Updated run_deap.py to send to MLFlow server. Also modified experiment name to be variable based on chosen dataset and start time

run_deap.py

@@ -5,6 +5,7 @@ from pathlib import Path
 
 import numpy as np
 import mlflow
+from datetime import datetime
 from deap import algorithms
 from deap.tools.emo import sortNondominated
 import pandas as pd
@@ -15,6 +16,16 @@ from src.preprocessing import build_preprocessor
 from src.models import make_model
 from src.stability import compute_shap_matrix
 
+# Main network
+# mlflow.set_tracking_uri("http://192.168.2.169:5000")
+
+# Cluster Subnet
+mlflow.set_tracking_uri("http://10.10.0.5:5000")
+
+# Network with DNS resolution (specified hosts or Tailnet)
+#mlflow.set_tracking_uri("http://medea:5000")
+
+
 
 def save_checkpoint(path, gen, pop, seed):
     state = {
@@ -43,7 +54,8 @@ def main():
     ap.add_argument("--pop-size", type=int, default=24)
     ap.add_argument("--seed", type=int, default=42)
     ap.add_argument("--cv-folds", type=int, default=3)
-    ap.add_argument("--experiment", default="deap_nsga_shap")
+    experiment_name = f"deap_nsga_shap_{datetime.now().strftime('%Y%m%d_%H%M%S')}"    
+    ap.add_argument("--experiment", default=experiment_name)
     ap.add_argument("--checkpoint-every", type=int, default=5)
     ap.add_argument(
         "--shap-pf-eval-rows",