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import pandas as pd
import gradio as gr
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import matplotlib.pyplot as plt
import seaborn as sns
def plot_kl_div_per_market(closed_markets: pd.DataFrame) -> gr.Plot:
# adding the total
all_markets = closed_markets.copy(deep=True)
all_markets["market_creator"] = "all"
# merging both dataframes
final_markets = pd.concat([closed_markets, all_markets], ignore_index=True)
final_markets = final_markets.sort_values(by="opening_datetime", ascending=True)
fig = px.box(
final_markets,
x="month_year_week",
y="kl_divergence",
color="market_creator",
color_discrete_sequence=["purple", "goldenrod", "darkgreen"],
category_orders={"market_creator": ["pearl", "quickstart", "all"]},
)
fig.update_traces(boxmean=True)
fig.update_layout(
xaxis_title="Markets closing Week",
yaxis_title="Kullback–Leibler divergence",
legend=dict(yanchor="top", y=0.5),
width=800, # Adjusted for better fit on laptop screens
height=600, # Adjusted for better fit on laptop screens
)
fig.update_xaxes(tickformat="%b %d\n%Y")
return gr.Plot(
value=fig,
)
def plot_kl_div_with_off_by(closed_markets: pd.DataFrame) -> gr.Plot:
# adding the total
all_markets = closed_markets.copy(deep=True)
all_markets["market_creator"] = "all"
# merging both dataframes
final_markets = pd.concat([closed_markets, all_markets], ignore_index=True)
final_markets = final_markets.sort_values(by="opening_datetime", ascending=True)
# Create the main figure and axis
fig, ax1 = plt.subplots(figsize=(10, 6))
# Create the boxplot using seaborn
sns.boxplot(
data=final_markets,
x="month_year_week",
y="kl_divergence",
ax=ax1,
hue="market_creator",
)
# Set labels and title for the main axis
ax1.set_xlabel("Week")
ax1.set_ylabel("KL Divergence")
ax1.set_title("KL Divergence Boxplot with Off-by Percentage")
# Create a secondary y-axis
ax2 = ax1.twinx()
# Plot the off_by_perc values on the secondary y-axis
for i, week in enumerate(closed_markets["month_year_week"].unique()):
off_by_perc = closed_markets[closed_markets["month_year_week"] == week][
"off_by_perc"
]
ax2.scatter([i] * len(off_by_perc), off_by_perc, color="red", alpha=0.01)
# Set label for the secondary y-axis
ax2.set_ylabel("Off-by Percentage")
# Adjust the layout and display the plot
plt.tight_layout()
return gr.Plot(
value=fig,
)
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