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import pandas as pd
import gradio as gr
from typing import List
from tabs.metrics import tool_metric_choices
import plotly.express as px
HEIGHT = 600
WIDTH = 1000
def prepare_tools(tools: pd.DataFrame) -> pd.DataFrame:
tools["request_time"] = pd.to_datetime(tools["request_time"])
tools = tools.sort_values(by="request_time", ascending=True)
tools["request_month_year_week"] = (
pd.to_datetime(tools["request_time"]).dt.to_period("W").dt.strftime("%b-%d")
)
# preparing the tools graph
# adding the total
tools_all = tools.copy(deep=True)
tools_all["market_creator"] = "all"
# merging both dataframes
tools = pd.concat([tools, tools_all], ignore_index=True)
tools = tools.sort_values(by="request_time", ascending=True)
return tools
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_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 sort_key(date_str):
month, year_week = date_str.split("-")
month_order = [
"Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec",
]
month_num = month_order.index(month) + 1
week = int(year_week)
return (week // 100, month_num, week % 100) # year, month, week
def integrated_plot_tool_winnings_overall_per_market_by_week(
winning_df: pd.DataFrame,
winning_selector: str = "Weekly Mean Mech Tool Accuracy as (Accurate Responses/All) %",
) -> gr.Plot:
# get the column name from the metric name
column_name = tool_metric_choices.get(winning_selector)
wins_df = get_overall_winning_rate_by_market(winning_df)
# Sort the unique values of request_month_year_week
sorted_categories = sorted(
wins_df["request_month_year_week"].unique(), key=sort_key
)
# Create a categorical type with a specific order
wins_df["request_month_year_week"] = pd.Categorical(
wins_df["request_month_year_week"], categories=sorted_categories, ordered=True
)
# Sort the DataFrame based on the new categorical column
wins_df = wins_df.sort_values("request_month_year_week")
fig = px.bar(
wins_df,
x="request_month_year_week",
y=column_name,
color="market_creator",
barmode="group",
color_discrete_sequence=["purple", "goldenrod", "darkgreen"],
category_orders={
"market_creator": ["pearl", "quickstart", "all"],
"request_month_year_week": sorted_categories,
},
)
fig.update_layout(
xaxis_title="Week",
yaxis_title=winning_selector,
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 integrated_tool_winnings_by_tool_per_market(
wins_df: pd.DataFrame, tool: str
) -> gr.Plot:
tool_wins_df = wins_df[wins_df["tool"] == tool]
# Sort the unique values of request_month_year_week
sorted_categories = sorted(
tool_wins_df["request_month_year_week"].unique(), key=sort_key
)
# Create a categorical type with a specific order
tool_wins_df["request_month_year_week"] = pd.Categorical(
tool_wins_df["request_month_year_week"],
categories=sorted_categories,
ordered=True,
)
# Sort the DataFrame based on the new categorical column
wins_df = wins_df.sort_values("request_month_year_week")
fig = px.bar(
tool_wins_df,
x="request_month_year_week",
y="win_perc",
color="market_creator",
barmode="group",
color_discrete_sequence=["purple", "goldenrod", "darkgreen"],
category_orders={
"market_creator": ["pearl", "quickstart", "all"],
"request_month_year_week": sorted_categories,
},
)
fig.update_layout(
xaxis_title="Week",
yaxis_title="Weekly Mean Mech Tool Accuracy as (Accurate Responses/All) %",
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)
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