pm4py.statistics.process_cube package#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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
Subpackages#
Submodules#
pm4py.statistics.process_cube.algorithm module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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
- class pm4py.statistics.process_cube.algorithm.Variants(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- CLASSIC = <module 'pm4py.statistics.process_cube.variants.classic' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\statistics\\process_cube\\variants\\classic.py'>#
- pm4py.statistics.process_cube.algorithm.apply(feature_table: DataFrame, x_col: str, y_col: str, agg_col: str, variant=Variants.CLASSIC, parameters: Dict[Any, Any] | None = None) Tuple[DataFrame, Dict[Any, Any]][source]#
Constructs a process cube by slicing data along two dimensions (x_col, y_col) and aggregating a third (agg_col). Additionally:
If x_col (or y_col) is an actual column in df, we do numeric binning. Otherwise, we do ‘prefix-based’ binning (include any column starting with x_col, and assign a row to that bin if >= 1).
We return both the pivoted DataFrame and a dict associating each cell (x_bin, y_bin) -> set of case IDs.
Parameters#
- feature_tablepd.DataFrame
A feature table that must contain ‘case:concept:name’ and agg_col, plus the columns for x_col, y_col (if in numeric mode) or the columns that start with x_col, y_col (if in prefix mode).
- x_colstr
The X dimension. If x_col in df.columns, use numeric binning. Otherwise, treat it as a prefix for ‘prefix-based’ binning.
- y_colstr
The Y dimension. If y_col in df.columns, use numeric binning. Otherwise, treat it as a prefix for ‘prefix-based’ binning.
- agg_colstr
The column to aggregate (mean, sum, etc.).
- variant
Variant of the algorithm to be used, possible values: - Variants.CLASSIC
- parameters: Dict[Any, Any]
Variant-specific parameters, including potentially: * Parameters.MAX_DIVISIONS_X: If x_col is numeric, how many bins to divide it into. * Parameters.MAX_DIVISIONS_Y: If y_col is numeric, how many bins to divide it into. * Parameters.AGGREGATION_FUNCTION: The aggregation function, e.g., ‘mean’, ‘sum’, ‘min’, ‘max’.
Returns#
- pivot_dfpd.DataFrame
A pivoted DataFrame representing the process cube, with x bins as rows and y bins as columns, containing aggregated values of agg_col.
- cell_case_dictdict
A dictionary mapping (x_bin, y_bin) -> set of case IDs that fall in that cell.