pm4py.algo.filtering.pandas.ltl.ltl_checker module#

class pm4py.algo.filtering.pandas.ltl.ltl_checker.Parameters(*values)[source]#

Bases: Enum

CASE_ID_KEY = 'pm4py:param:case_id_key'#
ATTRIBUTE_KEY = 'pm4py:param:attribute_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
RESOURCE_KEY = 'pm4py:param:resource_key'#
POSITIVE = 'positive'#
ENABLE_TIMESTAMP = 'enable_timestamp'#
TIMESTAMP_DIFF_BOUNDARIES = 'timestamp_diff_boundaries'#
pm4py.algo.filtering.pandas.ltl.ltl_checker.eventually_follows(df0: DataFrame, attribute_values: List[str], parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Applies the eventually follows rule

Parameters:

  • df0 – Dataframe

  • attribute_values – A list of attribute_values attribute_values[n] follows attribute_values[n-1] follows … follows attribute_values[0]

  • parameters – Parameters of the algorithm, including the attribute key and the positive parameter: - If True, returns all the cases containing all attribute_values and in which attribute_values[i] was eventually followed by attribute_values[i + 1] - If False, returns all the cases not containing all attribute_values, or in which an instance of attribute_values[i] was not eventually followed by an instance of attribute_values[i + 1]

Returns:

Filtered dataframe

Return type:

filtered_df

pm4py.algo.filtering.pandas.ltl.ltl_checker.A_next_B_next_C(df0: DataFrame, A: str, B: str, C: str, parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Applies the A net B next C rule

Parameters:

  • df0 – Dataframe

  • A – A Attribute value

  • B – B Attribute value

  • C – C Attribute value

  • parameters – Parameters of the algorithm, including the attribute key and the positive parameter: - If True, returns all the cases containing A, B and C and in which A was directly followed by B and B was directly followed by C - If False, returns all the cases not containing A or B or C, or in which none instance of A was directly followed by an instance of B and B was directly followed by C

Returns:

Filtered dataframe

Return type:

filtered_df

pm4py.algo.filtering.pandas.ltl.ltl_checker.four_eyes_principle(df0: DataFrame, A: str, B: str, parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Verifies the Four Eyes Principle given A and B

Parameters:

  • df0 – Dataframe

  • A – A attribute value

  • B – B attribute value

  • parameters – Parameters of the algorithm, including the attribute key and the positive parameter: - if True, then filters all the cases containing A and B which have empty intersection between the set

    of resources doing A and B

    • if False, then filters all the cases containing A and B which have no empty intersection between the set of resources doing A and B

Returns:

Filtered dataframe

Return type:

filtered_df

pm4py.algo.filtering.pandas.ltl.ltl_checker.attr_value_different_persons(df0: DataFrame, A: str, parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Checks whether an attribute value is assumed on events done by different resources

Parameters:

  • df0 – Dataframe

  • A – A attribute value

  • parameters –

    Parameters of the algorithm, including the attribute key and the positive parameter:

    • if True, then filters all the cases containing occurrences of A done by different resources

    • if False, then filters all the cases not containing occurrences of A done by different resources

Returns:

Filtered dataframe

Return type:

filtered_df