Replace obselete pw.Model.create_or_get with get_or_create

Author: bnaul

cesium_app/models.py

@@ -96,7 +96,7 @@ def add(name, project, file_uris=[], file_names=[], meta_features=[]):
             d = Dataset.create(name=name, project=project,
                                meta_features=meta_features)
             for fname, uri in zip(file_names, file_uris):
-                f, created = File.create_or_get(name=fname, uri=uri)
+                f, created = File.get_or_create(name=fname, uri=uri)
                 DatasetFile.create(dataset=d, file=f)
         return d
 

cesium_app/tests/fixtures.py

@@ -89,7 +89,7 @@ def create_test_featureset(project, label_type='class'):
     fset_path = pjoin(cfg['paths']['features_folder'],
                       '{}.nc'.format(str(uuid.uuid4())))
     fset_data.to_netcdf(fset_path)
-    f, created = m.File.create_or_get(uri=fset_path)
+    f, created = m.File.get_or_create(uri=fset_path)
     fset = m.Featureset.create(name='test_featureset', file=f, project=project,
                                features_list=features_to_use,
                                custom_features_script=None,
@@ -133,7 +133,7 @@ def create_test_model(fset, model_type='RandomForestClassifier'):
         model_path = pjoin(cfg['paths']['models_folder'],
                            '{}.pkl'.format(str(uuid.uuid4())))
         joblib.dump(model_data, model_path)
-    f, created = m.File.create_or_get(uri=model_path)
+    f, created = m.File.get_or_create(uri=model_path)
     model = m.Model.create(name='test_model',
                            file=f, featureset=fset, project=fset.project,
                            params=model_params[model_type], type=model_type,
@@ -163,7 +163,7 @@ def create_test_prediction(dataset, model):
     pred_path = pjoin(cfg['paths']['predictions_folder'],
                       '{}.nc'.format(str(uuid.uuid4())))
     pred_data.to_netcdf(pred_path)
-    f, created = m.File.create_or_get(uri=pred_path)
+    f, created = m.File.get_or_create(uri=pred_path)
     pred = m.Prediction.create(file=f, dataset=dataset, project=dataset.project,
                                model=model, finished=datetime.datetime.now())
     pred.save()