Splunk® Machine Learning Toolkit


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Custom algorithm template

You can use the following custom algorithm template to help get you started.

BaseAlgo class

From base import BaseAlgo.

class CustomAlgoTemplate(BaseAlgo):
    def __init__(self, options):
        # Option checking & initializations here

    def fit(self, df, options):
        # Fit an estimator to df, a pandas DataFrame of the search results

    def partial_fit(self, df, options):
        # Incrementally fit a model

    def apply(self, df, options):
        # Apply a saved model
        # Modify df, a pandas DataFrame of the search results
        return df

    def register_codecs():
        # Add codecs to the codec manager

Using the template above in a search, as in the example below, reflects the input data back to the search.

| fit CustomAlgoTemplate *

These are all described in detail in the $SPLUNK_HOME/etc/apps/Splunk_ML_Toolkit/bin/base.py BaseAlgo class as shown below.


Last modified on 01 August, 2019
Write a Python algorithm class
Running process and method calling conventions

This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 4.4.0, 4.4.1, 4.4.2, 4.5.0, 5.0.0, 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

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