elasticsearch/docs/reference/ml/df-analytics/apis/put-inference.asciidoc

[role="xpack"]
[testenv="basic"]
[[put-inference]]
=== Create {infer} trained model API
[subs="attributes"]
++++
<titleabbrev>Create {infer} trained model</titleabbrev>
++++

Creates an {infer} trained model.

WARNING: 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.


experimental[]


[[ml-put-inference-request]]
==== {api-request-title}

`PUT _ml/inference/<model_id>`


[[ml-put-inference-prereq]]
==== {api-prereq-title}

If the {es} {security-features} are enabled, you must have the following
built-in roles and privileges:

* `machine_learning_admin`

For more information, see <<security-privileges>> and <<built-in-roles>>.


[[ml-put-inference-desc]]
==== {api-description-title}

The create {infer} trained model API enables you to supply a trained model that
is not created by {dfanalytics}.


[[ml-put-inference-path-params]]
==== {api-path-parms-title}

`<model_id>`::
(Required, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]

[role="child_attributes"]
[[ml-put-inference-request-body]]
==== {api-request-body-title}

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

//Begin definition
`definition`::
(Required, object)
The {infer} definition for the model. If `definition` is specified, then
`compressed_definition` cannot be specified.
+
.Properties of `definition`
[%collapsible%open]
====
//Begin preprocessors
`preprocessors`::
(Optional, object)
Collection of preprocessors. See <<ml-put-inference-preprocessor-example>>.
+
.Properties of `preprocessors`
[%collapsible%open]
=====
//Begin frequency encoding
`frequency_encoding`::
(Required, object)
Defines a frequency encoding for a field.
+
.Properties of `frequency_encoding`
[%collapsible%open]
======
`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.
======
//End frequency encoding

//Begin one hot encoding
`one_hot_encoding`::
(Required, object)
Defines a one hot encoding map for a field.
+
.Properties of `one_hot_encoding`
[%collapsible%open]
======
`field`::
(Required, string)
The field name to encode.

`hot_map`::
(Required, object map of strings)
String map of "field_value: one_hot_column_name".
======
//End one hot encoding

//Begin target mean encoding
`target_mean_encoding`::
(Required, object)
Defines a target mean encoding for a field.
+
.Properties of `target_mean_encoding`
[%collapsible%open]
======
`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.
======
//End target mean encoding
=====
//End preprocessors

//Begin trained model
`trained_model`::
(Required, object)
The definition of the trained model.
+
.Properties of `trained_model`
[%collapsible%open]
=====
//Begin tree
`tree`::
(Required, object)
The definition for a binary decision tree.
+
.Properties of `tree`
[%collapsible%open]
======
`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.
======
//End tree

//Begin tree node
`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`
[%collapsible%open]
======
`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.
======
//End tree node

//Begin ensemble
`ensemble`::
(Optional, object)
The definition for an ensemble model. See <<ml-put-inference-model-example>>.
+
.Properties of `ensemble`
[%collapsible%open]
======
//Begin aggregate output
`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 <<ml-put-inference-aggregated-output-example>>.
+
.Properties of `aggregate_output`
[%collapsible%open]
=======
//Begin logistic regression
`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
https://en.wikipedia.org/wiki/Sigmoid_function[`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
https://en.wikipedia.org/wiki/Logistic_regression[this wiki article].
+
.Properties of `logistic_regression`
[%collapsible%open]
========
`weights`:::
(Required, double)
The weights to multiply by the input values (the inference values of the trained
models).
========
//End logistic regression

//Begin weighted sum
`weighted_sum`::
(Optional, object)
This `aggregated_output` type works with regression. The weighted sum of the
input values.
+
.Properties of `weighted_sum`
[%collapsible%open]
========
`weights`:::
(Required, double)
The weights to multiply by the input values (the inference values of the trained
models).
========
//End weighted sum

//Begin weighted mode
`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`
[%collapsible%open]
========
`weights`:::
(Required, double)
The weights to multiply by the input values (the inference values of the trained
models).
========
//End weighted mode

//Begin exponent
`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`
[%collapsible%open]
========
`weights`:::
(Required, double)
The weights to multiply by the input values (the inference values of the trained
models).
========
//End exponent
=======
//End aggregate output

`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`.
======
//End ensemble

=====
//End trained model

====
//End definition

`description`::
(Optional, string)
A human-readable description of the {infer} trained model.

//Begin inference_config
`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`
[%collapsible%open]
====
`regression`:::
(Optional, object)
Regression configuration for inference.
+
.Properties of regression inference
[%collapsible%open]
=====
`num_top_feature_importance_values`::::
(Optional, integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-regression-num-top-feature-importance-values]

`results_field`::::
(Optional, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-results-field]
=====

`classification`:::
(Optional, object)
Classification configuration for inference.
+
.Properties of classification inference
[%collapsible%open]
=====
`num_top_classes`::::
(Optional, integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-num-top-classes]

`num_top_feature_importance_values`::::
(Optional, integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-num-top-feature-importance-values]

`prediction_field_type`::::
(Optional, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-prediction-field-type]

`results_field`::::
(Optional, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-results-field]

`top_classes_results_field`::::
(Optional, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-top-classes-results-field]
=====
====
//End of inference_config

//Begin input
`input`::
(Required, object)
The input field names for the model definition.
+
.Properties of `input`
[%collapsible%open]
====
`field_names`:::
(Required, string)
An array of input field names for the model.
====
//End input

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

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


[[ml-put-inference-example]]
==== {api-examples-title}

[[ml-put-inference-preprocessor-example]]
===== Preprocessor examples

The example below shows a `frequency_encoding` preprocessor object:

[source,js]
----------------------------------
{
   "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
      }
   }
}
----------------------------------
//NOTCONSOLE


The next example shows a `one_hot_encoding` preprocessor object:

[source,js]
----------------------------------
{
   "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"
      }
   }
}
----------------------------------
//NOTCONSOLE


This example shows a `target_mean_encoding` preprocessor object:

[source,js]
----------------------------------
{
   "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
   }
}
----------------------------------
//NOTCONSOLE


[[ml-put-inference-model-example]]
===== Model examples

The first example shows a `trained_model` object:

[source,js]
----------------------------------
{
   "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"
   }
}
----------------------------------
//NOTCONSOLE


The following example shows an `ensemble` model object:

[source,js]
----------------------------------
"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"
}
----------------------------------
//NOTCONSOLE


[[ml-put-inference-aggregated-output-example]]
===== Aggregated output example

Example of a `logistic_regression` object:

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


Example of a `weighted_sum` object:

[source,js]
----------------------------------
"aggregate_output" : {
  "weighted_sum" : {
    "weights" : [1.0, -1.0, .5, 1.0, 5.0]
  }
}
----------------------------------
//NOTCONSOLE


Example of a `weighted_mode` object:

[source,js]
----------------------------------
"aggregate_output" : {
  "weighted_mode" : {
    "weights" : [1.0, 1.0, 1.0, 1.0, 1.0]
  }
}
----------------------------------
//NOTCONSOLE


Example of an `exponent` object:

[source,js]
----------------------------------
"aggregate_output" : {
  "exponent" : {
    "weights" : [1.0, 1.0, 1.0, 1.0, 1.0]
  }
}
----------------------------------
//NOTCONSOLE


[[ml-put-inference-json-schema]]
===== {infer-cap} JSON schema

For the full JSON schema of model {infer},
https://github.com/elastic/ml-json-schemas[click here].