mirror of
https://github.com/Aider-AI/aider.git
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refactored plots
This commit is contained in:
Paul Gauthier
parent
3bb237bdc1
commit
5fb7a323ec
2 changed files with 418 additions and 415 deletions
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@ -16,12 +16,10 @@ from typing import List
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import git
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import lox
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import prompts
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import typer
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from imgcat import imgcat
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from plots import plot_refactoring
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from rich.console import Console
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from aider import models
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@ -130,418 +128,6 @@ def show_stats(dirnames, graphs):
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plot_refactoring(df)
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def plot_timing(df):
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"""plot a graph showing the average duration of each (model, edit_format)"""
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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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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)
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zorder = 1
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grouped = df.groupby(["model", "edit_format"])["avg_duration"].mean().unstack()
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num_models, num_formats = grouped.shape
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pos = np.array(range(num_models))
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width = 0.8 / num_formats
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formats = grouped.columns
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models = grouped.index
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for i, fmt in enumerate(formats):
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edge = dict(edgecolor="#ffffff", linewidth=1.5)
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color = "#b3e6a8" if "diff" in fmt else "#b3d1e6"
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hatch = "////" if "func" in fmt else ""
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rects = ax.bar(
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pos + i * width,
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grouped[fmt],
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width * 0.95,
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label=fmt,
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color=color,
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hatch=hatch,
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zorder=zorder + 1,
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**edge,
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)
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ax.bar_label(rects, padding=4, labels=[f"{v:.1f}s" for v in grouped[fmt]], size=6)
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ax.set_xticks([p + 0.5 * width for p in pos])
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ax.set_xticklabels(models)
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ax.set_ylabel("Average GPT response time\nper exercise (sec)")
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ax.set_title("GPT Code Editing Speed\n(time per coding task)")
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ax.legend(
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title="Edit Format",
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loc="upper left",
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)
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ax.set_ylim(top=max(grouped.max()) * 1.1) # Set y-axis limit to 10% more than the max value
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plt.tight_layout()
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plt.savefig("tmp_timing.svg")
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imgcat(fig)
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def plot_outcomes(df, repeats, repeat_hi, repeat_lo, repeat_avg):
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tries = [df.groupby(["model", "edit_format"])["pass_rate_2"].mean()]
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if True:
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tries += [df.groupby(["model", "edit_format"])["pass_rate_1"].mean()]
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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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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)
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zorder = 1
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for grouped in tries:
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zorder += 1
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df = grouped.unstack()
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num_models, num_formats = df.shape
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pos = np.array(range(num_models))
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width = 0.8 / num_formats
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formats = df.columns
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models = df.index
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for i, fmt in enumerate(formats):
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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:
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edge = dict()
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if zorder == 2:
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edge["label"] = fmt
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color = "#b3e6a8" if "diff" in fmt else "#b3d1e6"
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hatch = "////" if "func" in fmt else ""
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rects = ax.bar(
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pos + i * width,
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df[fmt],
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width * 0.95,
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color=color,
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hatch=hatch,
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zorder=zorder,
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**edge,
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)
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if zorder == 2:
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ax.bar_label(rects, padding=4, labels=[f"{v:.0f}%" for v in df[fmt]], size=6)
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if len(repeats):
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ax.errorbar(
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1.4,
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repeat_avg,
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yerr=[[repeat_lo], [repeat_hi]],
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fmt="none",
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zorder=5,
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capsize=2.5,
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elinewidth=1,
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markeredgewidth=1,
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)
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ax.set_xticks([p + 0.5 * width for p in pos])
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model_labels = []
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for model in models:
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pieces = model.split("-")
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ml = "-".join(pieces[:2]) + "-\n" + "-".join(pieces[2:])
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model_labels.append(ml)
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ax.set_xticklabels(model_labels)
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top = 95
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ax.annotate(
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"First attempt,\nbased on\nnatural language\ninstructions",
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xy=(2.20, 41),
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xytext=(2, top),
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horizontalalignment="center",
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verticalalignment="top",
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arrowprops={"arrowstyle": "->", "connectionstyle": "arc3,rad=0.3"},
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)
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ax.annotate(
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"Second attempt,\nincluding unit test\nerror output",
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xy=(2.55, 56),
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xytext=(3.5, top),
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horizontalalignment="center",
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verticalalignment="top",
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arrowprops={"arrowstyle": "->", "connectionstyle": "arc3,rad=0.3"},
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)
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ax.set_ylabel("Percent of exercises completed successfully")
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# ax.set_xlabel("Model")
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ax.set_title("GPT Code Editing Skill\n(percent coding tasks correct)")
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ax.legend(
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title="Edit Format",
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loc="upper left",
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# bbox_to_anchor=(0.95, 0.95),
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)
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ax.set_ylim(top=100)
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plt.tight_layout()
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plt.savefig("tmp.svg")
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imgcat(fig)
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# df.to_csv("tmp.benchmarks.csv")
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def plot_outcomes_claude(df):
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print(df)
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# Fix wrong column label
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df["model"] = df["model"].replace("gpt-4-0314", "gpt-4-0613")
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tries = [
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df[["model", "pass_rate_2"]],
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df[["model", "pass_rate_1"]],
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]
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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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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)
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zorder = 1
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for df in tries:
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zorder += 1
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print(df)
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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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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:
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edge = dict()
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if zorder == 2:
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edge["label"] = "??"
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color = [
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"#b3e6a8",
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"#b3e6a8",
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"#b3e6a8",
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"#b3d1e6",
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]
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hatch = [ # noqa: F841
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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 = [ # noqa: F841
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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(
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pos + 0.5 * width,
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df.iloc[:, 1],
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width * 0.95,
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color=color,
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# hatch=hatch,
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# zorder=zorder,
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**edge,
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)
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if zorder == 2:
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ax.bar_label(rects, padding=4, labels=[f"{v:.0f}%" for v in df.iloc[:, 1]], size=6)
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ax.set_xticks([p + 0.5 * width for p in pos])
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models = df.iloc[:, 0]
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model_map = {
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"gpt-4-0613": "gpt-4-\n0613",
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"gpt-4-0125-preview": "gpt-4-\n0125-preview",
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"gpt-4-1106-preview": "gpt-4-\n1106-preview",
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"gpt-4-turbo-2024-04-09": "gpt-4-turbo-\n2024-04-09\n(GPT-4 Turbo with Vision)",
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}
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model_labels = []
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for model in models:
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ml = model_map.get(model, model)
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model_labels.append(ml)
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ax.set_xticklabels(model_labels, rotation=0)
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top = 95
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ax.annotate(
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"First attempt,\nbased on\nnatural language\ninstructions",
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xy=(1.0, 53),
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xytext=(0.75, top),
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horizontalalignment="center",
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verticalalignment="top",
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arrowprops={"arrowstyle": "->", "connectionstyle": "arc3,rad=0.3"},
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)
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ax.annotate(
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"Second attempt,\nincluding unit test\nerror output",
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xy=(1.55, 65),
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xytext=(1.9, top),
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horizontalalignment="center",
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verticalalignment="top",
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arrowprops={"arrowstyle": "->", "connectionstyle": "arc3,rad=0.3"},
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)
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ax.set_ylabel("Percent of exercises completed successfully")
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# ax.set_xlabel("Model")
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ax.set_title("Code Editing Skill")
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# ax.legend(
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# title="Model family",
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# loc="upper left",
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# )
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ax.set_ylim(top=100)
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plt.tight_layout()
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plt.savefig("tmp.svg")
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imgcat(fig)
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# df.to_csv("tmp.benchmarks.csv")
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def plot_refactoring(df):
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tries = [df.groupby(["model", "edit_format"])["pass_rate_1"].mean()]
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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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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)
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zorder = 1
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for grouped in tries:
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zorder += 1
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df = grouped.unstack()
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i, j = 0, 1
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temp = df.iloc[i].copy()
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df.iloc[i], df.iloc[j] = df.iloc[j], temp
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dump(df)
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# df.sort_values(by=["model"], ascending=False, inplace=True)
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num_models, num_formats = df.shape
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pos = np.array(range(num_models))
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width = 0.8 / num_formats
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formats = df.columns
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models = df.index
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dump(df)
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dump(models)
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dump(formats)
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for i, fmt in enumerate(formats):
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hatch = ""
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if fmt == "diff":
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color = "#b3e6a8"
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label = "Search/replace blocks"
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elif fmt == "udiff":
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color = "#b3d1e6"
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label = "Unified diffs"
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elif fmt == "difffolk":
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label = "Baseline + blind, no hands, $2k tip, etc"
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color = "#b3e6a8"
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hatch = "////"
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elif fmt == "udifffolk":
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label = "Unified diffs + blind, no hands, $2k tip, etc"
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color = "#b3d1e6"
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hatch = "////"
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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:
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edge = dict()
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if zorder == 2:
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edge["label"] = label
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color = [
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"#b3e6a8",
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"#b3e6a8",
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"#b3d1e6",
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]
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rects = ax.bar(
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pos + i * width,
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df[fmt],
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width * 0.95,
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color=color,
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hatch=hatch,
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zorder=zorder,
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**edge,
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)
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if zorder == 2:
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ax.bar_label(rects, padding=4, labels=[f"{v:.0f}%" for v in df[fmt]], size=6)
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ax.set_xticks([p + 0 * width for p in pos])
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model_map = {
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"gpt-4-0125-preview": "gpt-4-\n0125-preview",
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"gpt-4-1106-preview": "gpt-4-\n1106-preview",
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"gpt-4-turbo-2024-04-09": "gpt-4-turbo-\n2024-04-09\n(GPT-4 Turbo with Vision)",
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}
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model_labels = []
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for model in models:
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ml = model_map.get(model, model)
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model_labels.append(ml)
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model_labels = [
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"gpt-4-\n1106-preview",
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"gpt-4-\n0125-preview",
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"gpt-4-turbo-\n2024-04-09\n(GPT-4 Turbo with Vision)",
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]
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ax.set_xticklabels(model_labels, rotation=0)
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ax.set_ylabel("Percent of exercises completed successfully")
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# ax.set_xlabel("Model")
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ax.set_title('Refactoring "Laziness" Benchmark')
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# ax.legend(
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# title="Edit Format",
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# loc="upper left",
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# bbox_to_anchor=(0.95, 0.95),
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# )
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ax.set_ylim(top=100)
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plt.tight_layout()
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plt.savefig("tmp.svg")
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imgcat(fig)
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# df.to_csv("tmp.benchmarks.csv")
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def resolve_dirname(dirname, use_single_prior, make_new):
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if len(dirname.parts) > 1:
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return dirname
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|
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417
benchmark/plots.py
Normal file
417
benchmark/plots.py
Normal file
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@ -0,0 +1,417 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from imgcat import imgcat
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from aider.dump import dump # noqa: F401
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def plot_timing(df):
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"""plot a graph showing the average duration of each (model, edit_format)"""
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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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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)
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zorder = 1
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grouped = df.groupby(["model", "edit_format"])["avg_duration"].mean().unstack()
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num_models, num_formats = grouped.shape
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pos = np.array(range(num_models))
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width = 0.8 / num_formats
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formats = grouped.columns
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models = grouped.index
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for i, fmt in enumerate(formats):
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edge = dict(edgecolor="#ffffff", linewidth=1.5)
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color = "#b3e6a8" if "diff" in fmt else "#b3d1e6"
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hatch = "////" if "func" in fmt else ""
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rects = ax.bar(
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pos + i * width,
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grouped[fmt],
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width * 0.95,
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label=fmt,
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color=color,
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hatch=hatch,
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zorder=zorder + 1,
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**edge,
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)
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ax.bar_label(rects, padding=4, labels=[f"{v:.1f}s" for v in grouped[fmt]], size=6)
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ax.set_xticks([p + 0.5 * width for p in pos])
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ax.set_xticklabels(models)
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ax.set_ylabel("Average GPT response time\nper exercise (sec)")
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ax.set_title("GPT Code Editing Speed\n(time per coding task)")
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ax.legend(
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title="Edit Format",
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loc="upper left",
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)
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ax.set_ylim(top=max(grouped.max()) * 1.1) # Set y-axis limit to 10% more than the max value
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||||
|
||||
plt.tight_layout()
|
||||
plt.savefig("tmp_timing.svg")
|
||||
imgcat(fig)
|
||||
|
||||
|
||||
def plot_outcomes(df, repeats, repeat_hi, repeat_lo, repeat_avg):
|
||||
tries = [df.groupby(["model", "edit_format"])["pass_rate_2"].mean()]
|
||||
if True:
|
||||
tries += [df.groupby(["model", "edit_format"])["pass_rate_1"].mean()]
|
||||
|
||||
plt.rcParams["hatch.linewidth"] = 0.5
|
||||
plt.rcParams["hatch.color"] = "#444444"
|
||||
|
||||
from matplotlib import rc
|
||||
|
||||
rc("font", **{"family": "sans-serif", "sans-serif": ["Helvetica"], "size": 10})
|
||||
|
||||
fig, ax = plt.subplots(figsize=(6, 4))
|
||||
ax.grid(axis="y", zorder=0, lw=0.2)
|
||||
|
||||
zorder = 1
|
||||
for grouped in tries:
|
||||
zorder += 1
|
||||
df = grouped.unstack()
|
||||
num_models, num_formats = df.shape
|
||||
|
||||
pos = np.array(range(num_models))
|
||||
width = 0.8 / num_formats
|
||||
|
||||
formats = df.columns
|
||||
models = df.index
|
||||
|
||||
for i, fmt in enumerate(formats):
|
||||
if zorder > 1:
|
||||
edge = dict(
|
||||
edgecolor="#ffffff",
|
||||
linewidth=1.5,
|
||||
)
|
||||
else:
|
||||
edge = dict()
|
||||
if zorder == 2:
|
||||
edge["label"] = fmt
|
||||
|
||||
color = "#b3e6a8" if "diff" in fmt else "#b3d1e6"
|
||||
hatch = "////" if "func" in fmt else ""
|
||||
rects = ax.bar(
|
||||
pos + i * width,
|
||||
df[fmt],
|
||||
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[fmt]], size=6)
|
||||
|
||||
if len(repeats):
|
||||
ax.errorbar(
|
||||
1.4,
|
||||
repeat_avg,
|
||||
yerr=[[repeat_lo], [repeat_hi]],
|
||||
fmt="none",
|
||||
zorder=5,
|
||||
capsize=2.5,
|
||||
elinewidth=1,
|
||||
markeredgewidth=1,
|
||||
)
|
||||
|
||||
ax.set_xticks([p + 0.5 * width for p in pos])
|
||||
model_labels = []
|
||||
for model in models:
|
||||
pieces = model.split("-")
|
||||
ml = "-".join(pieces[:2]) + "-\n" + "-".join(pieces[2:])
|
||||
model_labels.append(ml)
|
||||
|
||||
ax.set_xticklabels(model_labels)
|
||||
|
||||
top = 95
|
||||
ax.annotate(
|
||||
"First attempt,\nbased on\nnatural language\ninstructions",
|
||||
xy=(2.20, 41),
|
||||
xytext=(2, 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=(3.5, 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("GPT Code Editing Skill\n(percent coding tasks correct)")
|
||||
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")
|
||||
|
||||
|
||||
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
|
||||
plt.rcParams["hatch.color"] = "#444444"
|
||||
|
||||
from matplotlib import rc
|
||||
|
||||
rc("font", **{"family": "sans-serif", "sans-serif": ["Helvetica"], "size": 10})
|
||||
|
||||
fig, ax = plt.subplots(figsize=(6, 4))
|
||||
ax.grid(axis="y", zorder=0, lw=0.2)
|
||||
|
||||
zorder = 1
|
||||
for df in tries:
|
||||
zorder += 1
|
||||
print(df)
|
||||
|
||||
num_models, _ = df.shape
|
||||
num_formats = 1
|
||||
|
||||
pos = np.array(range(num_models))
|
||||
width = 0.6 / num_formats
|
||||
|
||||
if zorder > 1:
|
||||
edge = dict(
|
||||
edgecolor="#ffffff",
|
||||
linewidth=1.5,
|
||||
)
|
||||
else:
|
||||
edge = dict()
|
||||
if zorder == 2:
|
||||
edge["label"] = "??"
|
||||
|
||||
color = [
|
||||
"#b3e6a8",
|
||||
"#b3e6a8",
|
||||
"#b3e6a8",
|
||||
"#b3d1e6",
|
||||
]
|
||||
hatch = [ # noqa: F841
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"////",
|
||||
"////",
|
||||
"////",
|
||||
"",
|
||||
"////",
|
||||
]
|
||||
hatch = [ # noqa: F841
|
||||
"////",
|
||||
"////",
|
||||
"////",
|
||||
"////",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"////",
|
||||
"",
|
||||
]
|
||||
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])
|
||||
|
||||
models = df.iloc[:, 0]
|
||||
model_map = {
|
||||
"gpt-4-0613": "gpt-4-\n0613",
|
||||
"gpt-4-0125-preview": "gpt-4-\n0125-preview",
|
||||
"gpt-4-1106-preview": "gpt-4-\n1106-preview",
|
||||
"gpt-4-turbo-2024-04-09": "gpt-4-turbo-\n2024-04-09\n(GPT-4 Turbo with Vision)",
|
||||
}
|
||||
model_labels = []
|
||||
for model in models:
|
||||
ml = model_map.get(model, model)
|
||||
model_labels.append(ml)
|
||||
ax.set_xticklabels(model_labels, rotation=0)
|
||||
|
||||
top = 95
|
||||
ax.annotate(
|
||||
"First attempt,\nbased on\nnatural language\ninstructions",
|
||||
xy=(1.0, 53),
|
||||
xytext=(0.75, top),
|
||||
horizontalalignment="center",
|
||||
verticalalignment="top",
|
||||
arrowprops={"arrowstyle": "->", "connectionstyle": "arc3,rad=0.3"},
|
||||
)
|
||||
ax.annotate(
|
||||
"Second attempt,\nincluding unit test\nerror output",
|
||||
xy=(1.55, 65),
|
||||
xytext=(1.9, 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()]
|
||||
|
||||
plt.rcParams["hatch.linewidth"] = 0.5
|
||||
plt.rcParams["hatch.color"] = "#444444"
|
||||
|
||||
from matplotlib import rc
|
||||
|
||||
rc("font", **{"family": "sans-serif", "sans-serif": ["Helvetica"], "size": 10})
|
||||
|
||||
fig, ax = plt.subplots(figsize=(6, 4))
|
||||
ax.grid(axis="y", zorder=0, lw=0.2)
|
||||
|
||||
zorder = 1
|
||||
for grouped in tries:
|
||||
zorder += 1
|
||||
df = grouped.unstack()
|
||||
|
||||
i, j = 0, 1
|
||||
temp = df.iloc[i].copy()
|
||||
df.iloc[i], df.iloc[j] = df.iloc[j], temp
|
||||
dump(df)
|
||||
|
||||
# df.sort_values(by=["model"], ascending=False, inplace=True)
|
||||
num_models, num_formats = df.shape
|
||||
|
||||
pos = np.array(range(num_models))
|
||||
width = 0.8 / num_formats
|
||||
|
||||
formats = df.columns
|
||||
models = df.index
|
||||
|
||||
dump(df)
|
||||
dump(models)
|
||||
dump(formats)
|
||||
for i, fmt in enumerate(formats):
|
||||
hatch = ""
|
||||
|
||||
if fmt == "diff":
|
||||
color = "#b3e6a8"
|
||||
label = "Search/replace blocks"
|
||||
elif fmt == "udiff":
|
||||
color = "#b3d1e6"
|
||||
label = "Unified diffs"
|
||||
elif fmt == "difffolk":
|
||||
label = "Baseline + blind, no hands, $2k tip, etc"
|
||||
color = "#b3e6a8"
|
||||
hatch = "////"
|
||||
elif fmt == "udifffolk":
|
||||
label = "Unified diffs + blind, no hands, $2k tip, etc"
|
||||
color = "#b3d1e6"
|
||||
hatch = "////"
|
||||
|
||||
if zorder > 1:
|
||||
edge = dict(
|
||||
edgecolor="#ffffff",
|
||||
linewidth=1.5,
|
||||
)
|
||||
else:
|
||||
edge = dict()
|
||||
if zorder == 2:
|
||||
edge["label"] = label
|
||||
|
||||
color = [
|
||||
"#b3e6a8",
|
||||
"#b3e6a8",
|
||||
"#b3d1e6",
|
||||
]
|
||||
|
||||
rects = ax.bar(
|
||||
pos + i * width,
|
||||
df[fmt],
|
||||
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[fmt]], size=6)
|
||||
|
||||
ax.set_xticks([p + 0 * width for p in pos])
|
||||
|
||||
model_map = {
|
||||
"gpt-4-0125-preview": "gpt-4-\n0125-preview",
|
||||
"gpt-4-1106-preview": "gpt-4-\n1106-preview",
|
||||
"gpt-4-turbo-2024-04-09": "gpt-4-turbo-\n2024-04-09\n(GPT-4 Turbo with Vision)",
|
||||
}
|
||||
model_labels = []
|
||||
|
||||
for model in models:
|
||||
ml = model_map.get(model, model)
|
||||
model_labels.append(ml)
|
||||
|
||||
model_labels = [
|
||||
"gpt-4-\n1106-preview",
|
||||
"gpt-4-\n0125-preview",
|
||||
"gpt-4-turbo-\n2024-04-09\n(GPT-4 Turbo with Vision)",
|
||||
]
|
||||
ax.set_xticklabels(model_labels, rotation=0)
|
||||
|
||||
ax.set_ylabel("Percent of exercises completed successfully")
|
||||
# ax.set_xlabel("Model")
|
||||
ax.set_title('Refactoring "Laziness" Benchmark')
|
||||
# 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")
|
||||
Loading…
Add table
Add a link
Reference in a new issue