Generic Models

Introduction

A Generic model 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 (Distributd Random Forest)
  • IRF (Isolation Random Forest)
  • GLM (Generalized Linear Model)

Importing a Generic Model

Importing a Generic model is available from Python, R, and Flow. As the model is generic, there are no hyperparameters and there is no real training phase for Generic models. 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)
generic_model <- h2o.genericModel(mojo_destination)

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)

generic_model = H2OGenericEstimator.from_file(path)
new_observations = h2o.import_file(path='new_observations.csv')
predictions = generic_model.predict(new_observations)

Retrieving a Generic Model in Flow

To retrieve a Generic model in Flow:

  1. Import or upload the Generic model (most likely a MOJO). To do this, click on Data in the top menu and select either Import Files or Upload File.
  2. Retrieve the Generic model by clicking Models in the top menu and selecting Generic.

Advanced Generic Model Initialization

It is also possible to construct a Generic model from already uploaded MOJO bytes. In this case, there is no need to import the MOJO every time a Generic model is created.

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)