Download openevolve-fixed/examples/math/signal_processing/evaluator.py from zhouk777/evolve: direct link, hf CLI and curl.
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https://huggingface.co/datasets/zhouk777/evolve/resolve/main/openevolve-fixed/examples/math/signal_processing/evaluator.py
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hf download hf://datasets/zhouk777/evolve/openevolve-fixed/examples/math/signal_processing/evaluator.py
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curl -L -o evaluator.py https://huggingface.co/datasets/zhouk777/evolve/resolve/main/openevolve-fixed/examples/math/signal_processing/evaluator.py
2.51 kB
| """Evaluator for adaptive signal processing.""" | |
| import importlib.util | |
| import numpy as np | |
| import time | |
| import traceback | |
| import json | |
| def evaluate(program_path): | |
| try: | |
| spec = importlib.util.spec_from_file_location("program", program_path) | |
| program = importlib.util.module_from_spec(spec) | |
| spec.loader.exec_module(program) | |
| if not hasattr(program, "run_signal_processing"): | |
| return {"combined_score": 0.0, "error": "Missing run_signal_processing"} | |
| np.random.seed(42) | |
| test_signals = [] | |
| # Generate 5 diverse test signals | |
| for t_type in range(5): | |
| n = 500 | |
| t = np.linspace(0, 10, n) | |
| if t_type == 0: | |
| clean = np.sin(2*np.pi*t) + 0.5*np.sin(6*np.pi*t) | |
| elif t_type == 1: | |
| clean = np.where(t < 5, np.sin(2*np.pi*t), np.sin(10*np.pi*t)) | |
| elif t_type == 2: | |
| clean = np.cumsum(np.random.randn(n)) / np.sqrt(n) | |
| elif t_type == 3: | |
| clean = np.sin(2*np.pi*t*(1+t/10)) | |
| else: | |
| clean = np.sign(np.sin(2*np.pi*t)) | |
| noise = np.random.randn(n) * 0.5 | |
| test_signals.append((clean, clean + noise)) | |
| scores = [] | |
| for clean, noisy in test_signals: | |
| try: | |
| result = program.run_signal_processing(noisy, window_size=20) | |
| filtered = np.array(result.get("filtered_signal", [])) | |
| if len(filtered) == 0: | |
| scores.append(0.0); continue | |
| # Align lengths - filter may produce shorter output due to windowing | |
| min_len = min(len(filtered), len(clean)) | |
| filtered = filtered[:min_len] | |
| clean_trimmed = clean[:min_len] | |
| noisy_trimmed = noisy[:min_len] | |
| corr = np.corrcoef(clean_trimmed, filtered)[0, 1] if np.std(filtered) > 0 else 0 | |
| mse = np.mean((clean_trimmed - filtered) ** 2) | |
| noise_var = np.mean((clean_trimmed - noisy_trimmed) ** 2) | |
| noise_red = 1 - mse / noise_var if noise_var > 0 else 0 | |
| scores.append(max(0, 0.5 * max(0, corr) + 0.3 * max(0, noise_red) + 0.2)) | |
| except: | |
| scores.append(0.0) | |
| combined = float(np.mean(scores)) if scores else 0.0 | |
| return {"combined_score": combined, "per_signal_scores": [float(s) for s in scores]} | |
| except Exception as e: | |
| return {"combined_score": 0.0, "error": str(e)} | |