Package an algorithm for Splunkbase
Complete the following steps to package an algorithm for Splunkbase:
For more information on Splunkbase, see Publish apps for Splunk Cloud Platform or Splunk Enterprise to Splunkbase on the Splunk Developer Portal.
Create the app in Splunkbase
To learn how to build an app in Splunkbase, see Create a Splunk app in the Splunk Developer Portal. And before choosing a name for your app, see Naming Conventions for apps and add-ons.
You do not need to load upload assets in the app.
The following fields are required for any app being added to Splunkbase:
Required field | Example input |
---|---|
Name | application name
|
Folder name | folder name
|
Template | barebones
|
Add the custom algorithm
The process of adding a custom algorithm to an app is similar to adding an algorithm to the Splunk Machine Learning Toolkit (MLTK). Complete the following steps to add an algorithm to your Splunkbase app:
You need access to the application's file system to add a custom algorithm to the app.
Name the custom algorithm
There are restrictions on algorithm names in MLTK. These namespace constraints apply to individual packaging in the application, but only affect the user of the application.
- The algorithm name must be unique across all of MLTK and its add-ons.
- You cannot use
algos
as apackage_name
, becausealgos
is the default folder for MLTK. - Any references to algorithm source files in the
register_codecs
method must also reference the same package name.
Example
Following installation of the SVR_app
application, there must be no other instances of SVR.py
within MLTK environment. If there is more than one instance, the most recently added copy takes precedence.
Add the implementation file
The following example uses the algorithm Support Vector Regression
, which is referred to as SVR
:
- Open the directory
SPLUNK_HOME/etc/apps/SVR_app/bin/
- Create a folder inside the bin folder of the app. For example,
app_algos
. The folder name must conform to the namespace constraints. - Create an empty file within the folder. For example,
__init__.py
. This step converts the directory into a python package, and lets you import modules such asSVR
. - Create another empty file within that same folder. For example,
SVR.py
. - Add the following lines of code to
SVR.py
:from sklearn.svm import SVR as _SVR
from base import BaseAlgo, RegressorMixin
from util.param_util import convert_params
class SVR(RegressorMixin, BaseAlgo):
def __init__(self, options): self.handle_options(options)
params = options.get('params', {}) out_params = convert_params( params, floats=['C', 'gamma'], strs=['kernel'], ints=['degree'], )
self.estimator = _SVR(**out_params)
@staticmethod def register_codecs(): from codec.codecs import SimpleObjectCodec from codec import codecs_manager codecs_manager.add_codec('app_algos.SVR', 'SVR', SimpleObjectCodec) codecs_manager.add_codec('sklearn.svm.classes', 'SVR', SimpleObjectCodec)
Modify the algorithm configuration file
The following example registers the algorithm SVR
and identifies the location of algorithm.py
in the directory of the Splunk Machine Learning Toolkit. Complete the following steps to modify the algorithm configuration file:
- Add a configuration file name
algos.conf
to the directorySPLUNK_HOME/etc/apps/SVR_app/local/
. - Add the following code to the
algos.conf
file:[SVR] package=app_algos disabled=false
The stanza
algorithm class name
, must always match the name of thealgorithm.py
. So, in this example[SVR]
matches with theSVR.py
file contained in the packageSPLUNK_HOME/etc/apps/<app_name>/bin/<app_algos>/
.For the Splunk Machine Learning Toolkit to find the
algos.conf
file, you must export its content system-wide. - Open the
SPLUNK_HOME/etc/apps/SVR_app/metadata/local.meta
file and add the following code:[algos] export = system
[algos]
is not configurable. Any other name will not be recognized by the Splunk Machine Learning Toolkit. - Restart Splunk Enterprise.
Test the custom algorithm
When you export algos.conf
system-wide, you can then use ML-SPL commands to reference the algorithm in MLTK and any MLTK add-on.
Test in the MLTK default search application
When you create and export an algorithm, you can call it the same way you call any algorithm shipped with MLTK.
Complete the following steps to test the algorithm in the search application:
- Navigate to the search tab in MLTK.
- Input the following SPL:
|inputlookup iris.csv | fit SVR petal_width from sepal_length
If your code executes without errors, then your custom algorithm is working correctly.
Test in an add-on
The process for calling an algorithm is the same when working within an add-on as in the MLTK search tab.
To test the example algorithm in the add on:
- Navigate to an application
app_name
from Splunk Enterprise home page. - Enter the following SPL:
index=_internal | head 1000 | fit SVR data_hour from cpu_seconds
If your code executes without errors, then your custom algorithm is working correctly.
Using codecs | Correlation Matrix example |
This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 5.1.0, 5.2.0, 5.2.1, 5.2.2, 5.3.0, 5.3.1, 5.3.3, 5.4.0, 5.4.1, 5.4.2, 5.5.0
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