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Create trained models API

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Create trained models API

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Creates a trained model.

Models created in version 7.8.0 are not backwards compatible with older node versions. If in a mixed cluster environment, all nodes must be at least 7.8.0 to use a model stored by a 7.8.0 node.

This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

Request

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PUT _ml/trained_models/<model_id>

Prerequisites

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If the Elasticsearch security features are enabled, you must have the following built-in roles or equivalent privileges:

machine_learning_admin

For more information, see Built-in roles and Machine learning security privileges.

Description

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The create trained model API enables you to supply a trained model that is not created by data frame analytics.

Path parameters

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<model_id>: (Required, string) The unique identifier of the trained model.

Request body

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compressed_definition: (Required, string) The compressed (GZipped and Base64 encoded) inference definition of the model. If compressed_definition is specified, then definition cannot be specified.

definition

(Required, object) The inference definition for the model. If definition is specified, then compressed_definition cannot be specified.

Properties of definition

preprocessors

(Optional, object) Collection of preprocessors. See Preprocessor examples.

Properties of preprocessors

frequency_encoding

(Required, object) Defines a frequency encoding for a field.

Properties of frequency_encoding

feature_name: (Required, string) The name of the resulting feature.
field: (Required, string) The field name to encode.
frequency_map: (Required, object map of string:double) Object that maps the field value to the frequency encoded value.
custom: (Optional, Boolean) Boolean value indicating if the analytics job created the preprocessor or if a user provided it. This adjusts the feature importance calculation. When true, the feature importance calculation returns importance for the processed feature. When false, the total importance of the original field is returned. Default is false.

one_hot_encoding

(Required, object) Defines a one hot encoding map for a field.

Properties of one_hot_encoding

field: (Required, string) The field name to encode.
hot_map: (Required, object map of strings) String map of "field_value: one_hot_column_name".
custom: (Optional, Boolean) Boolean value indicating if the analytics job created the preprocessor or if a user provided it. This adjusts the feature importance calculation. When true, the feature importance calculation returns importance for the processed feature. When false, the total importance of the original field is returned. Default is false.

target_mean_encoding

(Required, object) Defines a target mean encoding for a field.

Properties of target_mean_encoding

default_value

(Required, double) The feature value if the field value is not in the target_map.

feature_name

(Required, string) The name of the resulting feature.

field

(Required, string) The field name to encode.

target_map

(Required, object map of string:double) Object that maps the field value to the target mean value.

custom: (Optional, Boolean) Boolean value indicating if the analytics job created the preprocessor or if a user provided it. This adjusts the feature importance calculation. When true, the feature importance calculation returns importance for the processed feature. When false, the total importance of the original field is returned. Default is false.

trained_model

(Required, object) The definition of the trained model.

Properties of trained_model

tree

(Required, object) The definition for a binary decision tree.

Properties of tree

classification_labels: (Optional, string) An array of classification labels (used for classification).
feature_names: (Required, string) Features expected by the tree, in their expected order.
target_type: (Required, string) String indicating the model target type; regression or classification.
tree_structure: (Required, object) An array of tree_node objects. The nodes must be in ordinal order by their tree_node.node_index value.

tree_node

(Required, object) The definition of a node in a tree.

There are two major types of nodes: leaf nodes and not-leaf nodes.

Leaf nodes only need node_index and leaf_value defined.
All other nodes need split_feature, left_child, right_child, threshold, decision_type, and default_left defined.

Properties of tree_node

decision_type: (Optional, string) Indicates the positive value (in other words, when to choose the left node) decision type. Supported lt, lte, gt, gte. Defaults to lte.
default_left: (Optional, Boolean) Indicates whether to default to the left when the feature is missing. Defaults to true.
leaf_value: (Optional, double) The leaf value of the of the node, if the value is a leaf (in other words, no children).
left_child: (Optional, integer) The index of the left child.
node_index: (Integer) The index of the current node.
right_child: (Optional, integer) The index of the right child.
split_feature: (Optional, integer) The index of the feature value in the feature array.
split_gain: (Optional, double) The information gain from the split.
threshold: (Optional, double) The decision threshold with which to compare the feature value.

ensemble

(Optional, object) The definition for an ensemble model. See Model examples.

Properties of ensemble

aggregate_output

(Required, object) An aggregated output object that defines how to aggregate the outputs of the trained_models. Supported objects are weighted_mode, weighted_sum, and logistic_regression. See Aggregated output example.

Properties of aggregate_output

logistic_regression

(Optional, object) This aggregated_output type works with binary classification (classification for values [0, 1]). It multiplies the outputs (in the case of the ensemble model, the inference model values) by the supplied weights. The resulting vector is summed and passed to a sigmoid function. The result of the sigmoid function is considered the probability of class 1 (P_1), consequently, the probability of class 0 is 1 - P_1. The class with the highest probability (either 0 or 1) is then returned. For more information about logistic regression, see this wiki article.

Properties of logistic_regression

weights: (Required, double) The weights to multiply by the input values (the inference values of the trained models).

weighted_sum

(Optional, object) This aggregated_output type works with regression. The weighted sum of the input values.

Properties of weighted_sum

weights: (Required, double) The weights to multiply by the input values (the inference values of the trained models).

weighted_mode

(Optional, object) This aggregated_output type works with regression or classification. It takes a weighted vote of the input values. The most common input value (taking the weights into account) is returned.

Properties of weighted_mode

weights: (Required, double) The weights to multiply by the input values (the inference values of the trained models).

exponent

(Optional, object) This aggregated_output type works with regression. It takes a weighted sum of the input values and passes the result to an exponent function (e^x where x is the sum of the weighted values).

Properties of exponent

weights: (Required, double) The weights to multiply by the input values (the inference values of the trained models).

classification_labels

(Optional, string) An array of classification labels.

feature_names

(Optional, string) Features expected by the ensemble, in their expected order.

target_type

(Required, string) String indicating the model target type; regression or classification.

trained_models

(Required, object) An array of trained_model objects. Supported trained models are tree and ensemble.

description

(Optional, string) A human-readable description of the inference trained model.

inference_config

(Required, object) The default configuration for inference. This can be either a regression or classification configuration. It must match the underlying definition.trained_model's target_type.

Properties of inference_config

regression

(Optional, object) Regression configuration for inference.

Properties of regression inference

num_top_feature_importance_values: (Optional, integer) Specifies the maximum number of feature importance values per document. By default, it is zero and no feature importance calculation occurs.
results_field: (Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

classification

(Optional, object) Classification configuration for inference.

Properties of classification inference

num_top_classes: (Optional, integer) Specifies the number of top class predictions to return. Defaults to 0.
num_top_feature_importance_values: (Optional, integer) Specifies the maximum number of feature importance values per document. By default, it is zero and no feature importance calculation occurs.
prediction_field_type: (Optional, string) Specifies the type of the predicted field to write. Acceptable values are: string, number, boolean. When boolean is provided 1.0 is transformed to true and 0.0 to false.
results_field: (Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
top_classes_results_field: (Optional, string) Specifies the field to which the top classes are written. Defaults to top_classes.

input

(Required, object) The input field names for the model definition.

Properties of input

field_names: (Required, string) An array of input field names for the model.

metadata

(Optional, object) An object map that contains metadata about the model.

tags

(Optional, string) An array of tags to organize the model.

Examples

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Preprocessor examples

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The example below shows a frequency_encoding preprocessor object:

{
   "frequency_encoding":{
      "field":"FlightDelayType",
      "feature_name":"FlightDelayType_frequency",
      "frequency_map":{
         "Carrier Delay":0.6007414737092798,
         "NAS Delay":0.6007414737092798,
         "Weather Delay":0.024573576178086153,
         "Security Delay":0.02476631010889467,
         "No Delay":0.6007414737092798,
         "Late Aircraft Delay":0.6007414737092798
      }
   }
}

The next example shows a one_hot_encoding preprocessor object:

{
   "one_hot_encoding":{
      "field":"FlightDelayType",
      "hot_map":{
         "Carrier Delay":"FlightDelayType_Carrier Delay",
         "NAS Delay":"FlightDelayType_NAS Delay",
         "No Delay":"FlightDelayType_No Delay",
         "Late Aircraft Delay":"FlightDelayType_Late Aircraft Delay"
      }
   }
}

This example shows a target_mean_encoding preprocessor object:

{
   "target_mean_encoding":{
      "field":"FlightDelayType",
      "feature_name":"FlightDelayType_targetmean",
      "target_map":{
         "Carrier Delay":39.97465788139886,
         "NAS Delay":39.97465788139886,
         "Security Delay":203.171206225681,
         "Weather Delay":187.64705882352948,
         "No Delay":39.97465788139886,
         "Late Aircraft Delay":39.97465788139886
      },
      "default_value":158.17995752420433
   }
}

Model examples

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The first example shows a trained_model object:

{
   "tree":{
      "feature_names":[
         "DistanceKilometers",
         "FlightTimeMin",
         "FlightDelayType_NAS Delay",
         "Origin_targetmean",
         "DestRegion_targetmean",
         "DestCityName_targetmean",
         "OriginAirportID_targetmean",
         "OriginCityName_frequency",
         "DistanceMiles",
         "FlightDelayType_Late Aircraft Delay"
      ],
      "tree_structure":[
         {
            "decision_type":"lt",
            "threshold":9069.33437193022,
            "split_feature":0,
            "split_gain":4112.094574306927,
            "node_index":0,
            "default_left":true,
            "left_child":1,
            "right_child":2
         },
         ...
         {
            "node_index":9,
            "leaf_value":-27.68987349695448
         },
         ...
      ],
      "target_type":"regression"
   }
}

The following example shows an ensemble model object:

"ensemble":{
   "feature_names":[
      ...
   ],
   "trained_models":[
      {
         "tree":{
            "feature_names":[],
            "tree_structure":[
               {
                  "decision_type":"lte",
                  "node_index":0,
                  "leaf_value":47.64069875778043,
                  "default_left":false
               }
            ],
            "target_type":"regression"
         }
      },
      ...
   ],
   "aggregate_output":{
      "weighted_sum":{
         "weights":[
            ...
         ]
      }
   },
   "target_type":"regression"
}

Aggregated output example

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Example of a logistic_regression object:

"aggregate_output" : {
  "logistic_regression" : {
    "weights" : [2.0, 1.0, .5, -1.0, 5.0, 1.0, 1.0]
  }
}

Example of a weighted_sum object:

"aggregate_output" : {
  "weighted_sum" : {
    "weights" : [1.0, -1.0, .5, 1.0, 5.0]
  }
}

Example of a weighted_mode object:

"aggregate_output" : {
  "weighted_mode" : {
    "weights" : [1.0, 1.0, 1.0, 1.0, 1.0]
  }
}

Example of an exponent object:

"aggregate_output" : {
  "exponent" : {
    "weights" : [1.0, 1.0, 1.0, 1.0, 1.0]
  }
}

Trained models JSON schema

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For the full JSON schema of trained models, click here.

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