MOJO Capabilities¶

This section describes the basics of working with the MOJO model in H2O-3.

About H2O Generated MOJO Models¶

For information about quick starting and building MOJOs, look here. For information on how to use the h2o-genmodel, look here.

The h2o-genmodel API contains:

hex.genmodel.algos

All algorithms that support the MOJO model can be found here. These models can be loaded and directly used to score or perform other model-specific actions. For further documentation of the methods, refer to the javadoc of the GenModel class.

hex.genmodel.easy.EasyPredictModelWrapper

This wrapper gives MOJO models an easy, readable interface for scoring and other model-specific actions.

hex.genmodel.easy.prediction

This gives the predictions that can be called from the EasyPredictModelWrapper.predict() command. For more information, refer to the javadoc of the prediction classes.

hex.genmodel.easy.CategoricalEncoder

Classes from this interface can be used to preprocess raw data values to the proper categorical values expected by the model.

hex.genmodel.attributes.metrics

This package provides the different metrics for model-specific needs.

hex.genmodel.tools

These java command line tools can be used for various types of applications: printing decision trees, reading a CSV file and making predictions, reading a CSV file and munging it, etc.

Predicting Values with MOJO¶

MojoModel mojoModel = MojoModel.load("isolation_forest.zip");
double [] predictions = new double[]{Double.NaN, Double.NaN};
mojoModel.score0(new double[]{100, 100}, predictions);
System.out.println(Arrays.toString(predictions));

IsolationForestMojoModel mojoModel = (IsolationForestMojoModel) MojoModel.load("isolation_forest.zip");

EasyPredictModelWrapper.Config config = new EasyPredictModelWrapper.Config()
        .setModel(mojoModel);
EasyPredictModelWrapper model = new EasyPredictModelWrapper(config);

RowData row = new RowData();
row.put("x", "100");
row.put("y", "100");

AnomalyDetectionPrediction p = (AnomalyDetectionPrediction) model.predict(row);
System.out.println("[" + p.normalizedScore + ", " + p.score + "]");

Metadata Contained in the MOJO Model¶

All h2o-3 models contains some metadata. To access this, type the following respective commands:

Type the <model variable name> within the console.

# All metadata
model._model_json

# Useful metadata
model._model_json['output']

# Description of available keys
model._model_json['output']['help']

// Must call with metadata flag set to True
MojoModel model = MojoModel.load("GBM_model.zip", true);
ModelAttributes attributes = model._modelAttributes;

All MOJO models contain the following attributes:

// Correspond to model._model_json['output']['model_summary'] (Number of trees, Size of model,..)
attributes.getModelSummary();

// Correspond to model._model_json['output']['scoring_history']
attributes.getScoringHistory();

// Correspond to model._model_json['output']['training_metrics']
// but only some values are available (MSE, RMSE,...)
// and for example confusion Matrix and other is omitted.
attributes.getTrainingMetrics();

// Correspond to model._model_json['output']['validation_metrics']
// but only some values are available (MSE, RMSE,...)
// and for example confusion Matrix and other is omitted.
attributes.getValidationMetrics();

// Correspond to model._model_json['output']['cross_validation_metrics']
// but only some values are available (MSE, RMSE,...)
// and for example confusion Matrix and other is omitted.
attributes.getCrossValidationMetrics();

// Correspond to model._model_json['output']['cross_validation_metrics_summary']
attributes.getCrossValidationMetricsSummary();

// Model parameters setting when the model was built
attributes.getModelParameters();

Accessing Model Trees¶

The following example shows a way to access the number of trees from the model:

# Build and train your model
model <- h2o.randomForest(...)

# Print the number of trees
print(paste("Number of Trees: ", model@allparameters$ntrees))

# Build and train your model
model = H2ORandomForestEstimator(...)
model.train(...)

# Print the number of trees
print("Number of Trees: {}".format(model._model_json["output"]["model_summary"]["number_of_trees"]))

// Load the MOJO model
MojoModel model = MojoModel.load("rf_model.zip", true);

// Retrieve the model attributes
ModelAttributes attributes = model._modelAttributes;
System.out.print(attributes.getModelSummary().getColHeaders()[1] + ": ");
System.out.println(attributes.getModelSummary().getCell(1,0));

ModelAttributes Subclasses¶

Subclasses of ModelAttributes are used to handle model-specific attributes (e.g. Variable Importance).

// Must call with metadata flag set to True
MojoModel model = MojoModel.load("GBM_model.zip", true);
SharedTreeModelAttributes attributes = ((SharedTreeModelAttributes) model._modelAttributes);
String[] variables = attributes.getVariableImportances()._variables;
double[] importances = attributes.getVariableImportances()._importances;
System.out.print(variables[0] + ": ");
System.out.println(importances[0]);

MojoModel modelMojo = MojoModel.load("GBM_model.zip", true);
EasyPredictModelWrapper.Config config = new EasyPredictModelWrapper.Config().setModel(modelMojo);
EasyPredictModelWrapper model = new EasyPredictModelWrapper(config);
KeyValue[] importances = model.varimp();
System.out.print(importances[0].getKey() + ": ");
System.out.println(importances[0].getValue());