# Aider chat conversation:
USER: add a cmd line switch --graphs and only draw them if its provided
ASSISTANT: To add a command-line switch `--graphs` and conditionally generate graphs based on its presence, we need to modify the `benchmark.py` file. Specifically, we will add a new option to the `main` function to accept the `--graphs` switch and then use this option to control the calls to `plot_timing` and `plot_outcomes` functions.
Here are the steps we will follow:
1. Add the `--graphs` option to the `typer` command-line interface in the `main` function.
2. Use the value of this option to conditionally call the `plot_timing` and `plot_outcomes` functions.
Let's make these changes:
<source>python
benchmark/benchmark.py
<<<<<<< SEARCH
@app.command()
def main(
dirnames: List[str] = typer.Argument(..., help="Directory names"),
=======
@app.command()
def main(
dirnames: List[str] = typer.Argument(..., help="Directory names"),
graphs: bool = typer.Option(False, "--graphs", help="Generate graphs"),
>>>>>>> REPLACE
...
<<<<<<< SEARCH
plot_timing(df)
plot_outcomes(df, repeats, repeat_hi, repeat_lo, repeat_avg)
=======
if graphs:
plot_timing(df)
plot_outcomes(df, repeats, repeat_hi, repeat_lo, repeat_avg)
>>>>>>> REPLACE
</source>
With these changes, the `--graphs` switch will be available when running the `benchmark.py` script, and the graph generation will only occur if this switch is provided.
# Aider chat conversation:
ASSISTANT: To address the issue with the empty legend for the edit format in the `plot_timing` function, we need to add labels to the bars so that the legend can display them correctly. We will modify the `benchmark/benchmark.py` file to include labels for each edit format.
Here's the change that needs to be made:
<source>python
benchmark/benchmark.py
<<<<<<< SEARCH
for i, fmt in enumerate(formats):
edge = dict(edgecolor="#ffffff", linewidth=1.5)
color = "#b3e6a8" if "diff" in fmt else "#b3d1e6"
hatch = "////" if "func" in fmt else ""
rects = ax.bar(
pos + i * width,
grouped[fmt],
width * 0.95,
color=color,
hatch=hatch,
zorder=zorder + 1,
**edge,
)
=======
# Create a mapping for the labels and colors to ensure consistency
format_labels = {
'diff': 'Diff',
'func': 'Function',
'whole': 'Whole',
'diff-func': 'Diff-Function',
'diff-whole': 'Diff-Whole',
'func-whole': 'Function-Whole',
# Add other edit formats here if necessary
}
format_colors = {
'diff': '#b3e6a8',
'func': '#b3d1e6',
'whole': '#e6b3b3',
'diff-func': '#e6d1b3',
'diff-whole': '#b3e6d1',
'func-whole': '#d1b3e6',
# Add other edit format colors here if necessary
}
format_hatches = {
'diff': '////',
'func': '....',
'whole': '',
'diff-func': '\\\\\\\\',
'diff-whole': 'xxxx',
'func-whole': '----',
# Add other edit format hatches here if necessary
}
for i, fmt in enumerate(formats):
label = format_labels.get(fmt, fmt)
color = format_colors.get(fmt, '#b3d1e6')
hatch = format_hatches.get(fmt, '')
edge = dict(edgecolor="#ffffff", linewidth=1.5)
rects = ax.bar(
pos + i * width,
grouped[fmt],
width * 0.95,
label=label,
color=color,
hatch=hatch,
zorder=zorder + 1,
**edge,
)
>>>>>>> REPLACE
</source>
This change adds a mapping for labels, colors, and hatches for each edit format. It also ensures that each bar in the bar chart has a label associated with it, which will be used to generate the legend. The colors and hatches are placeholders and should be adjusted to match the desired styling.
# Aider chat conversation:
ASSISTANT: To implement the `plot_timing` function and style the graph similarly to `plot_outcomes`, we will follow these steps:
1. Define the `plot_timing` function to create a bar chart that shows the average duration for each combination of model and edit format.
2. Use similar styling options as used in `plot_outcomes` for consistency.
Let's start by implementing the `plot_timing` function in the `benchmark/benchmark.py` file.
<source>python
benchmark/benchmark.py
<<<<<<< SEARCH
def plot_timing(df):
"""plot a graph showing the average duration of each (model, edit_format)"""
=======
def plot_timing(df):
"""plot a graph showing the average duration of each (model, edit_format)"""
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
grouped = df.groupby(["model", "edit_format"])["avg_duration"].mean().unstack()
num_models, num_formats = grouped.shape
pos = np.array(range(num_models))
width = 0.8 / num_formats
formats = grouped.columns
models = grouped.index
for i, fmt in enumerate(formats):
edge = dict(edgecolor="#ffffff", linewidth=1.5)
color = "#b3e6a8" if "diff" in fmt else "#b3d1e6"
hatch = "////" if "func" in fmt else ""
rects = ax.bar(
pos + i * width,
grouped[fmt],
width * 0.95,
color=color,
hatch=hatch,
zorder=zorder + 1,
**edge,
)
ax.bar_label(rects, padding=4, labels=[f"{v:.1f}s" for v in grouped[fmt]], size=6)
ax.set_xticks([p + 0.5 * width for p in pos])
ax.set_xticklabels(models)
ax.set_ylabel("Average duration (seconds)")
ax.set_title("GPT Code Editing - Timing")
ax.legend(
title="Edit Format",
loc="upper left",
)
ax.set_ylim(top=max(grouped.max()) * 1.1) # Set y-axis limit to 10% more than the max value
plt.tight_layout()
plt.savefig("tmp_timing.svg")
imgcat(fig)
>>>>>>> REPLACE
</source>
This code block adds the implementation of the `plot_timing` function, which creates a bar chart to visualize the average duration of tests for each model and edit format. The styling is kept consistent with the `plot_outcomes` function, using similar colors, hatches, and font settings. The resulting graph is saved as an SVG file and displayed in the terminal using `imgcat`.
