pm4py.algo.filtering.log.attributes.attributes_filter module#

class pm4py.algo.filtering.log.attributes.attributes_filter.Parameters(*values)[source]#

Bases: Enum

ATTRIBUTE_KEY = 'pm4py:param:attribute_key'#
ACTIVITY_KEY = 'pm4py:param:activity_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
PARAMETER_KEY_CASE_GLUE = 'case_id_glue'#
DECREASING_FACTOR = 'decreasingFactor'#
POSITIVE = 'positive'#
STREAM_FILTER_KEY1 = 'stream_filter_key1'#
STREAM_FILTER_VALUE1 = 'stream_filter_value1'#
STREAM_FILTER_KEY2 = 'stream_filter_key2'#
STREAM_FILTER_VALUE2 = 'stream_filter_value2'#
KEEP_ONCE_PER_CASE = 'keep_once_per_case'#
pm4py.algo.filtering.log.attributes.attributes_filter.apply_numeric(log: EventLog, int1: float, int2: float, parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Apply a filter on cases (numerical filter)

Parameters:

  • log – Log

  • int1 – Lower bound of the interval

  • int2 – Upper bound of the interval

  • parameters – Possible parameters of the algorithm

Returns:

Filtered dataframe

Return type:

filtered_df

pm4py.algo.filtering.log.attributes.attributes_filter.apply_numeric_events(log: EventLog, int1: float, int2: float, parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Apply a filter on events (numerical filter)

Parameters:

  • log – Log

  • int1 – Lower bound of the interval

  • int2 – Upper bound of the interval

  • parameters –

    Possible parameters of the algorithm:

    Parameters.ATTRIBUTE_KEY => indicates which attribute to filter Parameters.POSITIVE => keep or remove traces with such events?

Returns:

Filtered log

Return type:

filtered_log

pm4py.algo.filtering.log.attributes.attributes_filter.apply_events(log: EventLog, values: List[str], parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Filter log by keeping only events with an attribute value that belongs to the provided values list

Parameters:

  • log – log

  • values – Allowed attributes

  • parameters –

    Parameters of the algorithm, including:

    Parameters.ACTIVITY_KEY -> Attribute identifying the activity in the log Parameters.POSITIVE -> Indicate if events should be kept/removed

Returns:

Filtered log

Return type:

filtered_log

pm4py.algo.filtering.log.attributes.attributes_filter.apply(log: EventLog, values: List[str], parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Filter log by keeping only traces that has/has not events with an attribute value that belongs to the provided values list

Parameters:

  • log – Trace log

  • values – Allowed attributes

  • parameters –

    Parameters of the algorithm, including:

    Parameters.ACTIVITY_KEY -> Attribute identifying the activity in the log Parameters.POSITIVE -> Indicate if events should be kept/removed

Returns:

Filtered log

Return type:

filtered_log

pm4py.algo.filtering.log.attributes.attributes_filter.apply_trace_attribute(log: EventLog, values: List[str], parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Filter a log on the trace attribute values

Parameters:

  • log – Event log

  • values – Allowed/forbidden values

  • parameters –

    Parameters of the algorithm, including:

    • Parameters.ATTRIBUTE_KEY: the attribute at the trace level to filter

    • Parameters.POSITIVE: boolean (keep/discard values)

Returns:

Filtered log

Return type:

filtered_log

pm4py.algo.filtering.log.attributes.attributes_filter.filter_log_on_max_no_activities(log: EventLog, max_no_activities: int = 25, parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Filter a log on a maximum number of activities

Parameters:

  • log – Log

  • max_no_activities – Maximum number of activities

  • parameters – Parameters of the algorithm

Returns:

Filtered version of the event log

Return type:

filtered_log

pm4py.algo.filtering.log.attributes.attributes_filter.filter_log_by_attributes_threshold(log, attributes, variants, vc, threshold, attribute_key='concept:name')[source]#

Keep only attributes which number of occurrences is above the threshold (or they belong to the first variant)

Parameters:

  • log – Log

  • attributes – Dictionary of attributes associated with their count

  • variants – (If specified) Dictionary with variant as the key and the list of traces as the value

  • vc – List of variant names along with their count

  • threshold – Cutting threshold (remove attributes which number of occurrences is below the threshold)

  • attribute_key – (If specified) Specify the activity key in the log (default concept:name)

Returns:

Filtered log

Return type:

filtered_log

pm4py.algo.filtering.log.attributes.attributes_filter.filter_log_relative_occurrence_event_attribute(log: EventLog, min_relative_stake: float, parameters: Dict[Any, Any] | None = None) → EventLog[source]#

Filters the event log keeping only the events having an attribute value which occurs: - in at least the specified (min_relative_stake) percentage of events, when Parameters.KEEP_ONCE_PER_CASE = False - in at least the specified (min_relative_stake) percentage of cases, when Parameters.KEEP_ONCE_PER_CASE = True

Parameters:

  • log – Event log

  • min_relative_stake – Minimum percentage of cases (expressed as a number between 0 and 1) in which the attribute should occur.

  • parameters – Parameters of the algorithm, including: - Parameters.ATTRIBUTE_KEY => the attribute to use (default: concept:name) - Parameters.KEEP_ONCE_PER_CASE => decides the level of the filter to apply (if the filter should be applied on the cases, set it to True).

Returns:

Filtered event log

Return type:

filtered_log