Fix smodel initialization

Fix kwargs use for mpl in contributivity

S-model initialization could fail if the confusion matrix has not the right shape, which can be the case if some labels are not included in the dataset of a partner. btw I noticed that smodel can only work with datasets with 10 labels, that's only cifar and mnist. I opened an issue about that
Signed-off-by: arthurPignet <arthur.pignet@mines-paristech.fr>

Author: arthurPignet

.coverage

@@ -1124,12 +1124,7 @@ def compute_relative_perf_matrix(self):
     def statistcal_distances_via_smodel(self):
 
         start = timer()
-        try:
-            mpl_pretrain = self.scenario.mpl.pretrain_epochs
-        except AttributeError:
-            mpl_pretrain = 2
-
-        mpl = fast_mpl.FastFedAvgSmodel(self.scenario, mpl_pretrain)
+        mpl = fast_mpl.FastFedAvgSmodel(self.scenario, **self.scenario.mpl_kwargs)
         mpl.fit()
         cross_entropy = tf.keras.metrics.CategoricalCrossentropy()
         self.contributivity_scores = {'Kullback Leiber divergence': [0 for _ in mpl.partners_list],

mplc/contributivity.py

@@ -544,7 +544,8 @@ def fit(self):
             for p in self.partners_list:
                 confusion = confusion_matrix(np.argmax(p.y_train, axis=1),
                                              np.argmax(pretrain_model.predict(p.x_train), axis=1),
-                                             normalize='pred')
+                                             normalize='pred',
+                                             labels=list(range(10)))
                 p.noise_layer_weights = [np.log(confusion.T + 1e-8)]
             self.model_weights[:-1] = self.pretrain_mpl.model_weights[:-1]
         else:

mplc/multi_partner_learning/basic_mpl.py

@@ -376,7 +376,7 @@ def fit_minibatch(model, partners_minibatches, partners_optimizers, partners_wei
             for p in self.partners_list:
                 confusion = confusion_matrix(np.argmax(p.y_train, axis=1),
                                              np.argmax(self.model.predict(p.x_train), axis=1),
-                                             normalize='pred')
+                                             normalize='pred', labels=list(range(10)))
                 p.noise_layer_weights = [np.log(confusion.T + 1e-8)]
         else:
             for p in self.partners_list:
@@ -549,7 +549,7 @@ def fit_epoch(model, train_dataset, partners_grads, smodel_list, global_grad, ag
             for p in self.partners_list:
                 confusion = confusion_matrix(np.argmax(p.y_train, axis=1),
                                              np.argmax(self.model.predict(p.x_train), axis=1),
-                                             normalize='pred')
+                                             normalize='pred', labels=list(range(10)))
                 p.noise_layer_weights = [np.log(confusion.T + 1e-8)]
         else:
             for p in self.partners_list: