pm4py.algo.discovery.inductive.variants.im.data_structures package

Submodules

pm4py.algo.discovery.inductive.variants.im.data_structures.subtree_plain module

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

class pm4py.algo.discovery.inductive.variants.im.data_structures.subtree_plain.Parameters

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'

CASE_ID_KEY = 'pm4py:param:case_id_key'

CONCURRENT_KEY = 'concurrent'

EMPTY_TRACE_KEY = 'empty_trace'

NOISE_THRESHOLD = 'noiseThreshold'

ONCE_PER_TRACE_KEY = 'once_per_trace'

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'

STRICT_TAU_LOOP_KEY = 'strict_tau_loop'

TAU_LOOP_KEY = 'tau_loop'

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'

class pm4py.algo.discovery.inductive.variants.im.data_structures.subtree_plain.SubtreePlain(log, dfg, master_dfg, initial_dfg, activities, counts, rec_depth, noise_threshold=0, start_activities=None, end_activities=None, initial_start_activities=None, initial_end_activities=None, parameters=None, real_init=True)

Bases: object

apply_fall_through(parameters=None)

check_for_cut(test_log, deleted_activity=None, parameters=None)

contains_empty_trace()

create_dfg(parameters=None)

detect_concurrent()

detect_cut(second_iteration=False, parameters=None)

detect_loop()

detect_xor(conn_components)

Detects xor cut :Parameters: * conn_components – Connected components

• this_nx_graph – NX graph calculated on the DFG
• strongly_connected_components – Strongly connected components

find_set_with_x(x, list_of_sets)

get_index_of_x_in_list(x, li)

initialize_tree(dfg, log, initial_dfg, activities, second_iteration=False, end_call=True, parameters=None)

Initialize the tree

Parameters:

• dfg – Directly follows graph of this subtree
• log – the event log
• initial_dfg – Referral directly follows graph that should be taken in account adding hidden/loop transitions
• activities – Activities of this subtree
• second_iteration – Boolean that indicates if we are executing this method for the second time

is_followed_by(dfg, activity_a, activity_b)

check if Activity A is followed by Activity B in the dfg of self, returns bool.

pm4py.algo.discovery.inductive.variants.im.data_structures.subtree_plain.make_tree(log, dfg, master_dfg, initial_dfg, activities, c, recursion_depth, noise_threshold, start_activities, end_activities, initial_start_activities, initial_end_activities, parameters=None)

Module contents

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.