Remove SVM classifier from evaluation

code_mario/clef18_task1_base.py

@@ -83,41 +83,42 @@ class Clef18Task1Base(LoggingMixin):
             ("SGD", lambda label, input_dim, output_dim, val_data: SGDClassifier(verbose=1, random_state=42)),
             ("DT", lambda label, input_dim, output_dim, val_data: DecisionTreeClassifier(random_state=42)),
             ("RF", lambda label, input_dim, output_dim, val_data: RandomForestClassifier(verbose=1, random_state=42)),
-            ("LinearSVM", lambda label, input_dim, output_dim, val_data: LinearSVC(max_iter=10000, verbose=1, random_state=42)),
+            #("LinearSVM", lambda label, input_dim, output_dim, val_data: LinearSVC(max_iter=10000, verbose=1, random_state=42)),
 
             ("DNN-200", lambda label, input_dim, output_dim, val_data:
                 self.create_dnn_classifier("dnn-200", label, val_data, input_dim=input_dim, output_dim=output_dim, hidden_layer_sizes=[200],
-                                            batch_normalization=False, dropout_rate=0.0, epochs=10, batch_size=2)),
+                                            batch_normalization=False, dropout_rate=0.0, epochs=10, batch_size=16)),
+
             ("DNN-300", lambda label, input_dim, output_dim, val_data:
                 self.create_dnn_classifier("dnn-300", label, val_data, input_dim=input_dim, output_dim=output_dim, hidden_layer_sizes=[300],
-                                            batch_normalization=False, dropout_rate=0.0, epochs=50, batch_size=2)),
+                                            batch_normalization=False, dropout_rate=0.0, epochs=50, batch_size=16)),
 
             ("DNN-200-BN-DO", lambda label, input_dim, output_dim, val_data:
                 self.create_dnn_classifier("dnn-200-bn-do", label, val_data, input_dim=input_dim, output_dim=output_dim, hidden_layer_sizes=[200],
-                                            batch_normalization=True, dropout_rate=0.5, epochs=50, batch_size=2)),
+                                            batch_normalization=True, dropout_rate=0.5, epochs=50, batch_size=16)),
             ("DNN-300-BN-DO", lambda label, input_dim, output_dim, val_data:
                 self.create_dnn_classifier("dnn-300-bn-do", label, val_data, input_dim=input_dim, output_dim=output_dim, hidden_layer_sizes=[300],
-                                            batch_normalization=True, dropout_rate=0.5, epochs=50, batch_size=2)),
+                                            batch_normalization=True, dropout_rate=0.5, epochs=50, batch_size=16)),
 
             ("DNN-200-100", lambda label, input_dim, output_dim, val_data:
                 self.create_dnn_classifier("dnn-200-100", label, val_data, input_dim=input_dim, output_dim=output_dim, hidden_layer_sizes=[200, 100],
-                                            batch_normalization=False, dropout_rate=0.0, epochs=50, batch_size=2)),
+                                            batch_normalization=False, dropout_rate=0.0, epochs=50, batch_size=16)),
             ("DNN-200-200", lambda label, input_dim, output_dim, val_data:
                 self.create_dnn_classifier("dnn-200-200", label, val_data, input_dim=input_dim, output_dim=output_dim, hidden_layer_sizes=[200, 200],
-                                            batch_normalization=False, dropout_rate=0.0, epochs=50, batch_size=2)),
+                                            batch_normalization=False, dropout_rate=0.0, epochs=50, batch_size=16)),
             ("DNN-300-200", lambda label, input_dim, output_dim, val_data:
                 self.create_dnn_classifier("dnn-300-200", label, val_data, input_dim=input_dim, output_dim=output_dim, hidden_layer_sizes=[300, 200],
-                                            batch_normalization=False, dropout_rate=0.0, epochs=50, batch_size=2)),
+                                            batch_normalization=False, dropout_rate=0.0, epochs=50, batch_size=16)),
 
             ("DNN-200-100-BN-DO", lambda label, input_dim, output_dim, val_data:
                 self.create_dnn_classifier("dnn-200-100-bn-do", label, val_data, input_dim=input_dim, output_dim=output_dim, hidden_layer_sizes=[200, 100],
-                                            batch_normalization=True, dropout_rate=0.5, epochs=50, batch_size=2)),
+                                            batch_normalization=True, dropout_rate=0.5, epochs=50, batch_size=16)),
             ("DNN-200-200-BN-DO", lambda label, input_dim, output_dim, val_data:
                 self.create_dnn_classifier("dnn-200-200-bn-do", label, val_data, input_dim=input_dim, output_dim=output_dim, hidden_layer_sizes=[200, 200],
-                                            batch_normalization=True, dropout_rate=0.5, epochs=50, batch_size=2)),
+                                            batch_normalization=True, dropout_rate=0.5, epochs=50, batch_size=16)),
             ("DNN-300-200-BN-DO", lambda label, input_dim, output_dim, val_data:
                 self.create_dnn_classifier("dnn-300-200-bn-do", label, val_data, input_dim=input_dim, output_dim=output_dim,hidden_layer_sizes=[300, 200],
-                                            batch_normalization=True, dropout_rate=0.5, epochs=50, batch_size=2)),
+                                            batch_normalization=True, dropout_rate=0.5, epochs=50, batch_size=16)),
 
             ('DU1', lambda label, input_dim, output_dim, val_data: DummyClassifier(strategy="stratified")),
             ('DU2', lambda label, input_dim, output_dim, val_data: DummyClassifier(strategy="most_frequent"))