January 18th, 2019

H2O New Year releases

Category: H2O, H2O Release, Python, R

There were two releases shortly after each other. First, on December 21st, there was a minor (fix) release Immediately followed by a more major release (but still on 3.22 branch) codename Xu, named after mathematician Jinchao Xu, whose work is focused on deep neural networks, besides many other fields of research.

Of course, the new release with codename Xu contains all the fixes present in previous fix release The following points are highlighting the most impactful changes. For a complete list of changes, fixes and improvements, please read the recent changes section.

Support for CDH 6.x

H2O now declares support for Cloudera distributions based on Hadoop 3. This includes releases by Cloudera CDH 6.0, CDH 6.1. Support for releases by Hortonworks HDP 3.0 and HDP 3.1 are going to be added shortly in one of the releases to follow.

Partial dependence plots can be exported

The API to plot partial dependence graphs has been enhanced with the ability to directly export (save to disk) the plots. For both R and Python API, a new parameter named save_to_file has been added. The parameter accepts a string representing a path on a filesystem.


The existing method partial_plot callable on any Model able has been modified by adding the new save_to_file parameter. All the plots are saved into one image file.

model.partial_plot(data = data, cols = ['AGE', 'RACE', 'DCAPS'],
                   server = True, plot = True, 
                   save_to_file = "/home/username/pdp.png")


H2O provides function h2o.partialPlot in R to create the plots. A new optional parameter named save_to has been introduced for this function. In R, different plots are traditionally represented by a separate pointer to a separate image. To honor this contract in H2O R API, the plots are not saved into one file, but one file is created per each feature.

h2o.partialPlot(object = model, data = data, save_to = "/home/username/pdp")

If the model mentioned in the showcase above has been trained using a set of three features: [Age, RACE, DCAPS], then H2O is going to save three files onto the filesystem:

  • /home/username/pdp_AGE.png
  • /home/username/pdp_RACE.png
  • /home/username/pdp_DCAPS.png

That is the reason why the string containing the filesystem path does not include png suffix. It is added automatically. If the suffix is added accidentally, it is stripped by H2O automatically.

Monotonicity constraints for GBM

H2O users now have the ability to affect GBM splits by stating monotonic relationships of a feature to the predicted variable. Any subset of features used during model training phase might be restricted with monotonicity constraints.


A new argument named monotone_constraints has been added to the H2OXGBoostEstimator’s constructor. This argument is optional and accepts a list of key:value. The key is the name of the feature and value is one of {-1,0,1}, where -1 represents decreasing constraint, 0 represents no constraint and 1 represents increasing constraint.

feature_names = ['MedInc', 'AveOccup', 'HouseAge']
monotone_constraints = {"MedInc": 1, "AveOccup": -1, "HouseAge": 1}
xgb_mono = H2OXGBoostEstimator(monotone_constraints = monotone_constraints)
xgb_mono.train(x = feature_names, y = "target", 
               training_frame = train, validation_frame = test)

Full Python demo is available on H2O GitHub.


A new argument named monotone_constraints has been added to h2o.gbm(...) function. This argument is optional and accepts a list of key:value. The key is the name of the feature and value is one of {-1,0,1}, where -1 represents decreasing constraint, 0 represents no constraint and 1 represents increasing constraint.

features <- ("Origin", "Dest", "Distance")
features.constraints <- c(1, 0, -1)
monotonicity.constraints <- setNames(features.constraints, features)
gbm.model <- h2o.gbm(y = "IsDepDelayed",
                     training_frame = training_frame,
                     monotone_constraints = monotonicity.constraints,
                     validation_frame = validation_frame)

AutoML performance improvement

In version, the performance of all models in the AutoML run has been improved. AutoML previously automatically partitioned the training set, setting aside 10% of the data to be used for an early stopping. This dataset was not being used, so now AutoML simply uses the full training dataset to train the models, leading to better model performance. All details, including the actual fixes done, are to be found in PUBDEV-6079 JIRA.

Custom metrics for early stopping

Specification for custom stoppic metrics is now supported for GBM, DRF and GLM algorithms. There are two new stopping criteria:

  • custom – for custom metric functions where “less is better”, it is expected that the lower bound is 0
  • custom_increasing – for custom metric functions where “more is better”


Custom stopping function

def custom_stopping_metric_function():
  return h2o.upload_custom_metric(
    CustomMaeFunc, func_name = "mae", func_file = "mm_mae.py")

model_actual = 
    model_id="prostate", ntrees = 10, max_depth = 5,
    score_each_iteration = True,
    custom_metric_func = custom_stopping_metric_function(),
    stopping_metric = "custom", stopping_tolerance = 0.1, stopping_rounds = 3)

model_actual.train(y = "AGE", x = ftrain.names, 
                   training_frame = ftrain, validation_frame = fvalid)

Exporting checkpoints in AutoML

Throughout the AutoML experiments and Grid searches, resulting models can now be checkpointed. By specifying export_checkpoints_dir value, which is a string pointing to a directory int he filesystem, checkpoints are saved automatically as new models are created.

AutoML Python

model = H2OAutoML(project_name = "ExampleProject", stopping_rounds = 3, 
                  export_checkpoints_dir = "/home/username/example/checkpoints")
model.train(y = "CAPSULE", training_frame = training_frame)

Grid Search Python

air_grid = H2OGridSearch(H2OGradientBoostingEstimator, 
                         hyper_params = hyper_parameters, 
                         search_criteria = search_crit)
air_grid.train(x = ["Origin", "Distance"], y = "IsDepDelayed", 
               training_frame = training_frame, 
               export_checkpoints_dir = checkpoints_dir)

AutoML R

model <- h2o.automl(y = y, training_frame = train, project_name = "AutoMLTest", 
                    export_checkpoints_dir = "/home/username/example/checkpoints")

Grid Search R

grid <- h2o.grid(algorithm = "glm", grid_id = "glm_grid_cars_test", 
                 x = predictors, y = "economy", training_frame = train,
                 family = "gaussian", hyper_params = hyper_params, 
                 export_checkpoints_dir = "checkpoints_dir")

About the Author

Pavel Pscheidl

Pavel is a machine learning engineer at H2O. Holding a master's degree in Applied Informatics, his main focus during his studies was applied statistics & stochastic methods, agent-based simulations and optimization. He joined a research team as a Ph.D. candidate while working on various problems like the effectiveness of fraud detection methods in highly-distributed systems. Due to his roots in computer science, his commercial focus was on enterprise Java systems and related standards. He also wrote a book in this field. In 2017, Pavel joined H2O's awesome team, abandoning all other activities, including research at the university. At H2O, he is proud of being able to leverage his passion for algorithms and optimization while diving deeper into statistics every single day.

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