updating the weighted accuracy formula

app.py

@@ -292,6 +292,10 @@ with demo:
 
             with gr.Row():
                 gr.Markdown("# Weighted accuracy ranking per tool")
+            with gr.Row():
+                gr.Markdown(
+                    "This metric is an approximation to the real metric used by the trader since some parameters are only dynamically generated."
+                )
             with gr.Row():
                 gr.Markdown(
                     "The data used for this metric is from the past two months. This metric is computed using both the tool accuracy and the volume of requests received by the tool"

tabs/tool_accuracy.py

@@ -2,11 +2,39 @@ 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 (row["tool_accuracy"] / 100.0) * (row["total_requests"] / global_requests)
+    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):