Statistics

Design

Contains utilities for the design of experiments.

This module defines utility functions for various experimental design methods.

calisim.statistics.design.get_full_factorial_design(parameter_spec: ParameterSpecification) → ndarray[source]

Get a full factorial design from a parameter specification.

Parameters:

parameter_spec (ParameterSpecification) – The parameter specification.

Returns:

The full factorial design.

Return type:

np.ndarray

Distance Metrics

Contains utilities for calculating distance values.

This module defines utility functions for distance metrics that can be used to compare simulated and observational data.

class calisim.statistics.distance_metrics.DistanceMetricBase[source]

The distance metric abstract class.

abstract calculate(observed: ndarray, simulated: ndarray) → float | ndarray[source]

Calculate the distance between observed and simulated data.

Parameters:

  • observed (np.ndarray) – The observed data.

  • simulated (np.ndarray) – The simulated data.

Raises:

NotImplementedError – Error raised for the unimplemented abstract method.

class calisim.statistics.distance_metrics.L1Norm[source]

The L1 norm distance.

calculate(observed: ndarray, simulated: ndarray) → float | ndarray[source]

Calculate the distance between observed and simulated data.

Parameters:

  • observed (np.ndarray) – The observed data.

  • simulated (np.ndarray) – The simulated data.

class calisim.statistics.distance_metrics.MeanAbsoluteError[source]

The mean absolute error distance.

calculate(observed: ndarray, simulated: ndarray) → float | ndarray[source]

Calculate the distance between observed and simulated data.

Parameters:

  • observed (np.ndarray) – The observed data.

  • simulated (np.ndarray) – The simulated data.

class calisim.statistics.distance_metrics.MeanAbsolutePercentageError[source]

The mean absolute percentage error distance.

calculate(observed: ndarray, simulated: ndarray) → float | ndarray[source]

Calculate the distance between observed and simulated data.

Parameters:

  • observed (np.ndarray) – The observed data.

  • simulated (np.ndarray) – The simulated data.

class calisim.statistics.distance_metrics.MeanPinballLoss[source]

The mean pinball loss distance.

calculate(observed: ndarray, simulated: ndarray) → float | ndarray[source]

Calculate the distance between observed and simulated data.

Parameters:

  • observed (np.ndarray) – The observed data.

  • simulated (np.ndarray) – The simulated data.

class calisim.statistics.distance_metrics.MeanSquaredError[source]

The mean squared error distance.

calculate(observed: ndarray, simulated: ndarray) → float | ndarray[source]

Calculate the distance between observed and simulated data.

Parameters:

  • observed (np.ndarray) – The observed data.

  • simulated (np.ndarray) – The simulated data.

class calisim.statistics.distance_metrics.MedianAbsoluteError[source]

The median absolute error distance.

calculate(observed: ndarray, simulated: ndarray) → float | ndarray[source]

Calculate the distance between observed and simulated data.

Parameters:

  • observed (np.ndarray) – The observed data.

  • simulated (np.ndarray) – The simulated data.

class calisim.statistics.distance_metrics.RootMeanSquaredError[source]

The root mean squared error distance.

calculate(observed: ndarray, simulated: ndarray) → float | ndarray[source]

Calculate the distance between observed and simulated data.

Parameters:

  • observed (np.ndarray) – The observed data.

  • simulated (np.ndarray) – The simulated data.

calisim.statistics.distance_metrics.get_distance_metric_func(distance_metric: str) → Callable[source]

Get the distance metric function by name.

Parameters:

distance_metric (str) – The distance metric name.

Returns:

The distance metric function.

Return type:

Callable

calisim.statistics.distance_metrics.get_distance_metrics() → list[dict][source]

Get a list of available distance metrics and labels.

Returns:

A list of available distance metrics and labels.

Return type:

list[dict]