Agent Conversation: anthropic/claude-opus-4.6
Model: anthropic/claude-opus-4.6
Total steps: 6
Messages: 13
UserStep 0
Task: optimization-hyperparameter-search
Hyperparameter Optimization: Custom Search Strategy Design
Research Question
Design a novel hyperparameter optimization (HPO) strategy that achieves better final validation scores and faster convergence than standard approaches like Random Search, TPE, Hyperband, and their combinations (BOHB, DEHB).
Background
Hyperparameter optimization is a fundamental problem in machine learning: given a model and dataset, find the hyperparameter configuration that maximizes validation performance within a limited evaluation budget. This is a black-box optimization problem where each function evaluation (training + validation) is expensive.
Classic strategies include:
- Random Search: Samples configurations uniformly. Simple but surprisingly effective, especially when some hyperparameters are more important than others (Bergstra & Bengio, 2012).
- TPE (Tree-structured Parzen Estimator): Models p(x|y<y*) and p(x|y>=y*) using kernel density estimation and maximizes their ratio (Bergstra et al., 2011).
- Hyperband: Uses multi-fidelity evaluation (early stopping) with successive halving to allocate resources to promising configurations (Li et al., 2017).
State-of-the-art methods combine these ideas:
- BOHB: Replaces random sampling in Hyperband with TPE-guided suggestions (Falkner et al., 2018).
- DEHB: Uses Differential Evolution within Hyperband's multi-fidelity framework (Awad et al., 2021).
- CMA-ES: Adapts a full covariance matrix of a Gaussian distribution for efficient continuous optimization (Hansen & Ostermeier, 2001).
There is ongoing research into strategies that better adapt to the optimization landscape, leverage multi-fidelity evaluations more effectively, or combine model-based search with evolutionary approaches.
Task
Implement a custom HPO strategy by modifying the CustomHPOStrategy class in scikit-learn/custom_hpo.py. You should implement both __init__ and suggest methods. The class is called repeatedly in a sequential loop where each call proposes one configuration to evaluate.
Interface
class CustomHPOStrategy:
def __init__(self, seed: int = 42):
"""Initialize the strategy with a random seed."""
self.seed = seed
self.rng = np.random.RandomState(seed)
def suggest(
self,
space: SearchSpace,
history: List[Trial],
budget_left: int,
) -> Tuple[Dict[str, Any], float]:
"""Propose the next configuration to evaluate.
Args:
space: SearchSpace with .params (list of HParam), .dim,
.sample_uniform(rng), .clip(config)
history: list of Trial(config, score, budget) from past evals
budget_left: remaining budget in full-fidelity units
Returns:
config: dict mapping hyperparameter names to values
fidelity: float in (0, 1] for multi-fidelity evaluation
"""The search space pro
AssistantStep 1edit
Tool Input
1{
2 "op": "replace",
3 "filename": "scikit-learn/custom_hpo.py",
4 "start_line": 255,
5 "end_line": 326,
6 "content": "class CustomHPOStrategy:\n \"\"\"AMF-TPE-TR: Adaptive Multi-Fidelity TPE with Trust-Region Local Search.\n Novelties: (1) per-dim KDE bandwidth via Silverman's rule, (2) mixed candidate\n generation from good KDE + trust-region perturbation, (3) continuous adaptive\n fidelity, (4) promotion of promising low-fidelity configs, (5) adaptive gamma.\"\"\"\n def __init__(self, seed=42):\n self.seed = seed\n self.rng = np.random.RandomState(seed)\n self.tb = None; self.pq = []; self.promoted = False\n def _enc(self, c, sp):\n v = []\n for p in sp.params:\n val = c[p.name]\n if p.type == \"categorical\": v.append(p.choices.index(val) / max(len(p.choices) - 1, 1))\n elif p.log_scale: v.append(np.clip((np.log(val) - np.log(p.low)) / (np.log(p.high) - np.log(p.low)), 0, 1))\n else: v.append(np.clip((val - p.low) / (p.high - p.low), 0, 1))\n return np.array(v)\n def _dec(self, v, sp):\n c = {}\n for i, p in enumerate(sp.params):\n u = float(np.clip(v[i], 0, 1))\n if p.type == \"categorical\":\n c[p.name] = p.choices[min(int(round(u * max(len(p.choices) - 1, 1))), len(p.choices) - 1)]\n elif p.log_scale:\n raw = np.exp(np.log(p.low) + u * (np.log(p.high) - np.log(p.low)))\n c[p.name] = float(raw) if p.type == \"float\" else int(round(raw))\n else:\n raw = p.low + u * (p.high - p.low)\n c[p.name] = float(raw) if p.type == \"float\" else int(round(raw))\n return c\n def suggest(self, space, history, budget_left):\n if self.tb is None: self.tb = budget_left\n n = len(history); frac = 1.0 - budget_left / self.tb if self.tb > 0 else 1.0\n if self.pq: return self.pq.pop(0), 1.0\n fid = np.clip(0.3 + frac, 0.3, 1.0) if budget_left > 3 else 1.0\n # Promote top low-fidelity configs to full fidelity at transition\n if fid >= 0.85 and not self.promoted and n > 10:\n self.promoted = True\n low = sorted([t for t in history if t.budget < 0.7], key=lambda t: t.score, reverse=True)\n self.pq = [t.config for t in low[:3]]\n if self.pq: return self.pq.pop(0), 1.0\n if n < 10: return space.sample_uniform(self.rng), fid\n # Build TPE model with per-dim bandwidth and adaptive gamma\n sc = np.array([t.score for t in history])\n gamma = max(0.15, 0.30 - 0.15 * frac)\n ng = max(1, int(gamma * n)); thr = np.sort(sc)[-ng]\n gv = np.array([self._enc(t.config, space) for t in history if t.score >= thr])\n bv = np.array([self._enc(t.config, space) for t in history if t.score < thr])\n if len(bv) == 0: bv = gv.copy()\n d = space.dim\n bg = np.maximum(np.std(gv, 0) * len(gv) ** (-1. / (d + 4)), 0.02)\n bb = np.maximum(np.std(bv, 0) * len(bv) ** (-1. / (d + 4)), 0.02)\n def kde(x, S, b):\n df = (x[None, :] - S) / b[None, :]; lp = -0.5 * np.sum(df ** 2, 1)\n mx = np.max(lp); return mx + np.log(np.mean(np.exp(lp - mx)) + 1e-30)\n bvec = self._enc(max(history, key=lambda t: t.score).config, space)\n be, bc = -np.inf, None\n for _ in range(96):\n r = self.rng.rand()\n if r < 0.2: c = space.sample_uniform(self.rng)\n elif r < 0.55: # Sample from good KDE\n c = self._dec(np.clip(gv[self.rng.randint(len(gv))] + self.rng.randn(d) * bg, 0, 1), space)\n else: # Trust-region local search around best\n s = max(0.02, 0.12 * (1 - frac))\n c = self._dec(np.clip(bvec + self.rng.randn(d) * s, 0, 1), space)\n x = self._enc(c, space); ei = kde(x, gv, bg) - kde(x, bv, bb)\n if ei > be: be, bc = ei, c\n return bc, fid\n"
7}Tool ResultStep 1
OK: Replaced lines 255..326 with 68 line(s) in scikit-learn/custom_hpo.py. Editable range: 255–322. [Current file: scikit-learn/custom_hpo.py | editable: 255–322 | total: 451 lines] 252| # ================================================================ 253| 254| 255 class CustomHPOStrategy: 256 """AMF-TPE-TR: Adaptive Multi-Fidelity TPE with Trust-Region Local Search. 257 Novelties: (1) per-dim KDE bandwidth via Silverman's rule, (2) mixed candidate 258 generation from good KDE + trust-region perturbation, (3) continuous adaptive 259 fidelity, (4) promotion of promising low-fidelity configs, (5) adaptive gamma.""" 260 def __init__(self, seed=42): 261 self.seed = seed 262 self.rng = np.random.RandomState(seed) 263 self.tb = None; self.pq = []; self.promoted = False 264 def _enc(self, c, sp): 265 v = [] 266 for p in sp.params: 267 val = c[p.name]
AssistantStep 2test
Tool ResultStep 2
[Test #1] (2 tests remaining; call submit(n=1) to submit this result) ### xgboost (scripts/xgboost.sh) Training progress (last evaluations): TRAIN_METRICS eval=55 cost=30.8/50 best_score=-0.392528 elapsed=783.5s TRAIN_METRICS eval=60 cost=35.6/50 best_score=-0.392528 elapsed=906.8s TRAIN_METRICS eval=65 cost=40.6/50 best_score=-0.392528 elapsed=1034.1s TRAIN_METRICS eval=70 cost=45.6/50 best_score=-0.392528 elapsed=1160.9s TRAIN_METRICS eval=75 cost=50.6/50 best_score=-0.392528 elapsed=1288.9s Test results (xgboost): best_val_score: -0.392528 convergence_auc: 0.943962 total_evals: 75.000000 ### svm (scripts/svm.sh) Training progress (last evaluations): TRAIN_METRICS eval=40 cost=21.0/40 best_score=0.975414 elapsed=0.5s TRAIN_METRICS eval=45 cost=25.8/40 best_score=0.975414 elapsed=0.6s TRAIN_METRICS eval=50 cost=30.7/40 best_score=0.975414 elapsed=0.7s TRAIN_METRICS eval=55 cost=35.7/40 best_score=0.975414 elapsed=0.8s TRAIN_METRICS eval=60 cost=40.7/40 best_score=0.975414 elap
AssistantStep 3edit
Tool Input
1{
2 "op": "replace",
3 "filename": "scikit-learn/custom_hpo.py",
4 "start_line": 255,
5 "end_line": 322,
6 "content": "class CustomHPOStrategy:\n \"\"\"GP-EI-TR: Gaussian Process BO with adaptive-lengthscale Expected Improvement\n and trust-region candidate generation. Novel contributions: (1) marginal-\n likelihood-based lengthscale selection, (2) mixed candidate generation from\n random + elite perturbation + shrinking trust-region, (3) robust GP fitting.\"\"\"\n def __init__(self, seed=42):\n self.seed = seed; self.rng = np.random.RandomState(seed)\n def _enc(self, c, sp):\n v = []\n for p in sp.params:\n val = c[p.name]\n if p.type == \"categorical\": v.append(p.choices.index(val) / max(len(p.choices) - 1, 1))\n elif p.log_scale: v.append(np.clip((np.log(val) - np.log(p.low)) / (np.log(p.high) - np.log(p.low)), 0, 1))\n else: v.append(np.clip((val - p.low) / (p.high - p.low), 0, 1))\n return np.array(v)\n def _dec(self, v, sp):\n c = {}\n for i, p in enumerate(sp.params):\n u = float(np.clip(v[i], 0, 1))\n if p.type == \"categorical\":\n c[p.name] = p.choices[min(int(round(u * max(len(p.choices) - 1, 1))), len(p.choices) - 1)]\n elif p.log_scale:\n raw = np.exp(np.log(p.low) + u * (np.log(p.high) - np.log(p.low)))\n c[p.name] = float(raw) if p.type == \"float\" else int(round(raw))\n else:\n raw = p.low + u * (p.high - p.low)\n c[p.name] = float(raw) if p.type == \"float\" else int(round(raw))\n return c\n def suggest(self, space, history, budget_left):\n n = len(history); d = space.dim\n if n < 2 * d + 1: return space.sample_uniform(self.rng), 1.0\n try:\n X = np.array([self._enc(t.config, space) for t in history])\n y = np.array([t.score for t in history])\n ym, ys = y.mean(), max(y.std(), 1e-8)\n yn = (y - ym) / ys; by = yn.max()\n sq = np.sum((X[:, None] - X[None, :]) ** 2, axis=2)\n bl, bls = -1e30, 0.5\n for ls in [0.1, 0.2, 0.5, 1.0, 2.0]:\n Kc = np.exp(-0.5 * sq / ls**2) + 0.01 * np.eye(n)\n try:\n Lc = np.linalg.cholesky(Kc)\n ac = np.linalg.solve(Lc.T, np.linalg.solve(Lc, yn))\n ll = -0.5 * float(yn @ ac) - float(np.sum(np.log(np.diag(Lc))))\n if ll > bl: bl, bls = ll, ls\n except: pass\n K = np.exp(-0.5 * sq / bls**2) + 0.01 * np.eye(n)\n L = np.linalg.cholesky(K)\n alpha = np.linalg.solve(L.T, np.linalg.solve(L, yn))\n bx = X[np.argmax(y)]; fr = n / (budget_left + n)\n be, bc = -1e30, None\n for _ in range(200):\n r = self.rng.rand()\n if r < 0.25: c = space.sample_uniform(self.rng)\n elif r < 0.55:\n ti = np.argsort(y)[-max(1, n // 4):]\n c = self._dec(np.clip(X[self.rng.choice(ti)] + self.rng.randn(d) * 0.15, 0, 1), space)\n else:\n s = max(0.02, 0.15 * (1 - fr))\n c = self._dec(np.clip(bx + self.rng.randn(d) * s, 0, 1), space)\n x = self._enc(c, space)\n ks = np.exp(-0.5 * np.sum((x - X)**2, 1) / bls**2)\n mu = float(ks @ alpha); vv = np.linalg.solve(L, ks)\n var = max(1.0 - float(vv @ vv), 1e-6)\n sig = np.sqrt(var); z = (mu - by) / sig\n ei = float((mu - by) * scipy_norm.cdf(z) + sig * scipy_norm.pdf(z))\n if ei > be: be, bc = ei, c\n return bc, 1.0\n except: return space.sample_uniform(self.rng), 1.0\n"
7}Tool ResultStep 3
OK: Replaced lines 255..322 with 69 line(s) in scikit-learn/custom_hpo.py. Editable range: 255–323. [Current file: scikit-learn/custom_hpo.py | editable: 255–323 | total: 452 lines] 252| # ================================================================ 253| 254| 255 class CustomHPOStrategy: 256 """GP-EI-TR: Gaussian Process BO with adaptive-lengthscale Expected Improvement 257 and trust-region candidate generation. Novel contributions: (1) marginal- 258 likelihood-based lengthscale selection, (2) mixed candidate generation from 259 random + elite perturbation + shrinking trust-region, (3) robust GP fitting.""" 260 def __init__(self, seed=42): 261 self.seed = seed; self.rng = np.random.RandomState(seed) 262 def _enc(self, c, sp): 263 v = [] 264 for p in sp.params: 265 val = c[p.name] 266 if p.type == "categorical": v.append(p.choices.index(val) / max(len(p
AssistantStep 4test
Tool ResultStep 4
[Test #2] (1 test remaining; call submit(n=2) to submit this result) ### xgboost (scripts/xgboost.sh) Training progress (last evaluations): TRAIN_METRICS eval=30 cost=30.0/50 best_score=-0.394014 elapsed=852.9s TRAIN_METRICS eval=35 cost=35.0/50 best_score=-0.390702 elapsed=1018.2s TRAIN_METRICS eval=40 cost=40.0/50 best_score=-0.390702 elapsed=1121.5s TRAIN_METRICS eval=45 cost=45.0/50 best_score=-0.389088 elapsed=1304.1s TRAIN_METRICS eval=50 cost=50.0/50 best_score=-0.389088 elapsed=1483.3s Test results (xgboost): best_val_score: -0.389088 convergence_auc: 0.907739 total_evals: 50.000000 ### svm (scripts/svm.sh) Training progress (last evaluations): TRAIN_METRICS eval=20 cost=20.0/40 best_score=0.977162 elapsed=0.8s TRAIN_METRICS eval=25 cost=25.0/40 best_score=0.977162 elapsed=1.0s TRAIN_METRICS eval=30 cost=30.0/40 best_score=0.977162 elapsed=1.3s TRAIN_METRICS eval=35 cost=35.0/40 best_score=0.977162 elapsed=1.5s TRAIN_METRICS eval=40 cost=40.0/40 best_score=0.977162 elap
AssistantStep 5edit
Tool Input
1{
2 "op": "replace",
3 "filename": "scikit-learn/custom_hpo.py",
4 "start_line": 255,
5 "end_line": 323,
6 "content": "class CustomHPOStrategy:\n \"\"\"GP-EI-D: GP BO with adaptive lengthscale, diversity-augmented EI, trust-region.\"\"\"\n def __init__(self, seed=42):\n self.seed = seed; self.rng = np.random.RandomState(seed)\n def _enc(self, c, sp):\n v = []\n for p in sp.params:\n val = c[p.name]\n if p.type == \"categorical\": v.append(p.choices.index(val) / max(len(p.choices) - 1, 1))\n elif p.log_scale: v.append(np.clip((np.log(val) - np.log(p.low)) / (np.log(p.high) - np.log(p.low)), 0, 1))\n else: v.append(np.clip((val - p.low) / (p.high - p.low), 0, 1))\n return np.array(v)\n def _dec(self, v, sp):\n c = {}\n for i, p in enumerate(sp.params):\n u = float(np.clip(v[i], 0, 1))\n if p.type == \"categorical\":\n c[p.name] = p.choices[min(int(round(u * max(len(p.choices) - 1, 1))), len(p.choices) - 1)]\n elif p.log_scale:\n raw = np.exp(np.log(p.low) + u * (np.log(p.high) - np.log(p.low)))\n c[p.name] = float(raw) if p.type == \"float\" else int(round(raw))\n else:\n raw = p.low + u * (p.high - p.low)\n c[p.name] = float(raw) if p.type == \"float\" else int(round(raw))\n return c\n def suggest(self, space, history, budget_left):\n n = len(history); d = space.dim\n if n < 8: return space.sample_uniform(self.rng), 1.0\n try:\n X = np.array([self._enc(t.config, space) for t in history])\n y = np.array([t.score for t in history])\n ym, ys = y.mean(), max(y.std(), 1e-8)\n yn = (y - ym) / ys; by = yn.max()\n sq = np.sum((X[:, None] - X[None, :]) ** 2, axis=2)\n bl, bls = -1e30, 0.5\n for ls in [0.1, 0.2, 0.5, 1.0, 2.0]:\n Kc = np.exp(-0.5 * sq / ls**2) + 0.01 * np.eye(n)\n try:\n Lc = np.linalg.cholesky(Kc)\n ac = np.linalg.solve(Lc.T, np.linalg.solve(Lc, yn))\n ll = -0.5 * float(yn @ ac) - float(np.sum(np.log(np.diag(Lc))))\n if ll > bl: bl, bls = ll, ls\n except: pass\n K = np.exp(-0.5 * sq / bls**2) + 0.01 * np.eye(n)\n L = np.linalg.cholesky(K); alpha = np.linalg.solve(L.T, np.linalg.solve(L, yn))\n bx = X[np.argmax(y)]; fr = n / (budget_left + n)\n be, bc = -1e30, None\n for _ in range(200):\n r = self.rng.rand()\n if r < 0.30: c = space.sample_uniform(self.rng)\n elif r < 0.60:\n ti = np.argsort(y)[-max(1, n // 4):]\n c = self._dec(np.clip(X[self.rng.choice(ti)] + self.rng.randn(d) * 0.15, 0, 1), space)\n else:\n s = max(0.02, 0.15 * (1 - fr))\n c = self._dec(np.clip(bx + self.rng.randn(d) * s, 0, 1), space)\n x = self._enc(c, space)\n ks = np.exp(-0.5 * np.sum((x - X)**2, 1) / bls**2)\n mu = float(ks @ alpha); vv = np.linalg.solve(L, ks)\n var = max(1.0 - float(vv @ vv), 1e-6)\n sig = np.sqrt(var); z = (mu - by) / sig\n ei = float((mu - by) * scipy_norm.cdf(z) + sig * scipy_norm.pdf(z))\n ei += 0.05 * (1 - fr) * np.sqrt(np.min(np.sum((x - X)**2, 1)) + 1e-10)\n if ei > be: be, bc = ei, c\n return bc, 1.0\n except: return space.sample_uniform(self.rng), 1.0\n"
7}Tool ResultStep 5
OK: Replaced lines 255..323 with 66 line(s) in scikit-learn/custom_hpo.py. Editable range: 255–320. [Current file: scikit-learn/custom_hpo.py | editable: 255–320 | total: 449 lines] 252| # ================================================================ 253| 254| 255 class CustomHPOStrategy: 256 """GP-EI-D: GP BO with adaptive lengthscale, diversity-augmented EI, trust-region.""" 257 def __init__(self, seed=42): 258 self.seed = seed; self.rng = np.random.RandomState(seed) 259 def _enc(self, c, sp): 260 v = [] 261 for p in sp.params: 262 val = c[p.name] 263 if p.type == "categorical": v.append(p.choices.index(val) / max(len(p.choices) - 1, 1)) 264 elif p.log_scale: v.append(np.clip((np.log(val) - np.log(p.low)) / (np.log(p.high) - np.log(p.low)), 0, 1)) 265 else: v.append(np.clip((val - p.low) / (p.high - p.low), 0, 1)) 266 return np.arr
AssistantStep 6test
Tool ResultStep 6
[Test #3] (0 tests remaining) ### xgboost (scripts/xgboost.sh) Training progress (last evaluations): TRAIN_METRICS eval=30 cost=30.0/50 best_score=-0.394943 elapsed=702.6s TRAIN_METRICS eval=35 cost=35.0/50 best_score=-0.394943 elapsed=846.8s TRAIN_METRICS eval=40 cost=40.0/50 best_score=-0.394943 elapsed=919.5s TRAIN_METRICS eval=45 cost=45.0/50 best_score=-0.394943 elapsed=1025.0s TRAIN_METRICS eval=50 cost=50.0/50 best_score=-0.392503 elapsed=1140.6s Test results (xgboost): best_val_score: -0.392503 convergence_auc: 0.922670 total_evals: 50.000000 ### svm (scripts/svm.sh) Training progress (last evaluations): TRAIN_METRICS eval=20 cost=20.0/40 best_score=0.975392 elapsed=0.7s TRAIN_METRICS eval=25 cost=25.0/40 best_score=0.975392 elapsed=0.9s TRAIN_METRICS eval=30 cost=30.0/40 best_score=0.975392 elapsed=1.2s TRAIN_METRICS eval=35 cost=35.0/40 best_score=0.975408 elapsed=1.4s TRAIN_METRICS eval=40 cost=40.0/40 best_score=0.978932 elapsed=1.6s Test results (svm): best_val_s