pm4py.algo.conformance.tokenreplay.diagnostics package

Submodules

pm4py.algo.conformance.tokenreplay.diagnostics.duration_diagnostics module

class pm4py.algo.conformance.tokenreplay.diagnostics.duration_diagnostics.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'
pm4py.algo.conformance.tokenreplay.diagnostics.duration_diagnostics.diagnose_from_notexisting_activities(log, notexisting_activities_in_model, parameters=None)[source]

Provide some conformance diagnostics related to activities that are not present in the model

Parameters
  • log – Trace log

  • notexisting_activities_in_model – Not existing activities in the model

  • parameters

    Possible parameters of the algorithm, including:

    Parameters.TIMESTAMP_KEY -> attribute of the event containing the timestamp

Returns

For each problematic activity, diagnostics about case duration

Return type

diagnostics

pm4py.algo.conformance.tokenreplay.diagnostics.duration_diagnostics.diagnose_from_trans_fitness(log, trans_fitness, parameters=None)[source]

Provide some conformance diagnostics related to transitions that are executed in a unfit manner

Parameters
  • log – Trace log

  • trans_fitness – For each transition, keeps track of unfit executions

  • parameters

    Possible parameters of the algorithm, including:

    Parameters.TIMESTAMP_KEY -> attribute of the event containing the timestamp

Returns

For each problematic transition, diagnostics about case duration

Return type

diagnostics

pm4py.algo.conformance.tokenreplay.diagnostics.duration_diagnostics.get_case_duration(case, timestamp_key='time:timestamp')[source]

Gets the duration of a case

Parameters
  • case – Case

  • timestamp_key – Attribute of the event to use as timestamp

Returns

Case duration

Return type

case_duration

pm4py.algo.conformance.tokenreplay.diagnostics.duration_diagnostics.get_median_case_duration(list_cases, timestamp_key='time:timestamp')[source]

Gets the median case duration of a list of cases

Parameters
  • list_cases – List of cases

  • timestamp_key – Attribute of the event to use as timestamp

Returns

Median case duration

Return type

median_case_duration

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

class pm4py.algo.conformance.tokenreplay.diagnostics.root_cause_analysis.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ENABLE_MULTIPLIER = 'enable_multiplier'
NUMERIC_ATTRIBUTES = 'numeric_attributes'
STRING_ATTRIBUTES = 'string_attributes'
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

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.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

Module contents