[role="xpack"] [testenv="platinum"] [[put-dfanalytics]] === Create {dfanalytics-jobs} API [subs="attributes"] ++++ Create {dfanalytics-jobs} ++++ Instantiates a {dfanalytics-job}. experimental[] [[ml-put-dfanalytics-request]] ==== {api-request-title} `PUT _ml/data_frame/analytics/` [[ml-put-dfanalytics-prereq]] ==== {api-prereq-title} If the {es} {security-features} are enabled, you must have the following built-in roles and privileges: * `machine_learning_admin` * `kibana_user` (UI only) * source index: `read`, `view_index_metadata` * destination index: `read`, `create_index`, `manage` and `index` * cluster: `monitor` (UI only) For more information, see <> and <>. [[ml-put-dfanalytics-desc]] ==== {api-description-title} This API creates a {dfanalytics-job} that performs an analysis on the source index and stores the outcome in a destination index. The destination index will be automatically created if it does not exist. The `index.number_of_shards` and `index.number_of_replicas` settings of the source index will be copied over the destination index. When the source index matches multiple indices, these settings will be set to the maximum values found in the source indices. The mappings of the source indices are also attempted to be copied over to the destination index, however, if the mappings of any of the fields don't match among the source indices, the attempt will fail with an error message. If the destination index already exists, then it will be use as is. This makes it possible to set up the destination index in advance with custom settings and mappings. [discrete] [[ml-hyperparam-optimization]] ===== Hyperparameter optimization If you don't supply {regression} or {classification} parameters, _hyperparameter optimization_ occurs, which sets a value for the undefined parameters. The starting point is calculated for data dependent parameters by examining the loss on the training data. Subject to the size constraint, this operation provides an upper bound on the improvement in validation loss. A fixed number of rounds is used for optimization which depends on the number of parameters being optimized. The optimization starts with random search, then Bayesian optimization is performed that is targeting maximum expected improvement. If you override any parameters by explicitely setting it, the optimization calculates the value of the remaining parameters accordingly and uses the value you provided for the overridden parameter. The number of rounds are reduced respectively. The validation error is estimated in each round by using 4-fold cross validation. [[ml-put-dfanalytics-path-params]] ==== {api-path-parms-title} ``:: (Required, string) include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-data-frame-analytics-define] [[ml-put-dfanalytics-request-body]] ==== {api-request-body-title} `allow_lazy_start`:: (Optional, boolean) include::{docdir}/ml/ml-shared.asciidoc[tag=allow-lazy-start] `analysis`:: (Required, object) The analysis configuration, which contains the information necessary to perform one of the following types of analysis: {classification}, {oldetection}, or {regression}. `analysis`.`classification`::: (Required^*^, object) The configuration information necessary to perform {ml-docs}/dfa-classification.html[{classification}]. + -- TIP: Advanced parameters are for fine-tuning {classanalysis}. They are set automatically by <> to give minimum validation error. It is highly recommended to use the default values unless you fully understand the function of these parameters. -- `analysis`.`classification`.`dependent_variable`:::: (Required, string) + -- include::{docdir}/ml/ml-shared.asciidoc[tag=dependent-variable] The data type of the field must be numeric (`integer`, `short`, `long`, `byte`), categorical (`ip`, `keyword`, `text`), or boolean. -- `analysis`.`classification`.`eta`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=eta] `analysis`.`classification`.`feature_bag_fraction`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=feature-bag-fraction] `analysis`.`classification`.`max_trees`:::: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=max-trees] `analysis`.`classification`.`gamma`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=gamma] `analysis`.`classification`.`lambda`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=lambda] `analysis`.`classification`.`num_top_classes`:::: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=num-top-classes] `analysis`.`classification`.`prediction_field_name`:::: (Optional, string) include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name] `analysis`.`classification`.`randomize_seed`:::: (Optional, long) include::{docdir}/ml/ml-shared.asciidoc[tag=randomize-seed] `analysis`.`classification`.`num_top_feature_importance_values`:::: (Optional, integer) Advanced configuration option. Specifies the maximum number of {ml-docs}/dfa-classification.html#dfa-classification-feature-importance[feature importance] values per document to return. By default, it is zero and no feature importance calculation occurs. `analysis`.`classification`.`training_percent`:::: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=training-percent] `analysis`.`outlier_detection`::: (Required^*^, object) The configuration information necessary to perform {ml-docs}/dfa-outlier-detection.html[{oldetection}]: `analysis`.`outlier_detection`.`compute_feature_influence`:::: (Optional, boolean) include::{docdir}/ml/ml-shared.asciidoc[tag=compute-feature-influence] `analysis`.`outlier_detection`.`feature_influence_threshold`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=feature-influence-threshold] `analysis`.`outlier_detection`.`method`:::: (Optional, string) include::{docdir}/ml/ml-shared.asciidoc[tag=method] `analysis`.`outlier_detection`.`n_neighbors`:::: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=n-neighbors] `analysis`.`outlier_detection`.`outlier_fraction`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=outlier-fraction] `analysis`.`outlier_detection`.`standardization_enabled`:::: (Optional, boolean) include::{docdir}/ml/ml-shared.asciidoc[tag=standardization-enabled] `analysis`.`regression`::: (Required^*^, object) The configuration information necessary to perform {ml-docs}/dfa-regression.html[{regression}]. + -- TIP: Advanced parameters are for fine-tuning {reganalysis}. They are set automatically by <> to give minimum validation error. It is highly recommended to use the default values unless you fully understand the function of these parameters. -- `analysis`.`regression`.`dependent_variable`:::: (Required, string) + -- include::{docdir}/ml/ml-shared.asciidoc[tag=dependent-variable] The data type of the field must be numeric. -- `analysis`.`regression`.`eta`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=eta] `analysis`.`regression`.`feature_bag_fraction`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=feature-bag-fraction] `analysis`.`regression`.`max_trees`:::: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=max-trees] `analysis`.`regression`.`gamma`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=gamma] `analysis`.`regression`.`lambda`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=lambda] `analysis`.`regression`.`prediction_field_name`:::: (Optional, string) include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name] `analysis`.`regression`.`num_top_feature_importance_values`:::: (Optional, integer) Advanced configuration option. Specifies the maximum number of {ml-docs}/dfa-regression.html#dfa-regression-feature-importance[feature importance] values per document to return. By default, it is zero and no feature importance calculation occurs. `analysis`.`regression`.`training_percent`:::: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=training-percent] `analysis`.`regression`.`randomize_seed`:::: (Optional, long) include::{docdir}/ml/ml-shared.asciidoc[tag=randomize-seed] `analyzed_fields`:: (Optional, object) include::{docdir}/ml/ml-shared.asciidoc[tag=analyzed-fields] `analyzed_fields`.`excludes`::: (Optional, array) include::{docdir}/ml/ml-shared.asciidoc[tag=analyzed-fields-excludes] `analyzed_fields`.`includes`::: (Optional, array) include::{docdir}/ml/ml-shared.asciidoc[tag=analyzed-fields-includes] `description`:: (Optional, string) include::{docdir}/ml/ml-shared.asciidoc[tag=description-dfa] `dest`:: (Required, object) include::{docdir}/ml/ml-shared.asciidoc[tag=dest] `model_memory_limit`:: (Optional, string) include::{docdir}/ml/ml-shared.asciidoc[tag=model-memory-limit-dfa] `source`:: (object) include::{docdir}/ml/ml-shared.asciidoc[tag=source-put-dfa] [[ml-put-dfanalytics-example]] ==== {api-examples-title} [[ml-put-dfanalytics-example-preprocess]] ===== Preprocessing actions example The following example shows how to limit the scope of the analysis to certain fields, specify excluded fields in the destination index, and use a query to filter your data before analysis. [source,console] -------------------------------------------------- PUT _ml/data_frame/analytics/model-flight-delays-pre { "source": { "index": [ "kibana_sample_data_flights" <1> ], "query": { <2> "range": { "DistanceKilometers": { "gt": 0 } } }, "_source": { <3> "includes": [], "excludes": [ "FlightDelay", "FlightDelayType" ] } }, "dest": { <4> "index": "df-flight-delays", "results_field": "ml-results" }, "analysis": { "regression": { "dependent_variable": "FlightDelayMin", "training_percent": 90 } }, "analyzed_fields": { <5> "includes": [], "excludes": [ "FlightNum" ] }, "model_memory_limit": "100mb" } -------------------------------------------------- // TEST[skip:setup kibana sample data] <1> The source index to analyze. <2> This query filters out entire documents that will not be present in the destination index. <3> The `_source` object defines fields in the dataset that will be included or excluded in the destination index. In this case, `includes` does not specify any fields, so the default behavior takes place: all the fields of the source index will included except the ones that are explicitly specified in `excludes`. <4> Defines the destination index that contains the results of the analysis and the fields of the source index specified in the `_source` object. Also defines the name of the `results_field`. <5> Specifies fields to be included in or excluded from the analysis. This does not affect whether the fields will be present in the destination index, only affects whether they are used in the analysis. In this example, we can see that all the fields of the source index are included in the destination index except `FlightDelay` and `FlightDelayType` because these are defined as excluded fields by the `excludes` parameter of the `_source` object. The `FlightNum` field is included in the destination index, however it is not included in the analysis because it is explicitly specified as excluded field by the `excludes` parameter of the `analyzed_fields` object. [[ml-put-dfanalytics-example-od]] ===== {oldetection-cap} example The following example creates the `loganalytics` {dfanalytics-job}, the analysis type is `outlier_detection`: [source,console] -------------------------------------------------- PUT _ml/data_frame/analytics/loganalytics { "description": "Outlier detection on log data", "source": { "index": "logdata" }, "dest": { "index": "logdata_out" }, "analysis": { "outlier_detection": { "compute_feature_influence": true, "outlier_fraction": 0.05, "standardization_enabled": true } } } -------------------------------------------------- // TEST[setup:setup_logdata] The API returns the following result: [source,console-result] ---- { "id": "loganalytics", "description": "Outlier detection on log data", "source": { "index": ["logdata"], "query": { "match_all": {} } }, "dest": { "index": "logdata_out", "results_field": "ml" }, "analysis": { "outlier_detection": { "compute_feature_influence": true, "outlier_fraction": 0.05, "standardization_enabled": true } }, "model_memory_limit": "1gb", "create_time" : 1562265491319, "version" : "7.6.0", "allow_lazy_start" : false } ---- // TESTRESPONSE[s/1562265491319/$body.$_path/] // TESTRESPONSE[s/"version" : "7.6.0"/"version" : $body.version/] [[ml-put-dfanalytics-example-r]] ===== {regression-cap} examples The following example creates the `house_price_regression_analysis` {dfanalytics-job}, the analysis type is `regression`: [source,console] -------------------------------------------------- PUT _ml/data_frame/analytics/house_price_regression_analysis { "source": { "index": "houses_sold_last_10_yrs" }, "dest": { "index": "house_price_predictions" }, "analysis": { "regression": { "dependent_variable": "price" } } } -------------------------------------------------- // TEST[skip:TBD] The API returns the following result: [source,console-result] ---- { "id" : "house_price_regression_analysis", "source" : { "index" : [ "houses_sold_last_10_yrs" ], "query" : { "match_all" : { } } }, "dest" : { "index" : "house_price_predictions", "results_field" : "ml" }, "analysis" : { "regression" : { "dependent_variable" : "price", "training_percent" : 100 } }, "model_memory_limit" : "1gb", "create_time" : 1567168659127, "version" : "8.0.0", "allow_lazy_start" : false } ---- // TESTRESPONSE[s/1567168659127/$body.$_path/] // TESTRESPONSE[s/"version": "8.0.0"/"version": $body.version/] The following example creates a job and specifies a training percent: [source,console] -------------------------------------------------- PUT _ml/data_frame/analytics/student_performance_mathematics_0.3 { "source": { "index": "student_performance_mathematics" }, "dest": { "index":"student_performance_mathematics_reg" }, "analysis": { "regression": { "dependent_variable": "G3", "training_percent": 70, <1> "randomize_seed": 19673948271 <2> } } } -------------------------------------------------- // TEST[skip:TBD] <1> The `training_percent` defines the percentage of the data set that will be used for training the model. <2> The `randomize_seed` is the seed used to randomly pick which data is used for training. [[ml-put-dfanalytics-example-c]] ===== {classification-cap} example The following example creates the `loan_classification` {dfanalytics-job}, the analysis type is `classification`: [source,console] -------------------------------------------------- PUT _ml/data_frame/analytics/loan_classification { "source" : { "index": "loan-applicants" }, "dest" : { "index": "loan-applicants-classified" }, "analysis" : { "classification": { "dependent_variable": "label", "training_percent": 75, "num_top_classes": 2 } } } -------------------------------------------------- // TEST[skip:TBD]