174 lines
7.4 KiB
Plaintext
174 lines
7.4 KiB
Plaintext
--
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:api: put-data-frame-analytics
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:request: PutDataFrameAnalyticsRequest
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:response: PutDataFrameAnalyticsResponse
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--
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[role="xpack"]
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[id="{upid}-{api}"]
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=== Put {dfanalytics-jobs} API
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Creates a new {dfanalytics-job}.
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The API accepts a +{request}+ object as a request and returns a +{response}+.
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[id="{upid}-{api}-request"]
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==== Put {dfanalytics-jobs} request
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A +{request}+ requires the following argument:
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-request]
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--------------------------------------------------
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<1> The configuration of the {dfanalytics-job} to create
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[id="{upid}-{api}-config"]
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==== {dfanalytics-cap} configuration
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The `DataFrameAnalyticsConfig` object contains all the details about the {dfanalytics-job}
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configuration and contains the following arguments:
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-config]
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--------------------------------------------------
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<1> The {dfanalytics-job} ID
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<2> The source index and query from which to gather data
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<3> The destination index
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<4> The analysis to be performed
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<5> The fields to be included in / excluded from the analysis
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<6> The memory limit for the model created as part of the analysis process
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<7> Optionally, a human-readable description
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<8> The maximum number of threads to be used by the analysis. Defaults to 1.
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[id="{upid}-{api}-query-config"]
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==== SourceConfig
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The index and the query from which to collect data.
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-source-config]
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--------------------------------------------------
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<1> Constructing a new DataFrameAnalyticsSource
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<2> The source index
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<3> The query from which to gather the data. If query is not set, a `match_all` query is used by default.
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<4> Source filtering to select which fields will exist in the destination index.
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===== QueryConfig
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The query with which to select data from the source.
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-query-config]
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--------------------------------------------------
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==== DestinationConfig
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The index to which data should be written by the {dfanalytics-job}.
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-dest-config]
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--------------------------------------------------
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<1> Constructing a new DataFrameAnalyticsDest
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<2> The destination index
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==== Analysis
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The analysis to be performed.
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Currently, the supported analyses include: +OutlierDetection+, +Classification+, +Regression+.
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===== Outlier detection
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+OutlierDetection+ analysis can be created in one of two ways:
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-outlier-detection-default]
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--------------------------------------------------
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<1> Constructing a new OutlierDetection object with default strategy to determine outliers
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or
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-outlier-detection-customized]
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--------------------------------------------------
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<1> Constructing a new OutlierDetection object
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<2> The method used to perform the analysis
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<3> Number of neighbors taken into account during analysis
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<4> The min `outlier_score` required to compute feature influence
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<5> Whether to compute feature influence
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<6> The proportion of the data set that is assumed to be outlying prior to outlier detection
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<7> Whether to apply standardization to feature values
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===== Classification
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+Classification+ analysis requires to set which is the +dependent_variable+ and
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has a number of other optional parameters:
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-classification]
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--------------------------------------------------
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<1> Constructing a new Classification builder object with the required dependent variable
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<2> The lambda regularization parameter. A non-negative double.
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<3> The gamma regularization parameter. A non-negative double.
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<4> The applied shrinkage. A double in [0.001, 1].
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<5> The maximum number of trees the forest is allowed to contain. An integer in [1, 2000].
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<6> The fraction of features which will be used when selecting a random bag for each candidate split. A double in (0, 1].
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<7> If set, feature importance for the top most important features will be computed.
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<8> The name of the prediction field in the results object.
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<9> The percentage of training-eligible rows to be used in training. Defaults to 100%.
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<10> The seed to be used by the random generator that picks which rows are used in training.
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<11> The optimization objective to target when assigning class labels. Defaults to maximize_minimum_recall.
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<12> The number of top classes to be reported in the results. Defaults to 2.
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<13> Custom feature processors that will create new features for analysis from the included document
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fields. Note, automatic categorical {ml-docs}/ml-feature-encoding.html[feature encoding] still occurs for all features.
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===== Regression
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+Regression+ analysis requires to set which is the +dependent_variable+ and
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has a number of other optional parameters:
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-regression]
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--------------------------------------------------
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<1> Constructing a new Regression builder object with the required dependent variable
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<2> The lambda regularization parameter. A non-negative double.
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<3> The gamma regularization parameter. A non-negative double.
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<4> The applied shrinkage. A double in [0.001, 1].
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<5> The maximum number of trees the forest is allowed to contain. An integer in [1, 2000].
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<6> The fraction of features which will be used when selecting a random bag for each candidate split. A double in (0, 1].
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<7> If set, feature importance for the top most important features will be computed.
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<8> The name of the prediction field in the results object.
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<9> The percentage of training-eligible rows to be used in training. Defaults to 100%.
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<10> The seed to be used by the random generator that picks which rows are used in training.
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<11> The loss function used for regression. Defaults to `mse`.
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<12> An optional parameter to the loss function.
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<13> Custom feature processors that will create new features for analysis from the included document
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fields. Note, automatic categorical {ml-docs}/ml-feature-encoding.html[feature encoding] still occurs for all features.
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==== Analyzed fields
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FetchContext object containing fields to be included in / excluded from the analysis
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-analyzed-fields]
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--------------------------------------------------
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include::../execution.asciidoc[]
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[id="{upid}-{api}-response"]
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==== Response
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The returned +{response}+ contains the newly created {dfanalytics-job}.
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-response]
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--------------------------------------------------
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