test_regressiom_model succesfully run

Author: tugrulguner

main.py

@@ -117,13 +117,8 @@ def go(config: DictConfig):
                 f"{config['main']['components_repository']}/test_regression_model",
                 "main",
                 parameters={
-                    "trainval_artifact": "trainval_data.csv:latest",
-                    "val_size": config['modeling']['val_size'],
-                    "random_seed": config['modeling']['random_seed'],
-                    "stratify_by": config['modeling']['stratify_by'],
-                    'rf_config': rf_config,
-                    'max_tfidf_features': config['modeling']['max_tfidf_features'],
-                    'output_artifact': 'random_forest_export'
+                    "mlflow_model": "random_forest_export:latest",
+                    "test_dataset": "test_data.csv:latest"
                 },
             )
 

src/train_random_forest/run.py

@@ -94,13 +94,12 @@ def go(args):
     # Save model package in the MLFlow sklearn format
     if os.path.exists("random_forest_dir"):
         shutil.rmtree("random_forest_dir")
-
     ######################################
     # Save the sk_pipe pipeline as a mlflow.sklearn model in the directory "random_forest_dir"
     # HINT: use mlflow.sklearn.save_model
     # YOUR CODE HERE
     ######################################
-    mlflow.sklearn.save_model(sk_pipe, 'random_forest_dir')
+    mlflow.sklearn.save_model(sk_pipe, "random_forest_dir")
 
     logger.info("Saving model")   ######################################
     # Upload the model we just exported to W&B
@@ -113,10 +112,9 @@ def go(args):
     sklearn_artifact = wandb.Artifact(
         args.output_artifact,
         type="model_export",
-        description="Sklearn Trained model",
-        metadata=args.rf_config
+        description="Sklearn Trained model"
     )
-    sklearn_artifact.add_dir('random_forest_dir')
+    sklearn_artifact.add_dir("random_forest_dir")
     run.log_artifact(sklearn_artifact)
     logger.info("Model Uploaded")
     # Plot feature importance