Adding first prototype of top three tools percentage of reqs

app.py

@@ -34,6 +34,7 @@ from tabs.tool_accuracy import (
     plot_tools_weighted_accuracy_rotated_graph,
     plot_tools_accuracy_rotated_graph,
     compute_weighted_accuracy,
+    plot_mech_requests_topthree_tools,
 )
 
 from tabs.invalid_markets import (
@@ -536,6 +537,17 @@ with demo:
             with gr.Row():
                 _ = plot_tools_weighted_accuracy_rotated_graph(tools_accuracy_info)
 
+            with gr.Row():
+                gr.Markdown(
+                    "# Mech requests percentage of the top three tools from the daily total"
+                )
+            with gr.Row():
+                _ = plot_mech_requests_topthree_tools(
+                    daily_mech_requests=daily_mech_requests,
+                    tools_accuracy_info=tools_accuracy_info,
+                    top=3,
+                )
+
         with gr.TabItem("⛔ Invalid Markets Dashboard"):
             with gr.Row():
                 gr.Markdown("# Daily distribution of invalid trades")

tabs/tool_accuracy.py

@@ -4,6 +4,7 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 from typing import Tuple
 import plotly.express as px
+import numpy as np
 
 VOLUME_FACTOR_REGULARIZATION = 0.5
 UNSCALED_WEIGHTED_ACCURACY_INTERVAL = (-0.5, 100.5)
@@ -150,3 +151,88 @@ def plot_tools_weighted_accuracy_rotated_graph(
     return gr.Plot(
         value=fig,
     )
+
+
+def plot_mech_requests_topthree_tools(
+    daily_mech_requests: pd.DataFrame, tools_accuracy_info: pd.DataFrame, top: int
+):
+    """Function to plot the percentage of mech requests from the top three tools"""
+    # Get the top three tools
+    top_tools = tools_accuracy_info.sort_values(
+        by="tool_accuracy", ascending=False
+    ).head(top)
+    top_tools = top_tools.tool.tolist()
+    # Filter the daily mech requests for the top three tools
+    daily_mech_requests_local_copy = daily_mech_requests.copy()
+    daily_mech_requests_local_copy = daily_mech_requests_local_copy[
+        daily_mech_requests_local_copy["market_creator"] == "all"
+    ]
+    # Get the daily total of mech requests no matter the tool
+    total_daily_mech_requests = (
+        daily_mech_requests_local_copy.groupby(["request_date"])
+        .agg({"total_mech_requests": "sum"})
+        .reset_index()
+    )
+    print("total_daily_mech_requests", total_daily_mech_requests.head())
+    total_daily_mech_requests.rename(
+        columns={"total_mech_requests": "total_daily_mech_requests"},
+        inplace=True,
+    )
+    # Merge the total daily mech requests with the daily mech requests
+    daily_mech_requests_local_copy = pd.merge(
+        daily_mech_requests_local_copy,
+        total_daily_mech_requests,
+        on="request_date",
+        how="left",
+    )
+    # Compute the percentage of mech requests for each tool
+    daily_mech_requests_local_copy["percentage"] = (
+        daily_mech_requests_local_copy["total_mech_requests"]
+        / daily_mech_requests_local_copy["total_daily_mech_requests"]
+    ) * 100
+
+    daily_mech_requests_local_copy = daily_mech_requests_local_copy[
+        daily_mech_requests_local_copy.tool.isin(top_tools)
+    ]
+
+    # remove the market_creator column
+    daily_mech_requests_local_copy = daily_mech_requests_local_copy.drop(
+        columns=["market_creator"]
+    )
+
+    # Create a pivot table to get the total mech requests per tool
+    pivoted = daily_mech_requests_local_copy.pivot(
+        index="request_date", columns="tool", values="percentage"
+    )
+    # Sort the columns for each row independently
+    sorted_values = np.sort(pivoted.values, axis=1)[
+        :, ::-1
+    ]  # sort and reverse (descending)
+    sorted_columns = np.argsort(pivoted.values, axis=1)[:, ::-1]  # get sorting indices
+
+    sorted_df = pd.DataFrame(
+        sorted_values,
+        index=pivoted.index,
+        columns=[
+            pivoted.columns[i] for i in sorted_columns[0]
+        ],  # use first row's order
+    )
+
+    sorted_long = sorted_df.reset_index().melt(
+        id_vars=["request_date"], var_name="tool", value_name="percentage"
+    )
+
+    fig = px.bar(
+        sorted_long,
+        x="request_date",
+        y="percentage",
+        color="tool",
+        color_discrete_map=tools_palette,
+    )
+    fig.update_layout(
+        xaxis_title="Day of the request",
+        yaxis_title="Percentage of Total daily mech requests",
+        legend=dict(yanchor="top", y=0.5),
+    )
+    fig.update_layout(width=WIDTH, height=HEIGHT)
+    return gr.Plot(value=fig)