Remove warnings from tests. Issue 264

mcfly/find_architecture.py

@@ -75,7 +75,7 @@ def train_models_on_samples(X_train, y_train, X_val, y_val, models,
           or `(inputs, targets, sample_weights)`
         - generator or `keras.utils.Sequence`. Should return a tuple of
           `(inputs, targets)` or `(inputs, targets, sample_weights)`
-          
+
         The input dataset for validation of shape
         (num_samples_val, num_timesteps, num_channels)
         More details can be found in the documentation for the Keras
@@ -185,7 +185,7 @@ def train_models_on_samples(X_train, y_train, X_val, y_val, models,
 
         if outputfile is not None:
             store_train_hist_as_json(params, model_types, history.history,
-                                     outputfile, model.metrics_names[0])
+                                     outputfile)
         if model_path is not None:
             model.save(os.path.join(model_path, 'model_{}.h5'.format(i)))
 

tests/test_integration.py

@@ -19,7 +19,7 @@ def test_integration(self):
                                           number_of_classes=num_classes,
                                           number_of_models=2,
                                           metrics=[metric],
-                                          model_type='CNN')  # Because CNNs are quick to train.
+                                          model_types=['CNN'])  # Because CNNs are quick to train.
         histories, val_accuracies, val_losses = find_architecture.train_models_on_samples(X_train, y_train,
                                                                                           X_val, y_val,
                                                                                           models, nr_epochs=5,

tests/test_storage.py

@@ -53,7 +53,10 @@ def create_dummy_model():
     num_time_steps = 100
     num_channels = 2
     num_samples_train = 5
-    model, _parameters, _type = modelgen.generate_models((num_samples_train, num_time_steps, num_channels), 5, 1)[0]
+    model, _parameters, _type = modelgen.generate_models(
+        (num_samples_train, num_time_steps, num_channels), 5, 1,
+        ['CNN'] # Chosen one model type to avoid warnings
+        )[0]
     return model