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This commit is contained in:
Lisa Cawley
GitHub
parent
c9db2de41d
commit
922ec8e961
@ -57,14 +57,13 @@ they are not included in the explanation.
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(Optional, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-data-frame-analytics]
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[[ml-explain-dfanalytics-request-body]]
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==== {api-request-body-title}
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A {dataframe-analytics-config} as described in <<put-dfanalytics>>.
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Note that `id` and `dest` don't need to be provided in the context of this API.
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[role="child_attributes"]
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[[ml-explain-dfanalytics-results]]
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==== {api-response-body-title}
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@ -70,7 +70,7 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=from]
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(Optional, integer)
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include::{docdir}/ml/ml-shared.asciidoc[tag=size]
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[role="child_attributes"]
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[[ml-get-dfanalytics-results]]
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==== {api-response-body-title}
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@ -78,7 +78,7 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=size]
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(Optional, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=tags]
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[role="child_attributes"]
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[[ml-get-inference-results]]
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==== {api-response-body-title}
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@ -80,7 +80,7 @@ using 4-fold cross validation.
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(Required, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-data-frame-analytics-define]
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[role="child_attributes"]
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[[ml-put-dfanalytics-request-body]]
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==== {api-request-body-title}
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@ -88,184 +88,197 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-data-frame-analytics-define]
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(Optional, boolean)
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include::{docdir}/ml/ml-shared.asciidoc[tag=allow-lazy-start]
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//Begin analysis
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`analysis`::
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(Required, object)
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The analysis configuration, which contains the information necessary to perform
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one of the following types of analysis: {classification}, {oldetection}, or
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{regression}.
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`analysis`.`classification`:::
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+
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.Properties of `analysis`
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[%collapsible%open]
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====
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//Begin classification
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`classification`:::
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(Required^*^, object)
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The configuration information necessary to perform
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{ml-docs}/dfa-classification.html[{classification}].
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+
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--
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TIP: Advanced parameters are for fine-tuning {classanalysis}. They are set
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automatically by <<ml-hyperparam-optimization,hyperparameter optimization>>
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to give minimum validation error. It is highly recommended to use the default
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values unless you fully understand the function of these parameters.
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--
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`analysis`.`classification`.`dependent_variable`::::
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+
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.Properties of `classification`
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[%collapsible%open]
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=====
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`dependent_variable`::::
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(Required, string)
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+
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--
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include::{docdir}/ml/ml-shared.asciidoc[tag=dependent-variable]
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+
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The data type of the field must be numeric (`integer`, `short`, `long`, `byte`),
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categorical (`ip`, `keyword`, `text`), or boolean.
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--
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`analysis`.`classification`.`eta`::::
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`eta`::::
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(Optional, double)
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include::{docdir}/ml/ml-shared.asciidoc[tag=eta]
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`analysis`.`classification`.`feature_bag_fraction`::::
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`feature_bag_fraction`::::
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(Optional, double)
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include::{docdir}/ml/ml-shared.asciidoc[tag=feature-bag-fraction]
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`analysis`.`classification`.`max_trees`::::
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(Optional, integer)
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include::{docdir}/ml/ml-shared.asciidoc[tag=max-trees]
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`analysis`.`classification`.`gamma`::::
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`gamma`::::
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(Optional, double)
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include::{docdir}/ml/ml-shared.asciidoc[tag=gamma]
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`analysis`.`classification`.`lambda`::::
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`lambda`::::
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(Optional, double)
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include::{docdir}/ml/ml-shared.asciidoc[tag=lambda]
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`analysis`.`classification`.`class_assignment_objective`::::
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(Optional, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=class-assignment-objective]
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`max_trees`::::
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(Optional, integer)
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include::{docdir}/ml/ml-shared.asciidoc[tag=max-trees]
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`analysis`.`classification`.`num_top_classes`::::
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`num_top_classes`::::
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(Optional, integer)
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include::{docdir}/ml/ml-shared.asciidoc[tag=num-top-classes]
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`analysis`.`classification`.`prediction_field_name`::::
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(Optional, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name]
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`analysis`.`classification`.`randomize_seed`::::
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(Optional, long)
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include::{docdir}/ml/ml-shared.asciidoc[tag=randomize-seed]
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`analysis`.`classification`.`num_top_feature_importance_values`::::
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`num_top_feature_importance_values`::::
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(Optional, integer)
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Advanced configuration option. Specifies the maximum number of
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{ml-docs}/dfa-classification.html#dfa-classification-feature-importance[feature
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importance] values per document to return. By default, it is zero and no feature importance
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calculation occurs.
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`analysis`.`classification`.`training_percent`::::
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`prediction_field_name`::::
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(Optional, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name]
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`randomize_seed`::::
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(Optional, long)
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include::{docdir}/ml/ml-shared.asciidoc[tag=randomize-seed]
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`training_percent`::::
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(Optional, integer)
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include::{docdir}/ml/ml-shared.asciidoc[tag=training-percent]
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`analysis`.`outlier_detection`:::
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//End classification
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=====
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//Begin outlier_detection
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`outlier_detection`:::
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(Required^*^, object)
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The configuration information necessary to perform
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{ml-docs}/dfa-outlier-detection.html[{oldetection}]:
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`analysis`.`outlier_detection`.`compute_feature_influence`::::
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+
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.Properties of `outlier_detection`
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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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`analysis`.`outlier_detection`.`feature_influence_threshold`::::
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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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`analysis`.`outlier_detection`.`method`::::
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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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`analysis`.`outlier_detection`.`n_neighbors`::::
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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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`analysis`.`outlier_detection`.`outlier_fraction`::::
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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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`analysis`.`outlier_detection`.`standardization_enabled`::::
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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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`analysis`.`regression`:::
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//End outlier_detection
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=====
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//Begin regression
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`regression`:::
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(Required^*^, object)
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The configuration information necessary to perform
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{ml-docs}/dfa-regression.html[{regression}].
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+
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--
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TIP: Advanced parameters are for fine-tuning {reganalysis}. They are set
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automatically by <<ml-hyperparam-optimization,hyperparameter optimization>>
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to give minimum validation error. It is highly recommended to use the default
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values unless you fully understand the function of these parameters.
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--
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`analysis`.`regression`.`dependent_variable`::::
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+
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.Properties of `regression`
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[%collapsible%open]
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=====
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`dependent_variable`::::
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(Required, string)
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+
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--
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include::{docdir}/ml/ml-shared.asciidoc[tag=dependent-variable]
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+
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The data type of the field must be numeric.
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--
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`analysis`.`regression`.`eta`::::
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`eta`::::
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(Optional, double)
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include::{docdir}/ml/ml-shared.asciidoc[tag=eta]
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`analysis`.`regression`.`feature_bag_fraction`::::
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`feature_bag_fraction`::::
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(Optional, double)
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include::{docdir}/ml/ml-shared.asciidoc[tag=feature-bag-fraction]
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`analysis`.`regression`.`max_trees`::::
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(Optional, integer)
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include::{docdir}/ml/ml-shared.asciidoc[tag=max-trees]
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`analysis`.`regression`.`gamma`::::
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`gamma`::::
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(Optional, double)
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include::{docdir}/ml/ml-shared.asciidoc[tag=gamma]
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`analysis`.`regression`.`lambda`::::
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`lambda`::::
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(Optional, double)
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include::{docdir}/ml/ml-shared.asciidoc[tag=lambda]
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`analysis`.`regression`.`prediction_field_name`::::
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(Optional, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name]
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`max_trees`::::
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(Optional, integer)
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include::{docdir}/ml/ml-shared.asciidoc[tag=max-trees]
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`analysis`.`regression`.`num_top_feature_importance_values`::::
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`num_top_feature_importance_values`::::
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(Optional, integer)
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Advanced configuration option. Specifies the maximum number of
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{ml-docs}/dfa-regression.html#dfa-regression-feature-importance[feature importance]
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values per document to return. By default, it is zero and no feature importance calculation
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occurs.
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values per document to return. By default, it is zero and no feature importance
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calculation occurs.
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`analysis`.`regression`.`training_percent`::::
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(Optional, integer)
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include::{docdir}/ml/ml-shared.asciidoc[tag=training-percent]
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`prediction_field_name`::::
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(Optional, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name]
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`analysis`.`regression`.`randomize_seed`::::
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`randomize_seed`::::
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(Optional, long)
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include::{docdir}/ml/ml-shared.asciidoc[tag=randomize-seed]
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`training_percent`::::
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(Optional, integer)
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include::{docdir}/ml/ml-shared.asciidoc[tag=training-percent]
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=====
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//End regression
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====
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//End analysis
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//Begin analyzed_fields
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`analyzed_fields`::
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(Optional, object)
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include::{docdir}/ml/ml-shared.asciidoc[tag=analyzed-fields]
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`analyzed_fields`.`excludes`:::
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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)
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include::{docdir}/ml/ml-shared.asciidoc[tag=analyzed-fields-excludes]
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`analyzed_fields`.`includes`:::
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(Optional, array)
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`includes`:::
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(Optional, array)
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include::{docdir}/ml/ml-shared.asciidoc[tag=analyzed-fields-includes]
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//End analyzed_fields
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====
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`description`::
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(Optional, string)
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@ -443,32 +443,46 @@ 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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`analyzed_fields`.`excludes`:::
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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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|
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`analyzed_fields`.`includes`:::
|
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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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`dest`.`index`:::
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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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|
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`dest`.`results_field`:::
|
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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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@ -479,29 +493,40 @@ to `ml`.
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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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`source`.`index`:::
|
||||
+
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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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`source`.`query`:::
|
||||
`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
|
||||
the query object in an {es} search POST body. By default, this property has the
|
||||
following value: `{"match_all": {}}`.
|
||||
|
||||
`source`.`_source`:::
|
||||
`_source`:::
|
||||
(object) Contains the specified `includes` and/or `excludes` patterns that
|
||||
select which fields are present in the destination. Fields that are excluded
|
||||
cannot be included in the analysis.
|
||||
|
||||
`source`.`_source`.`excludes`:::
|
||||
+
|
||||
.Properties of `_source`
|
||||
[%collapsible%open]
|
||||
======
|
||||
`excludes`:::
|
||||
(array) An array of strings that defines the fields that are excluded from the
|
||||
destination.
|
||||
|
||||
`source`.`_source`.`includes`:::
|
||||
`includes`:::
|
||||
(array) An array of strings that defines the fields that are included in the
|
||||
destination.
|
||||
======
|
||||
//End of _source
|
||||
=====
|
||||
//End source
|
||||
====
|
||||
end::data-frame-analytics[]
|
||||
|
||||
tag::data-frame-analytics-stats[]
|
||||
@ -970,16 +995,20 @@ A description of the job.
|
||||
end::description-dfa[]
|
||||
|
||||
tag::dest[]
|
||||
The destination configuration, consisting of `index` and
|
||||
optionally `results_field` (`ml` by default).
|
||||
|
||||
`index`:::
|
||||
(Required, string) Defines the _destination index_ to store the results of
|
||||
the {dfanalytics-job}.
|
||||
The destination configuration, consisting of `index` and optionally
|
||||
`results_field` (`ml` by default).
|
||||
+
|
||||
.Properties of `dest`
|
||||
[%collapsible%open]
|
||||
====
|
||||
`index`:::
|
||||
(Required, string) Defines the _destination index_ to store the results of the
|
||||
{dfanalytics-job}.
|
||||
|
||||
`results_field`:::
|
||||
(Optional, string) Defines the name of the field in which to store the
|
||||
results of the analysis. Default to `ml`.
|
||||
`results_field`:::
|
||||
(Optional, string) Defines the name of the field in which to store the results
|
||||
of the analysis. Defaults to `ml`.
|
||||
====
|
||||
end::dest[]
|
||||
|
||||
tag::detector-description[]
|
||||
@ -1045,14 +1074,11 @@ end::feature-influence-threshold[]
|
||||
|
||||
tag::field-selection[]
|
||||
An array of objects that explain selection for each field, sorted by
|
||||
the field names. Each object in the array has the following properties:
|
||||
|
||||
`name`:::
|
||||
(string) The field name.
|
||||
|
||||
`mapping_types`:::
|
||||
(string) The mapping types of the field.
|
||||
|
||||
the field names.
|
||||
+
|
||||
.Properties of `field_selection` objects
|
||||
[%collapsible%open]
|
||||
====
|
||||
`is_included`:::
|
||||
(boolean) Whether the field is selected to be included in the analysis.
|
||||
|
||||
@ -1063,8 +1089,15 @@ the field names. Each object in the array has the following properties:
|
||||
(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[]
|
||||
@ -1297,18 +1330,21 @@ allowed to contain. The maximum value is 2000.
|
||||
end::max-trees[]
|
||||
|
||||
tag::memory-estimation[]
|
||||
An object containing the memory estimates. The object has the
|
||||
following properties:
|
||||
|
||||
`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).
|
||||
|
||||
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[]
|
||||
@ -1648,38 +1684,44 @@ 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.
|
||||
|
||||
`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.
|
||||
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": {}}`.
|
||||
(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.
|
||||
|
||||
`includes`::::
|
||||
(array) An array of strings that defines the fields that will be
|
||||
included in the destination.
|
||||
(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.
|
||||
`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[]
|
||||
@ -1811,32 +1853,27 @@ 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.
|
||||
|
||||
`model_id`:::
|
||||
(string)
|
||||
Idetifier for the trained model.
|
||||
|
||||
+
|
||||
.Properties of trained model resources
|
||||
[%collapsible%open]
|
||||
====
|
||||
`created_by`:::
|
||||
(string)
|
||||
Information on the creator of the trained model.
|
||||
|
||||
`version`:::
|
||||
(string)
|
||||
The {es} version number in which the trained model was created.
|
||||
|
||||
`create_time`:::
|
||||
(<<time-units,time units>>)
|
||||
The time when the trained model was created.
|
||||
|
||||
`tags`:::
|
||||
(string)
|
||||
A comma delimited string of tags. A {infer} model can have many tags, or none.
|
||||
|
||||
`metadata`:::
|
||||
`default_field_map` :::
|
||||
(object)
|
||||
An object containing metadata about the trained model. For example, models
|
||||
created by {dfanalytics} contain an `analysis_config` and an `input`
|
||||
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)
|
||||
@ -1850,16 +1887,23 @@ The estimated number of operations to use the trained model.
|
||||
(string)
|
||||
The license level of the trained model.
|
||||
|
||||
`default_field_map` :::
|
||||
`metadata`:::
|
||||
(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"`.
|
||||
An object containing metadata about the trained model. For example, models
|
||||
created by {dfanalytics} contain `analysis_config` and `input` objects.
|
||||
|
||||
Any field map described in the inference configuration takes precedence.
|
||||
`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[]
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user