDictionary = getClassicDBhdf5(views, pathF, nameDB, NB_CLASS, askedLabelsNames, randomState)
+    datasetFile, labelsDictionary = getClassicDBhdf5(views, pathF, nameDB, NB_CLASS, askedLabelsNames, randomState, full)
 
     return datasetFile, labelsDictionary
 

Code/MonoMultiViewClassifiers/utils/Interpret.py

@@ -16,7 +16,7 @@ def getFeatureImportance(classifier, directory, interpretString=""):
     featureImportancesSorted = featureImportances[sortedArgs][:50]
     featureIndicesSorted = sortedArgs[:50]
     fig, ax = plt.subplots()
-    x = np.arange(50)
+    x = np.arange(len(featureIndicesSorted))
     formatter = FuncFormatter(percent)
     ax.yaxis.set_major_formatter(formatter)
     plt.bar(x, featureImportancesSorted)

Code/MonoMultiViewClassifiers/utils/execution.py

@@ -38,6 +38,8 @@ def parseTheArgs(arguments):
                                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')
+
 
     groupClass = parser.add_argument_group('Classification arguments')
     groupClass.add_argument('--CL_multiclassMethod', metavar='STRING', action='store',