Machine Learning Models in Fusion

Fusion provides the following tools required for the model training process:

  • Solr can easily store all your training data.

  • Spark jobs perform the iterative machine learning training tasks.

  • Fusion’s blob store facility makes the final model available for processing new data.

Training Models

Note: The approach for training models explained in this section still works in Fusion 3.1. A new approach introduced in Fusion 3.1 lets you create model-training jobs in the Fusion UI. See Machine Learning in Lucidworks Fusion for more information.

An example Scala script to train an SVM-based sentiment classifier for tweets is provided in the Lucidworks GitHub repo for the spark-solr project, see file SVMExample.scala. The SVMExample code illustrates how to read training data from Solr into Spark using the SparkSQL DataFrame API, how to run the iterations necessary to train an SVM model, and how to store the final model.

The following diagram depicts this process:

Model Training Processes

The class com.lucidworks.spark.fusion.FusionMLModelSupport is used to save the final model into Fusion’s blob store. It uploads the Spark MLlib model directory together with a file named spark-mllib.json.

Model Prediction

Fusion’s blob store requires all stored objects have a unique ID. Once the model is stored in the Fusion blobstore, it is available to Fusion’s index and query Machine Learning pipeline stages, which use the model to make predictions for new data in pipeline documents and queries. The following diagram shows how this process works:

Model Serving Processes

Model Checking

To test the goodness of your model in Fusion, first create either a document indexing pipeline or a query processing pipeline which contains a Machine Learning stage that uses your model to make predictions on your data, and then send a document or query through that pipeline pipeline which contains data for which you know what the predicted value should be. For example, given a trained sentiment classifier and an index stage configured to use it, the following document should be classified as a highly positive tweet, with a value of (close to) 1.0 in the "sentiment_d" field:

{ "id":"tweets-2",
  "fields": [
    { "name": "tweet_txt",
      "value": "I am super excited that spring is finally here, yay! #happy" }

Metadata file "spark-mllib.json"

The file "spark-mllib.json" contains metadata about the model implementation, in particular, how it derives feature vectors from a document or query. The JSON object has the following attributes:

  • "id" - a string label that used a unique ID for the Fusion blobstore, e.g., "tweets_sentiment_svm".

  • "modelClassName" - the name of the spark-mllib class or the custom Java class that implements the interface.

  • "featureFields" - a list of one or more field names.

  • "vectorizer" - specified the processing required to derive a vector of features from the contents of the document fields listed in the "featureFields" entry.

The following example shows the "spark-mllib.json" file for the model with id "tweets_sentiment_svm":

      "lucene-analyzer": {
    }, {

The "vectorizer" consists of two steps: a lucene-analyzer step followed by a hashingTF step. The lucene-analyzer step can use any Lucene analyzer to perform text analysis. For more information about using the lucene-analyzer, see:

Other available vectorizer operations include: the MLlib normalizer, standard scaler, and ChiSq selector. See SVMExample.scala for an example of how to use the standard scaler.