In [1]:
import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
In [2]:
# Connect to a pre-existing cluster
h2o.init()
H2O cluster uptime:
5 seconds 730 milliseconds H2O cluster version:
3.7.0.99999 H2O cluster name:
spIdea H2O cluster total nodes:
1 H2O cluster total free memory:
12.44 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 H2O Connection proxy:
None Python Version:
3.5.0
In [3]:
from h2o.utils.shared_utils import _locate # private function. used to find files within h2o git project directory.
df = h2o.import_file(path=_locate("smalldata/logreg/prostate.csv"))
Parse Progress: [##################################################] 100%
In [4]:
df.describe()
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 172.16.2.84:54321
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
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 0.4026315789473684 66.03947368421049 1.0868421052631572 2.2710526315789488 1.1078947368421048 15.408631578947375 15.812921052631573 6.3842105263157904 maxs 380.0 1.0 79.0 2.0 4.0 2.0 139.70000000000002 97.60000000000001 9.0 sigma 109.84079387914127 0.4910743389630552 6.527071269173311 0.3087732580252793 1.0001076181502861 0.3106564493514939 19.99757266856046 18.347619967271175 1.0919533744261092 zeros 0 227 0 3 0 0 0 167 2 missing 0 0 0 0 0 0 0 0 0 0 1.0 0.0 65.0 1.0 2.0 1.0 1.4000000000000001 0.0 6.0 1 2.0 0.0 72.0 1.0 3.0 2.0 6.7 0.0 7.0 2 3.0 0.0 70.0 1.0 1.0 2.0 4.9 0.0 6.0 3 4.0 0.0 76.0 2.0 2.0 1.0 51.2 20.0 7.0 4 5.0 0.0 69.0 1.0 1.0 1.0 12.3 55.9 6.0 5 6.0 1.0 71.0 1.0 3.0 2.0 3.3000000000000003 0.0 8.0 6 7.0 0.0 68.0 2.0 4.0 2.0 31.900000000000002 0.0 7.0 7 8.0 0.0 61.0 2.0 4.0 2.0 66.7 27.2 7.0 8 9.0 0.0 69.0 1.0 1.0 1.0 3.9 24.0 7.0 9 10.0 0.0 68.0 2.0 1.0 2.0 13.0 0.0 6.0
In [5]:
# Remove ID from training frame
train = df.drop("ID")
In [6]:
# For VOL & GLEASON, a zero really means "missing"
vol = train['VOL']
vol[vol == 0] = None
gle = train['GLEASON']
gle[gle == 0] = None
In [7]:
# Convert CAPSULE to a logical factor
train['CAPSULE'] = train['CAPSULE'].asfactor()
In [8]:
# See that the data is ready
train.describe()
Rows:380 Cols:8
Chunk compression summary:
chunk_type
chunk_name
count
count_percentage
size
size_percentage CBS
Bits
1
12.5
118 B
2.9164608 C1N
1-Byte Integers (w/o NAs)
5
62.5
2.2 KB
55.363323 C2S
2-Byte Fractions
2
25.0
1.6 KB
41.72022
Frame distribution summary:
size
number_of_rows
number_of_chunks_per_column
number_of_chunks 172.16.2.84:54321
4.0 KB
380.0
1.0
8.0 mean
4.0 KB
380.0
1.0
8.0 min
4.0 KB
380.0
1.0
8.0 max
4.0 KB
380.0
1.0
8.0 stddev
0 B
0.0
0.0
0.0 total
4.0 KB
380.0
1.0
8.0
CAPSULE AGE RACE DPROS DCAPS PSA VOL GLEASON type enum int int int int real real int mins 0.0 43.0 0.0 1.0 1.0 0.3 0.0 0.0 mean 0.4026315789473684 66.03947368421049 1.0868421052631572 2.2710526315789488 1.1078947368421048 15.408631578947375 15.812921052631573 6.3842105263157904 maxs 1.0 79.0 2.0 4.0 2.0 139.70000000000002 97.60000000000001 9.0 sigma 0.4910743389630552 6.527071269173311 0.3087732580252793 1.0001076181502861 0.3106564493514939 19.99757266856046 18.347619967271175 1.0919533744261092 zeros 227 0 3 0 0 0 167 2 missing 0 0 0 0 0 0 0 0 0 0 65.0 1.0 2.0 1.0 1.4000000000000001 0.0 6.0 1 0 72.0 1.0 3.0 2.0 6.7 0.0 7.0 2 0 70.0 1.0 1.0 2.0 4.9 0.0 6.0 3 0 76.0 2.0 2.0 1.0 51.2 20.0 7.0 4 0 69.0 1.0 1.0 1.0 12.3 55.9 6.0 5 1 71.0 1.0 3.0 2.0 3.3000000000000003 0.0 8.0 6 0 68.0 2.0 4.0 2.0 31.900000000000002 0.0 7.0 7 0 61.0 2.0 4.0 2.0 66.7 27.2 7.0 8 0 69.0 1.0 1.0 1.0 3.9 24.0 7.0 9 0 68.0 2.0 1.0 2.0 13.0 0.0 6.0
In [9]:
# Run GBM
my_gbm = H2OGradientBoostingEstimator(distribution = "bernoulli", ntrees=50, learn_rate=0.1)
my_gbm.train(x=list(range(1,train.ncol)), y="CAPSULE", training_frame=train, validation_frame=train)
gbm Model Build Progress: [##################################################] 100%
In [10]:
my_gbm_metrics = my_gbm.model_performance(train)
my_gbm_metrics.show()
ModelMetricsBinomial: gbm
** Reported on test data. **
MSE: 0.07584147467507414
R^2: 0.6846762562816877
LogLoss: 0.2744668128481441
AUC: 0.9780311537243385
Gini: 0.9560623074486769
Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.4549496668047897:
0
1
Error
Rate 0
216.0
11.0
0.0485
(11.0/227.0) 1
14.0
139.0
0.0915
(14.0/153.0) Total
230.0
150.0
0.0658
(25.0/380.0)
Maximum Metrics: Maximum metrics at their respective thresholds
metric
threshold
value
idx max f1
0.4549497
0.9174917
149.0 max f2
0.3032010
0.9394314
196.0 max f0point5
0.4728313
0.9244265
146.0 max accuracy
0.4549497
0.9342105
149.0 max precision
0.9747938
1.0
0.0 max absolute_MCC
0.4549497
0.8629130
149.0 max min_per_class_accuracy
0.4373995
0.9215686
156.0
Gains/Lift Table: Avg response rate: 40.26 %
group
lower_threshold
cumulative_data_fraction
response_rate
cumulative_response_rate
capture_rate
cumulative_capture_rate
lift
cumulative_lift
gain
cumulative_gain 1
0.9405750
0.05
1.0
1.0
0.1241830
0.1241830
2.4836601
2.4836601
148.3660131
148.3660131 2
0.8921980
0.1
1.0
1.0
0.1241830
0.2483660
2.4836601
2.4836601
148.3660131
148.3660131 3
0.8263695
0.15
1.0
1.0
0.1241830
0.3725490
2.4836601
2.4836601
148.3660131
148.3660131 4
0.7595460
0.2
0.9473684
0.9868421
0.1176471
0.4901961
2.3529412
2.4509804
135.2941176
145.0980392 5
0.7081926
0.25
1.0
0.9894737
0.1241830
0.6143791
2.4836601
2.4575163
148.3660131
145.7516340 6
0.6364312
0.3
0.8947368
0.9736842
0.1111111
0.7254902
2.2222222
2.4183007
122.2222222
141.8300654 7
0.5478651
0.35
0.6842105
0.9323308
0.0849673
0.8104575
1.6993464
2.3155929
69.9346405
131.5592904 8
0.4499827
0.4
0.7894737
0.9144737
0.0980392
0.9084967
1.9607843
2.2712418
96.0784314
127.1241830 9
0.3927870
0.45
0.2105263
0.8362573
0.0261438
0.9346405
0.5228758
2.0769789
-47.7124183
107.6978940 10
0.3207657
0.5
0.3157895
0.7842105
0.0392157
0.9738562
0.7843137
1.9477124
-21.5686275
94.7712418 11
0.2425744
0.55
0.1578947
0.7272727
0.0196078
0.9934641
0.3921569
1.8062983
-60.7843137
80.6298277 12
0.1977616
0.6
0.0
0.6666667
0.0
0.9934641
0.0
1.6557734
-100.0
65.5773420 13
0.1586941
0.65
0.0526316
0.6194332
0.0065359
1.0
0.1307190
1.5384615
-86.9281046
53.8461538 14
0.1353591
0.7
0.0
0.5751880
0.0
1.0
0.0
1.4285714
-100.0
42.8571429 15
0.1094101
0.75
0.0
0.5368421
0.0
1.0
0.0
1.3333333
-100.0
33.3333333 16
0.0923828
0.8
0.0
0.5032895
0.0
1.0
0.0
1.25
-100.0
25.0 17
0.0665933
0.85
0.0
0.4736842
0.0
1.0
0.0
1.1764706
-100.0
17.6470588 18
0.0477968
0.9
0.0
0.4473684
0.0
1.0
0.0
1.1111111
-100.0
11.1111111 19
0.0276973
0.95
0.0
0.4238227
0.0
1.0
0.0
1.0526316
-100.0
5.2631579 20
0.0125566
1.0
0.0
0.4026316
0.0
1.0
0.0
1.0
-100.0
0.0
Content source: jangorecki/h2o-3
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