add load and save in json

pipeline_lib/core/data_container.py

@@ -33,6 +33,7 @@ class DataContainer:
     PREDICTIONS = "predictions"
     EXPLAINER = "explainer"
     TUNING_PARAMS = "tuning_params"
+    TARGET = "target"
 
     def __init__(self, initial_data: Optional[dict] = None):
         """
@@ -184,6 +185,7 @@ def save(self, file_path: str, keys: Optional[Union[str, list[str]]] = None):
 
         with open(file_path, "wb") as file:
             file.write(serialized_data)
+
         self.logger.info(
             f"{self.__class__.__name__} serialized and saved to {file_path}. Size:"
             f" {data_size_mb:.2f} MB"

pipeline_lib/core/pipeline.py

@@ -22,6 +22,8 @@ def __init__(self, initial_data: Optional[DataContainer] = None):
         if not all(isinstance(step, PipelineStep) for step in self.steps):
             raise TypeError("All steps must be instances of PipelineStep")
         self.initial_data = initial_data
+        self.save_path = None
+        self.load_path = None
 
     @classmethod
     def register_step(cls, step_class):
@@ -102,11 +104,15 @@ def load_and_register_custom_steps(cls, custom_steps_path: str) -> None:
     def run(self) -> DataContainer:
         """Run the pipeline on the given data."""
 
-        data = DataContainer()
+        data = DataContainer.from_pickle(self.load_path) if self.load_path else DataContainer()
 
         for i, step in enumerate(self.steps):
             Pipeline.logger.info(f"Running {step.__class__.__name__} - {i + 1} / {len(self.steps)}")
             data = step.execute(data)
+
+        if self.save_path:
+            data.save(self.save_path)
+
         return data
 
     @classmethod
@@ -124,6 +130,10 @@ def from_json(cls, path: str) -> Pipeline:
             Pipeline.load_and_register_custom_steps(custom_steps_path)
 
         pipeline = Pipeline()
+
+        pipeline.load_path = config.get("load_path")
+        pipeline.save_path = config.get("save_path")
+
         steps = []
 
         for step_config in config["pipeline"]["steps"]:

pipeline_lib/core/steps/calculate_metrics.py

@@ -19,11 +19,11 @@ def __init__(self, config: Optional[dict] = None) -> None:
     def execute(self, data: DataContainer) -> DataContainer:
         self.logger.debug("Starting metric calculation")
         model_output = data[DataContainer.MODEL_OUTPUT]
-        model_configs = data[DataContainer.MODEL_CONFIGS]
-        target_column_name = model_configs.get("target")
+
+        target_column_name = data.get(DataContainer.TARGET)
 
         if target_column_name is None:
-            raise ValueError("Target column not found in model_configs.")
+            raise ValueError("Target column not found on any configuration.")
 
         true_values = model_output[target_column_name]
         predictions = model_output[DataContainer.PREDICTIONS]
@@ -32,5 +32,6 @@ def execute(self, data: DataContainer) -> DataContainer:
         rmse = np.sqrt(mean_squared_error(true_values, predictions))
 
         results = {"MAE": str(mae), "RMSE": str(rmse)}
+        self.logger.info(results)
         data[DataContainer.METRICS] = results
         return data

pipeline_lib/implementation/tabular/xgboost/fit_model.py

@@ -27,6 +27,8 @@ def execute(self, data: DataContainer) -> DataContainer:
         if target is None:
             raise ValueError("Target column not found in model_configs.")
 
+        data[DataContainer.TARGET] = target
+
         df_train = data[DataContainer.TRAIN]
         df_valid = data[DataContainer.VALIDATION]
 

pipeline_lib/implementation/tabular/xgboost/predict.py

@@ -19,14 +19,15 @@ def execute(self, data: DataContainer) -> DataContainer:
         if not isinstance(model_input, pd.DataFrame):
             raise ValueError("model_input must be a pandas DataFrame.")
 
-        model_configs = data.get(DataContainer.MODEL_CONFIGS)
-        if model_configs:
-            drop_columns: list[str] = model_configs.get("drop_columns")
+        if self.config:
+            drop_columns = self.config.get("drop_columns")
             if drop_columns:
                 model_input = model_input.drop(columns=drop_columns)
-            target = model_configs.get("target")
+
+            target = self.config.get("target")
             if target is None:
                 raise ValueError("Target column not found in model_configs.")
+            data[DataContainer.TARGET] = target
 
             predictions = model.predict(model_input.drop(columns=[target]))
         else: