This documentation is for Machine Learner 1.1.0. View documentation for the latest release.
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Introduction

This sample demonstrates how a model is generated out of an unlabeled dataset using the k-means anomaly detection algorithm. The sample uses a dataset to generate a model.

Prerequisites

Follow the steps below to set up the prerequisites before you start.

  1. Download WSO2 Machine Learner, and start the server. For information on setting up and running WSO2 ML, see Getting Started.
  2. Download and install jq (CLI JSON Processor). For detailed instructions, see jq Documentation.
  3. If you are using Mac OS X, download and install GNU stream editor (sed). For instructions, see GNU sed Documentation.

Executing the sample

Follow the steps below to execute the sample.

  1. Navigate to the <ML_HOME>/samples/default/anomaly-detection-unlabeled-data directory in your CLI.
  2. Issue the following command to execute the sample.
    ./model-generation.sh 

Analyzing the output

Once the sample is successfully executed, you can view the summary and the prediction of the model  as described below.

By default, the sample generates the model in the <ML_HOME>/models/  directory of your machine. For example, the generated file is in the following format denoting the date and time when it was generated:  wso2-ml-anomaly-detection-labeled-data-sample-analysis.Model.2015-11-20_11-00-12.

  1. Open the ML UI using the https://<ML_HOST>:<ML_PORT>/ml URL. Enter admin as both the user name and the password to log in.
  2. Click Projects.
     
  3. Click Models to view the models of the wso2-ml-anomaly-detection-unlabeled-data-sample-analysis analysis that was created when the sample was executed.
     
  4. Click View on the model. The generated model is displayed as shown below.
     

Viewing the model prediction

The sample executes the generated model on the <ML_HOME>/samples/default/anomaly-detection-unlabeled-data/prediction-test data set, and it prints the value ["anomaly"] as the prediction result in the CLI logs.

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