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import pandas as pd
import gradio as gr
from typing import List
HEIGHT = 600
WIDTH = 1000
def get_tool_winning_rate(tools_df: pd.DataFrame, inc_tools: List[str]) -> pd.DataFrame:
"""Gets the tool winning rate data for the given tools and calculates the winning percentage."""
tools_inc = tools_df[tools_df["tool"].isin(inc_tools)]
# tools_inc['error'] = tools_inc.apply(set_error, axis=1)
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", "win"])
.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_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 = (
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(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:
"""Plots the overall winning rate data for the given tools and calculates the winning percentage."""
return gr.BarPlot(
title="Winning Rate",
x_title="Date",
y_title=winning_selector,
show_label=True,
interactive=True,
show_actions_button=True,
tooltip=["request_month_year_week", winning_selector],
value=wins_df,
x="request_month_year_week",
y=winning_selector,
height=HEIGHT,
width=WIDTH,
)
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(
title="Winning Rate",
x_title="Week",
y_title="Winning Rate",
x="request_month_year_week",
y="win_perc",
value=wins_df[wins_df["tool"] == tool],
show_label=True,
interactive=True,
show_actions_button=True,
tooltip=["request_month_year_week", "win_perc"],
height=HEIGHT,
width=WIDTH,
)
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