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, 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

over_time(metric: str = 'Performance', by: polars.Expr | str = 'ModelID', *, every: str | datetime.timedelta = '1d', cumulative: bool = False, 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”
cumulative (bool, optional) – Whether to take the cumulative value or the absolute one, by default False
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, *, return_df: bool = False)¶

Parameters:

model_id (str)
return_df (bool)

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)¶

Parameters:

model_id (str)
predictor_name (str)
return_df (bool)

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

Parameters:

model_id (str)
query (Optional[pdstools.utils.types.QUERY])

Return type:

List[Figure]

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

Plots a bar chart of the performance of the predictors

By default, shows the performance over all models. 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, sorted by their median performance.

Parameters:

metric (str, optional) – The metric to plot, by default “Performance” This is more for future-proofing, once FeatureImportant 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
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