Search commands for machine learning

The Splunk Machine Learning Toolkit (MLTK) contains several custom search commands, referred to as ML-SPL commands. You can use these custom search commands on any Splunk platform instance on which the MLTK is installed.

ML-SPL commands implement classic machine learning and statistical learning tasks including:

ML-SPL command	Description
fit	Fit and apply a machine learning model to search results.
apply	Apply a machine learning model that was learned using the fit command.
summary	Return a summary of a machine learning model that was learned using the fit command.
listmodels	Return a list of machine learning models that were learned using the fit command.
deletemodel	Delete a machine learning model that was learned using the fit command.
sample	Randomly sample or partition events.
score	Run statistical tests to validate model outcomes.

ML-SPL commands follow the same syntax as other SPL commands in the Splunk platform. For more details on this syntax, see Understanding SPL syntax.

You can also configure the performance costs of the fit and apply commands. For details, see Configure algorithm performance costs.

You can download the Machine Learning Toolkit Quick Reference Guide (also available in Japanese) for a handy cheat sheet of ML-SPL commands and machine learning algorithms used in the Splunk Machine Learning Toolkit.

fit

Use the fit command to fit and apply a machine learning model to search results. The syntax is the same for supervised (labeled data) and unsupervised (unlabeled data) learning.

Syntax

The first argument for the algorithm is required. The options following the algorithm vary depending on the algorithm chosen.

    fit <algorithm> [option_name]=[option_value]... [into <model_name>]

Some algorithms require a response-field.

    fit <algorithm> [option_name]=[option_value]...<response-field> [into <model_name>]

Some algorithms require an explanatory-field.

    fit <algorithm> [option_name]=[option_value]...<explanatory-field> [into <model_name>]

Some algorithms require both a response-field and an explanatory-field. The from field is only required if both the response-field and explanatory-field are present.

    fit <algorithm> [option_name]=[option_value]...<response-field> from <explanatory-field> [into <model_name>]

Use the into keyword to store the learned model in an artifact that can later be applied to new search results with the apply command.

Not all algorithms support saved models. For details on all the algorithms that ship with the MLTK, see Algorithms in the Machine Learning Toolkit.

Examples

The following example fits a LinearRegression model to predict errors using _time.

   ... | fit LinearRegression errors from _time

The following example fits a LinearRegression model to predict errors using _time and saves it into a model named errors_over_time.

   ... | fit LinearRegression errors from _time into errors_over_time

The following example fits a LogisticRegression model to predict a categorical response from numerical measurements.

   ... | fit LogisticRegression species from petal_length petal_width sepal_length sepal_width

apply

Use the apply command to compute predictions for the current search results based on a model that was learned by the fit command. The apply command can be used on different search results than those used when fitting the model, but the results should have an identical list of fields.

Syntax

   apply <mode_name> [as <output_field>] 

Use the as keyword to rename the field added to search results by the model.

Examples

The following example applies a learned LinearRegression model named errors_over_time.

   ... | apply errors_over_time

The following example renames the output of the model to predicted_errors.

   ... | apply errors_over_time as predicted_errors

summary

Use the summary command to return a summary of a machine learning model that was learned using the fit command. The summary is algorithm specific. For example, the summary for the LinearRegression algorithm is a list of coefficients. The summary for the LogisticRegression algorithm is a list of coefficients for each class.

Syntax

   summary <model_name>

Examples

The following example inspects a learned LinearRegression model named errors_over_time.

   | summary errors_over_time

listmodels

Use the listmodels command to return a list of machine learning models that were learned using the fit command. The algorithm and arguments given when fit was invoked are displayed for each model.

Syntax

   listmodels

Example

The following example lists all models.

   | listmodels

deletemodel

Use the deletemodel command to delete a machine learning model learned using the fit command.

Syntax

   deletemodel <model_name>

Example

The following example deletes the model named errors_over_time.

   | deletemodel errors_over_time

sample

Use the sample command to randomly sample or partition events. The command samples in one of three modes:

• ratio: Returns an event with the given probability
• count: Returns exactly that number of events
• proportional: Samples each event with probability specified by a field value

A fourth mode of partitioning randomly divides events into a given number of partitions.

Refer to the following table for more details on the sample command modes and additional options.

Mode or option	Name	Description
Sampling mode	ratio	A float between 0 and 1 indicating the probability as a percentage that each event has of being included in the result set. For example, a ratio of 0.01 means that events have a 1% probability of being included in the results. Use ratio when you want an approximation.
	count	A number that indicates the exact number of randomly-chosen events to return. If the sample count exceeds the total number of events in the search, all events are returned.
	proportional	The name of a numeric field to use to determine the sampling probability of each event, which yields a biased sampling. Each event is sampled with a probability specified by this field value.
Partitioning mode	partitions	Use partitions to specify the number of partitions in which to randomly divide events, approximately split. Use partitions when you want to divide your results into groups for different purposes, such as using results for testing and training.
Additional option	seed	A number that specifies a random seed. Using seed ensures reproducible results. If unspecified, a pseudorandom value is used.
	count by <field>	Specifies a field by which to split events, returning the count number of events for each value of the specified field. If there are more events than count, all events are included in the results.
	inverse	Use with proportional. Inverts the probability, returning samples with one minus the probability specified in the proportional field.
	fieldname	The name of the field in which to store the partition number. Defaults to partition_number.

This sample command is not identical to using sampling options on the Event Sampling menu on the Search page in Splunk Web:

• Options from the Event Sampling menu perform sampling before the data is collected from indexes, at the beginning of the search pipeline.
• The sample command is applied after data is collected, accessing everything in the search pipeline.

Using the Event Sampling menu option is faster, but the sample command is usable anywhere in the search command and provides several modes that are not available to the Event Sampling feature. For example, the sample command supports partitioning, biased sampling, and the ability to retrieve an exact number of results.

Syntax

 sample [ratio=<float between 0 and 1>] [count=<positive integer>] [proportional=<name of numeric field> [inverse]] [partitions=<natural number  greater than 1> [fieldname=<string>]] [seed=<number>] [by <split_by_field>]

Examples

The following example uses the ratio keyword and retrieves approximately 1% of all events at random.

   ... | sample ratio=0.01

The following example uses the count keyword and retrieves exactly 20 events at random.

   ... | sample count=20

The following example uses the count keyword and retrieves exactly 20 events at random from each host.

   ... | sample count=20 by host

The following example uses the proportional keyword and returns each event with a probability determined by the value of some_field.

   ... | sample proportional="some_field"

The following example partitions events into seven groups, with the chosen group returned in a field called partition_number.

   ... | sample partitions=7 fieldname="partition_number"

score

The score command runs statistical tests to validate model outcomes. Use the score command to validate models and statistical tests for any use case. Choose the scoring method best suited to your data and problem you want to solve with score.

Syntax

The first argument for the scoring method is required. The options following the scoring method vary depending on the scoring method chosen.

Some scoring methods support pairwise comparisons between two sets of fields.

   ... | score <scoring-method-name> a_field_1 a_field_2 ... a_field_n against b_field_1 b_field_2 … b_field_m

Some scoring methods support pairwise comparisons between two sets of arrays.

  ... | score <scoring-method-name> array_a against array_b [options]

Some scoring methods are specific to the evaluation of clustering models.

   ... | score <scoring-method-name> <label_field> against <feature_field_1> ... <feature_field_n> metric=<options>

Example

The following example uses the score command on test data.

   ... | score confusion_matrix true="species" pred="predicted(species)"

The MLTK includes the following classes of the score command, each with their own sets of methods.

• Classification
• Clustering scoring
• Pairwise distances scoring
• Regression scoring
• Statistical functions (statsfunctions)
• Statistical testing (statstest)

Score commands are not customizable within the Splunk Machine Learning Toolkit.

The MLTK helps you test for model overfitting through the K-fold scoring option. For details, see K-fold scoring.