pm4py.algo.filtering.log.attributes package#

PM4Py – A Process Mining Library for Python

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

Submodules#

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

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

class pm4py.algo.filtering.log.attributes.attributes_filter.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[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_df: Filtered dataframe

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: 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: 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: 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: 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_log: Filtered version of the event 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: 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_log: Filtered event log