pm4py.objects.stochastic_petri package#

PM4Py â€“ A Process Mining Library for Python

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

Submodules#

pm4py.objects.stochastic_petri.ctmc module#

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.objects.stochastic_petri.ctmc.get_corr_hex(num)[source]#

Gets correspondence between a number and an hexadecimal string

Parameters#

num: Number

Returns#

hex_string: Hexadecimal string

pm4py.objects.stochastic_petri.ctmc.get_color_from_probabilities(prob_dictionary)[source]#

Returns colors from a dictionary of probabilities

Parameters#

prob_dictionary: Dictionary of probabilities

Returns#

color_dictionary: Dictionary of colors

pm4py.objects.stochastic_petri.ctmc.get_tangible_reachability_and_q_matrix_from_dfg_performance(dfg_performance, invisible_firing_rate=1000.0, parameters=None)[source]#

Get the tangible reachability graph and the Q matrix from the performance DFG

Parameters#

dfg_performance: Performance DFG
invisible_firing_rate: Firing rate for invisible transitions
parameters: Parameters

Returns#

reachab_graph: Reachability graph
tangible_reach_graph: Tangible reachability graph
stochastic_info: Stochastic information
q_matrix: Q-matrix from the tangible reachability graph

pm4py.objects.stochastic_petri.ctmc.get_tangible_reachability_and_q_matrix_from_log_net(log, net, im, fm, parameters=None)[source]#

Gets the tangible reachability graph from a log and an accepting Petri net

Parameters#

log: Event log
net: Petri net
im: Initial marking
fm: Final marking

Returns#

reachab_graph: Reachability graph
tangible_reach_graph: Tangible reachability graph
stochastic_info: Stochastic information
q_matrix: Q-matrix from the tangible reachability graph

pm4py.objects.stochastic_petri.ctmc.transient_analysis_from_petri_net_and_smap(net, im, s_map, delay, parameters=None)[source]#

Gets the transient analysis from a Petri net, a stochastic map and a delay

Parameters#

log: Event log
delay: Time delay
parameters: Parameters of the algorithm

Returns#

transient_result: Transient analysis result

pm4py.objects.stochastic_petri.ctmc.get_q_matrix_from_tangible_exponential(tangible_reach_graph, stochastic_info)[source]#

Gets Q matrix from tangible reachability graph and stochastic map where the distribution type has been forced to be exponential

Parameters#

tangible_reach_graph: Tangible reachability graph
stochastic_info: Stochastic map for each transition

Returns#

q_matrix: Q-matrix from the tangible reachability graph

pm4py.objects.stochastic_petri.ctmc.transient_analysis_from_tangible_q_matrix_and_single_state(tangible_reach_graph, q_matrix, source_state, time_diff)[source]#

Do transient analysis from tangible reachability graph, Q matrix and a single state to start from

Parameters#

tangible_reach_graph: Tangible reachability graph
q_matrix: Q matrix
source_state: Source state to consider
time_diff: Time interval we want to investigate

Returns#

transient_result: Transient analysis result

pm4py.objects.stochastic_petri.ctmc.transient_analysis_from_tangible_q_matrix_and_states_vector(tangible_reach_graph, q_matrix, states_vector, time_diff)[source]#

Do transient analysis from tangible reachability graph, Q matrix and a vector of probability of states

Parameters#

tangible_reach_graph: Tangible reachability graph
q_matrix: Q matrix
states_vector: Vector of states probabilities to start from
time_diff: Time interval we want to investigate

Returns#

transient_result: Transient analysis result

pm4py.objects.stochastic_petri.ctmc.nullspace(a_matrix, atol=1e-13, rtol=0)[source]#

Compute an approximate basis for the nullspace of A.

The algorithm used by this function is based on the singular value decomposition of A.

Parameters#

a_matrixndarray: A should be at most 2-D. A 1-D array with length k will be treated as a 2-D with shape (1, k)
atolfloat: The absolute tolerance for a zero singular value. Singular values smaller than atol are considered to be zero.
rtolfloat: The relative tolerance. Singular values less than rtol*smax are considered to be zero, where smax is the largest singular value.

If both atol and rtol are positive, the combined tolerance is the maximum of the two; that is:

tol = max(atol, rtol * smax)

Singular values smaller than tol are considered to be zero.

Returns#

nsndarray: If A is an array with shape (m, k), then ns will be an array with shape (k, n), where n is the estimated dimension of the nullspace of A. The columns of ns are a basis for the nullspace; each element in numpy.dot(A, ns) will be approximately zero.

pm4py.objects.stochastic_petri.ctmc.perform_steadystate(q_matrix, tangible_reach_graph)[source]#: Performs steady state analysis given the :param q_matrix: :return:

pm4py.objects.stochastic_petri.tangible_reachability module#

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.objects.stochastic_petri.tangible_reachability.get_tangible_reachability_from_log_net_im_fm(log, net, im, fm, parameters=None)[source]#

Gets the tangible reachability graph from a log and an accepting Petri net

Parameters#

log: Event log
net: Petri net
im: Initial marking
fm: Final marking

Returns#

reachab_graph: Reachability graph
tangible_reach_graph: Tangible reachability graph
stochastic_info: Stochastic information

pm4py.objects.stochastic_petri.tangible_reachability.get_tangible_reachability_from_net_im_sinfo(net, im, stochastic_info, parameters=None)[source]#

Gets the tangible reacahbility graph from a Petri net, an initial marking and a stochastic map

Parameters#

net: Petri net
im: Initial marking
fm: Final marking
stochastic_info: Stochastic information

Returns#

reachab_graph: Reachability graph
tangible_reach_graph: Tangible reachability graph

pm4py.objects.stochastic_petri.tangible_reachability.get_tangible_reachability_from_reachability(reach_graph, stochastic_info)[source]#

Gets the tangible reachability graph from the reachability graph and the stochastic transition map

Parameters#

reach_graph: Reachability graph
stochastic_info: Stochastic information

Returns#

tangible_reach_graph: Tangible reachability graph

pm4py.objects.stochastic_petri.utils module#

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.objects.stochastic_petri.utils.pick_transition(et, smap)[source]#

Pick a transition in a set of transitions based on the weights specified by the stochastic map

Parameters#

et: Enabled transitions
smap: Stochastic map

Returns#

trans: Transition chosen according to the weights