280 lines
9.7 KiB
Plaintext
280 lines
9.7 KiB
Plaintext
[role="xpack"]
|
|
[testenv="platinum"]
|
|
[[ml-dfanalytics-resources]]
|
|
=== {dfanalytics-cap} job resources
|
|
|
|
{dfanalytics-cap} resources relate to APIs such as <<put-dfanalytics>> and
|
|
<<get-dfanalytics>>.
|
|
|
|
[discrete]
|
|
[[ml-dfanalytics-properties]]
|
|
==== {api-definitions-title}
|
|
|
|
`analysis`::
|
|
(object) The type of analysis that is performed on the `source`. For example:
|
|
`outlier_detection` or `regression`. For more information, see
|
|
<<dfanalytics-types>>.
|
|
|
|
`analyzed_fields`::
|
|
(object) You can specify both `includes` and/or `excludes` patterns. If
|
|
`analyzed_fields` is not set, only the relevant fields will be included. For
|
|
example, all the numeric fields for {oldetection}. For the supported field
|
|
types, see <<ml-put-dfanalytics-supported-fields>>.
|
|
|
|
`includes`:::
|
|
(array) An array of strings that defines the fields that will be included in
|
|
the analysis.
|
|
|
|
`excludes`:::
|
|
(array) An array of strings that defines the fields that will be excluded
|
|
from the analysis.
|
|
|
|
|
|
[source,console]
|
|
--------------------------------------------------
|
|
PUT _ml/data_frame/analytics/loganalytics
|
|
{
|
|
"source": {
|
|
"index": "logdata"
|
|
},
|
|
"dest": {
|
|
"index": "logdata_out"
|
|
},
|
|
"analysis": {
|
|
"outlier_detection": {
|
|
}
|
|
},
|
|
"analyzed_fields": {
|
|
"includes": [ "request.bytes", "response.counts.error" ],
|
|
"excludes": [ "source.geo" ]
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// TEST[setup:setup_logdata]
|
|
|
|
`description`::
|
|
(Optional, string) A description of the job.
|
|
|
|
`dest`::
|
|
(object) The destination configuration of the analysis.
|
|
|
|
`index`:::
|
|
(Required, string) Defines the _destination index_ to store the results of
|
|
the {dfanalytics-job}.
|
|
|
|
`results_field`:::
|
|
(Optional, string) Defines the name of the field in which to store the
|
|
results of the analysis. Default to `ml`.
|
|
|
|
`id`::
|
|
(string) The unique identifier for the {dfanalytics-job}. This identifier can
|
|
contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and
|
|
underscores. It must start and end with alphanumeric characters. This property
|
|
is informational; you cannot change the identifier for existing jobs.
|
|
|
|
`model_memory_limit`::
|
|
(string) The approximate maximum amount of memory resources that are
|
|
permitted for analytical processing. The default value for {dfanalytics-jobs}
|
|
is `1gb`. If your `elasticsearch.yml` file contains an
|
|
`xpack.ml.max_model_memory_limit` setting, an error occurs when you try to
|
|
create {dfanalytics-jobs} that have `model_memory_limit` values greater than
|
|
that setting. For more information, see <<ml-settings>>.
|
|
|
|
`source`::
|
|
(object) The source configuration consisting an `index` and optionally a
|
|
`query` object.
|
|
|
|
`index`:::
|
|
(Required, string or array) Index or indices on which to perform the
|
|
analysis. It can be a single index or index pattern as well as an array of
|
|
indices or patterns.
|
|
|
|
`query`:::
|
|
(Optional, object) The {es} query domain-specific language
|
|
(<<query-dsl,DSL>>). This value corresponds to the query object in an {es}
|
|
search POST body. All the options that are supported by {es} can be used,
|
|
as this object is passed verbatim to {es}. By default, this property has
|
|
the following value: `{"match_all": {}}`.
|
|
|
|
[[dfanalytics-types]]
|
|
==== Analysis objects
|
|
|
|
{dfanalytics-cap} resources contain `analysis` objects. For example, when you
|
|
create a {dfanalytics-job}, you must define the type of analysis it performs.
|
|
|
|
[discrete]
|
|
[[oldetection-resources]]
|
|
==== {oldetection-cap} configuration objects
|
|
|
|
An `outlier_detection` configuration object has the following properties:
|
|
|
|
`compute_feature_influence`::
|
|
(boolean) If `true`, the feature influence calculation is enabled. Defaults to
|
|
`true`.
|
|
|
|
`feature_influence_threshold`::
|
|
(double) The minimum {olscore} that a document needs to have in order to
|
|
calculate its {fiscore}. Value range: 0-1 (`0.1` by default).
|
|
|
|
`method`::
|
|
(string) Sets the method that {oldetection} uses. If the method is not set
|
|
{oldetection} uses an ensemble of different methods and normalises and
|
|
combines their individual {olscores} to obtain the overall {olscore}. We
|
|
recommend to use the ensemble method. Available methods are `lof`, `ldof`,
|
|
`distance_kth_nn`, `distance_knn`.
|
|
|
|
`n_neighbors`::
|
|
(integer) Defines the value for how many nearest neighbors each method of
|
|
{oldetection} will use to calculate its {olscore}. When the value is not set,
|
|
different values will be used for different ensemble members. This helps
|
|
improve diversity in the ensemble. Therefore, only override this if you are
|
|
confident that the value you choose is appropriate for the data set.
|
|
|
|
`outlier_fraction`::
|
|
(double) Sets the proportion of the data set that is assumed to be outlying prior to
|
|
{oldetection}. For example, 0.05 means it is assumed that 5% of values are real outliers
|
|
and 95% are inliers.
|
|
|
|
`standardization_enabled`::
|
|
(boolean) If `true`, then the following operation is performed on the columns
|
|
before computing outlier scores: (x_i - mean(x_i)) / sd(x_i). Defaults to
|
|
`true`. For more information, see
|
|
https://en.wikipedia.org/wiki/Feature_scaling#Standardization_(Z-score_Normalization)[this wiki page about standardization].
|
|
|
|
|
|
[discrete]
|
|
[[regression-resources]]
|
|
==== {regression-cap} configuration objects
|
|
|
|
[source,console]
|
|
--------------------------------------------------
|
|
PUT _ml/data_frame/analytics/house_price_regression_analysis
|
|
{
|
|
"source": {
|
|
"index": "houses_sold_last_10_yrs" <1>
|
|
},
|
|
"dest": {
|
|
"index": "house_price_predictions" <2>
|
|
},
|
|
"analysis":
|
|
{
|
|
"regression": { <3>
|
|
"dependent_variable": "price" <4>
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// TEST[skip:TBD]
|
|
|
|
<1> Training data is taken from source index `houses_sold_last_10_yrs`.
|
|
<2> Analysis results will be output to destination index
|
|
`house_price_predictions`.
|
|
<3> The regression analysis configuration object.
|
|
<4> Regression analysis will use field `price` to train on. As no other
|
|
parameters have been specified it will train on 100% of eligible data, store its
|
|
prediction in destination index field `price_prediction` and use in-built
|
|
hyperparameter optimization to give minimum validation errors.
|
|
|
|
|
|
[float]
|
|
[[regression-resources-standard]]
|
|
===== Standard parameters
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=dependent_variable]
|
|
+
|
|
--
|
|
The data type of the field must be numeric.
|
|
--
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=prediction_field_name]
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=training_percent]
|
|
|
|
|
|
[float]
|
|
[[regression-resources-advanced]]
|
|
===== Advanced parameters
|
|
|
|
Advanced parameters are for fine-tuning {reganalysis}. They are set
|
|
automatically by <<ml-hyperparameter-optimization,hyperparameter optimization>>
|
|
to give minimum validation error. It is highly recommended to use the default
|
|
values unless you fully understand the function of these parameters. If these
|
|
parameters are not supplied, their values are automatically tuned to give
|
|
minimum validation error.
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=eta]
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=feature_bag_fraction]
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=maximum_number_trees]
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=gamma]
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=lambda]
|
|
|
|
|
|
[discrete]
|
|
[[classification-resources]]
|
|
==== {classification-cap} configuration objects
|
|
|
|
|
|
[float]
|
|
[[classification-resources-standard]]
|
|
===== Standard parameters
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=dependent_variable]
|
|
+
|
|
--
|
|
The data type of the field must be numeric or boolean.
|
|
--
|
|
|
|
`num_top_classes`::
|
|
(Optional, integer) Defines the number of categories for which the predicted
|
|
probabilities are reported. It must be non-negative. If it is greater than the
|
|
total number of categories (in the {version} version of the {stack}, it's two)
|
|
to predict then we will report all category probabilities. Defaults to 2.
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=prediction_field_name]
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=training_percent]
|
|
|
|
|
|
[float]
|
|
[[classification-resources-advanced]]
|
|
===== Advanced parameters
|
|
|
|
Advanced parameters are for fine-tuning {classanalysis}. They are set
|
|
automatically by <<ml-hyperparameter-optimization,hyperparameter optimization>>
|
|
to give minimum validation error. It is highly recommended to use the default
|
|
values unless you fully understand the function of these parameters. If these
|
|
parameters are not supplied, their values are automatically tuned to give
|
|
minimum validation error.
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=eta]
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=feature_bag_fraction]
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=maximum_number_trees]
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=gamma]
|
|
|
|
include::{docdir}/ml/ml-shared.asciidoc[tag=lambda]
|
|
|
|
|
|
[[ml-hyperparameter-optimization]]
|
|
===== Hyperparameter optimization
|
|
|
|
If you don't supply {regression} or {classification} parameters, hyperparameter
|
|
optimization will be performed by default to set a value for the undefined
|
|
parameters. The starting point is calculated for data dependent parameters by
|
|
examining the loss on the training data. Subject to the size constraint, this
|
|
operation provides an upper bound on the improvement in validation loss.
|
|
|
|
A fixed number of rounds is used for optimization which depends on the number of
|
|
parameters being optimized. The optimitazion starts with random search, then
|
|
Bayesian Optimisation is performed that is targeting maximum expected
|
|
improvement. If you override any parameters, then the optimization will
|
|
calculate the value of the remaining parameters accordingly and use the value
|
|
you provided for the overridden parameter. The number of rounds are reduced
|
|
respectively. The validation error is estimated in each round by using 4-fold
|
|
cross validation. |