pdstools.decision_analyzer.plots

Classes

Plot

Functions

offer_quality_piecharts(df, propensityTH, ...[, ...])

getTrendChart(df[, stage, return_df, level])

plot_priority_component_distribution(value_data, ...)

create_win_distribution_plot(...)

Create a win distribution bar chart with highlighted selected items.

create_parameter_distribution_boxplots(...)

Create box plots comparing parameter distributions between selected actions and others.

Module Contents

class Plot(decision_data)
_decision_data
threshold_deciles(thresholding_on, thresholding_name, return_df=False)
distribution_as_treemap(df: polars.LazyFrame, stage: str, scope_options: List[str])
Parameters:

  • df (polars.LazyFrame)

  • stage (str)

  • scope_options (List[str])

sensitivity(win_rank: int = 1, hide_priority=True, limit_xaxis_range=True, return_df=False, reference_group=None)
Parameters:

win_rank (int)

global_winloss_distribution(level, win_rank, return_df=False)
propensity_vs_optionality(stage='Arbitration', df=None, return_df=False)
optionality_funnel(df)
action_variation(stage='Final', return_df=False)
trend_chart(stage: str, scope: str, return_df=False) → Tuple[plotly.graph_objects.Figure, str | None]
Parameters:
Return type:

Tuple[plotly.graph_objects.Figure, Optional[str]]

decision_funnel(scope: str, additional_filters: polars.Expr | List[polars.Expr] | None = None, return_df=False)
Parameters:

  • scope (str)

  • additional_filters (Optional[Union[polars.Expr, List[polars.Expr]]])

filtering_components(stages: List[str], top_n, AvailableNBADStages, additional_filters: polars.Expr | List[polars.Expr] | None = None, return_df=False)
Parameters:

  • stages (List[str])

  • additional_filters (Optional[Union[polars.Expr, List[polars.Expr]]])

distribution(df: polars.LazyFrame, scope: str, breakdown: str, metric: str = 'Decisions', horizontal=False)
Parameters:

  • df (polars.LazyFrame)

  • scope (str)

  • breakdown (str)

  • metric (str)

prio_factor_boxplots(reference: polars.Expr | List[polars.Expr] | None = None, return_df=False) → Tuple[plotly.graph_objects.Figure, str | None]
Parameters:

reference (Optional[Union[polars.Expr, List[polars.Expr]]])

Return type:

Tuple[plotly.graph_objects.Figure, Optional[str]]

rank_boxplot(reference: polars.Expr | List[polars.Expr] | None = None, return_df=False)
Parameters:

reference (Optional[Union[polars.Expr, List[polars.Expr]]])

optionality_per_stage(return_df=False)
optionality_trend(df: polars.LazyFrame, return_df=False)
Parameters:

df (polars.LazyFrame)

offer_quality_piecharts(df: polars.LazyFrame, propensityTH, AvailableNBADStages, return_df=False, level='StageGroup')
Parameters:

df (polars.LazyFrame)

getTrendChart(df: polars.LazyFrame, stage: str = 'Output', return_df=False, level='StageGroup')
Parameters:

  • df (polars.LazyFrame)

  • stage (str)

plot_priority_component_distribution(value_data: polars.LazyFrame, component: str, granularity: str)
Parameters:

  • value_data (polars.LazyFrame)

  • component (str)

  • granularity (str)

create_win_distribution_plot(data: polars.DataFrame, win_count_col: str, scope_config: Dict[str, str | List[str]], title_suffix: str, y_axis_title: str) → Tuple[plotly.graph_objects.Figure, polars.DataFrame]

Create a win distribution bar chart with highlighted selected items.

This function creates a bar chart showing win counts across actions, groups, or issues based on the scope configuration. It automatically aggregates data appropriately and highlights the selected item in red while showing others in grey.

Parameters:

  • data (pl.DataFrame) – DataFrame containing win distribution data with action identifiers and win counts

  • win_count_col (str) – Column name containing win counts to plot (e.g., “original_win_count”, “new_win_count”)

  • scope_config (Dict[str, Union[str, List[str]]]) – Configuration dictionary from get_scope_config() containing: - level: “Action”, “Group”, or “Issue” - group_cols: List of columns for grouping - x_col: Column name for x-axis - selected_value: Value to highlight in red - plot_title_prefix: Prefix for plot title

  • title_suffix (str) – Suffix to add to plot title (e.g., “Current Performance”, “After Lever Adjustment”)

  • y_axis_title (str) – Title for y-axis (e.g., “Current Win Count”, “New Win Count”)

Returns:

  • Plotly figure with bar chart

  • Processed plot data (aggregated if needed)

Return type:

Tuple[go.Figure, pl.DataFrame]

Notes

  • For Action level: Shows individual actions

  • For Group/Issue level: Automatically aggregates data by summing win counts

  • Selected item is highlighted in red (#FF0000), others in grey

  • “No Winner” bar (if present in data) is shown in orange (#FFA500) to highlight interactions without winners

  • If selected item not found, uses light blue as fallback color

  • X-axis labels are hidden to avoid clutter, scope level shown as x-axis title

  • “No Winner” data is calculated and added by get_win_distribution_data() when all_interactions parameter is provided

Examples

>>> scope_config = get_scope_config("Service", "Cards", "MyAction")
>>> fig, plot_data = create_win_distribution_plot(
...     distribution_data,
...     "new_win_count",
...     scope_config,
...     "After Lever Adjustment",
...     "New Win Count"
... )
create_parameter_distribution_boxplots(segmented_df: polars.DataFrame, parameters: List[str] = ['Propensity', 'Value', 'Context Weight', 'Levers'], title: str = 'Parameter Distributions: Selected Actions vs Competitors') → plotly.graph_objects.Figure

Create box plots comparing parameter distributions between selected actions and others.

Parameters:

  • segmented_df (pl.DataFrame) – DataFrame with columns for parameters and a ‘segment’ column containing “Selected Actions” or “Others”

  • parameters (List[str], optional) – List of parameter column names to plot

  • title (str, optional) – Title for the plot

Returns:

Plotly figure with box plots

Return type:

go.Figure