pm4py.algo.transformation.log_to_target.variants 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.transformation.log_to_target.variants.next_activity 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.transformation.log_to_target.variants.next_activity.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

ACTIVITIES = 'activities'#

ACTIVITY_KEY = 'pm4py:param:activity_key'#

ENABLE_PADDING = 'enable_padding'#

PAD_SIZE = 'pad_size'#

pm4py.algo.transformation.log_to_target.variants.next_activity.apply(log: EventLog | EventStream | DataFrame, parameters: Dict[Any, Any] | None = None) → Tuple[List[List[List[int]]], List[str]][source]#

Returns a list of matrixes (one for every case). Every matrix contains as many rows as many events are contained in the case (an automatic padding option is also available), and as many columns as many distinct activities are in the log.

The corresponding activity to the given event is assigned to the value 1; the remaining activities are assigned to the value 0.

Parameters#

log: Event log / Event stream / Pandas dataframe
parameters: Parameters of the algorithm, including: - Parameters.ACTIVITIES => list of activities to consider - Parameters.ACTIVITY_KEY => attribute that should be used as activity - Parameters.ENABLE_PADDING => enables the padding (the length of cases is normalized) - Parameters.PAD_SIZE => the size of the padding

Returns#

target: The aforementioned list of matrixes.
activities: The considered list of activities

pm4py.algo.transformation.log_to_target.variants.next_time 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.transformation.log_to_target.variants.next_time.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#

ENABLE_PADDING = 'enable_padding'#

PAD_SIZE = 'pad_size'#

pm4py.algo.transformation.log_to_target.variants.next_time.apply(log: EventLog | EventStream | DataFrame, parameters: Dict[Any, Any] | None = None) → Tuple[List[List[int]], List[str]][source]#

Returns a list of lists (one for every case of the log) containing the difference between the timestamp of the current event and the timestamp of the next event of the case (an automatic padding option is also available). For the last event of the case, the difference is defaulted to 0.

Parameters#

log: Event log
parameters: Parameters of the algorithm, including: - Parameters.TIMESTAMP_KEY => the attribute of the log to be used as timestamp - Parameters.ENABLE_PADDING => enables the padding (the length of cases is normalized) - Parameters.PAD_SIZE => the size of the padding

Returns#

target: The aforementioned list
classes: Dummy list (of classes)

pm4py.algo.transformation.log_to_target.variants.remaining_time 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.transformation.log_to_target.variants.remaining_time.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#

ENABLE_PADDING = 'enable_padding'#

PAD_SIZE = 'pad_size'#

pm4py.algo.transformation.log_to_target.variants.remaining_time.apply(log: EventLog | EventStream | DataFrame, parameters: Dict[Any, Any] | None = None) → Tuple[List[List[int]], List[str]][source]#

Returns a list of lists (one for every case of the log) containing the remaining time in seconds from an event to the end of the case (an automatic padding option is also available).

Parameters#

log: Event log
parameters: Parameters of the algorithm, including: - Parameters.TIMESTAMP_KEY => the attribute of the log to be used as timestamp - Parameters.ENABLE_PADDING => enables the padding (the length of cases is normalized) - Parameters.PAD_SIZE => the size of the padding

Returns#

target: The aforementioned list
classes: Dummy list (of classes)