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# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
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# http://www.apache.org/licenses/LICENSE-2.0
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#
import collections
import math
import statistics
from collections.abc import Callable
from collections.abc import Iterable
from typing import Any
from typing import Optional
from apache_beam.ml.anomaly.base import DEFAULT_MISSING_LABEL
from apache_beam.ml.anomaly.base import DEFAULT_NORMAL_LABEL
from apache_beam.ml.anomaly.base import DEFAULT_OUTLIER_LABEL
from apache_beam.ml.anomaly.base import AggregationFn
from apache_beam.ml.anomaly.base import AnomalyPrediction
from apache_beam.ml.anomaly.specifiable import specifiable
class _AggModelIdMixin:
def __init__(self, agg_model_id: Optional[str] = None):
self._agg_model_id = agg_model_id
def _set_agg_model_id_if_unset(self, agg_model_id: str) -> None:
if self._agg_model_id is None:
self._agg_model_id = agg_model_id
def add_model_id(self, result_dict):
result_dict["model_id"] = self._agg_model_id
class _SourcePredictionMixin:
def __init__(self, include_source_predictions):
self._include_source_predictions = include_source_predictions
def add_source_predictions(self, result_dict, source_predictions):
if self._include_source_predictions:
result_dict["source_predictions"] = list(source_predictions)
[docs]
class LabelAggregation(AggregationFn, _AggModelIdMixin, _SourcePredictionMixin):
"""Aggregates anomaly predictions based on their labels.
This is an abstract base class for `AggregationFn`s that combine multiple
`AnomalyPrediction` objects into a single `AnomalyPrediction` based on
the labels of the input predictions.
Args:
agg_func (Callable[[Iterable[int]], int]): A function that aggregates
a collection of anomaly labels (integers) into a single label.
agg_model_id (Optional[str]): The model id used in aggregated predictions.
Defaults to None.
include_source_predictions (bool): If True, include the input predictions in
the `source_predictions` of the output. Defaults to False.
"""
def __init__(
self,
agg_func: Callable[[Iterable[int]], int],
agg_model_id: Optional[str] = None,
include_source_predictions: bool = False,
normal_label: int = DEFAULT_NORMAL_LABEL,
outlier_label: int = DEFAULT_OUTLIER_LABEL,
missing_label: int = DEFAULT_MISSING_LABEL,
):
self._agg = agg_func
self._normal_label = normal_label
self._outlier_label = outlier_label
self._missing_label = missing_label
_AggModelIdMixin.__init__(self, agg_model_id)
_SourcePredictionMixin.__init__(self, include_source_predictions)
[docs]
def apply(
self, predictions: Iterable[AnomalyPrediction]) -> AnomalyPrediction:
"""Applies the label aggregation function to a list of predictions.
Args:
predictions (Iterable[AnomalyPrediction]): A collection of
`AnomalyPrediction` objects to be aggregated.
Returns:
AnomalyPrediction: A single `AnomalyPrediction` object with the
aggregated label. The aggregated label is determined as follows:
- If there are any non-missing and non-error labels, the `agg_func` is
applied to aggregate them.
- If all labels are error labels (`None`), the aggregated label is also
`None`.
- If there are a mix of missing and error labels, the aggregated label
is the `missing_label`.
"""
result_dict: dict[str, Any] = {}
_AggModelIdMixin.add_model_id(self, result_dict)
_SourcePredictionMixin.add_source_predictions(
self, result_dict, predictions)
labels = [
prediction.label for prediction in predictions if
prediction.label is not None and prediction.label != self._missing_label
]
if len(labels) > 0:
# apply aggregation_fn if there is any non-None and non-missing label
result_dict["label"] = self._agg(labels)
elif all(map(lambda x: x.label is None, predictions)):
# all are error labels (None) -- all scores are error
result_dict["label"] = None
else:
# some missing labels with some error labels (None)
result_dict["label"] = self._missing_label
return AnomalyPrediction(**result_dict)
[docs]
class ScoreAggregation(AggregationFn, _AggModelIdMixin, _SourcePredictionMixin):
"""Aggregates anomaly predictions based on their scores.
This is an abstract base class for `AggregationFn`s that combine multiple
`AnomalyPrediction` objects into a single `AnomalyPrediction` based on
the scores of the input predictions.
Args:
agg_func (Callable[[Iterable[float]], float]): A function that aggregates
a collection of anomaly scores (floats) into a single score.
agg_model_id (Optional[str]): The model id used in aggregated predictions.
Defaults to None.
include_source_predictions (bool): If True, include the input predictions in
the `source_predictions` of the output. Defaults to False.
"""
def __init__(
self,
agg_func: Callable[[Iterable[float]], float],
agg_model_id: Optional[str] = None,
include_source_predictions: bool = False):
self._agg = agg_func
_AggModelIdMixin.__init__(self, agg_model_id)
_SourcePredictionMixin.__init__(self, include_source_predictions)
[docs]
def apply(
self, predictions: Iterable[AnomalyPrediction]) -> AnomalyPrediction:
"""Applies the score aggregation function to a list of predictions.
Args:
predictions (Iterable[AnomalyPrediction]): A collection of
`AnomalyPrediction` objects to be aggregated.
Returns:
AnomalyPrediction: A single `AnomalyPrediction` object with the
aggregated score. The aggregated score is determined as follows:
- If there are any non-missing and non-error scores, the `agg_func` is
applied to aggregate them.
- If all scores are error scores (`None`), the aggregated score is also
`None`.
- If there are a mix of missing (`NaN`) and error scores (`None`), the
aggregated score is `NaN`.
"""
result_dict: dict[str, Any] = {}
_AggModelIdMixin.add_model_id(self, result_dict)
_SourcePredictionMixin.add_source_predictions(
self, result_dict, predictions)
scores = [
prediction.score for prediction in predictions
if prediction.score is not None and not math.isnan(prediction.score)
]
if len(scores) > 0:
# apply aggregation_fn if there is any non-None and non-NaN score
result_dict["score"] = self._agg(scores)
elif all(map(lambda x: x.score is None, predictions)):
# all are error scores (None)
result_dict["score"] = None
else:
# some missing scores (NaN) with some error scores (None)
result_dict["score"] = float("NaN")
return AnomalyPrediction(**result_dict)
[docs]
@specifiable
class MajorityVote(LabelAggregation):
"""Aggregates anomaly labels using majority voting.
This `AggregationFn` implements a majority voting strategy to combine
anomaly labels from multiple `AnomalyPrediction` objects. It counts the
occurrences of normal and outlier labels and selects the label with the
higher count as the aggregated label. In case of a tie, a tie-breaker
label is used.
Example:
If input labels are [normal, outlier, outlier, normal, outlier], and
normal_label=0, outlier_label=1, then the aggregated label will be
outlier (1) because outliers have a majority (3 vs 2).
Args:
normal_label (int): The integer label for normal predictions. Defaults to 0.
outlier_label (int): The integer label for outlier predictions. Defaults to
1.
tie_breaker (int): The label to return if there is a tie in votes.
Defaults to 0 (normal_label).
**kwargs: Additional keyword arguments to pass to the base
`LabelAggregation` class.
"""
def __init__(self, tie_breaker=DEFAULT_NORMAL_LABEL, **kwargs):
self._tie_breaker = tie_breaker
def inner(predictions: Iterable[int]) -> int:
counters = collections.Counter(predictions)
if counters[self._normal_label] < counters[self._outlier_label]:
vote = self._outlier_label
elif counters[self._normal_label] > counters[self._outlier_label]:
vote = self._normal_label
else:
vote = self._tie_breaker
return vote
super().__init__(agg_func=inner, **kwargs)
# And scheme
[docs]
@specifiable
class AllVote(LabelAggregation):
"""Aggregates anomaly labels using an "all vote" (AND) scheme.
This `AggregationFn` implements an "all vote" strategy. It aggregates
anomaly labels such that the result is considered an outlier only if all
input `AnomalyPrediction` objects are labeled as outliers.
Example:
If input labels are [outlier, outlier, outlier], and outlier_label=1,
then the aggregated label will be outlier (1).
If input labels are [outlier, normal, outlier], and outlier_label=1,
then the aggregated label will be normal (0).
Args:
normal_label (int): The integer label for normal predictions. Defaults to 0.
outlier_label (int): The integer label for outlier predictions. Defaults to
1.
**kwargs: Additional keyword arguments to pass to the base
`LabelAggregation` class.
"""
def __init__(self, **kwargs):
def inner(predictions: Iterable[int]) -> int:
return self._outlier_label if all(
map(lambda p: p == self._outlier_label,
predictions)) else self._normal_label
super().__init__(agg_func=inner, **kwargs)
# Or scheme
[docs]
@specifiable
class AnyVote(LabelAggregation):
"""Aggregates anomaly labels using an "any vote" (OR) scheme.
This `AggregationFn` implements an "any vote" strategy. It aggregates
anomaly labels such that the result is considered an outlier if at least
one of the input `AnomalyPrediction` objects is labeled as an outlier.
Example:
If input labels are [normal, normal, outlier], and outlier_label=1,
then the aggregated label will be outlier (1).
If input labels are [normal, normal, normal], and outlier_label=1,
then the aggregated label will be normal (0).
Args:
normal_label (int): The integer label for normal predictions. Defaults to 0.
outlier_label (int): The integer label for outlier predictions. Defaults to
1.
**kwargs: Additional keyword arguments to pass to the base
`LabelAggregation` class.
"""
def __init__(self, **kwargs):
def inner(predictions: Iterable[int]) -> int:
return self._outlier_label if any(
map(lambda p: p == self._outlier_label,
predictions)) else self._normal_label
super().__init__(agg_func=inner, **kwargs)
[docs]
@specifiable
class AverageScore(ScoreAggregation):
"""Aggregates anomaly scores by calculating their average.
This `AggregationFn` computes the average of the anomaly scores from a
collection of `AnomalyPrediction` objects.
Args:
**kwargs: Additional keyword arguments to pass to the base
`ScoreAggregation` class.
"""
def __init__(self, **kwargs):
super().__init__(agg_func=statistics.mean, **kwargs)
[docs]
@specifiable
class MaxScore(ScoreAggregation):
"""Aggregates anomaly scores by selecting the maximum score.
This `AggregationFn` selects the highest anomaly score from a collection
of `AnomalyPrediction` objects as the aggregated score.
Args:
**kwargs: Additional keyword arguments to pass to the base
`ScoreAggregation` class.
"""
def __init__(self, **kwargs):
super().__init__(agg_func=max, **kwargs)