pm4py.algo.discovery.dfg package#

PM4Py – A Process Mining Library for Python

Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

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

Subpackages#

Submodules#

pm4py.algo.discovery.dfg.algorithm module#

PM4Py – A Process Mining Library for Python

Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

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

class pm4py.algo.discovery.dfg.algorithm.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

ACTIVITY_KEY = 'pm4py:param:activity_key'#
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
class pm4py.algo.discovery.dfg.algorithm.Variants(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

NATIVE = <module 'pm4py.algo.discovery.dfg.variants.native' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\native.py'>#
FREQUENCY = <module 'pm4py.algo.discovery.dfg.variants.native' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\native.py'>#
PERFORMANCE = <module 'pm4py.algo.discovery.dfg.variants.performance' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\performance.py'>#
FREQUENCY_GREEDY = <module 'pm4py.algo.discovery.dfg.variants.native' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\native.py'>#
PERFORMANCE_GREEDY = <module 'pm4py.algo.discovery.dfg.variants.performance' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\performance.py'>#
FREQ_TRIPLES = <module 'pm4py.algo.discovery.dfg.variants.freq_triples' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\freq_triples.py'>#
CASE_ATTRIBUTES = <module 'pm4py.algo.discovery.dfg.variants.case_attributes' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\case_attributes.py'>#
CLEAN = <module 'pm4py.algo.discovery.dfg.variants.clean' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\discovery\\dfg\\variants\\clean.py'>#
pm4py.algo.discovery.dfg.algorithm.apply(log: EventLog | EventStream | DataFrame, parameters: Dict[Any, Any] | None = None, variant=Variants.NATIVE) → Dict[Tuple[str, str], float][source]#

Calculates DFG graph (frequency or performance) starting from a log

Parameters#

log

Log

parameters
Possible parameters passed to the algorithms:

Parameters.AGGREGATION_MEASURE -> performance aggregation measure (min, max, mean, median) Parameters.ACTIVITY_KEY -> Attribute to use as activity Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp

variant
Variant of the algorithm to use, possible values:

  • Variants.NATIVE

  • Variants.FREQUENCY

  • Variants.FREQUENCY_GREEDY

  • Variants.PERFORMANCE

  • Variants.PERFORMANCE_GREEDY

  • Variants.FREQ_TRIPLES

Returns#

dfg

DFG graph

pm4py.algo.discovery.dfg.replacement module#

PM4Py – A Process Mining Library for Python

Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

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

pm4py.algo.discovery.dfg.replacement.replace_values(dfg1, dfg2)[source]#

Replace edge values specified in a DFG by values from a (potentially bigger) DFG

Parameters#

dfg1

First specified DFG (where values of edges should be replaces)

dfg2

Second specified DFG (from which values should be taken)

Returns#

dfg1

First specified DFG with overrided values

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