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[DOCS] Adds PUT inference API docs (#51231) 

Co-authored-by: Benjamin Trent <ben.w.trent@gmail.com>
Co-authored-by: Lisa Cawley <lcawley@elastic.co>
This commit is contained in:
István Zoltán Szabó 2020-01-31 13:12:24 +01:00
parent 9600ab4f57
commit dfc9f2330c
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2
docs/reference/ml/df-analytics/apis/index.asciidoc
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@ -17,6 +17,7 @@ You can use the following APIs to perform {ml} {dfanalytics} activities.
You can use the following APIs to perform {infer} operations. You can use the following APIs to perform {infer} operations.
* <<put-inference>>
* <<get-inference>> * <<get-inference>>
* <<get-inference-stats>> * <<get-inference-stats>>
* <<delete-inference>> * <<delete-inference>>
@ -26,6 +27,7 @@ See also <<ml-apis>>.
//CREATE //CREATE
include::put-dfanalytics.asciidoc[] include::put-dfanalytics.asciidoc[]
include::put-inference.asciidoc[]
//DELETE //DELETE
include::delete-dfanalytics.asciidoc[] include::delete-dfanalytics.asciidoc[]
include::delete-inference-trained-model.asciidoc[] include::delete-inference-trained-model.asciidoc[]

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docs/reference/ml/df-analytics/apis/put-inference.asciidoc Normal file
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@ -0,0 +1,494 @@
[role="xpack"]
[testenv="basic"]
[[put-inference]]
=== Create {infer} trained model API
[subs="attributes"]
++++
<titleabbrev>Create {infer} trained model</titleabbrev>
++++
Creates an {infer} trained model.
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::{docdir}/ml/ml-shared.asciidoc[tag=model-id]
[[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.
`definition`::
(Required, object)
The {infer} definition for the model. If `definition` is specified, then
`compressed_definition` cannot be specified.
`definition`.`preprocessors`:::
(Optional, object)
Collection of preprocessors. See <<ml-put-inference-preprocessors>> for the full
list of available preprocessors.
`definition`.`trained_model`:::
(Required, object)
The definition of the trained model. See <<ml-put-inference-trained-model>> for
details.
`description`::
(Optional, string)
A human-readable description of the {infer} trained model.
`input`::
(Required, object)
The input field names for the model definition.
`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.
[[ml-put-inference-preprocessors]]
===== {infer-cap} preprocessor definitions
`frequency_encoding`::
(Required, object)
Defines a frequency encoding for a field.
`frequency_encoding`.`field`:::
(Required, string)
The field name to encode.
`frequency_encoding`.`feature_name`:::
(Required, string)
The name of the resulting feature.
`frequency_encoding`.`frequency_map`:::
(Required, object map of string:double)
Object that maps the field value to the frequency encoded value.
`one_hot_encoding`::
(Required, object)
Defines a one hot encoding map for a field.
`one_hot_encoding`.`field`:::
(Required, string)
The field name to encode.
`one_hot_encoding`.`hot_map`:::
(Required, object map of strings)
String map of "field_value: one_hot_column_name".
`target_mean_encoding`::
(Required, object)
Defines a target mean encoding for a field.
`target_mean_encoding`.`field`:::
(Required, string)
The field name to encode.
`target_mean_encoding`.`feature_name`:::
(Required, string)
The name of the resulting feature.
`target_mean_encoding`.`target_map`:::
(Required, object map of string:double)
Object that maps the field value to the target mean value.
`target_mean_encoding`.`default_value`:::
(Required, double)
The feature value if the field value is not in the `target_map`.
See <<ml-put-inference-preprocessor-example>> for more details.
[[ml-put-inference-trained-model]]
===== {infer-cap} trained model definitions
`tree`::
(Required, object)
The definition for a binary decision tree.
`tree`.`feature_names`:::
(Required, string)
Features expected by the tree, in their expected order.
`tree`.`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`.`classification_labels`:::
(Optional, string) An array of classification labels (used for
`classification`).
`tree`.`target_type`:::
(Required, string)
String indicating the model target type; `regression` or `classification`.
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.
`tree_node`::
(Required, object)
The definition of a node in a tree.
`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`.
`tree_node`.`threshold`:::
(Optional, double)
The decision threshold with which to compare the feature value.
`tree_node`.`left_child`:::
(Optional, integer)
The index of the left child.
`tree_node`.`right_child`:::
(Optional, integer)
The index of the right child.
`tree_node`.`default_left`:::
(Optional, boolean)
Indicates whether to default to the left when the feature is missing. Defaults
to `true`.
`tree_node`.`split_feature`:::
(Optional, integer)
The index of the feature value in the feature array.
`tree_node`.`node_index`:::
(Integer)
The index of the current node.
`tree_node`.`split_gain`:::
(Optional, double) The information gain from the split.
`tree_node`.`leaf_value`:::
(Optional, double)
The leaf value of the of the node, if the value is a leaf (in other words, no
children).
`ensemble`::
(Optional, object)
The definition for an ensemble model.
`ensemble`.`feature_names`:::
(Required, string)
Features expected by the ensemble, in their expected order.
`ensemble`.`trained_models`:::
(Required, object)
An array of `trained_model` objects. Supported trained models are `tree` and
`ensemble`.
`ensemble`.`classification_labels`:::
(Optional, string)
An array of classification labels.
`ensemble`.`target_type`:::
(Required, string)
String indicating the model target type; `regression` or `classification.`
`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 <<ml-put-inference-model-example>> for more details.
[[ml-put-inference-aggregated-output]]
===== Aggregated output types
`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].
`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.
`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.
`weighted_mode`.`weights`:::
(Required, double)
The weights to multiply by the input values (the inference values of the trained
models).
See <<ml-put-inference-aggregated-output-example>> for more details.
[[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
[[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].