Wrote some doc

Code/MonoMultiViewClassifiers/utils/Dataset.py

@@ -10,6 +10,7 @@ from . import GetMultiviewDb as DB
 
 
 def getV(DATASET, viewIndex, usedIndices=None):
+    """Used to extract a view as a numpy array or a sparse mat from the HDF5 dataset"""
     if usedIndices is None:
         usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
     if type(usedIndices) is int:
@@ -32,6 +33,7 @@ def getV(DATASET, viewIndex, usedIndices=None):
 
 
 def getShape(DATASET, viewIndex):
+    """Used to get the dataset shape even if it's sparse"""
     if not DATASET.get("View" + str(viewIndex)).attrs["sparse"]:
         return DATASET.get("View" + str(viewIndex)).shape
     else:
@@ -39,6 +41,7 @@ def getShape(DATASET, viewIndex):
 
 
 def getValue(DATASET):
+    """Used to get the value of a view in the HDF5 dataset even if it sparse"""
     if not DATASET.attrs["sparse"]:
         return DATASET.value
     else:
@@ -50,6 +53,7 @@ def getValue(DATASET):
 
 
 def extractSubset(matrix, usedIndices):
+    """Used to extract a subset of a matrix even if it's sparse"""
     if sparse.issparse(matrix):
         newIndptr = np.zeros(len(usedIndices) + 1, dtype=int)
         oldindptr = matrix.indptr
@@ -69,8 +73,7 @@ def extractSubset(matrix, usedIndices):
 
 
 def initMultipleDatasets(args, nbCores):
-    """Used to create copies of the dataset if multicore computation is used
-    Needs arg.pathF and arg.name"""
+    """Used to create copies of the dataset if multicore computation is used"""
     if nbCores > 1:
         if DB.datasetsAlreadyExist(args.pathF, args.name, nbCores):
             logging.debug("Info:\t Enough copies of the dataset are already available")
@@ -90,6 +93,7 @@ def initMultipleDatasets(args, nbCores):
 
 
 def confirm(resp=True, timeout=15):
+    """Used to process answer"""
     ans = input_(timeout)
     if not ans:
         return resp
@@ -102,6 +106,7 @@ def confirm(resp=True, timeout=15):
 
 
 def input_(timeout=15):
+    """used as a UI to stop if too much HDD space will be used"""
     print("You have " + str(timeout) + " seconds to stop the script by typing n")
     i, o, e = select.select([sys.stdin], [], [], timeout)
     if i:

Code/MonoMultiViewClassifiers/utils/HyperParameterSearch.py

@@ -8,6 +8,7 @@ from .. import Metrics
 
 def searchBestSettings(dataset, classifierPackage, classifierName, metrics, iLearningIndices, iKFolds, randomState, viewsIndices=None,
                        searchingTool="hyperParamSearch", nIter=1, **kwargs):
+    """Used to select the right hyperparam optimization function to optimize hyper parameters"""
     if viewsIndices is None:
         viewsIndices = range(dataset.get("Metadata").attrs["nbView"])
     thismodule = sys.modules[__name__]
@@ -18,12 +19,13 @@ def searchBestSettings(dataset, classifierPackage, classifierName, metrics, iLea
 
 
 def gridSearch(dataset, classifierName, viewsIndices=None, kFolds=None, nIter=1, **kwargs):
-    # si grid search est selectionne, on veut tester certaines valeurs
+    """Used to perfom gridsearch on the classifiers"""
     pass
 
 
 def randomizedSearch(dataset, classifierPackage, classifierName, metrics, learningIndices, KFolds, randomState, viewsIndices=None, nIter=1,
                      nbCores=1, **classificationKWARGS):
+    """Used to perform a random search on the classifiers to optimize hyper parameters"""
     if viewsIndices is None:
         viewsIndices = range(dataset.get("Metadata").attrs["nbView"])
     metric = metrics[0]
@@ -75,10 +77,12 @@ def randomizedSearch(dataset, classifierPackage, classifierName, metrics, learni
 
 
 def spearMint(dataset, classifierName, viewsIndices=None, kFolds=None, nIter=1, **kwargs):
+    """Used to perform spearmint on the classifiers to optimize hyper parameters"""
     pass
 
 
 def genHeatMaps(params, scoresArray, outputFileName):
+    """Used to generate a heat map for each doublet of hyperparms optimized on the previous function"""
     nbParams = len(params)
     if nbParams > 2:
         combinations = itertools.combinations(range(nbParams), 2)

Code/MonoMultiViewClassifiers/utils/Interpret.py

@@ -5,11 +5,12 @@ import pickle
 
 
 def percent(x, pos):
-    'The two args are the value and tick position'
+    """Used to print percentage of importance on the y axis"""
     return '%1.1f %%' % (x * 100)
 
 
 def getFeatureImportance(classifier, directory, interpretString=""):
+    """Used to generate a graph and a pickle dictionary representing feature importances"""
     featureImportances = classifier.feature_importances_
     sortedArgs = np.argsort(-featureImportances)
     featureImportancesSorted = featureImportances[sortedArgs][:50]

Code/MonoMultiViewClassifiers/utils/execution.py

@@ -9,6 +9,7 @@ import sklearn
 
 
 def parseTheArgs(arguments):
+    """Used to parse the args entered by the user"""
 
     parser = argparse.ArgumentParser(
         description='This file is used to benchmark the scores fo multiple classification algorithm on multiview data.',
@@ -183,6 +184,7 @@ def parseTheArgs(arguments):
     return args
 
 def initRandomState(randomStateArg, directory):
+    """Used to init a random state and multiple if needed (multicore)"""
     if randomStateArg is None:
         randomState = np.random.RandomState(randomStateArg)
     else:
@@ -199,6 +201,7 @@ def initRandomState(randomStateArg, directory):
 
 
 def initLogFile(args):
+    """Used to init the directory where the results will be stored and the log file"""
     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"
@@ -226,11 +229,14 @@ def initLogFile(args):
 
 
 def genSplits(statsIter, datasetlength, DATASET, splitRatio, statsIterRandomStates):
+    """Used to gen the train/test splits using one or multiple random states"""
     indices = np.arange(datasetlength)
     if statsIter > 1:
         splits = []
         for randomState in statsIterRandomStates:
-            foldsObj = sklearn.model_selection.StratifiedShuffleSplit(n_splits=1, random_state=randomState, test_size=splitRatio)
+            foldsObj = sklearn.model_selection.StratifiedShuffleSplit(n_splits=1,
+                                                                      random_state=randomState,
+                                                                      test_size=splitRatio)
             folds = foldsObj.split(indices, DATASET.get("Labels").value)
             for fold in folds:
                 train_fold, test_fold = fold
@@ -249,6 +255,7 @@ def genSplits(statsIter, datasetlength, DATASET, splitRatio, statsIterRandomStat
 
 
 def genKFolds(statsIter, nbFolds, statsIterRandomStates):
+    """Used to generate folds indices for cross validation and multiple if needed"""
     if statsIter > 1:
         foldsList = []
         for randomState in statsIterRandomStates:
@@ -259,8 +266,7 @@ def genKFolds(statsIter, nbFolds, 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"""
+    """Used to return the views names that will be used by the algos, their indices and all the views names"""
     NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
     if args.views != [""]:
         allowedViews = args.views
@@ -278,6 +284,7 @@ def initViews(DATASET, args):
 
 
 def genDirecortiesNames(directory, statsIter):
+    """Used to generate the different directories of each iteration if needed"""
     if statsIter > 1:
         directories = []
         for i in range(statsIter):