calibr8.contrib.studentt

This module implements reusable calibration models with Students-t distributions for the dependent variable.

class calibr8.contrib.studentt.BaseAsymmetricLogisticT(*, independent_key: str, dependent_key: str, scale_degree: int = 0, theta_names: Sequence[str] | None = None)

Bases: ContinuousUnivariateModel, StudentTNoise

Attributes:

pymc_dist
theta_fitted: The parameter vector that describes the fitted model.
theta_guess: Initial guess that was used to fit the model.
theta_names: Names of model parameters in a fixed order.
theta_timestamp: The timestamp when theta_fitted was set.

Methods

`infer_independent`(y, *, lower, upper[, ...])	Infer the posterior distribution of the independent variable given the observations of the dependent variable.
`likelihood`(*, y, x[, theta, scan_x])	Likelihood of observation (dependent variable) given the independent variable.
`load`(filepath)	Instantiates a model from a JSON file of key properties.
`loglikelihood`(*, y, x[, name, replicate_id, ...])	Loglikelihood of observation (dependent variable) given the independent variable.
`objective`(independent, dependent[, minimize])	Creates an objective function for fitting to data.
`predict_dependent`(x, *[, theta])	Predicts the parameters mu and scale of a Student-t distribution which characterizes the dependent variable given values of the independent variable.
`predict_independent`(y, *[, theta])	Predict the independent variable using the inverse trend model.
`save`(filepath)	Save key properties of the calibration model to a JSON file.
`scipy_dist`

to_pymc
to_scipy

predict_dependent(x, *, theta=None)

Predicts the parameters mu and scale of a Student-t distribution which characterizes the dependent variable given values of the independent variable.

Parameters:

xarray-like

values of the independent variable

thetaoptional, array-like

parameter vector of the calibration model:: 5 parameters of asymmetric logistic model for mu [scale_degree] parameters for scale (lowest degree first) 1 parameter for degree of freedom

Returns:

muarray-like: values for the mu parameter of a Student-t distribution describing the dependent variable
scalearray-like or float: values for the scale parameter of a Student-t distribution describing the dependent variable
dffloat: degree of freedom of Student-t distribution

predict_independent(y, *, theta=None)

Predict the independent variable using the inverse trend model.

Parameters:

yarray-like

observations

thetaoptional, array-like

parameter vector of the calibration model:: 5 parameters of asymmetric logistic model for mu [scale_degree] parameters for scale (lowest degree first) 1 parameter for degree of freedom

Returns:

xarray-like: predicted independent values given the observations

class calibr8.contrib.studentt.BaseExponentialModelT(*, independent_key: str, dependent_key: str, scale_degree: int = 0, fixed_intercept: float | None = None, theta_names: Sequence[str] | None = None)

Bases: ContinuousUnivariateModel, StudentTNoise

Attributes:

pymc_dist
theta_fitted: The parameter vector that describes the fitted model.
theta_guess: Initial guess that was used to fit the model.
theta_names: Names of model parameters in a fixed order.
theta_timestamp: The timestamp when theta_fitted was set.

Methods

`infer_independent`(y, *, lower, upper[, ...])	Infer the posterior distribution of the independent variable given the observations of the dependent variable.
`likelihood`(*, y, x[, theta, scan_x])	Likelihood of observation (dependent variable) given the independent variable.
`load`(filepath)	Instantiates a model from a JSON file of key properties.
`loglikelihood`(*, y, x[, name, replicate_id, ...])	Loglikelihood of observation (dependent variable) given the independent variable.
`objective`(independent, dependent[, minimize])	Creates an objective function for fitting to data.
`predict_dependent`(x, *[, theta])	Predicts the parameters mu and scale of a Student-t distribution which characterizes the dependent variable given values of the independent variable.
`predict_independent`(y, *[, theta])	Predict the independent variable using the inverse trend model.
`save`(filepath)	Save key properties of the calibration model to a JSON file.
`scipy_dist`

to_pymc
to_scipy

predict_dependent(x, *, theta=None)

Predicts the parameters mu and scale of a Student-t distribution which characterizes the dependent variable given values of the independent variable.

Parameters:

xarray-like: Values of the independent variable.
thetaoptional, array-like: Parameter vector of the calibration model. Depending on the fixed_intercept setting these are [I, L, k] or [L, k] parameters of exponential model for mu. Followed by parameters for the model for scale (lowest degree first). Followed by the degree of freedom parameter.

Returns:

muarray-like: Values for the mu parameter of a Student-t distribution describing the dependent variable.
scalearray-like or float: Values for the scale parameter of a Student-t distribution describing the dependent variable.
dffloat: Degree of freedom of Student-t distribution.

predict_independent(y, *, theta=None)

Predict the independent variable using the inverse trend model.

Parameters:

yarray-like: Observations
thetaoptional, array-like: Parameter vector of the calibration model. Depending on the fixed_intercept setting these are [I, L, k] or [L, k] parameters of exponential model for mu. Followed by parameters for the model for scale (lowest degree first). Followed by the degree of freedom parameter.

Returns:

xarray-like: Predicted independent values given the observations.

class calibr8.contrib.studentt.BaseLogIndependentAsymmetricLogisticT(*, independent_key: str, dependent_key: str, scale_degree: int = 0, theta_names: Sequence[str] | None = None)

Bases: ContinuousUnivariateModel, StudentTNoise

Attributes:

pymc_dist
theta_fitted: The parameter vector that describes the fitted model.
theta_guess: Initial guess that was used to fit the model.
theta_names: Names of model parameters in a fixed order.
theta_timestamp: The timestamp when theta_fitted was set.

Methods

`infer_independent`(y, *, lower, upper[, ...])	Infer the posterior distribution of the independent variable given the observations of the dependent variable.
`likelihood`(*, y, x[, theta, scan_x])	Likelihood of observation (dependent variable) given the independent variable.
`load`(filepath)	Instantiates a model from a JSON file of key properties.
`loglikelihood`(*, y, x[, name, replicate_id, ...])	Loglikelihood of observation (dependent variable) given the independent variable.
`objective`(independent, dependent[, minimize])	Creates an objective function for fitting to data.
`predict_dependent`(x, *[, theta])	Predicts the parameters mu and scale of a Student-t distribution which characterizes the dependent variable given values of the independent variable.
`predict_independent`(y, *[, theta])	Predict the independent variable using the inverse trend model.
`save`(filepath)	Save key properties of the calibration model to a JSON file.
`scipy_dist`

to_pymc
to_scipy

predict_dependent(x, *, theta=None)

Predicts the parameters mu and scale of a Student-t distribution which characterizes the dependent variable given values of the independent variable.

Parameters:

xarray-like

values of the independent variable

thetaoptional, array-like

parameter vector of the calibration model:: 5 parameters of log-independent asymmetric logistic model for mu [scale_degree] parameters for scale (lowest degree first) 1 parameter for degree of freedom

Returns:

muarray-like: values for the mu parameter of a Student-t distribution describing the dependent variable
scalearray-like or float: values for the scale parameter of a Student-t distribution describing the dependent variable
dffloat: degree of freedom of Student-t distribution

predict_independent(y, *, theta=None)

Predict the independent variable using the inverse trend model.

Parameters:

yarray-like

observations

thetaoptional, array-like

parameter vector of the calibration model:: 5 parameters of log-independent asymmetric logistic model for mu [scale_degree] parameters for scale (lowest degree first) 1 parameter for degree of freedom

Returns:

xarray-like: predicted independent values given the observations

class calibr8.contrib.studentt.BaseModelT(independent_key: str, dependent_key: str, *, theta_names: Tuple[str])

Bases: ContinuousUnivariateModel, StudentTNoise

Attributes:

pymc_dist
theta_fitted: The parameter vector that describes the fitted model.
theta_guess: Initial guess that was used to fit the model.
theta_names: Names of model parameters in a fixed order.
theta_timestamp: The timestamp when theta_fitted was set.

Methods

`infer_independent`(y, *, lower, upper[, ...])	Infer the posterior distribution of the independent variable given the observations of the dependent variable.
`likelihood`(*, y, x[, theta, scan_x])	Likelihood of observation (dependent variable) given the independent variable.
`load`(filepath)	Instantiates a model from a JSON file of key properties.
`loglikelihood`(*, y, x[, name, replicate_id, ...])	Loglikelihood of observation (dependent variable) given the independent variable.
`objective`(independent, dependent[, minimize])	Creates an objective function for fitting to data.
`predict_dependent`(x, *[, theta])	Predicts the parameters of a probability distribution which characterises
`predict_independent`(y, *[, theta])	Predict the independent variable using the inverse trend model.
`save`(filepath)	Save key properties of the calibration model to a JSON file.
`scipy_dist`

to_pymc
to_scipy

class calibr8.contrib.studentt.BasePolynomialModelT(*, independent_key: str, dependent_key: str, mu_degree: int, scale_degree: int = 0, theta_names: Sequence[str] | None = None)

Bases: ContinuousUnivariateModel, StudentTNoise

Attributes:

pymc_dist
theta_fitted: The parameter vector that describes the fitted model.
theta_guess: Initial guess that was used to fit the model.
theta_names: Names of model parameters in a fixed order.
theta_timestamp: The timestamp when theta_fitted was set.

Methods

`infer_independent`(y, *, lower, upper[, ...])	Infer the posterior distribution of the independent variable given the observations of the dependent variable.
`likelihood`(*, y, x[, theta, scan_x])	Likelihood of observation (dependent variable) given the independent variable.
`load`(filepath)	Instantiates a model from a JSON file of key properties.
`loglikelihood`(*, y, x[, name, replicate_id, ...])	Loglikelihood of observation (dependent variable) given the independent variable.
`objective`(independent, dependent[, minimize])	Creates an objective function for fitting to data.
`predict_dependent`(x, *[, theta])	Predicts the parameters mu and scale of a Student-t distribution which characterizes the dependent variable given values of the independent variable.
`predict_independent`(y, *[, theta])	Predict the independent variable using the inverse trend model.
`save`(filepath)	Save key properties of the calibration model to a JSON file.
`scipy_dist`

to_pymc
to_scipy

predict_dependent(x, *, theta=None)

Predicts the parameters mu and scale of a Student-t distribution which characterizes the dependent variable given values of the independent variable.

Parameters:

xarray-like

values of the independent variable

thetaoptional, array-like

parameter vector of the calibration model:: [mu_degree] parameters for mu (lowest degree first) [scale_degree] parameters for scale (lowest degree first) 1 parameter for degree of freedom

Returns:

muarray-like: values for the mu parameter of a Student-t distribution describing the dependent variable
scalearray-like or float: values for the scale parameter of a Student-t distribution describing the dependent variable
dffloat: degree of freedom of Student-t distribution

predict_independent(y, *, theta=None)

Predict the independent variable using the inverse trend model.

Parameters:

yarray-like

observations

thetaoptional, array-like

parameter vector of the calibration model:: [mu_degree] parameters for mu (lowest degree first) [scale_degree] parameters for scale (lowest degree first) 1 parameter for degree of freedom

Returns:

xarray-like: predicted independent values given the observations