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import pandas as pd
import gradio as gr
import matplotlib.pyplot as plt
import seaborn as sns
from typing import Tuple
VOLUME_FACTOR_REGULARIZATION = 0.5
UNSCALED_WEIGHTED_ACCURACY_INTERVAL = (-0.5, 100.5)
SCALED_WEIGHTED_ACCURACY_INTERVAL = (0, 1)
def scale_value(
value: float,
min_max_bounds: Tuple[float, float],
scale_bounds: Tuple[float, float] = (0, 1),
) -> float:
"""Perform min-max scaling on a value."""
min_, max_ = min_max_bounds
current_range = max_ - min_
# normalize between 0-1
std = (value - min_) / current_range
# scale between min_bound and max_bound
min_bound, max_bound = scale_bounds
target_range = max_bound - min_bound
return std * target_range + min_bound
def get_weighted_accuracy(row, global_requests: int):
"""Function to compute the weighted accuracy of a tool"""
return scale_value(
(
row["tool_accuracy"]
+ (row["total_requests"] / global_requests) * VOLUME_FACTOR_REGULARIZATION
),
UNSCALED_WEIGHTED_ACCURACY_INTERVAL,
SCALED_WEIGHTED_ACCURACY_INTERVAL,
)
def compute_weighted_accuracy(tools_accuracy: pd.DataFrame):
global_requests = tools_accuracy.total_requests.sum()
tools_accuracy["weighted_accuracy"] = tools_accuracy.apply(
lambda x: get_weighted_accuracy(x, global_requests), axis=1
)
return tools_accuracy
def plot_tools_accuracy_graph(tools_accuracy_info: pd.DataFrame):
tools_accuracy_info = tools_accuracy_info.sort_values(
by="tool_accuracy", ascending=False
)
plt.figure(figsize=(25, 10))
plot = sns.barplot(
tools_accuracy_info,
x="tool_accuracy",
y="tool",
hue="tool",
dodge=False,
palette="viridis",
)
return gr.Plot(value=plot.get_figure())
def plot_tools_weighted_accuracy_graph(tools_accuracy_info: pd.DataFrame):
tools_accuracy_info = tools_accuracy_info.sort_values(
by="weighted_accuracy", ascending=False
)
# Create the Seaborn bar plot
sns.set_theme(palette="viridis")
plt.figure(figsize=(25, 10))
plot = sns.barplot(
tools_accuracy_info,
x="weighted_accuracy",
y="tool",
hue="tool",
dodge=False,
)
# Display the plot using gr.Plot
return gr.Plot(value=plot.get_figure())
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