Modified base stumps

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/AdaboostGraalpy.py

@@ -143,13 +143,13 @@ class AdaBoostGP(BaseEstimator, ClassifierMixin, BaseBoost):
 
 class AdaboostGraalpy(AdaBoostGP, BaseMonoviewClassifier):
 
-    def __init__(self, random_state=None, n_iterations=200, n_stumps=10, **kwargs):
+    def __init__(self, random_state=None, n_iterations=200, n_stumps=1, **kwargs):
         super(AdaboostGraalpy, self).__init__(
             n_iterations=n_iterations,
             n_stumps=n_stumps
         )
-        self.param_names = []
-        self.distribs = []
+        self.param_names = ["n_iterations",]
+        self.distribs = [CustomRandint(low=1, high=500)]
         self.classed_params = []
         self.weird_strings = {}
         self.n_stumps = n_stumps

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/AdaboostPregen.py

@@ -17,7 +17,7 @@ __status__ = "Prototype"  # Production, Development, Prototype
 class AdaboostPregen(AdaBoostClassifier, BaseBoost, BaseMonoviewClassifier):
 
     def __init__(self, random_state=None, n_estimators=50,
-                 base_estimator=None, n_stumps=10, self_complemeted=True , **kwargs):
+                 base_estimator=None, n_stumps=1, self_complemeted=True , **kwargs):
         super(AdaboostPregen, self).__init__(
             random_state=random_state,
             n_estimators=n_estimators,

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGDesc.py

@@ -5,7 +5,7 @@ from ..Monoview.Additions.QarBoostUtils import ColumnGenerationClassifierQar
 
 class CGDesc(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
 
-    def __init__(self, random_state=None, n_max_iterations=500, n_stumps_per_attribute=10, **kwargs):
+    def __init__(self, random_state=None, n_max_iterations=500, n_stumps_per_attribute=1, **kwargs):
         super(CGDesc, self).__init__(n_max_iterations=n_max_iterations,
             random_state=random_state,
             self_complemented=True,

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoost.py

@@ -7,7 +7,7 @@ import os
 
 class CQBoost(ColumnGenerationClassifier, BaseMonoviewClassifier):
 
-    def __init__(self, random_state=None, mu=0.01, epsilon=1e-06, n_stumps=10, **kwargs):
+    def __init__(self, random_state=None, mu=0.01, epsilon=1e-06, n_stumps=1, **kwargs):
         super(CQBoost, self).__init__(
             random_state=random_state,
             mu=mu,

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/MinCQGraalpy.py

@@ -240,7 +240,9 @@ class RegularizedBinaryMinCqClassifier(MinCqClassifier):
         n_examples, n_voters = np.shape(classification_matrix)
 
         if self.zeta == 0:
-            ftf = classification_matrix.T.dot(classification_matrix)
+            print(classification_matrix.shape)
+            np.transpose(classification_matrix)
+            ftf = np.dot(np.transpose(classification_matrix),classification_matrix)
         else:
             I = np.eye(n_examples)
             L = build_laplacian(X, n_neighbors=self.n_neighbors)
@@ -324,11 +326,11 @@ def build_laplacian(X, n_neighbors=None):
 
 class MinCQGraalpy(RegularizedBinaryMinCqClassifier, BaseMonoviewClassifier):
 
-    def __init__(self, random_state=None, mu=0.01, self_complemented=True, n_stumps_per_attribute=10 , **kwargs):
+    def __init__(self, random_state=None, mu=0.01, self_complemented=True, n_stumps_per_attribute=1, **kwargs):
         super(MinCQGraalpy, self).__init__(mu=mu,
             estimators_generator=StumpsClassifiersGenerator(n_stumps_per_attribute=n_stumps_per_attribute, self_complemented=self_complemented),
         )
-        self.param_names = ["mu"]
+        self.param_names = ["mu",]
         self.distribs = [CustomUniform(loc=0.5, state=2.0, multiplier="e-"),
                          ]
         self.n_stumps_per_attribute = n_stumps_per_attribute