Pretty-print the features produced by buildFeatures

feathr_project/feathr/client.py

@@ -5,6 +5,8 @@
 from datetime import datetime, timedelta
 from pathlib import Path
 from typing import Dict, List, Optional, Union
+from pprint import pprint
+from xmlrpc.client import Boolean
 
 import redis
 from azure.identity import DefaultAzureCredential
@@ -210,7 +212,7 @@ def register_features(self, from_context: bool = True):
         else:
             self.registry.register_features(self.local_workspace_dir, from_context=from_context)
 
-    def build_features(self, anchor_list: List[FeatureAnchor] = [], derived_feature_list: List[DerivedFeature] = []):
+    def build_features(self, anchor_list: List[FeatureAnchor] = [], derived_feature_list: List[DerivedFeature] = [], pprint_flag: bool = False):
         """Build features based on the current workspace. all actions that triggers a spark job will be based on the
         result of this action.
         """
@@ -235,6 +237,13 @@ def build_features(self, anchor_list: List[FeatureAnchor] = [], derived_feature_
         self.registry.save_to_feature_config_from_context(anchor_list, derived_feature_list, self.local_workspace_dir)
         self.anchor_list = anchor_list
         self.derived_feature_list = derived_feature_list
+
+        # Pretty print anchor list and derived_feature_list
+        if pprint_flag:
+            if self.anchor_list:
+                pprint(self.anchor_list)
+            if self.derived_feature_list:
+                pprint(self.derived_feature_list)
 
     def list_registered_features(self, project_name: str = None) -> List[str]:
         """List all the already registered features. If project_name is not provided or is None, it will return all
@@ -396,6 +405,7 @@ def get_offline_features(self,
                              output_path: str,
                              execution_configuratons: Union[SparkExecutionConfiguration ,Dict[str,str]] = None,
                              udf_files = None,
+                             pprint_flag: bool = False
                              ):
         """
         Get offline features for the observation dataset
@@ -434,6 +444,13 @@ def get_offline_features(self,
             _FeatureRegistry.save_to_feature_config_from_context(self.anchor_list, self.derived_feature_list, self.local_workspace_dir)
         else:
             raise RuntimeError("Please call FeathrClient.build_features() first in order to get offline features")
+
+        # Pretty print anchor list and derived_feature_list
+        if pprint_flag:
+            if self.anchor_list:
+                pprint(self.anchor_list)
+            if self.derived_feature_list:
+                pprint(self.derived_feature_list)
 
         write_to_file(content=config, full_file_name=config_file_path)
         return self._get_offline_features_with_config(config_file_path, execution_configuratons, udf_files=udf_files)
@@ -512,7 +529,7 @@ def wait_job_to_finish(self, timeout_sec: int = 300):
         else:
             raise RuntimeError('Spark job failed.')
 
-    def materialize_features(self, settings: MaterializationSettings, execution_configuratons: Union[SparkExecutionConfiguration ,Dict[str,str]] = None):
+    def materialize_features(self, settings: MaterializationSettings, execution_configuratons: Union[SparkExecutionConfiguration ,Dict[str,str]] = None, pprint_flag: bool = False):
         """Materialize feature data
 
         Args:
@@ -541,6 +558,13 @@ def materialize_features(self, settings: MaterializationSettings, execution_conf
             self._materialize_features_with_config(config_file_path, execution_configuratons, udf_files)
             if os.path.exists(config_file_path):
                 os.remove(config_file_path)
+
+        # Pretty print anchor_list and derived_feature_list
+        if pprint_flag:
+            if self.anchor_list:
+                pprint(self.anchor_list)
+            if self.derived_feature_list:
+                pprint(self.derived_feature_list)
 
     def _materialize_features_with_config(self, feature_gen_conf_path: str = 'feature_gen_conf/feature_gen.conf',execution_configuratons: Dict[str,str] = None, udf_files=[]):
         """Materializes feature data based on the feature generation config. The feature
@@ -698,4 +722,4 @@ def _get_kafka_config_str(self):
         config_str = """
             KAFKA_SASL_JAAS_CONFIG: "{sasl}"
             """.format(sasl=sasl)
-        return config_str
+        return config_str