copulas.multivariate.tree module¶

Multivariate trees module.

class copulas.multivariate.tree.CenterTree(random_state=None)[source]¶

Bases: copulas.multivariate.tree.Tree

Tree for a C-vine copula.

get_anchor()[source]¶

Find anchor variable with highest sum of dependence with the rest.

Returns: Anchor variable.
Return type: int

tree_type = 0¶

class copulas.multivariate.tree.DirectTree(random_state=None)[source]¶

Bases: copulas.multivariate.tree.Tree

DirectTree class.

tree_type = 1¶

class copulas.multivariate.tree.Edge(index, left, right, copula_name, copula_theta)[source]¶

Bases: object

Represents an edge in the copula.

classmethod from_dict(edge_dict)[source]¶

Create a new instance from a parameters dictionary.

Parameters: params (dict) – Parameters of the Edge, in the same format as the one returned by the to_dict method.
Returns: Instance of the edge defined on the parameters.
Return type: Edge

classmethod get_child_edge(index, left_parent, right_parent)[source]¶

Construct a child edge from two parent edges.

Parameters

index (int) – Index of the new Edge.
left_parent (Edge) – Left parent
right_parent (Edge) – Right parent

Returns

The new child edge.

Return type

Edge

classmethod get_conditional_uni(left_parent, right_parent)[source]¶

Identify pair univariate value from parents.

Parameters

left_parent (Edge) – left parent
right_parent (Edge) – right parent

Returns

left and right parents univariate.

Return type

tuple[np.ndarray, np.ndarray]

get_likelihood(uni_matrix)[source]¶

Compute likelihood given a U matrix.

Parameters: uni_matrix (numpy.array) – Matrix to compute the likelihood.
Returns: likelihood and conditional values.
Return type: tuple (np.ndarray, np.ndarray, np.array)

is_adjacent(another_edge)[source]¶

Check if two edges are adjacent.

Parameters: another_edge (Edge) – edge object of another edge
Returns: True if the two edges are adjacent.
Return type: bool

static sort_edge(edges)[source]¶

Sort iterable of edges first by left node indices then right.

Parameters: edges (list[Edge]) – List of edges to be sorted.
Returns: Sorted list by left and right node indices.
Return type: list[Edge]

to_dict()[source]¶

Return a dict with the parameters to replicate this Edge.

Returns: Parameters of this Edge.
Return type: dict

class copulas.multivariate.tree.RegularTree(random_state=None)[source]¶

Bases: copulas.multivariate.tree.Tree

RegularTree class.

tree_type = 2¶

class copulas.multivariate.tree.Tree(random_state=None)[source]¶

Bases: copulas.multivariate.base.Multivariate

Helper class to instantiate a single tree in the vine model.

fit(index, n_nodes, tau_matrix, previous_tree, edges=None)[source]¶

Fit this tree object.

Parameters

index (int) – index of the tree.
n_nodes (int) – number of nodes in the tree.
tau_matrix (numpy.array) – kendall’s tau matrix of the data, shape (n_nodes, n_nodes).
previous_tree (Tree) – tree object of previous level.

fitted = False¶

classmethod from_dict(tree_dict, previous=None)[source]¶

Create a new instance from a parameters dictionary.

Parameters: params (dict) – Parameters of the Tree, in the same format as the one returned by the to_dict method.
Returns: Instance of the tree defined on the parameters.
Return type: Tree

get_adjacent_matrix()[source]¶

Get adjacency matrix.

Returns: adjacency matrix
Return type: numpy.ndarray

get_likelihood(uni_matrix)[source]¶

Compute likelihood of the tree given an U matrix.

Parameters: uni_matrix (numpy.array) – univariate matrix to evaluate likelihood on.
Returns: likelihood of the current tree, next level conditional univariate matrix
Return type: tuple[float, numpy.array]

get_tau_matrix()[source]¶

Get tau matrix for adjacent pairs.

Returns: tau matrix for the current tree
Return type: tau (numpy.ndarray)

prepare_next_tree()[source]¶: Prepare conditional U matrix for next tree.

to_dict()[source]¶

Return a dict with the parameters to replicate this Tree.

Returns: Parameters of this Tree.
Return type: dict

tree_type = None¶

class copulas.multivariate.tree.TreeTypes[source]¶

Bases: enum.Enum

The available types of trees.

CENTER = 0¶

DIRECT = 1¶

REGULAR = 2¶

copulas.multivariate.tree.get_tree(tree_type)[source]¶

Get a Tree instance of the specified type.

Parameters: tree_type (str or TreeTypes) – Type of tree of which to get an instance.
Returns: Instance of a Tree of the specified type.
Return type: Tree