change: make model_config optional when predicted labels are provided for bias detection

src/sagemaker/clarify.py

@@ -1423,8 +1423,8 @@ def run_post_training_bias(
         self,
         data_config: DataConfig,
         data_bias_config: BiasConfig,
-        model_config: ModelConfig,
-        model_predicted_label_config: ModelPredictedLabelConfig,
+        model_config: Optional[ModelConfig] = None,
+        model_predicted_label_config: Optional[ModelPredictedLabelConfig] = None,
         methods: Union[str, List[str]] = "all",
         wait: bool = True,
         logs: bool = True,
@@ -1444,7 +1444,8 @@ def run_post_training_bias(
             data_config (:class:`~sagemaker.clarify.DataConfig`): Config of the input/output data.
             data_bias_config (:class:`~sagemaker.clarify.BiasConfig`): Config of sensitive groups.
             model_config (:class:`~sagemaker.clarify.ModelConfig`): Config of the model and its
-                endpoint to be created.
+                endpoint to be created. This is required unless``predicted_label_dataset_uri`` or
+                ``predicted_label`` is provided in ``data_config``.
             model_predicted_label_config (:class:`~sagemaker.clarify.ModelPredictedLabelConfig`):
                 Config of how to extract the predicted label from the model output.
             methods (str or list[str]): Selector of a subset of potential metrics:
@@ -1508,7 +1509,7 @@ def run_bias(
         self,
         data_config: DataConfig,
         bias_config: BiasConfig,
-        model_config: ModelConfig,
+        model_config: Optional[ModelConfig] = None,
         model_predicted_label_config: Optional[ModelPredictedLabelConfig] = None,
         pre_training_methods: Union[str, List[str]] = "all",
         post_training_methods: Union[str, List[str]] = "all",
@@ -1529,7 +1530,8 @@ def run_bias(
             data_config (:class:`~sagemaker.clarify.DataConfig`): Config of the input/output data.
             bias_config (:class:`~sagemaker.clarify.BiasConfig`): Config of sensitive groups.
             model_config (:class:`~sagemaker.clarify.ModelConfig`): Config of the model and its
-                endpoint to be created.
+                endpoint to be created. This is required unless``predicted_label_dataset_uri`` or
+                ``predicted_label`` is provided in ``data_config``.
             model_predicted_label_config (:class:`~sagemaker.clarify.ModelPredictedLabelConfig`):
                 Config of how to extract the predicted label from the model output.
             pre_training_methods (str or list[str]): Selector of a subset of potential metrics:
@@ -1930,16 +1932,30 @@ def _add_predictor(
     ):
         """Extends analysis config with predictor."""
         analysis_config = {**analysis_config}
-        analysis_config["predictor"] = model_config.get_predictor_config()
+        if isinstance(model_config, ModelConfig):
+            analysis_config["predictor"] = model_config.get_predictor_config()
+        else:
+            if "shap" in analysis_config["methods"] or "pdp" in analysis_config["methods"]:
+                raise ValueError(
+                    "model_config must be provided when explainability methods are selected."
+                )
+            if (
+                "predicted_label_dataset_uri" not in analysis_config
+                and "predicted_label" not in analysis_config
+            ):
+                raise ValueError(
+                    "model_config must be provided when `predicted_label_dataset_uri` or "
+                    "`predicted_label` are not provided in data_config."
+                )
         if isinstance(model_predicted_label_config, ModelPredictedLabelConfig):
             (
                 probability_threshold,
                 predictor_config,
             ) = model_predicted_label_config.get_predictor_config()
-            if predictor_config:
+            if predictor_config and "predictor" in analysis_config:
                 analysis_config["predictor"].update(predictor_config)
             _set(probability_threshold, "probability_threshold", analysis_config)
-        else:
+        elif "predictor" in analysis_config:
             _set(model_predicted_label_config, "label", analysis_config["predictor"])
         return analysis_config
 

tests/integ/test_clarify.py

@@ -474,6 +474,37 @@ def data_config_facets_not_included_pred_labels(
     )
 
 
+@pytest.fixture
+def data_config_pred_labels(
+    sagemaker_session,
+    pred_data_path,
+    data_path,
+    headers,
+    pred_label_headers,
+):
+    test_run = utils.unique_name_from_base("test_run")
+    output_path = "s3://{}/{}/{}".format(
+        sagemaker_session.default_bucket(), "linear_learner_analysis_result", test_run
+    )
+    pred_label_data_s3_uri = "s3://{}/{}/{}/{}".format(
+        sagemaker_session.default_bucket(),
+        "linear_learner_analysis_resources",
+        test_run,
+        "predicted_labels.csv",
+    )
+    _upload_dataset(pred_data_path, pred_label_data_s3_uri, sagemaker_session)
+    return DataConfig(
+        s3_data_input_path=data_path,
+        s3_output_path=output_path,
+        label="Label",
+        headers=headers,
+        dataset_type="text/csv",
+        predicted_label_dataset_uri=pred_label_data_s3_uri,
+        predicted_label_headers=pred_label_headers,
+        predicted_label="PredictedLabel",
+    )
+
+
 @pytest.fixture(scope="module")
 def data_bias_config():
     return BiasConfig(
@@ -692,6 +723,39 @@ def test_post_training_bias_excluded_columns(
         check_analysis_config(data_config_excluded_columns, sagemaker_session, "post_training_bias")
 
 
+def test_post_training_bias_predicted_labels(
+    clarify_processor,
+    data_config_pred_labels,
+    data_bias_config,
+    model_predicted_label_config,
+    sagemaker_session,
+):
+    model_config = None
+    with timeout.timeout(minutes=CLARIFY_DEFAULT_TIMEOUT_MINUTES):
+        clarify_processor.run_post_training_bias(
+            data_config_pred_labels,
+            data_bias_config,
+            model_config,
+            model_predicted_label_config,
+            job_name=utils.unique_name_from_base("clarify-posttraining-bias-pred-labels"),
+            wait=True,
+        )
+        analysis_result_json = s3.S3Downloader.read_file(
+            data_config_pred_labels.s3_output_path + "/analysis.json",
+            sagemaker_session,
+        )
+        analysis_result = json.loads(analysis_result_json)
+        assert (
+            math.fabs(
+                analysis_result["post_training_bias_metrics"]["facets"]["F1"][0]["metrics"][0][
+                    "value"
+                ]
+            )
+            <= 1.0
+        )
+        check_analysis_config(data_config_pred_labels, sagemaker_session, "post_training_bias")
+
+
 def test_shap(clarify_processor, data_config, model_config, shap_config, sagemaker_session):
     with timeout.timeout(minutes=CLARIFY_DEFAULT_TIMEOUT_MINUTES):
         clarify_processor.run_explainability(

tests/unit/test_clarify.py

@@ -1575,3 +1575,89 @@ def test_analysis_config_generator_for_bias(data_config, data_bias_config, model
         },
     }
     assert actual == expected
+
+
+def test_analysis_config_for_bias_no_model_config(data_bias_config):
+    s3_data_input_path = "s3://path/to/input.csv"
+    s3_output_path = "s3://path/to/output"
+    predicted_labels_uri = "s3://path/to/predicted_labels.csv"
+    label_name = "Label"
+    headers = [
+        "Label",
+        "F1",
+        "F2",
+        "F3",
+        "F4",
+    ]
+    dataset_type = "text/csv"
+    data_config = DataConfig(
+        s3_data_input_path=s3_data_input_path,
+        s3_output_path=s3_output_path,
+        label=label_name,
+        headers=headers,
+        dataset_type=dataset_type,
+        predicted_label_dataset_uri=predicted_labels_uri,
+        predicted_label_headers=["PredictedLabel"],
+        predicted_label="PredictedLabel",
+    )
+    model_config = None
+    model_predicted_label_config = ModelPredictedLabelConfig(
+        probability="pr",
+        probability_threshold=0.8,
+        label_headers=["success"],
+    )
+    actual = _AnalysisConfigGenerator.bias(
+        data_config,
+        data_bias_config,
+        model_config,
+        model_predicted_label_config,
+        pre_training_methods="all",
+        post_training_methods="all",
+    )
+    expected = {
+        "dataset_type": "text/csv",
+        "headers": ["Label", "F1", "F2", "F3", "F4"],
+        "label": "Label",
+        "predicted_label_dataset_uri": "s3://path/to/predicted_labels.csv",
+        "predicted_label_headers": ["PredictedLabel"],
+        "predicted_label": "PredictedLabel",
+        "label_values_or_threshold": [1],
+        "facet": [{"name_or_index": "F1"}],
+        "group_variable": "F2",
+        "methods": {
+            "report": {"name": "report", "title": "Analysis Report"},
+            "pre_training_bias": {"methods": "all"},
+            "post_training_bias": {"methods": "all"},
+        },
+        "probability_threshold": 0.8,
+    }
+    assert actual == expected
+
+
+def test_invalid_analysis_config(data_config, data_bias_config, model_config):
+    with pytest.raises(
+        ValueError, match="model_config must be provided when explainability methods are selected."
+    ):
+        _AnalysisConfigGenerator.bias_and_explainability(
+            data_config=data_config,
+            model_config=None,
+            model_predicted_label_config=ModelPredictedLabelConfig(),
+            explainability_config=SHAPConfig(),
+            bias_config=data_bias_config,
+            pre_training_methods="all",
+            post_training_methods="all",
+        )
+
+    with pytest.raises(
+        ValueError,
+        match="model_config must be provided when `predicted_label_dataset_uri` or "
+        "`predicted_label` are not provided in data_config.",
+    ):
+        _AnalysisConfigGenerator.bias(
+            data_config=data_config,
+            model_config=None,
+            model_predicted_label_config=ModelPredictedLabelConfig(),
+            bias_config=data_bias_config,
+            pre_training_methods="all",
+            post_training_methods="all",
+        )