pm4py.algo.filtering.pandas.attributes.attributes_filter module#

class pm4py.algo.filtering.pandas.attributes.attributes_filter.Parameters(*values)[source]#

Bases: Enum

ATTRIBUTE_KEY = 'pm4py:param:attribute_key'#
ACTIVITY_KEY = 'pm4py:param:activity_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
DECREASING_FACTOR = 'decreasingFactor'#
POSITIVE = 'positive'#
STREAM_FILTER_KEY1 = 'stream_filter_key1'#
STREAM_FILTER_VALUE1 = 'stream_filter_value1'#
STREAM_FILTER_KEY2 = 'stream_filter_key2'#
STREAM_FILTER_VALUE2 = 'stream_filter_value2'#
KEEP_ONCE_PER_CASE = 'keep_once_per_case'#
pm4py.algo.filtering.pandas.attributes.attributes_filter.apply_numeric_events(df: DataFrame, int1: float, int2: float, parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Apply a filter on events (numerical filter)

Parameters:

  • df – Dataframe

  • int1 – Lower bound of the interval

  • int2 – Upper bound of the interval

  • parameters –

    Possible parameters of the algorithm:

    Parameters.ATTRIBUTE_KEY => indicates which attribute to filter positive => keep or remove events?

Returns:

Filtered dataframe

Return type:

filtered_df

pm4py.algo.filtering.pandas.attributes.attributes_filter.apply_numeric(df: DataFrame, int1: float, int2: float, parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Filter dataframe on attribute values (filter cases)

Parameters:

  • df – Dataframe

  • int1 – Lower bound of the interval

  • int2 – Upper bound of the interval

  • parameters –

    Possible parameters of the algorithm:

    Parameters.ATTRIBUTE_KEY => indicates which attribute to filter Parameters.POSITIVE => keep or remove traces with such events?

Returns:

Filtered dataframe

Return type:

filtered_df

pm4py.algo.filtering.pandas.attributes.attributes_filter.apply_events(df: DataFrame, values: List[str], parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Filter dataframe on attribute values (filter events)

Parameters:

  • df – Dataframe

  • values – Values to filter on

  • parameters –

    Possible parameters of the algorithm, including:

    Parameters.ATTRIBUTE_KEY -> Attribute we want to filter Parameters.POSITIVE -> Specifies if the filter should be applied including traces (positive=True) or excluding traces (positive=False)

Returns:

Filtered dataframe

Return type:

df

pm4py.algo.filtering.pandas.attributes.attributes_filter.apply(df: DataFrame, values: List[str], parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Filter dataframe on attribute values (filter traces)

Parameters:

  • df – Dataframe

  • values – Values to filter on

  • parameters –

    Possible parameters of the algorithm, including:

    Parameters.CASE_ID_KEY -> Case ID column in the dataframe Parameters.ATTRIBUTE_KEY -> Attribute we want to filter Parameters.POSITIVE -> Specifies if the filter should be applied including traces (positive=True) or excluding traces (positive=False)

Returns:

Filtered dataframe

Return type:

df

pm4py.algo.filtering.pandas.attributes.attributes_filter.filter_df_on_attribute_values(df, values, case_id_glue='case:concept:name', attribute_key='concept:name', positive=True)[source]#

Filter dataframe on attribute values

Parameters:

  • df – Dataframe

  • values – Values to filter on

  • case_id_glue – Case ID column in the dataframe

  • attribute_key – Attribute we want to filter

  • positive – Specifies if the filtered should be applied including traces (positive=True) or excluding traces (positive=False)

Returns:

Filtered dataframe

Return type:

df

pm4py.algo.filtering.pandas.attributes.attributes_filter.filter_df_keeping_activ_exc_thresh(df, thresh, act_count0=None, activity_key='concept:name', most_common_variant=None)[source]#

Filter a dataframe keeping activities exceeding the threshold

Parameters:

  • df – Pandas dataframe

  • thresh – Threshold to use to cut activities

  • act_count0 – (If provided) Dictionary that associates each activity with its count

  • activity_key – Column in which the activity is present

Returns:

Filtered dataframe

Return type:

df

pm4py.algo.filtering.pandas.attributes.attributes_filter.filter_df_keeping_spno_activities(df: DataFrame, activity_key: str = 'concept:name', max_no_activities: int = 25)[source]#

Filter a dataframe on the specified number of attributes

Parameters:

  • df – Dataframe

  • activity_key – Activity key in dataframe (must be specified if different from concept:name)

  • max_no_activities – Maximum allowed number of attributes

Returns:

Filtered dataframe

Return type:

df

pm4py.algo.filtering.pandas.attributes.attributes_filter.filter_df_relative_occurrence_event_attribute(df: DataFrame, min_relative_stake: float, parameters: Dict[Any, Any] | None = None) → DataFrame[source]#

Filters the event log keeping only the events having an attribute value which occurs: - in at least the specified (min_relative_stake) percentage of events, when Parameters.KEEP_ONCE_PER_CASE = False - in at least the specified (min_relative_stake) percentage of cases, when Parameters.KEEP_ONCE_PER_CASE = True

Parameters:

  • df – Pandas dataframe

  • min_relative_stake – Minimum percentage of cases (expressed as a number between 0 and 1) in which the attribute should occur.

  • parameters – Parameters of the algorithm, including: - Parameters.ATTRIBUTE_KEY => the attribute to use (default: concept:name) - Parameters.KEEP_ONCE_PER_CASE => decides the level of the filter to apply (if the filter should be applied on the cases, set it to True).

Returns:

Filtered Pandas dataframe

Return type:

filtered_df