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h2o_gbm_prostate

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h2o_gbm_prostate / H2O_Example.py

Rajiv Shah

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e87dc49 almost 2 years ago

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	# %%
	import h2o

	# %%
	h2o.__version__

	# %%
	h2o.init()

	# %%
	from h2o.estimators import H2OGradientBoostingEstimator
	h2o.init(jvm_custom_args=["sys.ai.h2o.debug.allowJavaVersions", "18"])

	# Import the prostate dataset into H2O:
	prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv")

	# Set the predictors and response; set the factors:
	prostate["CAPSULE"] = prostate["CAPSULE"].asfactor()
	predictors = ["ID","AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON"]
	response = "CAPSULE"

	# Build and train the model:
	pros_gbm = H2OGradientBoostingEstimator(nfolds=5,
	seed=1111,
	keep_cross_validation_predictions = True)
	pros_gbm.train(x=predictors, y=response, training_frame=prostate)

	# Eval performance:
	perf = pros_gbm.model_performance()

	# Generate predictions on a test set (if necessary):
	pred = pros_gbm.predict(prostate)

	# Extract feature interactions:
	feature_interactions = pros_gbm.feature_interaction()

	# %%
	feature_interactions

	# %%
	#save model
	h2o.save_model(model=pros_gbm, force=True)

	# %%
	pros_gbm.save_mojo('mojo')