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import matplotlib.pyplot as plt
import pandas as pd
from .utils import undo_hyperlink
def plot_avg_correlation(df1, df2):
"""
Plots the "average" column for each unique model that appears in both dataframes.
Parameters:
- df1: pandas DataFrame containing columns "model" and "average".
- df2: pandas DataFrame containing columns "model" and "average".
"""
# Identify the unique models that appear in both DataFrames
common_models = pd.Series(list(set(df1["model"]) & set(df2["model"])))
# Set up the plot
plt.figure(figsize=(13, 6), constrained_layout=True)
# axes from 0 to 1 for x and y
plt.xlim(0.475, 0.8)
plt.ylim(0.475, 0.8)
# larger font (16)
plt.rcParams.update({"font.size": 12, "axes.labelsize": 14, "axes.titlesize": 14})
# plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)
# plt.tight_layout()
# plt.margins(0,0)
for model in common_models:
# Filter data for the current model
df1_model_data = df1[df1["model"] == model]["average"].values
df2_model_data = df2[df2["model"] == model]["average"].values
# Plotting
plt.scatter(df1_model_data, df2_model_data, label=model)
m_name = undo_hyperlink(model)
if m_name == "No text found":
m_name = "Random"
# Add text above each point like
# plt.text(x[i] + 0.1, y[i] + 0.1, label, ha='left', va='bottom')
plt.text(
df1_model_data - 0.005, df2_model_data, m_name, horizontalalignment="right", verticalalignment="center"
)
# add correlation line to scatter plot
# first, compute correlation
corr = df1["average"].corr(df2["average"])
# add correlation line based on corr
plt.xlabel("HERM Eval. Set Avg.", fontsize=16)
plt.ylabel("Pref. Test Sets Avg.", fontsize=16)
# plt.legend(title='Model', bbox_to_anchor=(1.05, 1), loc='upper left')
return plt
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