copulas.univariate package

Submodules

• copulas.univariate.base module
  • BoundedType
    • BoundedType.BOUNDED
    • BoundedType.SEMI_BOUNDED
    • BoundedType.UNBOUNDED
  • ParametricType
    • ParametricType.NON_PARAMETRIC
    • ParametricType.PARAMETRIC
  • ScipyModel
    • ScipyModel.MODEL_CLASS
    • ScipyModel.cumulative_distribution()
    • ScipyModel.fit()
    • ScipyModel.log_probability_density()
    • ScipyModel.percent_point()
    • ScipyModel.probability_density()
    • ScipyModel.sample()
  • Univariate
    • Univariate.BOUNDED
    • Univariate.PARAMETRIC
    • Univariate.cdf()
    • Univariate.check_fit()
    • Univariate.cumulative_distribution()
    • Univariate.fit()
    • Univariate.fitted
    • Univariate.from_dict()
    • Univariate.load()
    • Univariate.log_probability_density()
    • Univariate.pdf()
    • Univariate.percent_point()
    • Univariate.ppf()
    • Univariate.probability_density()
    • Univariate.sample()
    • Univariate.save()
    • Univariate.set_random_state()
    • Univariate.to_dict()
• copulas.univariate.beta module
  • BetaUnivariate
    • BetaUnivariate.BOUNDED
    • BetaUnivariate.MODEL_CLASS
    • BetaUnivariate.PARAMETRIC
• copulas.univariate.gamma module
  • GammaUnivariate
    • GammaUnivariate.BOUNDED
    • GammaUnivariate.MODEL_CLASS
    • GammaUnivariate.PARAMETRIC
• copulas.univariate.gaussian module
  • GaussianUnivariate
    • GaussianUnivariate.BOUNDED
    • GaussianUnivariate.MODEL_CLASS
    • GaussianUnivariate.PARAMETRIC
• copulas.univariate.gaussian_kde module
  • GaussianKDE
    • GaussianKDE.BOUNDED
    • GaussianKDE.MODEL_CLASS
    • GaussianKDE.PARAMETRIC
    • GaussianKDE.cumulative_distribution()
    • GaussianKDE.percent_point()
    • GaussianKDE.probability_density()
    • GaussianKDE.sample()
• copulas.univariate.log_laplace module
  • LogLaplace
    • LogLaplace.BOUNDED
    • LogLaplace.MODEL_CLASS
    • LogLaplace.PARAMETRIC
• copulas.univariate.selection module
  • select_univariate()
• copulas.univariate.student_t module
  • StudentTUnivariate
    • StudentTUnivariate.BOUNDED
    • StudentTUnivariate.MODEL_CLASS
    • StudentTUnivariate.PARAMETRIC
• copulas.univariate.truncated_gaussian module
  • TruncatedGaussian
    • TruncatedGaussian.BOUNDED
    • TruncatedGaussian.MODEL_CLASS
    • TruncatedGaussian.PARAMETRIC
• copulas.univariate.uniform module
  • UniformUnivariate
    • UniformUnivariate.BOUNDED
    • UniformUnivariate.MODEL_CLASS
    • UniformUnivariate.PARAMETRIC

Module contents

Univariate copulas module.

class copulas.univariate.BetaUnivariate(random_state=None)

Bases: ScipyModel

Wrapper around scipy.stats.beta.

Documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.beta.html

BOUNDED = 2

MODEL_CLASS = <scipy.stats._continuous_distns.beta_gen object>

PARAMETRIC = 1

class copulas.univariate.BoundedType(*values)

Bases: Enum

Bounded Enum.

BOUNDED = 2

SEMI_BOUNDED = 1

UNBOUNDED = 0

class copulas.univariate.GammaUnivariate(random_state=None)

Bases: ScipyModel

Wrapper around scipy.stats.gamma.

Documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gamma.html

BOUNDED = 1

MODEL_CLASS = <scipy.stats._continuous_distns.gamma_gen object>

PARAMETRIC = 1

class copulas.univariate.GaussianKDE(sample_size=None, random_state=None, bw_method=None, weights=None)

Bases: ScipyModel

A wrapper for gaussian Kernel density estimation.

It was implemented in scipy.stats toolbox. gaussian_kde is slower than statsmodels but allows more flexibility.

When a sample_size is provided the fit method will sample the data, and mask the real information. Also, ensure the number of entries will be always the value of sample_size.

Parameters:

sample_size (int) – amount of parameters to sample

BOUNDED = 0

MODEL_CLASS

alias of gaussian_kde

PARAMETRIC = 0

cumulative_distribution(X)

Compute the cumulative distribution value for each point in X.

Parameters:

X (numpy.ndarray) – Values for which the cumulative distribution will be computed. It must have shape (n, 1).

Returns:

Cumulative distribution values for points in X.

Return type:

numpy.ndarray

Raises:

NotFittedError – if the model is not fitted.

percent_point(U, method='chandrupatla')

Compute the inverse cumulative distribution value for each point in U.

Parameters:

• U (numpy.ndarray) – Values for which the cumulative distribution will be computed. It must have shape (n, 1) and values must be in [0,1].

• method (str) – Whether to use the chandrupatla or bisect solver.

Returns:

Inverse cumulative distribution values for points in U.

Return type:

numpy.ndarray

Raises:

NotFittedError – if the model is not fitted.

probability_density(X)

Compute the probability density for each point in X.

Parameters:

X (numpy.ndarray) – Values for which the probability density will be computed. It must have shape (n, 1).

Returns:

Probability density values for points in X.

Return type:

numpy.ndarray

Raises:

NotFittedError – if the model is not fitted.

sample(n_samples=1)

Sample values from this model.

Argument:

n_samples (int):

Number of values to sample

Returns:

Array of shape (n_samples, 1) with values randomly sampled from this model distribution.

Return type:

numpy.ndarray

Raises:

NotFittedError – if the model is not fitted.

class copulas.univariate.GaussianUnivariate(random_state=None)

Bases: ScipyModel

Gaussian univariate model.

BOUNDED = 0

MODEL_CLASS = <scipy.stats._continuous_distns.norm_gen object>

PARAMETRIC = 1

class copulas.univariate.LogLaplace(random_state=None)

Bases: ScipyModel

Wrapper around scipy.stats.loglaplace.

Documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.loglaplace.html

BOUNDED = 1

MODEL_CLASS = <scipy.stats._continuous_distns.loglaplace_gen object>

PARAMETRIC = 1

class copulas.univariate.ParametricType(*values)

Bases: Enum

Parametric Enum.

NON_PARAMETRIC = 0

PARAMETRIC = 1

class copulas.univariate.StudentTUnivariate(random_state=None)

Bases: ScipyModel

Wrapper around scipy.stats.t.

Documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.t.html

BOUNDED = 0

MODEL_CLASS = <scipy.stats._continuous_distns.t_gen object>

PARAMETRIC = 1

class copulas.univariate.TruncatedGaussian(minimum=None, maximum=None, random_state=None)

Bases: ScipyModel

Wrapper around scipy.stats.truncnorm.

Documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.truncnorm.html

BOUNDED = 2

MODEL_CLASS = <scipy.stats._continuous_distns.truncnorm_gen object>

PARAMETRIC = 1

class copulas.univariate.UniformUnivariate(random_state=None)

Bases: ScipyModel

Uniform univariate model.

BOUNDED = 2

MODEL_CLASS = <scipy.stats._continuous_distns.uniform_gen object>

PARAMETRIC = 1

class copulas.univariate.Univariate(candidates=None, parametric=None, bounded=None, random_state=None, selection_sample_size=None)

Bases: object

Univariate Distribution.

Parameters:

• candidates (list[str or type or Univariate]) – List of candidates to select the best univariate from. It can be a list of strings representing Univariate FQNs, or a list of Univariate subclasses or a list of instances.

• parametric (ParametricType) – If not None, only select subclasses of this type. Ignored if candidates is passed.

• bounded (BoundedType) – If not None, only select subclasses of this type. Ignored if candidates is passed.

• random_state (int or np.random.RandomState) – Random seed or RandomState to use.

• selection_sample_size (int) – Size of the subsample to use for candidate selection. If None, all the data is used.

BOUNDED = 0

PARAMETRIC = 0

cdf(X)

Compute the cumulative distribution value for each point in X.

Parameters:

X (numpy.ndarray) – Values for which the cumulative distribution will be computed. It must have shape (n, 1).

Returns:

Cumulative distribution values for points in X.

Return type:

numpy.ndarray

check_fit()

Check whether this model has already been fit to a random variable.

Raise a NotFittedError if it has not.

Raises:

NotFittedError – if the model is not fitted.

cumulative_distribution(X)

Compute the cumulative distribution value for each point in X.

Parameters:

X (numpy.ndarray) – Values for which the cumulative distribution will be computed. It must have shape (n, 1).

Returns:

Cumulative distribution values for points in X.

Return type:

numpy.ndarray

Raises:

NotFittedError – if the model is not fitted.

fit(X)

Fit the model to a random variable.

Parameters:

X (numpy.ndarray) – Values of the random variable. It must have shape (n, 1).

fitted = False

classmethod from_dict(params)

Build a distribution from its params dict.

Parameters:

params (dict) – Dictionary containing the FQN of the distribution and the necessary parameters to rebuild it. The input format is exactly the same that is outputted by the distribution class to_dict method.

Returns:

Distribution instance.

Return type:

Univariate

classmethod load(path)

Load a Univariate instance from a pickle file.

Parameters:

path (str) – Path to the pickle file where the distribution has been serialized.

Returns:

Loaded instance.

Return type:

Univariate

log_probability_density(X)

Compute the log of the probability density for each point in X.

It should be overridden with numerically stable variants whenever possible.

Parameters:

X (numpy.ndarray) – Values for which the log probability density will be computed. It must have shape (n, 1).

Returns:

Log probability density values for points in X.

Return type:

numpy.ndarray

Raises:

NotFittedError – if the model is not fitted.

pdf(X)

Compute the probability density for each point in X.

Parameters:

X (numpy.ndarray) – Values for which the probability density will be computed. It must have shape (n, 1).

Returns:

Probability density values for points in X.

Return type:

numpy.ndarray

percent_point(U)

Compute the inverse cumulative distribution value for each point in U.

Parameters:

U (numpy.ndarray) – Values for which the cumulative distribution will be computed. It must have shape (n, 1) and values must be in [0,1].

Returns:

Inverse cumulative distribution values for points in U.

Return type:

numpy.ndarray

Raises:

NotFittedError – if the model is not fitted.

ppf(U)

Compute the inverse cumulative distribution value for each point in U.

Parameters:

U (numpy.ndarray) – Values for which the cumulative distribution will be computed. It must have shape (n, 1) and values must be in [0,1].

Returns:

Inverse cumulative distribution values for points in U.

Return type:

numpy.ndarray

probability_density(X)

Compute the probability density for each point in X.

Parameters:

X (numpy.ndarray) – Values for which the probability density will be computed. It must have shape (n, 1).

Returns:

Probability density values for points in X.

Return type:

numpy.ndarray

Raises:

NotFittedError – if the model is not fitted.

sample(n_samples=1)

Sample values from this model.

Argument:

n_samples (int):

Number of values to sample

Returns:

Array of shape (n_samples, 1) with values randomly sampled from this model distribution.

Return type:

numpy.ndarray

Raises:

NotFittedError – if the model is not fitted.

save(path)

Serialize this univariate instance using pickle.

Parameters:

path (str) – Path to where this distribution will be serialized.

set_random_state(random_state)

Set the random state.

Parameters:

random_state (int, np.random.RandomState, or None) – Seed or RandomState for the random generator.

to_dict()

Return the parameters of this model in a dict.

Returns:

Dictionary containing the distribution type and all the parameters that define the distribution.

Return type:

dict

Raises:

NotFittedError – if the model is not fitted.