H statistic: Improve input validation #16759

from h2o.estimators import H2OXGBoostEstimator

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

# Set the predictors and response; set the response as a factor:
titanic["survived"] = titanic["survived"].asfactor()
predictors = titanic.columns
response = "survived"

# Split the dataset into a train and valid set:
train, valid = titanic.split_frame(ratios=[.8], seed=1234)

# Build and train the model:
titanic_xgb = H2OXGBoostEstimator(booster='dart',
                                  normalize_type="tree",
                                  seed=1234)
titanic_xgb.train(x=predictors,
                  y=response,
                  training_frame=train,
                  validation_frame=valid)

# Eval performance:
perf = titanic_xgb.model_performance()

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

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

# Get Friedman and Popescu's H statistics
h = titanic_xgb.h(train, ['fair','age'])
print(h)

Server error java.lang.NullPointerException:
  Error: Cannot read field "_key" because "vecs[i]" is null
  Request: None