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ee2fc0d | 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 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | """Classification example on synthetic data: Claude fixes a logistic model's residuals.
Each example belongs to the class with the largest score, with 2% label noise:
s0 = -2 x2 x5
s1 = 2 x2 x5
s2 = 1.5 (x4^2 - 1) + 0.8 x1 - 0.6 x3 + 0.5
Here x ~ N(0, I_6), N = 1000 (600 / 200 / 200), and x6 is irrelevant. Logistic regression can use
the linear x1 and x3 terms, but it cannot represent the x2 x5 interaction or the even x4 term.
Protocol (paper Sec. 3.5): residuals, the high-residual pool, critiques and p_k use the training
split; the retained iterate m_k* and the blend beta are chosen on validation; test is used only
for final metrics. Predictions follow Eq. 2.
Usage:
python classification.py --seed 0 # real run (needs Anthropic credentials)
python classification.py --seed 0 --mock --T 3 # offline pipeline check, no API calls
"""
from __future__ import annotations
import argparse
import contextlib
import json
import os
import random
import sys
import time
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, f1_score, log_loss
from xgboost import XGBClassifier
from claude_backend import ClaudeLLM, install_import_shims
install_import_shims()
import maricl_core as mc # noqa: E402 (released MARICL implementation, unmodified)
from maricl_headline import maricl_predict_proba, select_beta, select_classification # noqa: E402
from regression import FEATURE_DESCS, FEATURES, load_dotenv, term_flags # noqa: E402
DOMAIN = ("Synthetic 3-class classification benchmark with standardised inputs. Feature semantics "
"are unknown and no domain knowledge applies.")
N, D, C = 1000, 6, 3
def true_scores(X):
x1, x2, x3, x4, x5 = (X[:, i] for i in range(5))
return np.stack([-2.0 * x2 * x5, 2.0 * x2 * x5,
1.5 * (x4**2 - 1.0) + 0.8 * x1 - 0.6 * x3 + 0.5], axis=1)
def make_data(seed):
rng = np.random.default_rng(seed)
X = rng.standard_normal((N, D))
clean = true_scores(X).argmax(axis=1)
y = clean.copy()
flip = rng.random(N) < 0.02
y[flip] = rng.integers(0, C, size=int(flip.sum()))
idx = np.random.RandomState(seed).permutation(N)
split = {"train": idx[:600], "val": idx[600:800], "test": idx[800:]}
return X, y, clean, split
def metrics(y, proba):
pred = proba.argmax(axis=1)
return {"accuracy": float(accuracy_score(y, pred)),
"macro_f1": float(f1_score(y, pred, average="macro")),
"log_loss": float(log_loss(y, proba, labels=list(range(C))))}
def label_metrics(y, pred):
return {"accuracy": float(accuracy_score(y, pred)),
"macro_f1": float(f1_score(y, pred, average="macro"))}
def recovered_structure(model, correction, X):
"""How closely one correction's class scores track the two true nonlinear terms."""
delta = model._apply_single(correction, X, FEATURES)
x2, x4, x5 = X[:, 1], X[:, 3], X[:, 4]
with np.errstate(divide="ignore", invalid="ignore"):
inter = np.corrcoef(delta[:, 1] - delta[:, 0], x2 * x5)[0, 1]
even = np.corrcoef(delta[:, 2] - 0.5 * (delta[:, 0] + delta[:, 1]), x4**2)[0, 1]
return {"r_interaction_x2x5": float(np.nan_to_num(inter)), "r_even_x4": float(np.nan_to_num(even))}
# ---------------------------------------------------------------- offline stand-in
class _MockRunnable:
def __init__(self, schema, rng):
self.schema, self.rng = schema, rng
def invoke(self, messages):
name = self.schema.__name__
if name == "Hypothesis":
return self.schema(hypothesised_pattern="mock", implicated_features=["x2", "x5", "x4"],
functional_form_guess="[-a*x2*x5, a*x2*x5, b*(x4**2 - 1)]",
rationale="offline mock")
if name == "Correction":
a, b = self.rng.uniform(0.5, 2.0), self.rng.uniform(0.2, 1.5)
return self.schema(T_k="mock", f_k=f"[-{a:.3f}*x2*x5, {a:.3f}*x2*x5, {b:.3f}*(x4**2 - 1)]")
return self.schema(diagnosis="mock", structural_or_coefficient="coefficient", suggested_change="mock")
class MockLLM:
def __init__(self, seed):
self.rng = random.Random(seed)
def with_structured_output(self, schema):
return _MockRunnable(schema, self.rng)
# ---------------------------------------------------------------- main
def run_experiment(seed, T, llm, llm_creative, out_dir, tag, calls_path=None, fit_log=None):
"""Run one seed end to end; returns the results dict (also written to out_dir/tag.json)."""
X, y, clean, sp = make_data(seed)
tr, va, te = sp["train"], sp["val"], sp["test"]
base = LogisticRegression(max_iter=1000).fit(X[tr], y[tr])
P_tr, P_va, P_te = base.predict_proba(X[tr]), base.predict_proba(X[va]), base.predict_proba(X[te])
mc.configure_llms(llm, llm_creative)
model = mc.MARICL(task="classification", K=2, kappa=0.3, T=T, B=10, p_min=0.1,
gamma=2.0, tau_fail=0.5, verbose=True)
t0 = time.time()
sink = open(fit_log, "w") if fit_log else None
try:
with contextlib.redirect_stdout(sink) if sink else contextlib.nullcontext():
model.fit(X[tr], y[tr], base_model=base, feature_names=FEATURES,
feature_descs=FEATURE_DESCS, domain_context=DOMAIN, n_classes=C)
finally:
if sink:
sink.close()
fit_seconds = time.time() - t0
xgb = XGBClassifier(random_state=seed).fit(X[tr], y[tr])
def maricl(t_max):
chosen, scores, ts = select_classification(model, X[tr], y[tr], X[va], y[va], P_tr, P_va, t_max)
beta = select_beta(model, X[va], y[va], P_va, chosen, scores)
return chosen, scores, ts, beta, maricl_predict_proba(model, X[te], P_te, chosen, scores, beta)
chosen, scores, ts, beta, proba = maricl(T)
res = {
"seed": seed, "T": T, "fit_seconds": round(fit_seconds, 1), "class_counts": np.bincount(y).tolist(),
"logistic": metrics(y[te], P_te), "xgboost": metrics(y[te], xgb.predict_proba(X[te])),
"oracle": label_metrics(y[te], clean[te]), "maricl": metrics(y[te], proba), "beta": beta,
"corrections": [], "budget_curve": [],
}
for k, (s, c, p, t) in enumerate(zip(model.states_, chosen, scores, ts)):
res["corrections"].append({
"k": k + 1, "p_k": p, "retained_t": t,
"hypothesis": s.hypothesis.hypothesised_pattern,
"form_guess": s.hypothesis.functional_form_guess,
"T_k": c.T_k if c else None, "f_k": c.f_k if c else None,
"flags": term_flags(c.f_k) if c else None,
"structure": recovered_structure(model, c, X[te]) if c else None,
"history": [{"t": e["t"], "f_k": e["correction"].f_k, "train_log_loss": e["loss"]}
for e in s.history],
})
for t_max in range(T + 1):
res["budget_curve"].append({"T": t_max, **metrics(y[te], maricl(t_max)[4])})
if calls_path and os.path.exists(calls_path):
calls = [json.loads(line) for line in open(calls_path)]
res["calls"] = {"n": len(calls), "input_tokens": sum(c["input_tokens"] for c in calls),
"output_tokens": sum(c["output_tokens"] for c in calls)}
os.makedirs(out_dir, exist_ok=True)
with open(os.path.join(out_dir, f"{tag}.json"), "w") as fh:
json.dump(res, fh, indent=2)
return res
def main():
load_dotenv(os.path.join(os.path.dirname(os.path.abspath(__file__)), ".env"))
ap = argparse.ArgumentParser()
ap.add_argument("--seed", type=int, required=True)
ap.add_argument("--T", type=int, default=10)
ap.add_argument("--model", default="claude-opus-5-5")
ap.add_argument("--effort", default="medium")
ap.add_argument("--mock", action="store_true")
ap.add_argument("--out", default="results_api/classification")
args = ap.parse_args()
os.makedirs(args.out, exist_ok=True)
tag = f"{'mock_' if args.mock else ''}seed{args.seed}"
calls_path = os.path.join(args.out, f"calls_{tag}.jsonl")
if os.path.exists(calls_path):
os.remove(calls_path)
if args.mock:
llm = llm_creative = MockLLM(args.seed)
else:
llm = ClaudeLLM(args.model, args.effort, calls_path, role="base")
llm_creative = ClaudeLLM(args.model, args.effort, calls_path, role="encoder")
res = run_experiment(args.seed, args.T, llm, llm_creative, args.out, tag, calls_path)
print(json.dumps({k: res[k] for k in ("logistic", "xgboost", "maricl", "oracle")}))
if __name__ == "__main__":
sys.exit(main())
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