Grid (Hyperparameter) Search¶
Supported Grid Search Hyperparameters¶
The following hyperparameters are supported by grid search.
Common Hyperparameters¶
weights_column
offset_column
fold_column
fold_assignment
stopping_rounds
max_runtime_secs
stopping_metric
stopping_tolerance
DRF Hyperparameters¶
mtries
categorical_encoding
GBM Hyperparameters¶
learn_rate
learn_rate_annealing
col_sample_rate
max_abs_leafnode_pred
pred_noise_bandwidth
distribution
tweedie_power
quantile_alpha
huber_alpha
categorical_encoding
K-Means Hyperparameters¶
max_iterations
standardize
seed
init
estimate_k
k
categorical_encoding
GLM Hyperparameters¶
seed
tweedie_variance_power
tweedie_link_power
alpha
lambda
missing_values_handling
standardize
GLRM Hyperparameters¶
transform
k
loss
multi_loss
loss_by_col
period
regularization_x
regularization_y
gamma_x
gamma_y
max_iterations
max_updates
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
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
overwrite_with_best_model
use_all_factor_levels
standardize
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
categorical_encoding
Aggregator Hyperparameters¶
radius_scale
transform
pca_method
k
max_iterations
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 MSEGET /<version>/Grids/<grid_id>
: Return specified gridPOST /<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.
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 searchh2o.getGrid(<grid_id>, sort_by, decreasing)
: Display the specified gridh2o.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
- model builder name (e.g.,
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 anH2OGridSearch
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 parametershyper_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 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."); // ... } }
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 classGBMGrid 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
get_grid
(Python) orgetGrid
(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, andnfolds = 0
, then the validation metrics will display. However, ifnfolds
> 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.