fixed shapconfig docstring, added shapconfig unit test

Author: Pranav Krishnan

src/sagemaker/clarify.py

@@ -318,20 +318,23 @@ def __init__(
                 calculated automatically by using K-means or K-prototypes in the input dataset.
             num_samples (None or int): Number of samples to be used in the Kernel SHAP algorithm.
                 This number determines the size of the generated synthetic dataset to compute the
-                SHAP values.
+                SHAP values. If not provided then Clarify job will choose a proper value according
+                to the count of features.
             agg_method (None or str): Aggregation method for global SHAP values. Valid values are
                 "mean_abs" (mean of absolute SHAP values for all instances),
                 "median" (median of SHAP values for all instances) and
                 "mean_sq" (mean of squared SHAP values for all instances).
+                If not provided then Clarify job uses method "mean_abs"
             use_logit (bool): Indicator of whether the logit function is to be applied to the model
                 predictions. Default is False. If "use_logit" is true then the SHAP values will
                 have log-odds units.
             save_local_shap_values (bool): Indicator of whether to save the local SHAP values
                 in the output location. Default is True.
             seed (int): seed value to get deterministic SHAP values. Default is None.
             num_clusters (None or int): If a baseline is not provided, Clarify automatically
-                computes a baseline dataset via a clustering algorithm. num_clusters is a parameter
-                for K-means/K-prototypes. Default is None.
+                computes a baseline dataset via a clustering algorithm (K-means/K-prototypes).
+                num_clusters is a parameter for this algorithm. num_clusters will be the resulting
+                size of the baseline dataset. If not provided, Clarify job will use a default value.
         """
         if agg_method is not None and agg_method not in ["mean_abs", "median", "mean_sq"]:
             raise ValueError(
@@ -342,17 +345,19 @@ def __init__(
                 "Baseline and num_clusters cannot both be provided. Please specify one of the two."
             )
         self.shap_config = {
-            "num_samples": num_samples,
-            "agg_method": agg_method,
             "use_logit": use_logit,
             "save_local_shap_values": save_local_shap_values,
         }
         if baseline is not None:
             self.shap_config["baseline"] = baseline
-        if num_clusters is not None:
-            self.shap_config["num_clusters"] = num_clusters
+        if num_samples is not None:
+            self.shap_config["num_samples"] = num_samples
+        if agg_method is not None:
+            self.shap_config["agg_method"] = agg_method
         if seed is not None:
             self.shap_config["seed"] = seed
+        if num_clusters is not None:
+            self.shap_config["num_clusters"] = num_clusters
 
     def get_explainability_config(self):
         """Returns config."""

tests/unit/test_clarify.py

@@ -292,6 +292,15 @@ def test_shap_config_no_baseline():
     }
     assert expected_config == shap_config.get_explainability_config()
 
+def test_shap_config_no_parameters():
+    shap_config = SHAPConfig()
+    expected_config = {
+        "shap": {
+            "use_logit": False,
+            "save_local_shap_values": True,
+        }
+    }
+    assert expected_config == shap_config.get_explainability_config()
 
 def test_invalid_shap_config():
     with pytest.raises(ValueError) as error:
@@ -303,6 +312,12 @@ def test_invalid_shap_config():
     assert "Invalid agg_method invalid. Please choose mean_abs, median, or mean_sq." in str(
         error.value
     )
+    with pytest.raises(ValueError) as error:
+        SHAPConfig(baseline=[[1]], num_samples=1, agg_method="mean_abs", num_clusters=2)
+    assert (
+        "Baseline and num_clusters cannot both be provided. Please specify one of the two."
+        in str(error.value)
+    )
 
 
 @pytest.fixture(scope="module")