Grid (Hyperparameter) Search¶

H2O supports two types of grid search – traditional (or “cartesian”) grid search and random grid search. In a cartesian grid search, users specify a set of values for each hyperparameter that they want to search over, and H2O will train a model for every combination of the hyperparameter values. This means that if you have three hyperparameters and you specify 5, 10 and 2 values for each, your grid will contain a total of 5*10*2 = 100 models.

In random grid search, the user specifies the hyperparameter space in the exact same way, except H2O will sample uniformly from the set of all possible hyperparameter value combinations. In random grid search, the user also specifies a stopping criterion, which controls when the random grid search is completed. The user can tell the random grid search to stop by specifying a maximum number of models or the maximum number of seconds allowed for the search. The user may also specify a performance-metric-based stopping criterion, which will stop the random grid search when the performance stops improving by a specified amount.

Once the grid search is complete, the user can query the grid object and sort the models by a particular performance metric (for example, “AUC”). All models are stored in the H2O cluster and are accessible by model id.

Examples of how to perform cartesian and random grid search in all of H2O’s APIs follow below. There are also longer grid search tutorials available for R and Python.

Grid Search in R and Python¶

Grid search in R provides the following capabilities:

H2OGrid class: Represents the results of the grid search
h2o.getGrid(<grid_id>, sort_by, decreasing): Displays the specified grid
h2o.grid(): Starts 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 a more advanced search strategy
- parallelism The number of models to build in parallel. Parallelism allows the leader node to search the hyperspace and build models in a parallel way, which ultimately speeds up grid search on small data. A value of 1 (default) specifies sequential building. Specify 0 for adaptive parallelism, which is decided by H2O. Any number >1 sets the exact number of models built in parallel.

More about search_criteria:

This is a named list of control parameters for smarter hyperparameter search. The list can include values for: strategy, max_models, max_runtime_secs, stopping_metric, stopping_tolerance, stopping_rounds and seed. The default value for strategy, “Cartesian”, covers the entire space of hyperparameter combinations. If you want to use cartesian grid search, you can leave the search_criteria argument unspecified. Specify the “RandomDiscrete” strategy to perform a random search of all the combinations of your hyperparameters. RandomDiscrete should be usually combined with at least one early stopping criterion, max_models and/or max_runtime_secs. Some examples below:

list(strategy = "RandomDiscrete", max_models = 10, seed = 1)
list(strategy = "RandomDiscrete", max_runtime_secs = 3600)
list(strategy = "RandomDiscrete", max_models = 42, max_runtime_secs = 28800)
list(strategy = "RandomDiscrete", stopping_tolerance = 0.001, stopping_rounds = 10)
list(strategy = "RandomDiscrete", stopping_metric = "misclassification", stopping_tolerance = 0.0005, stopping_rounds = 5)

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 is the optional dictionary for specifying more a advanced search strategy
- parallelism The number of models to build in parallel. Parallelism allows the leaer node to search the hyperspace and build models in a parallel way, which ultimately speeds up grid search on small data. A value of 1 (default) specifies sequential building. Specify 0 for adaptive parallelism, which is decided by H2O. Any number >1 sets the exact number of models built in parallel.

More about search_criteria:

This is a dictionary of control parameters for smarter hyperparameter search. The dictionary can include values for: strategy, max_models, max_runtime_secs, stopping_metric, stopping_tolerance, stopping_rounds and seed. The default value for strategy, “Cartesian”, covers the entire space of hyperparameter combinations. If you want to use cartesian grid search, you can leave the search_criteria argument unspecified. Specify the “RandomDiscrete” strategy to perform a random search of all the combinations of your hyperparameters. RandomDiscrete should be usually combined with at least one early stopping criterion, max_models and/or max_runtime_secs. Some examples below:

{'strategy': "RandomDiscrete", 'max_models': 10, 'seed': 1}
{'strategy': "RandomDiscrete", 'max_runtime_secs': 3600}
{'strategy': "RandomDiscrete", 'max_models': 42, 'max_runtime_secs': 28800}
{'strategy': "RandomDiscrete", 'stopping_tolerance': 0.001, 'stopping_rounds': 10}
{'strategy': "RandomDiscrete", 'stopping_metric': "misclassification", 'stopping_tolerance': 0.0005, 'stopping_rounds': 5}

Grid Search Examples¶

library(h2o)

h2o.init()

# Import a sample binary outcome dataset into H2O
data <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
test <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv")

# Identify predictors and response
y <- "response"
x <- setdiff(names(data), y)

# For binary classification, response should be a factor
data[, y] <- as.factor(data[, y])
test[, y] <- as.factor(test[, y])

# Split data into train & validation
ss <- h2o.splitFrame(data, seed = 1)
train <- ss[[1]]
valid <- ss[[2]]

# GBM hyperparameters
gbm_params1 <- list(learn_rate = c(0.01, 0.1),
                    max_depth = c(3, 5, 9),
                    sample_rate = c(0.8, 1.0),
                    col_sample_rate = c(0.2, 0.5, 1.0))

# Train and validate a cartesian grid of GBMs
gbm_grid1 <- h2o.grid("gbm", x = x, y = y,
                      grid_id = "gbm_grid1",
                      training_frame = train,
                      validation_frame = valid,
                      ntrees = 100,
                      seed = 1,
                      hyper_params = gbm_params1)

# Get the grid results, sorted by validation AUC
gbm_gridperf1 <- h2o.getGrid(grid_id = "gbm_grid1",
                             sort_by = "auc",
                             decreasing = TRUE)
print(gbm_gridperf1)

# Grab the top GBM model, chosen by validation AUC
best_gbm1 <- h2o.getModel(gbm_gridperf1@model_ids[[1]])

# Now let's evaluate the model performance on a test set
# so we get an honest estimate of top model performance
best_gbm_perf1 <- h2o.performance(model = best_gbm1,
                                  newdata = test)
h2o.auc(best_gbm_perf1)
# 0.7781779

# Look at the hyperparameters for the best model
print(best_gbm1@model[["model_summary"]])

import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.grid.grid_search import H2OGridSearch

h2o.init()

# Import a sample binary outcome dataset into H2O
data = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
test = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv")

# Identify predictors and response
x = data.columns
y = "response"
x.remove(y)

# For binary classification, response should be a factor
data[y] = data[y].asfactor()
test[y] = test[y].asfactor()

# Split data into train & validation
ss = data.split_frame(seed = 1)
train = ss[0]
valid = ss[1]

# GBM hyperparameters
gbm_params1 = {'learn_rate': [0.01, 0.1],
                'max_depth': [3, 5, 9],
                'sample_rate': [0.8, 1.0],
                'col_sample_rate': [0.2, 0.5, 1.0]}

# Train and validate a cartesian grid of GBMs
gbm_grid1 = H2OGridSearch(model=H2OGradientBoostingEstimator,
                          grid_id='gbm_grid1',
                          hyper_params=gbm_params1)
gbm_grid1.train(x=x, y=y,
                training_frame=train,
                validation_frame=valid,
                ntrees=100,
                seed=1)

# Get the grid results, sorted by validation AUC
gbm_gridperf1 = gbm_grid1.get_grid(sort_by='auc', decreasing=True)
gbm_gridperf1

# Grab the top GBM model, chosen by validation AUC
best_gbm1 = gbm_gridperf1.models[0]

# Now let's evaluate the model performance on a test set
# so we get an honest estimate of top model performance
best_gbm_perf1 = best_gbm1.model_performance(test)

best_gbm_perf1.auc()
# 0.7781778619721595

Random Grid Search Examples¶

# Use same data as previous example

# GBM hyperparameters (bigger grid than above)
gbm_params2 <- list(learn_rate = seq(0.01, 0.1, 0.01),
                    max_depth = seq(2, 10, 1),
                    sample_rate = seq(0.5, 1.0, 0.1),
                    col_sample_rate = seq(0.1, 1.0, 0.1))
search_criteria <- list(strategy = "RandomDiscrete", max_models = 36, seed = 1)

# Train and validate a random grid of GBMs
gbm_grid2 <- h2o.grid("gbm", x = x, y = y,
                      grid_id = "gbm_grid2",
                      training_frame = train,
                      validation_frame = valid,
                      ntrees = 100,
                      seed = 1,
                      hyper_params = gbm_params2,
                      search_criteria = search_criteria)

gbm_gridperf2 <- h2o.getGrid(grid_id = "gbm_grid2",
                             sort_by = "auc",
                             decreasing = TRUE)
print(gbm_gridperf2)

# Grab the top GBM model, chosen by validation AUC
best_gbm2 <- h2o.getModel(gbm_gridperf2@model_ids[[1]])

# Now let's evaluate the model performance on a test set
# so we get an honest estimate of top model performance
best_gbm_perf2 <- h2o.performance(model = best_gbm2,
                                  newdata = test)
h2o.auc(best_gbm_perf2)
# 0.7810757

# Look at the hyperparameters for the best model
print(best_gbm2@model[["model_summary"]])


For more information, refer to the `R grid search tutorial <https://github.com/h2oai/h2o-tutorials/blob/master/h2o-open-tour-2016/chicago/grid-search-model-selection.R>`__, `R grid search code <https://github.com/h2oai/h2o-3/blob/master/h2o-r/h2o-package/R/grid.R>`__, and `runit\_GBMGrid\_airlines.R <https://github.com/h2oai/h2o-3/blob/master/h2o-r/tests/testdir_algos/gbm/runit_GBMGrid_airlines.R>`__.

# Use same data as previous example

# GBM hyperparameters
gbm_params2 = {'learn_rate': [i * 0.01 for i in range(1, 11)],
                'max_depth': list(range(2, 11)),
                'sample_rate': [i * 0.1 for i in range(5, 11)],
                'col_sample_rate': [i * 0.1 for i in range(1, 11)]}

# Search criteria
search_criteria = {'strategy': 'RandomDiscrete', 'max_models': 36, 'seed': 1}

# Train and validate a random grid of GBMs
gbm_grid2 = H2OGridSearch(model=H2OGradientBoostingEstimator,
                          grid_id='gbm_grid2',
                          hyper_params=gbm_params2,
                          search_criteria=search_criteria)
gbm_grid2.train(x=x, y=y,
                training_frame=train,
                validation_frame=valid,
                ntrees=100,
                seed=1)

# Get the grid results, sorted by validation AUC
gbm_gridperf2 = gbm_grid2.get_grid(sort_by='auc', decreasing=True)
gbm_gridperf2

# Grab the top GBM model, chosen by validation AUC
best_gbm2 = gbm_gridperf2.models[0]

# Now let's evaluate the model performance on a test set
# so we get an honest estimate of top model performance
best_gbm_perf2 = best_gbm2.model_performance(test)

best_gbm_perf2.auc()
# 0.7810757307013204


For more information, refer to the `Python grid search tutorial <https://github.com/h2oai/h2o-tutorials/blob/master/h2o-open-tour-2016/chicago/grid-search-model-selection.ipynb>`__, `Python grid search code <https://github.com/h2oai/h2o-3/blob/master/h2o-py/h2o/grid/grid_search.py>`__, and `pyunit\_benign\_glm\_grid.py <https://github.com/h2oai/h2o-3/blob/master/h2o-py/tests/testdir_algos/glm/pyunit_benign_glm_grid.py>`__.

Saving and Loading a Grid Search¶

H2O supports saving and loading grids even after a cluster wipe or complete cluster restart. The save_grid function will export a grid and its models into a given folder while the load_grid function loads a previously saved grid and all its models from the given folder.

There are two modes to save a grid (in both R and Python):

Use auto-checkpointing and supply the export_checkpoints_dir parameter
Call the function h2o.save_grid for manual export

Checkpointing Example¶

Using the Grid Search example through the hyperparameters section, run the following additional commands to retrieve the checkpointed saved grid.

# Train and validate a cartesian grid of GBMs
gbm_grid1 <- h2o.grid("gbm", x = x,
                      y = y, grid_id = "gbm_grid_test",
                      training_frame = train,
                      validation_frame = valid,
                      ntrees = 100, seed = 1,
                      hyper_params = gbm_params1,
                      export_checkpoints_dir = tempdir())

# Identify the grid_id and model_ids
grid_id <- gbm_grid1@grid_id
gbm_grid_model_count <- length(gbm_grid1@model_ids)

# Wipe the cloud to simulate cluster restart
#(the models will no longer be available)
h2o.removeAll()

# Retrieve the saved grid
grid <- h2o.loadGrid(paste0(tempdir(), "/", grid_id))
grid

# Add the save location
import tempfile
checkpoints_dir = tempfile.mkdtemp()

# Train and validate a cartesian grid of GBMs
gbm_grid1 = H2OGridSearch(model=H2OGradientBoostingEstimator,
                          grid_id='gbm_grid',
                          hyper_params=gbm_params1,
                          export_checkpoints_dir=checkpoints_dir)
gbm_grid1.train(x=x, y=y,
                training_frame=train,
                validation_frame=valid,
                ntrees=100,
                seed=1)

# Identify the grid_id and model_ids
grid_id = gbm_grid1.grid_id
old_grid_model_count = len(gbm_grid1.model_ids)

# Wipe the cloud to simulate cluster restart
#(the models will no longer be available)
h2o.remove_all()

# Retrieve the saved grid
grid = h2o.load_grid(checkpoints_dir + "/" + grid_id)
grid

Manual Export Example¶

Using the Grid Search example through the hyperparameters section, run the following additional commands to retrieve the manually exported saved grid.

# Train and validate a cartesian grid of GBMs
gbm_grid1 <- h2o.grid("gbm", x = x,
                      y = y, grid_id = "gbm_grid1",
                      training_frame = train,
                      validation_frame = valid,
                      ntrees = 100, seed = 1,
                      hyper_params = gbm_params1)

# Identify the grid_id and model_ids
grid_id <- gbm_grid1@grid_id
gbm_grid1_model_count <- length(gbm_grid1@model_ids)

# Save the grid
saved_path <- h2o.saveGrid(grid_directory = tempdir(), grid_id = grid_id)

# Wipe the cloud to simulate cluster restart
#(the models will no longer be available)
h2o.removeAll()

# Retrieve the saved grid
grid <- h2o.loadGrid(saved_path)
grid

# Add the save location
import tempfile
checkpoints_dir = tempfile.mkdtemp()

# Train and validate a cartesian grid of GBMs
gbm_grid1 = H2OGridSearch(model=H2OGradientBoostingEstimator,
                          grid_id='gbm_grid1',
                          hyper_params=gbm_params1)
gbm_grid1.train(x=x, y=y,
                training_frame=train,
                validation_frame=valid,
                ntrees=100, seed=1)

# Identify the grid_id and model_ids
grid_id = gbm_grid1.grid_id
old_grid_model_count = len(gbm_grid1.model_ids)

# Save the grid
saved_path = h2o.save_grid(checkpoints_dir, grid_id)

# Wipe the cloud to simulate cluster restart
#(the models will no longer be available)
h2o.remove_all()

# Retrieve the saved grid
grid = h2o.load_grid(saved_path)
grid

Fault-Tolerant Grid Search¶

H2O supports progress recovery should the cluster fail during grid training. The recovery_dir parameter will cause the grid to save all its inputs and outputs into the given directory, and should the training fail, the grid progress can be resumed from the last model that was successfully trained.

iris <- h2o.importFile(
    "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris.csv",
    destination_frame="iris"
)
hyper_parameters <- list(
    learn_rate=c(0.01, 0.02, 0.03, 0.04),
    ntrees=c(100, 120, 130, 140)
)
# train a cartesian grid of GBMs
gbm_grid <- h2o.grid(
    "gbm", x=1:4, y=5,
    grid_id="gbm_grid", training_frame=iris,
    hyper_params=hyper_parameters,
    recovery_dir="hdfs://nameNode/user/john/gbm_grid_recovery"
)

# on a new cluster recover grid
# this will load the training frame and any other objects required for training
h2o.loadGrid(
    "hdfs://nameNode/user/john/gbm_grid_recovery/gbm_grid", # append grid ID to the recovery_dir
    load_params_references=TRUE
)
iris <- h2o.getFrame("iris") # get reference to re-loaded training frame
# continue grid training, same grid id will cause H2O to resume progress
grid <- h2o.grid(
    "gbm", grid_id="gbm_grid", x=1:4, y=5,
    training_frame=iris,
    hyper_params=hyper_parameters # use original hyper-parameters
)

iris = h2o.import_file(
    "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris.csv",
    destination_frame="iris"
)
hyper_parameters = {
    "learn_rate": [0.01, 0.02, 0.03, 0.04],
    "ntrees": [100, 120, 130, 140]
}

# train a cartesian grid of GBMs
gbm_grid = H2OGridSearch(
    model=H2OGradientBoostingEstimator,
    grid_id='gbm_grid',
    hyper_params=hyper_parameters,
    recovery_dir="hdfs://nameNode/user/john/gbm_grid_recovery"
)
gbm_grid.train(x=list(range(4)), y=4, training_frame=iris)

# on a new cluster recover grid
# this will load the training frame and any other objects required for training
grid = h2o.load_grid(
    "hdfs://nameNode/user/john/gbm_grid_recovery/gbm_grid",  # append grid ID to the recovery_dir
    load_params_references=True
)
train = h2o.get_frame("iris") # get reference to re-loaded training frame
grid.hyper_params = hyper_parameters # use original hyper-parameters
# continue grid training
grid.train(x=list(range(4)), y=4, training_frame=train)

Grid Search Java API¶

Each parameter exposed by the schema can specify if it is supported by grid search by including 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 hyperparameters 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 that 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 DistributionFamily[]{DistributionFamily.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.");
    // ...
  }
}

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 hyperparameters that 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.

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=[""]}

Supported Grid Search Hyperparameters¶

The following hyperparameters are supported by grid search.

Aggregator Hyperparameters¶

k
max_iterations
pca_method
radius_scale
transform

AutoML Hyperparameters¶

keep_cross_validation_models

Common Hyperparameters¶

fold_assignment
fold_column
max_runtime_secs
offset_column
stopping_metric
stopping_rounds
stopping_tolerance
weights_column

Deep Learning Hyperparameters¶

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

DRF Hyperparameters¶

categorical_encoding
mtries

GBM Hyperparameters¶

categorical_encoding
col_sample_rate
distribution
huber_alpha
learn_rate_annealing
learn_rate
max_abs_leafnode_pred
pred_noise_bandwidth
quantile_alpha
rand_family
rand_link
startval
tweedie_power

GLM Hyperparameters¶

alpha
lambda
missing_values_handling
seed
standardize
theta
tweedie_link_power
tweedie_variance_power

GAM Hyperparameters¶

alpha
bs
subspaces
gam_x
k
lambda
missing_values_handling
scale
seed
standardize
theta
tweedie_link_power
tweedie_variance_power

GLRM Hyperparameters¶

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

Isolation Forest Hyperparameters¶

categorical_encoding
max_depth
min_rows
mtries
ntrees
sample_rate
sample_size

K-Means Hyperparameters¶

categorical_encoding
estimate_k
init
k
max_iterations
seed
standardize

Naïve Bayes Hyperparameters¶

compute_metrics
eps_prob
eps_sdev
laplace
min_prob
min_sdev
seed

PCA Hyperparameters¶

k
max_iterations
transform

Shared Tree Hyperparameters¶

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

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

SVM Hyperparameters¶

gamma
hyper_param
rank_ratio
seed

XGBoost Hyperparameters¶

booster
categorical_encoding
col_sample_by_level
col_sample_rate_per tree
col_sample_rate
colsample_bytree
colsample_bynode
distribution
eta
gamma
grow_policy
learn_rate
max_abs_leafnode_pred
max_delta_step
max_depth
min_rows
min_split_improvement
normalize_type
ntrees
num_leaves
one_drop
rate_drop
reg_lambda
sample_rate
sample_type
seed
skip_drop
subsample
tree_method
tweedie_power

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.
The h2o.get_grid() (Python) or h2o.getGrid() (R) function can be called to retrieve a grid search instance. If neither cross-validation nor a validation frame is used in the grid search, then the training metrics will display in the “get grid” output. If a validation frame is passed to the grid, and nfolds = 0, then the validation metrics will display. However, if nfolds > 1, then cross-validation metrics will display even if a validation frame is provided.

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.

Grid (Hyperparameter) Search¶

Quick Links¶

Grid Search in R and Python¶

Grid Search Examples¶

Random Grid Search Examples¶

Saving and Loading a Grid Search¶

Checkpointing Example¶

Manual Export Example¶

Fault-Tolerant Grid Search¶

Grid Search Java API¶

Example¶

Exposing grid search end-point for a new algorithm¶

REST API¶

Example¶

Supported Grid Search Hyperparameters¶

Aggregator Hyperparameters¶

AutoML Hyperparameters¶

Common Hyperparameters¶

Deep Learning Hyperparameters¶

DRF Hyperparameters¶

GBM Hyperparameters¶

GLM Hyperparameters¶

GAM Hyperparameters¶

GLRM Hyperparameters¶

Isolation Forest Hyperparameters¶

K-Means Hyperparameters¶

Naïve Bayes Hyperparameters¶

PCA Hyperparameters¶

Shared Tree Hyperparameters¶

SVM Hyperparameters¶

XGBoost Hyperparameters¶

Grid Testing¶

Caveats/In Progress¶

Additional Documentation¶