[DOCS] Removes unshared sections from ml-shared.asciidoc (#55192)
This commit is contained in:
Lisa Cawley
GitHub
parent
f49354b7d7
commit
2910d01179
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@ -71,11 +71,50 @@ The API returns a response that contains the following:
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`field_selection`::
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(array)
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include::{docdir}/ml/ml-shared.asciidoc[tag=field-selection]
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An array of objects that explain selection for each field, sorted by
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the field names.
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+
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.Properties of `field_selection` objects
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[%collapsible%open]
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====
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`is_included`:::
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(boolean) Whether the field is selected to be included in the analysis.
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`is_required`:::
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(boolean) Whether the field is required.
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`feature_type`:::
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(string) The feature type of this field for the analysis. May be `categorical`
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or `numerical`.
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`mapping_types`:::
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(string) The mapping types of the field.
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`name`:::
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(string) The field name.
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`reason`:::
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(string) The reason a field is not selected to be included in the analysis.
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====
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`memory_estimation`::
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(object)
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include::{docdir}/ml/ml-shared.asciidoc[tag=memory-estimation]
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(object)
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An object containing the memory estimates.
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+
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.Properties of `memory_estimation`
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[%collapsible%open]
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====
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`expected_memory_with_disk`:::
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(string) Estimated memory usage under the assumption that overflowing to disk is
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allowed during {dfanalytics}. `expected_memory_with_disk` is usually smaller
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than `expected_memory_without_disk` as using disk allows to limit the main
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memory needed to perform {dfanalytics}.
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`expected_memory_without_disk`:::
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(string) Estimated memory usage under the assumption that the whole
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{dfanalytics} should happen in memory (i.e. without overflowing to disk).
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====
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[[ml-explain-dfanalytics-example]]
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@ -75,8 +75,93 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=size]
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==== {api-response-body-title}
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`data_frame_analytics`::
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(array)
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include::{docdir}/ml/ml-shared.asciidoc[tag=data-frame-analytics]
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(array)
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An array of {dfanalytics-job} resources, which are sorted by the `id` value in
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ascending order.
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+
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.Properties of {dfanalytics-job} resources
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[%collapsible%open]
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====
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`analysis`:::
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(object) The type of analysis that is performed on the `source`.
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//Begin analyzed_fields
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`analyzed_fields`:::
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(object) Contains `includes` and/or `excludes` patterns that select which fields
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are included in the analysis.
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+
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.Properties of `analyzed_fields`
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[%collapsible%open]
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=====
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`excludes`:::
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(Optional, array) An array of strings that defines the fields that are excluded
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from the analysis.
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`includes`:::
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(Optional, array) An array of strings that defines the fields that are included
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in the analysis.
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=====
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//End analyzed_fields
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//Begin dest
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`dest`:::
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(string) The destination configuration of the analysis.
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+
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.Properties of `dest`
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[%collapsible%open]
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=====
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`index`:::
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(string) The _destination index_ that stores the results of the
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{dfanalytics-job}.
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`results_field`:::
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(string) The name of the field that stores the results of the analysis. Defaults
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to `ml`.
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=====
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//End dest
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`id`:::
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(string) The unique identifier of the {dfanalytics-job}.
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`model_memory_limit`:::
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(string) The `model_memory_limit` that has been set to the {dfanalytics-job}.
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`source`:::
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(object) The configuration of how the analysis data is sourced. It has an
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`index` parameter and optionally a `query` and a `_source`.
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+
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.Properties of `source`
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[%collapsible%open]
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=====
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`index`:::
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(array) Index or indices on which to perform the analysis. It can be a single
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index or index pattern as well as an array of indices or patterns.
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`query`:::
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(object) The query that has been specified for the {dfanalytics-job}. The {es}
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query domain-specific language (<<query-dsl,DSL>>). This value corresponds to
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the query object in an {es} search POST body. By default, this property has the
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following value: `{"match_all": {}}`.
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`_source`:::
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(object) Contains the specified `includes` and/or `excludes` patterns that
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select which fields are present in the destination. Fields that are excluded
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cannot be included in the analysis.
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+
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.Properties of `_source`
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[%collapsible%open]
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======
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`excludes`:::
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(array) An array of strings that defines the fields that are excluded from the
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destination.
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`includes`:::
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(array) An array of strings that defines the fields that are included in the
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destination.
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======
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//End of _source
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=====
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//End source
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====
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[[ml-get-dfanalytics-response-codes]]
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@ -60,7 +60,8 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=allow-no-match]
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`decompress_definition`::
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(Optional, boolean)
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include::{docdir}/ml/ml-shared.asciidoc[tag=decompress-definition]
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Specifies whether the included model definition should be returned as a JSON map
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(`true`) or in a custom compressed format (`false`). Defaults to `true`.
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`from`::
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(Optional, integer)
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@ -68,7 +69,9 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=from]
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`include_model_definition`::
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(Optional, boolean)
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include::{docdir}/ml/ml-shared.asciidoc[tag=include-model-definition]
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Specifies if the model definition should be returned in the response. Defaults
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to `false`. When `true`, only a single model must match the ID patterns
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provided, otherwise a bad request is returned.
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`size`::
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(Optional, integer)
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@ -83,8 +86,60 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=tags]
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==== {api-response-body-title}
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`trained_model_configs`::
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(array)
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include::{docdir}/ml/ml-shared.asciidoc[tag=trained-model-configs]
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(array)
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An array of trained model resources, which are sorted by the `model_id` value in
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ascending order.
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+
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.Properties of trained model resources
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[%collapsible%open]
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====
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`created_by`:::
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(string)
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Information on the creator of the trained model.
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`create_time`:::
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(<<time-units,time units>>)
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The time when the trained model was created.
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`default_field_map` :::
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(object)
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A string to string object that contains the default field map to use
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when inferring against the model. For example, data frame analytics
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may train the model on a specific multi-field `foo.keyword`.
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The analytics job would then supply a default field map entry for
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`"foo" : "foo.keyword"`.
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+
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Any field map described in the inference configuration takes precedence.
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`estimated_heap_memory_usage_bytes`:::
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(integer)
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The estimated heap usage in bytes to keep the trained model in memory.
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`estimated_operations`:::
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(integer)
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The estimated number of operations to use the trained model.
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`license_level`:::
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(string)
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The license level of the trained model.
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`metadata`:::
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(object)
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An object containing metadata about the trained model. For example, models
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created by {dfanalytics} contain `analysis_config` and `input` objects.
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`model_id`:::
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(string)
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Idetifier for the trained model.
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`tags`:::
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(string)
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A comma delimited string of tags. A {infer} model can have many tags, or none.
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`version`:::
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(string)
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The {es} version number in which the trained model was created.
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====
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[[ml-get-inference-response-codes]]
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==== {api-response-codes-title}
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@ -182,28 +182,42 @@ The configuration information necessary to perform
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[%collapsible%open]
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=====
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`compute_feature_influence`::::
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(Optional, boolean)
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include::{docdir}/ml/ml-shared.asciidoc[tag=compute-feature-influence]
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(Optional, boolean)
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If `true`, the feature influence calculation is enabled. Defaults to `true`.
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`feature_influence_threshold`::::
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(Optional, double)
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include::{docdir}/ml/ml-shared.asciidoc[tag=feature-influence-threshold]
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(Optional, double)
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The minimum {olscore} that a document needs to have in order to calculate its
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{fiscore}. Value range: 0-1 (`0.1` by default).
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`method`::::
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(Optional, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=method]
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Sets the method that {oldetection} uses. If the method is not set {oldetection}
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uses an ensemble of different methods and normalises and combines their
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individual {olscores} to obtain the overall {olscore}. We recommend to use the
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ensemble method. Available methods are `lof`, `ldof`, `distance_kth_nn`,
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`distance_knn`.
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`n_neighbors`::::
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(Optional, integer)
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include::{docdir}/ml/ml-shared.asciidoc[tag=n-neighbors]
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Defines the value for how many nearest neighbors each method of
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{oldetection} will use to calculate its {olscore}. When the value is not set,
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different values will be used for different ensemble members. This helps
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improve diversity in the ensemble. Therefore, only override this if you are
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confident that the value you choose is appropriate for the data set.
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`outlier_fraction`::::
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(Optional, double)
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include::{docdir}/ml/ml-shared.asciidoc[tag=outlier-fraction]
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(Optional, double)
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Sets the proportion of the data set that is assumed to be outlying prior to
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{oldetection}. For example, 0.05 means it is assumed that 5% of values are real
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outliers and 95% are inliers.
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`standardization_enabled`::::
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(Optional, boolean)
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include::{docdir}/ml/ml-shared.asciidoc[tag=standardization-enabled]
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(Optional, boolean)
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If `true`, then the following operation is performed on the columns before
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computing outlier scores: (x_i - mean(x_i)) / sd(x_i). Defaults to `true`. For
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more information, see
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https://en.wikipedia.org/wiki/Feature_scaling#Standardization_(Z-score_Normalization)[this wiki page about standardization].
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//End outlier_detection
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=====
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//Begin regression
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@ -334,11 +348,54 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=dest]
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`model_memory_limit`::
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(Optional, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=model-memory-limit-dfa]
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The approximate maximum amount of memory resources that are permitted for
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analytical processing. The default value for {dfanalytics-jobs} is `1gb`. If
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your `elasticsearch.yml` file contains an `xpack.ml.max_model_memory_limit`
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setting, an error occurs when you try to create {dfanalytics-jobs} that have
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`model_memory_limit` values greater than that setting. For more information, see
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<<ml-settings>>.
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`source`::
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(object)
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include::{docdir}/ml/ml-shared.asciidoc[tag=source-put-dfa]
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The configuration of how to source the analysis data. It requires an `index`.
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Optionally, `query` and `_source` may be specified.
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+
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.Properties of `source`
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[%collapsible%open]
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====
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`index`:::
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(Required, string or array) Index or indices on which to perform the analysis.
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It can be a single index or index pattern as well as an array of indices or
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patterns.
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+
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WARNING: If your source indices contain documents with the same IDs, only the
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document that is indexed last appears in the destination index.
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`query`:::
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(Optional, object) The {es} query domain-specific language (<<query-dsl,DSL>>).
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This value corresponds to the query object in an {es} search POST body. All the
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options that are supported by {es} can be used, as this object is passed
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verbatim to {es}. By default, this property has the following value:
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`{"match_all": {}}`.
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`_source`:::
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(Optional, object) Specify `includes` and/or `excludes` patterns to select which
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fields will be present in the destination. Fields that are excluded cannot be
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included in the analysis.
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+
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.Properties of `_source`
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[%collapsible%open]
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=====
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`includes`::::
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(array) An array of strings that defines the fields that will be included in the
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destination.
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`excludes`::::
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(array) An array of strings that defines the fields that will be excluded from
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the destination.
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=====
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====
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[[ml-put-dfanalytics-example]]
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@ -278,10 +278,6 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=time-span]
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====
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end::chunking-config[]
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tag::compute-feature-influence[]
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If `true`, the feature influence calculation is enabled. Defaults to `true`.
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end::compute-feature-influence[]
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tag::custom-rules[]
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An array of custom rule objects, which enable you to customize the way detectors
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operate. For example, a rule may dictate to the detector conditions under which
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@ -375,95 +371,6 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=time-format]
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====
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end::data-description[]
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tag::data-frame-analytics[]
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An array of {dfanalytics-job} resources, which are sorted by the `id` value in
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ascending order.
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+
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.Properties of {dfanalytics-job} resources
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[%collapsible%open]
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====
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`analysis`:::
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(object) The type of analysis that is performed on the `source`.
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//Begin analyzed_fields
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`analyzed_fields`:::
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(object) Contains `includes` and/or `excludes` patterns that select which fields
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are included in the analysis.
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+
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.Properties of `analyzed_fields`
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[%collapsible%open]
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=====
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`excludes`:::
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(Optional, array) An array of strings that defines the fields that are excluded
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from the analysis.
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`includes`:::
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(Optional, array) An array of strings that defines the fields that are included
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in the analysis.
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=====
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//End analyzed_fields
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//Begin dest
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`dest`:::
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(string) The destination configuration of the analysis.
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+
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.Properties of `dest`
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[%collapsible%open]
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=====
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`index`:::
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(string) The _destination index_ that stores the results of the
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{dfanalytics-job}.
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`results_field`:::
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(string) The name of the field that stores the results of the analysis. Defaults
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to `ml`.
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=====
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//End dest
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`id`:::
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(string) The unique identifier of the {dfanalytics-job}.
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`model_memory_limit`:::
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(string) The `model_memory_limit` that has been set to the {dfanalytics-job}.
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`source`:::
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(object) The configuration of how the analysis data is sourced. It has an
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`index` parameter and optionally a `query` and a `_source`.
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+
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.Properties of `source`
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[%collapsible%open]
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=====
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`index`:::
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(array) Index or indices on which to perform the analysis. It can be a single
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index or index pattern as well as an array of indices or patterns.
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|
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`query`:::
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(object) The query that has been specified for the {dfanalytics-job}. The {es}
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query domain-specific language (<<query-dsl,DSL>>). This value corresponds to
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the query object in an {es} search POST body. By default, this property has the
|
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following value: `{"match_all": {}}`.
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|
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`_source`:::
|
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(object) Contains the specified `includes` and/or `excludes` patterns that
|
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select which fields are present in the destination. Fields that are excluded
|
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cannot be included in the analysis.
|
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+
|
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.Properties of `_source`
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[%collapsible%open]
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======
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`excludes`:::
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(array) An array of strings that defines the fields that are excluded from the
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destination.
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|
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`includes`:::
|
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(array) An array of strings that defines the fields that are included in the
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destination.
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======
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//End of _source
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=====
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//End source
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====
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end::data-frame-analytics[]
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tag::data-frame-analytics-stats[]
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An array of statistics objects for {dfanalytics-jobs}, which are
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sorted by the `id` value in ascending order.
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@ -906,11 +813,6 @@ category. (Dead categories are a side effect of the way categorization has no
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prior training.)
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end::dead-category-count[]
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tag::decompress-definition[]
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Specifies whether the included model definition should be returned as a JSON map
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(`true`) or in a custom compressed format (`false`). Defaults to `true`.
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end::decompress-definition[]
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tag::delayed-data-check-config[]
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Specifies whether the {dfeed} checks for missing data and the size of the
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window. For example: `{"enabled": true, "check_window": "1h"}`.
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|
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@ -1029,39 +931,6 @@ Advanced configuration option. Defines the fraction of features that will be
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used when selecting a random bag for each candidate split.
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end::feature-bag-fraction[]
|
||||
|
||||
tag::feature-influence-threshold[]
|
||||
The minimum {olscore} that a document needs to have in order to calculate its
|
||||
{fiscore}. Value range: 0-1 (`0.1` by default).
|
||||
end::feature-influence-threshold[]
|
||||
|
||||
tag::field-selection[]
|
||||
An array of objects that explain selection for each field, sorted by
|
||||
the field names.
|
||||
+
|
||||
.Properties of `field_selection` objects
|
||||
[%collapsible%open]
|
||||
====
|
||||
`is_included`:::
|
||||
(boolean) Whether the field is selected to be included in the analysis.
|
||||
|
||||
`is_required`:::
|
||||
(boolean) Whether the field is required.
|
||||
|
||||
`feature_type`:::
|
||||
(string) The feature type of this field for the analysis. May be `categorical`
|
||||
or `numerical`.
|
||||
|
||||
`mapping_types`:::
|
||||
(string) The mapping types of the field.
|
||||
|
||||
`name`:::
|
||||
(string) The field name.
|
||||
|
||||
`reason`:::
|
||||
(string) The reason a field is not selected to be included in the analysis.
|
||||
====
|
||||
end::field-selection[]
|
||||
|
||||
tag::filter[]
|
||||
One or more <<analysis-tokenfilters,token filters>>. In addition to the built-in
|
||||
token filters, other plugins can provide more token filters. This property is
|
||||
|
|
@ -1114,12 +983,6 @@ tag::groups[]
|
|||
A list of job groups. A job can belong to no groups or many.
|
||||
end::groups[]
|
||||
|
||||
tag::include-model-definition[]
|
||||
Specifies if the model definition should be returned in the response. Defaults
|
||||
to `false`. When `true`, only a single model must match the ID patterns
|
||||
provided, otherwise a bad request is returned.
|
||||
end::include-model-definition[]
|
||||
|
||||
tag::indices[]
|
||||
An array of index names. Wildcards are supported. For example:
|
||||
`["it_ops_metrics", "server*"]`.
|
||||
|
|
@ -1319,32 +1182,6 @@ Advanced configuration option. Defines the maximum number of trees the forest is
|
|||
allowed to contain. The maximum value is 2000.
|
||||
end::max-trees[]
|
||||
|
||||
tag::memory-estimation[]
|
||||
An object containing the memory estimates.
|
||||
+
|
||||
.Properties of `memory_estimation`
|
||||
[%collapsible%open]
|
||||
====
|
||||
`expected_memory_with_disk`:::
|
||||
(string) Estimated memory usage under the assumption that overflowing to disk is
|
||||
allowed during {dfanalytics}. `expected_memory_with_disk` is usually smaller
|
||||
than `expected_memory_without_disk` as using disk allows to limit the main
|
||||
memory needed to perform {dfanalytics}.
|
||||
|
||||
`expected_memory_without_disk`:::
|
||||
(string) Estimated memory usage under the assumption that the whole
|
||||
{dfanalytics} should happen in memory (i.e. without overflowing to disk).
|
||||
====
|
||||
end::memory-estimation[]
|
||||
|
||||
tag::method[]
|
||||
Sets the method that {oldetection} uses. If the method is not set {oldetection}
|
||||
uses an ensemble of different methods and normalises and combines their
|
||||
individual {olscores} to obtain the overall {olscore}. We recommend to use the
|
||||
ensemble method. Available methods are `lof`, `ldof`, `distance_kth_nn`,
|
||||
`distance_knn`.
|
||||
end::method[]
|
||||
|
||||
tag::missing-field-count[]
|
||||
The number of input documents that are missing a field that the {anomaly-job} is
|
||||
configured to analyze. Input documents with missing fields are still processed
|
||||
|
|
@ -1411,15 +1248,6 @@ tag::model-memory-limit-anomaly-jobs[]
|
|||
The upper limit for model memory usage, checked on increasing values.
|
||||
end::model-memory-limit-anomaly-jobs[]
|
||||
|
||||
tag::model-memory-limit-dfa[]
|
||||
The approximate maximum amount of memory resources that are permitted for
|
||||
analytical processing. The default value for {dfanalytics-jobs} is `1gb`. If
|
||||
your `elasticsearch.yml` file contains an `xpack.ml.max_model_memory_limit`
|
||||
setting, an error occurs when you try to create {dfanalytics-jobs} that have
|
||||
`model_memory_limit` values greater than that setting. For more information, see
|
||||
<<ml-settings>>.
|
||||
end::model-memory-limit-dfa[]
|
||||
|
||||
tag::model-memory-status[]
|
||||
The status of the mathematical models, which can have one of the following
|
||||
values:
|
||||
|
|
@ -1496,14 +1324,6 @@ NOTE: To use the `multivariate_by_fields` property, you must also specify
|
|||
--
|
||||
end::multivariate-by-fields[]
|
||||
|
||||
tag::n-neighbors[]
|
||||
Defines the value for how many nearest neighbors each method of
|
||||
{oldetection} will use to calculate its {olscore}. When the value is not set,
|
||||
different values will be used for different ensemble members. This helps
|
||||
improve diversity in the ensemble. Therefore, only override this if you are
|
||||
confident that the value you choose is appropriate for the data set.
|
||||
end::n-neighbors[]
|
||||
|
||||
tag::node-address[]
|
||||
The network address of the node.
|
||||
end::node-address[]
|
||||
|
|
@ -1538,12 +1358,6 @@ order documents are discarded, since jobs require time series data to be in
|
|||
ascending chronological order.
|
||||
end::out-of-order-timestamp-count[]
|
||||
|
||||
tag::outlier-fraction[]
|
||||
Sets the proportion of the data set that is assumed to be outlying prior to
|
||||
{oldetection}. For example, 0.05 means it is assumed that 5% of values are real
|
||||
outliers and 95% are inliers.
|
||||
end::outlier-fraction[]
|
||||
|
||||
tag::over-field-name[]
|
||||
The field used to split the data. In particular, this property is used for
|
||||
analyzing the splits with respect to the history of all splits. It is used for
|
||||
|
|
@ -1666,60 +1480,12 @@ tag::snapshot-id[]
|
|||
A numerical character string that uniquely identifies the model snapshot.
|
||||
end::snapshot-id[]
|
||||
|
||||
tag::source-put-dfa[]
|
||||
The configuration of how to source the analysis data. It requires an `index`.
|
||||
Optionally, `query` and `_source` may be specified.
|
||||
+
|
||||
.Properties of `source`
|
||||
[%collapsible%open]
|
||||
====
|
||||
`index`:::
|
||||
(Required, string or array) Index or indices on which to perform the analysis.
|
||||
It can be a single index or index pattern as well as an array of indices or
|
||||
patterns.
|
||||
+
|
||||
WARNING: If your source indices contain documents with the same IDs, only the
|
||||
document that is indexed last appears in the destination index.
|
||||
|
||||
`query`:::
|
||||
(Optional, object) The {es} query domain-specific language (<<query-dsl,DSL>>).
|
||||
This value corresponds to the query object in an {es} search POST body. All the
|
||||
options that are supported by {es} can be used, as this object is passed
|
||||
verbatim to {es}. By default, this property has the following value:
|
||||
`{"match_all": {}}`.
|
||||
|
||||
`_source`:::
|
||||
(Optional, object) Specify `includes` and/or `excludes` patterns to select which
|
||||
fields will be present in the destination. Fields that are excluded cannot be
|
||||
included in the analysis.
|
||||
+
|
||||
.Properties of `_source`
|
||||
[%collapsible%open]
|
||||
=====
|
||||
`includes`::::
|
||||
(array) An array of strings that defines the fields that will be included in the
|
||||
destination.
|
||||
|
||||
`excludes`::::
|
||||
(array) An array of strings that defines the fields that will be excluded from
|
||||
the destination.
|
||||
=====
|
||||
====
|
||||
end::source-put-dfa[]
|
||||
|
||||
tag::sparse-bucket-count[]
|
||||
The number of buckets that contained few data points compared to the expected
|
||||
number of data points. If your data contains many sparse buckets, consider using
|
||||
a longer `bucket_span`.
|
||||
end::sparse-bucket-count[]
|
||||
|
||||
tag::standardization-enabled[]
|
||||
If `true`, then the following operation is performed on the columns before
|
||||
computing outlier scores: (x_i - mean(x_i)) / sd(x_i). Defaults to `true`. For
|
||||
more information, see
|
||||
https://en.wikipedia.org/wiki/Feature_scaling#Standardization_(Z-score_Normalization)[this wiki page about standardization].
|
||||
end::standardization-enabled[]
|
||||
|
||||
tag::state-anomaly-job[]
|
||||
The status of the {anomaly-job}, which can be one of the following values:
|
||||
+
|
||||
|
|
@ -1833,62 +1599,6 @@ The number of `partition` field values that were analyzed by the models. This
|
|||
value is cumulative for all detectors in the job.
|
||||
end::total-partition-field-count[]
|
||||
|
||||
tag::trained-model-configs[]
|
||||
An array of trained model resources, which are sorted by the `model_id` value in
|
||||
ascending order.
|
||||
+
|
||||
.Properties of trained model resources
|
||||
[%collapsible%open]
|
||||
====
|
||||
`created_by`:::
|
||||
(string)
|
||||
Information on the creator of the trained model.
|
||||
|
||||
`create_time`:::
|
||||
(<<time-units,time units>>)
|
||||
The time when the trained model was created.
|
||||
|
||||
`default_field_map` :::
|
||||
(object)
|
||||
A string to string object that contains the default field map to use
|
||||
when inferring against the model. For example, data frame analytics
|
||||
may train the model on a specific multi-field `foo.keyword`.
|
||||
The analytics job would then supply a default field map entry for
|
||||
`"foo" : "foo.keyword"`.
|
||||
+
|
||||
Any field map described in the inference configuration takes precedence.
|
||||
|
||||
`estimated_heap_memory_usage_bytes`:::
|
||||
(integer)
|
||||
The estimated heap usage in bytes to keep the trained model in memory.
|
||||
|
||||
`estimated_operations`:::
|
||||
(integer)
|
||||
The estimated number of operations to use the trained model.
|
||||
|
||||
`license_level`:::
|
||||
(string)
|
||||
The license level of the trained model.
|
||||
|
||||
`metadata`:::
|
||||
(object)
|
||||
An object containing metadata about the trained model. For example, models
|
||||
created by {dfanalytics} contain `analysis_config` and `input` objects.
|
||||
|
||||
`model_id`:::
|
||||
(string)
|
||||
Idetifier for the trained model.
|
||||
|
||||
`tags`:::
|
||||
(string)
|
||||
A comma delimited string of tags. A {infer} model can have many tags, or none.
|
||||
|
||||
`version`:::
|
||||
(string)
|
||||
The {es} version number in which the trained model was created.
|
||||
====
|
||||
end::trained-model-configs[]
|
||||
|
||||
tag::training-percent[]
|
||||
Defines what percentage of the eligible documents that will
|
||||
be used for training. Documents that are ignored by the analysis (for example
|
||||
|
|
|
|||
Loading…
Reference in New Issue