max_depth¶

import h2o
h2o.init()
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.grid.grid_search import H2OGridSearch

# import the titanic dataset
df = h2o.import_file(path = "http://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")

# pick a response for the supervised problem
response = "survived"

# the response variable is an integer
# we will turn it into a categorical/factor for binary classification
df[response] = df[response].asfactor()

# use all other columns as predictors
# (except for the name & the response column ("survived"))
predictors = df.columns
del predictors[1:3]

# split the data for machine learning
train, valid, test = df.split_frame(
    ratios=[0.6,0.2],
    seed=1234,
    destination_frames=['train.hex','valid.hex','test.hex']
)

# Establish baseline performance
# We only provide the required parameters, everything else is default
gbm = H2OGradientBoostingEstimator()
gbm.train(x=predictors, y=response, training_frame=train)

# Get the AUC on the validation set
perf = gbm.model_performance(valid)
print perf.auc()
# The AUC is over 94%, so this model is highly predictive!
0.948013524937

# Determine the best max_depth value to use during a hyper-parameter search
# Depth 10 is usually plenty of depth for most datasets, but you never know
hyper_params = {'max_depth' : range(1,30,2)}
# hyper_params = {max_depth = [4,6,8,12,16,20]} may be faster for larger datasets

#Build initial GBM Model
gbm_grid = H2OGradientBoostingEstimator(
    # more trees is better if the learning rate is small enough
    # here, use "more than enough" trees - we have early stopping
    ntrees=10000,

    # smaller learning rate is better
    # since we have learning_rate_annealing, we can afford to start with a
    # bigger learning rate
    learn_rate=0.05,

    # learning rate annealing: learning_rate shrinks by 1% after every tree
    # (use 1.00 to disable, but then lower the learning_rate)
    learn_rate_annealing = 0.99,

    # sample 80% of rows per tree
    sample_rate = 0.8,

    # sample 80% of columns per split
    col_sample_rate = 0.8,

    # fix a random number generator seed for reproducibility
    seed = 1234,

    # score every 10 trees to make early stopping reproducible
    # (it depends on the scoring interval)
    score_tree_interval = 10,

    # early stopping once the validation AUC doesn't improve by at least 0.01% for
    # 5 consecutive scoring events
    stopping_rounds = 5,
    stopping_metric = "AUC",
    stopping_tolerance = 1e-4)

# Build grid search with previously made GBM and hyper parameters
grid = H2OGridSearch(gbm_grid,hyper_params,
                     grid_id = 'depth_grid',
                     search_criteria = {'strategy': "Cartesian"})

# Train grid search
grid.train(x=predictors,
           y=response,
           training_frame = train,
           validation_frame = valid)

# Display the grid search results
# Sorted by increasing logloss (because this is a classification task)
print grid

     max_depth            model_ids              logloss
0           17   depth_grid_model_8  0.20544094075930078
1           19   depth_grid_model_9  0.20584402503242194
2           27  depth_grid_model_13  0.20627418156921704
3           11   depth_grid_model_5   0.2069364255413584
4           13   depth_grid_model_6   0.2078569528636169
5           25  depth_grid_model_12  0.20834760530631993
6            9   depth_grid_model_4  0.20842232867415922
7           29  depth_grid_model_14  0.20904163538087436
8           15   depth_grid_model_7  0.20991531457742935
9           23  depth_grid_model_11   0.2104361858121492
10          21  depth_grid_model_10  0.21069590143686837
11           7   depth_grid_model_3  0.21127939637392396
12           5   depth_grid_model_2  0.21509420086032935
13           3   depth_grid_model_1  0.21854010261642962
14           1   depth_grid_model_0  0.23892331983893703

# Sort the grid models by decreasing AUC
sorted_grid = grid.get_grid(sort_by='auc',decreasing=True)
print(sorted_grid)

     max_depth            model_ids                 auc
0           13   depth_grid_model_6  0.9552831783601015
1           27  depth_grid_model_13  0.9547196393350239
2           17   depth_grid_model_8  0.9543251620174698
3           11   depth_grid_model_5  0.9538743307974078
4            9   depth_grid_model_4  0.9534798534798535
5           19   depth_grid_model_9  0.9534234995773457
6           25  depth_grid_model_12  0.9529726683572838
7           29  depth_grid_model_14  0.9528036066497605
8           21  depth_grid_model_10  0.9526908988447449
9           15   depth_grid_model_7  0.9526345449422373
10           7   depth_grid_model_3   0.951789236404621
11          23  depth_grid_model_11  0.9505494505494505
12           3   depth_grid_model_1   0.949084249084249
13           5   depth_grid_model_2  0.9484361792054099
14           1   depth_grid_model_0  0.9478162862778248