pm4py.algo.discovery.heuristics.variants package

Submodules

pm4py.algo.discovery.heuristics.variants.classic 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.heuristics.variants.classic.Parameters[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
AND_MEASURE_THRESH = 'and_measure_thresh'
CASE_ID_KEY = 'pm4py:param:case_id_key'
DEPENDENCY_THRESH = 'dependency_thresh'
DFG_PRE_CLEANING_NOISE_THRESH = 'dfg_pre_cleaning_noise_thresh'
HEU_NET_DECORATION = 'heu_net_decoration'
LOOP_LENGTH_TWO_THRESH = 'loop_length_two_thresh'
MIN_ACT_COUNT = 'min_act_count'
MIN_DFG_OCCURRENCES = 'min_dfg_occurrences'
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'
pm4py.algo.discovery.heuristics.variants.classic.apply(log: pm4py.objects.log.obj.EventLog, parameters: Optional[Dict[Union[str, pm4py.algo.discovery.heuristics.variants.classic.Parameters], Any]] = None) → Tuple[pm4py.objects.petri_net.obj.PetriNet, pm4py.objects.petri_net.obj.Marking, pm4py.objects.petri_net.obj.Marking][source]

Discovers a Petri net using Heuristics Miner

Parameters:

  • log – Event log

  • parameters – Possible parameters of the algorithm, including:

    • Parameters.ACTIVITY_KEY
    • Parameters.TIMESTAMP_KEY
    • Parameters.CASE_ID_KEY
    • Parameters.DEPENDENCY_THRESH
    • Parameters.AND_MEASURE_THRESH
    • Parameters.MIN_ACT_COUNT
    • Parameters.MIN_DFG_OCCURRENCES
    • Parameters.DFG_PRE_CLEANING_NOISE_THRESH
    • Parameters.LOOP_LENGTH_TWO_THRESH
Returns:

  • net – Petri net
  • im – Initial marking
  • fm – Final marking
pm4py.algo.discovery.heuristics.variants.classic.apply_dfg(dfg: Dict[Tuple[str, str], int], activities=None, activities_occurrences=None, start_activities=None, end_activities=None, parameters: Optional[Dict[Any, Any]] = None) → Tuple[pm4py.objects.petri_net.obj.PetriNet, pm4py.objects.petri_net.obj.Marking, pm4py.objects.petri_net.obj.Marking][source]

Discovers a Petri net using Heuristics Miner

Parameters:

  • dfg – Directly-Follows Graph

  • activities – (If provided) list of activities of the log

  • activities_occurrences – (If provided) dictionary of activities occurrences

  • start_activities – (If provided) dictionary of start activities occurrences

  • end_activities – (If provided) dictionary of end activities occurrences

  • parameters – Possible parameters of the algorithm, including:

    • Parameters.ACTIVITY_KEY
    • Parameters.TIMESTAMP_KEY
    • Parameters.CASE_ID_KEY
    • Parameters.DEPENDENCY_THRESH
    • Parameters.AND_MEASURE_THRESH
    • Parameters.MIN_ACT_COUNT
    • Parameters.MIN_DFG_OCCURRENCES
    • Parameters.DFG_PRE_CLEANING_NOISE_THRESH
    • Parameters.LOOP_LENGTH_TWO_THRESH
Returns:

  • net – Petri net
  • im – Initial marking
  • fm – Final marking
pm4py.algo.discovery.heuristics.variants.classic.apply_heu(log: pm4py.objects.log.obj.EventLog, parameters: Optional[Dict[Any, Any]] = None) → pm4py.objects.heuristics_net.obj.HeuristicsNet[source]

Discovers an Heuristics Net using Heuristics Miner

Parameters:

  • log – Event log

  • parameters – Possible parameters of the algorithm, including:

    • Parameters.ACTIVITY_KEY
    • Parameters.TIMESTAMP_KEY
    • Parameters.CASE_ID_KEY
    • Parameters.DEPENDENCY_THRESH
    • Parameters.AND_MEASURE_THRESH
    • Parameters.MIN_ACT_COUNT
    • Parameters.MIN_DFG_OCCURRENCES
    • Parameters.DFG_PRE_CLEANING_NOISE_THRESH
    • Parameters.LOOP_LENGTH_TWO_THRESH
Returns:

Heuristics Net
Return type:

heu
pm4py.algo.discovery.heuristics.variants.classic.apply_heu_dfg(dfg, activities=None, activities_occurrences=None, start_activities=None, end_activities=None, dfg_window_2=None, freq_triples=None, performance_dfg=None, parameters=None) → pm4py.objects.heuristics_net.obj.HeuristicsNet[source]

Discovers an Heuristics Net using Heuristics Miner

Parameters:

  • dfg – Directly-Follows Graph

  • activities – (If provided) list of activities of the log

  • activities_occurrences – (If provided) dictionary of activities occurrences

  • start_activities – (If provided) dictionary of start activities occurrences

  • end_activities – (If provided) dictionary of end activities occurrences

  • dfg_window_2 – (If provided) DFG of window 2

  • freq_triples – (If provided) Frequency triples

  • performance_dfg – (If provided) Performance DFG

  • parameters – Possible parameters of the algorithm, including:

    • Parameters.ACTIVITY_KEY
    • Parameters.TIMESTAMP_KEY
    • Parameters.CASE_ID_KEY
    • Parameters.DEPENDENCY_THRESH
    • Parameters.AND_MEASURE_THRESH
    • Parameters.MIN_ACT_COUNT
    • Parameters.MIN_DFG_OCCURRENCES
    • Parameters.DFG_PRE_CLEANING_NOISE_THRESH
    • Parameters.LOOP_LENGTH_TWO_THRESH
Returns:

Heuristics Net
Return type:

heu
pm4py.algo.discovery.heuristics.variants.classic.apply_pandas(df: pandas.core.frame.DataFrame, parameters: Optional[Dict[Union[str, pm4py.algo.discovery.heuristics.variants.classic.Parameters], Any]] = None) → Tuple[pm4py.objects.petri_net.obj.PetriNet, pm4py.objects.petri_net.obj.Marking, pm4py.objects.petri_net.obj.Marking][source]

Discovers a Petri net using Heuristics Miner

Parameters:
  • df – Pandas dataframe
  • parameters – Possible parameters of the algorithm, including: activity_key, case_id_glue, timestamp_key, dependency_thresh, and_measure_thresh, min_act_count, min_dfg_occurrences, dfg_pre_cleaning_noise_thresh, loops_length_two_thresh
Returns:

  • net – Petri net
  • im – Initial marking
  • fm – Final marking
pm4py.algo.discovery.heuristics.variants.classic.calculate(heu_net, dependency_thresh=0.5, and_measure_thresh=0.65, min_act_count=1, min_dfg_occurrences=1, dfg_pre_cleaning_noise_thresh=0.05, loops_length_two_thresh=0.5, parameters=None)[source]

Calculate the dependency matrix, populate the nodes

Parameters:
  • dependency_thresh – (Optional) dependency threshold
  • and_measure_thresh – (Optional) AND measure threshold
  • min_act_count – (Optional) minimum number of occurrences of an activity
  • min_dfg_occurrences – (Optional) minimum dfg occurrences
  • dfg_pre_cleaning_noise_thresh – (Optional) DFG pre cleaning noise threshold
  • loops_length_two_thresh – (Optional) loops length two threshold
  • parameters – Other parameters of the algorithm

pm4py.algo.discovery.heuristics.variants.plusplus 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.heuristics.variants.plusplus.Parameters[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
AND_MEASURE_THRESH = 'and_measure_thresh'
CASE_ID_KEY = 'pm4py:param:case_id_key'
DEPENDENCY_THRESH = 'dependency_thresh'
HEU_NET_DECORATION = 'heu_net_decoration'
MIN_ACT_COUNT = 'min_act_count'
MIN_DFG_OCCURRENCES = 'min_dfg_occurrences'
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'
pm4py.algo.discovery.heuristics.variants.plusplus.apply(log: pm4py.objects.log.obj.EventLog, parameters: Optional[Dict[Any, Any]] = None) → Tuple[pm4py.objects.petri_net.obj.PetriNet, pm4py.objects.petri_net.obj.Marking, pm4py.objects.petri_net.obj.Marking][source]

Discovers a Petri net using the Heuristics Miner ++ algorithm

Implements the approach described in Burattin, Andrea, and Alessandro Sperduti. “Heuristics Miner for Time Intervals.” ESANN. 2010.

https://andrea.burattin.net/public-files/publications/2010-esann-slides.pdf

Parameters:
  • log – Event log
  • parameters – Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.START_TIMESTAMP_KEY - Parameters.TIMESTAMP_KEY - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.HEU_NET_DECORATION
Returns:

  • net – Petri net
  • im – Initial marking
  • fm – Final marking
pm4py.algo.discovery.heuristics.variants.plusplus.apply_dfg(dfg, activities=None, activities_occurrences=None, start_activities=None, end_activities=None, parameters=None)[source]
pm4py.algo.discovery.heuristics.variants.plusplus.apply_heu(log: pm4py.objects.log.obj.EventLog, parameters: Optional[Dict[Any, Any]] = None) → pm4py.objects.heuristics_net.obj.HeuristicsNet[source]

Discovers an heuristics net using the Heuristics Miner ++ algorithm

Implements the approach described in Burattin, Andrea, and Alessandro Sperduti. “Heuristics Miner for Time Intervals.” ESANN. 2010.

https://andrea.burattin.net/public-files/publications/2010-esann-slides.pdf

Parameters:
  • log – Event log
  • parameters – Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.START_TIMESTAMP_KEY - Parameters.TIMESTAMP_KEY - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.HEU_NET_DECORATION
Returns:

Heuristics net
Return type:

heu_net
pm4py.algo.discovery.heuristics.variants.plusplus.apply_heu_dfg(dfg, activities=None, activities_occurrences=None, start_activities=None, end_activities=None, dfg_window_2=None, freq_triples=None, parameters=None)[source]
pm4py.algo.discovery.heuristics.variants.plusplus.apply_heu_pandas(df: pandas.core.frame.DataFrame, parameters: Optional[Dict[Any, Any]] = None) → pm4py.objects.heuristics_net.obj.HeuristicsNet[source]

Discovers an heuristics net using the Heuristics Miner ++ algorithm

Implements the approach described in Burattin, Andrea, and Alessandro Sperduti. “Heuristics Miner for Time Intervals.” ESANN. 2010.

https://andrea.burattin.net/public-files/publications/2010-esann-slides.pdf

Parameters:
  • df – Dataframe
  • parameters – Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.START_TIMESTAMP_KEY - Parameters.TIMESTAMP_KEY - Parameters.CASE_ID_KEY - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.HEU_NET_DECORATION
Returns:

Heuristics net
Return type:

heu_net
pm4py.algo.discovery.heuristics.variants.plusplus.apply_pandas(df: pandas.core.frame.DataFrame, parameters: Optional[Dict[Any, Any]] = None) → Tuple[pm4py.objects.petri_net.obj.PetriNet, pm4py.objects.petri_net.obj.Marking, pm4py.objects.petri_net.obj.Marking][source]

Discovers a Petri net using the Heuristics Miner ++ algorithm

Implements the approach described in Burattin, Andrea, and Alessandro Sperduti. “Heuristics Miner for Time Intervals.” ESANN. 2010.

https://andrea.burattin.net/public-files/publications/2010-esann-slides.pdf

Parameters:
  • df – Dataframe
  • parameters – Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.START_TIMESTAMP_KEY - Parameters.TIMESTAMP_KEY - Parameters.CASE_ID_KEY - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.HEU_NET_DECORATION
Returns:

  • net – Petri net
  • im – Initial marking
  • fm – Final marking
pm4py.algo.discovery.heuristics.variants.plusplus.calculate(heu_net: pm4py.objects.heuristics_net.obj.HeuristicsNet, dependency_thresh: float, and_measure_thresh: float, heu_net_decoration: str) → pm4py.objects.heuristics_net.obj.HeuristicsNet[source]

Calculates the dependency matrix and the AND measures using the Heuristics Miner ++ formulas

Parameters:
  • heu_net – Heuristics net
  • dependency_thresh – Dependency threshold
  • and_measure_thresh – AND measure threshold
  • heu_net_decoration – Decoration to use (frequency/performance)
Returns:

Heuristics net
Return type:

heu_net
pm4py.algo.discovery.heuristics.variants.plusplus.calculate_and_in_measure(heu_net: pm4py.objects.heuristics_net.obj.HeuristicsNet, and_measure_thresh: float) → pm4py.objects.heuristics_net.obj.HeuristicsNet[source]

Calculates the AND measure for incoming edges using the Heuristics Miner ++ formula

Parameters:
  • heu_net – Heuristics net
  • and_measure_thresh – And measure threshold
Returns:

Heuristics net (enriched)
Return type:

heu_net
pm4py.algo.discovery.heuristics.variants.plusplus.calculate_and_out_measure(heu_net: pm4py.objects.heuristics_net.obj.HeuristicsNet, and_measure_thresh: float) → pm4py.objects.heuristics_net.obj.HeuristicsNet[source]

Calculates the AND measure for outgoing edges using the Heuristics Miner ++ formula

Parameters:
  • heu_net – Heuristics net
  • and_measure_thresh – And measure threshold
Returns:

Heuristics net (enriched)
Return type:

heu_net
pm4py.algo.discovery.heuristics.variants.plusplus.calculate_dependency(heu_net: pm4py.objects.heuristics_net.obj.HeuristicsNet, dependency_thresh: float, heu_net_decoration: str) → pm4py.objects.heuristics_net.obj.HeuristicsNet[source]

Calculates the dependency matrix using the Heuristics Miner ++ formula

Parameters:
  • heu_net – Heuristics net
  • dependency_thresh – Dependency threshold
  • heu_net_decoration – Decoration to include (frequency/performance)
Returns:

Heuristics net (enriched)
Return type:

heu_net
pm4py.algo.discovery.heuristics.variants.plusplus.discover_abstraction_dataframe(df: pandas.core.frame.DataFrame, parameters: Optional[Dict[Any, Any]] = None) → Tuple[Any, Any, Any, Any, Any, Any, Any][source]

Discovers an abstraction from a dataframe that is useful for the Heuristics Miner ++ algorithm

Parameters:
  • df – Dataframe
  • parameters – Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.START_TIMESTAMP_KEY - Parameters.TIMESTAMP_KEY - Parameters.CASE_ID_KEY
Returns:

  • start_activities – Start activities
  • end_activities – End activities
  • activities_occurrences – Activities along with their number of occurrences
  • dfg – Directly-follows graph
  • performance_dfg – (Performance) Directly-follows graph
  • sojourn_time – Sojourn time for each activity
  • concurrent_activities – Concurrent activities
pm4py.algo.discovery.heuristics.variants.plusplus.discover_abstraction_log(log: pm4py.objects.log.obj.EventLog, parameters: Optional[Dict[Any, Any]] = None) → Tuple[Any, Any, Any, Any, Any, Any, Any][source]

Discovers an abstraction from a log that is useful for the Heuristics Miner ++ algorithm

Parameters:
  • log – Event log
  • parameters – Parameters of the algorithm, including: - Parameters.ACTIVITY_KEY - Parameters.START_TIMESTAMP_KEY - Parameters.TIMESTAMP_KEY - Parameters.CASE_ID_KEY
Returns:

  • start_activities – Start activities
  • end_activities – End activities
  • activities_occurrences – Activities along with their number of occurrences
  • dfg – Directly-follows graph
  • performance_dfg – (Performance) Directly-follows graph
  • sojourn_time – Sojourn time for each activity
  • concurrent_activities – Concurrent activities
pm4py.algo.discovery.heuristics.variants.plusplus.discover_heu_net_plus_plus(start_activities, end_activities, activities_occurrences, dfg, performance_dfg, sojourn_time, concurrent_activities, parameters: Optional[Dict[Any, Any]] = None)[source]

Discovers an heuristics net using the Heuristics Miner ++ algorithm

Implements the approach described in Burattin, Andrea, and Alessandro Sperduti. “Heuristics Miner for Time Intervals.” ESANN. 2010.

https://andrea.burattin.net/public-files/publications/2010-esann-slides.pdf

Parameters:
  • start_activities – Start activities
  • end_activities – End activities
  • activities_occurrences – Activities along with their number of occurrences
  • dfg – Directly-follows graph
  • performance_dfg – (Performance) Directly-follows graph
  • sojourn_time – Sojourn time for each activity
  • concurrent_activities – Concurrent activities
  • parameters – Parameters of the algorithm, including: - Parameters.DEPENDENCY_THRESH - Parameters.AND_MEASURE_THRESH - Parameters.MIN_ACT_COUNT - Parameters.MIN_DFG_OCCURRENCES - Parameters.HEU_NET_DECORATION
Returns:

Heuristics net
Return type:

heu_net

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/>.