cleaning non-used functions

tabs/error.py

@@ -9,21 +9,6 @@ HEIGHT = 600
 WIDTH = 1000
 
 
-def get_error_data(tools_df: pd.DataFrame, inc_tools: List[str]) -> pd.DataFrame:
-    """Gets the error data for the given tools and calculates the error percentage."""
-    tools_inc = tools_df[tools_df["tool"].isin(inc_tools)]
-    error = (
-        tools_inc.groupby(["tool", "request_month_year_week", "error"])
-        .size()
-        .unstack()
-        .fillna(0)
-        .reset_index()
-    )
-    error["error_perc"] = (error[1] / (error[0] + error[1])) * 100
-    error["total_requests"] = error[0] + error[1]
-    return error
-
-
 def get_error_data_by_market(
     tools_df: pd.DataFrame, inc_tools: List[str]
 ) -> pd.DataFrame:
@@ -43,19 +28,6 @@ def get_error_data_by_market(
     return error
 
 
-def get_error_data_overall(error_df: pd.DataFrame) -> pd.DataFrame:
-    """Gets the error data for the given tools and calculates the error percentage."""
-    error_total = (
-        error_df.groupby("request_month_year_week")
-        .agg({"total_requests": "sum", 1: "sum", 0: "sum"})
-        .reset_index()
-    )
-    error_total["error_perc"] = (error_total[1] / error_total["total_requests"]) * 100
-    error_total.columns = error_total.columns.astype(str)
-    error_total["error_perc"] = error_total["error_perc"].apply(lambda x: round(x, 4))
-    return error_total
-
-
 def get_error_data_overall_by_market(error_df: pd.DataFrame) -> pd.DataFrame:
     """Gets the error data for the given tools and calculates the error percentage."""
     error_total = (
@@ -69,24 +41,6 @@ def get_error_data_overall_by_market(error_df: pd.DataFrame) -> pd.DataFrame:
     return error_total
 
 
-def plot_error_data(error_all_df: pd.DataFrame) -> gr.BarPlot:
-    """Plots the error data for the given tools and calculates the error percentage."""
-    return gr.BarPlot(
-        value=error_all_df,
-        x="request_month_year_week",
-        y="error_perc",
-        title="Error Percentage",
-        x_title="Week",
-        y_title="Error Percentage",
-        show_label=True,
-        interactive=True,
-        show_actions_button=True,
-        tooltip=["request_month_year_week", "error_perc"],
-        height=HEIGHT,
-        width=WIDTH,
-    )
-
-
 def plot_error_data_by_market(error_all_df: pd.DataFrame) -> gr.Plot:
 
     # Sort the unique values of request_month_year_week
@@ -125,28 +79,6 @@ def plot_error_data_by_market(error_all_df: pd.DataFrame) -> gr.Plot:
     return gr.Plot(value=fig)
 
 
-def plot_tool_error_data(error_df: pd.DataFrame, tool: str) -> gr.BarPlot:
-    """Plots the error data for the given tool."""
-    error_tool = error_df[error_df["tool"] == tool]
-    error_tool.columns = error_tool.columns.astype(str)
-    error_tool["error_perc"] = error_tool["error_perc"].apply(lambda x: round(x, 4))
-
-    return gr.BarPlot(
-        title="Error Percentage",
-        x_title="Week",
-        y_title="Error Percentage %",
-        show_label=True,
-        interactive=True,
-        show_actions_button=True,
-        tooltip=["request_month_year_week", "error_perc"],
-        value=error_tool,
-        x="request_month_year_week",
-        y="error_perc",
-        height=HEIGHT,
-        width=WIDTH,
-    )
-
-
 def plot_tool_error_data_by_market(error_df: pd.DataFrame, tool: str) -> gr.Plot:
     error_tool = error_df[error_df["tool"] == tool]
     error_tool.columns = error_tool.columns.astype(str)
@@ -188,27 +120,6 @@ def plot_tool_error_data_by_market(error_df: pd.DataFrame, tool: str) -> gr.Plot
     return gr.Plot(value=fig)
 
 
-def plot_week_error_data(error_df: pd.DataFrame, week: str) -> gr.BarPlot:
-    """Plots the error data for the given week."""
-    error_week = error_df[error_df["request_month_year_week"] == week]
-    error_week.columns = error_week.columns.astype(str)
-    error_week["error_perc"] = error_week["error_perc"].apply(lambda x: round(x, 4))
-    return gr.BarPlot(
-        value=error_week,
-        x="tool",
-        y="error_perc",
-        title="Error Percentage",
-        x_title="Tool",
-        y_title="Error Percentage",
-        show_label=True,
-        interactive=True,
-        show_actions_button=True,
-        tooltip=["tool", "error_perc"],
-        height=HEIGHT,
-        width=WIDTH,
-    )
-
-
 def plot_week_error_data_by_market(error_df: pd.DataFrame, week: str) -> gr.Plot:
     error_week = error_df[error_df["request_month_year_week"] == week]
     error_week.columns = error_week.columns.astype(str)

tabs/tool_win.py

@@ -26,37 +26,6 @@ def prepare_tools(tools: pd.DataFrame) -> pd.DataFrame:
     return tools
 
 
-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:
@@ -91,17 +60,6 @@ def get_tool_winning_rate_by_market(
     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_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 = (
@@ -113,26 +71,6 @@ def get_overall_winning_rate_by_market(wins_df: pd.DataFrame) -> pd.DataFrame:
     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 sort_key(date_str):
     month, year_week = date_str.split("-")
     month_order = [
@@ -197,24 +135,6 @@ def integrated_plot_tool_winnings_overall_per_market_by_week(
     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,
-    )
-
-
 def integrated_tool_winnings_by_tool_per_market(
     wins_df: pd.DataFrame, tool: str
 ) -> gr.Plot: