pm4py.algo.transformation.log_to_features.util package#

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

Submodules#

pm4py.algo.transformation.log_to_features.util.locally_linear_embedding module#

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

class pm4py.algo.transformation.log_to_features.util.locally_linear_embedding.Parameters(value)[source]#

Bases: Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
pm4py.algo.transformation.log_to_features.util.locally_linear_embedding.smooth(y: ndarray, box_pts: int) ndarray[source]#

Smooths the points in y with a weighted average.

Parameters#

y

Points

box_pts

Size of the weighted average

Returns#

y_smooth

Smoothened y

pm4py.algo.transformation.log_to_features.util.locally_linear_embedding.apply(log: EventLog, parameters: Optional[Dict[str, Any]] = None) Tuple[List[datetime], ndarray][source]#

Analyse the evolution of the features over the time using a locally linear embedding.

Parameters#

log

Event log

parameters

Variant-specific parameters, including: - Parameters.ACTIVITY_KEY => the activity key - Parameters.TIMESTAMP_KEY => the timestamp key - Parameters.CASE_ID_KEY => the case ID key

Returns#

x

Date attributes (starting points of the cases)

y

Deviation from the standard behavior (higher absolute values of y signal a higher deviation from the standard behavior)