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#!/usr/bin/env python3
import argparse
import importlib.util
import json
import os
import sys
import tempfile
from pathlib import Path
from types import ModuleType
from typing import Any, Dict, List, Tuple
HERE = Path(__file__).resolve().parent
RESOURCES = HERE / "resources"
SPEC_PATH = RESOURCES / "submission_spec.json"
OUTPUT_PROGRAM = HERE / "output_program.py"
sys.path.insert(0, str(RESOURCES))
from simulator import BCSimulator # noqa: E402
from utils import make_nx_graph # noqa: E402
def load_solution_module(solution_path: Path) -> ModuleType:
if not solution_path.exists():
raise FileNotFoundError(f"solution.py not found at {solution_path}")
spec = importlib.util.spec_from_file_location("submitted_solution", solution_path)
if spec is None or spec.loader is None:
raise ImportError(f"Failed to load spec for {solution_path}")
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module
def materialize_program(result: Any) -> Path:
if isinstance(result, dict):
if "program_path" in result:
candidate = Path(result["program_path"]).expanduser()
if not candidate.exists():
raise FileNotFoundError(f"Provided program_path does not exist: {candidate}")
return candidate
if "code" in result:
OUTPUT_PROGRAM.write_text(result["code"], encoding="utf-8")
return OUTPUT_PROGRAM
if isinstance(result, str):
# treat as code snippet
OUTPUT_PROGRAM.write_text(result, encoding="utf-8")
return OUTPUT_PROGRAM
raise TypeError("Solution.solve must return dict with 'code' or 'program_path', or a raw code string.")
def load_program_module(program_path: Path) -> ModuleType:
spec = importlib.util.spec_from_file_location("candidate_program", program_path)
if spec is None or spec.loader is None:
raise ImportError(f"Failed to load candidate program from {program_path}")
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module
def evaluate_search_algorithm(program_module: ModuleType, config_files: List[Path], num_vms: int) -> Dict[str, Any]:
if not hasattr(program_module, "search_algorithm"):
return {
"score": 0.0,
"combined_score": 0.0,
"runs_successfully": 0.0,
"error": "Missing search_algorithm function",
}
search_algorithm = getattr(program_module, "search_algorithm")
total_cost = 0.0
total_transfer_time = 0.0
successful = 0
with tempfile.TemporaryDirectory() as temp_dir:
original_cwd = os.getcwd()
os.chdir(temp_dir)
try:
cost_csv = RESOURCES / "profiles" / "cost.csv"
throughput_csv = RESOURCES / "profiles" / "throughput.csv"
for config_path in config_files:
config = json.loads(config_path.read_text(encoding="utf-8"))
config_name = config_path.stem
graph = make_nx_graph(
cost_path=str(cost_csv),
throughput_path=str(throughput_csv),
num_vms=num_vms,
)
bc_topology = search_algorithm(
config["source_node"],
config["dest_nodes"],
graph,
config["num_partitions"],
)
bc_topology.set_num_partitions(config["num_partitions"])
simulator = BCSimulator(num_vms=num_vms, output_dir="evals")
transfer_time, cost = simulator.evaluate_path(bc_topology, config)
total_cost += cost
total_transfer_time += transfer_time
successful += 1
finally:
os.chdir(original_cwd)
if successful == 0:
return {
"score": 0.0,
"combined_score": 0.0,
"runs_successfully": 0.0,
"error": "No configurations evaluated successfully",
}
cost_score = 1.0 / (1.0 + total_cost)
combined_score = cost_score
score = combined_score * 100
return {
"score": score,
"combined_score": combined_score,
"runs_successfully": 1.0,
"cost_score": cost_score,
"time_score": time_score,
"total_cost": total_cost,
"total_transfer_time": total_transfer_time,
"successful_runs": successful,
}
class Evaluator:
def __init__(self):
"""Initialize evaluator with hard-coded environment setup and load test traces"""
# Hard code in evaluator, env setup (done in prepare_env.py)
self.spec_path = SPEC_PATH
self.output_program = OUTPUT_PROGRAM
# Load test traces (config files)
spec = json.loads(self.spec_path.read_text(encoding="utf-8"))
self.config_files = [RESOURCES / Path(cfg) for cfg in spec["config_files"]]
self.num_vms = spec["num_vms"]
def evaluate(self, solution):
"""
Evaluate the solution using the loaded traces
Args:
solution: Solution instance with solve() method
Returns:
Dict with score and other metrics
"""
# Call solution.solve() with trace config and traces
result = solution.solve(str(self.spec_path))
program_path = materialize_program(result)
program_module = load_program_module(program_path)
# Calculate score using the search algorithm
metrics = evaluate_search_algorithm(program_module, self.config_files, self.num_vms)
return metrics
def evaluate(solution_path: Path, spec_path: Path) -> Dict[str, Any]:
"""Legacy function for backward compatibility"""
solution_module = load_solution_module(solution_path)
if not hasattr(solution_module, "Solution"):
raise AttributeError("solution.py must define a Solution class with a solve method")
solution_obj = solution_module.Solution()
if not hasattr(solution_obj, "solve"):
raise AttributeError("Solution class must define a solve(spec_path: str) method")
spec = json.loads(spec_path.read_text(encoding="utf-8"))
result = solution_obj.solve(str(spec_path))
program_path = materialize_program(result)
program_module = load_program_module(program_path)
config_files = [RESOURCES / Path(cfg) for cfg in spec["config_files"]]
metrics = evaluate_search_algorithm(program_module, config_files, spec["num_vms"])
return metrics
def main() -> None:
parser = argparse.ArgumentParser(description="Evaluate cloudcast broadcast optimizer")
parser.add_argument("--solution", default="../../execution_env/solution_env/solution.py")
parser.add_argument("--spec", default=str(SPEC_PATH))
parser.add_argument("--out", default="results.json")
args = parser.parse_args()
solution_path = Path(args.solution).resolve()
spec_path = Path(args.spec).resolve()
out_path = Path(args.out).resolve()
try:
module = load_solution_module(solution_path)
# Use new Solution class format
solution_class = getattr(module, "Solution", None)
if solution_class is None:
raise AttributeError("Solution class not found in solution.py")
print("[evaluator] Using Solution class format", file=sys.stderr)
evaluator = Evaluator()
solution = solution_class()
payload = evaluator.evaluate(solution)
out_path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
print(json.dumps(payload))
except Exception as exc:
error_payload = {"score": 0.0, "error": str(exc)}
out_path.write_text(json.dumps(error_payload, indent=2), encoding="utf-8")
print(json.dumps(error_payload))
raise
if __name__ == "__main__":
main()
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