# Base classes and utility functions¶

## Base classes¶

Base Bayes model and A/B testing classes.

class cprior.cdist.base.BayesABTest(modelA, modelB, simulations=None, random_state=None)

Bases: object

Bayes A/B test abstract class.

Parameters: modelA (object) – The Bayes model for variant A. modelB (object) – The Bayes model for variant B. simulations (int or None (default=1000000)) – Number of Monte Carlo simulations. random_state (int or None (default=None)) – The seed used by the random number generator.
expected_loss()

Compute the expected loss. This is the expected uplift lost by choosing a given variant.

expected_loss_ci()

Compute credible intervals on the difference distribution of $$Z = B-A$$ and/or $$Z = A-B$$.

expected_loss_relative()

Compute expected relative loss for choosing a variant. This can be seen as the negative expected relative improvement or uplift.

expected_loss_relative_ci()

Compute credible intervals on the relative difference distribution of $$Z = (B-A)/A$$ and/or $$Z = (A-B)/B$$.

probability()

Compute the error probability or chance to beat control.

update_A(data)

Update posterior parameters for variant A with new data samples.

Parameters: data (array-like, shape = (n_samples)) –
update_B(data)

Update posterior parameters for variant B with new data samples.

Parameters: data (array-like, shape = (n_samples)) –
class cprior.cdist.base.BayesMVTest(models, simulations=None, random_state=None, n_jobs=None)

Bases: object

Bayesian Multivariate test abstract class.

Parameters: models (dict) – The Bayes models. simulations (int or None (default=1000000)) – Number of Monte Carlo simulations. random_state (int or None (default=None)) – The seed used by the random number generator.
expected_loss()

Compute the expected loss. This is the expected uplift lost by choosing a given variant.

expected_loss_ci()

Compute credible intervals on the difference between two variants.

expected_loss_relative()

Compute expected relative loss for choosing a variant. This can be seen as the negative expected relative improvement or uplift.

expected_loss_relative_ci()

Compute credible intervals on the relative difference between two variants.

expected_loss_vs_all()

Compute the expected loss against all variations.

probability()

Compute the error probability or chance to beat control.

probability_vs_all()

Compute the error probability or chance to beat all variations.

update(data, variant)

Update posterior parameters for a given variant with new data samples.

Parameters: data (array-like, shape = (n_samples)) – variant (str) –
class cprior.cdist.base.BayesModel(name='')

Bases: object

Bayes model class.

Parameters: name (str (default="")) – Model name.
cdf(x)

Cumulative distribution function of the posterior distribution.

Parameters: x (array-like) – Quantiles. cdf – Cumulative distribution function evaluated at x. numpy.ndarray
credible_interval(interval_length)

Credible interval of the posterior distribution.

Parameters: interval_length (float (default=0.9)) – Compute interval_length% credible interval. This is a value in [0, 1]. interval – Lower and upper credible interval limits. tuple
mean()

Mean of the posterior distribution.

pdf(x)

Probability density function of the posterior distribution.

Parameters: x (array-like) – Quantiles. pdf – Probability density function evaluated at x. numpy.ndarray
ppf(q)

Percent point function (quantile) of the posterior distribution.

Parameters: x (array-like) – Lower tail probability. ppf – Quantile corresponding to the lower tail probability q. numpy.ndarray
rvs(size=1, random_state=None)

Random variates of the posterior distribution.

Parameters: size (int (default=1)) – Number of random variates. random_state (int or None (default=None)) – The seed used by the random number generator. rvs – Random variates of given size. numpy.ndarray or scalar
std()

Standard deviation of the posterior distribution.

var()

Variance of the posterior distribution.

## Utility functions¶

Utilities to check methods and models.

cprior.cdist.utils.check_ab_method(method, method_options, variant, lift=0, interval_length=0.9)

Check parameters of A/B testing method.

Parameters: method (str) – The default computational method. method_options (list or tuple) – The list of supported computational methods. variant (str) – The chosen variant. Options are “A”, “B”, “all” lift (float (default=0.0)) – The amount of uplift.
cprior.cdist.utils.check_models(refclass, *models)

Check that models for A/B and multivariate testing belong to the correct class.

Parameters: refclass (object) – Reference class. models (objects) – Model instances to be checked.
cprior.cdist.utils.check_mv_method(method, method_options, control, variant, variants, lift=0, interval_length=0.9)

Check parameters of Multivariate testing method.

Parameters: method (str) – The default computational method. method_options (list or tuple) – The list of supported computational methods. control (str) – The control variant. variant (str) – The variation variant. variants (list or tuple) – The list of available variants. lift (float (default=0.0)) – The amount of uplift.
cprior.cdist.utils.check_mv_models(refclass, models)

Check models for Multivariate testing.

Parameters: refclass (object) – Reference class. models (dict) – Dictionary of model instances to be checked.