'''
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
'''
from pm4py.statistics.process_cube.variants import classic
from enum import Enum
from pm4py.util import exec_utils
import pandas as pd
from typing import Optional, Dict, Any, Tuple
[docs]
class Variants(Enum):
CLASSIC = classic
[docs]
def apply(feature_table: pd.DataFrame, x_col: str, y_col: str, agg_col: str, variant=Variants.CLASSIC, parameters: Optional[Dict[Any, Any]] = None) -> Tuple[pd.DataFrame, Dict[Any, Any]]:
"""
Constructs a process cube by slicing data along two dimensions (x_col, y_col) and aggregating a third (agg_col).
Additionally:
1) 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).
2) We return both the pivoted DataFrame and a dict associating each cell
(x_bin, y_bin) -> set of case IDs.
Parameters
----------
feature_table : pd.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_col : str
The X dimension. If x_col in df.columns, use numeric binning. Otherwise, treat
it as a prefix for 'prefix-based' binning.
y_col : str
The Y dimension. If y_col in df.columns, use numeric binning. Otherwise, treat
it as a prefix for 'prefix-based' binning.
agg_col : str
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_df : pd.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_dict : dict
A dictionary mapping (x_bin, y_bin) -> set of case IDs that fall in that cell.
"""
return exec_utils.get_variant(variant).apply(feature_table, x_col, y_col, agg_col, parameters=parameters)
