Grid (Hyperparameter) Search¶

REST API¶

The current implementation of the grid search REST API exposes the following endpoints:

GET /<version>/Grids: List available grids, with optional parameters to sort the list by model metric such as MSE
GET /<version>/Grids/<grid_id>: Return specified grid
POST /<version>/Grids/<algo_name>: Start a new grid search
- <algo_name>: Supported algorithm values are {glm, gbm, drf, kmeans, deeplearning}

Endpoints accept model-specific parameters (e.g., GBMParametersV3 and an additional parameter called hyper_parameters which contains a dictionary of the hyper parameters which will be searched. In this dictionary an array of values is specified for each searched hyperparameter.

{
  "ntrees":[1,5],
  "learn_rate":[0.1,0.01]
}

An optional search_criteria dictionary specifies options for controlling more advanced search strategies. Currently, full Cartesian is the default. RandomDiscrete allows a random search over the hyperparameter space, with three ways of specifying when to stop the search: max number of models, max time, and metric-based early stopping (e.g., stop if MSE hasn’t improved by 0.0001 over the 5 best models). An example is:

{
  "strategy": "RandomDiscrete",
  "max_runtime_secs": 600,
  "max_models": 100,
  "stopping_metric": "AUTO",
  "stopping_tolerance": 0.00001,
  "stopping_rounds": 5,
  "seed": 123456
}

With grid search, each model is built sequentially, allowing users to view each model as it is built.

Supported Grid Search Hyperparameters¶

The following hyperparameters are supported by grid search.

Common Hyperparameters¶

validation_frame
response_column
weights_column
offset_column
fold_column
fold_assignment
stopping_rounds
max_runtime_secs
stopping_metric
stopping_tolerance

Shared Tree Hyperparameters¶

Note: The Shared Tree hyperparameters apply to DRF and GBM.

balance_classes
class_sampling_factors
max_after_balance_size
max_hit_ratio_k
ntrees
max_depth
min_rows
nbins
nbins_top_level
nbins_cats
r2_stopping
seed
build_tree_one_node
sample_rate
sample_rate_per_class
col_sample_rate_per_tree
col_sample_rate_change_per_level
score_tree_interval
min_split_improvement
histogram_type

DRF Hyperparameters¶

mtries

GBM Hyperparameters¶

learn_rate
learn_rate_annealing
distribution
quantile_alpha
tweedie_power
col_sample_rate
max_abs_leafnode_pred

K-Means Hyperparameters¶

max_iterations
standardize
seed
init

GLM Hyperparameters¶

transform
k
loss
multi_loss
loss_by_col
period
regularization_x
regularization_y
gamma_x
gamma_y
max_iterations
max_updates
missing_values_handling
init_step_size
min_step_size
seed
init
svd_method

Naïve Bayes Hyperparameters¶

laplace
min_sdev
eps_sdev
min_prob
eps_prob
compute_metrics
seed

PCA Hyperparameters¶

transform
k
max_iterations

Deep Learning Hyperparameters¶

balance_classes
class_sampling_factors
max_after_balance_size
max_confusion_matrix_size
max_hit_ratio_k
overwrite_with_best_model
use_all_factor_levels
standardize
activation
hidden
epochs
train_samples_per_iteration
target_ratio_comm_to_comp
seed
adaptive_rate
rho
epsilon
rate
rate_annealing
rate_decay
momentum_start
momentum_ramp
momentum_stable
nesterov_accelerated_gradient
input_dropout_ratio
hidden_dropout_ratios
l1
l2
max_w2
initial_weight_distribution
initial_weight_scale
initial_weights
initial_biases
loss
distribution
tweedie_power
quantile_alpha
score_interval
score_training_samples
score_validation_samples
score_duty_cycle
classification_stop
regression_stop
quiet_mode
score_validation_sampling
variable_importances
fast_mode
force_load_balance
replicate_training_data
single_node_mode
shuffle_training_data
missing_values_handling
sparse
col_major
average_activation
sparsity_beta
max_categorical_features
reproducible
elastic_averaging
elastic_averaging_moving_rate
elastic_averaging_regularization

Aggregator Hyperparameters¶

radius_scale
transform
pca_method
k
max_iterations

Example¶

Invoke a new GBM model grid search by POSTing the following request to /99/Grid/gbm:

parms:{hyper_parameters={"ntrees":[1,5],"learn_rate":[0.1,0.01]}, training_frame="filefd41fe7ac0b_csv_1.hex_2", grid_id="gbm_grid_search", response_column="Species"", ignored_columns=[""]}

Grid Search in R¶

Grid search in R provides the following capabilities:

H2OGrid class: Represents the results of the grid search
h2o.getGrid(<grid_id>, sort_by, decreasing): Display the specified grid
h2o.grid: Start a new grid search parameterized by
- model builder name (e.g., gbm)
- model parameters (e.g., ntrees=100)
- hyper_parameters attribute for passing a list of hyper parameters (e.g., list(ntrees=c(1,100), learn_rate=c(0.1,0.001)))
- search_criteria optional attribute for specifying more a advanced search strategy

Example¶

ntrees_opts = c(1, 5)
learn_rate_opts = c(0.1, 0.01)
hyper_parameters = list(ntrees = ntrees_opts, learn_rate = learn_rate_opts)
grid <- h2o.grid("gbm", grid_id="gbm_grid_test", x=1:4, y=5, training_frame=iris.hex, hyper_params = hyper_parameters)
grid_models <- lapply(grid@model_ids, function(mid) {
    model = h2o.getModel(mid)
  })

Random Hyper-Parameter Grid Search Example¶

# The following two commands remove any previously installed H2O packages for R.
if ("package:h2o" %in% search()) { detach("package:h2o", unload=TRUE) }
if ("h2o" %in% rownames(installed.packages())) { remove.packages("h2o") }

# Next, we download packages that H2O depends on.
pkgs <- c("methods","statmod","stats","graphics","RCurl","jsonlite","tools","utils")
for (pkg in pkgs) {
  if (! (pkg %in% rownames(installed.packages()))) { install.packages(pkg) }
}

# Now we download, install and initialize the H2O package for R.
install.packages("h2o", type="source", repos=(c("http://h2o-release.s3.amazonaws.com/h2o/rel-tukey/7/R")))


library(h2o)
h2o.init(nthreads=-1)
train <- h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/flow_examples/arrhythmia.csv.gz")
dim(train)
response <- 1
predictors <- c(2:ncol(train))

splits<-h2o.splitFrame(train, 0.9, destination_frames = c("trainSplit","validSplit"), seed = 123456)
trainSplit <- splits[[1]]
validSplit <- splits[[2]]


## Hyper-Parameter Search

## Construct a large Cartesian hyper-parameter space
ntrees_opts <- c(10000) ## early stopping will stop earlier
max_depth_opts <- seq(1,20)
min_rows_opts <- c(1,5,10,20,50,100)
learn_rate_opts <- seq(0.001,0.01,0.001)
sample_rate_opts <- seq(0.3,1,0.05)
col_sample_rate_opts <- seq(0.3,1,0.05)
col_sample_rate_per_tree_opts = seq(0.3,1,0.05)
#nbins_cats_opts = seq(100,10000,100) ## no categorical features in this dataset

hyper_params = list( ntrees = ntrees_opts,
                     max_depth = max_depth_opts,
                     min_rows = min_rows_opts,
                     learn_rate = learn_rate_opts,
                     sample_rate = sample_rate_opts,
                     col_sample_rate = col_sample_rate_opts,
                     col_sample_rate_per_tree = col_sample_rate_per_tree_opts
                     #,nbins_cats = nbins_cats_opts
)


## Search a random subset of these hyper-parmameters (max runtime and max models are enforced, and the search will stop after we don't improve much over the best 5 random models)
search_criteria = list(strategy = "RandomDiscrete", max_runtime_secs = 600, max_models = 100, stopping_metric = "AUTO", stopping_tolerance = 0.00001, stopping_rounds = 5, seed = 123456)

gbm.grid <- h2o.grid("gbm",
                     grid_id = "mygrid",
                     x = predictors,
                     y = response,

                     # faster to use a 80/20 split
                     training_frame = trainSplit,
                     validation_frame = validSplit,
                     nfolds = 0,

                     # alternatively, use N-fold cross-validation
                     #training_frame = train,
                     #nfolds = 5,

                     distribution="gaussian", ## best for MSE loss, but can try other distributions ("laplace", "quantile")

                     ## stop as soon as mse doesn't improve by more than 0.1% on the validation set,
                     ## for 2 consecutive scoring events
                     stopping_rounds = 2,
                     stopping_tolerance = 1e-3,
                     stopping_metric = "MSE",

                     score_tree_interval = 100, ## how often to score (affects early stopping)
                     seed = 123456, ## seed to control the sampling of the Cartesian hyper-parameter space
                     hyper_params = hyper_params,
                     search_criteria = search_criteria)

gbm.sorted.grid <- h2o.getGrid(grid_id = "mygrid", sort_by = "mse")
print(gbm.sorted.grid)

best_model <- h2o.getModel(gbm.sorted.grid@model_ids[[1]])
summary(best_model)

scoring_history <- as.data.frame(best_model@model$scoring_history)
plot(scoring_history$number_of_trees, scoring_history$training_MSE, type="p") #training mse
points(scoring_history$number_of_trees, scoring_history$validation_MSE, type="l") #validation mse

## get the actual number of trees
ntrees <- best_model@model$model_summary$number_of_trees
print(ntrees)

For more information, refer to the R grid search code and runit_GBMGrid_airlines.R.

Grid Search in Python¶

Class is H2OGridSearch
<grid_name>.show(): Display a list of models (including model IDs, hyperparameters, and MSE) explored by grid search (where <grid_name> is an instance of an H2OGridSearch class)
grid_search = H2OGridSearch(<model_type), hyper_params=hyper_parameters): Start a new grid search parameterized by:
- model_type is the type of H2O estimator model with its unchanged parameters
- hyper_params in Python is a dictionary of string parameters (keys) and a list of values to be explored by grid search (values) (e.g., {'ntrees':[1,100], 'learn_rate':[0.1, 0.001]}
- search_criteria optional dictionary for specifying more a advanced search strategy

Example¶

hyper_parameters = {'ntrees':[10,50], 'max_depth':[20,10]}
grid_search = H2OGridSearch(H2ORandomForestEstimator, hyper_params=hyper_parameters)
grid_search.train(x=["x1", "x2"], y="y", training_frame=train)
grid_search.show()

For more information, refer to the Python grid search code and pyunit_benign_glm_grid.py.

Grid Search Java API¶

Each parameter exposed by the schema can specify if it is supported by grid search by specifying the attribute gridable=true in the schema @API annotation. In any case, the Java API does not restrict the parameters supported by grid search.

There are two core entities: Grid and GridSearch. GridSeach is a job-building Grid object and is defined by the user’s model factory and the hyperspace walk strategy. The model factory must be defined for each supported model type (DRF, GBM, DL, and K-means). The hyperspace walk strategy specifies how the user-defined space of hyper parameters is traversed. The space definition is not limited. For each point in hyperspace, model parameters of the specified type are produced.

The implementation supports a simple Cartesian grid search as well as random search with several different stopping criteria. Grid build triggers a new model builder job for each hyperspace point returned by the walk strategy. If the model builder job fails, the resulting model is ignored; however, it can still be tracked in the job list, and errors are returned in the grid build result.

Model builder jobs are run serially in sequential order. More advanced job scheduling schemes are under development. Note that in cases of true big data sequential scheduling will yield the highest performance. It is only with a large cluster and small data that concurrent scheduling will improve performance.

The grid object contains the results of the grid search: a list of model keys produced by the grid search as well as any errors, and a table of metrics for each succesful model. The grid object publishes a simple API to get the models.

Launch the grid search by specifying:

the common model hyperparameters (parameter values which will be common across all models in the search)
the search hyperparameters (a map <parameterName, listOfValues> that defines the parameter spaces to traverse)
optionally, search criteria (an instance of HyperSpaceSearchCriteria)

The Java API can grid search any parameters defined in the model parameter’s class (e.g., GBMParameters). Paramters that are appropriate for gridding are marked by the @API parameter, but this is not enforced by the framework.

Additional methods are available in the model builder to support creation of model parameters and configuration. This eliminates the requirement of the previous implementation where each gridable value was represented as a double. This also allows users to specify different building strategies for model parameters. For example, the REST layer uses a builder that validates parameters against the model parameter’s schema, where the Java API uses a simple reflective builder. Additional reflections support is provided by PojoUtils (methods setField, getFieldValue).

Example¶

HashMap<String, Object[]> hyperParms = new HashMap<>();
hyperParms.put("_ntrees", new Integer[]{1, 2});
hyperParms.put("_distribution", new Distribution.Family[]{Distribution.Family.multinomial});
hyperParms.put("_max_depth", new Integer[]{1, 2, 5});
hyperParms.put("_learn_rate", new Float[]{0.01f, 0.1f, 0.3f});

// Setup common model parameters
GBMModel.GBMParameters params = new GBMModel.GBMParameters();
params._train = fr._key;
params._response_column = "cylinders";
// Trigger new grid search job, block for results and get the resulting grid object
GridSearch gs =
 GridSearch.startGridSearch(params, hyperParms, GBM_MODEL_FACTORY, new HyperSpaceSearchCriteria.CartesianSearchCriteria());
Grid grid = (Grid) gs.get();

Exposing grid search end-point for a new algorithm¶

In the following example, the PCA algorithm has been implemented and we would like to expose the algorithm via REST API. The following aspects are assumed:

The PCA model builder is called PCA
The PCA parameters are defined in a class called PCAParameters
The PCA parameters schema is called PCAParametersV3

To add support for PCA grid search:

Add the PCA model build factory into the hex.grid.ModelFactories class:

class ModelFactories {
 /* ... */
 public static ModelFactory<PCAModel.PCAParameters>
   PCA_MODEL_FACTORY =
   new ModelFactory<PCAModel.PCAParametners>() {
     @Override
     public String getModelName() {
       return "PCA";
     }
     @Override
     public ModelBuilder buildModel(PCAModel.PCAParameters params) {
       return new PCA(params);
     }
  };
}

Add the PCA REST end-point schema:

public class PCAGridSearchV99 extends GridSearchSchema<PCAGridSearchHandler.PCAGrid,
 PCAGridSearchV99,
 PCAModel.PCAParameters,
 PCAV3.PCAParametersV3> {
}

Add the PCA REST end-point handler:

public class PCAGridSearchHandler
 extends GridSearchHandler<PCAGridSearchHandler.PCAGrid,
 PCAGridSearchV99,
 PCAModel.PCAParameters,
 PCAV3.PCAParametersV3> {

   public PCAGridSearchV99 train(int version, PCAGridSearchV99 gridSearchSchema) {
     return super.do_train(version, gridSearchSchema);
   }

   @Override
   protected ModelFactory<PCAModel.PCAParameters> getModelFactory() {
     return ModelFactories.PCA_MODEL_FACTORY;
   }

   @Deprecated
   public static class PCAGrid extends Grid<PCAModel.PCAParameters> {

     public PCAGrid() {
       super(null, null, null, null);
     }
   }
}

Register the REST end-point in the register factory hex.api.Register:

public class Register extends AbstractRegister {
  @Override
  public void register() {
    // ...
    H2O.registerPOST("/99/Grid/pca", PCAGridSearchHandler.class, "train", "Run grid search for PCA model.");
    // ...
  }
}

Grid Testing¶

The current test infrastructure includes:

R Tests

GBM grids using wine, airlines, and iris datasets verify the consistency of results
DL grid using the hidden parameter verifying the passing of structured parameters as a list of values
Minor R testing support verifying equality of the model’s parameters against a given list of hyper parameters.

JUnit Test

Basic tests verifying consistency of the results for DRF, GBM, and KMeans
JUnit test assertions for grid results

There are tests for the RandomDiscrete search criteria in runit_GBMGrid_airlines.R and pyunit_benign_glm_grid.py.

Caveats/In Progress¶

Currently, the schema system requires specific classes instead of parameterized classes. For example, the schema definition Grid<GBMParameters> is not supported unless your define the class GBMGrid extends Grid<GBMParameters>.
Grid Job scheduler is sequential only; schedulers for concurrent builds are under development. Note that in cases of true big data sequential scheduling will yield the highest performance. It is only with a large cluster and small data that concurrent scheduling will improve performance.
The model builder job and grid jobs are not associated.
There is no way to list the hyper space parameters that caused a model builder job failure.

Additional Documentation¶

H2O Core Java Developer Documentation: The definitive Java API guide for the core components of H2O.
H2O Algos Java Developer Documentation: The definitive Java API guide for the algorithms used by H2O.
Hyperparameter Optimization in H2O: A guide to Grid Search and Random Search in H2O.