gemseo / uncertainty / statistics

parametric module

Class for the parametric estimation of statistics from a dataset.

Overview

The ParametricStatistics class inherits from the abstract Statistics class and aims to estimate statistics from a Dataset, based on candidate parametric distributions calibrated from this Dataset.

For each variable,

  1. the parameters of these distributions are calibrated from the Dataset,

  2. the fitted parametric Distribution which is optimal in the sense of a goodness-of-fit criterion and a selection criterion is selected to estimate the statistics related to this variable.

The ParametricStatistics relies on the OpenTURNS library through the OTDistribution and OTDistributionFitter classes.

Construction

The ParametricStatistics is built from two mandatory arguments:

  • a dataset,

  • a list of distributions names,

and can consider optional arguments:

  • a subset of variables names (by default, statistics are computed for all variables),

  • a fitting criterion name (by default, BIC is used; see AVAILABLE_CRITERIA and AVAILABLE_SIGNIFICANCE_TESTS for more information),

  • a level associated with the fitting criterion,

  • a selection criterion:

    • ‘best’: select the distribution minimizing (or maximizing, depending on the criterion) the criterion,

    • ‘first’: select the first distribution for which the criterion is greater (or lower, depending on the criterion) than the level,

  • a name for the ParametricStatistics object (by default, the name is the concatenation of ‘ParametricStatistics’ and the name of the Dataset).

Capabilities

By inheritance, a ParametricStatistics object has the same capabilities as Statistics. Additional ones are:

  • get_fitting_matrix(): this method displays the values of the fitting criterion for the different variables and candidate probability distributions as well as the select probability distribution,

  • plot_criteria(): this method plots the criterion values for a given variable.

class gemseo.uncertainty.statistics.parametric.ParametricStatistics(dataset, distributions, variables_names=None, fitting_criterion='BIC', level=0.05, selection_criterion='best', name=None)[source]

Bases: Statistics

Parametric estimation of statistics.

Examples

>>> from gemseo.api import (
...     create_discipline,
...     create_parameter_space,
...     create_scenario
... )
>>> from gemseo.uncertainty.statistics.parametric import ParametricStatistics
>>>
>>> expressions = {"y1": "x1+2*x2", "y2": "x1-3*x2"}
>>> discipline = create_discipline(
...     "AnalyticDiscipline", expressions=expressions
... )
>>>
>>> parameter_space = create_parameter_space()
>>> parameter_space.add_random_variable(
...     "x1", "OTUniformDistribution", minimum=-1, maximum=1
... )
>>> parameter_space.add_random_variable(
...     "x2", "OTNormalDistribution", mu=0.5, sigma=2
... )
>>>
>>> scenario = create_scenario(
...     [discipline],
...     "DisciplinaryOpt",
...     "y1", parameter_space, scenario_type="DOE"
... )
>>> scenario.execute({'algo': 'OT_MONTE_CARLO', 'n_samples': 100})
>>>
>>> dataset = scenario.export_to_dataset(opt_naming=False)
>>>
>>> statistics = ParametricStatistics(
...     dataset, ['Normal', 'Uniform', 'Triangular']
... )
>>> fitting_matrix = statistics.get_fitting_matrix()
>>> mean = statistics.mean()
Parameters:

  • dataset (Dataset) – A dataset.

  • distributions (dict[str, dict[str, gemseo.uncertainty.distributions.openturns.distribution.OTDistribution]]) – The names of the distributions.

  • variables_names (Iterable[str] | None) – The variables of interest. Default: consider all the variables available in the dataset.

  • fitting_criterion (str) –

    The name of the goodness-of-fit criterion, measuring how the distribution fits the data. Use ParametricStatistics.get_criteria() to get the available criteria.

    By default it is set to “BIC”.

  • level (float) –

    A test level, i.e. the risk of committing a Type 1 error, that is an incorrect rejection of a true null hypothesis, for criteria based on test hypothesis.

    By default it is set to 0.05.

  • selection_criterion (str) –

    The name of the selection criterion to select a distribution from a list of candidates. Either ‘first’ or ‘best’.

    By default it is set to “best”.

  • name (str) – A name for the object. Default: use the concatenation of the class and dataset names.

compute_a_value()

Compute the A-value \(\text{Aval}[X]\).

Returns:

The A-value of the different variables.

Return type:

dict[str, numpy.ndarray]

compute_b_value()

Compute the B-value \(\text{Bval}[X]\).

Returns:

The B-value of the different variables.

Return type:

dict[str, numpy.ndarray]

classmethod compute_expression(variable_name, statistic_name, show_name=False, **options)

Return the expression of a statistical function applied to a variable.

E.g. “P[X >= 1.0]” for the probability that X exceeds 1.0.

Parameters:

  • variable_name (str) – The name of the variable, e.g. "X".

  • statistic_name (str) – The name of the statistic, e.g. "probability".

  • show_name (bool) –

    If True, show option names. Otherwise, only show option values.

    By default it is set to False.

  • **options (bool | float | int) – The options passed to the statistical function, e.g. {"greater": True, "thresh": 1.0}.

Returns:

The expression of the statistical function applied to the variable.

Return type:

str

compute_margin(std_factor)

Compute a margin \(\text{Margin}[X]=\mathbb{E}[X]+\kappa\mathbb{S}[X]\).

Parameters:

std_factor (float) – The weight \(\kappa\) of the standard deviation.

Returns:

The margin for the different variables.

Return type:

dict[str, numpy.ndarray]

compute_maximum()[source]

Compute the maximum \(\text{Max}[X]\).

Returns:

The maximum of the different variables.

Return type:

dict[str, numpy.ndarray]

compute_mean()[source]

Compute the mean \(\mathbb{E}[X]\).

Returns:

The mean of the different variables.

Return type:

dict[str, numpy.ndarray]

compute_mean_std(std_factor)

Compute a margin \(\text{Margin}[X]=\mathbb{E}[X]+\kappa\mathbb{S}[X]\).

Parameters:

std_factor (float) – The weight \(\kappa\) of the standard deviation.

Returns:

The margin for the different variables.

Return type:

dict[str, numpy.ndarray]

compute_median()

Compute the median \(\text{Med}[X]\).

Returns:

The median of the different variables.

Return type:

dict[str, numpy.ndarray]

compute_minimum()[source]

Compute the \(\text{Min}[X]\).

Returns:

The minimum of the different variables.

Return type:

dict[str, numpy.ndarray]

compute_moment(order)[source]

Compute the n-th moment \(M[X; n]\).

Parameters:

order (int) – The order \(n\) of the moment.

Returns:

The moment of the different variables.

Return type:

dict[str, numpy.ndarray]

compute_percentile(order)

Compute the n-th percentile \(\text{p}[X; n]\).

Parameters:

order (int) – The order \(n\) of the percentile. Either 0, 1, 2, … or 100.

Returns:

The percentile of the different variables.

Return type:

dict[str, numpy.ndarray]

compute_probability(thresh, greater=True)[source]

Compute the probability related to a threshold.

Either \(\mathbb{P}[X \geq x]\) or \(\mathbb{P}[X \leq x]\).

Parameters:

  • thresh (float) – A threshold \(x\).

  • greater (bool) –

    The type of probability. If True, compute the probability of exceeding the threshold. Otherwise, compute the opposite.

    By default it is set to True.

Returns:

The probability of the different variables

Return type:

dict[str, numpy.ndarray]

compute_quantile(prob)[source]

Compute the quantile \(\mathbb{Q}[X; \alpha]\) related to a probability.

Parameters:

prob (float) – A probability \(\alpha\) between 0 and 1.

Returns:

The quantile of the different variables.

Return type:

dict[str, numpy.ndarray]

compute_quartile(order)

Compute the n-th quartile \(q[X; n]\).

Parameters:

order (int) – The order \(n\) of the quartile. Either 1, 2 or 3.

Returns:

The quartile of the different variables.

Return type:

dict[str, numpy.ndarray]

compute_range()[source]

Compute the range \(R[X]\).

Returns:

The range of the different variables.

Return type:

dict[str, numpy.ndarray]

compute_standard_deviation()[source]

Compute the standard deviation \(\mathbb{S}[X]\).

Returns:

The standard deviation of the different variables.

Return type:

dict[str, numpy.ndarray]

compute_tolerance_interval(coverage, confidence=0.95, side=ToleranceIntervalSide.BOTH)[source]

Compute a tolerance interval \(\text{TI}[X]\).

This coverage level is the minimum percentage of belonging to the TI. The tolerance interval is computed with a confidence level and can be either lower-sided, upper-sided or both-sided.

Parameters:

  • coverage (float) – A minimum percentage of belonging to the TI.

  • confidence (float) –

    A level of confidence in [0,1].

    By default it is set to 0.95.

  • side (ToleranceIntervalSide) –

    The type of the tolerance interval characterized by its sides of interest, either a lower-sided tolerance interval \([a, +\infty[\), an upper-sided tolerance interval \(]-\infty, b]\), or a two-sided tolerance interval \([c, d]\).

    By default it is set to BOTH.

Returns:

The tolerance limits of the different variables.

Return type:

dict[str, tuple[numpy.ndarray, numpy.ndarray]]

compute_variance()[source]

Compute the variance \(\mathbb{V}[X]\).

Returns:

The variance of the different variables.

Return type:

dict[str, numpy.ndarray]

compute_variation_coefficient()

Compute the coefficient of variation \(CoV[X]\).

This is the standard deviation normalized by the expectation: \(CoV[X]=\mathbb{E}[S]/\mathbb{E}[X]\).

Returns:

The coefficient of variation of the different variables.

Return type:

dict[str, numpy.ndarray]

get_criteria(variable)[source]

Get criteria for a given variable name and the different distributions.

Parameters:

variable (str) – The name of the variable.

Returns:

The criterion for the different distributions. and an indicator equal to True is the criterion is a p-value.

Return type:

tuple[dict[str, float], bool]

get_fitting_matrix()[source]

Get the fitting matrix.

This matrix contains goodness-of-fit measures for each pair < variable, distribution >.

Returns:

The printable fitting matrix.

Return type:

str

plot_criteria(variable, title=None, save=False, show=True, n_legend_cols=4, directory='.')[source]

Plot criteria for a given variable name.

Parameters:

  • variable (str) – The name of the variable.

  • title (str | None) – A plot title.

  • save (bool) –

    If True, save the plot on the disk.

    By default it is set to False.

  • show (bool) –

    If True, show the plot.

    By default it is set to True.

  • n_legend_cols (int) –

    The number of text columns in the upper legend.

    By default it is set to 4.

  • directory (str | Path) –

    The directory path, either absolute or relative.

    By default it is set to “.”.

Raises:

ValueError – If the variable is missing from the dataset.

Return type:

None

AVAILABLE_CRITERIA = ['BIC', 'ChiSquared', 'Kolmogorov']
AVAILABLE_DISTRIBUTIONS = ['Arcsine', 'Beta', 'Burr', 'Chi', 'ChiSquare', 'Dirichlet', 'Exponential', 'FisherSnedecor', 'Frechet', 'Gamma', 'GeneralizedPareto', 'Gumbel', 'Histogram', 'InverseNormal', 'Laplace', 'LogNormal', 'LogUniform', 'Logistic', 'MeixnerDistribution', 'Normal', 'Pareto', 'Rayleigh', 'Rice', 'Student', 'Trapezoidal', 'Triangular', 'TruncatedNormal', 'Uniform', 'VonMises', 'WeibullMax', 'WeibullMin']
AVAILABLE_SIGNIFICANCE_TESTS = ['ChiSquared', 'Kolmogorov']
SYMBOLS = {'a_value': 'Aval', 'b_value': 'Bval', 'margin': 'Margin', 'maximum': 'Max', 'mean': 'E', 'mean_std': 'E_StD', 'median': 'Med', 'minimum': 'Min', 'moment': 'M', 'percentile': 'p', 'probability': 'P', 'quantile': 'Q', 'quartile': 'q', 'range': 'R', 'standard_deviation': 'StD', 'tolerance_interval': 'TI', 'variance': 'V', 'variation_coefficient': 'CoV'}
dataset: Dataset

The dataset.

distributions: dict[str, dict[str, gemseo.uncertainty.distributions.openturns.distribution.OTDistribution]]

The probability distributions of the random variables.

fitting_criterion: str

The name of the goodness-of-fit criterion, measuring how the distribution fits the data.

level: float

The test level, i.e. risk of committing a Type 1 error, that is an incorrect rejection of a true null hypothesis, for criteria based on test hypothesis.

n_samples: int

The number of samples.

n_variables: int

The number of variables.

name: str

The name of the object.

selection_criterion: str

The name of the selection criterion to select a distribution from a list of candidates.

Examples using ParametricStatistics

Parametric estimation of statistics

Parametric estimation of statistics

Parametric estimation of statistics