Create inference trained model APIedit
Creates an inference 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 experimental and may be changed or removed completely in a future release. Elastic will take a best effort approach to fix any issues, but experimental features are not subject to the support SLA of official GA features.
Requestedit
PUT _ml/inference/<model_id>
Prerequisitesedit
If the Elasticsearch security features are enabled, you must have the following builtin roles or equivalent privileges:

machine_learning_admin
For more information, see Builtin roles and Machine learning security privileges.
Descriptionedit
The create inference trained model API enables you to supply a trained model that is not created by data frame analytics.
Path parametersedit

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

compressed_definition

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

definition

(Required, object) The inference definition for the model. If
definition
is specified, thencompressed_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. Whenfalse
, the total importance of the original field is returned. Default isfalse
.


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. Whenfalse
, the total importance of the original field is returned. Default isfalse
.


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. Whenfalse
, the total importance of the original field is returned. Default isfalse
.




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
orclassification
. 
tree_structure

(Required, object)
An array of
tree_node
objects. The nodes must be in ordinal order by theirtree_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 notleaf nodes.

Leaf nodes only need
node_index
andleaf_value
defined. 
All other nodes need
split_feature
,left_child
,right_child
,threshold
,decision_type
, anddefault_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 tolte
. 
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.

Leaf nodes only need

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 areweighted_mode
,weighted_sum
, andlogistic_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 theensemble
model, the inference model values) by the suppliedweights
. The resulting vector is summed and passed to asigmoid
function. The result of thesigmoid
function is considered the probability of class 1 (P_1
), consequently, the probability of class 0 is1  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
wherex
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
orclassification.

trained_models

(Required, object)
An array of
trained_model
objects. Supported trained models aretree
andensemble
.




description
 (Optional, string) A humanreadable description of the inference trained model.

inference_config

(Required, object) The default configuration for inference. This can be either a
regression
orclassification
configuration. It must match the underlyingdefinition.trained_model
'starget_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
. Whenboolean
is provided1.0
is transformed totrue
and0.0
tofalse
. 
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.
Examplesedit
Preprocessor examplesedit
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 examplesedit
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 exampleedit
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] } }
Inference JSON schemaedit
For the full JSON schema of model inference, click here.