pm4py.objects.log.util.df_features_utils module#

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pm4py.objects.log.util.df_features_utils.automatic_feature_selection_df(df, parameters=None)[source]#

Performs an automatic feature selection on dataframes, keeping the features useful for ML purposes

Parameters:

  • df – Dataframe

  • parameters – Parameters of the algorithm

Returns:

Dataframe with only the features that have been selected

Return type:

featured_df

pm4py.objects.log.util.df_features_utils.select_number_column(df: DataFrame, fea_df: DataFrame, col: str, case_id_key='case:concept:name', enable_numeric_attribute_statistics=False, numeric_attribute_aggregations: Collection[str] | None = None) → DataFrame[source]#

Extract a column for the features dataframe for the given numeric attribute

Parameters:

  • df – Dataframe

  • fea_df – Feature dataframe

  • col – Numeric column

  • case_id_key – Case ID key

  • enable_numeric_attribute_statistics – If True, extract numeric aggregation columns.

  • numeric_attribute_aggregations – Aggregations to compute for numeric attributes. Supported values are last, first, min, max, mean, median, stdev, and sum. Applied only to attributes whose names do not start with @@.

Returns:

Feature dataframe (desidered output)

Return type:

fea_df

pm4py.objects.log.util.df_features_utils.select_string_column(df: DataFrame, fea_df: DataFrame, col: str, case_id_key='case:concept:name', count_occurrences=False) → DataFrame[source]#

Extract N columns (for N different attribute values; hotencoding) for the features dataframe for the given string attribute

Parameters:

  • df – Dataframe

  • fea_df – Feature dataframe

  • col – String column

  • case_id_key – Case ID key

  • count_occurrences – If True, count the number of occurrences of the attribute value in each case. If False (default), use binary encoding (1 if present, 0 if not present)

Returns:

Feature dataframe (desidered output)

Return type:

fea_df

pm4py.objects.log.util.df_features_utils.get_features_df(df: DataFrame, list_columns: List[str], parameters: Dict[Any, Any] | None = None) → DataFrame[source]#

Given a dataframe and a list of columns, performs an automatic feature extraction

Parameters:

  • df – Dataframe

  • list_column – List of column to consider in the feature extraction

  • parameters – Parameters of the algorithm, including: - Parameters.CASE_ID_KEY: the case ID - Parameters.COUNT_OCCURRENCES: if True, count occurrences of string attributes instead of binary encoding - Parameters.ENABLE_NUMERIC_ATTRIBUTE_STATISTICS: if True, extract aggregation statistics for numeric attributes - Parameters.NUMERIC_ATTRIBUTE_AGGREGATIONS: collection of aggregations to compute for numeric attributes

    whose names do not start with @@

Returns:

Feature dataframe (desidered output)

Return type:

fea_df

pm4py.objects.log.util.df_features_utils.automatic_feature_extraction_df(df: DataFrame, parameters: Dict[Any, Any] | None = None) → DataFrame[source]#

Performs an automatic feature extraction given a dataframe

Parameters:

  • df – Dataframe

  • parameters – Parameters of the algorithm, including: - Parameters.CASE_ID_KEY: the case ID - Parameters.MIN_DIFFERENT_OCC_STR_ATTR - Parameters.MAX_DIFFERENT_OCC_STR_ATTR

Returns:

Dataframe with the features

Return type:

fea_df