Sequences¶

Sequences are the point where a templated dataset becomes a modeling dataset.

This page covers the grouping builders, the TemplateSequence shape, and the split semantics that determine how sequences are assigned to train and test.

>>> from anomalog.sequences import SplitLabel, TemplateSequence
>>> sequence = TemplateSequence(
...     events=[("template <*>", ["x"], None), ("template <*>", ["y"], 10)],
...     label=0,
...     entity_ids=["node-1"],
...     window_id=7,
...     split_label=SplitLabel.TRAIN,
... )
>>> sequence.sole_entity_id
'node-1'
>>> sequence.templates
['template <*>', 'template <*>']
>>> sequence.split_label.value
'train'

`anomalog.sequences`¶

Utilities for building template sequences from structured log lines.

The module groups parsed log lines into windows (entity, fixed-size, or time-based) and decorates them with inferred templates and anomaly labels.

`EntitySequenceBuilder` `dataclass` ¶

Bases: SequenceBuilder

Sequence builder for per-entity grouping.

`mode` `property` ¶

Return the grouping strategy for this builder.

`iter()` ¶

Iterate over template sequences yielded by the configured grouping.

Yields:

Name	Type	Description
`TemplateSequence`	`TemplateSequence`	One grouped and template-enriched sequence.

Raises:

Type	Description
`ValueError`	If the requested train split is impossible for the configured grouping and constraints.

`build_split_summary(*, sequence_count, train_sequence_count, train_label_counts, test_label_counts)` ¶

Describe requested versus effective split semantics for one run.

Parameters:

Name	Type	Description	Default
`sequence_count`	`int`	Total number of generated sequences.	required
`train_sequence_count`	`int`	Number of sequences assigned to train.	required
`train_label_counts`	`dict[int, int]`	Label counts in the train split.	required
`test_label_counts`	`dict[int, int]`	Label counts in the test split.	required

Returns:

Name	Type	Description
`SequenceSplitSummary`	`SequenceSplitSummary`	Requested and effective split metrics.

`eligible_train_sequence_count(*, sequence_count, train_label_counts, test_label_counts)` ¶

Return the sequences eligible for train-fraction accounting.

Parameters:

Name	Type	Description	Default
`sequence_count`	`int`	Total number of generated entity sequences.	required
`train_label_counts`	`dict[int, int]`	Label counts assigned to the train split.	required
`test_label_counts`	`dict[int, int]`	Label counts assigned to the test split.	required

Returns:

Name	Type	Description
`int`	`int`	Eligible entity-sequence count under the current policy.

`from_dataset(td)` `classmethod` ¶

Create an entity-grouped builder from a templated dataset.

Parameters:

Name	Type	Description	Default
`td`	`TemplatedDataset`	Templated dataset to bind into the builder.	required

Returns:

Name	Type	Description
`Self`	`Self`	Builder bound to the templated dataset.

`represent_with(representation)` ¶

Return a lazy builder that applies a representation per sequence.

Parameters:

Name	Type	Description	Default
`representation`	`SequenceRepresentation[TRepresentation]`	Sequence representation to apply lazily to each built sequence.	required

Returns:

Type	Description
`SequenceRepresentationView[TRepresentation]`	SequenceRepresentationView[TRepresentation]: Lazy represented view of the generated sequences.

`with_train_fraction(train_frac)` ¶

Return a copy with an updated train/test split fraction.

Parameters:

Name	Type	Description	Default
`train_frac`	`float`	Requested fraction of eligible sequences to assign to the train split.	required

Returns:

Name	Type	Description
`Self`	`Self`	Copy with updated train/test split fraction.

`with_train_on_normal_entities_only(*, enabled=True)` ¶

Limit training sequences to entities without anomalies.

Parameters:

Name	Type	Description	Default
`enabled`	`bool`	Whether to restrict train sequences to normal entities only.	`True`

Returns:

Name	Type	Description
`Self`	`Self`	Copy with updated normal-only training behavior.

`FixedSequenceBuilder` `dataclass` ¶

Bases: SequenceBuilder

Sequence builder for fixed-size window grouping.

`mode` `property` ¶

Return the grouping strategy for this builder.

`iter()` ¶

Iterate over template sequences yielded by the configured grouping.

Yields:

Name	Type	Description
`TemplateSequence`	`TemplateSequence`	One grouped and template-enriched sequence.

Raises:

Type	Description
`ValueError`	If the requested train split is impossible for the configured grouping and constraints.

`build_split_summary(*, sequence_count, train_sequence_count, train_label_counts, test_label_counts)` ¶

Describe requested versus effective split semantics for one run.

Parameters:

Name	Type	Description	Default
`sequence_count`	`int`	Total number of generated sequences.	required
`train_sequence_count`	`int`	Number of sequences assigned to train.	required
`train_label_counts`	`dict[int, int]`	Label counts in the train split.	required
`test_label_counts`	`dict[int, int]`	Label counts in the test split.	required

Returns:

Name	Type	Description
`SequenceSplitSummary`	`SequenceSplitSummary`	Requested and effective split metrics.

`eligible_train_sequence_count(*, sequence_count, train_label_counts, test_label_counts)` ¶

Return the sequences eligible for train-fraction accounting.

Parameters:

Name	Type	Description	Default
`sequence_count`	`int`	Total number of generated sequences.	required
`train_label_counts`	`dict[int, int]`	Label counts assigned to the train split.	required
`test_label_counts`	`dict[int, int]`	Label counts assigned to the test split.	required

Returns:

Name	Type	Description
`int`	`int`	Count of sequences considered eligible for train-fraction calculations for this grouping strategy.

`represent_with(representation)` ¶

Return a lazy builder that applies a representation per sequence.

Parameters:

Name	Type	Description	Default
`representation`	`SequenceRepresentation[TRepresentation]`	Sequence representation to apply lazily to each built sequence.	required

Returns:

Type	Description
`SequenceRepresentationView[TRepresentation]`	SequenceRepresentationView[TRepresentation]: Lazy represented view of the generated sequences.

`with_train_fraction(train_frac)` ¶

Return a copy with an updated train/test split fraction.

Parameters:

Name	Type	Description	Default
`train_frac`	`float`	Requested fraction of eligible sequences to assign to the train split.	required

Returns:

Name	Type	Description
`Self`	`Self`	Copy with updated train/test split fraction.

`GroupingMode` ¶

Bases: str, Enum

Strategy for grouping structured lines into sequences.

`SequenceBuilder` `dataclass` ¶

Common sequence-building behavior shared across grouping strategies.

`mode` `property` ¶

Return the grouping strategy for this builder.

`iter()` ¶

Iterate over template sequences yielded by the configured grouping.

Yields:

Name	Type	Description
`TemplateSequence`	`TemplateSequence`	One grouped and template-enriched sequence.

Raises:

Type	Description
`ValueError`	If the requested train split is impossible for the configured grouping and constraints.

`build_split_summary(*, sequence_count, train_sequence_count, train_label_counts, test_label_counts)` ¶

Describe requested versus effective split semantics for one run.

Parameters:

Name	Type	Description	Default
`sequence_count`	`int`	Total number of generated sequences.	required
`train_sequence_count`	`int`	Number of sequences assigned to train.	required
`train_label_counts`	`dict[int, int]`	Label counts in the train split.	required
`test_label_counts`	`dict[int, int]`	Label counts in the test split.	required

Returns:

Name	Type	Description
`SequenceSplitSummary`	`SequenceSplitSummary`	Requested and effective split metrics.

`eligible_train_sequence_count(*, sequence_count, train_label_counts, test_label_counts)` ¶

Return the sequences eligible for train-fraction accounting.

Parameters:

Name	Type	Description	Default
`sequence_count`	`int`	Total number of generated sequences.	required
`train_label_counts`	`dict[int, int]`	Label counts assigned to the train split.	required
`test_label_counts`	`dict[int, int]`	Label counts assigned to the test split.	required

Returns:

Name	Type	Description
`int`	`int`	Count of sequences considered eligible for train-fraction calculations for this grouping strategy.

`represent_with(representation)` ¶

Return a lazy builder that applies a representation per sequence.

Parameters:

Name	Type	Description	Default
`representation`	`SequenceRepresentation[TRepresentation]`	Sequence representation to apply lazily to each built sequence.	required

Returns:

Type	Description
`SequenceRepresentationView[TRepresentation]`	SequenceRepresentationView[TRepresentation]: Lazy represented view of the generated sequences.

`with_train_fraction(train_frac)` ¶

Return a copy with an updated train/test split fraction.

Parameters:

Name	Type	Description	Default
`train_frac`	`float`	Requested fraction of eligible sequences to assign to the train split.	required

Returns:

Name	Type	Description
`Self`	`Self`	Copy with updated train/test split fraction.

`SequenceSplitSummary` `dataclass` ¶

Serializable summary of requested versus effective split behavior.

`as_dict()` ¶

Return a stable JSON-friendly representation.

Returns:

Type	Description
`dict[str, int \| float \| bool \| str]`	dict[str, int \| float \| bool \| str]: Serialized split summary.

`SplitLabel` ¶

Bases: str, Enum

Dataset split membership for a sequence.

`TemplateSequence` `dataclass` ¶

Grouped log window before any model-specific representation is applied.

This keeps sequence semantics such as event ordering, labels, and entity membership. Model inputs derived from it live in SequenceSample.

`sole_entity_id` `property` ¶

Return the entity id when the sequence belongs to exactly one entity.

If multiple entities appear in the window, None is returned to avoid implying a single owning entity.

`templates` `property` ¶

Return the ordered template strings for this sequence.

`TimeSequenceBuilder` `dataclass` ¶

Bases: SequenceBuilder

Sequence builder for time-window grouping.

`mode` `property` ¶

Return the grouping strategy for this builder.

`iter()` ¶

Iterate over template sequences yielded by the configured grouping.

Yields:

Name	Type	Description
`TemplateSequence`	`TemplateSequence`	One grouped and template-enriched sequence.

Raises:

Type	Description
`ValueError`	If the requested train split is impossible for the configured grouping and constraints.

`build_split_summary(*, sequence_count, train_sequence_count, train_label_counts, test_label_counts)` ¶

Describe requested versus effective split semantics for one run.

Parameters:

Name	Type	Description	Default
`sequence_count`	`int`	Total number of generated sequences.	required
`train_sequence_count`	`int`	Number of sequences assigned to train.	required
`train_label_counts`	`dict[int, int]`	Label counts in the train split.	required
`test_label_counts`	`dict[int, int]`	Label counts in the test split.	required

Returns:

Name	Type	Description
`SequenceSplitSummary`	`SequenceSplitSummary`	Requested and effective split metrics.

`eligible_train_sequence_count(*, sequence_count, train_label_counts, test_label_counts)` ¶

Return the sequences eligible for train-fraction accounting.

Parameters:

Name	Type	Description	Default
`sequence_count`	`int`	Total number of generated sequences.	required
`train_label_counts`	`dict[int, int]`	Label counts assigned to the train split.	required
`test_label_counts`	`dict[int, int]`	Label counts assigned to the test split.	required

Returns:

Name	Type	Description
`int`	`int`	Count of sequences considered eligible for train-fraction calculations for this grouping strategy.

`represent_with(representation)` ¶

Return a lazy builder that applies a representation per sequence.

Parameters:

Name	Type	Description	Default
`representation`	`SequenceRepresentation[TRepresentation]`	Sequence representation to apply lazily to each built sequence.	required

Returns:

Type	Description
`SequenceRepresentationView[TRepresentation]`	SequenceRepresentationView[TRepresentation]: Lazy represented view of the generated sequences.

`with_train_fraction(train_frac)` ¶

Return a copy with an updated train/test split fraction.

Parameters:

Name	Type	Description	Default
`train_frac`	`float`	Requested fraction of eligible sequences to assign to the train split.	required

Returns:

Name	Type	Description
`Self`	`Self`	Copy with updated train/test split fraction.

Sequences¶

anomalog.sequences¶

EntitySequenceBuilder dataclass ¶

mode property ¶

__iter__() ¶

build_split_summary(*, sequence_count, train_sequence_count, train_label_counts, test_label_counts) ¶

eligible_train_sequence_count(*, sequence_count, train_label_counts, test_label_counts) ¶

from_dataset(td) classmethod ¶

represent_with(representation) ¶

with_train_fraction(train_frac) ¶

with_train_on_normal_entities_only(*, enabled=True) ¶

FixedSequenceBuilder dataclass ¶

mode property ¶

__iter__() ¶

build_split_summary(*, sequence_count, train_sequence_count, train_label_counts, test_label_counts) ¶

eligible_train_sequence_count(*, sequence_count, train_label_counts, test_label_counts) ¶

represent_with(representation) ¶

with_train_fraction(train_frac) ¶

GroupingMode ¶

SequenceBuilder dataclass ¶

mode property ¶

__iter__() ¶

build_split_summary(*, sequence_count, train_sequence_count, train_label_counts, test_label_counts) ¶

eligible_train_sequence_count(*, sequence_count, train_label_counts, test_label_counts) ¶

represent_with(representation) ¶

with_train_fraction(train_frac) ¶

SequenceSplitSummary dataclass ¶

as_dict() ¶

SplitLabel ¶

TemplateSequence dataclass ¶

sole_entity_id property ¶

templates property ¶

TimeSequenceBuilder dataclass ¶

mode property ¶

__iter__() ¶

build_split_summary(*, sequence_count, train_sequence_count, train_label_counts, test_label_counts) ¶

eligible_train_sequence_count(*, sequence_count, train_label_counts, test_label_counts) ¶

represent_with(representation) ¶

with_train_fraction(train_frac) ¶

`anomalog.sequences`¶

`EntitySequenceBuilder` `dataclass` ¶

`mode` `property` ¶

`iter()` ¶

`build_split_summary(*, sequence_count, train_sequence_count, train_label_counts, test_label_counts)` ¶

`eligible_train_sequence_count(*, sequence_count, train_label_counts, test_label_counts)` ¶

`from_dataset(td)` `classmethod` ¶

`represent_with(representation)` ¶

`with_train_fraction(train_frac)` ¶

`with_train_on_normal_entities_only(*, enabled=True)` ¶

`FixedSequenceBuilder` `dataclass` ¶

`mode` `property` ¶

`iter()` ¶

`build_split_summary(*, sequence_count, train_sequence_count, train_label_counts, test_label_counts)` ¶

`eligible_train_sequence_count(*, sequence_count, train_label_counts, test_label_counts)` ¶

`represent_with(representation)` ¶

`with_train_fraction(train_frac)` ¶

`GroupingMode` ¶

`SequenceBuilder` `dataclass` ¶

`mode` `property` ¶

`iter()` ¶

`build_split_summary(*, sequence_count, train_sequence_count, train_label_counts, test_label_counts)` ¶

`eligible_train_sequence_count(*, sequence_count, train_label_counts, test_label_counts)` ¶

`represent_with(representation)` ¶

`with_train_fraction(train_frac)` ¶

`SequenceSplitSummary` `dataclass` ¶

`as_dict()` ¶

`SplitLabel` ¶

`TemplateSequence` `dataclass` ¶

`sole_entity_id` `property` ¶

`templates` `property` ¶

`TimeSequenceBuilder` `dataclass` ¶

`mode` `property` ¶

`iter()` ¶

`build_split_summary(*, sequence_count, train_sequence_count, train_label_counts, test_label_counts)` ¶

`eligible_train_sequence_count(*, sequence_count, train_label_counts, test_label_counts)` ¶

`represent_with(representation)` ¶

`with_train_fraction(train_frac)` ¶