tools accuracy updates

app.py

@@ -20,23 +20,29 @@ from tabs.trades import (
 )
 
 from tabs.metrics import (
-    metric_choices,
-    default_metric,
+    trade_metric_choices,
+    tool_metric_choices,
+    default_trade_metric,
+    default_tool_metric,
     plot_trade_details,
     plot2_trade_details,
     plot_trade_metrics,
     WIDTH,
     HEIGHT,
+    plot_tool_metrics,
 )
 
 from tabs.tool_win import (
     get_tool_winning_rate,
+    get_tool_winning_rate_by_market,
     get_overall_winning_rate,
     plot_tool_winnings_overall,
     plot_tool_winnings_by_tool,
 )
 
 from tabs.tool_accuracy import (
+    plot_tools_weighted_accuracy_rotated_graph,
+    plot_tools_accuracy_rotated_graph,
     compute_weighted_accuracy,
     plot_tools_accuracy_graph,
     plot_tools_weighted_accuracy_graph,
@@ -193,68 +199,7 @@ with demo:
     )
 
     with gr.Tabs():
-        with gr.TabItem("🔥Trades Dashboard v1"):
-            with gr.Row():
-                gr.Markdown("# Trend of weekly trades")
-            with gr.Row():
-                with gr.Column(min_width=380):
-                    qs_trades_by_week = plot_trades_per_market_by_week(
-                        trades_df=trades_by_market, market_type="quickstart"
-                    )
-                with gr.Column(min_width=380):
-                    pearl_trades_by_week = plot_trades_per_market_by_week(
-                        trades_df=trades_by_market, market_type="pearl"
-                    )
-                with gr.Column(min_width=380):
-                    all_trades_by_week = plot_trades_per_market_by_week(
-                        trades_df=trades_by_market, market_type="all"
-                    )
-
-            with gr.Row():
-                gr.Markdown("# Percentage of winning trades per week")
-            with gr.Row():
-                with gr.Column(min_width=380):
-                    qs_wtrades_by_week = plot_winning_trades_per_market_by_week(
-                        trades_df=winning_trades_by_market, market_type="quickstart"
-                    )
-                with gr.Column(min_width=380):
-                    pearl_wtrades_by_week = plot_winning_trades_per_market_by_week(
-                        trades_df=winning_trades_by_market, market_type="pearl"
-                    )
-                with gr.Column(min_width=380):
-                    all_wtrades_by_week = plot_winning_trades_per_market_by_week(
-                        trades_df=winning_trades_by_market, market_type="all"
-                    )
-
-            with gr.Row():
-                gr.Markdown("# ⚖️ Trading metrics")
-            with gr.Row():
-                trade_details_selector = gr.Dropdown(
-                    label="Select a trade metric",
-                    choices=metric_choices,
-                    value=default_metric,
-                )
-            with gr.Row():
-                trade_details_plot = plot_trade_metrics(
-                    metric_name=default_metric,
-                    trades_df=trades_df,
-                )
-
-            def update_trade_details(trade_detail):
-                return plot_trade_metrics(metric_name=trade_detail, trades_df=trades_df)
-
-            trade_details_selector.change(
-                update_trade_details,
-                inputs=trade_details_selector,
-                outputs=trade_details_plot,
-            )
-
-            with gr.Row():
-                trade_details_selector
-            with gr.Row():
-                trade_details_plot
-
-        with gr.TabItem("🔥Trades Dashboard v2"):
+        with gr.TabItem("🔥Trades Dashboard"):
             with gr.Row():
                 gr.Markdown("# Trend of weekly trades")
             with gr.Row():
@@ -274,12 +219,12 @@ with demo:
             with gr.Row():
                 trade_details_selector = gr.Dropdown(
                     label="Select a trade metric",
-                    choices=metric_choices,
-                    value=default_metric,
+                    choices=trade_metric_choices,
+                    value=default_trade_metric,
                 )
             with gr.Row():
                 trade_details_plot = plot_trade_metrics(
-                    metric_name=default_metric,
+                    metric_name=default_trade_metric,
                     trades_df=trades_df,
                     height=400,
                     width=1400,
@@ -311,13 +256,15 @@ with demo:
             with gr.Row():
                 winning_selector = gr.Dropdown(
                     label="Select the tool metric",
-                    choices=["losses", "wins", "total_request", "win_perc"],
-                    value="win_perc",
+                    choices=tool_metric_choices,
+                    value=default_tool_metric,
                 )
 
             with gr.Row():
+                # plot_tool_metrics
                 winning_plot = plot_tool_winnings_overall(
-                    wins_df=winning_rate_overall_df, winning_selector="win_perc"
+                    wins_df=winning_rate_overall_df,
+                    winning_selector=default_tool_metric,
                 )
 
             def update_tool_winnings_overall_plot(winning_selector):
@@ -371,7 +318,7 @@ with demo:
                 )
 
             with gr.Row():
-                _ = plot_tools_accuracy_graph(tools_accuracy_info)
+                _ = plot_tools_accuracy_rotated_graph(tools_accuracy_info)
 
             with gr.Row():
                 gr.Markdown("# Weighted accuracy ranking per tool")
@@ -384,7 +331,7 @@ with demo:
                     "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. The minimum value of this custom metric is 0 and the maximum value is 1. The higher the better is the tool."
                 )
             with gr.Row():
-                _ = plot_tools_weighted_accuracy_graph(tools_accuracy_info)
+                _ = plot_tools_weighted_accuracy_rotated_graph(tools_accuracy_info)
 
         with gr.TabItem("⛔ Invalid Markets Dashboard"):
             with gr.Row():

tabs/metrics.py

@@ -2,7 +2,7 @@ import pandas as pd
 import gradio as gr
 import plotly.express as px
 
-metric_choices = [
+trade_metric_choices = [
     "mech calls",
     "collateral amount",
     "earnings",
@@ -10,7 +10,10 @@ metric_choices = [
     "ROI",
 ]
 
-default_metric = "ROI"
+tool_metric_choices = ["losses", "wins", "total_request", "win_perc"]
+
+default_trade_metric = "ROI"
+default_tool_metric = "win_perc"
 
 HEIGHT = 600
 WIDTH = 1000
@@ -219,3 +222,17 @@ def plot_average_roi_per_market_by_week(trades_df: pd.DataFrame) -> gr.LinePlot:
         height=HEIGHT,
         width=WIDTH,
     )
+
+
+def plot_tool_metrics(wins_df: pd.DataFrame, winning_selector: str) -> gr.Plot:
+    print("under construction")
+    if winning_selector == "losses":
+        yaxis_title = "Nr of mech calls per trade"
+    elif winning_selector == "win_perc":
+        column_name = "roi"
+        yaxis_title = "ROI (net profit/cost)"
+    elif winning_selector == "wins":
+        yaxis_title = "Collateral amount per trade (xDAI)"
+    else:  # "total_request"
+        column_name = metric_name
+        yaxis_title = "Gross profit per trade (xDAI)"

tabs/tool_accuracy.py

@@ -3,6 +3,7 @@ import gradio as gr
 import matplotlib.pyplot as plt
 import seaborn as sns
 from typing import Tuple
+import plotly.express as px
 
 VOLUME_FACTOR_REGULARIZATION = 0.5
 UNSCALED_WEIGHTED_ACCURACY_INTERVAL = (-0.5, 100.5)
@@ -23,6 +24,9 @@ tools_palette = {
     "prediction-request-rag": "chocolate",
 }
 
+HEIGHT = 400
+WIDTH = 1100
+
 
 def scale_value(
     value: float,
@@ -79,6 +83,29 @@ def plot_tools_accuracy_graph(tools_accuracy_info: pd.DataFrame):
     return gr.Plot(value=plot.get_figure())
 
 
+def plot_tools_accuracy_rotated_graph(tools_accuracy_info: pd.DataFrame):
+    tools_accuracy_info = tools_accuracy_info.sort_values(
+        by="tool_accuracy", ascending=False
+    )
+    fig = px.bar(
+        tools_accuracy_info,
+        x="tool",
+        y="tool_accuracy",
+        color="tool",
+        color_discrete_map=tools_palette,
+    )
+    fig.update_layout(
+        xaxis_title="Tool",
+        yaxis_title="Mech tool_accuracy (%)",
+    )
+    fig.update_layout(width=WIDTH, height=HEIGHT)
+    # fig.update_xaxes(tickangle=45)
+    fig.update_xaxes(showticklabels=False)
+    return gr.Plot(
+        value=fig,
+    )
+
+
 def plot_tools_weighted_accuracy_graph(tools_accuracy_info: pd.DataFrame):
     tools_accuracy_info = tools_accuracy_info.sort_values(
         by="weighted_accuracy", ascending=False
@@ -98,3 +125,26 @@ def plot_tools_weighted_accuracy_graph(tools_accuracy_info: pd.DataFrame):
     plt.ylabel("tool", fontsize=20)
     plt.tick_params(axis="y", labelsize=12)
     return gr.Plot(value=plot.get_figure())
+
+
+def plot_tools_weighted_accuracy_rotated_graph(tools_accuracy_info: pd.DataFrame):
+    tools_accuracy_info = tools_accuracy_info.sort_values(
+        by="weighted_accuracy", ascending=False
+    )
+    fig = px.bar(
+        tools_accuracy_info,
+        x="tool",
+        y="weighted_accuracy",
+        color="tool",
+        color_discrete_map=tools_palette,
+    )
+    fig.update_layout(
+        xaxis_title="Tool",
+        yaxis_title="Weighted accuracy metric",
+    )
+    fig.update_layout(width=WIDTH, height=HEIGHT)
+    # fig.update_xaxes(tickangle=45)
+    fig.update_xaxes(showticklabels=False)
+    return gr.Plot(
+        value=fig,
+    )

tabs/tool_win.py

@@ -38,6 +38,40 @@ def get_tool_winning_rate(tools_df: pd.DataFrame, inc_tools: List[str]) -> pd.Da
     return wins
 
 
+def get_tool_winning_rate_by_market(
+    tools_df: pd.DataFrame, inc_tools: List[str]
+) -> pd.DataFrame:
+    """Gets the tool winning rate data for the given tools by market and calculates the winning percentage."""
+    tools_inc = tools_df[tools_df["tool"].isin(inc_tools)]
+    tools_non_error = tools_inc[tools_inc["error"] != 1]
+    tools_non_error.loc[:, "currentAnswer"] = tools_non_error["currentAnswer"].replace(
+        {"no": "No", "yes": "Yes"}
+    )
+    tools_non_error = tools_non_error[
+        tools_non_error["currentAnswer"].isin(["Yes", "No"])
+    ]
+    tools_non_error = tools_non_error[tools_non_error["vote"].isin(["Yes", "No"])]
+    tools_non_error["win"] = (
+        tools_non_error["currentAnswer"] == tools_non_error["vote"]
+    ).astype(int)
+    tools_non_error.columns = tools_non_error.columns.astype(str)
+    wins = (
+        tools_non_error.groupby(
+            ["tool", "request_month_year_week", "market_creator", "win"], sort=False
+        )
+        .size()
+        .unstack()
+        .fillna(0)
+    )
+    wins["win_perc"] = (wins[1] / (wins[0] + wins[1])) * 100
+    wins.reset_index(inplace=True)
+    wins["total_request"] = wins[0] + wins[1]
+    wins.columns = wins.columns.astype(str)
+    # Convert request_month_year_week to string and explicitly set type for Altair
+    wins["request_month_year_week"] = wins["request_month_year_week"].astype(str)
+    return wins
+
+
 def get_overall_winning_rate(wins_df: pd.DataFrame) -> pd.DataFrame:
     """Gets the overall winning rate data for the given tools and calculates the winning percentage."""
     overall_wins = (
@@ -49,6 +83,28 @@ def get_overall_winning_rate(wins_df: pd.DataFrame) -> pd.DataFrame:
     return overall_wins
 
 
+def get_overall_winning_rate(wins_df: pd.DataFrame) -> pd.DataFrame:
+    """Gets the overall winning rate data for the given tools and calculates the winning percentage."""
+    overall_wins = (
+        wins_df.groupby("request_month_year_week")
+        .agg({"0": "sum", "1": "sum", "win_perc": "mean", "total_request": "sum"})
+        .rename(columns={"0": "losses", "1": "wins"})
+        .reset_index()
+    )
+    return overall_wins
+
+
+def get_overall_winning_rate_by_market(wins_df: pd.DataFrame) -> pd.DataFrame:
+    """Gets the overall winning rate data for the given tools and calculates the winning percentage."""
+    overall_wins = (
+        wins_df.groupby(["request_month_year_week", "market_creator"], sort=False)
+        .agg({"0": "sum", "1": "sum", "win_perc": "mean", "total_request": "sum"})
+        .rename(columns={"0": "losses", "1": "wins"})
+        .reset_index()
+    )
+    return overall_wins
+
+
 def plot_tool_winnings_overall(
     wins_df: pd.DataFrame, winning_selector: str = "win_perc"
 ) -> gr.BarPlot:
@@ -69,6 +125,41 @@ def plot_tool_winnings_overall(
     )
 
 
+def integrated_plot_tool_winnings_overall(
+    tools_df: pd.DataFrame, winning_selector: str = "win_perc"
+) -> gr.Plot:
+    # TODO Pending final implementation
+    """Plots the overall winning rate data for the given tools and calculates the winning percentage."""
+    # adding the total
+    wins_df_all = tools_df.copy(deep=True)
+    wins_df_all["market_creator"] = "all"
+
+    # merging both dataframes
+    all_winning_tools = pd.concat([wins_df, wins_df_all], ignore_index=True)
+    all_winning_tools = all_winning_tools.sort_values(
+        by="creation_timestamp", ascending=True
+    )
+    final_df = get_overall_winning_rate_by_market(all_winning_tools)
+    fig = px.bar(
+        final_df,
+        x="request_month_year_week",
+        y=winning_selector,
+        color="market_creator",
+        barmode="group",
+        color_discrete_sequence=["goldenrod", "darkgreen", "purple"],
+    )
+    fig.update_layout(
+        xaxis_title="Week",
+        yaxis_title="Weekly % of winning rate",
+        legend=dict(yanchor="top", y=0.5),
+    )
+    fig.update_layout(width=WIDTH, height=HEIGHT)
+    fig.update_xaxes(tickformat="%b %d\n%Y")
+    return gr.Plot(
+        value=fig,
+    )
+
+
 def plot_tool_winnings_by_tool(wins_df: pd.DataFrame, tool: str) -> gr.BarPlot:
     """Plots the winning rate data for the given tool."""
     return gr.BarPlot(