In [13]:

    

import h2o


    

In [14]:

    

h2o.init()


    

    





H2O cluster uptime: 
1 minutes 47 seconds 826 milliseconds 
H2O cluster version: 
3.1.0.99999
H2O cluster name: 
ece
H2O cluster total nodes: 
1
H2O cluster total memory: 
4.44 GB
H2O cluster total cores: 
8
H2O cluster allowed cores: 
8
H2O cluster healthy: 
True
H2O Connection ip: 
127.0.0.1
H2O Connection port: 
54321

In [15]:

    

prostate = h2o.upload_file(path=h2o.locate("smalldata/logreg/prostate.csv"))
prostate.describe()


    

    




Parse Progress: [##################################################] 100%
Uploaded py9485de29-6e44-4cde-bd77-b480bdc9d6cb into cluster with 380 rows and 9 cols
Rows: 380 Cols: 9

Chunk compression summary:







    





chunk_type
chunk_name
count
count_percentage
size
size_percentage
CBS
Bits
1
11.111112
    118  B
2.4210093
C1N
1-Byte Integers (w/o NAs)
5
55.555557
    2.2 KB
45.958145
C2
2-Byte Integers
1
11.111112
    828  B
16.9881
C2S
2-Byte Fractions
2
22.222223
    1.6 KB
34.632744






    




Frame distribution summary:







    






size
number_of_rows
number_of_chunks_per_column
number_of_chunks
172.16.2.41:54321
    4.8 KB
380.0
1.0
9.0
mean
    4.8 KB
380.0
1.0
9.0
min
    4.8 KB
380.0
1.0
9.0
max
    4.8 KB
380.0
1.0
9.0
stddev
      0  B
0.0
0.0
0.0
total
    4.8 KB
380.0
1.0
9.0






    




Column-by-Column Summary:







    






ID
CAPSULE
AGE
RACE
DPROS
DCAPS
PSA
VOL
GLEASON
type
int
int
int
int
int
int
real
real
int
mins
1.0
0.0
43.0
0.0
1.0
1.0
0.3
0.0
0.0
mean
190.5
0.402631578947
66.0394736842
1.08684210526
2.27105263158
1.10789473684
15.4086315789
15.8129210526
6.38421052632
maxs
380.0
1.0
79.0
2.0
4.0
2.0
139.7
97.6
9.0
sigma
109.840793879
0.491074338963
6.52707126917
0.308773258025
1.00010761815
0.310656449351
19.9975726686
18.3476199673
1.09195337443
zero_count
0
227
0
3
0
0
0
167
2
missing_count
0
0
0
0
0
0
0
0
0

In [16]:

    

prostate["CAPSULE"] = prostate["CAPSULE"].asfactor()
model = h2o.deeplearning(x = prostate[list(set(prostate.columns) - set(["ID","CAPSULE"]))], y = prostate["CAPSULE"], training_frame = prostate, activation = "Tanh", hidden = [10, 10, 10], epochs = 10000)
model.show()


    

    




deeplearning Model Build Progress: [##################################################] 100%
Model Details
=============
H2OBinomialModel :  Deep Learning
Model Key:  DeepLearningModel__bd863374e41fb02662d41e05ea504f7e

Status of Neuron Layers:







    






layer
units
type
dropout
l1
l2
mean_rate
rate_RMS
momentum
mean_weight
weight_RMS
mean_bias
bias_RMS

1
7
Input
0.0










2
10
Tanh
0.0
0.0
0.0
0.0053118872
0.0064453385
0.0
-0.052273955
1.2920758
-0.015679162
0.6260032

3
10
Tanh
0.0
0.0
0.0
0.04846302
0.12402522
0.0
-0.18807818
1.5033592
-0.11971774
1.2407272

4
10
Tanh
0.0
0.0
0.0
0.022704514
0.043265637
0.0
-0.06319846
1.7067846
0.33969718
0.96845585

5
2
Softmax

0.0
0.0
0.0053924634
0.000767734
0.0
-0.3149048
4.5431895
-0.00843814
0.7755003






    





ModelMetricsBinomial: deeplearning
** Reported on train data. **

MSE: 0.00515095678079
R^2: 0.978640384798
LogLoss: 0.0189778247313
AUC: 0.999740865509
Gini: 0.999481731018

Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.558165788651:







    






0
1
Error
Rate
0
226.0
1.0
0.0044
 (1.0/227.0)
1
1.0
152.0
0.0065
 (1.0/153.0)
Total
227.0
153.0
0.0109
 (0.0109/380.0)






    




Maximum Metrics:







    





metric
threshold
value
idx
f1
0.558165788651
0.993464052288
95.0
f2
0.558165788651
0.993464052288
95.0
f0point5
0.784003376961
0.997357992074
93.0
accuracy
0.784003376961
0.994736842105
93.0
precision
1.0
1.0
0.0
absolute_MCC
0.784003376961
0.989094861551
93.0
min_per_class_accuracy
0.558165788651
0.993464052288
95.0
tns
1.0
227.0
0.0
fns
1.0
102.0
0.0
fps
1.6900796636e-24
227.0
322.0
tps
0.0501889586449
153.0
103.0
tnr
1.0
1.0
0.0
fnr
1.0
0.666666666667
0.0
fpr
1.6900796636e-24
1.0
322.0
tpr
0.0501889586449
1.0
103.0






    




Scoring History:







    






timestamp
duration
training_speed
epochs
samples
training_MSE
training_r2
training_logloss
training_AUC
training_classification_error

2015-05-22 13:22:58
 0.000 sec
None
0.0
0.0
nan
nan
nan
nan
nan

2015-05-22 13:22:58
 0.012 sec
316666.667 rows/sec
10.0
3800.0
0.204413234872
0.15235397523
0.597336218462
nan
0.313157894737

2015-05-22 13:23:03
 5.020 sec
372430.279 rows/sec
4920.0
1869600.0
0.0165889609441
0.931210096019
0.0492657643152
nan
0.0184210526316

2015-05-22 13:23:08
10.028 sec
355444.755 rows/sec
9380.0
3564400.0
0.00753752319028
0.968743943743
0.0230601929024
nan
0.00789473684211

2015-05-22 13:23:09
10.754 sec
353356.890 rows/sec
10000.0
3800000.0
0.00515095678079
0.978640384798
0.0189778247313
nan
0.00526315789474

In [17]:

    

predictions = model.predict(prostate)
predictions.show()


    

    




Displaying 10 row(s):






    





Row ID
predict
p0
p1
1
[u'0']
[0.9999997615814209]
[2.7966822813141334e-07]
2
[u'0']
[0.9999793767929077]
[2.0565463273669593e-05]
3
[u'0']
[0.9999675750732422]
[3.237894270569086e-05]
4
[u'0']
[0.9999769926071167]
[2.2972568331169896e-05]
5
[u'0']
[1.0]
[2.3572571168249332e-14]
6
[u'1']
[5.477976405821039e-10]
[1.0]
7
[u'0']
[0.9999896287918091]
[1.033373519021552e-05]
8
[u'0']
[0.9994649291038513]
[0.0005351081490516663]
9
[u'0']
[0.9906359314918518]
[0.009364110417664051]
10
[u'0']
[1.0]
[3.102281764810755e-10]

In [18]:

    

performance = model.model_performance(prostate)
performance.show()


    

    




ModelMetricsBinomial: deeplearning
** Reported on test data. **

MSE: 0.00515095678079
R^2: 0.978640384798
LogLoss: 0.0189778247313
AUC: 0.999740865509
Gini: 0.999481731018

Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.558165788651:







    






0
1
Error
Rate
0
226.0
1.0
0.0044
 (1.0/227.0)
1
1.0
152.0
0.0065
 (1.0/153.0)
Total
227.0
153.0
0.0109
 (0.0109/380.0)






    




Maximum Metrics:







    





metric
threshold
value
idx
f1
0.558165788651
0.993464052288
95.0
f2
0.558165788651
0.993464052288
95.0
f0point5
0.784003376961
0.997357992074
93.0
accuracy
0.784003376961
0.994736842105
93.0
precision
1.0
1.0
0.0
absolute_MCC
0.784003376961
0.989094861551
93.0
min_per_class_accuracy
0.558165788651
0.993464052288
95.0
tns
1.0
227.0
0.0
fns
1.0
102.0
0.0
fps
1.6900796636e-24
227.0
322.0
tps
0.0501889586449
153.0
103.0
tnr
1.0
1.0
0.0
fnr
1.0
0.666666666667
0.0
fpr
1.6900796636e-24
1.0
322.0
tpr
0.0501889586449
1.0
103.0