facet.simulation.base.BaseUnivariateSimulator#

class facet.simulation.base.BaseUnivariateSimulator(model, sample, *, confidence_level=0.95, n_jobs=None, shared_memory=None, pre_dispatch=None, verbose=None)[source]#

Base class for univariate simulations.

Bases

ParallelizableMixin

Generic types

~T_SupervisedLearnerDF(bound= SupervisedLearnerDF)

Metaclasses

ABCMeta

Parameters

model (BaseUnivariateSimulator) – a fitted learner to use for calculating simulated outputs
sample (Sample) – the sample to be used for baseline calculations and simulations
confidence_level (float) – the width \(\alpha\) of the confidence interval to be estimated for simulation results
n_jobs (Optional[int]) – number of jobs to use in parallel; if None, use joblib default (default: None)
shared_memory (Optional[bool]) – if True, use threads in the parallel runs; if False or None, use multiprocessing (default: None)
pre_dispatch (Union[int, str, None]) – number of batches to pre-dispatch; if None, use joblib default (default: None)
verbose (Optional[int]) – verbosity level used in the parallel computation; if None, use joblib default (default: None)

Method summary

`baseline`	Calculate the expectation value of the simulation result, based on historically observed actuals.
`expected_output`	Calculate the expectation value of the actual model output, based on historically observed actuals.
`simulate_feature`	Simulate the average target uplift when fixing the value of the given feature across all observations.

Attribute summary

`output_unit`	Unit of the output values calculated by the simulation.
`n_jobs`	Number of jobs to use in parallel; if `None`, use joblib default.
`shared_memory`	If `True`, use threads in the parallel runs; if `False` or `None`, use multiprocessing.
`pre_dispatch`	Number of batches to pre-dispatch; if `None`, use joblib default.
`verbose`	Verbosity level used in the parallel computation; if `None`, use joblib default.
`model`	The learner pipeline used to conduct simulations
`sample`	The sample to be used in baseline calculations and simulations
`confidence_level`	The width of the confidence interval used to calculate the lower/upper bound of the simulation

Definitions

baseline()[source]#

Calculate the expectation value of the simulation result, based on historically observed actuals.

Return type: float
Returns: the expectation value of the simulation results

abstract expected_output()[source]#

Calculate the expectation value of the actual model output, based on historically observed actuals.

Return type: float
Returns: the expectation value of the actual model output

simulate_feature(feature_name, *, partitioner, **partitioner_params)[source]#

Simulate the average target uplift when fixing the value of the given feature across all observations.

Simulations are run for a set of values determined by the given partitioner, which is fitted to the observed values for the feature being simulated.

Parameters

feature_name (str) – the feature to run the simulation for
partitioner (Partitioner[TypeVar(T_Value, bound= generic)]) – the partitioner of feature values to run simulations for
partitioner_params (Any) – additional parameters to pass to the partitioner

Return type

UnivariateSimulationResult[TypeVar(T_Value, bound= generic)]

Returns

a mapping of output names to simulation results

confidence_level: float#: The width of the confidence interval used to calculate the lower/upper bound of the simulation

model: T_SupervisedLearnerDF#: The learner pipeline used to conduct simulations

abstract property output_unit: str#

Unit of the output values calculated by the simulation.

Return type: str

sample: facet.data.Sample#: The sample to be used in baseline calculations and simulations

facet.simulation.base

facet.simulation.base.UnivariateRegressionSimulator