elasticsearch/_async/client/ml.py [5343:5476]:
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self,
*,
job_id: str,
allow_lazy_open: t.Optional[bool] = None,
analysis_limits: t.Optional[t.Mapping[str, t.Any]] = None,
background_persist_interval: t.Optional[
t.Union[str, t.Literal[-1], t.Literal[0]]
] = None,
categorization_filters: t.Optional[t.Sequence[str]] = None,
custom_settings: t.Optional[t.Mapping[str, t.Any]] = None,
daily_model_snapshot_retention_after_days: t.Optional[int] = None,
description: t.Optional[str] = None,
detectors: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None,
error_trace: t.Optional[bool] = None,
filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
groups: t.Optional[t.Sequence[str]] = None,
human: t.Optional[bool] = None,
model_plot_config: t.Optional[t.Mapping[str, t.Any]] = None,
model_prune_window: t.Optional[
t.Union[str, t.Literal[-1], t.Literal[0]]
] = None,
model_snapshot_retention_days: t.Optional[int] = None,
per_partition_categorization: t.Optional[t.Mapping[str, t.Any]] = None,
pretty: t.Optional[bool] = None,
renormalization_window_days: t.Optional[int] = None,
results_retention_days: t.Optional[int] = None,
body: t.Optional[t.Dict[str, t.Any]] = None,
) -> ObjectApiResponse[t.Any]:
"""
.. raw:: html
Update an anomaly detection job.
Updates certain properties of an anomaly detection job.
``_
:param job_id: Identifier for the job.
:param allow_lazy_open: Advanced configuration option. Specifies whether this
job can open when there is insufficient machine learning node capacity for
it to be immediately assigned to a node. If `false` and a machine learning
node with capacity to run the job cannot immediately be found, the open anomaly
detection jobs API returns an error. However, this is also subject to the
cluster-wide `xpack.ml.max_lazy_ml_nodes` setting. If this option is set
to `true`, the open anomaly detection jobs API does not return an error and
the job waits in the opening state until sufficient machine learning node
capacity is available.
:param analysis_limits:
:param background_persist_interval: Advanced configuration option. The time between
each periodic persistence of the model. The default value is a randomized
value between 3 to 4 hours, which avoids all jobs persisting at exactly the
same time. The smallest allowed value is 1 hour. For very large models (several
GB), persistence could take 10-20 minutes, so do not set the value too low.
If the job is open when you make the update, you must stop the datafeed,
close the job, then reopen the job and restart the datafeed for the changes
to take effect.
:param categorization_filters:
:param custom_settings: Advanced configuration option. Contains custom meta data
about the job. For example, it can contain custom URL information as shown
in Adding custom URLs to machine learning results.
:param daily_model_snapshot_retention_after_days: Advanced configuration option,
which affects the automatic removal of old model snapshots for this job.
It specifies a period of time (in days) after which only the first snapshot
per day is retained. This period is relative to the timestamp of the most
recent snapshot for this job. Valid values range from 0 to `model_snapshot_retention_days`.
For jobs created before version 7.8.0, the default value matches `model_snapshot_retention_days`.
:param description: A description of the job.
:param detectors: An array of detector update objects.
:param groups: A list of job groups. A job can belong to no groups or many.
:param model_plot_config:
:param model_prune_window:
:param model_snapshot_retention_days: Advanced configuration option, which affects
the automatic removal of old model snapshots for this job. It specifies the
maximum period of time (in days) that snapshots are retained. This period
is relative to the timestamp of the most recent snapshot for this job.
:param per_partition_categorization: Settings related to how categorization interacts
with partition fields.
:param renormalization_window_days: Advanced configuration option. The period
over which adjustments to the score are applied, as new data is seen.
:param results_retention_days: Advanced configuration option. The period of time
(in days) that results are retained. Age is calculated relative to the timestamp
of the latest bucket result. If this property has a non-null value, once
per day at 00:30 (server time), results that are the specified number of
days older than the latest bucket result are deleted from Elasticsearch.
The default value is null, which means all results are retained.
"""
if job_id in SKIP_IN_PATH:
raise ValueError("Empty value passed for parameter 'job_id'")
__path_parts: t.Dict[str, str] = {"job_id": _quote(job_id)}
__path = f'/_ml/anomaly_detectors/{__path_parts["job_id"]}/_update'
__query: t.Dict[str, t.Any] = {}
__body: t.Dict[str, t.Any] = body if body is not None else {}
if error_trace is not None:
__query["error_trace"] = error_trace
if filter_path is not None:
__query["filter_path"] = filter_path
if human is not None:
__query["human"] = human
if pretty is not None:
__query["pretty"] = pretty
if not __body:
if allow_lazy_open is not None:
__body["allow_lazy_open"] = allow_lazy_open
if analysis_limits is not None:
__body["analysis_limits"] = analysis_limits
if background_persist_interval is not None:
__body["background_persist_interval"] = background_persist_interval
if categorization_filters is not None:
__body["categorization_filters"] = categorization_filters
if custom_settings is not None:
__body["custom_settings"] = custom_settings
if daily_model_snapshot_retention_after_days is not None:
__body["daily_model_snapshot_retention_after_days"] = (
daily_model_snapshot_retention_after_days
)
if description is not None:
__body["description"] = description
if detectors is not None:
__body["detectors"] = detectors
if groups is not None:
__body["groups"] = groups
if model_plot_config is not None:
__body["model_plot_config"] = model_plot_config
if model_prune_window is not None:
__body["model_prune_window"] = model_prune_window
if model_snapshot_retention_days is not None:
__body["model_snapshot_retention_days"] = model_snapshot_retention_days
if per_partition_categorization is not None:
__body["per_partition_categorization"] = per_partition_categorization
if renormalization_window_days is not None:
__body["renormalization_window_days"] = renormalization_window_days
if results_retention_days is not None:
__body["results_retention_days"] = results_retention_days
__headers = {"accept": "application/json", "content-type": "application/json"}
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elasticsearch/_sync/client/ml.py [5343:5476]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
self,
*,
job_id: str,
allow_lazy_open: t.Optional[bool] = None,
analysis_limits: t.Optional[t.Mapping[str, t.Any]] = None,
background_persist_interval: t.Optional[
t.Union[str, t.Literal[-1], t.Literal[0]]
] = None,
categorization_filters: t.Optional[t.Sequence[str]] = None,
custom_settings: t.Optional[t.Mapping[str, t.Any]] = None,
daily_model_snapshot_retention_after_days: t.Optional[int] = None,
description: t.Optional[str] = None,
detectors: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None,
error_trace: t.Optional[bool] = None,
filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
groups: t.Optional[t.Sequence[str]] = None,
human: t.Optional[bool] = None,
model_plot_config: t.Optional[t.Mapping[str, t.Any]] = None,
model_prune_window: t.Optional[
t.Union[str, t.Literal[-1], t.Literal[0]]
] = None,
model_snapshot_retention_days: t.Optional[int] = None,
per_partition_categorization: t.Optional[t.Mapping[str, t.Any]] = None,
pretty: t.Optional[bool] = None,
renormalization_window_days: t.Optional[int] = None,
results_retention_days: t.Optional[int] = None,
body: t.Optional[t.Dict[str, t.Any]] = None,
) -> ObjectApiResponse[t.Any]:
"""
.. raw:: html
Update an anomaly detection job.
Updates certain properties of an anomaly detection job.
``_
:param job_id: Identifier for the job.
:param allow_lazy_open: Advanced configuration option. Specifies whether this
job can open when there is insufficient machine learning node capacity for
it to be immediately assigned to a node. If `false` and a machine learning
node with capacity to run the job cannot immediately be found, the open anomaly
detection jobs API returns an error. However, this is also subject to the
cluster-wide `xpack.ml.max_lazy_ml_nodes` setting. If this option is set
to `true`, the open anomaly detection jobs API does not return an error and
the job waits in the opening state until sufficient machine learning node
capacity is available.
:param analysis_limits:
:param background_persist_interval: Advanced configuration option. The time between
each periodic persistence of the model. The default value is a randomized
value between 3 to 4 hours, which avoids all jobs persisting at exactly the
same time. The smallest allowed value is 1 hour. For very large models (several
GB), persistence could take 10-20 minutes, so do not set the value too low.
If the job is open when you make the update, you must stop the datafeed,
close the job, then reopen the job and restart the datafeed for the changes
to take effect.
:param categorization_filters:
:param custom_settings: Advanced configuration option. Contains custom meta data
about the job. For example, it can contain custom URL information as shown
in Adding custom URLs to machine learning results.
:param daily_model_snapshot_retention_after_days: Advanced configuration option,
which affects the automatic removal of old model snapshots for this job.
It specifies a period of time (in days) after which only the first snapshot
per day is retained. This period is relative to the timestamp of the most
recent snapshot for this job. Valid values range from 0 to `model_snapshot_retention_days`.
For jobs created before version 7.8.0, the default value matches `model_snapshot_retention_days`.
:param description: A description of the job.
:param detectors: An array of detector update objects.
:param groups: A list of job groups. A job can belong to no groups or many.
:param model_plot_config:
:param model_prune_window:
:param model_snapshot_retention_days: Advanced configuration option, which affects
the automatic removal of old model snapshots for this job. It specifies the
maximum period of time (in days) that snapshots are retained. This period
is relative to the timestamp of the most recent snapshot for this job.
:param per_partition_categorization: Settings related to how categorization interacts
with partition fields.
:param renormalization_window_days: Advanced configuration option. The period
over which adjustments to the score are applied, as new data is seen.
:param results_retention_days: Advanced configuration option. The period of time
(in days) that results are retained. Age is calculated relative to the timestamp
of the latest bucket result. If this property has a non-null value, once
per day at 00:30 (server time), results that are the specified number of
days older than the latest bucket result are deleted from Elasticsearch.
The default value is null, which means all results are retained.
"""
if job_id in SKIP_IN_PATH:
raise ValueError("Empty value passed for parameter 'job_id'")
__path_parts: t.Dict[str, str] = {"job_id": _quote(job_id)}
__path = f'/_ml/anomaly_detectors/{__path_parts["job_id"]}/_update'
__query: t.Dict[str, t.Any] = {}
__body: t.Dict[str, t.Any] = body if body is not None else {}
if error_trace is not None:
__query["error_trace"] = error_trace
if filter_path is not None:
__query["filter_path"] = filter_path
if human is not None:
__query["human"] = human
if pretty is not None:
__query["pretty"] = pretty
if not __body:
if allow_lazy_open is not None:
__body["allow_lazy_open"] = allow_lazy_open
if analysis_limits is not None:
__body["analysis_limits"] = analysis_limits
if background_persist_interval is not None:
__body["background_persist_interval"] = background_persist_interval
if categorization_filters is not None:
__body["categorization_filters"] = categorization_filters
if custom_settings is not None:
__body["custom_settings"] = custom_settings
if daily_model_snapshot_retention_after_days is not None:
__body["daily_model_snapshot_retention_after_days"] = (
daily_model_snapshot_retention_after_days
)
if description is not None:
__body["description"] = description
if detectors is not None:
__body["detectors"] = detectors
if groups is not None:
__body["groups"] = groups
if model_plot_config is not None:
__body["model_plot_config"] = model_plot_config
if model_prune_window is not None:
__body["model_prune_window"] = model_prune_window
if model_snapshot_retention_days is not None:
__body["model_snapshot_retention_days"] = model_snapshot_retention_days
if per_partition_categorization is not None:
__body["per_partition_categorization"] = per_partition_categorization
if renormalization_window_days is not None:
__body["renormalization_window_days"] = renormalization_window_days
if results_retention_days is not None:
__body["results_retention_days"] = results_retention_days
__headers = {"accept": "application/json", "content-type": "application/json"}
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