Updated Qarboost

multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/BoostUtils.py

@@ -666,6 +666,7 @@ def get_accuracy_graph(train_accuracies, classifier_name, file_name):
     f.savefig(file_name)
     plt.close()
 
+
 class BaseBoost(object):
 
     def __init__(self):
@@ -702,6 +703,8 @@ def getInterpretBase(classifier, directory, classifier_name, weights,
     interpretString += np.array2string(weights[weights_sort], precision=4, separator=',', suppress_small=True)
     interpretString += "\n \t It generated {} columns by attributes and used {} iterations to converge, and selected {} couple(s) of opposed voters".format(classifier.n_stumps,
         len(weights_sort), classifier.nb_opposed_voters)
+    if max(weights) > 0.50:
+        interpretString += "\n \t The vote is useless in this context : voter n°{} is a dictator of weight > 0.50".format(classifier.chosen_columns_[np.argmax(np.array(weights))])
     if len(weights_sort) == classifier.n_max_iterations or len(weights) == classifier.n_total_hypotheses_:
         if len(weights) == classifier.n_max_iterations:
             interpretString += ", and used all available iterations, "

multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/CQBoostUtils.py

@@ -192,7 +192,7 @@ class ColumnGenerationClassifier(BaseEstimator, ClassifierMixin, BaseBoost):
 #     def __init__(self, mu=0.001, epsilon=1e-08, n_max_iterations=None, estimators_generator=None, save_iteration_as_hyperparameter_each=None):
 #         super(CqBoostClassifier, self).__init__(epsilon, n_max_iterations, estimators_generator, dual_constraint_rhs=0,
 #                                                 save_iteration_as_hyperparameter_each=save_iteration_as_hyperparameter_each)
-#         # TODO: Vérifier la valeur de nu (dual_constraint_rhs) à l'initialisation, mais de toute manière ignorée car
+#         # TODO: Verifier la valeur de nu (dual_constraint_rhs) a l'initialisation, mais de toute maniere ignoree car
 #         # on ne peut pas quitter la boucle principale avec seulement un votant.
 #         self.mu = mu
 #         self.train_time = 0

multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py

@@ -20,13 +20,13 @@ def randomizedSearch(X_train, y_train, randomState, outputFileName, classifierMo
     estimator = getattr(classifierModule, CL_type)(randomState)
     params_dict = estimator.genDistribs()
     if params_dict:
-        nb_possible_combinations = compute_possible_combinations(params_dict)
         metricModule = getattr(Metrics, metric[0])
         if metric[1] is not None:
             metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
         else:
             metricKWARGS = {}
         scorer = metricModule.get_scorer(**metricKWARGS)
+        nb_possible_combinations = compute_possible_combinations(params_dict)
         if nIter > nb_possible_combinations:
             nIter = nb_possible_combinations
         randomSearch = RandomizedSearchCV(estimator, n_iter=nIter, param_distributions=params_dict, refit=True,

multiview_platform/MonoMultiViewClassifiers/Monoview/__init__.py

-from . import ExecClassifMonoView, MonoviewUtils, analyzeResult
+# from . import ExecClassifMonoView, MonoviewUtils, analyzeResult

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py

@@ -16,6 +16,7 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
             random_state=random_state,
             n_estimators=n_estimators,
             base_estimator=base_estimator,
+            algorithm="SAMME"
             )
         self.param_names = ["n_estimators", "base_estimator"]
         self.classed_params = ["base_estimator"]
@@ -29,8 +30,8 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
     def getInterpret(self, directory):
         interpretString = ""
         interpretString += self.getFeatureImportance(directory)
-        interpretString += "\n\n"
-        interpretString += str(self.estimator_errors_)
+        interpretString += "\n\n Estimator error | Estimator weight\n"
+        interpretString += "\n".join([str(error) +" | "+ str(weight/sum(self.estimator_weights_)) for error, weight in zip(self.estimator_errors_, self.estimator_weights_)])
         return interpretString
 
 

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoostv21.py

@@ -79,7 +79,7 @@ class ColumnGenerationClassifierv21(BaseEstimator, ClassifierMixin, BaseBoost):
                 self.train_accuracies.append(accuracy_score(y, np.sign(self.previous_vote)))
                 continue
 
-            # ---- On résoud le problème à deux votants analytiquement.
+            # ---- On resoud le probleme a deux votants analytiquement.
             w = self._solve_two_weights_min_c(new_voter_margin, example_weights)
             if w[0] == "break":
                 self.chosen_columns_.pop()

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/KNN.py

@@ -19,7 +19,7 @@ class KNN(KNeighborsClassifier, BaseMonoviewClassifier):
             )
         self.param_names = ["n_neighbors", "weights", "algorithm", "p"]
         self.classed_params = []
-        self.distribs = [CustomRandint(low=1, high=20), ["uniform", "distance"],
+        self.distribs = [CustomRandint(low=1, high=10), ["uniform", "distance"],
                          ["auto", "ball_tree", "kd_tree", "brute"], [1, 2]]
         self.weird_strings = {}
         self.random_state=random_state

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoost.py

@@ -7,10 +7,12 @@ class QarBoost(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
 
     def __init__(self, random_state=None, **kwargs):
         super(QarBoost, self).__init__(
-            random_state=random_state,
-            )
-        self.param_names = []
-        self.distribs = []
+            random_state=random_state)
+
+        self.param_names = ["self_complemented", "twice_the_same", "old_fashioned", "previous_vote_weighted",
+                            "c_bound_choice", "random_start", "two_wieghts_problem"]
+        self.distribs = [[True, False], [True, False], [True, False], [True, False],
+                         [True, False], [True, False], [True, False]]
         self.classed_params = []
         self.weird_strings = {}
 
@@ -19,6 +21,7 @@ class QarBoost(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
         return True
 
     def getInterpret(self, directory):
+        self.getInterpretQar(directory)
         return getInterpretBase(self, directory, "QarBoost", self.weights_, self.break_cause)
 
 

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC3.py

@@ -9,7 +9,7 @@ class QarBoostNC3(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
         super(QarBoostNC3, self).__init__(
             random_state=random_state,
             self_complemented=False,
-            twice_the_same=True,
+            twice_the_same=False,
             previous_vote_weighted=False
             )
         self.param_names = []
@@ -22,6 +22,7 @@ class QarBoostNC3(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
         return True
 
     def getInterpret(self, directory):
+        self.getInterpretQar(directory)
         return getInterpretBase(self, directory, "QarBoostNC3", self.weights_, self.break_cause)
 
     def get_name_for_fusion(self):

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostv2.py

@@ -9,7 +9,8 @@ class QarBoostv2(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
         super(QarBoostv2, self).__init__(
             random_state=random_state,
             self_complemented=True,
-            twice_the_same=True
+            twice_the_same=True,
+            previous_vote_weighted=True
             )
         self.param_names = []
         self.distribs = []