fix: Correct Clarify API docstrings by changing JSONPath to JMESPath

SageMaker Clarify API docstrings use term JSONPath, but actually the
Clarify processing container expects JMESPath expression. The commit
corrects the term to avoid customer confusion.

Author: xgchena

src/sagemaker/clarify.py

@@ -331,11 +331,11 @@ def __init__(
             s3_analysis_config_output_path (str): S3 prefix to store the analysis config output.
                 If this field is None, then the ``s3_output_path`` will be used
                 to store the ``analysis_config`` output.
-            label (str): Target attribute of the model required by bias metrics.
-                Specified as column name or index for CSV dataset or as JSONPath for JSONLines.
+            label (str): Target attribute of the model required by bias metrics. Specified as
+                column name or index for CSV dataset or as JMESPath expression for JSONLines.
                 *Required parameter* except for when the input dataset does not contain the label.
-            features (List[str]): JSONPath for locating the feature columns for bias metrics if the
-                dataset format is JSONLines.
+            features (List[str]): JMESPath expression to locate the feature columns for
+                bias metrics if the dataset format is JSONLines.
             dataset_type (str): Format of the dataset. Valid values are ``"text/csv"`` for CSV,
                 ``"application/jsonlines"`` for JSONLines, and
                 ``"application/x-parquet"`` for Parquet.
@@ -717,11 +717,11 @@ def __init__(
             ``label_headers=['cat','dog','fish']`` and infer the predicted label to be ``'fish'``.
 
         Args:
-            label (str or int): Index or JSONPath location in the model output for the prediction.
-                In case, this is a predicted label of the same type as the label in the dataset,
-                no further arguments need to be specified.
-            probability (str or int): Index or JSONPath location in the model output
-                for the predicted score(s).
+            label (str or int): Index or JMESPath expression to locate the prediction
+                in the model output. In case, this is a predicted label of the same type
+                as the label in the dataset, no further arguments need to be specified.
+            probability (str or int): Index or JMESPath expression to locate the predicted score(s)
+                in the model output.
             probability_threshold (float): An optional value for binary prediction tasks in which
                 the model returns a probability, to indicate the threshold to convert the
                 prediction to a boolean value. Default is ``0.5``.
@@ -1646,9 +1646,9 @@ def run_explainability(
                 You can request multiple methods at once by passing in a list of
                 `~sagemaker.clarify.ExplainabilityConfig`.
             model_scores (int or str or :class:`~sagemaker.clarify.ModelPredictedLabelConfig`):
-                Index or JSONPath to locate the predicted scores in the model output. This is not
-                required if the model output is a single score. Alternatively, it can be an instance
-                of :class:`~sagemaker.clarify.SageMakerClarifyProcessor`
+                Index or JMESPath expression to locate the predicted scores in the model output.
+                This is not required if the model output is a single score. Alternatively,
+                it can be an instance of :class:`~sagemaker.clarify.SageMakerClarifyProcessor`
                 to provide more parameters like ``label_headers``.
             wait (bool): Whether the call should wait until the job completes (default: True).
             logs (bool): Whether to show the logs produced by the job.
@@ -1775,9 +1775,9 @@ def run_bias_and_explainability(
                 str or
                 :class:`~sagemaker.clarify.ModelPredictedLabelConfig`
             ):
-                Index or JSONPath to locate the predicted scores in the model output. This is not
-                required if the model output is a single score. Alternatively, it can be an instance
-                of :class:`~sagemaker.clarify.SageMakerClarifyProcessor`
+                Index or JMESPath expression to locate the predicted scores in the model output.
+                This is not required if the model output is a single score. Alternatively,
+                it can be an instance of :class:`~sagemaker.clarify.SageMakerClarifyProcessor`
                 to provide more parameters like ``label_headers``.
             wait (bool): Whether the call should wait until the job completes (default: True).
             logs (bool): Whether to show the logs produced by the job.

src/sagemaker/model_monitor/clarify_model_monitoring.py

@@ -842,8 +842,8 @@ def __init__(self, bias_config, headers=None, label=None):
             bias_config (sagemaker.clarify.BiasConfig): Config object related to bias
                 configurations.
             headers (list[str]): A list of column names in the input dataset.
-            label (str): Target attribute for the model required by bias metrics.
-                Specified as column name or index for CSV dataset, or as JSONPath for JSONLines.
+            label (str): Target attribute for the model required by bias metrics. Specified as
+                column name or index for CSV dataset, or as JMESPath expression for JSONLines.
         """
         self.analysis_config = bias_config.get_config()
         if headers is not None:
@@ -889,9 +889,10 @@ def suggest_baseline(
             model_config (:class:`~sagemaker.clarify.ModelConfig`): Config of the model and its
                 endpoint to be created.
             model_scores (int or str or :class:`~sagemaker.clarify.ModelPredictedLabelConfig`):
-                Index or JSONPath to locate the predicted scores in the model output. This is not
-                required if the model output is a single score. Alternatively, it can be an instance
-                of ModelPredictedLabelConfig to provide more parameters like label_headers.
+                Index or JMESPath expression to locate the predicted scores in the model output.
+                This is not required if the model output is a single score. Alternatively,
+                it can be an instance of ModelPredictedLabelConfig to provide more parameters
+                like label_headers.
             wait (bool): Whether the call should wait until the job completes (default: False).
             logs (bool): Whether to show the logs produced by the job.
                 Only meaningful when wait is True (default: False).
@@ -1302,12 +1303,12 @@ def __init__(
         Args:
             analysis_config (BiasAnalysisConfig or ExplainabilityAnalysisConfig): analysis config
                 from configurations of the baselining job.
-            features_attribute (str): JSONpath to locate features in predictor request payload.
-                Only required when predictor content type is JSONlines.
-            inference_attribute (str): Index, header or JSONpath to locate predicted label in
-                predictor response payload.
-            probability_attribute (str): Index or JSONpath location in the model output for
-                probabilities or scores to be used for explainability.
+            features_attribute (str): JMESPath expression to locate features in predictor request
+                payload. Only required when predictor content type is JSONlines.
+            inference_attribute (str): Index, header or JMESPath expression to locate predicted
+                label in predictor response payload.
+            probability_attribute (str): Index or JMESPath expression to locate probabilities or
+                scores in the model output for computing feature attribution.
             probability_threshold_attribute (float): Value to indicate the threshold to select
                 the binary label in the case of binary classification. Default is 0.5.
         """

src/sagemaker/model_monitor/model_monitoring.py

@@ -1061,12 +1061,13 @@ def _generate_env_map(
             dataset_format (dict): The format of the baseline_dataset.
             dataset_source_container_path (str): The path to the dataset source.
             inference_attribute (str): Index or JSONpath to locate predicted label(s).
-                Only used for ModelQualityMonitor, ModelBiasMonitor, and ModelExplainabilityMonitor
+                Only used for ModelQualityMonitor.
             probability_attribute (str or int): Index or JSONpath to locate probabilities.
-                Only used for ModelQualityMonitor, ModelBiasMonitor and ModelExplainabilityMonitor
-            ground_truth_attribute (str): Index or JSONpath to locate actual label(s).
+                Only used for ModelQualityMonitor.
+            ground_truth_attribute (str): Index to locate actual label(s).
+                Only used for ModelQualityMonitor.
             probability_threshold_attribute (float): threshold to convert probabilities to binaries
-                Only used for ModelQualityMonitor, ModelBiasMonitor and ModelExplainabilityMonitor
+                Only used for ModelQualityMonitor.
 
         Returns:
             dict: Dictionary of environment keys and values.
@@ -2600,10 +2601,13 @@ def suggest_baseline(
             problem_type (str): The type of problem of this model quality monitoring. Valid
                 values are "Regression", "BinaryClassification", "MulticlassClassification".
             inference_attribute (str): Index or JSONpath to locate predicted label(s).
+                Only used for ModelQualityMonitor.
             probability_attribute (str or int): Index or JSONpath to locate probabilities.
-            ground_truth_attribute (str): Index or JSONpath to locate actual label(s).
+                Only used for ModelQualityMonitor.
+            ground_truth_attribute (str): Index to locate actual label(s).
+                Only used for ModelQualityMonitor.
             probability_threshold_attribute (float): threshold to convert probabilities to binaries
-                Only used for ModelQualityMonitor, ModelBiasMonitor and ModelExplainabilityMonitor
+                Only used for ModelQualityMonitor.
             post_analytics_processor_script (str): The path to the record post-analytics processor
                 script. This can be a local path or an S3 uri.
             output_s3_uri (str): Desired S3 destination Destination of the constraint_violations

src/sagemaker/workflow/clarify_check_step.py

@@ -132,8 +132,8 @@ class ModelExplainabilityCheckConfig(ClarifyCheckConfig):
         model_config (ModelConfig): Config of the model and its endpoint to be created.
         explainability_config (SHAPConfig): Config of the specific explainability method.
             Currently, only SHAP is supported.
-        model_scores (str or int or ModelPredictedLabelConfig): Index or JSONPath location
-            in the model output for the predicted scores to be explained (default: None).
+        model_scores (str or int or ModelPredictedLabelConfig): Index or JMESPath expression
+            to locate the predicted scores in the model output (default: None).
             This is not required if the model output is a single score. Alternatively,
             an instance of ModelPredictedLabelConfig can be provided
             but this field CANNOT be any type of the `PipelineVariable`.