Merge branch 'master' of github.com:Hestia-Homes/ML into heat-dev-model

.github/workflows/Deploy.yml

@@ -2,7 +2,7 @@ name: Sap Change Model Deploy
 
 on:
   push:
-    branches: [ sap-dev, sap-prod ]
+    branches: [ sap-dev, sap-prod, heat-dev, heat-prod, carbon-dev, carbon-prod]
 
 jobs:
   deploy:

.github/workflows/MLPipelinePostMerge.yml

@@ -42,7 +42,14 @@ jobs:
         if [ -z "${latest_version}" ]; then
           increment_version="1.0.0"
         else
-          increment_version=$(echo ${latest_version} | awk -F'.' '{OFS="."; $1+=1; print}')
+          increment_version=$(echo ${latest_version} | awk 'BEGIN {
+              FS="\\."   # Set the field separator to a period
+              OFS="."    # Set the output field separator to a period
+          }
+          {
+              major = $1 + 1   # Increment the major version
+              print major, "0", "0"   # Print the new version
+          }')
         fi
 
         new_tag=${REGISTER_MODEL_NAME}@v${increment_version}
@@ -80,7 +87,14 @@ jobs:
         if [ -z "${latest_version}" ]; then
           increment_version="0.1.0"
         else
-          increment_version=$(echo ${latest_version} | awk 'BEGIN{FS=OFS="."} {$2++; print}')
+          increment_version=$(echo ${latest_version} | awk 'BEGIN {
+              FS="\\."   # Set the field separator to a period
+              OFS="."    # Set the output field separator to a period
+          }
+          {
+              minor = $2 + 1   # Increment the minor version
+              print $1, minor, "0"   # Print the new version
+          }')
         fi
 
         new_tag=${REGISTER_MODEL_NAME}@v${increment_version}
@@ -118,7 +132,14 @@ jobs:
         if [ -z "${latest_version}" ]; then
           increment_version="0.0.1"
         else
-          increment_version=$(echo ${latest_version} | awk 'BEGIN{FS=OFS="."} {$3++; print}')
+          increment_version=$(echo ${latest_version} | awk 'BEGIN {
+              FS="\\."   # Set the field separator to a period
+              OFS="."    # Set the output field separator to a period
+          }
+          {
+              patch = $3 + 1   # Increment the patch version
+              print $1, $2, patch   # Print the new version
+          }')
         fi
 
         new_tag=${REGISTER_MODEL_NAME}@v${increment_version}
@@ -188,7 +209,7 @@ jobs:
         git config user.name "Github-Bot"
         git config user.email "Github-Bot@no-reply.com"
 
-        latest_dev_version=$(gto history ${REGISTER_MODEL_NAME} --asc --plain | awk '{print $NF}' | awk '/dev/')
+        latest_dev_version=$(gto history ${REGISTER_MODEL_NAME} --asc --plain | awk '{print $NF}' | awk '/dev/' | awk 'END {print}')
         if [ -z "${latest_dev_version}" ]; then
           increment_version="1"
         else
@@ -196,7 +217,7 @@ jobs:
         fi
 
         new_tag=${REGISTER_MODEL_NAME}#dev#${increment_version}
-        latest_version=$(gto show model@latest --ref | awk -F"@" '{print $2}')
+        latest_version=$(gto show ${REGISTER_MODEL_NAME}@latest --ref | awk -F"@" '{print $2}')
 
         echo ${new_tag}
 

MODEL_REGISTRY.md

@@ -8,9 +8,17 @@
         "active": true
     },
     "sap": {
-        "version": "v0.0.3",
+        "version": "v0.1.0",
         "stage": {
-            "dev": "v0.0.3"
+            "dev": "v0.1.0"
+        },
+        "registered": true,
+        "active": true
+    },
+    "heat": {
+        "version": "v0.0.1",
+        "stage": {
+            "dev": "v0.0.1"
         },
         "registered": true,
         "active": true

README.md

@@ -10,9 +10,9 @@ tracking and a model registry
 	- A bolt-on service that can implement model monitoring
 
 There are multiple protected branches which adapt the generic pipeline to produce different models:
-- sap_change-**
-- heat_change-**
-- carbon_change-**
+- sap-{dev/staging/prod}-**
+- heat-{dev/staging/prod}-**
+- carbon-{dev/staging/prod}-**
 
 These branches will differ by the configuration files that define the data used and the outputs of the ML-pipeline
 - There can be different additional logic for each branch but the pipeline will be the same.

deployment/handlers/prediction_app.py

@@ -69,9 +69,7 @@ def handler(event, context):
 
         storage_filepath = f"s3://{PREDICTIONS_BUCKET}/{portfolio_id}/{property_id}/{created_at}.parquet"
 
-        logger.info("-------------------------")
         logger.info(f"--- Initiate MLModel ---")
-        logger.info("-------------------------")
 
         build_model_params = settings.build_model
         client_params = settings.client
@@ -80,17 +78,13 @@ def handler(event, context):
 
         model = model_factory(build_model_params["model_type"])
 
-        logger.info("----------------------------")
         logger.info(f"--- Initiate Input DataClient ---")
-        logger.info("----------------------------")
         input_dataclient = dataclient_factory(
             dataclient_type="aws-s3",
             dataclient_config=client_params["aws-s3"],
         )
 
-        logger.info("----------------------------")
         logger.info(f"--- Initiate Output DataClient ---")
-        logger.info("----------------------------")
         output_dataclient = dataclient_factory(
             dataclient_type="aws-s3",
             dataclient_config=client_params["aws-s3"],
@@ -107,6 +101,7 @@ def handler(event, context):
             predictions_column_name=generate_predictions_params[
                 "predictions_column_name"
             ],
+            identifier_column=generate_predictions_params["identifier_column"],
         )
 
         return {

modules/ml-pipeline/Makefile

@@ -9,16 +9,16 @@ init: dev-conda
 .PHONY: dev-conda
 dev-conda:
 	# conda deactivate || echo "Not in conda environment"
-	# conda remove --name $CONDA_ENV --all -y || echo "No environment created previously"
-	conda create --name $CONDA_ENV python=$(PYTHON_VERSION) -y
+	# conda remove --name ${CONDA_ENV} --all -y || echo "No environment created previously"
+	conda create --name ${CONDA_ENV} python=$(PYTHON_VERSION) -y
 	conda init bash
-	conda run -vvvv -n $CONDA_ENV pip install --upgrade pip
-	conda run -vvvv -n $CONDA_ENV pip install -r src/pipeline/requirements/training/requirements-dev.txt
-	conda run -vvvv -n $CONDA_ENV pip install -r src/pipeline/requirements/version_control/requirements.txt
-	conda run -vvvv -n $CONDA_ENV pre-commit install
-	conda run -vvvv -n $CONDA_ENV pip install ipykernel
+	conda run -v -n ${CONDA_ENV} pip install --upgrade pip
+	conda run -v -n ${CONDA_ENV} pip install -r src/pipeline/requirements/training/requirements-dev.txt
+	conda run -v -n ${CONDA_ENV} pip install -r src/pipeline/requirements/version_control/requirements.txt
+	conda run -v -n ${CONDA_ENV} pre-commit install
+	conda run -v -n ${CONDA_ENV} pip install ipykernel
 	echo "TO ACTIVATE ENVIRONMENT, USE THE FOLLOWING COMMAND"
-	echo "conda activate $CONDA_ENV"
+	echo "conda activate ${CONDA_ENV}"
 
 
 .PHONY: dev-pyenv

modules/ml-pipeline/src/pipeline/0_startup_cleanup.py

@@ -16,13 +16,9 @@ def run_cleanup(artefacts_directory: str, metrics_directory: str) -> None:
     Remove the directory where artefacts are stored
     """
 
-    logger.info("---------------------")
     logger.info(f"--- Run Clean up ---")
-    logger.info("---------------------")
 
-    logger.info("-------------------------")
     logger.info(f"--- Delete artefacts ---")
-    logger.info("-------------------------")
 
     artefact_directory_path = Path(artefacts_directory)
 
@@ -31,9 +27,7 @@ def run_cleanup(artefacts_directory: str, metrics_directory: str) -> None:
         logger.info(f"Removing the directory: {artefacts_directory}")
         shutil.rmtree(artefact_directory_path)
 
-    logger.info("-----------------------")
     logger.info(f"--- Delete metrics ---")
-    logger.info("-----------------------")
 
     metrics_directory_path = Path(metrics_directory)
 
@@ -45,15 +39,11 @@ def run_cleanup(artefacts_directory: str, metrics_directory: str) -> None:
 
 if __name__ == "__main__":
 
-    logger.info("----------------------------")
     logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
 
     run_cleanup(
         artefacts_directory=startup_cleanup_params["artefacts"],
         metrics_directory=startup_cleanup_params["metrics"],
     )
 
-    logger.info("-------------------------------")
     logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")

modules/ml-pipeline/src/pipeline/1_prepare_data.py

@@ -17,9 +17,7 @@ from core.DataClient import dataclient_factory
 from core.FeatureProcessor import feature_processor_factory
 from config import settings
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate Parameters ---")
-logger.info("----------------------------")
 
 RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
 
@@ -33,9 +31,7 @@ output_train_filepath = prepare_data_params["output_train_filepath"]
 output_test_filepath = prepare_data_params["output_test_filepath"]
 feature_processor_config = feature_process_params["feature_processor_config"]
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate DataClient ---")
-logger.info("----------------------------")
 
 input_dataclient_type = prepare_data_params["input_dataclient_type"]
 output_dataclient_type = prepare_data_params["output_dataclient_type"]
@@ -49,9 +45,7 @@ output_dataclient = dataclient_factory(
     dataclient_config=client_params[output_dataclient_type],
 )
 
-logger.info("----------------------------------")
 logger.info(f"--- Initiate FeatureProcessor ---")
-logger.info("----------------------------------")
 
 feature_processor = feature_processor_factory(
     feature_process_params["feature_processor_type"]
@@ -76,15 +70,11 @@ def prepare_data(
     :param pipeline_mode: bool, Default False, this caches out the file for experimentation, objects returned in pipeline mode
     """
 
-    logger.info("--------------------")
     logger.info("--- Loading data ---")
-    logger.info("--------------------")
 
     data = input_dataclient.load_data(location=data_filepath, load_config={})
 
-    logger.info("--------------------------")
     logger.info("--- Feature Processing ---")
-    logger.info("--------------------------")
 
     data = feature_processor.feature_process(
         data,
@@ -93,9 +83,7 @@ def prepare_data(
         new_feature_funcs=new_feature_funcs,
     )
 
-    logger.info("----------------------")
     logger.info("--- Splitting data ---")
-    logger.info("----------------------")
 
     if train_proportion == 1:
         train = data
@@ -108,9 +96,7 @@ def prepare_data(
 
     train = train.reset_index(drop=True)
 
-    logger.info("-----------------------")
     logger.info("--- Outputting data ---")
-    logger.info("-----------------------")
 
     output_dataclient.save_data(
         obj=train, location=output_train_filepath, save_config=None
@@ -126,13 +112,9 @@ def prepare_data(
 
 if __name__ == "__main__":
 
-    logger.info("----------------------------")
     logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
 
-    logger.info("---------------------------")
     logger.info(f"--- Prepare Data Stage ---")
-    logger.info("---------------------------")
 
     prepare_data(
         input_dataclient=input_dataclient,
@@ -147,6 +129,4 @@ if __name__ == "__main__":
         new_feature_funcs=new_feature_funcs,
     )
 
-    logger.info("-------------------------------")
     logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")

modules/ml-pipeline/src/pipeline/2_build_model.py

@@ -18,9 +18,7 @@ from core.MLMetrics import metrics_factory
 from configs.post_prediction_logic import post_prediction_logic
 from config import settings
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate Parameters ---")
-logger.info("----------------------------")
 
 RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
 
@@ -40,22 +38,16 @@ train_filepath = prepare_data_params["output_train_filepath"]
 test_filepath = prepare_data_params["output_test_filepath"]
 fit_metrics_filepath = build_model_params["fit_metrics_filepath"]
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate DataClient ---")
-logger.info("----------------------------")
 
 # Output of previous prepare data step, will be where the data is
 dataclient = dataclient_factory(prepare_data_params["output_dataclient_type"])
 
-logger.info("-------------------------")
 logger.info(f"--- Initiate MLModel ---")
-logger.info("-------------------------")
 
 model = model_factory(model_type)
 
-logger.info("-------------------------")
 logger.info(f"--- Initiate Metrics ---")
-logger.info("-------------------------")
 
 metrics = metrics_factory(generate_metrics_params["metrics_type"])
 
@@ -75,9 +67,7 @@ def build_model(
     test_data: Union[pd.DataFrame, None] = None,
     pipeline_mode: bool = False,
 ):
-    logger.info("--------------------------------------")
     logger.info("--- Loading Data for build process ---")
-    logger.info("--------------------------------------")
 
     if train_data is None:
         if train_filepath is None:
@@ -89,9 +79,7 @@ def build_model(
             raise ValueError(f"Need {test_filepath} if no data supplied")
         test_data = dataclient.load_data(location=test_filepath, load_config=None)
 
-    logger.info("----------------------")
     logger.info("--- Training model ---")
-    logger.info("----------------------")
 
     model.train_model(
         data=train_data.drop(columns=identifier_columns),
@@ -99,32 +87,24 @@ def build_model(
         model_hyperparameters=model_hyperparameters,
     )
 
-    logger.info("----------------------------------")
     logger.info("--- Generating fit predictions ---")
-    logger.info("----------------------------------")
 
     fit_predictions = model.predict(
         data=train_data, post_prediction_logic=post_prediction_logic
     )
 
-    logger.info("------------------------------")
     logger.info("--- Generating fit metrics ---")
-    logger.info("------------------------------")
 
     metrics_output = metrics.generate_metrics(
         target=train_data[target],
         predictions=pd.Series(fit_predictions),
     )
 
-    logger.info("--------------------")
     logger.info("--- Saving model ---")
-    logger.info("--------------------")
 
     model.save_model(path=Path(model_save_location))
 
-    logger.info("--------------------------")
     logger.info("--- Saving fit metrics ---")
-    logger.info("--------------------------")
 
     dataclient.save_data(
         obj=metrics_output, location=fit_metrics_filepath, save_config=None
@@ -133,13 +113,9 @@ def build_model(
 
 if __name__ == "__main__":
 
-    logger.info("----------------------------")
     logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
 
-    logger.info("--------------------------")
     logger.info(f"--- Build Model Stage ---")
-    logger.info("--------------------------")
 
     build_model(
         dataclient=dataclient,
@@ -154,6 +130,4 @@ if __name__ == "__main__":
         fit_metrics_filepath=fit_metrics_filepath,
     )
 
-    logger.info("-------------------------------")
     logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")

modules/ml-pipeline/src/pipeline/3_generate_predictions.py

@@ -10,9 +10,7 @@ from core.Logger import logger
 from config import settings
 from generate_predictions import generate_predictions
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate Parameters ---")
-logger.info("----------------------------")
 
 RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
 
@@ -33,15 +31,11 @@ model_filepath = build_model_params["model_save_filepath"]
 predictions_output_filepath = generate_predictions_params["predictions_output_filepath"]
 predictions_column_name = generate_predictions_params["predictions_column_name"]
 
-logger.info("-------------------------")
 logger.info(f"--- Initiate MLModel ---")
-logger.info("-------------------------")
 
 model = model_factory(build_model_params["model_type"])
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate DataClient ---")
-logger.info("----------------------------")
 
 # We may have different locations of loading hence why we use one specified in generate_predictions.yaml
 # I.e. for metric runs, this will be a local data client
@@ -59,13 +53,9 @@ output_dataclient = dataclient_factory(
 
 if __name__ == "__main__":
 
-    logger.info("----------------------------")
     logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
 
-    logger.info("----------------------------------")
     logger.info(f"--- Generate Predictions Stage---")
-    logger.info("----------------------------------")
 
     generate_predictions(
         input_dataclient=input_dataclient,
@@ -78,6 +68,4 @@ if __name__ == "__main__":
         predictions_column_name=predictions_column_name,
     )
 
-    logger.info("-------------------------------")
     logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")

modules/ml-pipeline/src/pipeline/4_generate_metrics.py

@@ -14,9 +14,7 @@ from core.MLMetrics import metrics_factory
 from core.Logger import logger
 from config import settings
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate Parameters ---")
-logger.info("----------------------------")
 
 RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
 
@@ -34,15 +32,11 @@ predictions_column_name = generate_predictions_params["predictions_column_name"]
 metrics_output_filepath = generate_metrics_params["metrics_output_filepath"]
 
 
-logger.info("-------------------------")
 logger.info(f"--- Initiate MLModel ---")
-logger.info("-------------------------")
 
 model = model_factory(build_model_params["model_type"])
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate DataClient ---")
-logger.info("----------------------------")
 
 # Use data client for input and output, as we use dvc to cache later to the cloud
 dataclient_type = generate_metrics_params["dataclient_type"]
@@ -51,9 +45,7 @@ dataclient = dataclient_factory(
     dataclient_config=client_params[dataclient_type],
 )
 
-logger.info("---------------------------")
 logger.info(f"--- Initiate MLMetrics ---")
-logger.info("---------------------------")
 
 metrics = metrics_factory(generate_metrics_params["metrics_type"])
 
@@ -73,34 +65,26 @@ def generate_metrics(
     For a given model, we generate prediction and evaluate this against the true target
     """
 
-    logger.info("-------------------------")
     logger.info("--- Loading test data ---")
-    logger.info("-------------------------")
 
     test_data = input_dataclient.load_data(
         location=test_data_filepath, load_config=None
     )
 
-    logger.info("---------------------------")
     logger.info("--- Loading predictions ---")
-    logger.info("---------------------------")
 
     predictions = input_dataclient.load_data(
         location=predictions_output_filepath, load_config=None
     )
 
-    logger.info("--------------------------")
     logger.info("--- Generating metrics ---")
-    logger.info("--------------------------")
 
     metrics_output = metrics.generate_metrics(
         target=test_data[target],
         predictions=pd.Series(predictions[predictions_column_name]),
     )
 
-    logger.info("----------------------")
     logger.info("--- Saving metrics ---")
-    logger.info("----------------------")
 
     output_dataclient.save_data(
         obj=metrics_output, location=metrics_output_filepath, save_config=None
@@ -109,13 +93,9 @@ def generate_metrics(
 
 if __name__ == "__main__":
 
-    logger.info("----------------------------")
     logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
 
-    logger.info("------------------------------")
     logger.info(f"--- Generate Metrics Stage---")
-    logger.info("------------------------------")
 
     generate_metrics(
         input_dataclient=dataclient,
@@ -129,6 +109,4 @@ if __name__ == "__main__":
         metrics_output_filepath=metrics_output_filepath,
     )
 
-    logger.info("-------------------------------")
     logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")

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

@@ -16,3 +16,5 @@ default:
       time_limit: 4000
       presets: medium_quality
       excluded_model_types: ['KNN', 'RF']
+      infer_limit: 0.05
+      infer_limit_batch_size: 10000

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

@@ -21,7 +21,7 @@ default:
     # data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_with_differencing.parquet
     # data_filepath: s3://retrofit-data-dev/sap_change_model/floor_area_clean_test.parquet
     # data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_without_differencing.parquet
-    data_filepath: s3://retrofit-data-dev/sap_change_model/dataset.parquet
+    data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_test.parquet
     train_proportion: 0.9
     output_train_filepath: ./data/prepared_data/train.parquet
     output_test_filepath: ./data/prepared_data/test.parquet
@@ -43,6 +43,7 @@ default:
     test_data_filepath: ./data/prepared_data/test.parquet
     predictions_output_filepath: ./data/predictions/predictions.parquet
     predictions_column_name: predictions
+    identifier_column: id
 
   generate_metrics:
     dataclient_type: local

modules/ml-pipeline/src/pipeline/core/DataClient.py

@@ -142,9 +142,15 @@ class AWSS3Client:
         buffer = BytesIO()
         obj.to_parquet(buffer, index=False)
 
+        # Reset the buffer position to the beginning
+        buffer.seek(0)
+
         bucket, key = location.strip("s3://").split("/", 1)
         self.client.upload_fileobj(buffer, bucket, key)
 
+        # Close the buffer
+        buffer.close()
+
     def _load_parquet(self, location: str, load_config: dict) -> pd.DataFrame:
         """
         Load a parquet file

modules/ml-pipeline/src/pipeline/core/Logger.py

@@ -21,6 +21,7 @@ def setup_logger():
 
     # Add the stream handler to the logger
     logger.addHandler(stream_handler)
+    logger.propagate = False
 
     return logger
 

modules/ml-pipeline/src/pipeline/core/MLModels.py

@@ -149,6 +149,8 @@ class AutogluonAutoML:
         "time_limit",
         "presets",
         "excluded_model_types",
+        "infer_limit",
+        "infer_limit_batch_size",
     ]
 
     def load_model(self, path: Union[Path, str]) -> None:
@@ -203,6 +205,8 @@ class AutogluonAutoML:
             time_limit=model_hyperparameters["time_limit"],
             presets=model_hyperparameters["presets"],
             excluded_model_types=model_hyperparameters["excluded_model_types"],
+            infer_limit=model_hyperparameters["infer_limit"],
+            infer_limit_batch_size=model_hyperparameters["infer_limit_batch_size"],
         )
 
     def predict(

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

@@ -14,28 +14,23 @@ def generate_predictions(
     test_data_filepath: str,
     predictions_output_filepath: str,
     predictions_column_name: str,
+    identifier_column: str = "id",
 ):
     """
     For a given model, we generate prediction and evaluate this against the true target
     """
 
-    logger.info("-------------------------")
     logger.info("--- Loading test data ---")
-    logger.info("-------------------------")
 
     test_data = input_dataclient.load_data(
         location=test_data_filepath, load_config=None
     )
 
-    logger.info("---------------------")
     logger.info("--- Loading model ---")
-    logger.info("---------------------")
 
     model.load_model(model_filepath)
 
-    logger.info("------------------------------")
     logger.info("--- Generating predictions ---")
-    logger.info("------------------------------")
 
     prediction_data = (
         test_data.drop(columns=target) if target in test_data.columns else test_data
@@ -45,13 +40,17 @@ def generate_predictions(
         data=prediction_data, post_prediction_logic=post_prediction_logic
     )
 
-    logger.info("--------------------------")
     logger.info("--- Saving predictions ---")
-    logger.info("--------------------------")
 
     predictions_df = pd.DataFrame(predictions)
     predictions_df.columns = [predictions_column_name]
 
-    output_dataclient.save_data(
-        obj=predictions_df, location=predictions_output_filepath, save_config=None
+    output_df = (
+        pd.concat([test_data[identifier_column], predictions_df], axis=1)
+        if identifier_column in test_data.columns
+        else predictions_df
+    )
+
+    output_dataclient.save_data(
+        obj=output_df, location=predictions_output_filepath, save_config=None
     )