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c395f6a 8204dc0 c395f6a 8204dc0 c395f6a 8204dc0 c395f6a 8204dc0 c395f6a 8204dc0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | #!/usr/bin/env python3
"""
GridMind-RL Baseline Comparison Script
Loads heuristic + LLM baseline scores and prints a delta table.
"""
import json
import os
import sys
def load_json(path):
if not os.path.exists(path):
print(f"Warning: {path} not found", file=sys.stderr)
return None
with open(path) as f:
return json.load(f)
heuristic = load_json("results/baseline_scores_heuristic.json")
llm_baseline = load_json("baseline_scores.json")
if not heuristic:
print("Run: python inference.py --fast-mode --episodes 3 --output results/baseline_scores_heuristic.json")
sys.exit(1)
tasks = [1, 2, 3, 4]
task_names = {1: "Cost Minimization", 2: "Temperature Mgmt", 3: "Demand Response", 4: "Instruction Following"}
print("\n" + "=" * 72)
print(" GridMind-RL — Baseline Comparison")
print("=" * 72)
print(f" {'Policy':<25} {'Task 1':>9} {'Task 2':>9} {'Task 3':>9} {'Task 4':>9} {'Avg':>9}")
print("-" * 72)
h_averages = heuristic.get("task_averages", {})
h_row = ["Heuristic Baseline"]
h_total = 0
for t in tasks:
s = h_averages.get(str(t), 0.0)
h_row.append(f"{s:.3f}")
h_total += s
h_row.append(f"{h_total/4:.3f}")
print(f" {''.join(h_row):<25} {' '.join(h_row[1:])}")
if llm_baseline and llm_baseline.get("model") not in ("<your-active-model>", None):
llm_averages = llm_baseline.get("task_averages", {})
llm_row = [llm_baseline.get("model", "LLM Baseline")]
llm_total = 0
for t in tasks:
s = llm_averages.get(str(t), 0.0)
llm_row.append(f"{s:.3f}")
llm_total += s
llm_row.append(f"{llm_total/4:.3f}")
print(f" {llm_row[0]:<25} {' '.join(llm_row[1:])}")
else:
print(f" {'LLM Baseline':<25} {'--':>9} {'--':>9} {'--':>9} {'--':>9} {'--':>9}")
print(" (Run: python inference.py --task N --episodes 3 --output baseline_scores.json)")
print("-" * 72)
print("\n Delta vs Heuristic:")
if llm_baseline and llm_baseline.get("model") not in ("<your-active-model>", None):
for t in tasks:
h_s = h_averages.get(str(t), 0.0)
l_s = llm_averages.get(str(t), 0.0)
delta = l_s - h_s
sign = "+" if delta >= 0 else ""
print(f" Task {t}: {sign}{delta:.3f} ({delta/h_s*100:+.1f}%)")
else:
print(" Run LLM baseline to compute delta.")
print("=" * 72 + "\n") |