# Licensed to Elasticsearch B.V. under one or more contributor # license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright # ownership. Elasticsearch B.V. licenses this file to you under # the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import typing as t from elastic_transport import ObjectApiResponse from ._base import NamespacedClient from .utils import ( SKIP_IN_PATH, Stability, _quote, _rewrite_parameters, _stability_warning, ) class MlClient(NamespacedClient): @_rewrite_parameters( body_fields=("allow_no_match", "force", "timeout"), ) def close_job( self, *, job_id: str, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, force: t.Optional[bool] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Close anomaly detection jobs.

A job can be opened and closed multiple times throughout its lifecycle. A closed job cannot receive data or perform analysis operations, but you can still explore and navigate results. When you close a job, it runs housekeeping tasks such as pruning the model history, flushing buffers, calculating final results and persisting the model snapshots. Depending upon the size of the job, it could take several minutes to close and the equivalent time to re-open. After it is closed, the job has a minimal overhead on the cluster except for maintaining its meta data. Therefore it is a best practice to close jobs that are no longer required to process data. If you close an anomaly detection job whose datafeed is running, the request first tries to stop the datafeed. This behavior is equivalent to calling stop datafeed API with the same timeout and force parameters as the close job request. When a datafeed that has a specified end date stops, it automatically closes its associated job.

``_ :param job_id: 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. :param allow_no_match: Refer to the description for the `allow_no_match` query parameter. :param force: Refer to the descriptiion for the `force` query parameter. :param timeout: Refer to the description for the `timeout` query parameter. """ if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path_parts: t.Dict[str, str] = {"job_id": _quote(job_id)} __path = f'/_ml/anomaly_detectors/{__path_parts["job_id"]}/_close' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if allow_no_match is not None: __body["allow_no_match"] = allow_no_match if force is not None: __body["force"] = force if timeout is not None: __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} if __body is not None: __headers["content-type"] = "application/json" return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.close_job", path_parts=__path_parts, ) @_rewrite_parameters() def delete_calendar( self, *, calendar_id: str, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Delete a calendar.

Remove all scheduled events from a calendar, then delete it.

``_ :param calendar_id: A string that uniquely identifies a calendar. """ if calendar_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'calendar_id'") __path_parts: t.Dict[str, str] = {"calendar_id": _quote(calendar_id)} __path = f'/_ml/calendars/{__path_parts["calendar_id"]}' __query: t.Dict[str, t.Any] = {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers, endpoint_id="ml.delete_calendar", path_parts=__path_parts, ) @_rewrite_parameters() def delete_calendar_event( self, *, calendar_id: str, event_id: str, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Delete events from a calendar.

``_ :param calendar_id: A string that uniquely identifies a calendar. :param event_id: Identifier for the scheduled event. You can obtain this identifier by using the get calendar events API. """ if calendar_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'calendar_id'") if event_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'event_id'") __path_parts: t.Dict[str, str] = { "calendar_id": _quote(calendar_id), "event_id": _quote(event_id), } __path = f'/_ml/calendars/{__path_parts["calendar_id"]}/events/{__path_parts["event_id"]}' __query: t.Dict[str, t.Any] = {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers, endpoint_id="ml.delete_calendar_event", path_parts=__path_parts, ) @_rewrite_parameters() def delete_calendar_job( self, *, calendar_id: str, job_id: t.Union[str, t.Sequence[str]], error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Delete anomaly jobs from a calendar.

``_ :param calendar_id: A string that uniquely identifies a calendar. :param job_id: 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. """ if calendar_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'calendar_id'") if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path_parts: t.Dict[str, str] = { "calendar_id": _quote(calendar_id), "job_id": _quote(job_id), } __path = f'/_ml/calendars/{__path_parts["calendar_id"]}/jobs/{__path_parts["job_id"]}' __query: t.Dict[str, t.Any] = {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers, endpoint_id="ml.delete_calendar_job", path_parts=__path_parts, ) @_rewrite_parameters() def delete_data_frame_analytics( self, *, id: str, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, force: t.Optional[bool] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Delete a data frame analytics job.

``_ :param id: Identifier for the data frame analytics job. :param force: If `true`, it deletes a job that is not stopped; this method is quicker than stopping and deleting the job. :param timeout: The time to wait for the job to be deleted. """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") __path_parts: t.Dict[str, str] = {"id": _quote(id)} __path = f'/_ml/data_frame/analytics/{__path_parts["id"]}' __query: t.Dict[str, t.Any] = {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if force is not None: __query["force"] = force if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers, endpoint_id="ml.delete_data_frame_analytics", path_parts=__path_parts, ) @_rewrite_parameters() def delete_datafeed( self, *, datafeed_id: str, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, force: t.Optional[bool] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Delete a datafeed.

``_ :param datafeed_id: 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. :param force: Use to forcefully delete a started datafeed; this method is quicker than stopping and deleting the datafeed. """ if datafeed_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'datafeed_id'") __path_parts: t.Dict[str, str] = {"datafeed_id": _quote(datafeed_id)} __path = f'/_ml/datafeeds/{__path_parts["datafeed_id"]}' __query: t.Dict[str, t.Any] = {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if force is not None: __query["force"] = force if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers, endpoint_id="ml.delete_datafeed", path_parts=__path_parts, ) @_rewrite_parameters() def delete_filter( self, *, filter_id: str, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Delete a filter.

If an anomaly detection job references the filter, you cannot delete the filter. You must update or delete the job before you can delete the filter.

``_ :param filter_id: A string that uniquely identifies a filter. """ if filter_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'filter_id'") __path_parts: t.Dict[str, str] = {"filter_id": _quote(filter_id)} __path = f'/_ml/filters/{__path_parts["filter_id"]}' __query: t.Dict[str, t.Any] = {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers, endpoint_id="ml.delete_filter", path_parts=__path_parts, ) @_rewrite_parameters() def delete_job( self, *, job_id: str, delete_user_annotations: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, force: t.Optional[bool] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, wait_for_completion: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Delete an anomaly detection job.

All job configuration, model state and results are deleted. It is not currently possible to delete multiple jobs using wildcards or a comma separated list. If you delete a job that has a datafeed, the request first tries to delete the datafeed. This behavior is equivalent to calling the delete datafeed API with the same timeout and force parameters as the delete job request.

``_ :param job_id: Identifier for the anomaly detection job. :param delete_user_annotations: Specifies whether annotations that have been added by the user should be deleted along with any auto-generated annotations when the job is reset. :param force: Use to forcefully delete an opened job; this method is quicker than closing and deleting the job. :param wait_for_completion: Specifies whether the request should return immediately or wait until the job deletion completes. """ if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path_parts: t.Dict[str, str] = {"job_id": _quote(job_id)} __path = f'/_ml/anomaly_detectors/{__path_parts["job_id"]}' __query: t.Dict[str, t.Any] = {} if delete_user_annotations is not None: __query["delete_user_annotations"] = delete_user_annotations if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if force is not None: __query["force"] = force if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers, endpoint_id="ml.delete_job", path_parts=__path_parts, ) @_rewrite_parameters() def delete_trained_model( self, *, model_id: str, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, force: t.Optional[bool] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Delete an unreferenced trained model.

The request deletes a trained inference model that is not referenced by an ingest pipeline.

``_ :param model_id: The unique identifier of the trained model. :param force: Forcefully deletes a trained model that is referenced by ingest pipelines or has a started deployment. :param timeout: Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error. """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") __path_parts: t.Dict[str, str] = {"model_id": _quote(model_id)} __path = f'/_ml/trained_models/{__path_parts["model_id"]}' __query: t.Dict[str, t.Any] = {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if force is not None: __query["force"] = force if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers, endpoint_id="ml.delete_trained_model", path_parts=__path_parts, ) @_rewrite_parameters() def delete_trained_model_alias( self, *, model_id: str, model_alias: str, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Delete a trained model alias.

This API deletes an existing model alias that refers to a trained model. If the model alias is missing or refers to a model other than the one identified by the model_id, this API returns an error.

``_ :param model_id: The trained model ID to which the model alias refers. :param model_alias: The model alias to delete. """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") if model_alias in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_alias'") __path_parts: t.Dict[str, str] = { "model_id": _quote(model_id), "model_alias": _quote(model_alias), } __path = f'/_ml/trained_models/{__path_parts["model_id"]}/model_aliases/{__path_parts["model_alias"]}' __query: t.Dict[str, t.Any] = {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers, endpoint_id="ml.delete_trained_model_alias", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=( "analysis_config", "max_bucket_cardinality", "overall_cardinality", ), ) def estimate_model_memory( self, *, analysis_config: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, max_bucket_cardinality: t.Optional[t.Mapping[str, int]] = None, overall_cardinality: t.Optional[t.Mapping[str, int]] = None, pretty: t.Optional[bool] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Estimate job model memory usage.

Make an estimation of the memory usage for an anomaly detection job model. The estimate is based on analysis configuration details for the job and cardinality estimates for the fields it references.

``_ :param analysis_config: For a list of the properties that you can specify in the `analysis_config` component of the body of this API. :param max_bucket_cardinality: 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. :param overall_cardinality: 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`. """ __path_parts: t.Dict[str, str] = {} __path = "/_ml/anomaly_detectors/_estimate_model_memory" __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if analysis_config is not None: __body["analysis_config"] = analysis_config if max_bucket_cardinality is not None: __body["max_bucket_cardinality"] = max_bucket_cardinality if overall_cardinality is not None: __body["overall_cardinality"] = overall_cardinality __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.estimate_model_memory", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("evaluation", "index", "query"), ) def evaluate_data_frame( self, *, evaluation: t.Optional[t.Mapping[str, t.Any]] = None, index: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, query: t.Optional[t.Mapping[str, t.Any]] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Evaluate data frame analytics.

The API packages together commonly used evaluation metrics for various types of machine learning features. This has been designed for use on indexes created by data frame analytics. Evaluation requires both a ground truth field and an analytics result field to be present.

``_ :param evaluation: Defines the type of evaluation you want to perform. :param index: Defines the `index` in which the evaluation will be performed. :param query: A query clause that retrieves a subset of data from the source index. """ if evaluation is None and body is None: raise ValueError("Empty value passed for parameter 'evaluation'") if index is None and body is None: raise ValueError("Empty value passed for parameter 'index'") __path_parts: t.Dict[str, str] = {} __path = "/_ml/data_frame/_evaluate" __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if evaluation is not None: __body["evaluation"] = evaluation if index is not None: __body["index"] = index if query is not None: __body["query"] = query __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.evaluate_data_frame", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("advance_time", "calc_interim", "end", "skip_time", "start"), ) def flush_job( self, *, job_id: str, advance_time: t.Optional[t.Union[str, t.Any]] = None, calc_interim: t.Optional[bool] = None, end: t.Optional[t.Union[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, skip_time: t.Optional[t.Union[str, t.Any]] = None, start: t.Optional[t.Union[str, t.Any]] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Force buffered data to be processed. The flush jobs API is only applicable when sending data for analysis using the post data API. Depending on the content of the buffer, then it might additionally calculate new results. Both flush and close operations are similar, however the flush is more efficient if you are expecting to send more data for analysis. When flushing, the job remains open and is available to continue analyzing data. A close operation additionally prunes and persists the model state to disk and the job must be opened again before analyzing further data.

``_ :param job_id: Identifier for the anomaly detection job. :param advance_time: Refer to the description for the `advance_time` query parameter. :param calc_interim: Refer to the description for the `calc_interim` query parameter. :param end: Refer to the description for the `end` query parameter. :param skip_time: Refer to the description for the `skip_time` query parameter. :param start: Refer to the description for the `start` query parameter. """ if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path_parts: t.Dict[str, str] = {"job_id": _quote(job_id)} __path = f'/_ml/anomaly_detectors/{__path_parts["job_id"]}/_flush' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if advance_time is not None: __body["advance_time"] = advance_time if calc_interim is not None: __body["calc_interim"] = calc_interim if end is not None: __body["end"] = end if skip_time is not None: __body["skip_time"] = skip_time if start is not None: __body["start"] = start if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} if __body is not None: __headers["content-type"] = "application/json" return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.flush_job", path_parts=__path_parts, ) @_rewrite_parameters( parameter_aliases={"from": "from_"}, ) def get_calendar_events( self, *, calendar_id: str, end: t.Optional[t.Union[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, from_: t.Optional[int] = None, human: t.Optional[bool] = None, job_id: t.Optional[str] = None, pretty: t.Optional[bool] = None, size: t.Optional[int] = None, start: t.Optional[t.Union[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Get info about events in calendars.

``_ :param calendar_id: 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. :param end: Specifies to get events with timestamps earlier than this time. :param from_: Skips the specified number of events. :param job_id: 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 `*`. :param size: Specifies the maximum number of events to obtain. :param start: Specifies to get events with timestamps after this time. """ if calendar_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'calendar_id'") __path_parts: t.Dict[str, str] = {"calendar_id": _quote(calendar_id)} __path = f'/_ml/calendars/{__path_parts["calendar_id"]}/events' __query: t.Dict[str, t.Any] = {} if end is not None: __query["end"] = end if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if from_ is not None: __query["from"] = from_ if human is not None: __query["human"] = human if job_id is not None: __query["job_id"] = job_id if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size if start is not None: __query["start"] = start __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "GET", __path, params=__query, headers=__headers, endpoint_id="ml.get_calendar_events", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("page",), parameter_aliases={"from": "from_"}, ) def get_calendars( self, *, calendar_id: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, from_: t.Optional[int] = None, human: t.Optional[bool] = None, page: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, size: t.Optional[int] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Get calendar configuration info.

``_ :param calendar_id: 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. :param from_: Skips the specified number of calendars. This parameter is supported only when you omit the calendar identifier. :param page: This object is supported only when you omit the calendar identifier. :param size: Specifies the maximum number of calendars to obtain. This parameter is supported only when you omit the calendar identifier. """ __path_parts: t.Dict[str, str] if calendar_id not in SKIP_IN_PATH: __path_parts = {"calendar_id": _quote(calendar_id)} __path = f'/_ml/calendars/{__path_parts["calendar_id"]}' else: __path_parts = {} __path = "/_ml/calendars" __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if from_ is not None: __query["from"] = from_ if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size if not __body: if page is not None: __body["page"] = page if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} if __body is not None: __headers["content-type"] = "application/json" return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.get_calendars", path_parts=__path_parts, ) @_rewrite_parameters( parameter_aliases={"from": "from_"}, ) def get_data_frame_analytics( self, *, id: t.Optional[str] = None, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, exclude_generated: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, from_: t.Optional[int] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, size: t.Optional[int] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Get data frame analytics job configuration info. You can get information for multiple data frame analytics jobs in a single API request by using a comma-separated list of data frame analytics jobs or a wildcard expression.

``_ :param id: 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. :param allow_no_match: 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. :param exclude_generated: 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. :param from_: Skips the specified number of data frame analytics jobs. :param size: Specifies the maximum number of data frame analytics jobs to obtain. """ __path_parts: t.Dict[str, str] if id not in SKIP_IN_PATH: __path_parts = {"id": _quote(id)} __path = f'/_ml/data_frame/analytics/{__path_parts["id"]}' else: __path_parts = {} __path = "/_ml/data_frame/analytics" __query: t.Dict[str, t.Any] = {} if allow_no_match is not None: __query["allow_no_match"] = allow_no_match if error_trace is not None: __query["error_trace"] = error_trace if exclude_generated is not None: __query["exclude_generated"] = exclude_generated if filter_path is not None: __query["filter_path"] = filter_path if from_ is not None: __query["from"] = from_ if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "GET", __path, params=__query, headers=__headers, endpoint_id="ml.get_data_frame_analytics", path_parts=__path_parts, ) @_rewrite_parameters( parameter_aliases={"from": "from_"}, ) def get_data_frame_analytics_stats( self, *, id: t.Optional[str] = None, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, from_: t.Optional[int] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, size: t.Optional[int] = None, verbose: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Get data frame analytics jobs usage info.

``_ :param id: 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. :param allow_no_match: 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. :param from_: Skips the specified number of data frame analytics jobs. :param size: Specifies the maximum number of data frame analytics jobs to obtain. :param verbose: Defines whether the stats response should be verbose. """ __path_parts: t.Dict[str, str] if id not in SKIP_IN_PATH: __path_parts = {"id": _quote(id)} __path = f'/_ml/data_frame/analytics/{__path_parts["id"]}/_stats' else: __path_parts = {} __path = "/_ml/data_frame/analytics/_stats" __query: t.Dict[str, t.Any] = {} if allow_no_match is not None: __query["allow_no_match"] = allow_no_match if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if from_ is not None: __query["from"] = from_ if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size if verbose is not None: __query["verbose"] = verbose __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "GET", __path, params=__query, headers=__headers, endpoint_id="ml.get_data_frame_analytics_stats", path_parts=__path_parts, ) @_rewrite_parameters() def get_datafeed_stats( self, *, datafeed_id: t.Optional[t.Union[str, t.Sequence[str]]] = None, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Get datafeeds usage info. You can get statistics for multiple datafeeds in a single API request by using a comma-separated list of datafeeds or a wildcard expression. You can get statistics for all datafeeds by using _all, by specifying * as the <feed_id>, or by omitting the <feed_id>. If the datafeed is stopped, the only information you receive is the datafeed_id and the state. This API returns a maximum of 10,000 datafeeds.

``_ :param datafeed_id: 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. :param allow_no_match: 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. """ __path_parts: t.Dict[str, str] if datafeed_id not in SKIP_IN_PATH: __path_parts = {"datafeed_id": _quote(datafeed_id)} __path = f'/_ml/datafeeds/{__path_parts["datafeed_id"]}/_stats' else: __path_parts = {} __path = "/_ml/datafeeds/_stats" __query: t.Dict[str, t.Any] = {} if allow_no_match is not None: __query["allow_no_match"] = allow_no_match if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "GET", __path, params=__query, headers=__headers, endpoint_id="ml.get_datafeed_stats", path_parts=__path_parts, ) @_rewrite_parameters() def get_datafeeds( self, *, datafeed_id: t.Optional[t.Union[str, t.Sequence[str]]] = None, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, exclude_generated: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Get datafeeds configuration info. You can get information for multiple datafeeds in a single API request by using a comma-separated list of datafeeds or a wildcard expression. You can get information for all datafeeds by using _all, by specifying * as the <feed_id>, or by omitting the <feed_id>. This API returns a maximum of 10,000 datafeeds.

``_ :param datafeed_id: 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. :param allow_no_match: 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. :param exclude_generated: 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. """ __path_parts: t.Dict[str, str] if datafeed_id not in SKIP_IN_PATH: __path_parts = {"datafeed_id": _quote(datafeed_id)} __path = f'/_ml/datafeeds/{__path_parts["datafeed_id"]}' else: __path_parts = {} __path = "/_ml/datafeeds" __query: t.Dict[str, t.Any] = {} if allow_no_match is not None: __query["allow_no_match"] = allow_no_match if error_trace is not None: __query["error_trace"] = error_trace if exclude_generated is not None: __query["exclude_generated"] = exclude_generated if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "GET", __path, params=__query, headers=__headers, endpoint_id="ml.get_datafeeds", path_parts=__path_parts, ) @_rewrite_parameters( parameter_aliases={"from": "from_"}, ) def get_filters( self, *, filter_id: t.Optional[t.Union[str, t.Sequence[str]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, from_: t.Optional[int] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, size: t.Optional[int] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Get filters. You can get a single filter or all filters.

``_ :param filter_id: A string that uniquely identifies a filter. :param from_: Skips the specified number of filters. :param size: Specifies the maximum number of filters to obtain. """ __path_parts: t.Dict[str, str] if filter_id not in SKIP_IN_PATH: __path_parts = {"filter_id": _quote(filter_id)} __path = f'/_ml/filters/{__path_parts["filter_id"]}' else: __path_parts = {} __path = "/_ml/filters" __query: t.Dict[str, t.Any] = {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if from_ is not None: __query["from"] = from_ if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "GET", __path, params=__query, headers=__headers, endpoint_id="ml.get_filters", path_parts=__path_parts, ) @_rewrite_parameters() def get_job_stats( self, *, job_id: t.Optional[str] = None, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Get anomaly detection jobs usage info.

``_ :param job_id: 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. :param allow_no_match: 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. """ __path_parts: t.Dict[str, str] if job_id not in SKIP_IN_PATH: __path_parts = {"job_id": _quote(job_id)} __path = f'/_ml/anomaly_detectors/{__path_parts["job_id"]}/_stats' else: __path_parts = {} __path = "/_ml/anomaly_detectors/_stats" __query: t.Dict[str, t.Any] = {} if allow_no_match is not None: __query["allow_no_match"] = allow_no_match if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "GET", __path, params=__query, headers=__headers, endpoint_id="ml.get_job_stats", path_parts=__path_parts, ) @_rewrite_parameters() def get_jobs( self, *, job_id: t.Optional[t.Union[str, t.Sequence[str]]] = None, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, exclude_generated: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Get anomaly detection jobs configuration info. You can get information for 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 get information for all anomaly detection jobs by using _all, by specifying * as the <job_id>, or by omitting the <job_id>.

``_ :param job_id: 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. :param allow_no_match: 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. :param exclude_generated: 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. """ __path_parts: t.Dict[str, str] if job_id not in SKIP_IN_PATH: __path_parts = {"job_id": _quote(job_id)} __path = f'/_ml/anomaly_detectors/{__path_parts["job_id"]}' else: __path_parts = {} __path = "/_ml/anomaly_detectors" __query: t.Dict[str, t.Any] = {} if allow_no_match is not None: __query["allow_no_match"] = allow_no_match if error_trace is not None: __query["error_trace"] = error_trace if exclude_generated is not None: __query["exclude_generated"] = exclude_generated if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "GET", __path, params=__query, headers=__headers, endpoint_id="ml.get_jobs", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=( "allow_no_match", "bucket_span", "end", "exclude_interim", "overall_score", "start", "top_n", ), ) def get_overall_buckets( self, *, job_id: str, allow_no_match: t.Optional[bool] = None, bucket_span: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, end: t.Optional[t.Union[str, t.Any]] = None, error_trace: t.Optional[bool] = None, exclude_interim: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, overall_score: t.Optional[t.Union[float, str]] = None, pretty: t.Optional[bool] = None, start: t.Optional[t.Union[str, t.Any]] = None, top_n: t.Optional[int] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Get overall bucket results.

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

The overall_score is calculated by combining the scores of all the buckets within the overall bucket span. First, the maximum anomaly_score per anomaly detection job in the overall bucket is calculated. Then the top_n of those scores are averaged to result in the overall_score. This means that you can fine-tune the overall_score so that it is more or less sensitive to the number of jobs that detect an anomaly at the same time. For example, if you set top_n to 1, the overall_score is the maximum bucket score in the overall bucket. Alternatively, if you set top_n to the number of jobs, the overall_score is high only when all jobs detect anomalies in that overall bucket. If you set the bucket_span parameter (to a value greater than its default), the overall_score is the maximum overall_score of the overall buckets that have a span equal to the jobs' largest bucket span.

``_ :param job_id: 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 ``. :param allow_no_match: Refer to the description for the `allow_no_match` query parameter. :param bucket_span: Refer to the description for the `bucket_span` query parameter. :param end: Refer to the description for the `end` query parameter. :param exclude_interim: Refer to the description for the `exclude_interim` query parameter. :param overall_score: Refer to the description for the `overall_score` query parameter. :param start: Refer to the description for the `start` query parameter. :param top_n: Refer to the description for the `top_n` query parameter. """ if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path_parts: t.Dict[str, str] = {"job_id": _quote(job_id)} __path = ( f'/_ml/anomaly_detectors/{__path_parts["job_id"]}/results/overall_buckets' ) __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if allow_no_match is not None: __body["allow_no_match"] = allow_no_match if bucket_span is not None: __body["bucket_span"] = bucket_span if end is not None: __body["end"] = end if exclude_interim is not None: __body["exclude_interim"] = exclude_interim if overall_score is not None: __body["overall_score"] = overall_score if start is not None: __body["start"] = start if top_n is not None: __body["top_n"] = top_n if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} if __body is not None: __headers["content-type"] = "application/json" return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.get_overall_buckets", path_parts=__path_parts, ) @_rewrite_parameters( parameter_aliases={"from": "from_"}, ) def get_trained_models( self, *, model_id: t.Optional[t.Union[str, t.Sequence[str]]] = None, allow_no_match: t.Optional[bool] = None, decompress_definition: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, exclude_generated: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, from_: t.Optional[int] = None, human: t.Optional[bool] = None, include: t.Optional[ t.Union[ str, t.Literal[ "definition", "definition_status", "feature_importance_baseline", "hyperparameters", "total_feature_importance", ], ] ] = None, include_model_definition: t.Optional[bool] = None, pretty: t.Optional[bool] = None, size: t.Optional[int] = None, tags: t.Optional[t.Union[str, t.Sequence[str]]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Get trained model configuration info.

``_ :param model_id: The unique identifier of the trained model or a model alias. You can get information for multiple trained models in a single API request by using a comma-separated list of model IDs or a wildcard expression. :param allow_no_match: 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. :param decompress_definition: Specifies whether the included model definition should be returned as a JSON map (true) or in a custom compressed format (false). :param exclude_generated: 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. :param from_: Skips the specified number of models. :param include: A comma delimited string of optional fields to include in the response body. :param include_model_definition: parameter is deprecated! Use [include=definition] instead :param size: Specifies the maximum number of models to obtain. :param tags: 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. """ __path_parts: t.Dict[str, str] if model_id not in SKIP_IN_PATH: __path_parts = {"model_id": _quote(model_id)} __path = f'/_ml/trained_models/{__path_parts["model_id"]}' else: __path_parts = {} __path = "/_ml/trained_models" __query: t.Dict[str, t.Any] = {} if allow_no_match is not None: __query["allow_no_match"] = allow_no_match if decompress_definition is not None: __query["decompress_definition"] = decompress_definition if error_trace is not None: __query["error_trace"] = error_trace if exclude_generated is not None: __query["exclude_generated"] = exclude_generated if filter_path is not None: __query["filter_path"] = filter_path if from_ is not None: __query["from"] = from_ if human is not None: __query["human"] = human if include is not None: __query["include"] = include if include_model_definition is not None: __query["include_model_definition"] = include_model_definition if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size if tags is not None: __query["tags"] = tags __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "GET", __path, params=__query, headers=__headers, endpoint_id="ml.get_trained_models", path_parts=__path_parts, ) @_rewrite_parameters( parameter_aliases={"from": "from_"}, ) def get_trained_models_stats( self, *, model_id: t.Optional[t.Union[str, t.Sequence[str]]] = None, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, from_: t.Optional[int] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, size: t.Optional[int] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Get trained models usage info. You can get usage information for multiple trained models in a single API request by using a comma-separated list of model IDs or a wildcard expression.

``_ :param model_id: The unique identifier of the trained model or a model alias. It can be a comma-separated list or a wildcard expression. :param allow_no_match: 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. :param from_: Skips the specified number of models. :param size: Specifies the maximum number of models to obtain. """ __path_parts: t.Dict[str, str] if model_id not in SKIP_IN_PATH: __path_parts = {"model_id": _quote(model_id)} __path = f'/_ml/trained_models/{__path_parts["model_id"]}/_stats' else: __path_parts = {} __path = "/_ml/trained_models/_stats" __query: t.Dict[str, t.Any] = {} if allow_no_match is not None: __query["allow_no_match"] = allow_no_match if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if from_ is not None: __query["from"] = from_ if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "GET", __path, params=__query, headers=__headers, endpoint_id="ml.get_trained_models_stats", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("docs", "inference_config"), ) def infer_trained_model( self, *, model_id: str, docs: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, inference_config: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Evaluate a trained model.

``_ :param model_id: The unique identifier of the trained model. :param docs: 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. :param inference_config: The inference configuration updates to apply on the API call :param timeout: Controls the amount of time to wait for inference results. """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") if docs is None and body is None: raise ValueError("Empty value passed for parameter 'docs'") __path_parts: t.Dict[str, str] = {"model_id": _quote(model_id)} __path = f'/_ml/trained_models/{__path_parts["model_id"]}/_infer' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout if not __body: if docs is not None: __body["docs"] = docs if inference_config is not None: __body["inference_config"] = inference_config __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.infer_trained_model", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("timeout",), ) def open_job( self, *, job_id: str, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Open anomaly detection jobs.

An anomaly detection job must be opened to be ready to receive and analyze data. It can be opened and closed multiple times throughout its lifecycle. When you open a new job, it starts with an empty model. When you open an existing job, the most recent model state is automatically loaded. The job is ready to resume its analysis from where it left off, once new data is received.

``_ :param job_id: Identifier for the anomaly detection job. :param timeout: Refer to the description for the `timeout` query parameter. """ if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path_parts: t.Dict[str, str] = {"job_id": _quote(job_id)} __path = f'/_ml/anomaly_detectors/{__path_parts["job_id"]}/_open' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if timeout is not None: __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} if __body is not None: __headers["content-type"] = "application/json" return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.open_job", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("events",), ) def post_calendar_events( self, *, calendar_id: str, events: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Add scheduled events to the calendar.

``_ :param calendar_id: A string that uniquely identifies a calendar. :param events: 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. """ if calendar_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'calendar_id'") if events is None and body is None: raise ValueError("Empty value passed for parameter 'events'") __path_parts: t.Dict[str, str] = {"calendar_id": _quote(calendar_id)} __path = f'/_ml/calendars/{__path_parts["calendar_id"]}/events' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if events is not None: __body["events"] = events __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.post_calendar_events", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("config",), ) def preview_data_frame_analytics( self, *, id: t.Optional[str] = None, config: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Preview features used by data frame analytics. Preview the extracted features used by a data frame analytics config.

``_ :param id: Identifier for the data frame analytics job. :param config: 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. """ __path_parts: t.Dict[str, str] if id not in SKIP_IN_PATH: __path_parts = {"id": _quote(id)} __path = f'/_ml/data_frame/analytics/{__path_parts["id"]}/_preview' else: __path_parts = {} __path = "/_ml/data_frame/analytics/_preview" __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if config is not None: __body["config"] = config if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} if __body is not None: __headers["content-type"] = "application/json" return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.preview_data_frame_analytics", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("datafeed_config", "job_config"), ) def preview_datafeed( self, *, datafeed_id: t.Optional[str] = None, datafeed_config: t.Optional[t.Mapping[str, t.Any]] = None, end: t.Optional[t.Union[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, job_config: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, start: t.Optional[t.Union[str, t.Any]] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Preview a datafeed. This API returns the first "page" of search results from a datafeed. You can preview an existing datafeed or provide configuration details for a datafeed and anomaly detection job in the API. The preview shows the structure of the data that will be passed to the anomaly detection engine. IMPORTANT: When Elasticsearch security features are enabled, the preview uses the credentials of the user that called the API. However, when the datafeed starts it uses the roles of the last user that created or updated the datafeed. To get a preview that accurately reflects the behavior of the datafeed, use the appropriate credentials. You can also use secondary authorization headers to supply the credentials.

``_ :param datafeed_id: 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. :param datafeed_config: The datafeed definition to preview. :param end: The end time when the datafeed preview should stop :param job_config: 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. :param start: The start time from where the datafeed preview should begin """ __path_parts: t.Dict[str, str] if datafeed_id not in SKIP_IN_PATH: __path_parts = {"datafeed_id": _quote(datafeed_id)} __path = f'/_ml/datafeeds/{__path_parts["datafeed_id"]}/_preview' else: __path_parts = {} __path = "/_ml/datafeeds/_preview" __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if end is not None: __query["end"] = end if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if start is not None: __query["start"] = start if not __body: if datafeed_config is not None: __body["datafeed_config"] = datafeed_config if job_config is not None: __body["job_config"] = job_config if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} if __body is not None: __headers["content-type"] = "application/json" return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.preview_datafeed", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("description", "job_ids"), ) def put_calendar( self, *, calendar_id: str, description: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, job_ids: t.Optional[t.Sequence[str]] = None, pretty: t.Optional[bool] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Create a calendar.

``_ :param calendar_id: A string that uniquely identifies a calendar. :param description: A description of the calendar. :param job_ids: An array of anomaly detection job identifiers. """ if calendar_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'calendar_id'") __path_parts: t.Dict[str, str] = {"calendar_id": _quote(calendar_id)} __path = f'/_ml/calendars/{__path_parts["calendar_id"]}' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if description is not None: __body["description"] = description if job_ids is not None: __body["job_ids"] = job_ids if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} if __body is not None: __headers["content-type"] = "application/json" return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.put_calendar", path_parts=__path_parts, ) @_rewrite_parameters() def put_calendar_job( self, *, calendar_id: str, job_id: t.Union[str, t.Sequence[str]], error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Add anomaly detection job to calendar.

``_ :param calendar_id: A string that uniquely identifies a calendar. :param job_id: 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. """ if calendar_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'calendar_id'") if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path_parts: t.Dict[str, str] = { "calendar_id": _quote(calendar_id), "job_id": _quote(job_id), } __path = f'/_ml/calendars/{__path_parts["calendar_id"]}/jobs/{__path_parts["job_id"]}' __query: t.Dict[str, t.Any] = {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, endpoint_id="ml.put_calendar_job", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=( "analysis", "dest", "source", "allow_lazy_start", "analyzed_fields", "description", "headers", "max_num_threads", "meta", "model_memory_limit", "version", ), parameter_aliases={"_meta": "meta"}, ignore_deprecated_options={"headers"}, ) def put_data_frame_analytics( self, *, id: str, analysis: t.Optional[t.Mapping[str, t.Any]] = None, dest: t.Optional[t.Mapping[str, t.Any]] = None, source: t.Optional[t.Mapping[str, t.Any]] = None, allow_lazy_start: t.Optional[bool] = None, analyzed_fields: t.Optional[t.Mapping[str, t.Any]] = None, description: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, headers: t.Optional[t.Mapping[str, t.Union[str, t.Sequence[str]]]] = None, human: t.Optional[bool] = None, max_num_threads: t.Optional[int] = None, meta: t.Optional[t.Mapping[str, t.Any]] = None, model_memory_limit: t.Optional[str] = None, pretty: t.Optional[bool] = None, version: t.Optional[str] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Create a data frame analytics job. This API creates a data frame analytics job that performs an analysis on the source indices and stores the outcome in a destination index. By default, the query used in the source configuration is {"match_all": {}}.

If the destination index does not exist, it is created automatically when you start the job.

If you supply only a subset of the regression or classification parameters, hyperparameter optimization occurs. It determines a value for each of the undefined parameters.

``_ :param id: 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. :param analysis: The analysis configuration, which contains the information necessary to perform one of the following types of analysis: classification, outlier detection, or regression. :param dest: The destination configuration. :param source: The configuration of how to source the analysis data. :param allow_lazy_start: 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. :param analyzed_fields: 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. :param description: A description of the job. :param headers: :param max_num_threads: 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. :param meta: :param model_memory_limit: 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. :param version: """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") if analysis is None and body is None: raise ValueError("Empty value passed for parameter 'analysis'") if dest is None and body is None: raise ValueError("Empty value passed for parameter 'dest'") if source is None and body is None: raise ValueError("Empty value passed for parameter 'source'") __path_parts: t.Dict[str, str] = {"id": _quote(id)} __path = f'/_ml/data_frame/analytics/{__path_parts["id"]}' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if analysis is not None: __body["analysis"] = analysis if dest is not None: __body["dest"] = dest if source is not None: __body["source"] = source if allow_lazy_start is not None: __body["allow_lazy_start"] = allow_lazy_start if analyzed_fields is not None: __body["analyzed_fields"] = analyzed_fields if description is not None: __body["description"] = description if headers is not None: __body["headers"] = headers if max_num_threads is not None: __body["max_num_threads"] = max_num_threads if meta is not None: __body["_meta"] = meta if model_memory_limit is not None: __body["model_memory_limit"] = model_memory_limit if version is not None: __body["version"] = version __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.put_data_frame_analytics", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=( "aggregations", "aggs", "chunking_config", "delayed_data_check_config", "frequency", "headers", "indexes", "indices", "indices_options", "job_id", "max_empty_searches", "query", "query_delay", "runtime_mappings", "script_fields", "scroll_size", ), ignore_deprecated_options={"headers"}, ) def put_datafeed( self, *, datafeed_id: str, aggregations: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, aggs: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, allow_no_indices: t.Optional[bool] = None, chunking_config: t.Optional[t.Mapping[str, t.Any]] = None, delayed_data_check_config: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, expand_wildcards: t.Optional[ t.Union[ t.Sequence[ t.Union[str, t.Literal["all", "closed", "hidden", "none", "open"]] ], t.Union[str, t.Literal["all", "closed", "hidden", "none", "open"]], ] ] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, frequency: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, headers: t.Optional[t.Mapping[str, t.Union[str, t.Sequence[str]]]] = None, human: t.Optional[bool] = None, ignore_throttled: t.Optional[bool] = None, ignore_unavailable: t.Optional[bool] = None, indexes: t.Optional[t.Union[str, t.Sequence[str]]] = None, indices: t.Optional[t.Union[str, t.Sequence[str]]] = None, indices_options: t.Optional[t.Mapping[str, t.Any]] = None, job_id: t.Optional[str] = None, max_empty_searches: t.Optional[int] = None, pretty: t.Optional[bool] = None, query: t.Optional[t.Mapping[str, t.Any]] = None, query_delay: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, runtime_mappings: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, script_fields: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, scroll_size: t.Optional[int] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Create a datafeed. Datafeeds retrieve data from Elasticsearch for analysis by an anomaly detection job. You can associate only one datafeed with each anomaly detection job. The datafeed contains a query that runs at a defined interval (frequency). If you are concerned about delayed data, you can add a delay (query_delay') at each interval. By default, the datafeed uses the following query: {"match_all": {"boost": 1}}`.

When 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. You must use Kibana, this API, or the create anomaly detection jobs API to create a datafeed. Do not add a datafeed directly to the .ml-config index. Do not give users write privileges on the .ml-config index.

``_ :param datafeed_id: 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. :param aggregations: If set, the datafeed performs aggregation searches. Support for aggregations is limited and should be used only with low cardinality data. :param aggs: If set, the datafeed performs aggregation searches. Support for aggregations is limited and should be used only with low cardinality data. :param allow_no_indices: If true, wildcard indices expressions that resolve into no concrete indices are ignored. This includes the `_all` string or when no indices are specified. :param chunking_config: 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. :param delayed_data_check_config: 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. :param expand_wildcards: 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. :param frequency: 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. :param headers: :param ignore_throttled: If true, concrete, expanded, or aliased indices are ignored when frozen. :param ignore_unavailable: If true, unavailable indices (missing or closed) are ignored. :param indexes: 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. :param indices: 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. :param indices_options: Specifies index expansion options that are used during search :param job_id: Identifier for the anomaly detection job. :param max_empty_searches: 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. :param query: 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. :param query_delay: 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. :param runtime_mappings: Specifies runtime fields for the datafeed search. :param script_fields: 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. :param scroll_size: 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. """ if datafeed_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'datafeed_id'") __path_parts: t.Dict[str, str] = {"datafeed_id": _quote(datafeed_id)} __path = f'/_ml/datafeeds/{__path_parts["datafeed_id"]}' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable if pretty is not None: __query["pretty"] = pretty if not __body: if aggregations is not None: __body["aggregations"] = aggregations if aggs is not None: __body["aggs"] = aggs if chunking_config is not None: __body["chunking_config"] = chunking_config if delayed_data_check_config is not None: __body["delayed_data_check_config"] = delayed_data_check_config if frequency is not None: __body["frequency"] = frequency if headers is not None: __body["headers"] = headers if indexes is not None: __body["indexes"] = indexes if indices is not None: __body["indices"] = indices if indices_options is not None: __body["indices_options"] = indices_options if job_id is not None: __body["job_id"] = job_id if max_empty_searches is not None: __body["max_empty_searches"] = max_empty_searches if query is not None: __body["query"] = query if query_delay is not None: __body["query_delay"] = query_delay if runtime_mappings is not None: __body["runtime_mappings"] = runtime_mappings if script_fields is not None: __body["script_fields"] = script_fields if scroll_size is not None: __body["scroll_size"] = scroll_size __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.put_datafeed", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("description", "items"), ) def put_filter( self, *, filter_id: str, description: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, items: t.Optional[t.Sequence[str]] = None, pretty: t.Optional[bool] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Create a filter. A filter contains a list of strings. It can be used by one or more anomaly detection jobs. Specifically, filters are referenced in the custom_rules property of detector configuration objects.

``_ :param filter_id: A string that uniquely identifies a filter. :param description: A description of the filter. :param items: 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. """ if filter_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'filter_id'") __path_parts: t.Dict[str, str] = {"filter_id": _quote(filter_id)} __path = f'/_ml/filters/{__path_parts["filter_id"]}' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if description is not None: __body["description"] = description if items is not None: __body["items"] = items __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.put_filter", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=( "analysis_config", "data_description", "allow_lazy_open", "analysis_limits", "background_persist_interval", "custom_settings", "daily_model_snapshot_retention_after_days", "datafeed_config", "description", "groups", "model_plot_config", "model_snapshot_retention_days", "renormalization_window_days", "results_index_name", "results_retention_days", ), ) def put_job( self, *, job_id: str, analysis_config: t.Optional[t.Mapping[str, t.Any]] = None, data_description: t.Optional[t.Mapping[str, t.Any]] = None, allow_lazy_open: t.Optional[bool] = None, allow_no_indices: t.Optional[bool] = None, analysis_limits: t.Optional[t.Mapping[str, t.Any]] = None, background_persist_interval: t.Optional[ t.Union[str, t.Literal[-1], t.Literal[0]] ] = None, custom_settings: t.Optional[t.Any] = None, daily_model_snapshot_retention_after_days: t.Optional[int] = None, datafeed_config: t.Optional[t.Mapping[str, t.Any]] = None, description: t.Optional[str] = None, error_trace: t.Optional[bool] = None, expand_wildcards: t.Optional[ t.Union[ t.Sequence[ t.Union[str, t.Literal["all", "closed", "hidden", "none", "open"]] ], t.Union[str, t.Literal["all", "closed", "hidden", "none", "open"]], ] ] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, groups: t.Optional[t.Sequence[str]] = None, human: t.Optional[bool] = None, ignore_throttled: t.Optional[bool] = None, ignore_unavailable: t.Optional[bool] = None, model_plot_config: t.Optional[t.Mapping[str, t.Any]] = None, model_snapshot_retention_days: t.Optional[int] = None, pretty: t.Optional[bool] = None, renormalization_window_days: t.Optional[int] = None, results_index_name: t.Optional[str] = None, results_retention_days: t.Optional[int] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Create an anomaly detection job.

If you include a datafeed_config, you must have read index privileges on the source index. If you include a datafeed_config but do not provide a query, the datafeed uses {"match_all": {"boost": 1}}.

``_ :param job_id: 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. :param analysis_config: Specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational. :param data_description: 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. :param allow_lazy_open: Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node. 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. :param allow_no_indices: If `true`, wildcard indices expressions that resolve into no concrete indices are ignored. This includes the `_all` string or when no indices are specified. :param analysis_limits: 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. :param background_persist_interval: Advanced configuration option. The time between each periodic persistence of the model. The default value is a randomized value between 3 to 4 hours, which avoids all jobs persisting at exactly the same time. The smallest allowed value is 1 hour. For very large models (several GB), persistence could take 10-20 minutes, so do not set the `background_persist_interval` value too low. :param custom_settings: Advanced configuration option. Contains custom meta data about the job. :param daily_model_snapshot_retention_after_days: Advanced configuration option, which affects the automatic removal of old model snapshots for this job. It specifies a period of time (in days) after which only the first snapshot per day is retained. This period is relative to the timestamp of the most recent snapshot for this job. Valid values range from 0 to `model_snapshot_retention_days`. :param datafeed_config: 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. :param description: A description of the job. :param expand_wildcards: 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. :param groups: A list of job groups. A job can belong to no groups or many. :param ignore_throttled: If `true`, concrete, expanded or aliased indices are ignored when frozen. :param ignore_unavailable: If `true`, unavailable indices (missing or closed) are ignored. :param model_plot_config: 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. :param model_snapshot_retention_days: Advanced configuration option, which affects the automatic removal of old model snapshots for this job. It specifies the maximum period of time (in days) that snapshots are retained. This period is relative to the timestamp of the most recent snapshot for this job. By default, snapshots ten days older than the newest snapshot are deleted. :param renormalization_window_days: 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. :param results_index_name: A text string that affects the name of the machine learning results index. By default, the job generates an index named `.ml-anomalies-shared`. :param results_retention_days: Advanced configuration option. The period of time (in days) that results are retained. Age is calculated relative to the timestamp of the latest bucket result. If this property has a non-null value, once per day at 00:30 (server time), results that are the specified number of days older than the latest bucket result are deleted from Elasticsearch. The default value is null, which means all results are retained. 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. """ if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") if analysis_config is None and body is None: raise ValueError("Empty value passed for parameter 'analysis_config'") if data_description is None and body is None: raise ValueError("Empty value passed for parameter 'data_description'") __path_parts: t.Dict[str, str] = {"job_id": _quote(job_id)} __path = f'/_ml/anomaly_detectors/{__path_parts["job_id"]}' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable if pretty is not None: __query["pretty"] = pretty if not __body: if analysis_config is not None: __body["analysis_config"] = analysis_config if data_description is not None: __body["data_description"] = data_description if allow_lazy_open is not None: __body["allow_lazy_open"] = allow_lazy_open if analysis_limits is not None: __body["analysis_limits"] = analysis_limits if background_persist_interval is not None: __body["background_persist_interval"] = background_persist_interval if custom_settings is not None: __body["custom_settings"] = custom_settings if daily_model_snapshot_retention_after_days is not None: __body["daily_model_snapshot_retention_after_days"] = ( daily_model_snapshot_retention_after_days ) if datafeed_config is not None: __body["datafeed_config"] = datafeed_config if description is not None: __body["description"] = description if groups is not None: __body["groups"] = groups if model_plot_config is not None: __body["model_plot_config"] = model_plot_config if model_snapshot_retention_days is not None: __body["model_snapshot_retention_days"] = model_snapshot_retention_days if renormalization_window_days is not None: __body["renormalization_window_days"] = renormalization_window_days if results_index_name is not None: __body["results_index_name"] = results_index_name if results_retention_days is not None: __body["results_retention_days"] = results_retention_days __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.put_job", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=( "compressed_definition", "definition", "description", "inference_config", "input", "metadata", "model_size_bytes", "model_type", "platform_architecture", "prefix_strings", "tags", ), ) def put_trained_model( self, *, model_id: str, compressed_definition: t.Optional[str] = None, defer_definition_decompression: t.Optional[bool] = None, definition: t.Optional[t.Mapping[str, t.Any]] = None, description: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, inference_config: t.Optional[t.Mapping[str, t.Any]] = None, input: t.Optional[t.Mapping[str, t.Any]] = None, metadata: t.Optional[t.Any] = None, model_size_bytes: t.Optional[int] = None, model_type: t.Optional[ t.Union[str, t.Literal["lang_ident", "pytorch", "tree_ensemble"]] ] = None, platform_architecture: t.Optional[str] = None, prefix_strings: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, tags: t.Optional[t.Sequence[str]] = None, wait_for_completion: t.Optional[bool] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Create a trained model. Enable you to supply a trained model that is not created by data frame analytics.

``_ :param model_id: The unique identifier of the trained model. :param compressed_definition: The compressed (GZipped and Base64 encoded) inference definition of the model. If compressed_definition is specified, then definition cannot be specified. :param defer_definition_decompression: If set to `true` and a `compressed_definition` is provided, the request defers definition decompression and skips relevant validations. :param definition: The inference definition for the model. If definition is specified, then compressed_definition cannot be specified. :param description: A human-readable description of the inference trained model. :param inference_config: 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. :param input: The input field names for the model definition. :param metadata: An object map that contains metadata about the model. :param model_size_bytes: 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. :param model_type: The model type. :param platform_architecture: 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. :param prefix_strings: Optional prefix strings applied at inference :param tags: An array of tags to organize the model. :param wait_for_completion: Whether to wait for all child operations (e.g. model download) to complete. """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") __path_parts: t.Dict[str, str] = {"model_id": _quote(model_id)} __path = f'/_ml/trained_models/{__path_parts["model_id"]}' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if defer_definition_decompression is not None: __query["defer_definition_decompression"] = defer_definition_decompression if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion if not __body: if compressed_definition is not None: __body["compressed_definition"] = compressed_definition if definition is not None: __body["definition"] = definition if description is not None: __body["description"] = description if inference_config is not None: __body["inference_config"] = inference_config if input is not None: __body["input"] = input if metadata is not None: __body["metadata"] = metadata if model_size_bytes is not None: __body["model_size_bytes"] = model_size_bytes if model_type is not None: __body["model_type"] = model_type if platform_architecture is not None: __body["platform_architecture"] = platform_architecture if prefix_strings is not None: __body["prefix_strings"] = prefix_strings if tags is not None: __body["tags"] = tags __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.put_trained_model", path_parts=__path_parts, ) @_rewrite_parameters() def put_trained_model_alias( self, *, model_id: str, model_alias: str, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, reassign: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Create or update a trained model alias. A trained model alias is a logical name used to reference a single trained model. You can use aliases instead of trained model identifiers to make it easier to reference your models. For example, you can use aliases in inference aggregations and processors. An alias must be unique and refer to only a single trained model. However, you can have multiple aliases for each trained model. If you use this API to update an alias such that it references a different trained model ID and the model uses a different type of data frame analytics, an error occurs. For example, this situation occurs if you have a trained model for regression analysis and a trained model for classification analysis; you cannot reassign an alias from one type of trained model to another. If you use this API to update an alias and there are very few input fields in common between the old and new trained models for the model alias, the API returns a warning.

``_ :param model_id: The identifier for the trained model that the alias refers to. :param model_alias: The alias to create or update. This value cannot end in numbers. :param reassign: 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. """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") if model_alias in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_alias'") __path_parts: t.Dict[str, str] = { "model_id": _quote(model_id), "model_alias": _quote(model_alias), } __path = f'/_ml/trained_models/{__path_parts["model_id"]}/model_aliases/{__path_parts["model_alias"]}' __query: t.Dict[str, t.Any] = {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if reassign is not None: __query["reassign"] = reassign __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, endpoint_id="ml.put_trained_model_alias", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("definition", "total_definition_length", "total_parts"), ) def put_trained_model_definition_part( self, *, model_id: str, part: int, definition: t.Optional[str] = None, total_definition_length: t.Optional[int] = None, total_parts: t.Optional[int] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Create part of a trained model definition.

``_ :param model_id: The unique identifier of the trained model. :param part: 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`. :param definition: The definition part for the model. Must be a base64 encoded string. :param total_definition_length: The total uncompressed definition length in bytes. Not base64 encoded. :param total_parts: The total number of parts that will be uploaded. Must be greater than 0. """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") if part in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'part'") if definition is None and body is None: raise ValueError("Empty value passed for parameter 'definition'") if total_definition_length is None and body is None: raise ValueError( "Empty value passed for parameter 'total_definition_length'" ) if total_parts is None and body is None: raise ValueError("Empty value passed for parameter 'total_parts'") __path_parts: t.Dict[str, str] = { "model_id": _quote(model_id), "part": _quote(part), } __path = f'/_ml/trained_models/{__path_parts["model_id"]}/definition/{__path_parts["part"]}' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if definition is not None: __body["definition"] = definition if total_definition_length is not None: __body["total_definition_length"] = total_definition_length if total_parts is not None: __body["total_parts"] = total_parts __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.put_trained_model_definition_part", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("vocabulary", "merges", "scores"), ) def put_trained_model_vocabulary( self, *, model_id: str, vocabulary: t.Optional[t.Sequence[str]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, merges: t.Optional[t.Sequence[str]] = None, pretty: t.Optional[bool] = None, scores: t.Optional[t.Sequence[float]] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Create a trained model vocabulary. This API is supported only for natural language processing (NLP) models. The vocabulary is stored in the index as described in inference_config.*.vocabulary of the trained model definition.

``_ :param model_id: The unique identifier of the trained model. :param vocabulary: The model vocabulary, which must not be empty. :param merges: The optional model merges if required by the tokenizer. :param scores: The optional vocabulary value scores if required by the tokenizer. """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") if vocabulary is None and body is None: raise ValueError("Empty value passed for parameter 'vocabulary'") __path_parts: t.Dict[str, str] = {"model_id": _quote(model_id)} __path = f'/_ml/trained_models/{__path_parts["model_id"]}/vocabulary' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if vocabulary is not None: __body["vocabulary"] = vocabulary if merges is not None: __body["merges"] = merges if scores is not None: __body["scores"] = scores __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.put_trained_model_vocabulary", path_parts=__path_parts, ) @_rewrite_parameters() def reset_job( self, *, job_id: str, delete_user_annotations: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, wait_for_completion: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Reset an anomaly detection job. All model state and results are deleted. The job is ready to start over as if it had just been created. It is not currently possible to reset multiple jobs using wildcards or a comma separated list.

``_ :param job_id: The ID of the job to reset. :param delete_user_annotations: Specifies whether annotations that have been added by the user should be deleted along with any auto-generated annotations when the job is reset. :param wait_for_completion: Should this request wait until the operation has completed before returning. """ if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path_parts: t.Dict[str, str] = {"job_id": _quote(job_id)} __path = f'/_ml/anomaly_detectors/{__path_parts["job_id"]}/_reset' __query: t.Dict[str, t.Any] = {} if delete_user_annotations is not None: __query["delete_user_annotations"] = delete_user_annotations if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, endpoint_id="ml.reset_job", path_parts=__path_parts, ) @_rewrite_parameters() def start_data_frame_analytics( self, *, id: str, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Start a data frame analytics job. A data frame analytics job can be started and stopped multiple times throughout its lifecycle. If the destination index does not exist, it is created automatically the first time you start the data frame analytics job. The index.number_of_shards and index.number_of_replicas settings for the destination index are copied from the source index. If there are multiple source indices, the destination index copies the highest setting values. The mappings for the destination index are also copied from the source indices. If there are any mapping conflicts, the job fails to start. If the destination index exists, it is used as is. You can therefore set up the destination index in advance with custom settings and mappings.

``_ :param id: 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. :param timeout: Controls the amount of time to wait until the data frame analytics job starts. """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") __path_parts: t.Dict[str, str] = {"id": _quote(id)} __path = f'/_ml/data_frame/analytics/{__path_parts["id"]}/_start' __query: t.Dict[str, t.Any] = {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, endpoint_id="ml.start_data_frame_analytics", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("end", "start", "timeout"), ) def start_datafeed( self, *, datafeed_id: str, end: t.Optional[t.Union[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, start: t.Optional[t.Union[str, t.Any]] = None, timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Start datafeeds.

A datafeed must be started in order to retrieve data from Elasticsearch. A datafeed can be started and stopped multiple times throughout its lifecycle.

Before you can start a datafeed, the anomaly detection job must be open. Otherwise, an error occurs.

If you restart a stopped datafeed, it continues processing input data from the next millisecond after it was stopped. If new data was indexed for that exact millisecond between stopping and starting, it will be ignored.

When Elasticsearch security features are enabled, your datafeed remembers which roles the last user to create or update it had at the time of creation or update and runs the query using those same roles. If you provided secondary authorization headers when you created or updated the datafeed, those credentials are used instead.

``_ :param datafeed_id: 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. :param end: Refer to the description for the `end` query parameter. :param start: Refer to the description for the `start` query parameter. :param timeout: Refer to the description for the `timeout` query parameter. """ if datafeed_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'datafeed_id'") __path_parts: t.Dict[str, str] = {"datafeed_id": _quote(datafeed_id)} __path = f'/_ml/datafeeds/{__path_parts["datafeed_id"]}/_start' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if end is not None: __body["end"] = end if start is not None: __body["start"] = start if timeout is not None: __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} if __body is not None: __headers["content-type"] = "application/json" return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.start_datafeed", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("adaptive_allocations",), ) def start_trained_model_deployment( self, *, model_id: str, adaptive_allocations: t.Optional[t.Mapping[str, t.Any]] = None, cache_size: t.Optional[t.Union[int, str]] = None, deployment_id: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, number_of_allocations: t.Optional[int] = None, pretty: t.Optional[bool] = None, priority: t.Optional[t.Union[str, t.Literal["low", "normal"]]] = None, queue_capacity: t.Optional[int] = None, threads_per_allocation: t.Optional[int] = None, timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, wait_for: t.Optional[ t.Union[str, t.Literal["fully_allocated", "started", "starting"]] ] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Start a trained model deployment. It allocates the model to every machine learning node.

``_ :param model_id: The unique identifier of the trained model. Currently, only PyTorch models are supported. :param adaptive_allocations: Adaptive allocations configuration. When enabled, the number of allocations is set based on the current load. If adaptive_allocations is enabled, do not set the number of allocations manually. :param cache_size: 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. :param deployment_id: A unique identifier for the deployment of the model. :param number_of_allocations: 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. If adaptive_allocations is enabled, do not set this value, because it’s automatically set. :param priority: The deployment priority. :param queue_capacity: 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. :param threads_per_allocation: 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. :param timeout: Specifies the amount of time to wait for the model to deploy. :param wait_for: Specifies the allocation status to wait for before returning. """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") __path_parts: t.Dict[str, str] = {"model_id": _quote(model_id)} __path = f'/_ml/trained_models/{__path_parts["model_id"]}/deployment/_start' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if cache_size is not None: __query["cache_size"] = cache_size if deployment_id is not None: __query["deployment_id"] = deployment_id if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if number_of_allocations is not None: __query["number_of_allocations"] = number_of_allocations if pretty is not None: __query["pretty"] = pretty if priority is not None: __query["priority"] = priority if queue_capacity is not None: __query["queue_capacity"] = queue_capacity if threads_per_allocation is not None: __query["threads_per_allocation"] = threads_per_allocation if timeout is not None: __query["timeout"] = timeout if wait_for is not None: __query["wait_for"] = wait_for if not __body: if adaptive_allocations is not None: __body["adaptive_allocations"] = adaptive_allocations if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} if __body is not None: __headers["content-type"] = "application/json" return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.start_trained_model_deployment", path_parts=__path_parts, ) @_rewrite_parameters() def stop_data_frame_analytics( self, *, id: str, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, force: t.Optional[bool] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Stop data frame analytics jobs. A data frame analytics job can be started and stopped multiple times throughout its lifecycle.

``_ :param id: 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. :param allow_no_match: 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. :param force: If true, the data frame analytics job is stopped forcefully. :param timeout: Controls the amount of time to wait until the data frame analytics job stops. Defaults to 20 seconds. """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") __path_parts: t.Dict[str, str] = {"id": _quote(id)} __path = f'/_ml/data_frame/analytics/{__path_parts["id"]}/_stop' __query: t.Dict[str, t.Any] = {} if allow_no_match is not None: __query["allow_no_match"] = allow_no_match if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if force is not None: __query["force"] = force if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, endpoint_id="ml.stop_data_frame_analytics", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("allow_no_match", "force", "timeout"), ) def stop_datafeed( self, *, datafeed_id: str, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, force: t.Optional[bool] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Stop datafeeds. A datafeed that is stopped ceases to retrieve data from Elasticsearch. A datafeed can be started and stopped multiple times throughout its lifecycle.

``_ :param datafeed_id: 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. :param allow_no_match: Refer to the description for the `allow_no_match` query parameter. :param force: Refer to the description for the `force` query parameter. :param timeout: Refer to the description for the `timeout` query parameter. """ if datafeed_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'datafeed_id'") __path_parts: t.Dict[str, str] = {"datafeed_id": _quote(datafeed_id)} __path = f'/_ml/datafeeds/{__path_parts["datafeed_id"]}/_stop' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if allow_no_match is not None: __body["allow_no_match"] = allow_no_match if force is not None: __body["force"] = force if timeout is not None: __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} if __body is not None: __headers["content-type"] = "application/json" return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.stop_datafeed", path_parts=__path_parts, ) @_rewrite_parameters() def stop_trained_model_deployment( self, *, model_id: str, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, force: t.Optional[bool] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Stop a trained model deployment.

``_ :param model_id: The unique identifier of the trained model. :param allow_no_match: 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. :param force: Forcefully stops the deployment, even if it is used by ingest pipelines. You can't use these pipelines until you restart the model deployment. """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") __path_parts: t.Dict[str, str] = {"model_id": _quote(model_id)} __path = f'/_ml/trained_models/{__path_parts["model_id"]}/deployment/_stop' __query: t.Dict[str, t.Any] = {} if allow_no_match is not None: __query["allow_no_match"] = allow_no_match if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if force is not None: __query["force"] = force if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, endpoint_id="ml.stop_trained_model_deployment", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=( "allow_lazy_start", "description", "max_num_threads", "model_memory_limit", ), ) def update_data_frame_analytics( self, *, id: str, allow_lazy_start: t.Optional[bool] = None, description: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, max_num_threads: t.Optional[int] = None, model_memory_limit: t.Optional[str] = None, pretty: t.Optional[bool] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Update a data frame analytics job.

``_ :param id: 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. :param allow_lazy_start: Specifies whether this job can start when there is insufficient machine learning node capacity for it to be immediately assigned to a node. :param description: A description of the job. :param max_num_threads: 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. :param model_memory_limit: 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. """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") __path_parts: t.Dict[str, str] = {"id": _quote(id)} __path = f'/_ml/data_frame/analytics/{__path_parts["id"]}/_update' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if allow_lazy_start is not None: __body["allow_lazy_start"] = allow_lazy_start if description is not None: __body["description"] = description if max_num_threads is not None: __body["max_num_threads"] = max_num_threads if model_memory_limit is not None: __body["model_memory_limit"] = model_memory_limit __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.update_data_frame_analytics", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=( "aggregations", "chunking_config", "delayed_data_check_config", "frequency", "indexes", "indices", "indices_options", "job_id", "max_empty_searches", "query", "query_delay", "runtime_mappings", "script_fields", "scroll_size", ), ) def update_datafeed( self, *, datafeed_id: str, aggregations: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, allow_no_indices: t.Optional[bool] = None, chunking_config: t.Optional[t.Mapping[str, t.Any]] = None, delayed_data_check_config: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, expand_wildcards: t.Optional[ t.Union[ t.Sequence[ t.Union[str, t.Literal["all", "closed", "hidden", "none", "open"]] ], t.Union[str, t.Literal["all", "closed", "hidden", "none", "open"]], ] ] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, frequency: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, human: t.Optional[bool] = None, ignore_throttled: t.Optional[bool] = None, ignore_unavailable: t.Optional[bool] = None, indexes: t.Optional[t.Sequence[str]] = None, indices: t.Optional[t.Sequence[str]] = None, indices_options: t.Optional[t.Mapping[str, t.Any]] = None, job_id: t.Optional[str] = None, max_empty_searches: t.Optional[int] = None, pretty: t.Optional[bool] = None, query: t.Optional[t.Mapping[str, t.Any]] = None, query_delay: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None, runtime_mappings: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, script_fields: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, scroll_size: t.Optional[int] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Update a datafeed. You must stop and start the datafeed for the changes to be applied. When Elasticsearch security features are enabled, your datafeed remembers which roles the user who updated it had at the time of the update and runs the query using those same roles. If you provide secondary authorization headers, those credentials are used instead.

``_ :param datafeed_id: 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. :param aggregations: If set, the datafeed performs aggregation searches. Support for aggregations is limited and should be used only with low cardinality data. :param allow_no_indices: If `true`, wildcard indices expressions that resolve into no concrete indices are ignored. This includes the `_all` string or when no indices are specified. :param chunking_config: 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. :param delayed_data_check_config: 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. :param expand_wildcards: 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. :param frequency: 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. :param ignore_throttled: If `true`, concrete, expanded or aliased indices are ignored when frozen. :param ignore_unavailable: If `true`, unavailable indices (missing or closed) are ignored. :param indexes: 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. :param indices: 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. :param indices_options: Specifies index expansion options that are used during search. :param job_id: :param max_empty_searches: 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. :param query: 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. :param query_delay: 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. :param runtime_mappings: Specifies runtime fields for the datafeed search. :param script_fields: 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. :param scroll_size: 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`. """ if datafeed_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'datafeed_id'") __path_parts: t.Dict[str, str] = {"datafeed_id": _quote(datafeed_id)} __path = f'/_ml/datafeeds/{__path_parts["datafeed_id"]}/_update' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable if pretty is not None: __query["pretty"] = pretty if not __body: if aggregations is not None: __body["aggregations"] = aggregations if chunking_config is not None: __body["chunking_config"] = chunking_config if delayed_data_check_config is not None: __body["delayed_data_check_config"] = delayed_data_check_config if frequency is not None: __body["frequency"] = frequency if indexes is not None: __body["indexes"] = indexes if indices is not None: __body["indices"] = indices if indices_options is not None: __body["indices_options"] = indices_options if job_id is not None: __body["job_id"] = job_id if max_empty_searches is not None: __body["max_empty_searches"] = max_empty_searches if query is not None: __body["query"] = query if query_delay is not None: __body["query_delay"] = query_delay if runtime_mappings is not None: __body["runtime_mappings"] = runtime_mappings if script_fields is not None: __body["script_fields"] = script_fields if scroll_size is not None: __body["scroll_size"] = scroll_size __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.update_datafeed", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=("add_items", "description", "remove_items"), ) def update_filter( self, *, filter_id: str, add_items: t.Optional[t.Sequence[str]] = None, description: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, remove_items: t.Optional[t.Sequence[str]] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

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

``_ :param filter_id: A string that uniquely identifies a filter. :param add_items: The items to add to the filter. :param description: A description for the filter. :param remove_items: The items to remove from the filter. """ if filter_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'filter_id'") __path_parts: t.Dict[str, str] = {"filter_id": _quote(filter_id)} __path = f'/_ml/filters/{__path_parts["filter_id"]}/_update' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if add_items is not None: __body["add_items"] = add_items if description is not None: __body["description"] = description if remove_items is not None: __body["remove_items"] = remove_items __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.update_filter", path_parts=__path_parts, ) @_rewrite_parameters( body_fields=( "allow_lazy_open", "analysis_limits", "background_persist_interval", "categorization_filters", "custom_settings", "daily_model_snapshot_retention_after_days", "description", "detectors", "groups", "model_plot_config", "model_prune_window", "model_snapshot_retention_days", "per_partition_categorization", "renormalization_window_days", "results_retention_days", ), ) def update_job( self, *, job_id: str, allow_lazy_open: t.Optional[bool] = None, analysis_limits: t.Optional[t.Mapping[str, t.Any]] = None, background_persist_interval: t.Optional[ t.Union[str, t.Literal[-1], t.Literal[0]] ] = None, categorization_filters: t.Optional[t.Sequence[str]] = None, custom_settings: t.Optional[t.Mapping[str, t.Any]] = None, daily_model_snapshot_retention_after_days: t.Optional[int] = None, description: t.Optional[str] = None, detectors: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, groups: t.Optional[t.Sequence[str]] = None, human: t.Optional[bool] = None, model_plot_config: t.Optional[t.Mapping[str, t.Any]] = None, model_prune_window: t.Optional[ t.Union[str, t.Literal[-1], t.Literal[0]] ] = None, model_snapshot_retention_days: t.Optional[int] = None, per_partition_categorization: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, renormalization_window_days: t.Optional[int] = None, results_retention_days: t.Optional[int] = None, body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ .. raw:: html

Update an anomaly detection job. Updates certain properties of an anomaly detection job.

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

Update a trained model deployment.

``_ :param model_id: The unique identifier of the trained model. Currently, only PyTorch models are supported. :param adaptive_allocations: Adaptive allocations configuration. When enabled, the number of allocations is set based on the current load. If adaptive_allocations is enabled, do not set the number of allocations manually. :param number_of_allocations: 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. If adaptive_allocations is enabled, do not set this value, because it’s automatically set. """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") __path_parts: t.Dict[str, str] = {"model_id": _quote(model_id)} __path = f'/_ml/trained_models/{__path_parts["model_id"]}/deployment/_update' __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty if not __body: if adaptive_allocations is not None: __body["adaptive_allocations"] = adaptive_allocations if number_of_allocations is not None: __body["number_of_allocations"] = number_of_allocations if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} if __body is not None: __headers["content-type"] = "application/json" return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body, endpoint_id="ml.update_trained_model_deployment", path_parts=__path_parts, )