pm4py.algo.filtering.pandas.variants.variants_filter module#

class pm4py.algo.filtering.pandas.variants.variants_filter.Parameters(*values)[source]#

Bases: Enum

CASE_ID_KEY = 'pm4py:param:case_id_key'#
ACTIVITY_KEY = 'pm4py:param:activity_key'#
DECREASING_FACTOR = 'decreasingFactor'#
POSITIVE = 'positive'#
pm4py.algo.filtering.pandas.variants.variants_filter.apply(df: DataFrame, admitted_variants: List[List[str]], parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Apply a filter on variants

Parameters:

  • df – Dataframe

  • admitted_variants – List of admitted variants (to include/exclude)

  • parameters –

    Parameters of the algorithm, including:

    Parameters.CASE_ID_KEY -> Column that contains the Case ID Parameters.ACTIVITY_KEY -> Column that contains the activity Parameters.POSITIVE -> Specifies if the filter should be applied including traces (positive=True) or excluding traces (positive=False) variants_df -> If provided, avoid recalculation of the variants dataframe

Returns:

Filtered dataframe

Return type:

df

pm4py.algo.filtering.pandas.variants.variants_filter.filter_variants_top_k(log, k, parameters=None)[source]#

Keeps the top-k variants of the log

Parameters:

  • log – Event log

  • k – Number of variants that should be kept

  • parameters – Parameters

Returns:

Filtered log

Return type:

filtered_log

pm4py.algo.filtering.pandas.variants.variants_filter.filter_variants_by_coverage_percentage(log, min_coverage_percentage, parameters=None)[source]#

Filters the variants of the log by a coverage percentage (e.g., if min_coverage_percentage=0.4, and we have a log with 1000 cases, of which 500 of the variant 1, 400 of the variant 2, and 100 of the variant 3, the filter keeps only the traces of variant 1 and variant 2).

Parameters:

  • log – Event log

  • min_coverage_percentage – Minimum allowed percentage of coverage

  • parameters – Parameters

Returns:

Filtered log

Return type:

filtered_log