pdstools.adm.Plots._binning

Predictor binning visualisations and lift charts.

Classes

_BinningPlotsMixin

Common attribute surface used by every plot mixin.

Module Contents

class _BinningPlotsMixin

Bases: pdstools.adm.Plots._base._PlotsBase

Common attribute surface used by every plot mixin.

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

Examples

>>> # Predictor binning for a single predictor on a model
>>> fig = dm.plot.predictor_binning(
...     model_id="M-1001",
...     predictor_name="Customer.Age",
... )

>>> # Retrieve the underlying binning data
>>> df = dm.plot.predictor_binning(
...     model_id="M-1001",
...     predictor_name="Customer.Age",
...     return_df=True,
... )
multiple_predictor_binning(model_id: str, query: pdstools.utils.types.QUERY | None = None, show_all=True) → list[pdstools.utils.plot_utils.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]

Examples

>>> # Plot binning for every predictor in a model
>>> figs = dm.plot.multiple_predictor_binning(model_id="M-1001")

>>> # Collect figures without displaying, filtered to IH predictors
>>> figs = dm.plot.multiple_predictor_binning(
...     model_id="M-1001",
...     query={"PredictorCategory": "IH"},
...     show_all=False,
... )
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]

Examples

>>> # Lift chart for a single predictor on a model
>>> fig = dm.plot.binning_lift(
...     model_id="M-1001",
...     predictor_name="Customer.Age",
... )

>>> # Return the lift data for further analysis
>>> df = dm.plot.binning_lift(
...     model_id="M-1001",
...     predictor_name="Customer.Age",
...     return_df=True,
... )