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This commit is contained in:
Paul Gauthier 2023-06-30 06:44:37 -07:00
parent cba75a3bfe
commit a9cc3f81b2
1 changed files with 27 additions and 10 deletions
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37
benchmark/benchmark.py
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@ -50,6 +50,8 @@ def show_stats(dirnames):
if row.model == "gpt-3.5-turbo":
row.model = "gpt-3.5-turbo-0613"
if row.edit_format == "diff-func-string":
row.edit_format = "diff-func"
if row.completed_tests < 133:
print(f"Warning: {row.dir_name} is incomplete: {row.completed_tests}")
@ -69,12 +71,15 @@ def show_stats(dirnames):
# df_grouped1 = df.groupby(["model", "edit_format"])["pass_rate_1"].mean()
df_grouped2 = df.groupby(["model", "edit_format"])["pass_rate_2"].mean()
dump(df_grouped2.unstack())
plt.rcParams["hatch.linewidth"] = 0.5
plt.rcParams["hatch.color"] = "#444444"
fig, ax = plt.subplots(figsize=(10, 6))
from matplotlib import rc
rc("font", **{"family": "sans-serif", "sans-serif": ["Helvetica"], "size": 18})
fig, ax = plt.subplots(figsize=(12, 8))
ax.grid(axis="y", zorder=0, lw=0.2)
"""
colors = ["#007BFF", "#89CFF0", "#008000", "#32CD32"]
df_grouped2.unstack().plot(
@ -85,8 +90,7 @@ def show_stats(dirnames):
"""
df = df_grouped2.unstack()
num_models, num_formats = df.shape
dump(num_models)
dump(num_formats)
pos = np.array(range(num_models))
width = 0.8 / num_formats
@ -94,20 +98,33 @@ def show_stats(dirnames):
models = df.index
for i, fmt in enumerate(formats):
dump(df[fmt])
color = "#b3e6a8" if "diff" in fmt else "#b3d1e6"
hatch = "///" if "func" in fmt else ""
ax.bar(pos + i * width, df[fmt], width * 0.95, label=fmt, color=color, hatch=hatch)
rects = ax.bar(
pos + i * width,
df[fmt],
width * 0.95,
label=fmt,
color=color,
hatch=hatch,
zorder=3,
)
ax.bar_label(rects, padding=2, labels=[f"{v:.0f}%" for v in df[fmt]], size=12)
ax.set_xticks([p + 1.5 * width for p in pos])
ax.set_xticklabels(models)
ax.set_xticklabels(models, rotation=45)
ax.set_ylabel("Percent of passed unittests")
# ax.set_xlabel("Model")
ax.set_title("Code editing success rate by model & edit format")
ax.legend(title="Edit Format")
ax.legend(
title="Edit Format",
loc="upper left",
# bbox_to_anchor=(0.95, 0.95),
)
ax.set_ylim(top=100)
plt.tight_layout()
plt.savefig("tmp.svg")
imgcat(fig)
# df.to_csv("tmp.benchmarks.csv")