[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_admin` (UI only) * source indices: `read`, `view_index_metadata` * destination index: `read`, `create_index`, `manage` and `index` * cluster: `monitor` (UI only) For more information, see <> and <>. NOTE: It is possible that secondary authorization headers are supplied in the request. If this is the case, the secondary authorization headers are used instead of the primary headers. [[ml-put-dfanalytics-desc]] ==== {api-description-title} This API creates a {dfanalytics-job} that performs an analysis on the source indices and stores the outcome in a destination index. If the destination index does not exist, it is created automatically when you start the job. See <>. [[ml-hyperparam-optimization]] If you supply only a subset of the {regression} or {classification} parameters, _hyperparameter optimization_ occurs. It determines a value for each of 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. 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] [role="child_attributes"] [[ml-put-dfanalytics-request-body]] ==== {api-request-body-title} `allow_lazy_start`:: (Optional, boolean) Specifies whether this job can start when there is insufficient {ml} node capacity for it to be immediately assigned to a node. The default is `false`; if a {ml} node with capacity to run the job cannot immediately be found, the <> API returns an error. However, this is also subject to the cluster-wide `xpack.ml.max_lazy_ml_nodes` setting. See <>. If this option is set to `true`, the API does not return an error and the job waits in the `starting` state until sufficient {ml} node capacity is available. //Begin analysis `analysis`:: (Required, object) The analysis configuration, which contains the information necessary to perform one of the following types of analysis: {classification}, {oldetection}, or {regression}. + .Properties of `analysis` [%collapsible%open] ==== //Begin classification `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 hyperparameter optimization to give the minimum validation error. It is highly recommended to use the default values unless you fully understand the function of these parameters. + .Properties of `classification` [%collapsible%open] ===== `class_assignment_objective`:::: (Optional, string) Defines the objective to optimize when assigning class labels: `maximize_accuracy` or `maximize_minimum_recall`. When maximizing accuracy, class labels are chosen to maximize the number of correct predictions. When maximizing minimum recall, labels are chosen to maximize the minimum recall for any class. Defaults to `maximize_minimum_recall`. `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` or `keyword`), or boolean. There must be no more than 30 different values in this field. `eta`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=eta] `feature_bag_fraction`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=feature-bag-fraction] `gamma`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=gamma] `lambda`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=lambda] `max_trees`:::: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=max-trees] `num_top_classes`:::: (Optional, integer) Defines the number of categories for which the predicted probabilities are reported. It must be non-negative. If it is greater than the total number of categories, the API reports all category probabilities. Defaults to 2. `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. `prediction_field_name`:::: (Optional, string) include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name] `randomize_seed`:::: (Optional, long) include::{docdir}/ml/ml-shared.asciidoc[tag=randomize-seed] `training_percent`:::: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=training-percent] //End classification ===== //Begin outlier_detection `outlier_detection`::: (Required^*^, object) The configuration information necessary to perform {ml-docs}/dfa-outlier-detection.html[{oldetection}]: + .Properties of `outlier_detection` [%collapsible%open] ===== `compute_feature_influence`:::: (Optional, boolean) If `true`, the feature influence calculation is enabled. Defaults to `true`. `feature_influence_threshold`:::: (Optional, double) The minimum {olscore} that a document needs to have in order to calculate its {fiscore}. Value range: 0-1 (`0.1` by default). `method`:::: (Optional, string) 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`. `n_neighbors`:::: (Optional, integer) 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. `outlier_fraction`:::: (Optional, double) 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. `standardization_enabled`:::: (Optional, boolean) 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 outlier_detection ===== //Begin regression `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 hyperparameter optimization to give minimum validation error. It is highly recommended to use the default values unless you fully understand the function of these parameters. + .Properties of `regression` [%collapsible%open] ===== `dependent_variable`:::: (Required, string) + include::{docdir}/ml/ml-shared.asciidoc[tag=dependent-variable] + The data type of the field must be numeric. `eta`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=eta] `feature_bag_fraction`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=feature-bag-fraction] `gamma`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=gamma] `lambda`:::: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=lambda] `max_trees`:::: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=max-trees] `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. `prediction_field_name`:::: (Optional, string) include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name] `randomize_seed`:::: (Optional, long) include::{docdir}/ml/ml-shared.asciidoc[tag=randomize-seed] `training_percent`:::: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=training-percent] ===== //End regression ==== //End analysis //Begin analyzed_fields `analyzed_fields`:: (Optional, object) Specify `includes` and/or `excludes` patterns to select which fields will be included in the analysis. The patterns specified in `excludes` are applied last, therefore `excludes` takes precedence. In other words, if the same field is specified in both `includes` and `excludes`, then the field will not be included in the analysis. + -- [[dfa-supported-fields]] The supported fields for each type of analysis are as follows: * {oldetection-cap} requires numeric or boolean data to analyze. The algorithms don't support missing values therefore fields that have data types other than numeric or boolean are ignored. Documents where included fields contain missing values, null values, or an array are also ignored. Therefore the `dest` index may contain documents that don't have an {olscore}. * {regression-cap} supports fields that are numeric, `boolean`, `text`, `keyword`, and `ip`. It is also tolerant of missing values. Fields that are supported are included in the analysis, other fields are ignored. Documents where included fields contain an array with two or more values are also ignored. Documents in the `dest` index that don’t contain a results field are not included in the {reganalysis}. * {classification-cap} supports fields that are numeric, `boolean`, `text`, `keyword`, and `ip`. It is also tolerant of missing values. Fields that are supported are included in the analysis, other fields are ignored. Documents where included fields contain an array with two or more values are also ignored. Documents in the `dest` index that don’t contain a results field are not included in the {classanalysis}. {classanalysis-cap} can be improved by mapping ordinal variable values to a single number. For example, in case of age ranges, you can model the values as "0-14" = 0, "15-24" = 1, "25-34" = 2, and so on. If `analyzed_fields` is not set, only the relevant fields will be included. For example, all the numeric fields for {oldetection}. For more information about field selection, see <>. -- + .Properties of `analyzed_fields` [%collapsible%open] ==== `excludes`::: (Optional, array) An array of strings that defines the fields that will be excluded from the analysis. You do not need to add fields with unsupported data types to `excludes`, these fields are excluded from the analysis automatically. `includes`::: (Optional, array) An array of strings that defines the fields that will be included in the analysis. //End analyzed_fields ==== `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) 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 <>. `source`:: (object) 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 (<>). 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. ===== ==== [[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]