Rajiv Shah

add py script

e87dc49 over 4 years ago

1.17 kB

# %%
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')

	# %%
	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')