pdstools.adm.Plots

Classes

Plots

Module Contents

class Plots(datamart: pdstools.adm.ADMDatamart.ADMDatamart)

Bases: pdstools.utils.namespaces.LazyNamespace

Parameters:

datamart (pdstools.adm.ADMDatamart.ADMDatamart)

dependencies = ['plotly']

dependency_group = 'adm'

datamart

bubble_chart(*, last: bool = True, rounding: int = 5, query: pdstools.utils.types.QUERY | None = None, facet: str | polars.Expr | None = None, color: str | None = 'Performance', return_df: bool = False)

The Bubble Chart, as seen in Prediction Studio

Parameters:

• last (bool, optional) – Whether to only include the latest snapshot, by default True

• rounding (int, optional) – To how many digits to round the performance number

• query (Optional[QUERY], optional) – The query to apply to the data, by default None

• facet (Optional[Union[str, pl.Expr]], optional) – Column name or Polars expression to facet the plot into subplots, by default None

• return_df (bool, optional) – Whether to return a dataframe instead of a plot, by default False

• color (Optional[str])

over_time(metric: str = 'Performance', by: polars.Expr | str = 'ModelID', *, every: str | datetime.timedelta = '1d', cumulative: bool = True, query: pdstools.utils.types.QUERY | None = None, facet: str | None = None, return_df: bool = False)

Statistics over time

Parameters:

• metric (str, optional) – The metric to plot, by default “Performance”

• by (Union[pl.Expr, str], optional) – The column to group by, by default “ModelID”

• every (Union[str, timedelta], optional) – By what time period to group, by default “1d”, see https://docs.pola.rs/api/python/stable/reference/expressions/api/polars.Expr.dt.truncate.html for periods.

• cumulative (bool, optional) – Whether to show cumulative values or period-over-period changes, by default True

• query (Optional[QUERY], optional) – The query to apply to the data, by default None

• facet (Optional[str], optional) – Whether to facet the plot into subplots, by default None

• return_df (bool, optional) – Whether to return a dataframe instead of a plot, by default False

proposition_success_rates(metric: str = 'SuccessRate', by: str = 'Name', *, top_n: int = 0, query: pdstools.utils.types.QUERY | None = None, facet: str | None = None, return_df: bool = False)

Proposition Success Rates

Parameters:

• metric (str, optional) – The metric to plot, by default “SuccessRate”

• by (str, optional) – By which column to group the, by default “Name”

• top_n (int, optional) – Whether to take a top_n on the by column, by default 0

• query (Optional[QUERY], optional) – A query to apply to the data, by default None

• facet (Optional[str], optional) – What facetting column to apply to the graph, by default None

• return_df (bool, optional) – Whether to return a DataFrame instead of the graph, by default False

score_distribution(model_id: str, *, active_range: bool = True, return_df: bool = False)

Generate a score distribution plot for a specific model.

Parameters:

• model_id (str) – The ID of the model to generate the score distribution for

• active_range (bool, optional) – Whether to filter to active score range only, by default True

• return_df (bool, optional) – Whether to return a dataframe instead of a plot, by default False

Returns:

Plotly figure showing score distribution or DataFrame if return_df=True

Return type:

Union[Figure, pl.LazyFrame]

Raises:

ValueError – If no data is available for the provided model ID

multiple_score_distributions(query: pdstools.utils.types.QUERY | None = None, show_all: bool = True) → List[Figure]

Generate the score distribution plot for all models in the query

Parameters:

• query (Optional[QUERY], optional) – A query to apply to the data, by default None

• show_all (bool, optional) – Whether to ‘show’ all plots or just get a list of them, by default True

Returns:

A list of Plotly charts, one for each model instance

Return type:

List[go.Figure]

predictor_binning(model_id: str, predictor_name: str, return_df: bool = False)

Generate a predictor binning plot for a specific model and predictor.

Parameters:

• model_id (str) – The ID of the model containing the predictor

• predictor_name (str) – Name of the predictor to analyze

• return_df (bool, optional) – Whether to return a dataframe instead of a plot, by default False

Returns:

Plotly figure showing predictor binning or DataFrame if return_df=True

Return type:

Union[Figure, pl.LazyFrame]

Raises:

ValueError – If no data is available for the provided model ID and predictor name

multiple_predictor_binning(model_id: str, query: pdstools.utils.types.QUERY | None = None, show_all=True) → List[Figure]

Generate predictor binning plots for all predictors in a model.

Parameters:

• model_id (str) – The ID of the model to generate predictor binning plots for

• query (Optional[QUERY], optional) – A query to apply to the predictor data, by default None

• show_all (bool, optional) – Whether to display all plots or just return the list, by default True

Returns:

A list of Plotly figures, one for each predictor in the model

Return type:

List[Figure]

_boxplot_pre_aggregated(df: polars.LazyFrame, *, y_col: str, metric_col: str, metric_weight_col: str | None = None, legend_col: str | None = None, return_df: bool = False)

Parameters:

• df (polars.LazyFrame)

• y_col (str)

• metric_col (str)

• metric_weight_col (Optional[str])

• legend_col (Optional[str])

• return_df (bool)

predictor_performance(*, metric: str = 'Performance', top_n: int | None = None, active_only: bool = False, query: pdstools.utils.types.QUERY | None = None, return_df: bool = False)

Plots a box plot of the performance of the predictors

Use the query argument to drill down to a more specific subset If top n is given, chooses the top predictors based on the weighted average performance across models, ordered by their median performance.

Parameters:

• metric (str, optional) – The metric to plot, by default “Performance” This is more for future-proofing, once FeatureImportance gets more used.

• top_n (Optional[int], optional) – The top n predictors to plot, by default None

• active_only (bool, optional) – Whether to only consider active predictor performance, by default False

• query (Optional[QUERY], optional) – The query to apply to the data, by default None

• return_df (bool, optional) – Whether to return a dataframe instead of a plot, by default False

See also

pdstools.adm.ADMDatamart.apply_predictor_categorization

how to override the out of the box predictor categorization

predictor_category_performance(*, metric: str = 'Performance', active_only: bool = False, query: pdstools.utils.types.QUERY | None = None, return_df: bool = False)

Plot the predictor category performance

Parameters:

• metric (str, optional) – The metric to plot, by default “Performance”

• active_only (bool, optional) – Whether to only analyze active predictors, by default False

• query (Optional[QUERY], optional) – An optional query to apply, by default None

• return_df (bool, optional) – An optional flag to get the dataframe instead, by default False

Returns:

A Plotly figure

Return type:

px.Figure

See also

pdstools.adm.ADMDatamart.apply_predictor_categorization

how to override the out of the box predictor categorization

predictor_contribution(*, by: str = 'Configuration', query: pdstools.utils.types.QUERY | None = None, return_df: bool = False)

Plots the predictor contribution for each configuration

Parameters:

• by (str, optional) – By which column to plot the contribution, by default “Configuration”

• query (Optional[QUERY], optional) – An optional query to apply to the data, by default None

• return_df (bool, optional) – An optional flag to get a Dataframe instead, by default False

Returns:

A plotly figure

Return type:

px.Figure

See also

pdstools.adm.ADMDatamart.apply_predictor_categorization

how to override the out of the box predictor categorization

predictor_performance_heatmap(*, top_predictors: int = 20, top_groups: int | None = None, by: str = 'Name', active_only: bool = False, query: pdstools.utils.types.QUERY | None = None, return_df: bool = False)

Generate a heatmap showing predictor performance across different groups.

Parameters:

• top_predictors (int, optional) – Number of top-performing predictors to include, by default 20

• top_groups (int, optional) – Number of top groups to include, by default None (all groups)

• by (str, optional) – Column to group by for the heatmap, by default “Name”

• active_only (bool, optional) – Whether to only include active predictors, by default False

• query (Optional[QUERY], optional) – Optional query to filter the data, by default None

• return_df (bool, optional) – Whether to return a dataframe instead of a plot, by default False

Returns:

Plotly heatmap figure or DataFrame if return_df=True

Return type:

Union[Figure, pl.LazyFrame]

response_gain()

tree_map(metric: Literal['ResponseCount', 'Positives', 'Performance', 'SuccessRate', 'percentage_without_responses'] = 'Performance', *, by: str = 'Name', query: pdstools.utils.types.QUERY | None = None, return_df: bool = False)

Generate a tree map visualization showing hierarchical model metrics.

Parameters:

• metric (Literal["ResponseCount", "Positives", "Performance", "SuccessRate", "percentage_without_responses"], optional) – The metric to visualize in the tree map, by default “Performance”

• by (str, optional) – Column to group by for the tree map hierarchy, by default “Name”

• query (Optional[QUERY], optional) – Optional query to filter the data, by default None

• return_df (bool, optional) – Whether to return a dataframe instead of a plot, by default False

Returns:

Plotly treemap figure or DataFrame if return_df=True

Return type:

Union[Figure, pl.LazyFrame]

predictor_count(*, by: str | List[str] = ['EntryType', 'Type'], query: pdstools.utils.types.QUERY | None = None, return_df: bool = False)

Generate a box plot showing the distribution of predictor counts by type.

Parameters:

• by (Union[str, List[str]], optional) – Column(s) to group predictors by, by default [“EntryType”, “Type”]

• query (Optional[QUERY], optional) – Optional query to filter the data, by default None

• return_df (bool, optional) – Whether to return a dataframe instead of a plot, by default False

Returns:

Plotly box plot figure or DataFrame if return_df=True

Return type:

Union[Figure, pl.LazyFrame]

binning_lift(model_id: str, predictor_name: str, *, query: pdstools.utils.types.QUERY | None = None, return_df: bool = False)

Generate a binning lift plot for a specific predictor showing propensity lift per bin.

Parameters:

• model_id (str) – The ID of the model containing the predictor

• predictor_name (str) – Name of the predictor to analyze for lift

• query (Optional[QUERY], optional) – Optional query to filter the predictor data, by default None

• return_df (bool, optional) – Whether to return a dataframe instead of a plot, by default False

Returns:

Plotly bar chart showing binning lift or DataFrame if return_df=True

Return type:

Union[Figure, pl.LazyFrame]

action_overlap(group_col: str | list[str] | polars.Expr = 'Channel', overlap_col='Name', *, show_fraction=True, query: pdstools.utils.types.QUERY | None = None, return_df: bool = False)

Generate an overlap matrix heatmap showing shared actions across different groups.

Parameters:

• group_col (Union[str, list[str], pl.Expr], optional) – Column(s) to group by for overlap analysis, by default “Channel”

• overlap_col (str, optional) – Column containing values to analyze for overlap, by default “Name”

• show_fraction (bool, optional) – Whether to show overlap as fraction or absolute count, by default True

• query (Optional[QUERY], optional) – Optional query to filter the data, by default None

• return_df (bool, optional) – Whether to return a dataframe instead of a plot, by default False

Returns:

Plotly heatmap showing action overlap or DataFrame if return_df=True

Return type:

Union[Figure, pl.LazyFrame]

partitioned_plot(func: Callable, facets: List[Dict[str, str | None]], show_plots: bool = True, *args, **kwargs)

Execute a plotting function across multiple faceted subsets of data.

This method applies a given plotting function to multiple filtered subsets of data, where each subset is defined by the facet conditions. It’s useful for generating multiple plots with different filter conditions applied.

Parameters:

• func (Callable) – The plotting function to execute for each facet

• facets (List[Dict[str, Optional[str]]]) – List of dictionaries defining filter conditions for each facet

• show_plots (bool, optional) – Whether to display the plots as they are generated, by default True

• *args (tuple) – Additional positional arguments to pass to the plotting function

• **kwargs (dict) – Additional keyword arguments to pass to the plotting function

Returns:

List of Plotly figures, one for each facet condition

Return type:

List[Figure]