Didi some debug

multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_multiview_utils.py → multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py

@@ -16,8 +16,8 @@ class BaseJumboFusion(LateFusionClassifier):
         self.distribs += [CustomRandint(1,10)]
         self.nb_monoview_per_view = nb_monoview_per_view
 
-    def set_params(self, **params):
-        self.nb_monoview_per_view = params["nb_monoview_per_view"]
+    def set_params(self, nb_monoview_per_view=1, **params):
+        self.nb_monoview_per_view = nb_monoview_per_view
         super(BaseJumboFusion, self).set_params(**params)
 
     def predict(self, X, example_indices=None, view_indices=None):
@@ -28,7 +28,6 @@ class BaseJumboFusion(LateFusionClassifier):
     def fit(self, X, y, train_indices=None, view_indices=None):
         train_indices, view_indices = get_examples_views_indices(X, train_indices, view_indices)
         self.init_classifiers(len(view_indices), nb_monoview_per_view=self.nb_monoview_per_view)
-        print(self.classifiers_names, self.nb_monoview_per_view)
         self.fit_monoview_estimators(X, y, train_indices=train_indices, view_indices=view_indices)
         monoview_decisions = self.predict_monoview(X, example_indices=train_indices, view_indices=view_indices)
         self.aggregation_estimator.fit(monoview_decisions, y[train_indices])

multiview_platform/mono_multi_view_classifiers/multiview_classifiers/svm_jumbo_fusion.py

 from sklearn.svm import SVC
 
-from .additions.jumbo_multiview_utils import BaseJumboFusion
+from .additions.jumbo_fusion_utils import BaseJumboFusion
 from ..monoview.monoview_utils import CustomUniform, CustomRandint
 
 classifier_class_name = "SVMJumboFusion"
@@ -17,9 +17,9 @@ class SVMJumboFusion(BaseJumboFusion):
         self.distribs += [CustomUniform(), ["rbf", "poly", "linear"], CustomRandint(2, 5)]
         self.aggregation_estimator = SVC(C=C, kernel=kernel, degree=degree)
 
-    def set_params(self, **params):
+    def set_params(self, C=1.0, kernel="rbf", degree=1, **params):
         super(SVMJumboFusion, self).set_params(**params)
-        self.aggregation_estimator.set_params(**dict((key, value) for key, value in params.items() if key in ["C", "kernel", "degree"]))
+        self.aggregation_estimator.set_params(C=C, kernel=kernel, degree=degree)
         return self
 
 

multiview_platform/tests/test_multiview_classifiers/test_additions/test_jumbo_fusion_utils.py

+import unittest
+import numpy as np
+
+import  multiview_platform.mono_multi_view_classifiers.multiview_classifiers.additions.jumbo_fusion_utils  as ju
+
+
+class FakeDataset():
+
+    def __init__(self, views, labels):
+        self.nb_views = views.shape[0]
+        self.dataset_length = views.shape[2]
+        self.views = views
+        self.labels = labels
+
+    def get_v(self, view_index, example_indices):
+        return self.views[view_index, example_indices]
+
+    def get_nb_class(self, example_indices):
+        return np.unique(self.labels[example_indices])
+
+
+#TODO
\ No newline at end of file