MOJO Models

Introduction

The MOJO import functionality provides a means to use external, pre-trained models in H2O - mainly for the purpose of scoring. Depending on each external model, metrics and other model information might be obtained as well. Currently, only selected H2O MOJOs are supported. (See the MOJO Quick Start section for information about creating MOJOs.)

Supported MOJOs

Only a subset of H2O MOJO models is supported in this version.

  • GBM (Gradient Boosting Machines)
  • DRF (Distributed Random Forest)
  • IRF (Isolation Random Forest)
  • GLM (Generalized Linear Model)

Importing a MOJO

Importing a MOJO can be done from Python, R, and Flow. H2O imports the model and embraces it for the purpose of scoring. Information output about the model may be limited.

Importing in R or Python

data <- h2o.importFile(path = 'training_dataset.csv')
cols <- c("Some column", "Another column")
original_model <- h2o.glm(x=cols, y = "response", training_frame = data)

path <- "/path/to/model/directory"
mojo_destination <- h2o.download_mojo(model = original_model, path = path)
imported_model <- h2o.import_mojo(mojo_destination)

new_observations <- h2o.importFile(path = 'new_observations.csv')
h2o.predict(imported_model, new_observations)

data = h2o.import_file(path='training_dataset.csv')
original_model = H2OGeneralizedLinearEstimator()
original_model.train(x = ["Some column", "Another column"], y = "response", training_frame=data)

path = '/path/to/model/directory/model.zip'
original_model.download_mojo(path)

imported_model = h2o.import_mojo(path)
new_observations = h2o.import_file(path='new_observations.csv')
predictions = imported_model.predict(new_observations)

Importing a MOJO Model in Flow

To import a MOJO model in Flow:

  1. Import or upload the MOJO as a Generic model into H2O. To do this, click on Data in the top menu and select either Import Files or Upload File.
  2. Retrieve the imported MOJO by clicking Models in the top menu and selecting Generic Model.

Advanced Generic Model Initialization

It is also possible to import a MOJO from already uploaded MOJO bytes using Generic model. Generic model is the underlying mechanism behind MOJO import. In this case, there is no need to re-upload the MOJO every time a new MOJO imported model is created. The upload may occur only once.

Defining a Generic Model

The following options can be specified when using a Generic model:

  • model_id: Specify a custom name for the model to use as a reference.
  • model_key: Specify a key for the self-contained model archive.
  • path: Specify a path to the file with the self-contained model archive.

Examples

data <- h2o.importFile(path = 'training_dataset.csv')
cols <- c("Some column", "Another column")
original_model <- h2o.glm(x=cols, y = "response", training_frame = data)

path <- "/path/to/model/directory"
mojo_destination <- h2o.download_mojo(model = original_model, path = path)

# Only import or upload MOJO model data, do not initialize the generic model yet
imported_mojo_key <- h2o.importFile(mojo_destination, parse = FALSE)
# Build the generic model later, when needed
generic_model <- h2o.generic(model_key = imported_mojo_key)

new_observations <- h2o.importFile(path = 'new_observations.csv')
h2o.predict(generic_model, new_observations)

data = h2o.import_file(path='training_dataset.csv')
original_model = H2OGeneralizedLinearEstimator()
original_model.train(x = ["Some column", "Another column"], y = "response", training_frame=data)

path = '/path/to/model/directory/model.zip'
original_model.download_mojo(path)

imported_mojo_key = h2o.lazy_import(file)
generic_model = H2OGenericEstimator(model_key = get_frame(model_key[0]))
new_observations = h2o.import_file(path='new_observations.csv')
predictions = generic_model.predict(new_observations)