Register H2O Models

Assume we have fitted a H2O model outside PiML workflow

For demonstration, we fit a model using H2O

import h2o
h2o.no_progress()
h2o.init(verbose=False)

import numpy as np
import pandas as pd
from sklearn.datasets import fetch_california_housing
from h2o.estimators import H2OGradientBoostingEstimator

data = fetch_california_housing()
feature_names = data.feature_names
target_name = data.target_names[0]

h2o_data = h2o.H2OFrame(pd.DataFrame(np.hstack([data.data, data.target.reshape(-1, 1)]),
                                     columns=feature_names + [target_name])
                       )
h2o_data_train, h2o_data_test = h2o_data.split_frame(ratios=[0.8], seed=2023)

gbm_model = H2OGradientBoostingEstimator()
gbm_model.train(feature_names, target_name, training_frame=h2o_data_train)

# Save the model to file system
mojo_file_path = gbm_model.save_mojo(path="./")

Then, we can test this model using PiML

from piml import Experiment
exp = Experiment(highcode_only=True)

Load the MOJO model into memory and register it into PiML

imported_model = h2o.import_mojo(mojo_file_path)

pipeline = exp.make_pipeline(model=imported_model,
                             task_type="regression",
                             train_x=h2o_data_train[feature_names].as_data_frame().values,
                             train_y=h2o_data_train[target_name].as_data_frame().values.ravel(),
                             test_x=h2o_data_test[feature_names].as_data_frame().values,
                             test_y=h2o_data_test[target_name].as_data_frame().values.ravel(),
                             feature_names=feature_names,
                             target_name=target_name)
exp.register(pipeline, "H2O-GBM")

Check model performance

exp.model_diagnose(model="H2O-GBM", show="accuracy_table")

           MSE     MAE      R2

Train   0.2423  0.3396  0.8180
Test    0.2377  0.3429  0.8216
Gap    -0.0046  0.0033  0.0037

Explain using post-hoc explanation tools

exp.model_explain(model="H2O-GBM", show="pfi", figsize=(5, 4))

Run validataion tests

exp.model_diagnose(model="H2O-GBM", show="weakspot", slice_features=["MedInc"], figsize=(5, 4))

Estimated memory usage: 34 MB