Machine Learning (ML)

class elasticsearch.client.MlClient(client)
Parameters:

client (BaseClient)

clear_trained_model_deployment_cache(*, model_id, error_trace=None, filter_path=None, human=None, pretty=None)

Clear the cached results from a trained model deployment

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/clear-trained-model-deployment-cache.html

Parameters:
  • model_id (str) – The unique identifier of the trained model.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

close_job(*, job_id, allow_no_match=None, error_trace=None, filter_path=None, force=None, human=None, pretty=None, timeout=None, body=None)

Closes one or more anomaly detection jobs. A job can be opened and closed multiple times throughout its lifecycle.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-close-job.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job. It can be a job identifier, a group name, or a wildcard expression. You can close multiple anomaly detection jobs in a single API request by using a group name, a comma-separated list of jobs, or a wildcard expression. You can close all jobs by using _all or by specifying * as the job identifier.

  • allow_no_match (bool | None) – Refer to the description for the allow_no_match query parameter.

  • force (bool | None) – Refer to the descriptiion for the force query parameter.

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – Refer to the description for the timeout query parameter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

delete_calendar(*, calendar_id, error_trace=None, filter_path=None, human=None, pretty=None)

Deletes a calendar.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-delete-calendar.html

Parameters:
  • calendar_id (str) – A string that uniquely identifies a calendar.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

delete_calendar_event(*, calendar_id, event_id, error_trace=None, filter_path=None, human=None, pretty=None)

Deletes scheduled events from a calendar.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-delete-calendar-event.html

Parameters:
  • calendar_id (str) – A string that uniquely identifies a calendar.

  • event_id (str) – Identifier for the scheduled event. You can obtain this identifier by using the get calendar events API.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

delete_calendar_job(*, calendar_id, job_id, error_trace=None, filter_path=None, human=None, pretty=None)

Deletes anomaly detection jobs from a calendar.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-delete-calendar-job.html

Parameters:
  • calendar_id (str) – A string that uniquely identifies a calendar.

  • job_id (str | Sequence[str]) – An identifier for the anomaly detection jobs. It can be a job identifier, a group name, or a comma-separated list of jobs or groups.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

delete_data_frame_analytics(*, id, error_trace=None, filter_path=None, force=None, human=None, pretty=None, timeout=None)

Deletes an existing data frame analytics job.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/delete-dfanalytics.html

Parameters:
  • id (str) – Identifier for the data frame analytics job.

  • force (bool | None) – If true, it deletes a job that is not stopped; this method is quicker than stopping and deleting the job.

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – The time to wait for the job to be deleted.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

delete_datafeed(*, datafeed_id, error_trace=None, filter_path=None, force=None, human=None, pretty=None)

Deletes an existing datafeed.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-delete-datafeed.html

Parameters:
  • datafeed_id (str) – A numerical character string that uniquely identifies the datafeed. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

  • force (bool | None) – Use to forcefully delete a started datafeed; this method is quicker than stopping and deleting the datafeed.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

delete_expired_data(*, job_id=None, error_trace=None, filter_path=None, human=None, pretty=None, requests_per_second=None, timeout=None, body=None)

Deletes expired and unused machine learning data.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-delete-expired-data.html

Parameters:
  • job_id (str | None) – Identifier for an anomaly detection job. It can be a job identifier, a group name, or a wildcard expression.

  • requests_per_second (float | None) – The desired requests per second for the deletion processes. The default behavior is no throttling.

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – How long can the underlying delete processes run until they are canceled.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

delete_filter(*, filter_id, error_trace=None, filter_path=None, human=None, pretty=None)

Deletes a filter.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-delete-filter.html

Parameters:
  • filter_id (str) – A string that uniquely identifies a filter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

delete_forecast(*, job_id, forecast_id=None, allow_no_forecasts=None, error_trace=None, filter_path=None, human=None, pretty=None, timeout=None)

Deletes forecasts from a machine learning job.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-delete-forecast.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • forecast_id (str | None) – A comma-separated list of forecast identifiers. If you do not specify this optional parameter or if you specify _all or * the API deletes all forecasts from the job.

  • allow_no_forecasts (bool | None) – Specifies whether an error occurs when there are no forecasts. In particular, if this parameter is set to false and there are no forecasts associated with the job, attempts to delete all forecasts return an error.

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – Specifies the period of time to wait for the completion of the delete operation. When this period of time elapses, the API fails and returns an error.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

delete_job(*, job_id, delete_user_annotations=None, error_trace=None, filter_path=None, force=None, human=None, pretty=None, wait_for_completion=None)

Deletes an existing anomaly detection job.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-delete-job.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • delete_user_annotations (bool | None) – Specifies whether annotations that have been added by the user should be deleted along with any auto-generated annotations when the job is reset.

  • force (bool | None) – Use to forcefully delete an opened job; this method is quicker than closing and deleting the job.

  • wait_for_completion (bool | None) – Specifies whether the request should return immediately or wait until the job deletion completes.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

delete_model_snapshot(*, job_id, snapshot_id, error_trace=None, filter_path=None, human=None, pretty=None)

Deletes an existing model snapshot.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-delete-snapshot.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • snapshot_id (str) – Identifier for the model snapshot.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

delete_trained_model(*, model_id, error_trace=None, filter_path=None, force=None, human=None, pretty=None)

Deletes an existing trained inference model that is currently not referenced by an ingest pipeline.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/delete-trained-models.html

Parameters:
  • model_id (str) – The unique identifier of the trained model.

  • force (bool | None) – Forcefully deletes a trained model that is referenced by ingest pipelines or has a started deployment.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

delete_trained_model_alias(*, model_id, model_alias, error_trace=None, filter_path=None, human=None, pretty=None)

Deletes a model alias that refers to the trained model

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/delete-trained-models-aliases.html

Parameters:
  • model_id (str) – The trained model ID to which the model alias refers.

  • model_alias (str) – The model alias to delete.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

estimate_model_memory(*, analysis_config=None, error_trace=None, filter_path=None, human=None, max_bucket_cardinality=None, overall_cardinality=None, pretty=None, body=None)

Estimates the model memory

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-apis.html

Parameters:
  • analysis_config (Mapping[str, Any] | None) – For a list of the properties that you can specify in the analysis_config component of the body of this API.

  • max_bucket_cardinality (Mapping[str, int] | None) – Estimates of the highest cardinality in a single bucket that is observed for influencer fields over the time period that the job analyzes data. To produce a good answer, values must be provided for all influencer fields. Providing values for fields that are not listed as influencers has no effect on the estimation.

  • overall_cardinality (Mapping[str, int] | None) – Estimates of the cardinality that is observed for fields over the whole time period that the job analyzes data. To produce a good answer, values must be provided for fields referenced in the by_field_name, over_field_name and partition_field_name of any detectors. Providing values for other fields has no effect on the estimation. It can be omitted from the request if no detectors have a by_field_name, over_field_name or partition_field_name.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

evaluate_data_frame(*, evaluation=None, index=None, error_trace=None, filter_path=None, human=None, pretty=None, query=None, body=None)

Evaluates the data frame analytics for an annotated index.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/evaluate-dfanalytics.html

Parameters:
  • evaluation (Mapping[str, Any] | None) – Defines the type of evaluation you want to perform.

  • index (str | None) – Defines the index in which the evaluation will be performed.

  • query (Mapping[str, Any] | None) – A query clause that retrieves a subset of data from the source index.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

explain_data_frame_analytics(*, id=None, allow_lazy_start=None, analysis=None, analyzed_fields=None, description=None, dest=None, error_trace=None, filter_path=None, human=None, max_num_threads=None, model_memory_limit=None, pretty=None, source=None, body=None)

Explains a data frame analytics config.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/explain-dfanalytics.html

Parameters:
  • id (str | None) – Identifier for the data frame analytics job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

  • allow_lazy_start (bool | None) – Specifies whether this job can start when there is insufficient machine learning node capacity for it to be immediately assigned to a node.

  • analysis (Mapping[str, Any] | None) – The analysis configuration, which contains the information necessary to perform one of the following types of analysis: classification, outlier detection, or regression.

  • analyzed_fields (Mapping[str, Any] | None) – Specify includes and/or excludes patterns to select which fields will be included in the analysis. The patterns specified in excludes are applied last, therefore excludes takes precedence. In other words, if the same field is specified in both includes and excludes, then the field will not be included in the analysis.

  • description (str | None) – A description of the job.

  • dest (Mapping[str, Any] | None) – The destination configuration, consisting of index and optionally results_field (ml by default).

  • max_num_threads (int | None) – The maximum number of threads to be used by the analysis. Using more threads may decrease the time necessary to complete the analysis at the cost of using more CPU. Note that the process may use additional threads for operational functionality other than the analysis itself.

  • model_memory_limit (str | None) – The approximate maximum amount of memory resources that are permitted for analytical processing. If your elasticsearch.yml file contains an xpack.ml.max_model_memory_limit setting, an error occurs when you try to create data frame analytics jobs that have model_memory_limit values greater than that setting.

  • source (Mapping[str, Any] | None) – The configuration of how to source the analysis data. It requires an index. Optionally, query and _source may be specified.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

flush_job(*, job_id, advance_time=None, calc_interim=None, end=None, error_trace=None, filter_path=None, human=None, pretty=None, skip_time=None, start=None, body=None)

Forces any buffered data to be processed by the job.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-flush-job.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • advance_time (str | Any | None) – Refer to the description for the advance_time query parameter.

  • calc_interim (bool | None) – Refer to the description for the calc_interim query parameter.

  • end (str | Any | None) – Refer to the description for the end query parameter.

  • skip_time (str | Any | None) – Refer to the description for the skip_time query parameter.

  • start (str | Any | None) – Refer to the description for the start query parameter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

forecast(*, job_id, duration=None, error_trace=None, expires_in=None, filter_path=None, human=None, max_model_memory=None, pretty=None, body=None)

Predicts the future behavior of a time series by using its historical behavior.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-forecast.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job. The job must be open when you create a forecast; otherwise, an error occurs.

  • duration (Literal[-1] | ~typing.Literal[0] | str | None) – Refer to the description for the duration query parameter.

  • expires_in (Literal[-1] | ~typing.Literal[0] | str | None) – Refer to the description for the expires_in query parameter.

  • max_model_memory (str | None) – Refer to the description for the max_model_memory query parameter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

get_buckets(*, job_id, timestamp=None, anomaly_score=None, desc=None, end=None, error_trace=None, exclude_interim=None, expand=None, filter_path=None, from_=None, human=None, page=None, pretty=None, size=None, sort=None, start=None, body=None)

Retrieves anomaly detection job results for one or more buckets.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-bucket.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • timestamp (str | Any | None) – The timestamp of a single bucket result. If you do not specify this parameter, the API returns information about all buckets.

  • anomaly_score (float | None) – Refer to the description for the anomaly_score query parameter.

  • desc (bool | None) – Refer to the description for the desc query parameter.

  • end (str | Any | None) – Refer to the description for the end query parameter.

  • exclude_interim (bool | None) – Refer to the description for the exclude_interim query parameter.

  • expand (bool | None) – Refer to the description for the expand query parameter.

  • from – Skips the specified number of buckets.

  • page (Mapping[str, Any] | None)

  • size (int | None) – Specifies the maximum number of buckets to obtain.

  • sort (str | None) – Refer to the desription for the sort query parameter.

  • start (str | Any | None) – Refer to the description for the start query parameter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • from_ (int | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

get_calendar_events(*, calendar_id, end=None, error_trace=None, filter_path=None, from_=None, human=None, job_id=None, pretty=None, size=None, start=None)

Retrieves information about the scheduled events in calendars.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-calendar-event.html

Parameters:
  • calendar_id (str) – A string that uniquely identifies a calendar. You can get information for multiple calendars by using a comma-separated list of ids or a wildcard expression. You can get information for all calendars by using _all or * or by omitting the calendar identifier.

  • end (str | Any | None) – Specifies to get events with timestamps earlier than this time.

  • from – Skips the specified number of events.

  • job_id (str | None) – Specifies to get events for a specific anomaly detection job identifier or job group. It must be used with a calendar identifier of _all or *.

  • size (int | None) – Specifies the maximum number of events to obtain.

  • start (str | Any | None) – Specifies to get events with timestamps after this time.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • from_ (int | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

get_calendars(*, calendar_id=None, error_trace=None, filter_path=None, from_=None, human=None, page=None, pretty=None, size=None, body=None)

Retrieves configuration information for calendars.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-calendar.html

Parameters:
  • calendar_id (str | None) – A string that uniquely identifies a calendar. You can get information for multiple calendars by using a comma-separated list of ids or a wildcard expression. You can get information for all calendars by using _all or * or by omitting the calendar identifier.

  • from – Skips the specified number of calendars. This parameter is supported only when you omit the calendar identifier.

  • page (Mapping[str, Any] | None) – This object is supported only when you omit the calendar identifier.

  • size (int | None) – Specifies the maximum number of calendars to obtain. This parameter is supported only when you omit the calendar identifier.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • from_ (int | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

get_categories(*, job_id, category_id=None, error_trace=None, filter_path=None, from_=None, human=None, page=None, partition_field_value=None, pretty=None, size=None, body=None)

Retrieves anomaly detection job results for one or more categories.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-category.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • category_id (str | None) – Identifier for the category, which is unique in the job. If you specify neither the category ID nor the partition_field_value, the API returns information about all categories. If you specify only the partition_field_value, it returns information about all categories for the specified partition.

  • from – Skips the specified number of categories.

  • page (Mapping[str, Any] | None) – Configures pagination. This parameter has the from and size properties.

  • partition_field_value (str | None) – Only return categories for the specified partition.

  • size (int | None) – Specifies the maximum number of categories to obtain.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • from_ (int | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

get_data_frame_analytics(*, id=None, allow_no_match=None, error_trace=None, exclude_generated=None, filter_path=None, from_=None, human=None, pretty=None, size=None)

Retrieves configuration information for data frame analytics jobs.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/get-dfanalytics.html

Parameters:
  • id (str | None) – Identifier for the data frame analytics job. If you do not specify this option, the API returns information for the first hundred data frame analytics jobs.

  • allow_no_match (bool | None) – Specifies what to do when the request: 1. Contains wildcard expressions and there are no data frame analytics jobs that match. 2. Contains the _all string or no identifiers and there are no matches. 3. Contains wildcard expressions and there are only partial matches. The default value returns an empty data_frame_analytics array when there are no matches and the subset of results when there are partial matches. If this parameter is false, the request returns a 404 status code when there are no matches or only partial matches.

  • exclude_generated (bool | None) – Indicates if certain fields should be removed from the configuration on retrieval. This allows the configuration to be in an acceptable format to be retrieved and then added to another cluster.

  • from – Skips the specified number of data frame analytics jobs.

  • size (int | None) – Specifies the maximum number of data frame analytics jobs to obtain.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • from_ (int | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

get_data_frame_analytics_stats(*, id=None, allow_no_match=None, error_trace=None, filter_path=None, from_=None, human=None, pretty=None, size=None, verbose=None)

Retrieves usage information for data frame analytics jobs.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/get-dfanalytics-stats.html

Parameters:
  • id (str | None) – Identifier for the data frame analytics job. If you do not specify this option, the API returns information for the first hundred data frame analytics jobs.

  • allow_no_match (bool | None) – Specifies what to do when the request: 1. Contains wildcard expressions and there are no data frame analytics jobs that match. 2. Contains the _all string or no identifiers and there are no matches. 3. Contains wildcard expressions and there are only partial matches. The default value returns an empty data_frame_analytics array when there are no matches and the subset of results when there are partial matches. If this parameter is false, the request returns a 404 status code when there are no matches or only partial matches.

  • from – Skips the specified number of data frame analytics jobs.

  • size (int | None) – Specifies the maximum number of data frame analytics jobs to obtain.

  • verbose (bool | None) – Defines whether the stats response should be verbose.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • from_ (int | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

get_datafeed_stats(*, datafeed_id=None, allow_no_match=None, error_trace=None, filter_path=None, human=None, pretty=None)

Retrieves usage information for datafeeds.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-datafeed-stats.html

Parameters:
  • datafeed_id (str | Sequence[str] | None) – Identifier for the datafeed. It can be a datafeed identifier or a wildcard expression. If you do not specify one of these options, the API returns information about all datafeeds.

  • allow_no_match (bool | None) – Specifies what to do when the request: 1. Contains wildcard expressions and there are no datafeeds that match. 2. Contains the _all string or no identifiers and there are no matches. 3. Contains wildcard expressions and there are only partial matches. The default value is true, which returns an empty datafeeds array when there are no matches and the subset of results when there are partial matches. If this parameter is false, the request returns a 404 status code when there are no matches or only partial matches.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

get_datafeeds(*, datafeed_id=None, allow_no_match=None, error_trace=None, exclude_generated=None, filter_path=None, human=None, pretty=None)

Retrieves configuration information for datafeeds.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-datafeed.html

Parameters:
  • datafeed_id (str | Sequence[str] | None) – Identifier for the datafeed. It can be a datafeed identifier or a wildcard expression. If you do not specify one of these options, the API returns information about all datafeeds.

  • allow_no_match (bool | None) – Specifies what to do when the request: 1. Contains wildcard expressions and there are no datafeeds that match. 2. Contains the _all string or no identifiers and there are no matches. 3. Contains wildcard expressions and there are only partial matches. The default value is true, which returns an empty datafeeds array when there are no matches and the subset of results when there are partial matches. If this parameter is false, the request returns a 404 status code when there are no matches or only partial matches.

  • exclude_generated (bool | None) – Indicates if certain fields should be removed from the configuration on retrieval. This allows the configuration to be in an acceptable format to be retrieved and then added to another cluster.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

get_filters(*, filter_id=None, error_trace=None, filter_path=None, from_=None, human=None, pretty=None, size=None)

Retrieves filters.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-filter.html

Parameters:
  • filter_id (str | Sequence[str] | None) – A string that uniquely identifies a filter.

  • from – Skips the specified number of filters.

  • size (int | None) – Specifies the maximum number of filters to obtain.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • from_ (int | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

get_influencers(*, job_id, desc=None, end=None, error_trace=None, exclude_interim=None, filter_path=None, from_=None, human=None, influencer_score=None, page=None, pretty=None, size=None, sort=None, start=None, body=None)

Retrieves anomaly detection job results for one or more influencers.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-influencer.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • desc (bool | None) – If true, the results are sorted in descending order.

  • end (str | Any | None) – Returns influencers with timestamps earlier than this time. The default value means it is unset and results are not limited to specific timestamps.

  • exclude_interim (bool | None) – If true, the output excludes interim results. By default, interim results are included.

  • from – Skips the specified number of influencers.

  • influencer_score (float | None) – Returns influencers with anomaly scores greater than or equal to this value.

  • page (Mapping[str, Any] | None) – Configures pagination. This parameter has the from and size properties.

  • size (int | None) – Specifies the maximum number of influencers to obtain.

  • sort (str | None) – Specifies the sort field for the requested influencers. By default, the influencers are sorted by the influencer_score value.

  • start (str | Any | None) – Returns influencers with timestamps after this time. The default value means it is unset and results are not limited to specific timestamps.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • from_ (int | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

get_job_stats(*, job_id=None, allow_no_match=None, error_trace=None, filter_path=None, human=None, pretty=None)

Retrieves usage information for anomaly detection jobs.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-job-stats.html

Parameters:
  • job_id (str | None) – Identifier for the anomaly detection job. It can be a job identifier, a group name, a comma-separated list of jobs, or a wildcard expression. If you do not specify one of these options, the API returns information for all anomaly detection jobs.

  • allow_no_match (bool | None) – Specifies what to do when the request: 1. Contains wildcard expressions and there are no jobs that match. 2. Contains the _all string or no identifiers and there are no matches. 3. Contains wildcard expressions and there are only partial matches. If true, the API returns an empty jobs array when there are no matches and the subset of results when there are partial matches. If false, the API returns a 404 status code when there are no matches or only partial matches.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

get_jobs(*, job_id=None, allow_no_match=None, error_trace=None, exclude_generated=None, filter_path=None, human=None, pretty=None)

Retrieves configuration information for anomaly detection jobs.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-job.html

Parameters:
  • job_id (str | Sequence[str] | None) – Identifier for the anomaly detection job. It can be a job identifier, a group name, or a wildcard expression. If you do not specify one of these options, the API returns information for all anomaly detection jobs.

  • allow_no_match (bool | None) – Specifies what to do when the request: 1. Contains wildcard expressions and there are no jobs that match. 2. Contains the _all string or no identifiers and there are no matches. 3. Contains wildcard expressions and there are only partial matches. The default value is true, which returns an empty jobs array when there are no matches and the subset of results when there are partial matches. If this parameter is false, the request returns a 404 status code when there are no matches or only partial matches.

  • exclude_generated (bool | None) – Indicates if certain fields should be removed from the configuration on retrieval. This allows the configuration to be in an acceptable format to be retrieved and then added to another cluster.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

get_memory_stats(*, node_id=None, error_trace=None, filter_path=None, human=None, master_timeout=None, pretty=None, timeout=None)

Returns information on how ML is using memory.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/get-ml-memory.html

Parameters:
  • node_id (str | None) – The names of particular nodes in the cluster to target. For example, nodeId1,nodeId2 or ml:true

  • master_timeout (Literal[-1] | ~typing.Literal[0] | str | None) – Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

get_model_snapshot_upgrade_stats(*, job_id, snapshot_id, allow_no_match=None, error_trace=None, filter_path=None, human=None, pretty=None)

Gets stats for anomaly detection job model snapshot upgrades that are in progress.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-job-model-snapshot-upgrade-stats.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • snapshot_id (str) – A numerical character string that uniquely identifies the model snapshot. You can get information for multiple snapshots by using a comma-separated list or a wildcard expression. You can get all snapshots by using _all, by specifying * as the snapshot ID, or by omitting the snapshot ID.

  • allow_no_match (bool | None) – Specifies what to do when the request: - Contains wildcard expressions and there are no jobs that match. - Contains the _all string or no identifiers and there are no matches. - Contains wildcard expressions and there are only partial matches. The default value is true, which returns an empty jobs array when there are no matches and the subset of results when there are partial matches. If this parameter is false, the request returns a 404 status code when there are no matches or only partial matches.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

get_model_snapshots(*, job_id, snapshot_id=None, desc=None, end=None, error_trace=None, filter_path=None, from_=None, human=None, page=None, pretty=None, size=None, sort=None, start=None, body=None)

Retrieves information about model snapshots.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-snapshot.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • snapshot_id (str | None) – A numerical character string that uniquely identifies the model snapshot. You can get information for multiple snapshots by using a comma-separated list or a wildcard expression. You can get all snapshots by using _all, by specifying * as the snapshot ID, or by omitting the snapshot ID.

  • desc (bool | None) – Refer to the description for the desc query parameter.

  • end (str | Any | None) – Refer to the description for the end query parameter.

  • from – Skips the specified number of snapshots.

  • page (Mapping[str, Any] | None)

  • size (int | None) – Specifies the maximum number of snapshots to obtain.

  • sort (str | None) – Refer to the description for the sort query parameter.

  • start (str | Any | None) – Refer to the description for the start query parameter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • from_ (int | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

get_overall_buckets(*, job_id, allow_no_match=None, bucket_span=None, end=None, error_trace=None, exclude_interim=None, filter_path=None, human=None, overall_score=None, pretty=None, start=None, top_n=None, body=None)

Retrieves overall bucket results that summarize the bucket results of multiple anomaly detection jobs.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-overall-buckets.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job. It can be a job identifier, a group name, a comma-separated list of jobs or groups, or a wildcard expression. You can summarize the bucket results for all anomaly detection jobs by using _all or by specifying * as the <job_id>.

  • allow_no_match (bool | None) – Refer to the description for the allow_no_match query parameter.

  • bucket_span (Literal[-1] | ~typing.Literal[0] | str | None) – Refer to the description for the bucket_span query parameter.

  • end (str | Any | None) – Refer to the description for the end query parameter.

  • exclude_interim (bool | None) – Refer to the description for the exclude_interim query parameter.

  • overall_score (float | str | None) – Refer to the description for the overall_score query parameter.

  • start (str | Any | None) – Refer to the description for the start query parameter.

  • top_n (int | None) – Refer to the description for the top_n query parameter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

get_records(*, job_id, desc=None, end=None, error_trace=None, exclude_interim=None, filter_path=None, from_=None, human=None, page=None, pretty=None, record_score=None, size=None, sort=None, start=None, body=None)

Retrieves anomaly records for an anomaly detection job.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-get-record.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • desc (bool | None) – Refer to the description for the desc query parameter.

  • end (str | Any | None) – Refer to the description for the end query parameter.

  • exclude_interim (bool | None) – Refer to the description for the exclude_interim query parameter.

  • from – Skips the specified number of records.

  • page (Mapping[str, Any] | None)

  • record_score (float | None) – Refer to the description for the record_score query parameter.

  • size (int | None) – Specifies the maximum number of records to obtain.

  • sort (str | None) – Refer to the description for the sort query parameter.

  • start (str | Any | None) – Refer to the description for the start query parameter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • from_ (int | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

get_trained_models(*, model_id=None, allow_no_match=None, decompress_definition=None, error_trace=None, exclude_generated=None, filter_path=None, from_=None, human=None, include=None, pretty=None, size=None, tags=None)

Retrieves configuration information for a trained inference model.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/get-trained-models.html

Parameters:
  • model_id (str | None) – The unique identifier of the trained model.

  • allow_no_match (bool | None) – Specifies what to do when the request: - Contains wildcard expressions and there are no models that match. - Contains the _all string or no identifiers and there are no matches. - Contains wildcard expressions and there are only partial matches. If true, it returns an empty array when there are no matches and the subset of results when there are partial matches.

  • decompress_definition (bool | None) – Specifies whether the included model definition should be returned as a JSON map (true) or in a custom compressed format (false).

  • exclude_generated (bool | None) – Indicates if certain fields should be removed from the configuration on retrieval. This allows the configuration to be in an acceptable format to be retrieved and then added to another cluster.

  • from – Skips the specified number of models.

  • include (Literal['definition', 'definition_status', 'feature_importance_baseline', 'hyperparameters', 'total_feature_importance'] | str | None) – A comma delimited string of optional fields to include in the response body.

  • size (int | None) – Specifies the maximum number of models to obtain.

  • tags (str | None) – A comma delimited string of tags. A trained model can have many tags, or none. When supplied, only trained models that contain all the supplied tags are returned.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • from_ (int | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

get_trained_models_stats(*, model_id=None, allow_no_match=None, error_trace=None, filter_path=None, from_=None, human=None, pretty=None, size=None)

Retrieves usage information for trained inference models.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/get-trained-models-stats.html

Parameters:
  • model_id (str | Sequence[str] | None) – The unique identifier of the trained model or a model alias. It can be a comma-separated list or a wildcard expression.

  • allow_no_match (bool | None) – Specifies what to do when the request: - Contains wildcard expressions and there are no models that match. - Contains the _all string or no identifiers and there are no matches. - Contains wildcard expressions and there are only partial matches. If true, it returns an empty array when there are no matches and the subset of results when there are partial matches.

  • from – Skips the specified number of models.

  • size (int | None) – Specifies the maximum number of models to obtain.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • from_ (int | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

infer_trained_model(*, model_id, docs=None, error_trace=None, filter_path=None, human=None, inference_config=None, pretty=None, timeout=None, body=None)

Evaluate a trained model.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/infer-trained-model.html

Parameters:
  • model_id (str) – The unique identifier of the trained model.

  • docs (Sequence[Mapping[str, Any]] | None) – An array of objects to pass to the model for inference. The objects should contain a fields matching your configured trained model input. Typically, for NLP models, the field name is text_field. Currently, for NLP models, only a single value is allowed.

  • inference_config (Mapping[str, Any] | None) – The inference configuration updates to apply on the API call

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – Controls the amount of time to wait for inference results.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

info(*, error_trace=None, filter_path=None, human=None, pretty=None)

Returns defaults and limits used by machine learning.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/get-ml-info.html

Parameters:
Return type:

ObjectApiResponse[Any]

open_job(*, job_id, error_trace=None, filter_path=None, human=None, pretty=None, timeout=None, body=None)

Opens one or more anomaly detection jobs.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-open-job.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – Refer to the description for the timeout query parameter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

post_calendar_events(*, calendar_id, events=None, error_trace=None, filter_path=None, human=None, pretty=None, body=None)

Posts scheduled events in a calendar.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-post-calendar-event.html

Parameters:
  • calendar_id (str) – A string that uniquely identifies a calendar.

  • events (Sequence[Mapping[str, Any]] | None) – A list of one of more scheduled events. The event’s start and end times can be specified as integer milliseconds since the epoch or as a string in ISO 8601 format.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

post_data(*, job_id, data=None, body=None, error_trace=None, filter_path=None, human=None, pretty=None, reset_end=None, reset_start=None)

Sends data to an anomaly detection job for analysis.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-post-data.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job. The job must have a state of open to receive and process the data.

  • data (Sequence[Any] | None)

  • reset_end (str | Any | None) – Specifies the end of the bucket resetting range.

  • reset_start (str | Any | None) – Specifies the start of the bucket resetting range.

  • body (Sequence[Any] | None)

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

preview_data_frame_analytics(*, id=None, config=None, error_trace=None, filter_path=None, human=None, pretty=None, body=None)

Previews that will be analyzed given a data frame analytics config.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/preview-dfanalytics.html

Parameters:
  • id (str | None) – Identifier for the data frame analytics job.

  • config (Mapping[str, Any] | None) – A data frame analytics config as described in create data frame analytics jobs. Note that id and dest don’t need to be provided in the context of this API.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

preview_datafeed(*, datafeed_id=None, datafeed_config=None, end=None, error_trace=None, filter_path=None, human=None, job_config=None, pretty=None, start=None, body=None)

Previews a datafeed.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-preview-datafeed.html

Parameters:
  • datafeed_id (str | None) – A numerical character string that uniquely identifies the datafeed. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters. NOTE: If you use this path parameter, you cannot provide datafeed or anomaly detection job configuration details in the request body.

  • datafeed_config (Mapping[str, Any] | None) – The datafeed definition to preview.

  • end (str | Any | None) – The end time when the datafeed preview should stop

  • job_config (Mapping[str, Any] | None) – The configuration details for the anomaly detection job that is associated with the datafeed. If the datafeed_config object does not include a job_id that references an existing anomaly detection job, you must supply this job_config object. If you include both a job_id and a job_config, the latter information is used. You cannot specify a job_config object unless you also supply a datafeed_config object.

  • start (str | Any | None) – The start time from where the datafeed preview should begin

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

put_calendar(*, calendar_id, description=None, error_trace=None, filter_path=None, human=None, job_ids=None, pretty=None, body=None)

Instantiates a calendar.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-put-calendar.html

Parameters:
  • calendar_id (str) – A string that uniquely identifies a calendar.

  • description (str | None) – A description of the calendar.

  • job_ids (Sequence[str] | None) – An array of anomaly detection job identifiers.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

put_calendar_job(*, calendar_id, job_id, error_trace=None, filter_path=None, human=None, pretty=None)

Adds an anomaly detection job to a calendar.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-put-calendar-job.html

Parameters:
  • calendar_id (str) – A string that uniquely identifies a calendar.

  • job_id (str) – An identifier for the anomaly detection jobs. It can be a job identifier, a group name, or a comma-separated list of jobs or groups.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

put_data_frame_analytics(*, id, analysis=None, dest=None, source=None, allow_lazy_start=None, analyzed_fields=None, description=None, error_trace=None, filter_path=None, headers=None, human=None, max_num_threads=None, model_memory_limit=None, pretty=None, version=None, body=None)

Instantiates a data frame analytics job.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/put-dfanalytics.html

Parameters:
  • id (str) – Identifier for the data frame analytics job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

  • analysis (Mapping[str, Any] | None) – The analysis configuration, which contains the information necessary to perform one of the following types of analysis: classification, outlier detection, or regression.

  • dest (Mapping[str, Any] | None) – The destination configuration.

  • source (Mapping[str, Any] | None) – The configuration of how to source the analysis data.

  • allow_lazy_start (bool | None) – Specifies whether this job can start when there is insufficient machine learning node capacity for it to be immediately assigned to a node. If set to false and a machine learning node with capacity to run the job cannot be immediately found, the API returns an error. If set to true, the API does not return an error; the job waits in the starting state until sufficient machine learning node capacity is available. This behavior is also affected by the cluster-wide xpack.ml.max_lazy_ml_nodes setting.

  • analyzed_fields (Mapping[str, Any] | None) – Specifies includes and/or excludes patterns to select which fields will be included in the analysis. The patterns specified in excludes are applied last, therefore excludes takes precedence. In other words, if the same field is specified in both includes and excludes, then the field will not be included in the analysis. If analyzed_fields is not set, only the relevant fields will be included. For example, all the numeric fields for outlier detection. The supported fields vary for each type of analysis. Outlier detection requires numeric or boolean data to analyze. The algorithms don’t support missing values therefore fields that have data types other than numeric or boolean are ignored. Documents where included fields contain missing values, null values, or an array are also ignored. Therefore the dest index may contain documents that don’t have an outlier score. Regression supports fields that are numeric, boolean, text, keyword, and ip data types. It is also tolerant of missing values. Fields that are supported are included in the analysis, other fields are ignored. Documents where included fields contain an array with two or more values are also ignored. Documents in the dest index that don’t contain a results field are not included in the regression analysis. Classification supports fields that are numeric, boolean, text, keyword, and ip data types. It is also tolerant of missing values. Fields that are supported are included in the analysis, other fields are ignored. Documents where included fields contain an array with two or more values are also ignored. Documents in the dest index that don’t contain a results field are not included in the classification analysis. Classification analysis can be improved by mapping ordinal variable values to a single number. For example, in case of age ranges, you can model the values as 0-14 = 0, 15-24 = 1, 25-34 = 2, and so on.

  • description (str | None) – A description of the job.

  • headers (Mapping[str, str | Sequence[str]] | None)

  • max_num_threads (int | None) – The maximum number of threads to be used by the analysis. Using more threads may decrease the time necessary to complete the analysis at the cost of using more CPU. Note that the process may use additional threads for operational functionality other than the analysis itself.

  • model_memory_limit (str | None) – The approximate maximum amount of memory resources that are permitted for analytical processing. If your elasticsearch.yml file contains an xpack.ml.max_model_memory_limit setting, an error occurs when you try to create data frame analytics jobs that have model_memory_limit values greater than that setting.

  • version (str | None)

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

put_datafeed(*, datafeed_id, aggregations=None, allow_no_indices=None, chunking_config=None, delayed_data_check_config=None, error_trace=None, expand_wildcards=None, filter_path=None, frequency=None, headers=None, human=None, ignore_throttled=None, ignore_unavailable=None, indexes=None, indices=None, indices_options=None, job_id=None, max_empty_searches=None, pretty=None, query=None, query_delay=None, runtime_mappings=None, script_fields=None, scroll_size=None, body=None)

Instantiates a datafeed.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-put-datafeed.html

Parameters:
  • datafeed_id (str) – A numerical character string that uniquely identifies the datafeed. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

  • aggregations (Mapping[str, Mapping[str, Any]] | None) – If set, the datafeed performs aggregation searches. Support for aggregations is limited and should be used only with low cardinality data.

  • allow_no_indices (bool | None) – If true, wildcard indices expressions that resolve into no concrete indices are ignored. This includes the _all string or when no indices are specified.

  • chunking_config (Mapping[str, Any] | None) – Datafeeds might be required to search over long time periods, for several months or years. This search is split into time chunks in order to ensure the load on Elasticsearch is managed. Chunking configuration controls how the size of these time chunks are calculated; it is an advanced configuration option.

  • delayed_data_check_config (Mapping[str, Any] | None) – Specifies whether the datafeed checks for missing data and the size of the window. The datafeed can optionally search over indices that have already been read in an effort to determine whether any data has subsequently been added to the index. If missing data is found, it is a good indication that the query_delay is set too low and the data is being indexed after the datafeed has passed that moment in time. This check runs only on real-time datafeeds.

  • expand_wildcards (Sequence[Literal['all', 'closed', 'hidden', 'none', 'open'] | str] | ~typing.Literal['all', 'closed', 'hidden', 'none', 'open'] | str | None) – Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values.

  • frequency (Literal[-1] | ~typing.Literal[0] | str | None) – The interval at which scheduled queries are made while the datafeed runs in real time. The default value is either the bucket span for short bucket spans, or, for longer bucket spans, a sensible fraction of the bucket span. When frequency is shorter than the bucket span, interim results for the last (partial) bucket are written then eventually overwritten by the full bucket results. If the datafeed uses aggregations, this value must be divisible by the interval of the date histogram aggregation.

  • headers (Mapping[str, str | Sequence[str]] | None)

  • ignore_throttled (bool | None) – If true, concrete, expanded, or aliased indices are ignored when frozen.

  • ignore_unavailable (bool | None) – If true, unavailable indices (missing or closed) are ignored.

  • indexes (str | Sequence[str] | None) – An array of index names. Wildcards are supported. If any of the indices are in remote clusters, the machine learning nodes must have the remote_cluster_client role.

  • indices (str | Sequence[str] | None) – An array of index names. Wildcards are supported. If any of the indices are in remote clusters, the machine learning nodes must have the remote_cluster_client role.

  • indices_options (Mapping[str, Any] | None) – Specifies index expansion options that are used during search

  • job_id (str | None) – Identifier for the anomaly detection job.

  • max_empty_searches (int | None) – If a real-time datafeed has never seen any data (including during any initial training period), it automatically stops and closes the associated job after this many real-time searches return no documents. In other words, it stops after frequency times max_empty_searches of real-time operation. If not set, a datafeed with no end time that sees no data remains started until it is explicitly stopped. By default, it is not set.

  • query (Mapping[str, Any] | None) – The Elasticsearch query domain-specific language (DSL). This value corresponds to the query object in an Elasticsearch search POST body. All the options that are supported by Elasticsearch can be used, as this object is passed verbatim to Elasticsearch.

  • query_delay (Literal[-1] | ~typing.Literal[0] | str | None) – The number of seconds behind real time that data is queried. For example, if data from 10:04 a.m. might not be searchable in Elasticsearch until 10:06 a.m., set this property to 120 seconds. The default value is randomly selected between 60s and 120s. This randomness improves the query performance when there are multiple jobs running on the same node.

  • runtime_mappings (Mapping[str, Mapping[str, Any]] | None) – Specifies runtime fields for the datafeed search.

  • script_fields (Mapping[str, Mapping[str, Any]] | None) – Specifies scripts that evaluate custom expressions and returns script fields to the datafeed. The detector configuration objects in a job can contain functions that use these script fields.

  • scroll_size (int | None) – The size parameter that is used in Elasticsearch searches when the datafeed does not use aggregations. The maximum value is the value of index.max_result_window, which is 10,000 by default.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

put_filter(*, filter_id, description=None, error_trace=None, filter_path=None, human=None, items=None, pretty=None, body=None)

Instantiates a filter.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-put-filter.html

Parameters:
  • filter_id (str) – A string that uniquely identifies a filter.

  • description (str | None) – A description of the filter.

  • items (Sequence[str] | None) – The items of the filter. A wildcard * can be used at the beginning or the end of an item. Up to 10000 items are allowed in each filter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

put_job(*, job_id, analysis_config=None, data_description=None, allow_lazy_open=None, analysis_limits=None, background_persist_interval=None, custom_settings=None, daily_model_snapshot_retention_after_days=None, datafeed_config=None, description=None, error_trace=None, filter_path=None, groups=None, human=None, model_plot_config=None, model_snapshot_retention_days=None, pretty=None, renormalization_window_days=None, results_index_name=None, results_retention_days=None, body=None)

Instantiates an anomaly detection job.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-put-job.html

Parameters:
  • job_id (str) – The identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

  • analysis_config (Mapping[str, Any] | None) – Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.

  • data_description (Mapping[str, Any] | None) – Defines the format of the input data when you send data to the job by using the post data API. Note that when configure a datafeed, these properties are automatically set. When data is received via the post data API, it is not stored in Elasticsearch. Only the results for anomaly detection are retained.

  • allow_lazy_open (bool | None) – 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. By default, if 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.

  • analysis_limits (Mapping[str, Any] | None) – Limits can be applied for the resources required to hold the mathematical models in memory. These limits are approximate and can be set per job. They do not control the memory used by other processes, for example the Elasticsearch Java processes.

  • background_persist_interval (Literal[-1] | ~typing.Literal[0] | str | None) – 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 background_persist_interval value too low.

  • custom_settings (Any | None) – Advanced configuration option. Contains custom meta data about the job.

  • daily_model_snapshot_retention_after_days (int | None) – 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.

  • datafeed_config (Mapping[str, Any] | None) – Defines a datafeed for the anomaly detection job. If Elasticsearch security features are enabled, your datafeed remembers which roles the user who created it had at the time of creation and runs the query using those same roles. If you provide secondary authorization headers, those credentials are used instead.

  • description (str | None) – A description of the job.

  • groups (Sequence[str] | None) – A list of job groups. A job can belong to no groups or many.

  • model_plot_config (Mapping[str, Any] | None) – This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection. If you enable model plot it can add considerable overhead to the performance of the system; it is not feasible for jobs with many entities. Model plot provides a simplified and indicative view of the model and its bounds. It does not display complex features such as multivariate correlations or multimodal data. As such, anomalies may occasionally be reported which cannot be seen in the model plot. Model plot config can be configured when the job is created or updated later. It must be disabled if performance issues are experienced.

  • model_snapshot_retention_days (int | None) – 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. By default, snapshots ten days older than the newest snapshot are deleted.

  • renormalization_window_days (int | None) – Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen. The default value is the longer of 30 days or 100 bucket spans.

  • results_index_name (str | None) – A text string that affects the name of the machine learning results index. By default, the job generates an index named .ml-anomalies-shared.

  • results_retention_days (int | None) – 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. Annotations generated by the system also count as results for retention purposes; they are deleted after the same number of days as results. Annotations added by users are retained forever.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

put_trained_model(*, model_id, compressed_definition=None, defer_definition_decompression=None, definition=None, description=None, error_trace=None, filter_path=None, human=None, inference_config=None, input=None, metadata=None, model_size_bytes=None, model_type=None, platform_architecture=None, prefix_strings=None, pretty=None, tags=None, wait_for_completion=None, body=None)

Creates an inference trained model.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/put-trained-models.html

Parameters:
  • model_id (str) – The unique identifier of the trained model.

  • compressed_definition (str | None) – The compressed (GZipped and Base64 encoded) inference definition of the model. If compressed_definition is specified, then definition cannot be specified.

  • defer_definition_decompression (bool | None) – If set to true and a compressed_definition is provided, the request defers definition decompression and skips relevant validations.

  • definition (Mapping[str, Any] | None) – The inference definition for the model. If definition is specified, then compressed_definition cannot be specified.

  • description (str | None) – A human-readable description of the inference trained model.

  • inference_config (Mapping[str, Any] | None) – The default configuration for inference. This can be either a regression or classification configuration. It must match the underlying definition.trained_model’s target_type. For pre-packaged models such as ELSER the config is not required.

  • input (Mapping[str, Any] | None) – The input field names for the model definition.

  • metadata (Any | None) – An object map that contains metadata about the model.

  • model_size_bytes (int | None) – The estimated memory usage in bytes to keep the trained model in memory. This property is supported only if defer_definition_decompression is true or the model definition is not supplied.

  • model_type (Literal['lang_ident', 'pytorch', 'tree_ensemble'] | str | None) – The model type.

  • platform_architecture (str | None) – The platform architecture (if applicable) of the trained mode. If the model only works on one platform, because it is heavily optimized for a particular processor architecture and OS combination, then this field specifies which. The format of the string must match the platform identifiers used by Elasticsearch, so one of, linux-x86_64, linux-aarch64, darwin-x86_64, darwin-aarch64, or windows-x86_64. For portable models (those that work independent of processor architecture or OS features), leave this field unset.

  • prefix_strings (Mapping[str, Any] | None) – Optional prefix strings applied at inference

  • tags (Sequence[str] | None) – An array of tags to organize the model.

  • wait_for_completion (bool | None) – Whether to wait for all child operations (e.g. model download) to complete.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

put_trained_model_alias(*, model_id, model_alias, error_trace=None, filter_path=None, human=None, pretty=None, reassign=None)

Creates a new model alias (or reassigns an existing one) to refer to the trained model

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/put-trained-models-aliases.html

Parameters:
  • model_id (str) – The identifier for the trained model that the alias refers to.

  • model_alias (str) – The alias to create or update. This value cannot end in numbers.

  • reassign (bool | None) – Specifies whether the alias gets reassigned to the specified trained model if it is already assigned to a different model. If the alias is already assigned and this parameter is false, the API returns an error.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

put_trained_model_definition_part(*, model_id, part, definition=None, total_definition_length=None, total_parts=None, error_trace=None, filter_path=None, human=None, pretty=None, body=None)

Creates part of a trained model definition

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/put-trained-model-definition-part.html

Parameters:
  • model_id (str) – The unique identifier of the trained model.

  • part (int) – The definition part number. When the definition is loaded for inference the definition parts are streamed in the order of their part number. The first part must be 0 and the final part must be total_parts - 1.

  • definition (str | None) – The definition part for the model. Must be a base64 encoded string.

  • total_definition_length (int | None) – The total uncompressed definition length in bytes. Not base64 encoded.

  • total_parts (int | None) – The total number of parts that will be uploaded. Must be greater than 0.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

put_trained_model_vocabulary(*, model_id, vocabulary=None, error_trace=None, filter_path=None, human=None, merges=None, pretty=None, scores=None, body=None)

Creates a trained model vocabulary

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/put-trained-model-vocabulary.html

Parameters:
  • model_id (str) – The unique identifier of the trained model.

  • vocabulary (Sequence[str] | None) – The model vocabulary, which must not be empty.

  • merges (Sequence[str] | None) – The optional model merges if required by the tokenizer.

  • scores (Sequence[float] | None) – The optional vocabulary value scores if required by the tokenizer.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

reset_job(*, job_id, delete_user_annotations=None, error_trace=None, filter_path=None, human=None, pretty=None, wait_for_completion=None)

Resets an existing anomaly detection job.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-reset-job.html

Parameters:
  • job_id (str) – The ID of the job to reset.

  • delete_user_annotations (bool | None) – Specifies whether annotations that have been added by the user should be deleted along with any auto-generated annotations when the job is reset.

  • wait_for_completion (bool | None) – Should this request wait until the operation has completed before returning.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

revert_model_snapshot(*, job_id, snapshot_id, delete_intervening_results=None, error_trace=None, filter_path=None, human=None, pretty=None, body=None)

Reverts to a specific snapshot.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-revert-snapshot.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • snapshot_id (str) – You can specify empty as the <snapshot_id>. Reverting to the empty snapshot means the anomaly detection job starts learning a new model from scratch when it is started.

  • delete_intervening_results (bool | None) – Refer to the description for the delete_intervening_results query parameter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

set_upgrade_mode(*, enabled=None, error_trace=None, filter_path=None, human=None, pretty=None, timeout=None)

Sets a cluster wide upgrade_mode setting that prepares machine learning indices for an upgrade.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-set-upgrade-mode.html

Parameters:
  • enabled (bool | None) – When true, it enables upgrade_mode which temporarily halts all job and datafeed tasks and prohibits new job and datafeed tasks from starting.

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – The time to wait for the request to be completed.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

start_data_frame_analytics(*, id, error_trace=None, filter_path=None, human=None, pretty=None, timeout=None)

Starts a data frame analytics job.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/start-dfanalytics.html

Parameters:
  • id (str) – Identifier for the data frame analytics job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – Controls the amount of time to wait until the data frame analytics job starts.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

start_datafeed(*, datafeed_id, end=None, error_trace=None, filter_path=None, human=None, pretty=None, start=None, timeout=None, body=None)

Starts one or more datafeeds.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-start-datafeed.html

Parameters:
  • datafeed_id (str) – A numerical character string that uniquely identifies the datafeed. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

  • end (str | Any | None) – Refer to the description for the end query parameter.

  • start (str | Any | None) – Refer to the description for the start query parameter.

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – Refer to the description for the timeout query parameter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

start_trained_model_deployment(*, model_id, cache_size=None, deployment_id=None, error_trace=None, filter_path=None, human=None, number_of_allocations=None, pretty=None, priority=None, queue_capacity=None, threads_per_allocation=None, timeout=None, wait_for=None)

Start a trained model deployment.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/start-trained-model-deployment.html

Parameters:
  • model_id (str) – The unique identifier of the trained model. Currently, only PyTorch models are supported.

  • cache_size (int | str | None) – The inference cache size (in memory outside the JVM heap) per node for the model. The default value is the same size as the model_size_bytes. To disable the cache, 0b can be provided.

  • deployment_id (str | None) – A unique identifier for the deployment of the model.

  • number_of_allocations (int | None) – The number of model allocations on each node where the model is deployed. All allocations on a node share the same copy of the model in memory but use a separate set of threads to evaluate the model. Increasing this value generally increases the throughput. If this setting is greater than the number of hardware threads it will automatically be changed to a value less than the number of hardware threads.

  • priority (Literal['low', 'normal'] | str | None) – The deployment priority.

  • queue_capacity (int | None) – Specifies the number of inference requests that are allowed in the queue. After the number of requests exceeds this value, new requests are rejected with a 429 error.

  • threads_per_allocation (int | None) – Sets the number of threads used by each model allocation during inference. This generally increases the inference speed. The inference process is a compute-bound process; any number greater than the number of available hardware threads on the machine does not increase the inference speed. If this setting is greater than the number of hardware threads it will automatically be changed to a value less than the number of hardware threads.

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – Specifies the amount of time to wait for the model to deploy.

  • wait_for (Literal['fully_allocated', 'started', 'starting'] | str | None) – Specifies the allocation status to wait for before returning.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

stop_data_frame_analytics(*, id, allow_no_match=None, error_trace=None, filter_path=None, force=None, human=None, pretty=None, timeout=None)

Stops one or more data frame analytics jobs.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/stop-dfanalytics.html

Parameters:
  • id (str) – Identifier for the data frame analytics job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

  • allow_no_match (bool | None) – Specifies what to do when the request: 1. Contains wildcard expressions and there are no data frame analytics jobs that match. 2. Contains the _all string or no identifiers and there are no matches. 3. Contains wildcard expressions and there are only partial matches. The default value is true, which returns an empty data_frame_analytics array when there are no matches and the subset of results when there are partial matches. If this parameter is false, the request returns a 404 status code when there are no matches or only partial matches.

  • force (bool | None) – If true, the data frame analytics job is stopped forcefully.

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – Controls the amount of time to wait until the data frame analytics job stops. Defaults to 20 seconds.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

stop_datafeed(*, datafeed_id, allow_no_match=None, error_trace=None, filter_path=None, force=None, human=None, pretty=None, timeout=None, body=None)

Stops one or more datafeeds.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-stop-datafeed.html

Parameters:
  • datafeed_id (str) – Identifier for the datafeed. You can stop multiple datafeeds in a single API request by using a comma-separated list of datafeeds or a wildcard expression. You can close all datafeeds by using _all or by specifying * as the identifier.

  • allow_no_match (bool | None) – Refer to the description for the allow_no_match query parameter.

  • force (bool | None) – Refer to the description for the force query parameter.

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – Refer to the description for the timeout query parameter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

stop_trained_model_deployment(*, model_id, allow_no_match=None, error_trace=None, filter_path=None, force=None, human=None, pretty=None)

Stop a trained model deployment.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/stop-trained-model-deployment.html

Parameters:
  • model_id (str) – The unique identifier of the trained model.

  • allow_no_match (bool | None) – Specifies what to do when the request: contains wildcard expressions and there are no deployments that match; contains the _all string or no identifiers and there are no matches; or contains wildcard expressions and there are only partial matches. By default, it returns an empty array when there are no matches and the subset of results when there are partial matches. If false, the request returns a 404 status code when there are no matches or only partial matches.

  • force (bool | None) – Forcefully stops the deployment, even if it is used by ingest pipelines. You can’t use these pipelines until you restart the model deployment.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

update_data_frame_analytics(*, id, allow_lazy_start=None, description=None, error_trace=None, filter_path=None, human=None, max_num_threads=None, model_memory_limit=None, pretty=None, body=None)

Updates certain properties of a data frame analytics job.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/update-dfanalytics.html

Parameters:
  • id (str) – Identifier for the data frame analytics job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

  • allow_lazy_start (bool | None) – Specifies whether this job can start when there is insufficient machine learning node capacity for it to be immediately assigned to a node.

  • description (str | None) – A description of the job.

  • max_num_threads (int | None) – The maximum number of threads to be used by the analysis. Using more threads may decrease the time necessary to complete the analysis at the cost of using more CPU. Note that the process may use additional threads for operational functionality other than the analysis itself.

  • model_memory_limit (str | None) – The approximate maximum amount of memory resources that are permitted for analytical processing. If your elasticsearch.yml file contains an xpack.ml.max_model_memory_limit setting, an error occurs when you try to create data frame analytics jobs that have model_memory_limit values greater than that setting.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

update_datafeed(*, datafeed_id, aggregations=None, allow_no_indices=None, chunking_config=None, delayed_data_check_config=None, error_trace=None, expand_wildcards=None, filter_path=None, frequency=None, human=None, ignore_throttled=None, ignore_unavailable=None, indexes=None, indices=None, indices_options=None, job_id=None, max_empty_searches=None, pretty=None, query=None, query_delay=None, runtime_mappings=None, script_fields=None, scroll_size=None, body=None)

Updates certain properties of a datafeed.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-update-datafeed.html

Parameters:
  • datafeed_id (str) – A numerical character string that uniquely identifies the datafeed. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

  • aggregations (Mapping[str, Mapping[str, Any]] | None) – If set, the datafeed performs aggregation searches. Support for aggregations is limited and should be used only with low cardinality data.

  • allow_no_indices (bool | None) – If true, wildcard indices expressions that resolve into no concrete indices are ignored. This includes the _all string or when no indices are specified.

  • chunking_config (Mapping[str, Any] | None) – Datafeeds might search over long time periods, for several months or years. This search is split into time chunks in order to ensure the load on Elasticsearch is managed. Chunking configuration controls how the size of these time chunks are calculated; it is an advanced configuration option.

  • delayed_data_check_config (Mapping[str, Any] | None) – Specifies whether the datafeed checks for missing data and the size of the window. The datafeed can optionally search over indices that have already been read in an effort to determine whether any data has subsequently been added to the index. If missing data is found, it is a good indication that the query_delay is set too low and the data is being indexed after the datafeed has passed that moment in time. This check runs only on real-time datafeeds.

  • expand_wildcards (Sequence[Literal['all', 'closed', 'hidden', 'none', 'open'] | str] | ~typing.Literal['all', 'closed', 'hidden', 'none', 'open'] | str | None) – Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values. Valid values are: * all: Match any data stream or index, including hidden ones. * closed: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed. * hidden: Match hidden data streams and hidden indices. Must be combined with open, closed, or both. * none: Wildcard patterns are not accepted. * open: Match open, non-hidden indices. Also matches any non-hidden data stream.

  • frequency (Literal[-1] | ~typing.Literal[0] | str | None) – The interval at which scheduled queries are made while the datafeed runs in real time. The default value is either the bucket span for short bucket spans, or, for longer bucket spans, a sensible fraction of the bucket span. When frequency is shorter than the bucket span, interim results for the last (partial) bucket are written then eventually overwritten by the full bucket results. If the datafeed uses aggregations, this value must be divisible by the interval of the date histogram aggregation.

  • ignore_throttled (bool | None) – If true, concrete, expanded or aliased indices are ignored when frozen.

  • ignore_unavailable (bool | None) – If true, unavailable indices (missing or closed) are ignored.

  • indexes (Sequence[str] | None) – An array of index names. Wildcards are supported. If any of the indices are in remote clusters, the machine learning nodes must have the remote_cluster_client role.

  • indices (Sequence[str] | None) – An array of index names. Wildcards are supported. If any of the indices are in remote clusters, the machine learning nodes must have the remote_cluster_client role.

  • indices_options (Mapping[str, Any] | None) – Specifies index expansion options that are used during search.

  • job_id (str | None)

  • max_empty_searches (int | None) – If a real-time datafeed has never seen any data (including during any initial training period), it automatically stops and closes the associated job after this many real-time searches return no documents. In other words, it stops after frequency times max_empty_searches of real-time operation. If not set, a datafeed with no end time that sees no data remains started until it is explicitly stopped. By default, it is not set.

  • query (Mapping[str, Any] | None) – The Elasticsearch query domain-specific language (DSL). This value corresponds to the query object in an Elasticsearch search POST body. All the options that are supported by Elasticsearch can be used, as this object is passed verbatim to Elasticsearch. Note that if you change the query, the analyzed data is also changed. Therefore, the time required to learn might be long and the understandability of the results is unpredictable. If you want to make significant changes to the source data, it is recommended that you clone the job and datafeed and make the amendments in the clone. Let both run in parallel and close one when you are satisfied with the results of the job.

  • query_delay (Literal[-1] | ~typing.Literal[0] | str | None) – The number of seconds behind real time that data is queried. For example, if data from 10:04 a.m. might not be searchable in Elasticsearch until 10:06 a.m., set this property to 120 seconds. The default value is randomly selected between 60s and 120s. This randomness improves the query performance when there are multiple jobs running on the same node.

  • runtime_mappings (Mapping[str, Mapping[str, Any]] | None) – Specifies runtime fields for the datafeed search.

  • script_fields (Mapping[str, Mapping[str, Any]] | None) – Specifies scripts that evaluate custom expressions and returns script fields to the datafeed. The detector configuration objects in a job can contain functions that use these script fields.

  • scroll_size (int | None) – The size parameter that is used in Elasticsearch searches when the datafeed does not use aggregations. The maximum value is the value of index.max_result_window.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

update_filter(*, filter_id, add_items=None, description=None, error_trace=None, filter_path=None, human=None, pretty=None, remove_items=None, body=None)

Updates the description of a filter, adds items, or removes items.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-update-filter.html

Parameters:
  • filter_id (str) – A string that uniquely identifies a filter.

  • add_items (Sequence[str] | None) – The items to add to the filter.

  • description (str | None) – A description for the filter.

  • remove_items (Sequence[str] | None) – The items to remove from the filter.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

update_job(*, job_id, allow_lazy_open=None, analysis_limits=None, background_persist_interval=None, categorization_filters=None, custom_settings=None, daily_model_snapshot_retention_after_days=None, description=None, detectors=None, error_trace=None, filter_path=None, groups=None, human=None, model_plot_config=None, model_prune_window=None, model_snapshot_retention_days=None, per_partition_categorization=None, pretty=None, renormalization_window_days=None, results_retention_days=None, body=None)

Updates certain properties of an anomaly detection job.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-update-job.html

Parameters:
  • job_id (str) – Identifier for the job.

  • allow_lazy_open (bool | None) – 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.

  • analysis_limits (Mapping[str, Any] | None)

  • background_persist_interval (Literal[-1] | ~typing.Literal[0] | str | None) – 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.

  • categorization_filters (Sequence[str] | None)

  • custom_settings (Mapping[str, Any] | None) – 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.

  • daily_model_snapshot_retention_after_days (int | None) – 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.

  • description (str | None) – A description of the job.

  • detectors (Sequence[Mapping[str, Any]] | None) – An array of detector update objects.

  • groups (Sequence[str] | None) – A list of job groups. A job can belong to no groups or many.

  • model_plot_config (Mapping[str, Any] | None)

  • model_prune_window (Literal[-1] | ~typing.Literal[0] | str | None)

  • model_snapshot_retention_days (int | None) – 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.

  • per_partition_categorization (Mapping[str, Any] | None) – Settings related to how categorization interacts with partition fields.

  • renormalization_window_days (int | None) – Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen.

  • results_retention_days (int | None) – 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.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

update_model_snapshot(*, job_id, snapshot_id, description=None, error_trace=None, filter_path=None, human=None, pretty=None, retain=None, body=None)

Updates certain properties of a snapshot.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-update-snapshot.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • snapshot_id (str) – Identifier for the model snapshot.

  • description (str | None) – A description of the model snapshot.

  • retain (bool | None) – If true, this snapshot will not be deleted during automatic cleanup of snapshots older than model_snapshot_retention_days. However, this snapshot will be deleted when the job is deleted.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

update_trained_model_deployment(*, model_id, error_trace=None, filter_path=None, human=None, number_of_allocations=None, pretty=None, body=None)

Updates certain properties of trained model deployment.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/update-trained-model-deployment.html

Parameters:
  • model_id (str) – The unique identifier of the trained model. Currently, only PyTorch models are supported.

  • number_of_allocations (int | None) – The number of model allocations on each node where the model is deployed. All allocations on a node share the same copy of the model in memory but use a separate set of threads to evaluate the model. Increasing this value generally increases the throughput. If this setting is greater than the number of hardware threads it will automatically be changed to a value less than the number of hardware threads.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

  • body (Dict[str, Any] | None)

Return type:

ObjectApiResponse[Any]

upgrade_job_snapshot(*, job_id, snapshot_id, error_trace=None, filter_path=None, human=None, pretty=None, timeout=None, wait_for_completion=None)

Upgrades a given job snapshot to the current major version.

https://www.elastic.co/guide/en/elasticsearch/reference/8.14/ml-upgrade-job-model-snapshot.html

Parameters:
  • job_id (str) – Identifier for the anomaly detection job.

  • snapshot_id (str) – A numerical character string that uniquely identifies the model snapshot.

  • timeout (Literal[-1] | ~typing.Literal[0] | str | None) – Controls the time to wait for the request to complete.

  • wait_for_completion (bool | None) – When true, the API won’t respond until the upgrade is complete. Otherwise, it responds as soon as the upgrade task is assigned to a node.

  • error_trace (bool | None)

  • filter_path (str | Sequence[str] | None)

  • human (bool | None)

  • pretty (bool | None)

Return type:

ObjectApiResponse[Any]

validate(*, analysis_config=None, analysis_limits=None, data_description=None, description=None, error_trace=None, filter_path=None, human=None, job_id=None, model_plot=None, model_snapshot_id=None, model_snapshot_retention_days=None, pretty=None, results_index_name=None, body=None)

Validates an anomaly detection job.

https://www.elastic.co/guide/en/machine-learning/8.14/ml-jobs.html

Parameters:
Return type:

ObjectApiResponse[Any]

validate_detector(*, detector=None, body=None, error_trace=None, filter_path=None, human=None, pretty=None)

Validates an anomaly detection detector.

https://www.elastic.co/guide/en/machine-learning/8.14/ml-jobs.html

Parameters:
Return type:

ObjectApiResponse[Any]