nearly there

modules/ml-pipeline/src/pipeline/configs/generate_metrics.yaml

@@ -1,5 +1,3 @@
 dataclient_type: local
-input_datahandler_type: parquet
-output_datahandler_type: json
 metrics_type: Regression
 metrics_output_filepath: ./metrics/metrics.json

modules/ml-pipeline/src/pipeline/model_analysis.py

@@ -79,7 +79,7 @@ def model_analysis(
     logger.info(f"--- Generate Feature Importance ---")
     logger.info("------------------------------------")
 
-    test_df = pd.read_parquet(output_test_filepath)
+    test_df = dataclient.load_data(output_test_filepath)
 
     test_df = test_df.head(permutation_subsample_amount)
 

modules/ml-pipeline/src/pipeline/prediction_analysis.py

@@ -36,30 +36,76 @@ feature_process_params = yaml.safe_load(open(feature_process_path))
 build_model_path = Path(__file__).parent / "configs" / "build_model.yaml"
 build_model_params = yaml.safe_load(open(build_model_path))
 
-model_analysis_path = Path(__file__).parent / "configs" / "model_analysis.yaml"
-model_analysis_params = yaml.safe_load(open(model_analysis_path))
-
-generate_predictions_path = (
-    Path(__file__).parent / "configs" / "generate_predictions.yaml"
+prediction_analysis_path = (
+    Path(__file__).parent / "configs" / "prediction_analysis.yaml"
 )
-generate_predictions_params = yaml.safe_load(open(generate_predictions_path))
+prediction_analysis_params = yaml.safe_load(open(prediction_analysis_path))
 
 model = model_factory(build_model_params["model_type"])
 model.load_model(build_model_params["model_save_filepath"])
 
-dataclient_type = model_analysis_params["dataclient_type"]
+dataclient_type = prediction_analysis_params["dataclient_type"]
 dataclient = dataclient_factory(
     dataclient_type=dataclient_type,
     dataclient_config=client_params[dataclient_type],
 )
 
+output_test_filepath = prepare_data_params["output_test_filepath"]
 
-def prediction_analysis(model: MLModel, dataclient: DataClient):
 
-    shap.kmeans()
+def prediction_analysis(
+    model: MLModel, dataclient: DataClient, output_test_filepath: str
+):
 
+    test_df = dataclient.load_data(output_test_filepath)
+    target = "SAP_ENDING"
+    test_df_without_target = test_df.drop(columns=[target])
+
+    # test_df_summary = shap.kmeans(test_df, 10)
+    # print("Baseline feature-values: \n", test_df_summary)
+    class AutogluonWrapper:
+        def __init__(self, predictor, feature_names):
+            self.ag_model = predictor
+            self.feature_names = feature_names
+
+        def predict(self, X):
+            if isinstance(X, pd.Series):
+                X = X.values.reshape(1, -1)
+            if not isinstance(X, pd.DataFrame):
+                X = pd.DataFrame(X, columns=self.feature_names)
+            return self.ag_model.predict(X)
+
+    ag_wrapper = AutogluonWrapper(
+        model.model, feature_names=test_df_without_target.columns
+    )
+    explainer = shap.KernelExplainer(ag_wrapper.predict, test_df_without_target)
+
+    NSHAP_SAMPLES = 100  # how many samples to use to approximate each Shapely value, larger values will be slower
+    N_VAL = 30  # how many datapoints from validation data should we interpret predictions for, larger values will be slower
+
+    ROW_INDEX = 0  # index of an example datapoint
+    single_datapoint = test_df_without_target.iloc[[ROW_INDEX]]
+    single_prediction = ag_wrapper.predict(single_datapoint)
+
+    shap_values_single = explainer.shap_values(single_datapoint, nsamples=NSHAP_SAMPLES)
+    shap.force_plot(
+        explainer.expected_value,
+        shap_values_single,
+        test_df_without_target.iloc[ROW_INDEX, :],
+    )
     ...
 
 
 if __name__ == "__main__":
-    prediction_analysis()
+
+    logger.info("----------------------------")
+    logger.info(f"--- {__file__} - Start! ---")
+    logger.info("----------------------------")
+
+    prediction_analysis(
+        model=model, dataclient=dataclient, output_test_filepath=output_test_filepath
+    )
+
+    logger.info("-------------------------------")
+    logger.info(f"--- {__file__} - Complete! ---")
+    logger.info("-------------------------------")

modules/ml-pipeline/src/pipeline/prepare_data.py

@@ -74,6 +74,9 @@ def prepare_data(
         train, test = train_test_split(
             data, train_size=train_proportion, test_size=(1 - train_proportion)
         )
+        test = test.reset_index(drop=True)
+
+    train = train.reset_index(drop=True)
 
     logger.info("-----------------------")
     logger.info("--- Outputting data ---")