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.
Quick Links¶
Grid Search in R and Python¶
Grid search in R provides the following capabilities:
H2OGrid class
: Represents the results of the grid searchh2o.getGrid(<grid_id>, sort_by, decreasing)
: Displays the specified gridh2o.grid()
: Starts a new grid search parameterized bymodel 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 strategyparallelism
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
.
You can also use “Sequential”, which goes through the specified parameters in sequence and requires the specified parameter lists to have the same length. “Sequential” strategy exposes early_stopping
parameter (defaults to TRUE) that can be used to disable early stopping while still obeying the max_models
and 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)
list(strategy = "Sequential", max_runtime_secs = 3600)
list(strategy = "Sequential", max_models = 42, max_runtime_secs = 28800)
list(strategy = "Sequential", stopping_tolerance = 0.001, stopping_rounds = 10)
list(strategy = "Sequential", early_stopping = FALSE)
list(strategy = "Sequential", early_stopping = FALSE, max_models = 42, max_runtime_secs = 28800)
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
is the optional dictionary for specifying more a advanced search strategyparallelism
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
.
You can also use “Sequential”, which goes through the specified parameters in sequence and requires the specified parameter lists to have the same length. “Sequential” strategy exposes early_stopping
parameter (defaults to True) that can be used to disable early stopping while still obeying the max_models
and 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}
{'strategy': "Sequential", 'max_runtime_secs': 3600}
{'strategy': "Sequential", 'max_models': 42, 'max_runtime_secs': 28800}
{'strategy': "Sequential", 'stopping_tolerance': 0.001, 'stopping_rounds': 10}
{'strategy': "Sequential", 'early_stopping': False}
{'strategy': "Sequential", 'early_stopping': False, 'max_models': 42, 'max_runtime_secs': 28800}
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, Python grid search code, and 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
parameterCall 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 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 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.
Supervised Algorithms¶
AutoML Hyperparameters¶
No available hyperparameters.
CoxPH Hyperparameters¶
use_all_factor_levels
Deep Learning Hyperparameters¶
activation
average_activation
adaptive_rate
balance_classes
class_sampling_factors
classification_stop
col_major
elastic_averaging
elastic_averaging_moving_rate
elastic_averaging_regularization
epochs
epsilon
fast_mode
force_load_balance
hidden
hidden_dropout_ratios
initial_biases
initial_weight_distribution
initial_weight_scale
initial_weights
input_dropout_ratio
l1
l2
loss
max_categorical_features
max_w2
missing_values_handling
momentum_ramp
momentum_stable
nesterov_accelerated_gradient
overwrite_with_best_model
quiet_mode
rate
rate_annealing
rate_decay
regression_stop
replicate_training_data
reproducible
rho
seed
score_duty_cycle
score_interval
score_training_samples
score_validation_samples
score_validation_sampling
shuffle_training_data
sparse
sparsity_beta
standardize
target_ratio_comm_to_comp
train_samples_per_iteration
variable_importances
DRF Hyperparameters¶
balance_classes
class_sampling_factors
max_after_balance_size
seed
GLM Hyperparameters¶
alpha
dispersion_learning_rate
init_dispersion_parameter
lambda
rand_family
rand_link
startval
theta
tweedie_variance_power
tweedie_link_power
Isotonic Regression Hyperparameters¶
No available hyperparameters.
ModelSelection Hyperparameters¶
alpha
lambda
missing_values_handling
nparallelism
rand_family
seed
startval
tweedie_variance_power
GAM Hyperparameters¶
alpha
balance_classes
bs
gam_columns
lambda
missing_values_handling
num_knots
rand_family
scale
seed
splines_non_negative
spline_order
startval
theta
tweedie_variance_power
GBM Hyperparameters¶
balance_classes
class_sampling_factors
max_after_balance_size
seed
Naïve Bayes Hyperparameters¶
compute_metrics
eps_prob
eps_sdev
laplace
min_prob
min_sdev
seed
Rulefit Hyperparameters¶
seed
Stacked Ensemble Hyperparameters¶
seed
SVM Hyperparameters¶
gamma
hyper_param
rank_ratio
seed
Uplift DRF Hyperparameters¶
balance_classes
class_sampling_factors
max_after_balance_size
seed
XGBoost Hyperparameters¶
backend
booster
colsample_bylevel
colsample_bynode
colsample_bytree
dmatrix_type
eta
gamma
grow_policy
max_bins
max_delta_step
max_leaves
min_child_weight
normalize_type
one_drop
rate_drop
reg_alpha
reg_lambda
sample_type
scale_pos_weight
seed
skip_drop
subsample
tree_method
Unsupervised Hyperparameters¶
Aggregator Hyperparameters¶
k
max_iterations
pca_method
rel_tol_num_exemplars
target_num_exemplars
transform
use_all_factor_levels
GLRM Hyperparameters¶
estimate_k
gamma_x
gamma_y
init
init_step_size
k
loss
loss_by_col
loss_by_col_idx
max_iterations
max_updates
min_step_size
multi_loss
period
regularization_x
regularization_y
seed
svd_method
transform
PCA Hyperparameters¶
k
max_iterations
transform
K-Means Hyperparameters¶
estimate_k
init
max_iterations
seed
standardize
Isolation Forest Hyperparameters¶
seed
Extended Isolation Forest Hyperparameters¶
seed
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 valuesMinor 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
h2o.get_grid()
(Python) orh2o.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, 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.