o gZhS1@sHddlZddlmZddlmZddlmZmZm Z GdddeZ dS)N)ObjectApiResponse)NamespacedClient) SKIP_IN_PATH_quote_rewrite_parametersc5@sx1eZdZeddddddedejedejejeej efdejedejede ej f d d Z ed d dddddddd dedejedejedejejeej efdejedejedejedejejddefde ej fddZ eddddddedejedejejeej efdejedejede ej f ddZeddddddededejedejejeej efdejedejede ej fddZeddddddedejeej efdejedejejeej efdejedejede ej fddZeddddddddedejedejejeej efdejedejedejedejejddefde ej fd d!Zedddddd"d#edejedejejeej efdejedejedejede ej fd$d%Zed d dddddddd&dejedejedejejeej efdejedejed'ejedejejddefde ej fd(d)Zedddddd*edejedejejeej efdejedejede ej f d+d,Zedddddddd-ded.ejed/ejedejedejejeej efdejedejedejejddefde ej fd0d1Zedddddddd2ded3ejedejedejejeej efdejedejedejed4ejede ej fd5d6Zedddddded7edejedejejeej efdejedejede ej fd8d9Zedddddd"dedejedejejeej efdejedejedejede ej fd:d;ZeddddddedZed d dddddddd?d@ejejeej fdejedejejeej efdejedAejejeefdBejejeefdejede ej fdCdDZed d ddddddEdFejeej fdGedejedejejeej efdejedejedHejejeej fde ej fdIdJZed d ddddddddddddddK dejedLejedMejejeej fdNejejeej fdOejedPejejeej fdejedejejeej efdejedQejedRejedejedSejejeej fde ej fdTdUZed d ddddddddddV dedWejejeej fdXejedYejejeej fdejedejejeej efdejedejedZejejeej fd[ejejeej fde ej fd\d]Z ed d dddddddd^ded_ejejddefdejed`ejejddefdejejeej efdejedaejedejede ej fdbdcZ!ed dddeidfddddddddddddddddgdedhejejeej fdiejedjejedYejejeej fdejedkejedlejedejejeej efdeejedejedmejejeej fdejednejedoejed[ejejeej fde ej f"dpdqZ"edddeidrdddddddddds dedYejejeej fdejedejejeej efdeejedejedejedejednejed[ejejeej fde ej fdtduZ#ed dddeidfdddddddddvdejedejedejejeej efdeejedejedmejejeej fdejednejede ej fdwdxZ$ed dddeidfddddddddddy dedzejedejedejejeej efdeejedejedmejejeej fd{ejedejednejede ej fd|d}Z%edddeidrdddddddddd~ dejedejedejedejedejejeej efdeejedejedejednejede ej fddZ&edddeidrdddddddddd dejedejedejedejejeej efdeejedejedejednejedejede ej fddZ'edddddddd#ejejeej efdejedejedejejeej efdejedejede ej fddZ(eddddddddd#ejejeej efdejedejedejedejejeej efdejedejede ej fddZ)edddeidrddddddddd*ejejeej efdejedejejeej efdeejedejedejednejede ej fddZ*ed dddeidfdddddddddddddd dedjejedYejejeej fdejedkejedejejeej efdeejedejedejedmejejeej fdejednejedoejed[ejejeej fde ej fddZ+eddddddddejedejedejedejejeej efdejedejede ej fddZ,edddddddddejejeej efdejedejedejedejejeej efdejedejede ej fddZ-edddddddddejedejedejejeej efdejedejejddefdejedejejddefde ej fddZ.eddddddded7edejedejedejejeej efdejedejede ej fddZ/ed dddeidfddddddddddddd ded7ejedjejedYejejeej fdejedejejeej efdeejedejedmejejeej fdejednejedoejed[ejejeej fde ej fddZ0ed d dddddddddddd dedejedejejddefdYejejeej fdejedkejedejejeej efdejedejejeefdejed[ejejeej fdejede ej fddZ1ed dddeidfdddddddddddddd dedjejedYejejeej fdejedkejedejejeej efdeejedejedmejejeej fdejedejednejedoejed[ejejeej fde ej fddZ2edddeidrddddddddddddd dejedejedejedejedejedejejeej efdeejedejedejejdefdejednejedejede ej fddZ3edddeidrddddddddddejejeej efdejedejedejejeej efdeejedejedejednejede ej fddZ4ed d ddddddddedej ejeej fdejedejejeej efdejedejejeej fdejedejejddefde ej fddZ5eddddddejedejejeej efdejedejede ej f ddZ6ed d dddddddedejedejejeej efdejedejedejejddefde ej fddZ7ed d ddddddedej ejeej fdejedejejeej efdejedejede ej fddĄZ8eddƍdddddddǜdedej ej dejedejejeej efdejedejedejejeej fdejejeej fde ej fdd˄Z9ed d ddddddd̜dejedejejeej fdejedejejeej efdejedejede ej fddτZ:ed d ddddddddddМ d#ejedejejeej fdYejejeej fdejedejejeej efdejedejejeej fdejed[ejejeej fde ej fddԄZ;ed d ddddddd՜dedOejedejedejejeej efdejedejej edejede ej fdd؄Zed dhd܍ddddddddddddddddddddddddd#edejejeejeej ffdejedejejeej fdejejeej fdejedejejej ejdefejdeffdejejeej efdejejddefdejejeejeej effdejedejedejedejejeej efdejejeej efdejejeej fdejedejedejedHejejeej fdejejddefdejejeejeej ffdejejeejeej ffdejede ej f2ddZ?ed d dddddddd*edOejedejedejejeej efdejedejej edejede ej fddZ@ed d ddddddddddddddddddded@ejeej fdejeej fdejedejejeej fdejejddefdejej dejedejejeej fdOejedejedejejeej efdejej edejedejejeej fdejedejedejedejedejede ej f*ddZAed d ddddddddddddddddded ejed ejed ejejeej fdOejedejedejejeej efdejedejejeej fd ejejeej fd ejej dejedejejdefdejedejedejej ede ej f"ddZBedddddddedZKedddddddd dedejedejedejejeej efdejedejedejedejejddefde ej fd?d@ZLed d dddddddd d#edejedejedejejeej efdejedejedejedejejddefde ej fdAdBZMedddddddCdedejedejedejejeej efdejedejedejede ej fdDdEZNed d dddddddddFdedLejedOejedejedejejeej efdejedQejedRejedejede ej fdGdHZOed d dddddddddddddddddddddddId#edejejeejeej ffdejedejejeej fdejejeej fdejedejejej ejdefejdeffdejejeej efdejejddefdejedejedejedejej edejej edejejeej fdejedejedejedHejejeej fdejejddefdejejeejeej ffdejejeejeej ffdejede ej f0dJdKZPed d ddddddddLd*edMejej edOejedejedejejeej efdejedejedNejej ede ej fdOdPZQed d ddddddddddddddddddddQdedejedejejeej fdejejddefdRejej edejejeej fdejedOejedSejej ejeej fdejedejejeej efdejej edejedejejeej fdTejejddefdejedUejejeej fdejedejedejede ej f*dVdWZRed d dddddddXded7edOejedejedejejeej efdejedejedYejede ej fdZd[ZSeddddddd\ded7edejedejejeej efdejedejedejejddefd4ejede ej fd]d^ZTed d dddddddddddddd_ d@ejejeej fdejejeej fdejejeej fdOejedejedejejeej efdejedejed`ejejeej fdaejedejedejedejede ej fdbdcZUedddƍdddddddejeej fdejedejejeej efdejedejede ej f dedfZVdS(gMlClientN) error_trace filter_pathhumanprettymodel_idr r r r returnc Cs~|tvrtddt|d}i}|dur||d<|dur"||d<|dur*||d<|dur2||d<d d i}|jd |||d S) a Clear the cached results from a trained model deployment ``_ :param model_id: The unique identifier of the trained model. +Empty value passed for parameter 'model_id'/_ml/trained_models/z/deployment/cache/_clearNr r r r acceptapplication/jsonPOSTparamsheadersr ValueErrorrperform_request) selfr r r r r _MlClient__path_MlClient__query_MlClient__headersrT/var/www/html/lang_env/lib/python3.10/site-packages/elasticsearch/_sync/client/ml.py$clear_trained_model_deployment_caches z-MlClient.clear_trained_model_deployment_cacheT) body_fields)allow_no_matchr r forcer r timeoutjob_idr"r#r$z t.Literal[-1]z t.Literal[0]c C|tvrtddt|d} i} i} |dur|| d<|dur$|| d<|dur,|| d<|dur4|| d<|dur<|| d <|durD|| d <|durL|| d <| sPd} d d i} | dur\d | d<|jd| | | | dS)a Closes one or more anomaly detection jobs. A job can be opened and closed multiple times throughout its lifecycle. ``_ :param job_id: Identifier for the anomaly detection job. It can be a job identifier, a group name, or a wildcard expression. You can close multiple anomaly detection jobs in a single API request by using a group name, a comma-separated list of jobs, or a wildcard expression. You can close all jobs by using `_all` or by specifying `*` as the job identifier. :param allow_no_match: Refer to the description for the `allow_no_match` query parameter. :param force: Refer to the descriptiion for the `force` query parameter. :param timeout: Refer to the description for the `timeout` query parameter. )Empty value passed for parameter 'job_id'/_ml/anomaly_detectors/z/_closeNr"r r r#r r r$rr content-typerrrbodyr) rr%r"r r r#r r r$r_MlClient__bodyrrrrr close_job=s6 zMlClient.close_job calendar_idc C||tvrtddt|}i}|dur||d<|dur!||d<|dur)||d<|dur1||d<dd i}|jd |||d S) z Deletes a calendar. ``_ :param calendar_id: A string that uniquely identifies a calendar. .Empty value passed for parameter 'calendar_id'/_ml/calendars/Nr r r r rrDELETErr) rr.r r r r rrrrrrdelete_calendarx zMlClient.delete_calendarevent_idc C|tvrtd|tvrtddt|dt|}i}|dur&||d<|dur.||d<|dur6||d<|dur>||d <d d i} |jd ||| d S)a} Deletes scheduled events from a calendar. ``_ :param calendar_id: A string that uniquely identifies a calendar. :param event_id: Identifier for the scheduled event. You can obtain this identifier by using the get calendar events API. r0z+Empty value passed for parameter 'event_id'r1z/events/Nr r r r rrr2rr) rr.r5r r r r rrrrrrdelete_calendar_event$zMlClient.delete_calendar_eventc Cr6)a Deletes anomaly detection 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. r0r'r1/jobs/Nr r r r rrr2rr rr.r%r r r r rrrrrrdelete_calendar_jobr8zMlClient.delete_calendar_job)r r r#r r r$idc Cs|tvrtddt|}i} |dur|| d<|dur!|| d<|dur)|| d<|dur1|| d<|dur9|| d<|durA|| d <d d i} |jd || | d S)a Deletes an existing data frame analytics job. ``_ :param id: Identifier for the data frame analytics job. :param force: If `true`, it deletes a job that is not stopped; this method is quicker than stopping and deleting the job. :param timeout: The time to wait for the job to be deleted. %Empty value passed for parameter 'id'/_ml/data_frame/analytics/Nr r r#r r r$rrr2rr) rr<r r r#r r r$rrrrrrdelete_data_frame_analyticss(z$MlClient.delete_data_frame_analytics)r r r#r r datafeed_idc C|tvrtddt|}i}|dur||d<|dur!||d<|dur)||d<|dur1||d<|dur9||d<d d i} |jd ||| d S) aA Deletes an existing 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. .Empty value passed for parameter 'datafeed_id'/_ml/datafeeds/Nr r r#r r rrr2rr) rr@r r r#r r rrrrrrdelete_datafeeds$zMlClient.delete_datafeed)r%r r r r requests_per_secondr$rEc Cs|tvr dt|}nd}i} i} |dur|| d<|dur"|| d<|dur*|| d<|dur2|| d<|dur:|| d<|durB|| d <| sFd} d d i} | durRd | d <|jd || | | dS)a< Deletes expired and unused machine learning data. ``_ :param job_id: Identifier for an anomaly detection job. It can be a job identifier, a group name, or a wildcard expression. :param requests_per_second: The desired requests per second for the deletion processes. The default behavior is no throttling. :param timeout: How long can the underlying delete processes run until they are canceled. z/_ml/_delete_expired_data/z/_ml/_delete_expired_dataNr r r r rEr$rrr)r2r*rrr) rr%r r r r rEr$rrr,rrrrdelete_expired_data=s2 zMlClient.delete_expired_data filter_idc Cr/) z Deletes a filter. ``_ :param filter_id: A string that uniquely identifies a filter. ,Empty value passed for parameter 'filter_id' /_ml/filters/Nr r r r rrr2rr) rrHr r r r rrrrrr delete_filterrr4zMlClient.delete_filter) forecast_idallow_no_forecastsr r r r r$rLrMc Cs|tvrtd|tvr|tvrdt|dt|} n|tvr*dt|d} ntdi} |dur8|| d<|dur@|| d<|durH|| d <|durP|| d <|durX|| d <|dur`|| d <d di} |jd| | | dS)a Deletes forecasts from a machine learning job. ``_ :param job_id: Identifier for the anomaly detection job. :param forecast_id: A comma-separated list of forecast identifiers. If you do not specify this optional parameter or if you specify `_all` or `*` the API deletes all forecasts from the job. :param allow_no_forecasts: Specifies whether an error occurs when there are no forecasts. In particular, if this parameter is set to `false` and there are no forecasts associated with the job, attempts to delete all forecasts return an error. :param timeout: Specifies the period of time to wait for the completion of the delete operation. When this period of time elapses, the API fails and returns an error. r'r(z /_forecast/ /_forecast-Couldn't find a path for the given parametersNrMr r r r r$rrr2rr) rr%rLrMr r r r r$rrrrrrdelete_forecasts0zMlClient.delete_forecast)delete_user_annotationsr r r#r r wait_for_completionrQrRc Cs|tvrtddt|} i} |dur|| d<|dur!|| d<|dur)|| d<|dur1|| d<|dur9|| d<|durA|| d <|durI|| d <d d i} |jd | | | dS)a Deletes an existing anomaly detection job. ``_ :param job_id: Identifier for the anomaly detection job. :param delete_user_annotations: Specifies whether annotations that have been added by the user should be deleted along with any auto-generated annotations when the job is reset. :param force: Use to forcefully delete an opened job; this method is quicker than closing and deleting the job. :param wait_for_completion: Specifies whether the request should return immediately or wait until the job deletion completes. r'r(NrQr r r#r r rRrrr2rr) rr%rQr r r#r r rRrrrrrr delete_jobs,zMlClient.delete_job snapshot_idc Cr6)a Deletes an existing model snapshot. ``_ :param job_id: Identifier for the anomaly detection job. :param snapshot_id: Identifier for the model snapshot. r'.Empty value passed for parameter 'snapshot_id'r(/model_snapshots/Nr r r r rrr2rr) rr%rTr r r r rrrrrrdelete_model_snapshot$zMlClient.delete_model_snapshotc CrA) a Deletes an existing trained inference model that is currently 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. rrNr r r#r r rrr2rr) rr r r r#r r rrrrrrdelete_trained_model$s$zMlClient.delete_trained_model model_aliasc Cr6)a= Deletes a model alias that refers to the trained model ``_ :param model_id: The trained model ID to which the model alias refers. :param model_alias: The model alias to delete. r.Empty value passed for parameter 'model_alias'r/model_aliases/Nr r r r rrr2rr) rr rZr r r r rrrrrrdelete_trained_model_aliasLrXz#MlClient.delete_trained_model_alias)analysis_configr r r max_bucket_cardinalityoverall_cardinalityr r^r_r`c Csd}i} i} |dur|| d<|dur|| d<|dur|| d<|dur&|| d<|dur.|| d<|dur6|| d<|dur>|| d <d d d } |jd || | | d S)a Estimates the model memory ``_ :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`. z-/_ml/anomaly_detectors/_estimate_model_memoryNr^r r r r_r`r rrr)rr*r) rr^r r r r_r`r rr,rrrrrestimate_model_memoryrs*"  zMlClient.estimate_model_memory)r r r r query evaluationindexrdc Cs|durtd|durtdd}i} i} |dur|| d<|dur&|| d<|dur.|| d<|dur6|| d<|dur>|| d <|durF|| d <|durN|| d <d d d } |jd|| | | dS)a Evaluates the data frame analytics for an annotated index. ``_ :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. Nz-Empty value passed for parameter 'evaluation'z(Empty value passed for parameter 'index'z/_ml/data_frame/_evaluatererfr r r r rdrrarr*rr) rrerfr r r r rdrr,rrrrrevaluate_data_frames2  zMlClient.evaluate_data_frame) r<allow_lazy_startanalysisanalyzed_fields descriptiondestr r r max_num_threadsmodel_memory_limitr sourcerirjrkrlrmrnrorpc Cs|tvr dt|d}nd}i}i}|dur||d<|dur#||d<|dur+||d<|dur3||d<|dur;||d <|durC||d <|durK||d <| durS| |d <| dur[| |d <| durc| |d<| durk| |d<| durs| |d<|swd}ddi}|durd|d<|jd||||dS)a Explains a data frame analytics config. ``_ :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 analysis: The analysis configuration, which contains the information necessary to perform one of the following types of analysis: classification, outlier detection, or regression. :param analyzed_fields: Specify includes and/or excludes patterns to select which fields will be included in the analysis. The patterns specified in excludes are applied last, therefore excludes takes precedence. In other words, if the same field is specified in both includes and excludes, then the field will not be included in the analysis. :param description: A description of the job. :param dest: The destination configuration, consisting of index and optionally results_field (ml by default). :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. :param source: The configuration of how to source the analysis data. It requires an index. Optionally, query and _source may be specified. r>z /_explainz"/_ml/data_frame/analytics/_explainNrirjrkrlrmr r r rnror rprrr)rr*rF)rr<rirjrkrlrmr r r rnror rprr,rrrrrexplain_data_frame_analyticssJ5 z%MlClient.explain_data_frame_analytics) advance_time calc_interimendr r r r skip_timestartrrrsrtrurvc Cs|tvrtddt|d} i} i} |dur|| d<|dur$|| d<|dur,|| d<|dur4|| d<|dur<|| d <|durD|| d <|durL|| d <| durT| | d <| dur\| | d <| s`d} ddi}| durld|d<|jd| | || dS)a Forces any buffered data to be processed by the job. ``_ :param job_id: Identifier for the anomaly detection job. :param advance_time: Refer to the description for the `advance_time` query parameter. :param calc_interim: Refer to the description for the `calc_interim` query parameter. :param end: Refer to the description for the `end` query parameter. :param skip_time: Refer to the description for the `skip_time` query parameter. :param start: Refer to the description for the `start` query parameter. r'r(z/_flushNrrrsrtr r r r rurvrrr)rr*r)rr%rrrsrtr r r r rurvrr,rrrrr flush_job8s> zMlClient.flush_job)durationr expires_inr r max_model_memoryr rxryrzc Cs|tvrtddt|d} i} i} |dur|| d<|dur$|| d<|dur,|| d<|dur4|| d<|dur<|| d <|durD|| d <|durL|| d <| sPd} d d i} | dur\d | d<|jd| | | | dS)az Predicts the future behavior of a time series by using its historical behavior. ``_ :param job_id: Identifier for the anomaly detection job. The job must be open when you create a forecast; otherwise, an error occurs. :param duration: Refer to the description for the `duration` query parameter. :param expires_in: Refer to the description for the `expires_in` query parameter. :param max_model_memory: Refer to the description for the `max_model_memory` query parameter. r'r(rNNrxr ryr r rzr rrr)rr*r) rr%rxr ryr r rzr rr,rrrrrforecastus6 zMlClient.forecastfromfrom_)r!parameter_aliases) timestamp anomaly_scoredescrtr exclude_interimexpandr r}r pager sizesortrvrrrrrrrrcCsx|tvrtd|tvr|tvrdt|dt|}n|tvr*dt|d}ntdi}i}|dur:||d<|durB||d<|durJ||d <|durR||d <|durZ||d <|durb||d <| durj| |d <| durr| |d<| durz| |d<| dur| |d<| dur| |d<|dur||d<|dur||d<|dur||d<|sd}ddi}|durd|d<|jd||||dS)a Retrieves anomaly detection job results for one or more buckets. ``_ :param job_id: Identifier for the anomaly detection job. :param timestamp: The timestamp of a single bucket result. If you do not specify this parameter, the API returns information about all buckets. :param anomaly_score: Refer to the description for the `anomaly_score` query parameter. :param desc: Refer to the description for the `desc` 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 expand: Refer to the description for the `expand` query parameter. :param from_: Skips the specified number of buckets. :param page: :param size: Specifies the maximum number of buckets to obtain. :param sort: Refer to the desription for the `sort` query parameter. :param start: Refer to the description for the `start` query parameter. r'r(z/results/buckets/z/results/bucketsrONrrrtr rrr r|r rr rrrvrrr)rr*r)rr%rrrrtr rrr r}r rr rrrvrr,rrrrr get_bucketssZ- zMlClient.get_buckets)r~) rtr r r}r r%r rrvc Cs|tvrtddt|d} i} |dur|| d<|dur"|| d<|dur*|| d<|dur2|| d<|dur:|| d <|durB|| d <|durJ|| d <| durR| | d <| durZ| | d <ddi} |jd| | | dS)a Retrieves information about the scheduled 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. r0r1/eventsNrtr r r|r r%r rrvrrGETrr)rr.rtr r r}r r%r rrvrrrrrrget_calendar_eventss4!zMlClient.get_calendar_events)r.r r r}r rr rc Cs|tvr dt|} nd} i} i} |dur|| d<|dur"|| d<|dur*|| d<|dur2|| d<|dur:|| d<|durB|| d <|durJ|| d <| sNd} d d i} | durZd | d <|jd| | | | dS)aw Retrieves configuration information for 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 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. r1z/_ml/calendarsNr r r|r rr rrrr)rr*rF) rr.r r r}r rr rrrr,rrrr get_calendarsDs6 zMlClient.get_calendars) category_idr r r}r rpartition_field_valuer rrrc Cs|tvrtd|tvr|tvrdt|dt|} n|tvr*dt|d} ntdi} i} |dur:|| d<|durB|| d<|durJ|| d <|durR|| d <|durZ|| d <|durb|| d <| durj| | d <| durr| | d<| svd} ddi}| durd|d<|jd| | || dS)a Retrieves anomaly detection job results for one or more categories. ``_ :param job_id: Identifier for the anomaly detection job. :param category_id: Identifier for the category, which is unique in the job. If you specify neither the category ID nor the partition_field_value, the API returns information about all categories. If you specify only the partition_field_value, it returns information about all categories for the specified partition. :param from_: Skips the specified number of categories. :param page: Configures pagination. This parameter has the `from` and `size` properties. :param partition_field_value: Only return categories for the specified partition. :param size: Specifies the maximum number of categories to obtain. r'r(z/results/categories/z/results/categoriesrONr r r|r rrr rrrr)rr*r)rr%rr r r}r rrr rrrr,rrrrget_categoriessB" zMlClient.get_categories) r<r"r exclude_generatedr r}r r rrc Cs|tvr dt|} nd} i} |dur|| d<|dur || d<|dur(|| d<|dur0|| d<|dur8|| d<|dur@|| d <|durH|| d <| durP| | d <d d i} |jd| | | dS)a Retrieves configuration information for data frame analytics jobs. ``_ :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. r>z/_ml/data_frame/analyticsNr"r rr r|r r rrrrrrF) rr<r"r rr r}r r rrrrrrrget_data_frame_analyticss0&z!MlClient.get_data_frame_analytics) r<r"r r r}r r rverboserc Cs|tvr dt|d} nd} i} |dur|| d<|dur!|| d<|dur)|| d<|dur1|| d<|dur9|| d <|durA|| d <|durI|| d <| durQ| | d <d di} |jd| | | dS)a Retrieves usage information for data frame analytics jobs. ``_ :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. r>/_statsz /_ml/data_frame/analytics/_statsNr"r r r|r r rrrrrrrF) rr<r"r r r}r r rrrrrrrrget_data_frame_analytics_statss0$z'MlClient.get_data_frame_analytics_stats)r@r"r r r r c C|tvr dt|d}nd}i}|dur||d<|dur!||d<|dur)||d<|dur1||d<|dur9||d <d d i} |jd ||| d S)a Retrieves usage information for datafeeds. ``_ :param datafeed_id: Identifier for the datafeed. It can be a datafeed identifier or a wildcard expression. If you do not specify one of these options, the API returns information about all datafeeds. :param allow_no_match: Specifies what to do when the request: 1. Contains wildcard expressions and there are no datafeeds that match. 2. Contains the `_all` string or no identifiers and there are no matches. 3. Contains wildcard expressions and there are only partial matches. The default value is `true`, which returns an empty `datafeeds` array when there are no matches and the subset of results when there are partial matches. If this parameter is `false`, the request returns a `404` status code when there are no matches or only partial matches. rCrz/_ml/datafeeds/_statsNr"r r r r rrrrrF) rr@r"r r r r rrrrrrget_datafeed_statsCs$zMlClient.get_datafeed_stats)r@r"r rr r r c C|tvr dt|}nd}i} |dur|| d<|dur || d<|dur(|| d<|dur0|| d<|dur8|| d<|dur@|| d <d d i} |jd || | d S)a Retrieves configuration information for 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. rCz/_ml/datafeedsNr"r rr r r rrrrrF) rr@r"r rr r r rrrrrr get_datafeedsr(zMlClient.get_datafeeds)rHr r r}r r rc Cr)aN Retrieves 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. rJz /_ml/filtersNr r r|r r rrrrrrF) rrHr r r}r r rrrrrrr get_filterss(zMlClient.get_filters) rrtr rr r}r influencer_scorerr rrrvrcCs,|tvrtddt|d}i}i}|dur||d<|dur$||d<|dur,||d<|dur4||d<|dur<||d <|durD||d <|durL||d <| durT| |d <| dur\| |d <| durd| |d<| durl| |d<| durt| |d<|dur|||d<|sd}ddi}|durd|d<|jd||||dS)a@ Retrieves anomaly detection job results for one or more influencers. ``_ :param job_id: Identifier for the anomaly detection job. :param desc: If true, the results are sorted in descending order. :param end: Returns influencers with timestamps earlier than this time. The default value means it is unset and results are not limited to specific timestamps. :param exclude_interim: If true, the output excludes interim results. By default, interim results are included. :param from_: Skips the specified number of influencers. :param influencer_score: Returns influencers with anomaly scores greater than or equal to this value. :param page: Configures pagination. This parameter has the `from` and `size` properties. :param size: Specifies the maximum number of influencers to obtain. :param sort: Specifies the sort field for the requested influencers. By default, the influencers are sorted by the `influencer_score` value. :param start: Returns influencers with timestamps after this time. The default value means it is unset and results are not limited to specific timestamps. r'r(z/results/influencersNrrtr rr r|r rrr rrrvrrr)rr*r)rr%rrtr rr r}r rrr rrrvrrr,rrrrget_influencerssN, zMlClient.get_influencers)r%r"r r r r c Cr)a" Retrieves usage information for anomaly detection jobs. ``_ :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. r(rz/_ml/anomaly_detectors/_statsNr"r r r r rrrrrF) rr%r"r r r r rrrrrr get_job_stats(s$zMlClient.get_job_stats)r%r"r rr r r c Cr)a Retrieves configuration information for anomaly detection jobs. ``_ :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. r(z/_ml/anomaly_detectorsNr"r rr r r rrrrrF) rr%r"r rr r r rrrrrrget_jobsXrzMlClient.get_jobs)node_idr r r master_timeoutr r$rrc Cs|tvr dt|d}nd}i} |dur|| d<|dur!|| d<|dur)|| d<|dur1|| d<|dur9|| d <|durA|| d <d d i} |jd || | dS)a Returns information on how ML is using memory. ``_ :param node_id: The names of particular nodes in the cluster to target. For example, `nodeId1,nodeId2` or `ml:true` :param master_timeout: Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error. :param timeout: Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error. z /_ml/memory/rz/_ml/memory/_statsNr r r rr r$rrrrrF) rrr r r rr r$rrrrrrget_memory_statss(zMlClient.get_memory_stats)r"r r r r c Cs|tvrtd|tvrtddt|dt|d}i} |dur'|| d<|dur/|| d<|dur7|| d <|dur?|| d <|durG|| d <d d i} |jd|| | dS)a Gets stats for anomaly detection job model snapshot upgrades that are in progress. ``_ :param job_id: Identifier for the anomaly detection job. :param snapshot_id: A numerical character string that uniquely identifies the model snapshot. You can get information for multiple snapshots by using a comma-separated list or a wildcard expression. You can get all snapshots by using `_all`, by specifying `*` as the snapshot ID, or by omitting the snapshot ID. :param allow_no_match: Specifies what to do when the request: - Contains wildcard expressions and there are no jobs that match. - Contains the _all string or no identifiers and there are no matches. - Contains wildcard expressions and there are only partial matches. The default value is true, which returns an empty jobs array when there are no matches and the subset of results when there are partial matches. If this parameter is false, the request returns a 404 status code when there are no matches or only partial matches. r'rUr(rVz/_upgrade/_statsNr"r r r r rrrrr) rr%rTr"r r r r rrrrrr get_model_snapshot_upgrade_statss(z)MlClient.get_model_snapshot_upgrade_stats) rTrrtr r r}r rr rrrvc CsH|tvrtd|tvr|tvrdt|dt|}n|tvr*dt|d}ntdi}i}|dur:||d<|durB||d<|durJ||d <|durR||d <|durZ||d <|durb||d <| durj| |d <| durr| |d<| durz| |d<| dur| |d<| dur| |d<|sd}ddi}|durd|d<|jd||||dS)a& Retrieves information about model snapshots. ``_ :param job_id: Identifier for the anomaly detection job. :param snapshot_id: A numerical character string that uniquely identifies the model snapshot. You can get information for multiple snapshots by using a comma-separated list or a wildcard expression. You can get all snapshots by using `_all`, by specifying `*` as the snapshot ID, or by omitting the snapshot ID. :param desc: Refer to the description for the `desc` query parameter. :param end: Refer to the description for the `end` query parameter. :param from_: Skips the specified number of snapshots. :param page: :param size: Specifies the maximum number of snapshots to obtain. :param sort: Refer to the description for the `sort` query parameter. :param start: Refer to the description for the `start` query parameter. r'r(rVz/model_snapshotsrONrrtr r r|r rr rrrvrrr)rr*r)rr%rTrrtr r r}r rr rrrvrr,rrrrrget_model_snapshotssN( zMlClient.get_model_snapshots) r" bucket_spanrtr rr r overall_scorer rvtop_nrrrc Cs |tvrtddt|d} i}i}|dur||d<|dur$||d<|dur,||d<|dur4||d<|dur<||d <|durD||d <|durL||d <| durT| |d <| dur\| |d <| durd| |d<| durl| |d<|spd}ddi}|dur|d|d<|jd| |||dS)a Retrieves overall bucket results that summarize the bucket results of multiple anomaly detection jobs. ``_ :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. r'r(z/results/overall_bucketsNr"rrtr rr r rr rvrrrr)rr*r)rr%r"rrtr rr r rr rvrrr,rrrrrget_overall_bucketsCsF( zMlClient.get_overall_buckets) rrtr rr r}r rr record_scorerrrvrcCs,|tvrtddt|d}i}i}|dur||d<|dur$||d<|dur,||d<|dur4||d<|dur<||d <|durD||d <|durL||d <| durT| |d <| dur\| |d <| durd| |d<| durl| |d<| durt| |d<|dur|||d<|sd}ddi}|durd|d<|jd||||dS)a Retrieves anomaly records for an anomaly detection job. ``_ :param job_id: Identifier for the anomaly detection job. :param desc: Refer to the description for the `desc` 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 from_: Skips the specified number of records. :param page: :param record_score: Refer to the description for the `record_score` query parameter. :param size: Specifies the maximum number of records to obtain. :param sort: Refer to the description for the `sort` query parameter. :param start: Refer to the description for the `start` query parameter. r'r(z/results/recordsNrrtr rr r|r rr rrrrvrrr)rr*r)rr%rrtr rr r}r rr rrrrvrr,rrrrr get_recordssN' zMlClient.get_records) r r"decompress_definitionr rr r}r includer rtagsrrzzt.Literal['definition', 'definition_status', 'feature_importance_baseline', 'hyperparameters', 'total_feature_importance']rc Cs|tvr dt|} nd} i}|dur||d<|dur ||d<|dur(||d<|dur0||d<|dur8||d<|dur@||d <|durH||d <| durP| |d <| durX| |d <| dur`| |d <| durh| |d<ddi}|jd| ||dS)a9 Retrieves configuration information for a trained inference model. ``_ :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 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 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. rz/_ml/trained_modelsNr"rr rr r|r rr rrrrrrrF)rr r"rr rr r}r rr rrrrrrrrget_trained_modelss<1zMlClient.get_trained_models)r r"r r r}r r rc Cs|tvr dt|d} nd} i} |dur|| d<|dur!|| d<|dur)|| d<|dur1|| d<|dur9|| d <|durA|| d <|durI|| d <d d i} |jd| | | dS)a Retrieves usage information for trained inference models. ``_ :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. rrz/_ml/trained_models/_statsNr"r r r|r r rrrrrrF) rr r"r r r}r r rrrrrrrget_trained_models_stats/s,z!MlClient.get_trained_models_stats)r r r inference_configr r$docsrc Cs|tvrtd|durtddt|d} i} i} |dur$|| d<|dur,|| d<|dur4|| d<|dur<|| d <|durD|| d <|durL|| d <|durT|| d <d d d} |jd| | | | dS)a Evaluate a trained model. ``_ :param model_id: The unique identifier of the trained model. :param docs: An array of objects to pass to the model for inference. The objects should contain a fields matching your configured trained model input. Typically, for NLP models, the field name is `text_field`. Currently, for NLP models, only a single value is allowed. :param inference_config: The inference configuration updates to apply on the API call :param timeout: Controls the amount of time to wait for inference results. rNz'Empty value passed for parameter 'docs'rz/_inferrr r r rr r$rrarr*r) rr rr r r rr r$rr,rrrrrinfer_trained_modeles2  zMlClient.infer_trained_modelcCsbd}i}|dur ||d<|dur||d<|dur||d<|dur$||d<ddi}|jd |||d S) z Returns defaults and limits used by machine learning. ``_ z /_ml/infoNr r r r rrrrrb)rr r r r rrrrrrinfosz MlClient.info)r r r r r$c Cs|tvrtddt|d}i}i} |dur||d<|dur$||d<|dur,||d<|dur4||d<|dur<|| d <| s@d} d d i} | durLd | d <|jd ||| | dS)a- Opens one or more anomaly detection jobs. ``_ :param job_id: Identifier for the anomaly detection job. :param timeout: Refer to the description for the `timeout` query parameter. r'r(z/_openNr r r r r$rrr)rr*r) rr%r r r r r$rrr,rrrropen_jobs. zMlClient.open_jobeventsc Cs|tvrtd|durtddt|d}i}i} |dur$||d<|dur,|| d<|dur4|| d<|dur<|| d <|durD|| d <d d d } |jd || | |dS)u Posts scheduled events in a 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. r0Nz)Empty value passed for parameter 'events'r1rrr r r r rrarr*r) rr.rr r r r rr,rrrrrpost_calendar_eventss*  zMlClient.post_calendar_eventsdata)Z body_name)r r r r reset_end reset_startrrc Cs|tvrtd|durtddt|d} i} |dur"|| d<|dur*|| d<|dur2|| d<|dur:|| d <|durB|| d <|durJ|| d <|} d d d} |jd| | | | dS)a Sends data to an anomaly detection job for analysis. ``_ :param job_id: Identifier for the anomaly detection job. The job must have a state of open to receive and process the data. :param data: :param reset_end: Specifies the end of the bucket resetting range. :param reset_start: Specifies the start of the bucket resetting range. r'Nz'Empty value passed for parameter 'data'r(z/_datar r r r rrrzapplication/x-ndjsonrarr*r) rr%rr r r r rrrrr,rrrr post_data s2 zMlClient.post_data)r<configr r r r rc Cs|tvr dt|d}nd}i}i} |dur||d<|dur#|| d<|dur+|| d<|dur3|| d<|dur;|| d <|s?d}d d i} |durKd | d <|jd || | |dS)u Previews that will be analyzed given 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. r> /_previewz"/_ml/data_frame/analytics/_previewNrr r r r rrr)rr*rF) rr<rr r r r rr,rrrrrpreview_data_frame_analyticsH s. z%MlClient.preview_data_frame_analytics) r@datafeed_configrtr r r job_configr rvrrc Cs|tvr dt|d} nd} i} i} |dur|| d<|dur#|| d<|dur+|| d<|dur3|| d<|dur;|| d <|durC|| d <|durK|| d <| durS| | d <| sWd} d di} | durcd| d<|jd| | | | dS)a Previews 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. 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 rCrz/_ml/datafeeds/_previewNrrtr r r rr rvrrr)rr*rF)rr@rrtr r r rr rvrr,rrrrrpreview_datafeedx s:$ zMlClient.preview_datafeed)rlr r r job_idsr rc Cs|tvrtddt|}i} i} |dur|| d<|dur#|| d<|dur+|| d<|dur3|| d<|dur;|| d<|durC|| d <| sGd} d d i} | durSd | d <|jd || | | dS)aW Instantiates 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. r0r1Nrlr r r rr rrr)PUTr*r) rr.rlr r r rr rr,rrrrr put_calendar s2 zMlClient.put_calendarc Cr6)a Adds an anomaly detection job to 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. r0r'r1r9Nr r r r rrrrrr:rrrput_calendar_job r8zMlClient.put_calendar_jobr)r!Zignore_deprecated_options) rirkrlr r rr rnror versionrcCsT|tvrtd|durtd|durtd|dur tddt|}i}i}|dur3||d<|dur;||d<|durC||d <|durK||d <|durS||d <|dur[||d <|durc||d <| durk| |d<| durs| |d<| dur{| |d<| dur| |d<| dur| |d<|dur||d<|dur||d<ddd}|jd||||dS)u Instantiates 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 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 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: r=Nz+Empty value passed for parameter 'analysis'z'Empty value passed for parameter 'dest'z)Empty value passed for parameter 'source'r>rjrmrprirkrlr r rr rnror rrrarr*r)rr<rjrmrprirkrlr r rr rnror rrr,rrrrrput_data_frame_analytics sVS  z!MlClient.put_data_frame_analytics) aggregationsallow_no_indiceschunking_configdelayed_data_check_configr expand_wildcardsr frequencyrr ignore_throttledignore_unavailableindexesindicesindices_optionsr%max_empty_searchesr rd query_delayruntime_mappings script_fields scroll_sizerrrrrz4t.Literal['all', 'closed', 'hidden', 'none', 'open']rrrrrrrrrrrcCs|tvrtddt|}i}i}|dur||d<|dur#||d<|dur+||d<|dur3||d<|dur;||d<|durC||d <|durK||d <| durS| |d <| dur[| |d <| durc| |d <| durk| |d<| durs| |d<|dur{||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<ddd}|jd||||dS)a Instantiates 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 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 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. rBrCNrrrrr rr rrr rrrrrr%rr rdrrrrrrarr*r)rr@rrrrr rr rrr rrrrrr%rr rdrrrrrr,rrrrr put_datafeed snq  zMlClient.put_datafeed)rlr r r itemsr rc Cs|tvrtddt|}i} i} |dur|| d<|dur#|| d<|dur+|| d<|dur3|| d<|dur;|| d<|durC|| d <d d d } |jd || | | d S)a Instantiates a filter. ``_ :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. rIrJNrlr r r rr rrarr*r) rrHrlr r r rr rr,rrrrr put_filter; s*  zMlClient.put_filter)allow_lazy_openanalysis_limitsbackground_persist_intervalcustom_settings)daily_model_snapshot_retention_after_daysrrlr r groupsr model_plot_configmodel_snapshot_retention_daysr renormalization_window_daysresults_index_nameresults_retention_daysdata_descriptionrrrrrrrrrrrcCs|tvrtd|durtd|durtddt|}i}i}|dur+||d<|dur3||d<|dur;||d<|durC||d <|durK||d <|durS||d <|dur[||d <| durc| |d <| durk| |d<| durs| |d<| dur{| |d<| dur| |d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<ddd}|jd||||dS)a Instantiates an anomaly detection job. ``_ :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 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 groups: A list of job groups. A job can belong to no groups or many. :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. r'Nz2Empty value passed for parameter 'analysis_config'z3Empty value passed for parameter 'data_description'r(r^rrrrrrrrlr r rr rrr rrrrrarr*r)rr%r^rrrrrrrrlr r rr rrr rrrrr,rrrrrput_jobi slk  zMlClient.put_job)compressed_definitiondefer_definition_decompression definitionrlr r r rinputmetadatamodel_size_bytes model_typeplatform_architecturer rrrrrrrrz3t.Literal['lang_ident', 'pytorch', 'tree_ensemble']rcCs4|tvrtddt|}i}i}|dur||d<|dur#||d<|dur+||d<|dur3||d<|dur;||d<|durC||d <|durK||d <| durS| |d <| dur[| |d <| durc| |d <| durk| |d<| durs| |d<|dur{||d<|dur||d<|dur||d<ddd}|jd||||dS)a- Creates an inference trained model. ``_ :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 tags: An array of tags to organize the model. rrNrrrrlr r r rrrrrrr rrrarr*r)rr rrrrlr r r rrrrrrr rrr,rrrrrput_trained_model sN<  zMlClient.put_trained_model)r r r r reassignrc Cs|tvrtd|tvrtddt|dt|}i} |dur&|| d<|dur.|| d<|dur6|| d<|dur>|| d <|durF|| d <d d i} |jd || | dS)a Creates a new model alias (or reassigns an existing one) to refer to the trained model ``_ :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. rr[rr\Nr r r r rrrrrr) rr rZr r r r rrrrrrrput_trained_model_aliasn s(z MlClient.put_trained_model_aliasparttotal_definition_length total_partsc Cs|tvrtd|tvrtd|durtd|dur td|dur(tddt|dt|} i} i} |dur@|| d <|durH|| d <|durP|| d <|durX|| d <|dur`|| d <|durh|| d<| durp| | d<ddd} |jd| | | | dS)aN Creates 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. rz'Empty value passed for parameter 'part'Nz-Empty value passed for parameter 'definition'z:Empty value passed for parameter 'total_definition_length'z.Empty value passed for parameter 'total_parts'rz /definition/rrrr r r r rrarr*r)rr rrrrr r r r rr,rrrrr!put_trained_model_definition_part sB   z*MlClient.put_trained_model_definition_part)r r r mergesr scores vocabularyrrc Cs|tvrtd|durtddt|d} i} i} |dur$|| d<|dur,|| d<|dur4|| d<|dur<|| d <|durD|| d <|durL|| d <|durT|| d <d d d} |jd| | | | dS)a Creates a trained model vocabulary ``_ :param model_id: The unique identifier of the trained model. :param vocabulary: The model vocabulary, which must not be empty. :param merges: The optional model merges if required by the tokenizer. :param scores: The optional vocabulary value scores if required by the tokenizer. rNz-Empty value passed for parameter 'vocabulary'rz /vocabularyrr r r rr rrrarr*r) rr rr r r rr rrr,rrrrrput_trained_model_vocabulary s2  z%MlClient.put_trained_model_vocabulary)rQr r r r rRc C|tvrtddt|d}i} |dur|| d<|dur"|| d<|dur*|| d<|dur2|| d<|dur:|| d <|durB|| d <d d i} |jd || | dS)a Resets an existing anomaly detection job. ``_ :param job_id: The ID of the job to reset. :param delete_user_annotations: Specifies whether annotations that have been added by the user should be deleted along with any auto-generated annotations when the job is reset. :param wait_for_completion: Should this request wait until the operation has completed before returning. r'r(z/_resetNrQr r r r rRrrrrr) rr%rQr r r r rRrrrrrr reset_job s(zMlClient.reset_job)delete_intervening_resultsr r r r rc Cs|tvrtd|tvrtddt|dt|d}i} i} |dur)|| d<|dur1|| d<|dur9|| d <|durA|| d <|durI|| d <| sMd} d d i} | durYd | d<|jd|| | | dS)a; Reverts to a specific snapshot. ``_ :param job_id: Identifier for the anomaly detection job. :param snapshot_id: You can specify `empty` as the . Reverting to the empty snapshot means the anomaly detection job starts learning a new model from scratch when it is started. :param delete_intervening_results: Refer to the description for the `delete_intervening_results` query parameter. r'rUr(rVz/_revertNrr r r r rrr)rr*r) rr%rTrr r r r rr,rrrrrrevert_model_snapshot= s2 zMlClient.revert_model_snapshot)enabledr r r r r$rc Csd}i}|dur ||d<|dur||d<|dur||d<|dur$||d<|dur,||d<|dur4||d<d d i} |jd ||| d S) a Sets a cluster wide upgrade_mode setting that prepares machine learning indices for an upgrade. ``_ :param enabled: When `true`, it enables `upgrade_mode` which temporarily halts all job and datafeed tasks and prohibits new job and datafeed tasks from starting. :param timeout: The time to wait for the request to be completed. z/_ml/set_upgrade_modeNrr r r r r$rrrrrb) rrr r r r r$rrrrrrset_upgrade_modeq s$zMlClient.set_upgrade_modec Cs|tvrtddt|d}i}|dur||d<|dur"||d<|dur*||d<|dur2||d<|dur:||d <d d i} |jd ||| d S)a Starts 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 timeout: Controls the amount of time to wait until the data frame analytics job starts. r=r>/_startNr r r r r$rrrrr) rr<r r r r r$rrrrrrstart_data_frame_analytics s$z#MlClient.start_data_frame_analytics)rtr r r r rvr$c Cs|tvrtddt|d} i} i} |dur|| d<|dur$|| d<|dur,|| d<|dur4|| d<|dur<|| d <|durD|| d <|durL|| d <| sPd} d d i} | dur\d | d<|jd| | | | dS)a Starts one or more datafeeds. ``_ :param datafeed_id: A numerical character string that uniquely identifies the datafeed. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters. :param end: Refer to the description for the `end` query parameter. :param start: Refer to the description for the `start` query parameter. :param timeout: Refer to the description for the `timeout` query parameter. rBrCrNrtr r r r rvr$rrr)rr*r) rr@rtr r r r rvr$rr,rrrrrstart_datafeed s6 zMlClient.start_datafeed) cache_size deployment_idr r r number_of_allocationsr priorityqueue_capacitythreads_per_allocationr$wait_forrrrrzt.Literal['low', 'normal']rrrz3t.Literal['fully_allocated', 'started', 'starting']c Cs|tvrtddt|d}i}|dur||d<|dur"||d<|dur*||d<|dur2||d<|dur:||d <|durB||d <|durJ||d <| durR| |d <| durZ| |d <| durb| |d<| durj| |d<| durr| |d<ddi}|jd|||dS)a@ Start a trained model deployment. ``_ :param model_id: The unique identifier of the trained model. Currently, only PyTorch models are supported. :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. :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. rrz/deployment/_startNrrr r r rr rrrr$rrrrrr)rr rrr r r rr rrrr$rrrrrrrstart_trained_model_deployment s@3z'MlClient.start_trained_model_deploymentc Cs|tvrtddt|d} i} |dur|| d<|dur"|| d<|dur*|| d<|dur2|| d<|dur:|| d <|durB|| d <|durJ|| d <d d i} |jd| | | dS)a Stops one or more data frame analytics jobs. ``_ :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. r=r>/_stopNr"r r r#r r r$rrrrr) rr<r"r r r#r r r$rrrrrrstop_data_frame_analyticsOs,!z"MlClient.stop_data_frame_analyticsc Cr&)a Stops one or more datafeeds. ``_ :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. rBrCr Nr"r r r#r r r$rrr)rr*r) rr@r"r r r#r r r$rr,rrrrr stop_datafeeds6 zMlClient.stop_datafeed)r"r r r#r r c Cr)a 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. rrz/deployment/_stopNr"r r r#r r rrrrr) rr r"r r r#r r rrrrrrstop_trained_model_deployments(z&MlClient.stop_trained_model_deployment)rirlr r r rnror c Cs|tvrtddt|d} i} i} |dur|| d<|dur$|| d<|dur,|| d<|dur4|| d<|dur<|| d <|durD|| d <|durL|| d <| durT| | d <d d d} |jd| | | | dS)aw Updates certain properties of a data frame analytics job. ``_ :param id: Identifier for the data frame analytics job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters. :param allow_lazy_start: Specifies whether this job can start when there is insufficient machine learning node capacity for it to be immediately assigned to a node. :param description: A description of the job. :param max_num_threads: The maximum number of threads to be used by the analysis. Using more threads may decrease the time necessary to complete the analysis at the cost of using more CPU. Note that the process may use additional threads for operational functionality other than the analysis itself. :param model_memory_limit: The approximate maximum amount of memory resources that are permitted for analytical processing. If your `elasticsearch.yml` file contains an `xpack.ml.max_model_memory_limit` setting, an error occurs when you try to create data frame analytics jobs that have `model_memory_limit` values greater than that setting. r=r>/_updateNrirlr r r rnror rrarr*r)rr<rirlr r r rnror rr,rrrrrupdate_data_frame_analyticss2%  z$MlClient.update_data_frame_analytics)rrrrr rr rr rrrrrr%rr rdrrrrcCs|tvrtddt|d}i}i}|dur||d<|dur$||d<|dur,||d<|dur4||d<|dur<||d <|durD||d <|durL||d <| durT| |d <| dur\| |d <| durd| |d<| durl| |d<| durt| |d<|dur|||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<ddd}|jd||||dS)a{ Updates certain properties of 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 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`. rBrCr Nrrrrr rr rr rrrrrr%rr rdrrrrrrarr*r)rr@rrrrr rr rr rrrrrr%rr rdrrrrrr,rrrrrupdate_datafeed0sjz  zMlClient.update_datafeed) add_itemsrlr r r r remove_itemsrrc Cs|tvrtddt|d} i} i} |dur|| d<|dur$|| d<|dur,|| d<|dur4|| d<|dur<|| d <|durD|| d <|durL|| d <d d d } |jd| | | | dS)a Updates the description of a filter, adds items, or removes items. ``_ :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. rIrJr Nrrlr r r r rrrarr*r) rrHrrlr r r r rrr,rrrrr update_filters.  zMlClient.update_filter)rrrcategorization_filtersrrrl detectorsr r rr rmodel_prune_windowrper_partition_categorizationr rrrrrrcCsv|tvrtddt|d}i}i}|dur||d<|dur$||d<|dur,||d<|dur4||d<|dur<||d <|durD||d <|durL||d <| durT| |d <| dur\| |d <| durd| |d<| durl| |d<| durt| |d<|dur|||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<|dur||d<ddd}|jd||||dS)a7 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. r'r(r Nrrrrrrrlrr r rr rrrrr rrrrarr*r)rr%rrrrrrrlrr r rr rrrrr rrrr,rrrrr update_jobsdU  zMlClient.update_job)rlr r r r retainrc Cs|tvrtd|tvrtddt|dt|d} i} i} |dur)|| d<|dur1|| d<|dur9|| d <|durA|| d <|durI|| d <|durQ|| d <d d d} |jd| | | | dS)aN Updates certain properties of a snapshot. ``_ :param job_id: Identifier for the anomaly detection job. :param snapshot_id: Identifier for the model snapshot. :param description: A description of the model snapshot. :param retain: If `true`, this snapshot will not be deleted during automatic cleanup of snapshots older than `model_snapshot_retention_days`. However, this snapshot will be deleted when the job is deleted. r'rUr(rVr Nrlr r r r rrrarr*r) rr%rTrlr r r r rrr,rrrrrupdate_model_snapshots.  zMlClient.update_model_snapshot)r r r r r$rRc Cs|tvrtd|tvrtddt|dt|d} i} |dur'|| d<|dur/|| d<|dur7|| d <|dur?|| d <|durG|| d <|durO|| d <d di} |jd| | | dS)u Upgrades a given job snapshot to the current major version. ``_ :param job_id: Identifier for the anomaly detection job. :param snapshot_id: A numerical character string that uniquely identifies the model snapshot. :param timeout: Controls the time to wait for the request to complete. :param wait_for_completion: When true, the API won’t respond until the upgrade is complete. Otherwise, it responds as soon as the upgrade task is assigned to a node. r'rUr(rVz /_upgradeNr r r r r$rRrrrrr) rr%rTr r r r r$rRrrrrrrupgrade_job_snapshots,zMlClient.upgrade_job_snapshot) r^rrrlr r r r% model_plotmodel_snapshot_idrr rrrc Csd}i}i}|dur||d<|dur||d<|dur||d<|dur&||d<|dur.||d<|dur6||d<|dur>||d <|durF||d <| durN| |d <| durV| |d <| dur^| |d <| durf| |d<| durn| |d<ddd}|jd||||dS)a Validates an anomaly detection job. ``_ :param analysis_config: :param analysis_limits: :param data_description: :param description: :param job_id: :param model_plot: :param model_snapshot_id: :param model_snapshot_retention_days: :param results_index_name: z /_ml/anomaly_detectors/_validateNr^rrrlr r r r%rrrr rrrarr*rb)rr^rrrlr r r r%rrrr rrr,rrrrrvalidatesB#  zMlClient.validatedetectorc Csz|durtdd}i}|dur||d<|dur||d<|dur$||d<|dur,||d<|}ddd } |jd ||| |d S) z Validates an anomaly detection detector. ``_ :param detector: Nz+Empty value passed for parameter 'detector'z)/_ml/anomaly_detectors/_validate/detectorr r r r rrarr*rg) rrr r r r rrr,rrrrvalidate_detectorAs"  zMlClient.validate_detector)W__name__ __module__ __qualname__rstrtOptionalboolUnionSequencerAnyr r-r3r7r;r?rDfloatrGrKrPrSrWrYr]Mappingintrcrhrqrwr{rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr r r rrrrrrrrrrrrrs !   8 ! & &  * )  2 !   7   1 % ' %  5  /      Y     :   4      X     9   8     A    =    ; .  4  *      P /  4   0  3      M      I      K    N   3   4  * *    2 -    @  . &    "   {   " !" &  +         a  ,     ?   0  ,  1  ( (   5     S   7   6  0    <    !.   .        0   0     Br) typingr$Zelastic_transportr_baserutilsrrrrrrrrs