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fixed mislabelled gpt-4 column
This commit is contained in:
Paul Gauthier
parent
31909221cc
commit
ac39791fee
2 changed files with 184 additions and 51 deletions
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@ -6,7 +6,7 @@
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@ -1531,7 +1531,7 @@ L 166.556556 195.155104
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L 166.556556 125.076842
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L 143.983423 125.076842
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z
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L 183.58541 127.625143
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z
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L 223.187396 114.88364
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L 262.789382 105.964589
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L 341.993354 121.254392
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L 381.59534 103.416288
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<defs>
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<defs>
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<pattern id="h44c145aa42" patternUnits="userSpaceOnUse" x="0" y="0" width="72" height="72">
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<pattern id="h6479e354e1" patternUnits="userSpaceOnUse" x="0" y="0" width="72" height="72">
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L 36 -36
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L 108 36
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" style="fill: #ffffff; stroke: #ffffff; stroke-width: 0.5; stroke-linecap: butt; stroke-linejoin: miter"/>
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L 36 -36
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Before Width: | Height: | Size: 55 KiB After Width: | Height: | Size: 55 KiB |
|
|
@ -1,5 +1,4 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
import datetime
|
||||
import json
|
||||
import os
|
||||
|
|
@ -121,12 +120,13 @@ def show_stats(dirnames, graphs):
|
|||
repeat_hi = repeat_lo = repeat_avg = None # noqa: F841
|
||||
|
||||
df = pd.DataFrame.from_records(rows)
|
||||
df.sort_values(by=["model", "edit_format"], inplace=True)
|
||||
# df.sort_values(by=["model", "edit_format"], inplace=True)
|
||||
|
||||
# dump(df)
|
||||
if graphs:
|
||||
# plot_timing(df)
|
||||
plot_outcomes(df, repeats, repeat_hi, repeat_lo, repeat_avg)
|
||||
# plot_outcomes(df, repeats, repeat_hi, repeat_lo, repeat_avg)
|
||||
plot_outcomes_claude(df)
|
||||
# plot_refactoring(df)
|
||||
|
||||
|
||||
|
|
@ -292,6 +292,139 @@ def plot_outcomes(df, repeats, repeat_hi, repeat_lo, repeat_avg):
|
|||
# df.to_csv("tmp.benchmarks.csv")
|
||||
|
||||
|
||||
def plot_outcomes_claude(df):
|
||||
print(df)
|
||||
|
||||
# Fix wrong column label
|
||||
df["model"] = df["model"].replace("gpt-4-0314", "gpt-4-0613")
|
||||
|
||||
tries = [
|
||||
df[["model", "pass_rate_2"]],
|
||||
df[["model", "pass_rate_1"]],
|
||||
]
|
||||
|
||||
plt.rcParams["hatch.linewidth"] = 0.5
|
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plt.rcParams["hatch.color"] = "#444444"
|
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|
||||
from matplotlib import rc
|
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|
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rc("font", **{"family": "sans-serif", "sans-serif": ["Helvetica"], "size": 10})
|
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|
||||
fig, ax = plt.subplots(figsize=(6, 4))
|
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ax.grid(axis="y", zorder=0, lw=0.2)
|
||||
|
||||
zorder = 1
|
||||
for df in tries:
|
||||
zorder += 1
|
||||
print(df)
|
||||
|
||||
num_models, _ = df.shape
|
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num_formats = 1
|
||||
|
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pos = np.array(range(num_models))
|
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width = 0.6 / num_formats
|
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|
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if zorder > 1:
|
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edge = dict(
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edgecolor="#ffffff",
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linewidth=1.5,
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)
|
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else:
|
||||
edge = dict()
|
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if zorder == 2:
|
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edge["label"] = "??"
|
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|
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color = [
|
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"#b3e6a8",
|
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"#b3e6a8",
|
||||
"#b3e6a8",
|
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"#b3e6a8",
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"#b3d1e6",
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"#b3d1e6",
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"#b3d1e6",
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"#e6b3b3",
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"#d1b3e6",
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]
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hatch = [
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"",
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"",
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"",
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"",
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"////",
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"////",
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"////",
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"",
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"////",
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]
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hatch = [
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"////",
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"////",
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"////",
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"////",
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"",
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"",
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"",
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"////",
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"",
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]
|
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rects = ax.bar(
|
||||
pos + 0.5 * width,
|
||||
df.iloc[:, 1],
|
||||
width * 0.95,
|
||||
color=color,
|
||||
hatch=hatch,
|
||||
zorder=zorder,
|
||||
**edge,
|
||||
)
|
||||
if zorder == 2:
|
||||
ax.bar_label(rects, padding=4, labels=[f"{v:.0f}%" for v in df.iloc[:, 1]], size=6)
|
||||
|
||||
ax.set_xticks([p + 0.5 * width for p in pos])
|
||||
model_labels = []
|
||||
for model in df.iloc[:, 0]:
|
||||
pieces = model.split("-")
|
||||
N = 3
|
||||
ml = "-".join(pieces[:N])
|
||||
if pieces[N:]:
|
||||
ml += "-\n" + "-".join(pieces[N:])
|
||||
model_labels.append(ml)
|
||||
|
||||
ax.set_xticklabels(model_labels, rotation=60)
|
||||
|
||||
top = 95
|
||||
ax.annotate(
|
||||
"First attempt,\nbased on\nnatural language\ninstructions",
|
||||
xy=(2.0, 41),
|
||||
xytext=(1.75, top),
|
||||
horizontalalignment="center",
|
||||
verticalalignment="top",
|
||||
arrowprops={"arrowstyle": "->", "connectionstyle": "arc3,rad=0.3"},
|
||||
)
|
||||
ax.annotate(
|
||||
"Second attempt,\nincluding unit test\nerror output",
|
||||
xy=(2.55, 56),
|
||||
xytext=(3.9, top),
|
||||
horizontalalignment="center",
|
||||
verticalalignment="top",
|
||||
arrowprops={"arrowstyle": "->", "connectionstyle": "arc3,rad=0.3"},
|
||||
)
|
||||
|
||||
ax.set_ylabel("Percent of exercises completed successfully")
|
||||
# ax.set_xlabel("Model")
|
||||
ax.set_title("Code Editing Skill")
|
||||
# ax.legend(
|
||||
# title="Model family",
|
||||
# loc="upper left",
|
||||
# )
|
||||
ax.set_ylim(top=100)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig("tmp.svg")
|
||||
imgcat(fig)
|
||||
|
||||
# df.to_csv("tmp.benchmarks.csv")
|
||||
|
||||
|
||||
def plot_refactoring(df):
|
||||
tries = [df.groupby(["model", "edit_format"])["pass_rate_1"].mean()]
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue