copulas.univariate.base module

Base Univariate class.

class copulas.univariate.base.BoundedType(*values)[source]

Bases: Enum

Bounded Enum.

BOUNDED = 2

SEMI_BOUNDED = 1

UNBOUNDED = 0

class copulas.univariate.base.ParametricType(*values)[source]

Bases: Enum

Parametric Enum.

NON_PARAMETRIC = 0

PARAMETRIC = 1

class copulas.univariate.base.ScipyModel(random_state=None)[source]

Bases: Univariate, ABC

Wrapper for scipy models.

This class makes the probability_density, cumulative_distribution, percent_point and sample point at the underlying pdf, cdf, ppd and rvs methods respectively.

fit, _get_params and _set_params must be implemented by the subclasses.

MODEL_CLASS = None

cumulative_distribution(X)[source]

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)[source]

Fit the model to a random variable.

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

log_probability_density(X)[source]

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

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.

percent_point(U)[source]

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.

probability_density(X)[source]

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)[source]

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.base.Univariate(candidates=None, parametric=None, bounded=None, random_state=None, selection_sample_size=None)[source]

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)[source]

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()[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

Serialize this univariate instance using pickle.

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

set_random_state(random_state)[source]

Set the random state.

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

to_dict()[source]

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.