chore: rename test_data_samples_keys to data_samples_keys (#398)

Signed-off-by: ThibaultFy <[email protected]>
Substra · Feb 28, 2024 · 74b145c · 74b145c
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Showing 5 changed files with 18 additions and 21 deletions.
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Changed
 
+- Rename `test_data_sample_keys` to `data_sample_keys` on `TestDataNodes` after [SubstraFL #185](https://github.com/Substra/substrafl/pull/185) ([#398](https://github.com/Substra/substra-documentation/pull/398))
 - Test and predict tasks are now merged, after [SubstraFL #177](https://github.com/Substra/substrafl/pull/177)
 - Rename `predictions_path` to `predictions` in metrics ([#376](https://github.com/Substra/substra-documentation/pull/376))
 - Pass `metric_functions` to `Strategy` instead to `TestDataNodes` ([#376](https://github.com/Substra/substra-documentation/pull/376))

diff --git a/docs/source/examples/substrafl/get_started/run_mnist_torch.ipynb b/docs/source/examples/substrafl/get_started/run_mnist_torch.ipynb
@@ -229,11 +229,9 @@
         "**datasamples**.\n",
         "\n",
         "To add a metric, you need to define a function that computes and returns a performance\n",
-        "from the datasamples (as returned by the opener) and the predictions_path (to be loaded within the function).\n",
+        "from the datasamples (as returned by the opener) and the predictions of the model.\n",
         "\n",
-        "When using a Torch SubstraFL algorithm, the predictions are saved in the `predict` function in numpy format\n",
-        "so that you can simply load them using `np.load`.\n",
-        "\n"
+        "When using a Torch SubstraFL algorithm, the predictions are returned by the `predict` function.\n"
       ]
     },
     {
@@ -553,7 +551,7 @@
         "    TestDataNode(\n",
         "        organization_id=org_id,\n",
         "        data_manager_key=dataset_keys[org_id],\n",
-        "        test_data_sample_keys=[test_datasample_keys[org_id]],\n",
+        "        data_sample_keys=[test_datasample_keys[org_id]],\n",
         "    )\n",
         "    for org_id in DATA_PROVIDER_ORGS_ID\n",
         "]\n",
@@ -788,7 +786,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.17"
+      "version": "3.11.6"
     }
   },
   "nbformat": 4,

diff --git a/docs/source/examples/substrafl/go_further/run_diabetes_substrafl.ipynb b/docs/source/examples/substrafl/go_further/run_diabetes_substrafl.ipynb
@@ -205,7 +205,6 @@
         "    org_id: clients[org_id].add_data_sample(\n",
         "        DataSampleSpec(\n",
         "            data_manager_keys=[dataset_keys[org_id]],\n",
-        "            test_only=False,\n",
         "            path=data_path / f\"org_{i + 1}\",\n",
         "        ),\n",
         "        local=True,\n",
@@ -690,7 +689,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.17"
+      "version": "3.11.6"
     }
   },
   "nbformat": 4,

diff --git a/docs/source/examples/substrafl/go_further/run_iris_sklearn.ipynb b/docs/source/examples/substrafl/go_further/run_iris_sklearn.ipynb
@@ -390,7 +390,7 @@
         "\n",
         "    def predict(self, datasamples, shared_state):\n",
         "        \"\"\"The predict function to be executed by the evaluation function on\n",
-        "        data we want to test our model on. The predict method is mandatory and is \n",
+        "        data we want to test our model on. The predict method is mandatory and is\n",
         "        an `abstractmethod` of the `Algo` class.\n",
         "\n",
         "        Args:\n",
@@ -436,7 +436,7 @@
       "outputs": [],
       "source": [
         "from substrafl.strategies import FedAvg\n",
-        "        \n",
+        "\n",
         "strategy = FedAvg(algo=SklearnLogisticRegression(model=cls, seed=SEED), metric_functions=accuracy)"
       ]
     },
@@ -495,7 +495,7 @@
         "    TestDataNode(\n",
         "        organization_id=org_id,\n",
         "        data_manager_key=dataset_keys[org_id],\n",
-        "        test_data_sample_keys=[test_datasample_keys[org_id]],\n",
+        "        data_sample_keys=[test_datasample_keys[org_id]],\n",
         "    )\n",
         "    for org_id in DATA_PROVIDER_ORGS_ID\n",
         "]\n",
@@ -658,7 +658,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.17"
+      "version": "3.11.6"
     }
   },
   "nbformat": 4,

diff --git a/docs/source/examples/substrafl/go_further/run_mnist_cyclic.ipynb b/docs/source/examples/substrafl/go_further/run_mnist_cyclic.ipynb
@@ -234,10 +234,9 @@
         "**datasamples**.\n",
         "\n",
         "To add a metric, you need to define a function that computes and returns a performance\n",
-        "from the datasamples (as returned by the opener) and the predictions_path (to be loaded within the function).\n",
+        "from the datasamples (as returned by the opener) and the predictions of the model.\n",
         "\n",
-        "When using a Torch SubstraFL algorithm, the predictions are saved in the `predict` function in numpy format\n",
-        "so that you can simply load them using `np.load`.\n",
+        "When using a Torch SubstraFL algorithm, the predictions are returned by the `predict` function.\n",
         "\n"
       ]
     },
@@ -480,10 +479,10 @@
         "    \"\"\"\n",
         "\n",
         "    def __init__(\n",
-        "            self, \n",
-        "            algo: Algo, \n",
-        "            metric_functions: Optional[Dict[str, Callable]] = None, \n",
-        "            *args, \n",
+        "            self,\n",
+        "            algo: Algo,\n",
+        "            metric_functions: Optional[Dict[str, Callable]] = None,\n",
+        "            *args,\n",
         "            **kwargs,\n",
         "        ):\n",
         "        \"\"\"\n",
@@ -622,7 +621,7 @@
         "            test_node.update_states(\n",
         "                traintask_id=self._cyclic_local_state.key,\n",
         "                operation=self.evaluate(\n",
-        "                    data_samples=test_node.test_data_sample_keys,\n",
+        "                    data_samples=test_node.data_sample_keys,\n",
         "                    _algo_name=f\"Evaluating with {self.__class__.__name__}\",\n",
         "                ),\n",
         "                round_idx=round_idx,\n",
@@ -901,7 +900,7 @@
         "    TestDataNode(\n",
         "        organization_id=org_id,\n",
         "        data_manager_key=dataset_keys[org_id],\n",
-        "        test_data_sample_keys=[test_datasample_keys[org_id]],\n",
+        "        data_sample_keys=[test_datasample_keys[org_id]],\n",
         "    )\n",
         "    for org_id in DATA_PROVIDER_ORGS_ID\n",
         "]\n",