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``
Shared Tree Hyperparameters
~~~~~~~~~~~~~~~~~~~~~~~~~~~
**Note**: The Shared Tree hyperparameters apply to DRF and GBM.
- ``balance_classes``
- ``class_sampling_factors``
- ``max_after_balance_size``
- ``ntrees``
- ``max_depth``
- ``min_rows``
- ``nbins``
- ``nbins_top_level``
- ``nbins_cats``
- ``seed``
- ``sample_rate``
- ``sample_rate_per_class``
- ``col_sample_rate_per_tree``
- ``col_sample_rate_change_per_level``
- ``min_split_improvement``
- ``histogram_type``
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 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 <https://github.com/h2oai/h2o-3/blob/master/h2o-algos/src/main/java/hex/schemas/GBMV3.java>`__
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.
.. code:: json
{
"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:
.. code:: json
{
"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``:
.. code:: json
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
~~~~~~~
.. code:: r
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
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: r
# 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 <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>`__.
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
~~~~~~~
.. code:: python
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 <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>`__.
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 <https://en.wikipedia.org/wiki/Hyperparameter_optimization>`__.
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
~~~~~~~
.. code:: java
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:
1. 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);
}
};
}
2. Add the PCA REST end-point schema:
::
public class PCAGridSearchV99 extends GridSearchSchema<PCAGridSearchHandler.PCAGrid,
PCAGridSearchV99,
PCAModel.PCAParameters,
PCAV3.PCAParametersV3> {
}
3. 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);
}
}
}
4. 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 <https://github.com/h2oai/h2o-3/blob/master/h2o-r/tests/testdir_algos/gbm/runit_GBMGrid_airlines.R>`_
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>`_.
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 ``get_grid`` (Python) or ``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 <../h2o-core/javadoc/index.html>`_: The definitive Java API guide
for the core components of H2O.
- `H2O Algos Java Developer Documentation <../h2o-algos/javadoc/index.html>`_: The definitive Java API guide
for the algorithms used by H2O.
- `Hyperparameter Optimization in H2O <https://github.com/h2oai/h2o-3/blob/master/h2o-docs/src/product/tutorials/random%20hyperparmeter%20search%20and%20roadmap.md>`_: A guide to Grid Search and Random Search in H2O.