pm4py.algo.conformance.alignments.petri_net.variants 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.algo.conformance.alignments.petri_net.variants.dijkstra_less_memory 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

class pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_less_memory.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

PARAM_TRACE_COST_FUNCTION = 'trace_cost_function'#

PARAM_MODEL_COST_FUNCTION = 'model_cost_function'#

PARAM_STD_SYNC_COST = 'std_sync_cost'#

PARAM_MAX_ALIGN_TIME_TRACE = 'max_align_time_trace'#

PARAM_MAX_ALIGN_TIME = 'max_align_time'#

PARAMETER_VARIANT_DELIMITER = 'variant_delimiter'#

PARAM_ALIGNMENT_RESULT_IS_SYNC_PROD_AWARE = 'ret_tuple_as_trans_desc'#

ACTIVITY_KEY = 'pm4py:param:activity_key'#

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_less_memory.get_best_worst_cost(petri_net, initial_marking, final_marking, parameters=None)[source]#

Gets the best worst cost of an alignment

Parameters#

petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking

Returns#

best_worst_cost: Best worst cost of alignment

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_less_memory.apply_from_variants_list_petri_string(var_list, petri_net_string, parameters=None)[source]#

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_less_memory.apply_from_variants_list(var_list, petri_net, initial_marking, final_marking, parameters=None)[source]#

Apply the alignments from the specification of a list of variants in the log

Parameters#

var_list: List of variants (for each item, the first entry is the variant itself, the second entry may be the number of cases)
petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking
parameters: Parameters of the algorithm (same as ‘apply’ method, plus ‘variant_delimiter’ that is , by default)

Returns#

dictio_alignments: Dictionary that assigns to each variant its alignment

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_less_memory.apply_from_variant(variant, petri_net, initial_marking, final_marking, parameters=None)[source]#

Apply the alignments from the specification of a single variant

Parameters#

variant: Variant (as string delimited by the “variant_delimiter” parameter)
petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking
parameters: Parameters of the algorithm (same as ‘apply’ method, plus ‘variant_delimiter’ that is , by default)

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_less_memory.apply(trace: Trace, net: PetriNet, im: Marking, fm: Marking, parameters: Dict[str | Parameters, Any] | None = None) → Dict[str, Any][source]#: Performs the basic alignment search, given a trace and a net.

Parameters#

trace: list input trace, assumed to be a list of events (i.e. the code will use the activity key to get the attributes) petri_net: pm4py.objects.petri.net.PetriNet the Petri net to use in the alignment initial_marking: pm4py.objects.petri.net.Marking initial marking in the Petri net final_marking: pm4py.objects.petri.net.Marking final marking in the Petri net parameters: dict (optional) dictionary containing one of the following:

Parameters.PARAM_TRACE_COST_FUNCTION: list (parameter) mapping of each index of the trace to a positive cost value Parameters.PARAM_MODEL_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding model cost Parameters.ACTIVITY_KEY: str (parameter) key to use to identify the activity described by the events

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states and traversed_arcs

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_no_heuristics 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

class pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_no_heuristics.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

PARAM_TRACE_COST_FUNCTION = 'trace_cost_function'#

PARAM_MODEL_COST_FUNCTION = 'model_cost_function'#

PARAM_SYNC_COST_FUNCTION = 'sync_cost_function'#

PARAM_ALIGNMENT_RESULT_IS_SYNC_PROD_AWARE = 'ret_tuple_as_trans_desc'#

PARAM_TRACE_NET_COSTS = 'trace_net_costs'#

TRACE_NET_CONSTR_FUNCTION = 'trace_net_constr_function'#

TRACE_NET_COST_AWARE_CONSTR_FUNCTION = 'trace_net_cost_aware_constr_function'#

PARAM_MAX_ALIGN_TIME_TRACE = 'max_align_time_trace'#

PARAM_MAX_ALIGN_TIME = 'max_align_time'#

PARAMETER_VARIANT_DELIMITER = 'variant_delimiter'#

ACTIVITY_KEY = 'pm4py:param:activity_key'#

VARIANTS_IDX = 'variants_idx'#

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_no_heuristics.get_best_worst_cost(petri_net, initial_marking, final_marking, parameters=None)[source]#

Gets the best worst cost of an alignment

Parameters#

petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking

Returns#

best_worst_cost: Best worst cost of alignment

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_no_heuristics.apply(trace: Trace, petri_net: PetriNet, initial_marking: Marking, final_marking: Marking, parameters: Dict[str | Parameters, Any] | None = None) → Dict[str, Any][source]#: Performs the basic alignment search, given a trace and a net.

Parameters#

trace: list input trace, assumed to be a list of events (i.e. the code will use the activity key to get the attributes) petri_net: pm4py.objects.petri.net.PetriNet the Petri net to use in the alignment initial_marking: pm4py.objects.petri.net.Marking initial marking in the Petri net final_marking: pm4py.objects.petri.net.Marking final marking in the Petri net parameters: dict (optional) dictionary containing one of the following:

Parameters.PARAM_TRACE_COST_FUNCTION: list (parameter) mapping of each index of the trace to a positive cost value Parameters.PARAM_MODEL_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding model cost Parameters.PARAM_SYNC_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding synchronous costs Parameters.ACTIVITY_KEY: str (parameter) key to use to identify the activity described by the events

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states and traversed_arcs

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_no_heuristics.apply_from_variant(variant, petri_net, initial_marking, final_marking, parameters=None)[source]#

Apply the alignments from the specification of a single variant

Parameters#

variant: Variant (as string delimited by the “variant_delimiter” parameter)
petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking
parameters: Parameters of the algorithm (same as ‘apply’ method, plus ‘variant_delimiter’ that is , by default)

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states and traversed_arcs

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_no_heuristics.apply_from_variants_dictionary(var_dictio, petri_net, initial_marking, final_marking, parameters=None)[source]#

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_no_heuristics.apply_from_variants_list(var_list, petri_net, initial_marking, final_marking, parameters=None)[source]#

Apply the alignments from the specification of a list of variants in the log

Parameters#

var_list: List of variants (for each item, the first entry is the variant itself, the second entry may be the number of cases)
petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking
parameters: Parameters of the algorithm (same as ‘apply’ method, plus ‘variant_delimiter’ that is , by default)

Returns#

dictio_alignments: Dictionary that assigns to each variant its alignment

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_no_heuristics.apply_from_variants_list_petri_string(var_list, petri_net_string, parameters=None)[source]#

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_no_heuristics.apply_from_variants_list_petri_string_mprocessing(mp_output, var_list, petri_net_string, parameters=None)[source]#

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_no_heuristics.apply_trace_net(petri_net, initial_marking, final_marking, trace_net, trace_im, trace_fm, parameters=None)[source]#: Performs the basic alignment search, given a trace net and a net.

Parameters#

trace: list input trace, assumed to be a list of events (i.e. the code will use the activity key to get the attributes) petri_net: pm4py.objects.petri.net.PetriNet the Petri net to use in the alignment initial_marking: pm4py.objects.petri.net.Marking initial marking in the Petri net final_marking: pm4py.objects.petri.net.Marking final marking in the Petri net parameters: dict (optional) dictionary containing one of the following:

Parameters.PARAM_TRACE_COST_FUNCTION: list (parameter) mapping of each index of the trace to a positive cost value Parameters.PARAM_MODEL_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding model cost Parameters.PARAM_SYNC_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding synchronous costs Parameters.ACTIVITY_KEY: str (parameter) key to use to identify the activity described by the events Parameters.PARAM_TRACE_NET_COSTS: dict (parameter) mapping between transitions and costs

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states and traversed_arcs

pm4py.algo.conformance.alignments.petri_net.variants.dijkstra_no_heuristics.apply_sync_prod(sync_prod, initial_marking, final_marking, cost_function, skip, ret_tuple_as_trans_desc=False, max_align_time_trace=9223372036854775807)[source]#

pm4py.algo.conformance.alignments.petri_net.variants.discounted_a_star 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

class pm4py.algo.conformance.alignments.petri_net.variants.discounted_a_star.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

PARAM_TRACE_COST_FUNCTION = 'trace_cost_function'#

PARAM_MODEL_COST_FUNCTION = 'model_cost_function'#

PARAM_SYNC_COST_FUNCTION = 'sync_cost_function'#

PARAM_ALIGNMENT_RESULT_IS_SYNC_PROD_AWARE = 'ret_tuple_as_trans_desc'#

PARAM_TRACE_NET_COSTS = 'trace_net_costs'#

TRACE_NET_CONSTR_FUNCTION = 'trace_net_constr_function'#

TRACE_NET_COST_AWARE_CONSTR_FUNCTION = 'trace_net_cost_aware_constr_function'#

PARAM_MAX_ALIGN_TIME_TRACE = 'max_align_time_trace'#

PARAM_MAX_ALIGN_TIME = 'max_align_time'#

PARAMETER_VARIANT_DELIMITER = 'variant_delimiter'#

ACTIVITY_KEY = 'pm4py:param:activity_key'#

VARIANTS_IDX = 'variants_idx'#

SYNCHRONOUS = 'synchronous_dijkstra'#

EXPONENT = 'exponent'#

pm4py.algo.conformance.alignments.petri_net.variants.discounted_a_star.get_best_worst_cost(petri_net, initial_marking, final_marking, parameters=None)[source]#

Gets the best worst cost of an alignment

Parameters#

petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking

Returns#

best_worst_cost: Best worst cost of alignment

pm4py.algo.conformance.alignments.petri_net.variants.discounted_a_star.apply(trace, petri_net, initial_marking, final_marking, parameters=None)[source]#: Performs the basic alignment search, given a trace and a net.

Parameters#

trace: list input trace, assumed to be a list of events (i.e. the code will use the activity key to get the attributes) petri_net: pm4py.objects.petri.net.PetriNet the Petri net to use in the alignment initial_marking: pm4py.objects.petri.net.Marking initial marking in the Petri net final_marking: pm4py.objects.petri.net.Marking final marking in the Petri net parameters: dict (optional) dictionary containing one of the following:

Parameters.PARAM_TRACE_COST_FUNCTION: list (parameter) mapping of each index of the trace to a positive cost value Parameters.PARAM_MODEL_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding model cost Parameters.PARAM_SYNC_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding synchronous costs Parameters.ACTIVITY_KEY: str (parameter) key to use to identify the activity described by the events

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states and traversed_arcs

pm4py.algo.conformance.alignments.petri_net.variants.discounted_a_star.apply_from_variant(variant, petri_net, initial_marking, final_marking, parameters=None)[source]#

Apply the alignments from the specification of a single variant

Parameters#

variant: Variant (as string delimited by the “variant_delimiter” parameter)
petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking
parameters: Parameters of the algorithm (same as ‘apply’ method, plus ‘variant_delimiter’ that is , by default)

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states and traversed_arcs

pm4py.algo.conformance.alignments.petri_net.variants.discounted_a_star.apply_from_variants_dictionary(var_dictio, petri_net, initial_marking, final_marking, parameters=None)[source]#

pm4py.algo.conformance.alignments.petri_net.variants.discounted_a_star.apply_from_variants_list(var_list, petri_net, initial_marking, final_marking, parameters=None)[source]#

Apply the alignments from the specification of a list of variants in the log

Parameters#

var_list: List of variants (for each item, the first entry is the variant itself, the second entry may be the number of cases)
petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking
parameters: Parameters of the algorithm (same as ‘apply’ method, plus ‘variant_delimiter’ that is , by default)

Returns#

dictio_alignments: Dictionary that assigns to each variant its alignment

pm4py.algo.conformance.alignments.petri_net.variants.discounted_a_star.apply_from_variants_list_petri_string(var_list, petri_net_string, parameters=None)[source]#

pm4py.algo.conformance.alignments.petri_net.variants.discounted_a_star.apply_from_variants_list_petri_string_mprocessing(mp_output, var_list, petri_net_string, parameters=None)[source]#

pm4py.algo.conformance.alignments.petri_net.variants.discounted_a_star.apply_trace_net(petri_net, initial_marking, final_marking, trace_net, trace_im, trace_fm, parameters=None)[source]#: Performs the basic alignment search, given a trace net and a net.

Parameters#

trace: list input trace, assumed to be a list of events (i.e. the code will use the activity key to get the attributes) petri_net: pm4py.objects.petri.net.PetriNet the Petri net to use in the alignment initial_marking: pm4py.objects.petri.net.Marking initial marking in the Petri net final_marking: pm4py.objects.petri.net.Marking final marking in the Petri net parameters: dict (optional) dictionary containing one of the following:

Parameters.PARAM_TRACE_COST_FUNCTION: list (parameter) mapping of each index of the trace to a positive cost value Parameters.PARAM_MODEL_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding model cost Parameters.PARAM_SYNC_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding synchronous costs Parameters.ACTIVITY_KEY: str (parameter) key to use to identify the activity described by the events Parameters.PARAM_TRACE_NET_COSTS: dict (parameter) mapping between transitions and costs

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states and traversed_arcs

pm4py.algo.conformance.alignments.petri_net.variants.discounted_a_star.apply_sync_prod(sync_prod, initial_marking, final_marking, skip, ret_tuple_as_trans_desc=False, max_align_time_trace=9223372036854775807, expo=2)[source]#

pm4py.algo.conformance.alignments.petri_net.variants.generator_dijkstra_less_memory 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

class pm4py.algo.conformance.alignments.petri_net.variants.generator_dijkstra_less_memory.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

PARAM_TRACE_COST_FUNCTION = 'trace_cost_function'#

PARAM_MODEL_COST_FUNCTION = 'model_cost_function'#

PARAM_STD_SYNC_COST = 'std_sync_cost'#

PARAM_MAX_ALIGN_TIME_TRACE = 'max_align_time_trace'#

PARAM_MAX_ALIGN_TIME = 'max_align_time'#

PARAMETER_VARIANT_DELIMITER = 'variant_delimiter'#

PARAM_ALIGNMENT_RESULT_IS_SYNC_PROD_AWARE = 'ret_tuple_as_trans_desc'#

ACTIVITY_KEY = 'pm4py:param:activity_key'#

pm4py.algo.conformance.alignments.petri_net.variants.generator_dijkstra_less_memory.apply(trace: Trace, net: PetriNet, im: Marking, fm: Marking, parameters: Dict[str | Parameters, Any] | None = None) → Dict[str, Any][source]#: Performs the basic alignment search, given a trace and a net.

Parameters#

trace: list input trace, assumed to be a list of events (i.e. the code will use the activity key to get the attributes) petri_net: pm4py.objects.petri.net.PetriNet the Petri net to use in the alignment initial_marking: pm4py.objects.petri.net.Marking initial marking in the Petri net final_marking: pm4py.objects.petri.net.Marking final marking in the Petri net parameters: dict (optional) dictionary containing one of the following:

Parameters.PARAM_TRACE_COST_FUNCTION: list (parameter) mapping of each index of the trace to a positive cost value Parameters.PARAM_MODEL_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding model cost Parameters.ACTIVITY_KEY: str (parameter) key to use to identify the activity described by the events

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states and traversed_arcs

pm4py.algo.conformance.alignments.petri_net.variants.generator_dijkstra_no_heuristics 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

class pm4py.algo.conformance.alignments.petri_net.variants.generator_dijkstra_no_heuristics.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

PARAM_TRACE_COST_FUNCTION = 'trace_cost_function'#

PARAM_MODEL_COST_FUNCTION = 'model_cost_function'#

PARAM_SYNC_COST_FUNCTION = 'sync_cost_function'#

PARAM_ALIGNMENT_RESULT_IS_SYNC_PROD_AWARE = 'ret_tuple_as_trans_desc'#

PARAM_TRACE_NET_COSTS = 'trace_net_costs'#

TRACE_NET_CONSTR_FUNCTION = 'trace_net_constr_function'#

TRACE_NET_COST_AWARE_CONSTR_FUNCTION = 'trace_net_cost_aware_constr_function'#

PARAM_MAX_ALIGN_TIME_TRACE = 'max_align_time_trace'#

PARAM_MAX_ALIGN_TIME = 'max_align_time'#

PARAMETER_VARIANT_DELIMITER = 'variant_delimiter'#

ACTIVITY_KEY = 'pm4py:param:activity_key'#

VARIANTS_IDX = 'variants_idx'#

pm4py.algo.conformance.alignments.petri_net.variants.generator_dijkstra_no_heuristics.get_best_worst_cost(petri_net, initial_marking, final_marking, parameters=None)[source]#

Gets the best worst cost of an alignment

Parameters#

petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking

Returns#

best_worst_cost: Best worst cost of alignment

pm4py.algo.conformance.alignments.petri_net.variants.generator_dijkstra_no_heuristics.apply(trace: Trace, petri_net: PetriNet, initial_marking: Marking, final_marking: Marking, parameters: Dict[str | Parameters, Any] | None = None) → Iterator[Dict[str, Any]][source]#: Performs the basic alignment search, given a trace and a net.

Parameters#

trace: list input trace, assumed to be a list of events (i.e. the code will use the activity key to get the attributes) petri_net: pm4py.objects.petri.net.PetriNet the Petri net to use in the alignment initial_marking: pm4py.objects.petri.net.Marking initial marking in the Petri net final_marking: pm4py.objects.petri.net.Marking final marking in the Petri net parameters: dict (optional) dictionary containing one of the following:

Parameters.PARAM_TRACE_COST_FUNCTION: list (parameter) mapping of each index of the trace to a positive cost value Parameters.PARAM_MODEL_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding model cost Parameters.PARAM_SYNC_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding synchronous costs Parameters.ACTIVITY_KEY: str (parameter) key to use to identify the activity described by the events

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states and traversed_arcs

pm4py.algo.conformance.alignments.petri_net.variants.generator_dijkstra_no_heuristics.apply_trace_net(petri_net, initial_marking, final_marking, trace_net, trace_im, trace_fm, parameters=None)[source]#: Performs the basic alignment search, given a trace net and a net.

Parameters#

trace: list input trace, assumed to be a list of events (i.e. the code will use the activity key to get the attributes) petri_net: pm4py.objects.petri.net.PetriNet the Petri net to use in the alignment initial_marking: pm4py.objects.petri.net.Marking initial marking in the Petri net final_marking: pm4py.objects.petri.net.Marking final marking in the Petri net parameters: dict (optional) dictionary containing one of the following:

Parameters.PARAM_TRACE_COST_FUNCTION: list (parameter) mapping of each index of the trace to a positive cost value Parameters.PARAM_MODEL_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding model cost Parameters.PARAM_SYNC_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding synchronous costs Parameters.ACTIVITY_KEY: str (parameter) key to use to identify the activity described by the events Parameters.PARAM_TRACE_NET_COSTS: dict (parameter) mapping between transitions and costs

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states and traversed_arcs

pm4py.algo.conformance.alignments.petri_net.variants.generator_dijkstra_no_heuristics.apply_sync_prod(sync_prod, initial_marking, final_marking, cost_function, skip, ret_tuple_as_trans_desc=False, max_align_time_trace=9223372036854775807)[source]#

pm4py.algo.conformance.alignments.petri_net.variants.generator_dijkstra_no_heuristics.check_cycle_in_searchtuple(st: DijkstraSearchTuple) → bool[source]#

pm4py.algo.conformance.alignments.petri_net.variants.generator_dijkstra_no_heuristics.repr_searchtuple(st: DijkstraSearchTuple) → str[source]#

pm4py.algo.conformance.alignments.petri_net.variants.generator_dijkstra_no_heuristics.rec_hub(hub, curr_m) → List[List[DijkstraSearchTuple]][source]#

pm4py.algo.conformance.alignments.petri_net.variants.generator_dijkstra_no_heuristics.reconstruct_alignment_generator(hub, state: DijkstraSearchTuple, ret_tuple_as_trans_desc=False)[source]#

pm4py.algo.conformance.alignments.petri_net.variants.state_equation_a_star 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

class pm4py.algo.conformance.alignments.petri_net.variants.state_equation_a_star.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

PARAM_TRACE_COST_FUNCTION = 'trace_cost_function'#

PARAM_MODEL_COST_FUNCTION = 'model_cost_function'#

PARAM_SYNC_COST_FUNCTION = 'sync_cost_function'#

PARAM_ALIGNMENT_RESULT_IS_SYNC_PROD_AWARE = 'ret_tuple_as_trans_desc'#

PARAM_TRACE_NET_COSTS = 'trace_net_costs'#

TRACE_NET_CONSTR_FUNCTION = 'trace_net_constr_function'#

TRACE_NET_COST_AWARE_CONSTR_FUNCTION = 'trace_net_cost_aware_constr_function'#

PARAM_MAX_ALIGN_TIME_TRACE = 'max_align_time_trace'#

PARAM_MAX_ALIGN_TIME = 'max_align_time'#

PARAMETER_VARIANT_DELIMITER = 'variant_delimiter'#

ACTIVITY_KEY = 'pm4py:param:activity_key'#

VARIANTS_IDX = 'variants_idx'#

RETURN_SYNC_COST_FUNCTION = 'return_sync_cost_function'#

pm4py.algo.conformance.alignments.petri_net.variants.state_equation_a_star.get_best_worst_cost(petri_net, initial_marking, final_marking, parameters=None)[source]#

Gets the best worst cost of an alignment

Parameters#

petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking

Returns#

best_worst_cost: Best worst cost of alignment

pm4py.algo.conformance.alignments.petri_net.variants.state_equation_a_star.apply(trace: Trace, petri_net: PetriNet, initial_marking: Marking, final_marking: Marking, parameters: Dict[str | Parameters, Any] | None = None) → Dict[str, Any][source]#: Performs the basic alignment search, given a trace and a net.

Parameters#

trace: list input trace, assumed to be a list of events (i.e. the code will use the activity key to get the attributes) petri_net: pm4py.objects.petri.net.PetriNet the Petri net to use in the alignment initial_marking: pm4py.objects.petri.net.Marking initial marking in the Petri net final_marking: pm4py.objects.petri.net.Marking final marking in the Petri net parameters: dict (optional) dictionary containing one of the following:

Parameters.PARAM_TRACE_COST_FUNCTION: list (parameter) mapping of each index of the trace to a positive cost value Parameters.PARAM_MODEL_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding model cost Parameters.PARAM_SYNC_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding synchronous costs Parameters.ACTIVITY_KEY: str (parameter) key to use to identify the activity described by the events

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states and traversed_arcs

pm4py.algo.conformance.alignments.petri_net.variants.state_equation_a_star.apply_from_variant(variant, petri_net, initial_marking, final_marking, parameters=None)[source]#

Apply the alignments from the specification of a single variant

Parameters#

variant: Variant (as string delimited by the “variant_delimiter” parameter)
petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking
parameters: Parameters of the algorithm (same as ‘apply’ method, plus ‘variant_delimiter’ that is , by default)

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states and traversed_arcs

pm4py.algo.conformance.alignments.petri_net.variants.state_equation_a_star.apply_from_variants_dictionary(var_dictio, petri_net, initial_marking, final_marking, parameters=None)[source]#

Apply the alignments from the specification of a variants dictionary

Parameters#

var_dictio: Dictionary of variants (along possibly with their count, or the list of indexes, or the list of involved cases)
petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking
parameters: Parameters of the algorithm (same as ‘apply’ method, plus ‘variant_delimiter’ that is , by default)

Returns#

dictio_alignments: Dictionary that assigns to each variant its alignment

pm4py.algo.conformance.alignments.petri_net.variants.state_equation_a_star.apply_from_variants_list(var_list, petri_net, initial_marking, final_marking, parameters=None)[source]#

Apply the alignments from the specification of a list of variants in the log

Parameters#

var_list: List of variants (for each item, the first entry is the variant itself, the second entry may be the number of cases)
petri_net: Petri net
initial_marking: Initial marking
final_marking: Final marking
parameters: Parameters of the algorithm (same as ‘apply’ method, plus ‘variant_delimiter’ that is , by default)

Returns#

dictio_alignments: Dictionary that assigns to each variant its alignment

pm4py.algo.conformance.alignments.petri_net.variants.state_equation_a_star.apply_from_variants_list_petri_string(var_list, petri_net_string, parameters=None)[source]#

Apply the alignments from the specification of a list of variants in the log

Parameters#

var_list: List of variants (for each item, the first entry is the variant itself, the second entry may be the number of cases)
petri_net_string: String representing the accepting Petri net

Returns#

dictio_alignments: Dictionary that assigns to each variant its alignment

pm4py.algo.conformance.alignments.petri_net.variants.state_equation_a_star.apply_from_variants_list_petri_string_mprocessing(mp_output, var_list, petri_net_string, parameters=None)[source]#

Apply the alignments from the specification of a list of variants in the log

Parameters#

mp_output: Multiprocessing output
var_list: List of variants (for each item, the first entry is the variant itself, the second entry may be the number of cases)
petri_net_string: String representing the accepting Petri net

Returns#

dictio_alignments: Dictionary that assigns to each variant its alignment

pm4py.algo.conformance.alignments.petri_net.variants.state_equation_a_star.apply_trace_net(petri_net, initial_marking, final_marking, trace_net, trace_im, trace_fm, parameters=None)[source]#: Performs the basic alignment search, given a trace net and a net.

Parameters#

trace: list input trace, assumed to be a list of events (i.e. the code will use the activity key to get the attributes) petri_net: pm4py.objects.petri.net.PetriNet the Petri net to use in the alignment initial_marking: pm4py.objects.petri.net.Marking initial marking in the Petri net final_marking: pm4py.objects.petri.net.Marking final marking in the Petri net parameters: dict (optional) dictionary containing one of the following:

Parameters.PARAM_TRACE_COST_FUNCTION: list (parameter) mapping of each index of the trace to a positive cost value Parameters.PARAM_MODEL_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding model cost Parameters.PARAM_SYNC_COST_FUNCTION: dict (parameter) mapping of each transition in the model to corresponding synchronous costs Parameters.ACTIVITY_KEY: str (parameter) key to use to identify the activity described by the events Parameters.PARAM_TRACE_NET_COSTS: dict (parameter) mapping between transitions and costs

Returns#

dictionary: dict with keys alignment, cost, visited_states, queued_states and traversed_arcs

pm4py.algo.conformance.alignments.petri_net.variants.state_equation_a_star.apply_sync_prod(sync_prod, initial_marking, final_marking, cost_function, skip, ret_tuple_as_trans_desc=False, max_align_time_trace=9223372036854775807)[source]#: Performs the basic alignment search on top of the synchronous product net, given a cost function and skip-symbol

Parameters#

sync_prod: pm4py.objects.petri.net.PetriNet synchronous product net initial_marking: pm4py.objects.petri.net.Marking initial marking in the synchronous product net final_marking: pm4py.objects.petri.net.Marking final marking in the synchronous product net cost_function: dict cost function mapping transitions to the synchronous product net skip: Any symbol to use for skips in the alignment

Returns#

dictionary : dict with keys alignment, cost, visited_states, queued_states and traversed_arcs