diff --git a/code/interpretation/compute-shap.py b/code/interpretation/compute-shap.py
index d039a32..213f093 100644
--- a/code/interpretation/compute-shap.py
+++ b/code/interpretation/compute-shap.py
@@ -7,7 +7,7 @@
 from sklearn.cluster import AgglomerativeClustering
 from mlflow.sklearn import load_model, save_model
 import pyspark.sql.functions as f
-from pyspark.sql.types import StructField, StructType, IntegerType
+from pyspark.sql.types import StructField, StructType, IntegerType, FloatType
 
 # COMMAND ----------
 
@@ -48,7 +48,7 @@ def _get_data(dataset_name, time_step):
     df = spark.table(f"churndb.{dataset_name}_customer_model")
     train = df.where(f.col("time_step")>time_step)
     test = df.where(f.col("time_step")==time_step)
-    test = test.sample(fraction=1.0, seed=1)\
+    test = test.orderBy("row_id").sample(fraction=1.0, seed=1)\
         .limit(1000).repartition(20)
     return {"train":train, "test":test}
 
@@ -60,11 +60,14 @@ def _get_explainer(model, df):
 def _compute_shap(explainer, df):
     def _get_shap(iterator, explainer=explainer):
         for X in iterator:
+            # possibly add expected values here
+            shap_instance = explainer(X.loc[:,_get_features(X)])
             shap_values = np.column_stack((X.loc[:,"row_id"].values,
-                explainer(X.loc[:,_get_features(X)]).values))
+                shap_instance.values, shap_instance.base_values))
             yield pd.DataFrame(shap_values)
     schema = StructType([f for f in df.schema.fields\
-        if f.name in ["row_id"]+_get_features(df)])
+        if f.name in ["row_id"]+_get_features(df)]\
+            +[StructField("base_values", FloatType(), False)])
     return df.mapInPandas(_get_shap, schema=schema)
     
 def glue_shap(dataset_name, time_step, pipe):
@@ -85,6 +88,11 @@ def glue_shap(dataset_name, time_step, pipe):
 
 # COMMAND ----------
 
+# spark.table("churndb.retailrocket_shap_values").where(f.col("pipe")=="svm_rbf_class")\
+#     .toPandas().iloc[:,1:-1].sum(axis=1).sort_values()
+
+# COMMAND ----------
+
 spark.sql("DROP TABLE IF EXISTS churndb.rees46_shap_values;")
-glue_shap("rees46", 0, "rf_class")
+glue_shap("rees46", 0, "gbm_class")
 glue_shap("rees46", 0, "gbm_reg")