test: remove models and model from input output identifiers (#367)

Signed-off-by: ThibaultFy <[email protected]>
Substra · Jun 30, 2023 · a86fd7c · a86fd7c
1 parent cedbe2a
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Showing 4 changed files with 29 additions and 28 deletions.
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Changed
+
+- Remove `model` and `models` for input and output identifiers in tests. Replace by `shared` instead. ([#367](https://github.com/Substra/substra/pull/367))
+
 ## [0.45.0](https://github.com/Substra/substra/releases/tag/0.45.0) - 2023-06-12
 
 ### Added

diff --git a/tests/data_factory.py b/tests/data_factory.py
@@ -79,7 +79,7 @@ def _get_predictions(path):
 def train(inputs, outputs, task_properties):
     X = inputs['{InputIdentifiers.datasamples}'][0]
     y = inputs['{InputIdentifiers.datasamples}'][1]
-    models_path = inputs.get('{InputIdentifiers.models}', [])
+    models_path = inputs.get('{InputIdentifiers.shared}', [])
     models = [_load_model(model_path) for model_path in models_path]
 
     print(f'Train, get X: {{X}}, y: {{y}}, models: {{models}}')
@@ -95,12 +95,12 @@ def train(inputs, outputs, task_properties):
         res = dict(value=avg + err)
 
     print(f'Train, return {{res}}')
-    _save_model(res, outputs['{OutputIdentifiers.model}'])
+    _save_model(res, outputs['{OutputIdentifiers.shared}'])
 
 @tools.register
 def predict(inputs, outputs, task_properties):
     X = inputs['{InputIdentifiers.datasamples}'][0]
-    model = _load_model(inputs['{InputIdentifiers.model}'])
+    model = _load_model(inputs['{InputIdentifiers.shared}'])
 
     res = [x * model['value'] for x in X]
     print(f'Predict, get X: {{X}}, model: {{model}}, return {{res}}')
@@ -129,14 +129,14 @@ def _save_predictions(y_pred, path):
 
 @tools.register
 def aggregate(inputs, outputs, task_properties):
-    models_path = inputs.get('{InputIdentifiers.models}', [])
+    models_path = inputs.get('{InputIdentifiers.shared}', [])
     models = [_load_model(model_path) for model_path in models_path]
     print(f'Aggregate models: {{models}}')
     values = [m['value'] for m in models]
     avg = sum(values) / len(values)
     res = dict(value=avg)
     print(f'Aggregate result: {{res}}')
-    _save_model(res, outputs['{OutputIdentifiers.model}'])
+    _save_model(res, outputs['{OutputIdentifiers.shared}'])
 
 @tools.register
 def predict(inputs, outputs, task_properties):

diff --git a/tests/fl_interface.py b/tests/fl_interface.py
@@ -25,8 +25,6 @@ class FunctionCategory(str, Enum):
 class InputIdentifiers(str, Enum):
     local = "local"
     shared = "shared"
-    model = "model"
-    models = "models"
     predictions = "predictions"
     performance = "performance"
     opener = "opener"
@@ -36,7 +34,6 @@ class InputIdentifiers(str, Enum):
 class OutputIdentifiers(str, Enum):
     local = "local"
     shared = "shared"
-    model = "model"
     predictions = "predictions"
     performance = "performance"
 
@@ -45,7 +42,7 @@ class FLFunctionInputs(list, Enum):
     """Substra function inputs by function category based on the InputIdentifiers"""
 
     FUNCTION_AGGREGATE = [
-        FunctionInputSpec(identifier=InputIdentifiers.models, kind=AssetKind.model.value, optional=False, multiple=True)
+        FunctionInputSpec(identifier=InputIdentifiers.shared, kind=AssetKind.model.value, optional=False, multiple=True)
     ]
     FUNCTION_SIMPLE = [
         FunctionInputSpec(
@@ -57,7 +54,7 @@ class FLFunctionInputs(list, Enum):
         FunctionInputSpec(
             identifier=InputIdentifiers.opener, kind=AssetKind.data_manager.value, optional=False, multiple=False
         ),
-        FunctionInputSpec(identifier=InputIdentifiers.models, kind=AssetKind.model.value, optional=True, multiple=True),
+        FunctionInputSpec(identifier=InputIdentifiers.shared, kind=AssetKind.model.value, optional=True, multiple=True),
     ]
     FUNCTION_COMPOSITE = [
         FunctionInputSpec(
@@ -85,7 +82,7 @@ class FLFunctionInputs(list, Enum):
             identifier=InputIdentifiers.opener, kind=AssetKind.data_manager.value, optional=False, multiple=False
         ),
         FunctionInputSpec(
-            identifier=InputIdentifiers.model, kind=AssetKind.model.value, optional=False, multiple=False
+            identifier=InputIdentifiers.shared, kind=AssetKind.model.value, optional=False, multiple=False
         ),
     ]
     FUNCTION_PREDICT_COMPOSITE = [
@@ -125,10 +122,10 @@ class FLFunctionOutputs(list, Enum):
     """Substra function outputs by function category based on the OutputIdentifiers"""
 
     FUNCTION_AGGREGATE = [
-        FunctionOutputSpec(identifier=OutputIdentifiers.model, kind=AssetKind.model.value, multiple=False)
+        FunctionOutputSpec(identifier=OutputIdentifiers.shared, kind=AssetKind.model.value, multiple=False)
     ]
     FUNCTION_SIMPLE = [
-        FunctionOutputSpec(identifier=OutputIdentifiers.model, kind=AssetKind.model.value, multiple=False)
+        FunctionOutputSpec(identifier=OutputIdentifiers.shared, kind=AssetKind.model.value, multiple=False)
     ]
     FUNCTION_COMPOSITE = [
         FunctionOutputSpec(identifier=OutputIdentifiers.local, kind=AssetKind.model.value, multiple=False),
@@ -168,9 +165,9 @@ def task(opener_key, data_sample_keys):
     def trains_to_train(model_keys):
         return [
             InputRef(
-                identifier=InputIdentifiers.models,
+                identifier=InputIdentifiers.shared,
                 parent_task_key=model_key,
-                parent_task_output_identifier=OutputIdentifiers.model,
+                parent_task_output_identifier=OutputIdentifiers.shared,
             )
             for model_key in model_keys
         ]
@@ -179,9 +176,9 @@ def trains_to_train(model_keys):
     def trains_to_aggregate(model_keys):
         return [
             InputRef(
-                identifier=InputIdentifiers.models,
+                identifier=InputIdentifiers.shared,
                 parent_task_key=model_key,
-                parent_task_output_identifier=OutputIdentifiers.model,
+                parent_task_output_identifier=OutputIdentifiers.shared,
             )
             for model_key in model_keys
         ]
@@ -190,9 +187,9 @@ def trains_to_aggregate(model_keys):
     def train_to_predict(model_key):
         return [
             InputRef(
-                identifier=InputIdentifiers.model,
+                identifier=InputIdentifiers.shared,
                 parent_task_key=model_key,
-                parent_task_output_identifier=OutputIdentifiers.model,
+                parent_task_output_identifier=OutputIdentifiers.shared,
             )
         ]
 
@@ -252,15 +249,15 @@ def aggregate_to_shared(model_key):
             InputRef(
                 identifier=InputIdentifiers.shared,
                 parent_task_key=model_key,
-                parent_task_output_identifier=OutputIdentifiers.model,
+                parent_task_output_identifier=OutputIdentifiers.shared,
             )
         ]
 
     @staticmethod
     def composites_to_aggregate(model_keys):
         return [
             InputRef(
-                identifier=InputIdentifiers.models,
+                identifier=InputIdentifiers.shared,
                 parent_task_key=model_key,
                 parent_task_output_identifier=OutputIdentifiers.shared,
             )
@@ -271,17 +268,17 @@ def composites_to_aggregate(model_keys):
     def aggregate_to_predict(model_key):
         return [
             InputRef(
-                identifier=InputIdentifiers.models,
+                identifier=InputIdentifiers.shared,
                 parent_task_key=model_key,
-                parent_task_output_identifier=OutputIdentifiers.model,
+                parent_task_output_identifier=OutputIdentifiers.shared,
             )
         ]
 
     @staticmethod
     def local_to_aggregate(model_key):
         return [
             InputRef(
-                identifier=InputIdentifiers.models,
+                identifier=InputIdentifiers.shared,
                 parent_task_key=model_key,
                 parent_task_output_identifier=OutputIdentifiers.local,
             )
@@ -291,7 +288,7 @@ def local_to_aggregate(model_key):
     def shared_to_aggregate(model_key):
         return [
             InputRef(
-                identifier=InputIdentifiers.models,
+                identifier=InputIdentifiers.shared,
                 parent_task_key=model_key,
                 parent_task_output_identifier=OutputIdentifiers.shared,
             )
@@ -310,11 +307,11 @@ class FLTaskOutputGenerator:
 
     @staticmethod
     def traintask(authorized_ids=None):
-        return {OutputIdentifiers.model: ComputeTaskOutputSpec(permissions=_permission_from_ids(authorized_ids))}
+        return {OutputIdentifiers.shared: ComputeTaskOutputSpec(permissions=_permission_from_ids(authorized_ids))}
 
     @staticmethod
     def aggregatetask(authorized_ids=None):
-        return {OutputIdentifiers.model: ComputeTaskOutputSpec(permissions=_permission_from_ids(authorized_ids))}
+        return {OutputIdentifiers.shared: ComputeTaskOutputSpec(permissions=_permission_from_ids(authorized_ids))}
 
     @staticmethod
     def predicttask(authorized_ids=None):

diff --git a/tests/sdk/local/test_debug.py b/tests/sdk/local/test_debug.py
@@ -204,7 +204,7 @@ def test_tasks_extra_fields(self, asset_factory, clients):
 
         assert len(predicttask.inputs) == 3  # data sample + opener + input task
 
-        model_ref = [x for x in predicttask.inputs if x.identifier == InputIdentifiers.model]
+        model_ref = [x for x in predicttask.inputs if x.identifier == InputIdentifiers.shared]
         assert len(model_ref) == 1
         assert model_ref[0].parent_task_key == traintask_key