File size: 2,235 Bytes
6cf60f3 | 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 | import json
import numpy as np
import matplotlib.pyplot as plt
def load_graded(path):
with open(path) as f:
data = json.load(f)
return [item["graded_list"] for item in data]
datasets = [
("Qwen3-4B-Base", "output/Qwen3-4B-Base/Scenario.codegeneration_16_0.6_eval_all.json"),
("ftajwar (MaxRL 1000)", "output/ftajwar/qwen3_4B_Base_MaxRL_Polaris_1000_steps/Scenario.codegeneration_16_0.6_eval_all.json"),
("ftajwar (GRPO 1000)", "output/ftajwar/qwen3_4B_Base_GRPO_Polaris_1000_steps/Scenario.codegeneration_16_0.6_eval_all.json"),
]
def bootstrap_pass_at_k(graded_lists, k, n_bootstrap=10000, rng=None):
"""Bootstrapping estimator for pass@k averaged over problems."""
if rng is None:
rng = np.random.default_rng(42)
problem_scores = []
for outcomes in graded_lists:
outcomes_arr = np.array(outcomes, dtype=bool)
n = len(outcomes_arr)
# Sample k indices with replacement, check if any pass
samples = rng.integers(0, n, size=(n_bootstrap, k))
any_pass = outcomes_arr[samples].any(axis=1)
problem_scores.append(any_pass.mean())
return np.mean(problem_scores)
k_values = [1, 2, 4, 8, 16]
fig, ax = plt.subplots(figsize=(8, 5))
all_values = []
for label, path in datasets:
graded = load_graded(path)
rng = np.random.default_rng(42)
pass_at_k = [bootstrap_pass_at_k(graded, k, rng=rng) for k in k_values]
all_values.extend(pass_at_k)
print(f"\n{label}:")
for k, v in zip(k_values, pass_at_k):
print(f" pass@{k}: {v:.4f}")
line, = ax.plot(k_values, pass_at_k, marker="o", linewidth=2, markersize=8, label=label)
for k, v in zip(k_values, pass_at_k):
ax.annotate(f"{v:.3f}", (k, v), textcoords="offset points", xytext=(5, 6),
fontsize=8, color=line.get_color())
ax.set_xscale("log", base=2)
ax.set_xticks(k_values)
ax.set_xticklabels([str(k) for k in k_values])
ax.set_xlabel("k", fontsize=12)
ax.set_ylabel("pass@k", fontsize=12)
ax.set_title("pass@k (bootstrapping estimator)", fontsize=13)
ax.set_ylim(0, max(all_values) * 1.3)
ax.legend(fontsize=10)
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig("instruct.png", dpi=150)
print("\nSaved instruct.png")
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