# Context:
USER: how can i set the bar color/fill style for each edit-format
ASSISTANT: To set the bar color/fill style for each edit-format, you need to modify the `show_stats` function in the `benchmark/benchmark.py` file.
Here are the steps:
1. Define a dictionary mapping each edit-format to a specific color or fill style.
2. Use the `color` or `fill` parameter in the `plot` function to set the color or fill style for each bar.
Here is the code change:
```python
benchmark/benchmark.py
<<<<<<< ORIGINAL
fig, ax = plt.subplots(figsize=(10, 6))
df_grouped2.unstack().plot(kind="barh", ax=ax)
=======
fig, ax = plt.subplots(figsize=(10, 6))
color_map = {"edit_format1": "blue", "edit_format2": "green", "edit_format3": "red"} # Define your color map here
df_grouped2.unstack().plot(kind="barh", ax=ax, color=[color_map[i] for i in df_grouped2.index])
>>>>>>> UPDATED
```
Please replace `"edit_format1"`, `"edit_format2"`, and `"edit_format3"` with your actual edit formats, and `"blue"`, `"green"`, and `"red"` with your desired colors.
# Context:
USER: sort the grouped columns by descending average
ASSISTANT: To sort the grouped columns by descending average, we need to modify the `show_stats` function in the `benchmark/benchmark.py` file. Specifically, we need to sort the `df_grouped` DataFrame by its values in descending order.
Here is the change:
```python
benchmark/benchmark.py
<<<<<<< ORIGINAL
df_grouped = df.groupby(["model", "edit_format"])["pass_rate_1"].mean()
=======
df_grouped = df.groupby(["model", "edit_format"])["pass_rate_1"].mean().sort_values(ascending=False)
>>>>>>> UPDATED
```
This change will sort the grouped DataFrame `df_grouped` by its values in descending order. The `ascending=False` argument in the `sort_values` function ensures that the sorting is done in descending order.
# Context:
USER: switch the plt stuff to use an explicit fig
ASSISTANT: To switch the matplotlib plotting to use an explicit figure, we need to modify the `show_stats` function in the `benchmark/benchmark.py` file.
Here are the changes:
```python
benchmark/benchmark.py
<<<<<<< ORIGINAL
df_grouped.unstack().plot(kind="barh", figsize=(10, 6))
plt.xlabel("Pass Rate 1")
plt.ylabel("Model")
plt.title("Pass Rate 1 for each Model/Edit Format")
plt.legend(title="Edit Format")
plt.show()
=======
fig, ax = plt.subplots(figsize=(10, 6))
df_grouped.unstack().plot(kind="barh", ax=ax)
ax.set_xlabel("Pass Rate 1")
ax.set_ylabel("Model")
ax.set_title("Pass Rate 1 for each Model/Edit Format")
ax.legend(title="Edit Format")
plt.show()
>>>>>>> UPDATED
```
This change creates a new figure and axes before plotting, and then uses the axes for setting labels, title, and legend. This is a more explicit way of handling figures and axes in matplotlib.
# Context:
USER: don't combine the edit formats. plot a different bar for each model/edit_format combo. But group the bars for each model together.
ASSISTANT: To implement this change, we need to modify the `show_stats` function in the `benchmark/benchmark.py` file. We will use the pandas and matplotlib libraries to create the bar plot.
Here are the steps:
1. In the `show_stats` function, after creating the DataFrame `df`, sort it based on "model" and "edit_format".
2. Group the DataFrame by "model" and "edit_format" and plot a bar graph for "pass_rate_1".
Here are the changes:
```python
benchmark/benchmark.py
<<<<<<< ORIGINAL
def show_stats(dirnames):
rows = []
for dirname in dirnames:
row = summarize_results(dirname)
rows.append(vars(row))
df = pd.DataFrame.from_records(rows)
df.sort_values(by=["model", "edit_format"], inplace=True)
df_grouped = df.groupby("model")["pass_rate_1"].mean()
df_grouped.plot(kind='barh', figsize=(10, 6))
plt.xlabel('Pass Rate 1')
plt.ylabel('Model')
plt.title('Pass Rate 1 for each Model/Edit Format')
plt.show()
df.to_csv("tmp.benchmark.csv")
=======
def show_stats(dirnames):
rows = []
for dirname in dirnames:
row = summarize_results(dirname)
rows.append(vars(row))
df = pd.DataFrame.from_records(rows)
df.sort_values(by=["model", "edit_format"], inplace=True)
df_grouped = df.groupby(["model", "edit_format"])["pass_rate_1"].mean()
df_grouped.unstack().plot(kind='barh', figsize=(10, 6))
plt.xlabel('Pass Rate 1')
plt.ylabel('Model')
plt.title('Pass Rate 1 for each Model/Edit Format')
plt.legend(title='Edit Format')
plt.show()
df.to_csv("tmp.benchmark.csv")
>>>>>>> UPDATED
```
This will create a bar graph with the average of "pass_rate_1" for each "edit_format" within each model. The bars for each model are grouped together.
# Context:
USER: in `show_stats` plot a horizontal bar graph. sort on "model" and "edit_format". group the bars by model. plot the pass_rate_1 for each model/edit_format.
ASSISTANT: To implement this change, we need to modify the `show_stats` function in the `benchmark/benchmark.py` file. We will use the pandas and matplotlib libraries to create the bar plot.
Here are the steps:
1. Import the matplotlib library at the top of the file.
2. In the `show_stats` function, after creating the DataFrame `df`, sort it based on "model" and "edit_format".
3. Group the DataFrame by "model" and plot a bar graph for "pass_rate_1".
Here are the changes:
```python
benchmark/benchmark.py
<<<<<<< ORIGINAL
import pandas as pd
=======
import pandas as pd
import matplotlib.pyplot as plt
>>>>>>> UPDATED
```
```python
benchmark/benchmark.py
<<<<<<< ORIGINAL
def show_stats(dirnames):
rows = []
for dirname in dirnames:
row = summarize_results(dirname)
rows.append(vars(row))
df = pd.DataFrame.from_records(rows)
print(df)
df.to_csv("tmp.benchmark.csv")
=======
def show_stats(dirnames):
rows = []
for dirname in dirnames:
row = summarize_results(dirname)
rows.append(vars(row))
df = pd.DataFrame.from_records(rows)
df.sort_values(by=["model", "edit_format"], inplace=True)
df_grouped = df.groupby("model")["pass_rate_1"].mean()
df_grouped.plot(kind='barh', figsize=(10, 6))
plt.xlabel('Pass Rate 1')
plt.ylabel('Model')
plt.title('Pass Rate 1 for each Model/Edit Format')
plt.show()
df.to_csv("tmp.benchmark.csv")
>>>>>>> UPDATED
```
Please note that this will create a bar graph with the average of "pass_rate_1" for each model. If you want to plot "pass_rate_1" for each "edit_format" within each model, we would need to modify the grouping and plotting code.
