import h2o
import os.path
from collections import OrderedDict
from builtins import range
from h2o.estimators.deepwater import H2ODeepWaterEstimator
from h2o.grid.grid_search import H2OGridSearch
h2o.init(nthreads=-1)
PATH=os.path.expanduser("~/h2o-3/")
Checking whether there is an H2O instance running at http://localhost:54321. connected.
| H2O cluster uptime: | 57 mins 57 secs |
| H2O cluster version: | 3.11.0.99999 |
| H2O cluster version age: | 20 hours and 30 minutes |
| H2O cluster name: | arno |
| H2O cluster total nodes: | 1 |
| H2O cluster free memory: | 13.57 Gb |
| H2O cluster total cores: | 12 |
| H2O cluster allowed cores: | 12 |
| H2O cluster status: | locked, healthy |
| H2O connection url: | http://localhost:54321 |
| H2O connection proxy: | None |
| Python version: | 2.7.12 final |
!nvidia-smi
Sun Oct 23 23:16:00 2016
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 367.44 Driver Version: 367.44 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 980 Ti Off | 0000:01:00.0 On | N/A |
| 9% 62C P0 74W / 275W | 854MiB / 6076MiB | 2% Default |
+-------------------------------+----------------------+----------------------+
| 1 TITAN X (Pascal) Off | 0000:02:00.0 Off | N/A |
| 27% 48C P2 53W / 250W | 456MiB / 12189MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1642 G /usr/lib/xorg/Xorg 588MiB |
| 0 2590 G compiz 183MiB |
| 0 10779 G ...ivePortalInterstitial/Enabled/ChildAccoun 78MiB |
| 1 13724 C /usr/lib/jvm/java-8-oracle/bin/java 453MiB |
+-----------------------------------------------------------------------------+
train = h2o.import_file(PATH + "smalldata/iris/iris_wheader.csv")
predictors = list(range(0,4))
response_col = 4
Parse progress: |█████████████████████████████████████████████████████████████████████████████| 100%
hyper_parameters = {
'hidden' : [[20,20],[50,50,50],[200,200],[50,50,50,50,50]],
'activation' : ["tanh","rectifier"],
'learning_rate' : [lr/1e3 for lr in range(1,10)]
}
parameters = {
'seed' : 42,
'epochs' : 500,
'nfolds' : 3,
'stopping_rounds' : 3, ## enable early stopping of each model in the hyperparameter search
'stopping_metric' : "logloss",
'stopping_tolerance' : 1e-3 ## stop once validation logloss of the cv models doesn't improve enough
}
search_criteria = {
'strategy': "RandomDiscrete",
'max_runtime_secs': 30, ## limit the runtime to 30 seconds
'max_models': 100, ## build no more than 100 models
'seed' : 42,
'stopping_rounds' : 5, ## enable early stopping of the overall leaderboard
'stopping_metric' : "logloss",
'stopping_tolerance': 1e-4
}
print(hyper_parameters)
{'learning_rate': [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009], 'hidden': [[20, 20], [50, 50, 50], [200, 200], [50, 50, 50, 50, 50]], 'activation': ['tanh', 'rectifier']}
gs = H2OGridSearch(H2ODeepWaterEstimator,
hyper_params=hyper_parameters,
search_criteria=search_criteria)
gs.train(x=predictors, y=response_col, training_frame=train, **parameters)
deepwater Grid Build progress: |██████████████████████████████████████████████████████████████| 100%
gs.get_grid("logloss")
activation hidden learning_rate \
0 Tanh [20, 20] 0.009
1 Tanh [20, 20] 0.007
2 Tanh [50, 50, 50] 0.004
3 Tanh [200, 200] 0.006
4 Tanh [50, 50, 50, 50, 50] 0.003
5 Tanh [200, 200] 0.002
6 Rectifier [200, 200] 0.001
7 Rectifier [200, 200] 0.004
8 Rectifier [20, 20] 0.001
model_ids \
0 Grid_DeepWater_iris_wheader4.hex_model_python_1477286246751_9_model_3
1 Grid_DeepWater_iris_wheader4.hex_model_python_1477286246751_9_model_4
2 Grid_DeepWater_iris_wheader4.hex_model_python_1477286246751_9_model_1
3 Grid_DeepWater_iris_wheader4.hex_model_python_1477286246751_9_model_2
4 Grid_DeepWater_iris_wheader4.hex_model_python_1477286246751_9_model_7
5 Grid_DeepWater_iris_wheader4.hex_model_python_1477286246751_9_model_6
6 Grid_DeepWater_iris_wheader4.hex_model_python_1477286246751_9_model_0
7 Grid_DeepWater_iris_wheader4.hex_model_python_1477286246751_9_model_5
8 Grid_DeepWater_iris_wheader4.hex_model_python_1477286246751_9_model_8
logloss
0 0.174867823409404
1 0.2548120069902128
2 0.2847309554355027
3 0.5744841418551249
4 0.6931066094644429
5 0.6965300796568423
6 1.7458040234667165
7 2.747305024267217
8 6.0184638863365345