665 lines
16 KiB
Plaintext
665 lines
16 KiB
Plaintext
[role="xpack"]
|
|
[testenv="basic"]
|
|
[[put-trained-models]]
|
|
= Create trained models API
|
|
[subs="attributes"]
|
|
++++
|
|
<titleabbrev>Create trained models</titleabbrev>
|
|
++++
|
|
|
|
Creates a 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-trained-models-request]]
|
|
== {api-request-title}
|
|
|
|
`PUT _ml/trained_models/<model_id>`
|
|
|
|
|
|
[[ml-put-trained-models-prereq]]
|
|
== {api-prereq-title}
|
|
|
|
If the {es} {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 {ml-docs-setup-privileges}.
|
|
|
|
|
|
[[ml-put-trained-models-desc]]
|
|
== {api-description-title}
|
|
|
|
The create trained model API enables you to supply a trained model that is not
|
|
created by {dfanalytics}.
|
|
|
|
|
|
[[ml-put-trained-models-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-trained-models-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-trained-models-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.
|
|
|
|
`custom`::
|
|
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=custom-preprocessor]
|
|
|
|
======
|
|
//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".
|
|
|
|
`custom`::
|
|
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=custom-preprocessor]
|
|
|
|
======
|
|
//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.
|
|
|
|
`custom`::
|
|
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=custom-preprocessor]
|
|
|
|
======
|
|
//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-trained-models-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-trained-models-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
|
|
{wikipedia}/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
|
|
{wikipedia}/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-trained-models-example]]
|
|
== {api-examples-title}
|
|
|
|
[[ml-put-trained-models-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-trained-models-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-trained-models-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-trained-models-json-schema]]
|
|
=== Trained models JSON schema
|
|
|
|
For the full JSON schema of trained models,
|
|
https://github.com/elastic/ml-json-schemas[click here].
|