Hierarchical Partition¶

The API references for the hierarchical partition of the parameter space. Please see the general partition class in API Cheatsheet.

Binary Partition¶

class PyXAB.partition.BinaryPartition.BinaryPartition(domain=None, node=<class 'PyXAB.partition.Node.P_node'>)¶

Bases: PyXAB.partition.Partition.Partition

Implementation of Binary Partition

__init__(domain=None, node=<class 'PyXAB.partition.Node.P_node'>)¶

Initialization of the Binary Partition

Parameters

domain (list(list)) – The domain of the objective function to be optimized, should be in the form of list of lists (hypercubes), i.e., [[range1], [range2], … [range_d]], where [range_i] is a list indicating the domain’s projection on the i-th dimension, e.g., [-1, 1]
node – The node used in the partition, with the default choice to be P_node.

deepen()¶: The function to deepen the partition by one layer by making children to every node in the last layer

get_depth()¶

The function to get the depth of the partition

Returns: depth – The depth of the partition
Return type: int

get_layer_node_list(depth)¶

The function to get the all the nodes on the specified depth

Parameters: depth (int) – The depth of the layer in the partition
Returns: self.node_list – The list of nodes on the specified depth
Return type: list

get_node_list()¶

The function to get the list all nodes in the partition

Returns: self.node_list – The list of all nodes
Return type: list

get_root()¶

The function to get the root of the partition

Returns: The root node of the partition
Return type: self.root

make_children(parent, newlayer=False)¶

The function to make children for the parent node with a standard binary partition, i.e., split every parent node in the middle. If there are multiple dimensions, the dimension to split the parent is chosen randomly

Parameters

parent – The parent node to be expanded into children nodes
newlayer (bool) – Boolean variable that indicates whether or not a new layer is created

Random Binary Partition¶

class PyXAB.partition.RandomBinaryPartition.RandomBinaryPartition(domain=None, node=<class 'PyXAB.partition.Node.P_node'>)¶

Bases: PyXAB.partition.Partition.Partition

Implementation of Random Binary Partition

__init__(domain=None, node=<class 'PyXAB.partition.Node.P_node'>)¶

Initialization of the Random Binary Partition

Parameters

domain (list(list)) – The domain of the objective function to be optimized, should be in the form of list of lists (hypercubes), i.e., [[range1], [range2], … [range_d]], where [range_i] is a list indicating the domain’s projection on the i-th dimension, e.g., [-1, 1]
node – The node used in the partition, with the default choice to be P_node.

deepen()¶: The function to deepen the partition by one layer by making children to every node in the last layer

get_depth()¶

The function to get the depth of the partition

Returns: depth – The depth of the partition
Return type: int

get_layer_node_list(depth)¶

The function to get the all the nodes on the specified depth

Parameters: depth (int) – The depth of the layer in the partition
Returns: self.node_list – The list of nodes on the specified depth
Return type: list

get_node_list()¶

The function to get the list all nodes in the partition

Returns: self.node_list – The list of all nodes
Return type: list

get_root()¶

The function to get the root of the partition

Returns: The root node of the partition
Return type: self.root

make_children(parent, newlayer=False)¶

The function to make children for the parent node with a random binary partition, i.e., split every parent node randomly into two children. If there are multiple dimensions, the dimension to split the parent is chosen randomly

Parameters

parent – The parent node to be expanded into children nodes
newlayer (bool) – Boolean variable that indicates whether or not a new layer is created

Dimension-wise Binary Partition¶

class PyXAB.partition.DimensionBinaryPartition.DimensionBinaryPartition(domain=None, node=<class 'PyXAB.partition.Node.P_node'>)¶

Bases: PyXAB.partition.Partition.Partition

Implementation of Dimension-wise Binary Partition

__init__(domain=None, node=<class 'PyXAB.partition.Node.P_node'>)¶

Initialization of the Dimension-wise Binary Partition

Parameters

domain (list(list)) – The domain of the objective function to be optimized, should be in the form of list of lists (hypercubes), i.e., [[range1], [range2], … [range_d]], where [range_i] is a list indicating the domain’s projection on the i-th dimension, e.g., [-1, 1]
node – The node used in the partition, with the default choice to be P_node.

deepen()¶: The function to deepen the partition by one layer by making children to every node in the last layer

get_depth()¶

The function to get the depth of the partition

Returns: depth – The depth of the partition
Return type: int

get_layer_node_list(depth)¶

The function to get the all the nodes on the specified depth

Parameters: depth (int) – The depth of the layer in the partition
Returns: self.node_list – The list of nodes on the specified depth
Return type: list

get_node_list()¶

The function to get the list all nodes in the partition

Returns: self.node_list – The list of all nodes
Return type: list

get_root()¶

The function to get the root of the partition

Returns: The root node of the partition
Return type: self.root

make_children(parent, newlayer=False)¶

The function to make children for the parent node with a dimension-wise binary partition, i.e., split every parent node into 2^d children where d is the dimension of the parameter domain. Each dimension of the domain is split right in the middle.

Parameters

parent – The parent node to be expanded into children nodes
newlayer (bool) – Boolean variable that indicates whether or not a new layer is created

Kary Partition¶

class PyXAB.partition.KaryPartition.KaryPartition(domain=None, K=3, node=<class 'PyXAB.partition.Node.P_node'>)¶

Bases: PyXAB.partition.Partition.Partition

Implementation of K-ary Partition especially when K >= 3, i.e., Ternary, Quaternary, and so on

__init__(domain=None, K=3, node=<class 'PyXAB.partition.Node.P_node'>)¶

Initialization of the K-ary Partition

Parameters

domain (list(list)) – The domain of the objective function to be optimized, should be in the form of list of lists (hypercubes), i.e., [[range1], [range2], … [range_d]], where [range_i] is a list indicating the domain’s projection on the i-th dimension, e.g., [-1, 1]
K (int) – The number of children of each parent, with the default choice to be 3
node – The node used in the partition, with the default choice to be P_node.

deepen()¶: The function to deepen the partition by one layer by making children to every node in the last layer

get_depth()¶

The function to get the depth of the partition

Returns: depth – The depth of the partition
Return type: int

get_layer_node_list(depth)¶

The function to get the all the nodes on the specified depth

Parameters: depth (int) – The depth of the layer in the partition
Returns: self.node_list – The list of nodes on the specified depth
Return type: list

get_node_list()¶

The function to get the list all nodes in the partition

Returns: self.node_list – The list of all nodes
Return type: list

get_root()¶

The function to get the root of the partition

Returns: The root node of the partition
Return type: self.root

make_children(parent, newlayer=False)¶

The function to make children for the parent node with a standard K-ary partition, i.e., split every parent node into K children nodes of the same size. If there are multiple dimensions, the dimension to split the parent is chosen randomly

Parameters

parent – The parent node to be expanded into children nodes
newlayer (bool) – Boolean variable that indicates whether or not a new layer is created

RandomKary Partition¶

class PyXAB.partition.RandomKaryPartition.RandomKaryPartition(domain=None, K=3, node=<class 'PyXAB.partition.Node.P_node'>)¶

Bases: PyXAB.partition.Partition.Partition

Implementation of Random K-ary Partition especially when K >= 3, i.e., Ternary, Quaternary, and so on

__init__(domain=None, K=3, node=<class 'PyXAB.partition.Node.P_node'>)¶

Initialization of the Random K-ary Partition

Parameters

domain (list(list)) – The domain of the objective function to be optimized, should be in the form of list of lists (hypercubes), i.e., [[range1], [range2], … [range_d]], where [range_i] is a list indicating the domain’s projection on the i-th dimension, e.g., [-1, 1]
K (int) – The number of children of each parent
node – The node used in the partition, with the default choice to be P_node.

deepen()¶: The function to deepen the partition by one layer by making children to every node in the last layer

get_depth()¶

The function to get the depth of the partition

Returns: depth – The depth of the partition
Return type: int

get_layer_node_list(depth)¶

The function to get the all the nodes on the specified depth

Parameters: depth (int) – The depth of the layer in the partition
Returns: self.node_list – The list of nodes on the specified depth
Return type: list

get_node_list()¶

The function to get the list all nodes in the partition

Returns: self.node_list – The list of all nodes
Return type: list

get_root()¶

The function to get the root of the partition

Returns: The root node of the partition
Return type: self.root

make_children(parent, newlayer=False)¶

The function to make children for the parent node with a random K-ary partition, i.e., split every parent node into K children nodes of different sizes. If there are multiple dimensions, the dimension to split the parent is chosen randomly

Parameters

parent – The parent node to be expanded into children nodes
newlayer (bool) – Boolean variable that indicates whether or not a new layer is created

Hierarchical Partition¶

Binary Partition¶

Random Binary Partition¶

Dimension-wise Binary Partition¶

Kary Partition¶

RandomKary Partition¶

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