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

Bases: Enum

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: Dict[str, Any] | None = 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)