'''
PM4Py – A Process Mining Library for Python
Copyright (C) 2026 Process Intelligence Solutions GmbH
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 enum import Enum
from typing import Optional, Dict, Any, Iterable, List, Set
import polars as pl
from pm4py.util import constants, xes_constants, exec_utils
[docs]
class Parameters(Enum):
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
COMPUTE_EXTRA_TEMPORAL_FEATURES = "compute_extra_temporal_features"
def _drop_if_present(lf: pl.LazyFrame, cols: Iterable[str]) -> pl.LazyFrame:
"""Drop columns from a LazyFrame if they exist (schema-aware)."""
existing: Set[str] = set(lf.collect_schema().names())
to_drop = [c for c in cols if c in existing]
return lf.drop(to_drop) if to_drop else lf
def _prepare_case_features(
df: pl.LazyFrame,
case_id_key: str,
start_timestamp_key: str,
timestamp_key: str,
compute_extra_temporal_features: bool,
) -> pl.LazyFrame:
"""Compute per-case aggregates needed for feature enrichment."""
# Use more specific internal names to minimize collisions with user columns.
case_start_col = "__pm4py_case_start"
case_end_col = "__pm4py_case_end"
case_summary = df.group_by(case_id_key).agg(
[
pl.col(start_timestamp_key).first().alias(case_start_col),
pl.col(timestamp_key).last().alias(case_end_col),
]
)
case_summary = case_summary.with_columns(
(
(
pl.col(case_end_col).dt.timestamp("ns")
- pl.col(case_start_col).dt.timestamp("ns")
)
/ 1_000_000_000
).alias("@@case_throughput")
)
if compute_extra_temporal_features:
case_summary = case_summary.with_columns(
[
pl.col(case_start_col).dt.strftime("%Y").alias("@@case_start_year"),
pl.col(case_start_col).dt.strftime("%Y-%m").alias("@@case_start_ymonth"),
pl.concat_str(pl.lit("M"), pl.col(case_start_col).dt.strftime("%m")).alias(
"@@case_start_month"
),
pl.concat_str(
pl.lit("W"),
pl.col(case_start_col)
.dt.week()
.cast(pl.Utf8)
.str.pad_start(2, "0"),
).alias("@@case_start_week"),
pl.col(case_end_col).dt.strftime("%Y").alias("@@case_end_year"),
pl.col(case_end_col).dt.strftime("%Y-%m").alias("@@case_end_ymonth"),
pl.concat_str(pl.lit("M"), pl.col(case_end_col).dt.strftime("%m")).alias(
"@@case_end_month"
),
pl.concat_str(
pl.lit("W"),
pl.col(case_end_col)
.dt.week()
.cast(pl.Utf8)
.str.pad_start(2, "0"),
).alias("@@case_end_week"),
]
)
select_columns: List[pl.Expr] = [pl.col(case_id_key), pl.col("@@case_throughput")]
if compute_extra_temporal_features:
select_columns.extend(
[
pl.col("@@case_start_year"),
pl.col("@@case_start_ymonth"),
pl.col("@@case_start_month"),
pl.col("@@case_start_week"),
pl.col("@@case_end_year"),
pl.col("@@case_end_ymonth"),
pl.col("@@case_end_month"),
pl.col("@@case_end_week"),
]
)
return case_summary.select(select_columns)
[docs]
def compute_extra_columns(
dataframe: pl.LazyFrame,
parameters: Optional[Dict[Any, Any]] = None,
) -> pl.LazyFrame:
"""Enrich a Polars LazyFrame with additional case-level columns."""
if parameters is None:
parameters = {}
case_id_key = exec_utils.get_param_value(
Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME
)
start_timestamp_key = exec_utils.get_param_value(
Parameters.START_TIMESTAMP_KEY,
parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY,
)
timestamp_key = exec_utils.get_param_value(
Parameters.TIMESTAMP_KEY,
parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY,
)
compute_extra_temporal_features = exec_utils.get_param_value(
Parameters.COMPUTE_EXTRA_TEMPORAL_FEATURES, parameters, True
)
# Drop any previously-added enrichment columns to avoid duplicated columns
# (including join-suffix leftovers from older versions).
all_enrichment_columns = [
"@@count",
"@@case_throughput",
"@@case_start_year",
"@@case_start_ymonth",
"@@case_start_month",
"@@case_start_week",
"@@case_end_year",
"@@case_end_ymonth",
"@@case_end_month",
"@@case_end_week",
]
drop_candidates = list(all_enrichment_columns) + [
f"{c}_right" for c in all_enrichment_columns
]
dataframe = _drop_if_present(dataframe, drop_candidates)
df = dataframe.with_columns(pl.lit(1).alias("@@count"))
case_features = _prepare_case_features(
df,
case_id_key,
start_timestamp_key,
timestamp_key,
compute_extra_temporal_features,
)
enriched = df.join(case_features, on=case_id_key, how="left", coalesce=True)
return enriched
__all__ = ["Parameters", "compute_extra_columns"]
