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

class pm4py.algo.conformance.tokenreplay.diagnostics.root_cause_analysis.Parameters(*values)[source]#

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)[source]#

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)[source]#

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)[source]#

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)[source]#

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