import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib
# Enable LaTeX rendering in Matplotlib
matplotlib.rcParams["text.usetex"] = True
matplotlib.rcParams["text.latex.preamble"] = r"\usepackage{amsmath}"
[docs]
def plot_recognition_coverage_curve(
data,
coverage_col="Coverage",
recognition_col="Recognition",
f2_score_col="F2_score",
figsize=(8, 6),
show_f2=True,
show_legend=True,
):
"""Plots a Recognition-Coverage curve using provided data, including
optional F2 scores annotated. Styled with Seaborn for enhanced visual
appearance.
Parameters:
- data (dict): Nested dictionary containing the data for each radii,
formatted as shown in example.
- coverage_col (str): Key name for the coverage data in the dictionary.
- recognition_col (str): Key name for the recognition data in the dictionary.
- f2_score_col (str): Key name for the F2 score data in the dictionary.
- figsize (tuple): Figure size for the plot, default is (8, 6).
- show_f2 (bool): Whether to show F2 scores on the curve, default is True.
Example Data format:
{'radii_0': {'Novelty': 96.44, 'Coverage': 93.98, 'Recognition': 3.55, ...}}
"""
df = pd.DataFrame(data).T
sns.set_theme(style="whitegrid")
plt.figure(figsize=figsize)
ax = sns.lineplot(
data=df,
x=coverage_col,
y=recognition_col,
marker="o",
label="Recognition-Coverage Curve" if show_legend else None,
)
if show_f2:
for i, txt in enumerate(df[f2_score_col]):
ax.annotate(
r"$F_2=" + f"{txt:.2f}" + "$",
(df[coverage_col].iloc[i], df[recognition_col].iloc[i]),
textcoords="offset points",
xytext=(0, 10),
ha="center",
)
# Adding labels, title, and legend with LaTeX
plt.xlabel(r"$\mathrm{{Coverage\%}}$", fontsize=18)
plt.ylabel(r"$\mathrm{{Recognition\%}}$", fontsize=18)
plt.title(r"Recognition-Coverage Curve", fontsize=24)
plt.legend()
plt.grid(True)
plt.show()
[docs]
def plot_f2_scores_line(data, figsize=(8, 6), show_f2=True, show_legend=True):
"""Plots F2 scores across different radii using a line plot, showing the
trend of F2 score changes, and annotated with optional F2 scores.
Parameters:
- data (dict): Dictionary containing nested dictionaries with 'F2_score'
and possibly other metrics.
- figsize (tuple): Figure size for the plot, default is (8, 6).
- show_f2 (bool): Whether to show F2 scores on the curve, default is True.
- show_legend (bool): Whether to show the legend on the plot, default is True.
Example Data format:
{'radii_0': {'Novelty': 96.44, 'Coverage': 93.98, 'Recognition': 3.55,
'F2_score': 0.15}, ...}
"""
# Convert the nested dictionary into a DataFrame and prepare for plotting
df = pd.DataFrame(data).T
df["Radii"] = [int(key.split("_")[1]) for key in data.keys()]
df.sort_values("Radii", inplace=True) # Ensure data is sorted by radii
# Setting up Seaborn for enhanced plotting style
sns.set_theme(style="whitegrid")
# Create the plot
plt.figure(figsize=figsize)
sns.lineplot(
data=df,
x="Radii",
y="F2_score",
marker="o",
label="F2 Score Trend" if show_legend else None,
)
# Optionally annotate each point with its F2 score
if show_f2:
for i in range(len(df)):
plt.text(
df.iloc[i]["Radii"],
df.iloc[i]["F2_score"] + 0.01,
r"$F_2=" + f'{df.iloc[i]["F2_score"]:.2f}' + "$",
color="black",
ha="center",
va="bottom",
)
# Adding labels, title, and legend with LaTeX
plt.xlabel(r"$\text{Radii}$", fontsize=18)
plt.ylabel(r"$F_2\ \text{Score}$", fontsize=18)
plt.title(r"$F_2\ \text{Score}$ Across Different Radii", fontsize=24)
if show_legend:
plt.legend()
plt.grid(True)
plt.show()
