Added multiple kwargs per clf, to be tested

multiview_platform/MonoMultiViewClassifiers/ExecClassif.py

@@ -6,6 +6,7 @@ import pkgutil
 import time
 
 import matplotlib
+import itertools
 import numpy as np
 from joblib import Parallel, delayed
 
@@ -157,7 +158,7 @@ def initMonoviewExps(benchmark, viewsDictionary, nbClass, kwargsInit):
         for viewName, viewIndex in viewsDictionary.items():
             for classifier in benchmark["Monoview"]:
                 if multiple_args(classifier, kwargsInit):
-                    argumentDictionaries["Monoview"] += gen_multiple_args_dictionnaries(nbClass, kwargsInit)
+                    argumentDictionaries["Monoview"] += gen_multiple_args_dictionnaries(nbClass, kwargsInit, classifier, viewName, viewIndex)
                 else:
                     arguments = {
                         "args": {classifier + "KWARGS": kwargsInit[
@@ -168,14 +169,37 @@ def initMonoviewExps(benchmark, viewsDictionary, nbClass, kwargsInit):
     return argumentDictionaries
 
 def multiple_args(classifier, kwargsInit):
-    listed_args = [type(value) == list and  len(value)>1 for key, value in kwargsInit[classifier + "KWARGSInit"].items()]
+    listed_args = [type(value) == list and  len(value)>1 for key, value in
+                   kwargsInit[classifier + "KWARGSInit"].items()]
     if True in listed_args:
         return True
     else: 
         return False
 
-
-def gen_multiple_args_dictionnaries(nbClass, kwargsInit):
+def gen_multiple_kwargs_combinations(clKWARGS):
+    values = list(clKWARGS.values())
+    listed_values = [[_] if type(_) is not list else _ for _ in values]
+    values_cartesian_prod = [_ for _ in itertools.product(*listed_values)]
+    keys = clKWARGS.keys()
+    kwargs_combination = [dict((key, value) for key, value in zip(keys, values))
+                          for values in values_cartesian_prod]
+    return kwargs_combination
+
+
+def gen_multiple_args_dictionnaries(nbClass, kwargsInit,
+                                    classifier, viewName, viewIndex):
+    multiple_kwargs_list = gen_multiple_kwargs_combinations(kwargsInit[classifier + "KWARGSInit"])
+    multiple_kwargs_dict = dict(
+        (classifier+"_"+"_".join(map(str,list(dictionary.values()))), dictionary)
+        for dictionary in multiple_kwargs_list)
+    args_dictionnaries = [{
+                        "args": {classifier_name + "KWARGS": arguments,
+                                 "feat": viewName,
+                                 "CL_type": classifier_name,
+                                 "nbClass": nbClass},
+                        "viewIndex": viewIndex}
+        for classifier_name, arguments in multiple_kwargs_dict.items()]
+    return args_dictionnaries
 
 
 def initMonoviewKWARGS(args, classifiersNames):

multiview_platform/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py

@@ -77,7 +77,8 @@ def ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices,
     logging.debug("Done:\t Determine Train/Test split")
 
     logging.debug("Start:\t Generate classifier args")
-    classifierModule = getattr(MonoviewClassifiers, CL_type)
+    classifierModuleName = CL_type.split("_")[0]
+    classifierModule = getattr(MonoviewClassifiers, classifierModuleName)
     clKWARGS, testFoldsPreds = getHPs(classifierModule, hyperParamSearch,
                                       nIter, CL_type, X_train, y_train,
                                       randomState, outputFileName,
@@ -85,7 +86,7 @@ def ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices,
     logging.debug("Done:\t Generate classifier args")
 
     logging.debug("Start:\t Training")
-    classifier = getattr(classifierModule, CL_type)(randomState, **clKWARGS)
+    classifier = getattr(classifierModule, classifierModuleName)(randomState, **clKWARGS)
 
     classifier.fit(X_train, y_train)  # NB_CORES=nbCores,
     logging.debug("Done:\t Training")