logml.survival_analysis.artifacts.kaplan_meier

Functions

get_kaplan_meier_fitters(container)	Returns KaplanMeier model for each group within a given column.
get_logrank_test_summary(container)	Runs logrank test against a given column's values.

Classes

KaplanMeierArtifact(*args)

Object for conducting univariate analysis: KM plot and logRank test.

class logml.survival_analysis.artifacts.kaplan_meier.KaplanMeierArtifact(*args)

Bases: logml.survival_analysis.artifacts.base.BaseArtifact

Object for conducting univariate analysis: KM plot and logRank test.

LABEL = 'kaplan_meier'

property raw_logrank_p_value: float

Returns raw LogRank test p-value.

property logrank_p_value: float

Returns corrected p-value (if available), or raw logrank test one.

build(container: logml.data.datasets.survival_dataset.UnivariateSurvivalContainer)

Initializes artifact using a given survival container (univariate).

Contains of the following parts:

• KM models are fitted (per group)

• LogRanks test

plot() → matplotlib.figure.Figure

Artifact visualization: survival functions and LogRank test p-value.

column_name: str

threshold: float

logml.survival_analysis.artifacts.kaplan_meier.get_logrank_test_summary(container: logml.data.datasets.survival_dataset.UnivariateSurvivalContainer) → lifelines.statistics.StatisticalResult

Runs logrank test against a given column’s values.

logml.survival_analysis.artifacts.kaplan_meier.get_kaplan_meier_fitters(container: logml.data.datasets.survival_dataset.UnivariateSurvivalContainer) → Dict[str, lifelines.fitters.kaplan_meier_fitter.KaplanMeierFitter]

Returns KaplanMeier model for each group within a given column.