H2OPredict() using GBM model.¶
Setup¶
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import tempfile
import getpass
from teradataml import create_context, DataFrame, save_byom, retrieve_byom, \
delete_byom, list_byom, remove_context, load_example_data, db_drop_table
from teradataml.options.configure import configure
from teradataml.analytics.byom.H2OPredict import H2OPredict
import h2o
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# Create the connection.
host = getpass.getpass("Host: ")
username = getpass.getpass("Username: ")
password = getpass.getpass("Password: ")
con = create_context(host=host, username=username, password=password)
Load example data and use sample() for splitting input data into testing and training dataset.¶
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load_example_data("byom", "iris_input")
iris_input = DataFrame("iris_input")
# Create 2 samples of input data - sample 1 will have 80% of total rows and sample 2 will have 20% of total rows.
iris_sample = iris_input.sample(frac=[0.8, 0.2])
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# Create train dataset from sample 1 by filtering on "sampleid" and drop "sampleid" column as it is not required for training model.
iris_train = iris_sample[iris_sample.sampleid == "1"].drop("sampleid", axis = 1)
iris_train
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# Create test dataset from sample 2 by filtering on "sampleid" and drop "sampleid" column as it is not required for scoring.
iris_test = iris_sample[iris_sample.sampleid == "2"].drop("sampleid", axis = 1)
iris_test
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Prepare dataset for a creating Gradient Boosting Machine model.¶
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h2o.init()
# Since H2OFrame accepts pandas DataFrame, converting teradataml DataFrame to pandas DataFrame.
iris_train_pd = iris_train.to_pandas()
h2o_df = h2o.H2OFrame(iris_train_pd)
h2o_df
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Train Gradient Boosting Machine Model.¶
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# Import required libraries.
from h2o.estimators import H2OGradientBoostingEstimator
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# Add the code for training model.
h2o_df["species"] = h2o_df["species"].asfactor()
predictors = h2o_df.columns
response = "species"
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gbm_model = H2OGradientBoostingEstimator(nfolds=5, seed=1111, keep_cross_validation_predictions = True)
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gbm_model.train(x=predictors, y=response, training_frame=h2o_df)
Save the model in MOJO format.¶
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# Saving H2O Model to a file.
temp_dir = tempfile.TemporaryDirectory()
model_file_path = gbm_model.save_mojo(path=f"{temp_dir.name}", force=True)
Save the model in Vantage.¶
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# Save the H2O Model in Vantage.
save_byom(model_id="h2o_gbm_iris", model_file=model_file_path, table_name="byom_models")
List the models from Vantage.¶
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# List the models from "byom_models".
list_byom("byom_models")
Retrieve the model from Vantage.¶
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# Retrieve the model from vantage using the model name 'h2o_gbm_iris'.
model=retrieve_byom("h2o_gbm_iris", "byom_models")
Set "configure.byom_install_location" to the database where BYOM functions are installed.¶
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configure.byom_install_location = getpass.getpass("byom_install_location: ")
Score the model.¶
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# Score the model on 'iris_test' data.
result = H2OPredict(newdata=iris_test,
newdata_partition_column='id',
newdata_order_column='id',
modeldata=model,
modeldata_order_column='model_id',
model_output_fields=['label', 'classProbabilities'],
accumulate=['id', 'sepal_length', 'petal_length'],
overwrite_cached_models='*',
enable_options='stageProbabilities',
model_type='OpenSource'
)
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# Print the query.
print(result.show_query())
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# Print the result.
result.result
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Cleanup.¶
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# Delete the saved Model.
delete_byom("h2o_gbm_iris", table_name="byom_models")
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# Drop model table.
db_drop_table("byom_models")
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# Drop input data table.
db_drop_table("iris_input")
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# One must run remove_context() to close the connection and garbage collect internally generated objects.
remove_context()
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