save runs & metrics to folder

pipeline_lib/core/pipeline.py

@@ -2,7 +2,9 @@
 
 import json
 import logging
-from typing import Optional
+import os
+from datetime import datetime
+from typing import Any, Optional
 
 from pipeline_lib.core.data_container import DataContainer
 from pipeline_lib.core.model_registry import ModelRegistry
@@ -24,12 +26,13 @@ def __init__(self, initial_data: Optional[DataContainer] = None):
         self.save_path = None
         self.load_path = None
         self.model_path = None
+        self.config = None
 
     def add_steps(self, steps: list[PipelineStep]):
         """Add steps to the pipeline."""
         self.steps.extend(steps)
 
-    def run(self, is_train: bool) -> DataContainer:
+    def run(self, is_train: bool, save: bool = True) -> DataContainer:
         """Run the pipeline on the given data."""
 
         data = DataContainer.from_pickle(self.load_path) if self.load_path else DataContainer()
@@ -48,8 +51,8 @@ def run(self, is_train: bool) -> DataContainer:
             )
             data = step.execute(data)
 
-        if self.save_path:
-            data.save(self.save_path)
+        if save:
+            self.save_run(data)
 
         return data
 
@@ -79,6 +82,7 @@ def from_json(cls, path: str) -> Pipeline:
 
         pipeline.load_path = config.get("load_path")
         pipeline.save_path = config.get("save_path")
+        pipeline.config = config
 
         steps = []
 
@@ -116,6 +120,31 @@ def from_json(cls, path: str) -> Pipeline:
         pipeline.add_steps(steps)
         return pipeline
 
+    def save_run(
+        self,
+        data: DataContainer,
+        parent_folder: str = "runs",
+        logs: Optional[logging.LogRecord] = None,
+    ) -> None:
+        """Save the pipeline run."""
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        folder_name = f"{self.__class__.__name__}_{timestamp}"
+        run_folder = os.path.join(parent_folder, folder_name)
+
+        # Create the run folder
+        os.makedirs(run_folder, exist_ok=True)
+
+        # Save the JSON configuration
+        with open(os.path.join(run_folder, "pipeline_config.json"), "w") as f:
+            json.dump(self.config, f, indent=4, cls=CustomJSONEncoder)
+
+        # Save the training metrics
+        if data.metrics:
+            with open(os.path.join(run_folder, "metrics.json"), "w") as f:
+                json.dump(data.metrics, f, indent=4)
+
+        self.logger.info(f"Pipeline run saved to {run_folder}")
+
     def __str__(self) -> str:
         step_names = [f"{i + 1}. {step.__class__.__name__}" for i, step in enumerate(self.steps)]
         return f"{self.__class__.__name__} with steps:\n" + "\n".join(step_names)
@@ -124,3 +153,10 @@ def __repr__(self) -> str:
         """Return an unambiguous string representation of the pipeline."""
         step_names = [f"{step.__class__.__name__}()" for step in self.steps]
         return f"{self.__class__.__name__}({', '.join(step_names)})"
+
+
+class CustomJSONEncoder(json.JSONEncoder):
+    def default(self, obj: Any) -> Any:
+        if isinstance(obj, type):
+            return obj.__name__
+        return super().default(obj)