PyCTBN.PyCTBN.structure_graph package¶

Submodules¶

PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix module¶

class PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix(state_residence_times: Optional[numpy.array] = None, state_transition_matrix: Optional[numpy.array] = None, cim: Optional[numpy.array] = None)¶

Bases: object

Abstracts the Conditional Intesity matrix of a node as aggregation of the state residence times vector and state transition matrix and the actual CIM matrix.

Parameters

state_residence_times (numpy.array) – state residence times vector
state_transition_matrix (numpy.ndArray) – the transitions count matrix

_cim

the actual cim of the node

property cim: numpy.ndarray¶

compute_cim_coefficients() → None¶: Compute the coefficients of the matrix _cim by using the following equality q_xx’ = M[x, x’] / T[x]. The class member _cim will contain the computed cim

property state_residence_times: numpy.ndarray¶

property state_transition_matrix: numpy.ndarray¶

PyCTBN.PyCTBN.structure_graph.network_generator module¶

class PyCTBN.PyCTBN.structure_graph.network_generator.NetworkGenerator(labels, vals)¶

Bases: object

Provides the methods to generate a network graph and the CIMs related to it Items in _labels, _vals and _indxs are related, and therefore respect the same order

Parameters

_labels (List) – List of variables labels that will be part of the network
_vals (List) – List of cardinalities of the variables in network (defined in the same order as _labels)
_indxs (List) – List of the nodes indexes
_cims (Dict) – It contains, for each variable label (the key), the SetOfCims object related to it
_graph (NetworkGraph) – The NetworkGraph object representing the generated structure

property cims: dict¶

property dyn_str: list¶

generate_cims(min_val, max_val)¶

For each node, generate the corresponding SetOfCims. The objective is to group the CIMs: (actually generated by private method __generate_cim) according to parents possibles states of every node. This method must obviously be executed after the graph has been generated.

Parameters

min_val (float) – Minimum value allowed for the coefficients in the CIMs
max_val (float) – Maximum value allowed for the coefficients in the CIMs

generate_graph(density, fixed: bool = False)¶

Generate the edges according to specified density, and then instantiate the NetworkGraph object: to represent the network

Parameters

density (float) – Probability of an edge between two nodes to exist
fixed (bool) – Specifies whether the required density is mandatory or it’s just a probability

property graph: PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph¶

property variables: list¶

PyCTBN.PyCTBN.structure_graph.network_graph module¶

class PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph(graph_struct: PyCTBN.PyCTBN.structure_graph.structure.Structure)¶

Bases: object

Abstracts the infos contained in the Structure class in the form of a directed graph. Has the task of creating all the necessary filtering and indexing structures for parameters estimation

Parameters: graph_struct (Structure) – the Structure object from which infos about the net will be extracted
_graph: directed graph
_aggregated_info_about_nodes_parents: a structure that contains all the necessary infos about every parents of the node of which all the indexing and filtering structures will be constructed.
_time_scalar_indexing_structure: the indexing structure for state res time estimation
_transition_scalar_indexing_structure: the indexing structure for transition computation
_time_filtering: the columns filtering structure used in the computation of the state res times
_transition_filtering: the columns filtering structure used in the computation of the transition from one state to another
_p_combs_structure: all the possible parents states combination for the node of interest

add_edges(list_of_edges: List) → None¶

Add the edges to the _graph contained in the list list_of_edges.

Parameters: list_of_edges (List) – the list containing of tuples containing the edges

add_nodes(list_of_nodes: List) → None¶

Adds the nodes to the _graph contained in the list of nodes list_of_nodes. Sets all the properties that identify a nodes (index, positional index, cardinality)

Parameters: list_of_nodes (List) – the nodes to add to _graph

static build_p_comb_structure_for_a_node(parents_values: List) → numpy.ndarray¶

Builds the combinatorial structure that contains the combinations of all the values contained in parents_values.

Parameters: parents_values (List) – the cardinalities of the nodes
Returns: A numpy matrix containing a grid of the combinations
Return type: numpy.ndArray

static build_time_columns_filtering_for_a_node(node_indx: int, p_indxs: List) → numpy.ndarray¶: Builds the necessary structure to filter the desired columns indicated by node_indx and p_indxs in the dataset. This structute will be used in the computation of the state res times. :param node_indx: the index of the node :type node_indx: int :param p_indxs: the indexes of the node’s parents :type p_indxs: List :return: The filtering structure for times estimation :rtype: numpy.ndArray

static build_time_scalar_indexing_structure_for_a_node(node_states: int, parents_vals: List) → numpy.ndarray¶

Builds an indexing structure for the computation of state residence times values.

Parameters

node_states (int) – the node cardinality
parents_vals (List) – the caridinalites of the node’s parents

Returns

The time indexing structure

Return type

numpy.ndArray

static build_transition_filtering_for_a_node(node_indx: int, p_indxs: List, nodes_number: int) → numpy.ndarray¶: Builds the necessary structure to filter the desired columns indicated by node_indx and p_indxs in the dataset. This structure will be used in the computation of the state transitions values. :param node_indx: the index of the node :type node_indx: int :param p_indxs: the indexes of the node’s parents :type p_indxs: List :param nodes_number: the total number of nodes in the dataset :type nodes_number: int :return: The filtering structure for transitions estimation :rtype: numpy.ndArray

static build_transition_scalar_indexing_structure_for_a_node(node_states_number: int, parents_vals: List) → numpy.ndarray¶

Builds an indexing structure for the computation of state transitions values.

Parameters

node_states_number (int) – the node cardinality
parents_vals (List) – the caridinalites of the node’s parents

Returns

The transition indexing structure

Return type

numpy.ndArray

clear_indexing_filtering_structures() → None¶: Initialize all the filtering/indexing structures.

property edges: List¶

fast_init(node_id: str) → None¶

Initializes all the necessary structures for parameters estimation of the node identified by the label node_id

Parameters: node_id (string) – the label of the node

get_node_indx(node_id) → int¶

get_ordered_by_indx_set_of_parents(node: str) → Tuple¶

Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely (parents_labels, parents_indexes, parents_cardinalities).

Parameters: node (string) – the label of the node
Returns: a tuple containing all the parent set infos
Return type: Tuple

get_parents_by_id(node_id) → List¶

Returns a list of labels of the parents of the node node_id

Parameters: node_id (string) – the node label
Returns: a List of labels of the parents
Return type: List

get_positional_node_indx(node_id) → int¶

get_states_number(node_id) → int¶

has_edge(edge: tuple) → bool¶

Check if the graph contains a specific edge

Parameters:: edge: a tuple that rappresents the edge
Returns:: bool

property nodes: List¶

property nodes_indexes: numpy.ndarray¶

property nodes_values: numpy.ndarray¶

property p_combs: numpy.ndarray¶

remove_edges(list_of_edges: List) → None¶

Remove the edges to the graph contained in the list list_of_edges.

Parameters: list_of_edges (List) – The edges to remove from the graph

remove_node(node_id: str) → None¶: Remove the node node_id from all the class members. Initialize all the filtering/indexing structures.

property time_filtering: numpy.ndarray¶

property time_scalar_indexing_strucure: numpy.ndarray¶

property transition_filtering: numpy.ndarray¶

property transition_scalar_indexing_structure: numpy.ndarray¶

PyCTBN.PyCTBN.structure_graph.sample_path module¶

class PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath(importer: PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter)¶

Bases: object

Aggregates all the informations about the trajectories, the real structure of the sampled net and variables cardinalites. Has the task of creating the objects Trajectory and Structure that will contain the mentioned data.

Parameters: importer (AbstractImporter) – the Importer object which contains the imported and processed data
_trajectories: the Trajectory object that will contain all the concatenated trajectories
_structure: the Structure Object that will contain all the structural infos about the net
_total_variables_count: the number of variables in the net

build_structure() → None¶: Builds the Structure object that aggregates all the infos about the net.

build_trajectories() → None¶: Builds the Trajectory object that will contain all the trajectories. Clears all the unused dataframes in _importer Object

clear_memory()¶

property has_prior_net_structure: bool¶

property structure: PyCTBN.PyCTBN.structure_graph.structure.Structure¶

property total_variables_count: int¶

property trajectories: PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory¶

PyCTBN.PyCTBN.structure_graph.set_of_cims module¶

class PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims(node_id: str, parents_states_number: List, node_states_number: int, p_combs: numpy.ndarray, cims: Optional[numpy.ndarray] = None)¶

Bases: object

Aggregates all the CIMS of the node identified by the label _node_id.

Parameters

node_id – the node label
parents_states_number (List) – the cardinalities of the parents
node_states_number (int) – the caridinality of the node
p_combs (numpy.ndArray) – the p_comb structure bound to this node

_state_residence_time

matrix containing all the state residence time vectors for the node

_transition_matrices

matrix containing all the transition matrices for the node

_actual_cims

the cims of the node

property actual_cims: numpy.ndarray¶

build_cims(state_res_times: numpy.ndarray, transition_matrices: numpy.ndarray) → None¶

Build the ConditionalIntensityMatrix objects given the state residence times and transitions matrices. Compute the cim coefficients.The class member _actual_cims will contain the computed cims.

Parameters

state_res_times (numpy.ndArray) – the state residence times matrix
transition_matrices (numpy.ndArray) – the transition matrices

build_times_and_transitions_structures() → None¶: Initializes at the correct dimensions the state residence times matrix and the state transition matrices.

filter_cims_with_mask(mask_arr: numpy.ndarray, comb: List) → numpy.ndarray¶

Filter the cims contained in the array _actual_cims given the boolean mask mask_arr and the index comb.

Parameters

mask_arr (numpy.array) – the boolean mask that indicates which parent to consider
comb (numpy.array) – the state/s of the filtered parents

Returns

Array of ConditionalIntensityMatrix objects

Return type

numpy.array

get_cims_number()¶

property p_combs: numpy.ndarray¶

PyCTBN.PyCTBN.structure_graph.structure module¶

class PyCTBN.PyCTBN.structure_graph.structure.Structure(nodes_labels_list: List, nodes_indexes_arr: numpy.ndarray, nodes_vals_arr: numpy.ndarray, edges_list: List, total_variables_number: int)¶

Bases: object

Contains all the infos about the network structure(nodes labels, nodes caridinalites, edges, indexes)

Parameters

nodes_labels_list (List) – the symbolic names of the variables
nodes_indexes_arr (numpy.ndArray) – the indexes of the nodes
nodes_vals_arr (numpy.ndArray) – the cardinalites of the nodes
edges_list (List) – the edges of the network
total_variables_number (int) – the total number of variables in the dataset

add_edge(edge: tuple)¶

clean_structure_edges()¶

contains_edge(edge: tuple) → bool¶

property edges: List¶

get_node_id(node_indx: int) → str¶

Given the node_index returns the node label.

Parameters: node_indx (int) – the node index
Returns: the node label
Return type: string

get_node_indx(node_id: str) → int¶

Given the node_index returns the node label.

Parameters: node_id (string) – the node label
Returns: the node index
Return type: int

get_positional_node_indx(node_id: str) → int¶

get_states_number(node: str) → int¶

Given the node label node returns the cardinality of the node.

Parameters: node (string) – the node label
Returns: the node cardinality
Return type: int

property nodes_indexes: numpy.ndarray¶

property nodes_labels: List¶

property nodes_values: numpy.ndarray¶

remove_edge(edge: tuple)¶

remove_node(node_id: str) → None¶: Remove the node node_id from all the class members. The class member _total_variables_number since it refers to the total number of variables in the dataset.

property total_variables_number: int¶

PyCTBN.PyCTBN.structure_graph.trajectory module¶

class PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory(list_of_columns: List, original_cols_number: int)¶

Bases: object

Abstracts the infos about a complete set of trajectories, represented as a numpy array of doubles (the time deltas) and a numpy matrix of ints (the changes of states).

Parameters

list_of_columns (List) – the list containing the times array and values matrix
original_cols_number (int) – total number of cols in the data

_actual_trajectory

the trajectory containing also the duplicated/shifted values

_times

the array containing the time deltas

property complete_trajectory: numpy.ndarray¶

size()¶

property times¶

property trajectory: numpy.ndarray¶

PyCTBN.PyCTBN.structure_graph package¶

Submodules¶

PyCTBN.PyCTBN.structure_graph.conditional_intensity_matrix module¶

PyCTBN.PyCTBN.structure_graph.network_generator module¶

PyCTBN.PyCTBN.structure_graph.network_graph module¶

PyCTBN.PyCTBN.structure_graph.sample_path module¶

PyCTBN.PyCTBN.structure_graph.set_of_cims module¶

PyCTBN.PyCTBN.structure_graph.structure module¶

PyCTBN.PyCTBN.structure_graph.trajectory module¶

Module contents¶