pm4py.algo.conformance.tokenreplay.diagnostics.root_cause_analysis module

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class pm4py.algo.conformance.tokenreplay.diagnostics.root_cause_analysis.Parameters(*values)

Bases: Enum

STRING_ATTRIBUTES = 'string_attributes'

NUMERIC_ATTRIBUTES = 'numeric_attributes'

ENABLE_MULTIPLIER = 'enable_multiplier'

pm4py.algo.conformance.tokenreplay.diagnostics.root_cause_analysis.form_log_from_dictio_couple(first_cases_repr, second_cases_repr, enable_multiplier=False)

Form a log from a couple of dictionary, to use for root cause analysis

Parameters:

• first_cases_repr – First cases representation

• second_cases_repr – Second cases representation

• enable_multiplier – Enable balancing of classes

Returns:

Trace log object

Return type:

log

pm4py.algo.conformance.tokenreplay.diagnostics.root_cause_analysis.form_representation_from_dictio_couple(first_cases_repr, second_cases_repr, string_attributes, numeric_attributes, enable_multiplier=False)

Gets a log representation, useful for training the decision tree, from a couple of dictionaries along with the list of string attributes and numeric attributes to consider, to use for root cause analysis

Parameters:

• first_cases_repr – First cases representation

• second_cases_repr – Second cases representation

• string_attributes – String attributes contained in the log

• numeric_attributes – Numeric attributes contained in the log

• enable_multiplier – Enable balancing of classes

Returns:

• data – Matrix representation of the event log

• feature_names – Array of feature names

pm4py.algo.conformance.tokenreplay.diagnostics.root_cause_analysis.diagnose_from_trans_fitness(log, trans_fitness, parameters=None)

Perform root cause analysis starting from transition fitness knowledge

Parameters:

• log – Trace log object

• trans_fitness – Transition fitness object

• parameters –

  Possible parameters of the algorithm, including:

  string_attributes -> List of string event attributes to consider

  in building the decision tree

  numeric_attributes -> List of numeric event attributes to consider

  in building the decision tree

Returns:

For each problematic transition:

• a decision tree comparing fit and unfit executions

• feature names

• classes

Return type:

diagnostics

pm4py.algo.conformance.tokenreplay.diagnostics.root_cause_analysis.diagnose_from_notexisting_activities(log, notexisting_activities_in_model, parameters=None)

Perform root cause analysis related to activities that are not present in the model

Parameters:

• log – Trace log object

• notexisting_activities_in_model – Not existing activities in the model

• parameters –

  Possible parameters of the algorithm, including:

  string_attributes -> List of string event attributes to consider

  in building the decision tree

  numeric_attributes -> List of numeric event attributes to consider

  in building the decision tree

Returns:

For each problematic transition:

• a decision tree comparing fit and unfit executions

• feature names

• classes

Return type:

diagnostics