def apply_auto_filter(df, parameters=None):
"""
Apply auto filter on activity values
Parameters
------------
df
Dataframe
parameters
Possible parameters of the algorithm, including:
activity_key -> Column containing the activity
decreasingFactor -> Decreasing factor that should be passed to the algorithm
Returns
------------
df
Filtered dataframe
"""
if parameters is None:
parameters = {}
activity_key = parameters[
PARAMETER_CONSTANT_ACTIVITY_KEY] if PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else DEFAULT_NAME_KEY
decreasing_factor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else DECREASING_FACTOR
activities = get_attribute_values(df, activity_key)
alist = attributes_common.get_sorted_attributes_list(activities)
thresh = attributes_common.get_attributes_threshold(
alist, decreasing_factor)
return filter_df_keeping_activ_exc_thresh(df,
thresh,
activity_key=activity_key,
act_count0=activities)
def apply_auto_filter(df, parameters=None):
"""Applies auto filter on activity values
"""
if parameters is None:
parameters = {}
most_common_variant = parameters[
PARAM_MOST_COMMON_VARIANT] if PARAM_MOST_COMMON_VARIANT in parameters else None
if most_common_variant is None:
most_common_variant = []
activity_key = parameters[
PARAMETER_CONSTANT_ACTIVITY_KEY] if PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else DEFAULT_NAME_KEY
decreasing_factor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else DECREASING_FACTOR
if df.count() > 0:
activities = get_attribute_values(df, activity_key)
alist = attributes_common.get_sorted_attributes_list(activities)
thresh = attributes_common.get_attributes_threshold(
alist, decreasing_factor)
return filter_df_keeping_activ_exc_thresh(
df,
thresh,
activity_key=activity_key,
act_count0=activities,
most_common_variant=most_common_variant)
return df
def apply_auto_filter(log, variants=None, parameters=None):
"""
Apply an attributes filter detecting automatically a percentage
Parameters
----------
log
Log
variants
(If specified) Dictionary with variant as the key and the list of traces as the value
parameters
Parameters of the algorithm, including:
decreasingFactor -> Decreasing factor (stops the algorithm when the next activity by occurrence is
below this factor in comparison to previous)
attribute_key -> Attribute key (must be specified if different from concept:name)
Returns
---------
filtered_log
Filtered log
"""
if parameters is None:
parameters = {}
attribute_key = parameters[
PARAMETER_CONSTANT_ATTRIBUTE_KEY] if PARAMETER_CONSTANT_ATTRIBUTE_KEY in parameters else DEFAULT_NAME_KEY
decreasing_factor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else filtering_constants.DECREASING_FACTOR
parameters_variants = {
PARAMETER_CONSTANT_ATTRIBUTE_KEY: attribute_key,
PARAMETER_CONSTANT_ACTIVITY_KEY: attribute_key
}
if variants is None:
variants = variants_filter.get_variants(log,
parameters=parameters_variants)
vc = variants_filter.get_variants_sorted_by_count(variants)
attributes_values = get_attribute_values(log,
attribute_key,
parameters=parameters_variants)
alist = attributes_common.get_sorted_attributes_list(attributes_values)
thresh = attributes_common.get_attributes_threshold(
alist, decreasing_factor)
filtered_log = filter_log_by_attributes_threshold(log, attributes_values,
variants, vc, thresh,
attribute_key)
return filtered_log
