Copyright (C) 2017 J. Patrick Hall, jphall@gwu.edu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
In [1]:
# imports
import h2o
from h2o.estimators.deeplearning import H2ODeepLearningEstimator
from h2o.grid.grid_search import H2OGridSearch
In [2]:
# display matplotlib graphics in notebook
%matplotlib inline
In [3]:
# start and connect to h2o server
h2o.init()
Checking whether there is an H2O instance running at http://localhost:54321..... not found.
Attempting to start a local H2O server...
Java Version: java version "1.8.0_112"; Java(TM) SE Runtime Environment (build 1.8.0_112-b16); Java HotSpot(TM) 64-Bit Server VM (build 25.112-b16, mixed mode)
Starting server from /Users/phall/anaconda/lib/python3.5/site-packages/h2o/backend/bin/h2o.jar
Ice root: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmptx61xb53
JVM stdout: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmptx61xb53/h2o_phall_started_from_python.out
JVM stderr: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmptx61xb53/h2o_phall_started_from_python.err
Server is running at http://127.0.0.1:54321
Connecting to H2O server at http://127.0.0.1:54321... successful.
H2O cluster uptime:
01 secs
H2O cluster timezone:
America/New_York
H2O data parsing timezone:
UTC
H2O cluster version:
3.18.0.11
H2O cluster version age:
7 days, 15 hours and 45 minutes
H2O cluster name:
H2O_from_python_phall_2bf4zp
H2O cluster total nodes:
1
H2O cluster free memory:
3.556 Gb
H2O cluster total cores:
8
H2O cluster allowed cores:
8
H2O cluster status:
accepting new members, healthy
H2O connection url:
http://127.0.0.1:54321
H2O connection proxy:
None
H2O internal security:
False
H2O API Extensions:
XGBoost, Algos, AutoML, Core V3, Core V4
Python version:
3.5.2 final
In [4]:
# load clean data
path = '../data/'
In [5]:
# define input variable measurement levels
# strings automatically parsed as enums (nominal)
# numbers automatically parsed as numeric
col_types = {'label': 'enum'}
In [6]:
train = h2o.import_file(path=path + 'train.csv', col_types=col_types) # multi-threaded import
test = h2o.import_file(path=path + 'test.csv')
Parse progress: |█████████████████████████████████████████████████████████| 100%
Parse progress: |█████████████████████████████████████████████████████████| 100%
In [7]:
train.describe()
Rows:60000
Cols:785
pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 pixel8 pixel9 pixel10 pixel11 pixel12 pixel13 pixel14 pixel15 pixel16 pixel17 pixel18 pixel19 pixel20 pixel21 pixel22 pixel23 pixel24 pixel25 pixel26 pixel27 pixel28 pixel29 pixel30 pixel31 pixel32 pixel33 pixel34 pixel35 pixel36 pixel37 pixel38 pixel39 pixel40 pixel41 pixel42 pixel43 pixel44 pixel45 pixel46 pixel47 pixel48 pixel49 pixel50 pixel51 pixel52 pixel53 pixel54 pixel55 pixel56 pixel57 pixel58 pixel59 pixel60 pixel61 pixel62 pixel63 pixel64 pixel65 pixel66 pixel67 pixel68 pixel69 pixel70 pixel71 pixel72 pixel73 pixel74 pixel75 pixel76 pixel77 pixel78 pixel79 pixel80 pixel81 pixel82 pixel83 pixel84 pixel85 pixel86 pixel87 pixel88 pixel89 pixel90 pixel91 pixel92 pixel93 pixel94 pixel95 pixel96 pixel97 pixel98 pixel99 pixel100 pixel101 pixel102 pixel103 pixel104 pixel105 pixel106 pixel107 pixel108 pixel109 pixel110 pixel111 pixel112 pixel113 pixel114 pixel115 pixel116 pixel117 pixel118 pixel119 pixel120 pixel121 pixel122 pixel123 pixel124 pixel125 pixel126 pixel127 pixel128 pixel129 pixel130 pixel131 pixel132 pixel133 pixel134 pixel135 pixel136 pixel137 pixel138 pixel139 pixel140 pixel141 pixel142 pixel143 pixel144 pixel145 pixel146 pixel147 pixel148 pixel149 pixel150 pixel151 pixel152 pixel153 pixel154 pixel155 pixel156 pixel157 pixel158 pixel159 pixel160 pixel161 pixel162 pixel163 pixel164 pixel165 pixel166 pixel167 pixel168 pixel169 pixel170 pixel171 pixel172 pixel173 pixel174 pixel175 pixel176 pixel177 pixel178 pixel179 pixel180 pixel181 pixel182 pixel183 pixel184 pixel185 pixel186 pixel187 pixel188 pixel189 pixel190 pixel191 pixel192 pixel193 pixel194 pixel195 pixel196 pixel197 pixel198 pixel199 pixel200 pixel201 pixel202 pixel203 pixel204 pixel205 pixel206 pixel207 pixel208 pixel209 pixel210 pixel211 pixel212 pixel213 pixel214 pixel215 pixel216 pixel217 pixel218 pixel219 pixel220 pixel221 pixel222 pixel223 pixel224 pixel225 pixel226 pixel227 pixel228 pixel229 pixel230 pixel231 pixel232 pixel233 pixel234 pixel235 pixel236 pixel237 pixel238 pixel239 pixel240 pixel241 pixel242 pixel243 pixel244 pixel245 pixel246 pixel247 pixel248 pixel249 pixel250 pixel251 pixel252 pixel253 pixel254 pixel255 pixel256 pixel257 pixel258 pixel259 pixel260 pixel261 pixel262 pixel263 pixel264 pixel265 pixel266 pixel267 pixel268 pixel269 pixel270 pixel271 pixel272 pixel273 pixel274 pixel275 pixel276 pixel277 pixel278 pixel279 pixel280 pixel281 pixel282 pixel283 pixel284 pixel285 pixel286 pixel287 pixel288 pixel289 pixel290 pixel291 pixel292 pixel293 pixel294 pixel295 pixel296 pixel297 pixel298 pixel299 pixel300 pixel301 pixel302 pixel303 pixel304 pixel305 pixel306 pixel307 pixel308 pixel309 pixel310 pixel311 pixel312 pixel313 pixel314 pixel315 pixel316 pixel317 pixel318 pixel319 pixel320 pixel321 pixel322 pixel323 pixel324 pixel325 pixel326 pixel327 pixel328 pixel329 pixel330 pixel331 pixel332 pixel333 pixel334 pixel335 pixel336 pixel337 pixel338 pixel339 pixel340 pixel341 pixel342 pixel343 pixel344 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pixel567 pixel568 pixel569 pixel570 pixel571 pixel572 pixel573 pixel574 pixel575 pixel576 pixel577 pixel578 pixel579 pixel580 pixel581 pixel582 pixel583 pixel584 pixel585 pixel586 pixel587 pixel588 pixel589 pixel590 pixel591 pixel592 pixel593 pixel594 pixel595 pixel596 pixel597 pixel598 pixel599 pixel600 pixel601 pixel602 pixel603 pixel604 pixel605 pixel606 pixel607 pixel608 pixel609 pixel610 pixel611 pixel612 pixel613 pixel614 pixel615 pixel616 pixel617 pixel618 pixel619 pixel620 pixel621 pixel622 pixel623 pixel624 pixel625 pixel626 pixel627 pixel628 pixel629 pixel630 pixel631 pixel632 pixel633 pixel634 pixel635 pixel636 pixel637 pixel638 pixel639 pixel640 pixel641 pixel642 pixel643 pixel644 pixel645 pixel646 pixel647 pixel648 pixel649 pixel650 pixel651 pixel652 pixel653 pixel654 pixel655 pixel656 pixel657 pixel658 pixel659 pixel660 pixel661 pixel662 pixel663 pixel664 pixel665 pixel666 pixel667 pixel668 pixel669 pixel670 pixel671 pixel672 pixel673 pixel674 pixel675 pixel676 pixel677 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type int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int int enum
mins 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
mean 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0021 0.007833333333333333 0.0036 0.00015 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0002666666666666666 0.0009166666666666666 0.009283333333333333 0.024283333333333334 0.04371666666666667 0.0641 0.12013333333333333 0.16073333333333334 0.17418333333333333 0.17743333333333333 0.18931666666666666 0.17415 0.18693333333333334 0.15365 0.10011666666666667 0.07123333333333333 0.053816666666666665 0.021366666666666666 0.010083333333333333 0.003533333333333333 0.0 0.0 0.0 0.0 0.0 0.0 0.0010666666666666667 0.0007 0.00695 0.005483333333333333 0.0471 0.1384 0.2641833333333333 0.5066166666666667 0.8668 1.2900833333333332 1.87035 2.52995 3.2016166666666668 3.62555 3.721983333333333 3.39255 2.8029333333333333 2.044383333333333 1.2021166666666667 0.63345 0.2961666666666667 0.09398333333333334 0.03521666666666667 0.008633333333333333 0.0 0.0 0.0 0.0 0.0032333333333333333 0.00585 0.012016666666666667 0.06976666666666667 0.21208333333333335 0.5461 1.1544166666666666 2.2159 3.6396333333333333 5.438 7.40705 9.696733333333333 11.883083333333333 13.237366666666667 13.124616666666666 11.813516666666667 9.543666666666667 6.86305 4.19365 2.2746333333333335 1.0610333333333335 0.4130666666666667 0.16208333333333333 0.027766666666666665 0.0028000000000000004 0.0 0.0 0.0006333333333333333 0.005216666666666666 0.01435 0.08088333333333333 0.4102166666666667 1.0432333333333332 2.4192833333333335 4.77575 8.394416666666666 13.31026666666667 19.477300000000003 27.036683333333333 35.21216666666667 41.8408 45.25259999999999 44.33883333333333 39.14144999999999 31.328216666666655 22.923933333333334 14.831016666666667 8.6614 4.5479666666666665 2.137016666666667 0.8629 0.20775 0.02965 0.002033333333333333 0.0 0.0 0.010316666666666667 0.06335 0.3954666666666667 1.4634 3.5877833333333333 7.227816666666667 13.017116666666666 21.217916666666664 31.500400000000013 44.20636666666667 58.903850000000006 73.80415 85.1027333333333 90.59978333333333 88.92393333333337 80.18124999999996 65.94281666666669 49.79383333333333 34.34983333333332 21.51 12.390316666666665 6.639116666666666 2.9927166666666665 0.8436666666666667 0.14163333333333333 0.0047 0.0 0.00018333333333333334 0.0275 0.22143333333333334 1.1195166666666667 3.2899 7.436466666666667 14.254433333333335 24.13775 37.28851666666666 53.11539999999999 70.91281666666663 89.27846666666665 106.23296666666666 118.5061166666667 124.09814999999995 121.71133333333334 112.03333333333333 96.10811666666665 75.16536666666667 54.037116666666655 35.31248333333333 20.9919 11.442433333333334 5.387766666666667 1.8555833333333334 0.3727 0.0303 0.0007833333333333334 0.019733333333333332 0.10766666666666666 0.59565 2.3124 5.928766666666666 12.436050000000002 22.451800000000002 36.294783333333335 53.89841666666667 73.76471666666671 94.1030833333333 111.5794833333333 124.99991666666668 132.7591166666666 135.3987 133.33865000000006 126.67753333333334 113.54666666666667 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In [8]:
# split into 40% training, 30% validation, and 30% test
train, valid = train.split_frame([0.8])
In [9]:
# assign target and inputs
y = 'label'
X = [name for name in train.columns if name != y]
print(y)
print(X)
label
['pixel0', 'pixel1', 'pixel2', 'pixel3', 'pixel4', 'pixel5', 'pixel6', 'pixel7', 'pixel8', 'pixel9', 'pixel10', 'pixel11', 'pixel12', 'pixel13', 'pixel14', 'pixel15', 'pixel16', 'pixel17', 'pixel18', 'pixel19', 'pixel20', 'pixel21', 'pixel22', 'pixel23', 'pixel24', 'pixel25', 'pixel26', 'pixel27', 'pixel28', 'pixel29', 'pixel30', 'pixel31', 'pixel32', 'pixel33', 'pixel34', 'pixel35', 'pixel36', 'pixel37', 'pixel38', 'pixel39', 'pixel40', 'pixel41', 'pixel42', 'pixel43', 'pixel44', 'pixel45', 'pixel46', 'pixel47', 'pixel48', 'pixel49', 'pixel50', 'pixel51', 'pixel52', 'pixel53', 'pixel54', 'pixel55', 'pixel56', 'pixel57', 'pixel58', 'pixel59', 'pixel60', 'pixel61', 'pixel62', 'pixel63', 'pixel64', 'pixel65', 'pixel66', 'pixel67', 'pixel68', 'pixel69', 'pixel70', 'pixel71', 'pixel72', 'pixel73', 'pixel74', 'pixel75', 'pixel76', 'pixel77', 'pixel78', 'pixel79', 'pixel80', 'pixel81', 'pixel82', 'pixel83', 'pixel84', 'pixel85', 'pixel86', 'pixel87', 'pixel88', 'pixel89', 'pixel90', 'pixel91', 'pixel92', 'pixel93', 'pixel94', 'pixel95', 'pixel96', 'pixel97', 'pixel98', 'pixel99', 'pixel100', 'pixel101', 'pixel102', 'pixel103', 'pixel104', 'pixel105', 'pixel106', 'pixel107', 'pixel108', 'pixel109', 'pixel110', 'pixel111', 'pixel112', 'pixel113', 'pixel114', 'pixel115', 'pixel116', 'pixel117', 'pixel118', 'pixel119', 'pixel120', 'pixel121', 'pixel122', 'pixel123', 'pixel124', 'pixel125', 'pixel126', 'pixel127', 'pixel128', 'pixel129', 'pixel130', 'pixel131', 'pixel132', 'pixel133', 'pixel134', 'pixel135', 'pixel136', 'pixel137', 'pixel138', 'pixel139', 'pixel140', 'pixel141', 'pixel142', 'pixel143', 'pixel144', 'pixel145', 'pixel146', 'pixel147', 'pixel148', 'pixel149', 'pixel150', 'pixel151', 'pixel152', 'pixel153', 'pixel154', 'pixel155', 'pixel156', 'pixel157', 'pixel158', 'pixel159', 'pixel160', 'pixel161', 'pixel162', 'pixel163', 'pixel164', 'pixel165', 'pixel166', 'pixel167', 'pixel168', 'pixel169', 'pixel170', 'pixel171', 'pixel172', 'pixel173', 'pixel174', 'pixel175', 'pixel176', 'pixel177', 'pixel178', 'pixel179', 'pixel180', 'pixel181', 'pixel182', 'pixel183', 'pixel184', 'pixel185', 'pixel186', 'pixel187', 'pixel188', 'pixel189', 'pixel190', 'pixel191', 'pixel192', 'pixel193', 'pixel194', 'pixel195', 'pixel196', 'pixel197', 'pixel198', 'pixel199', 'pixel200', 'pixel201', 'pixel202', 'pixel203', 'pixel204', 'pixel205', 'pixel206', 'pixel207', 'pixel208', 'pixel209', 'pixel210', 'pixel211', 'pixel212', 'pixel213', 'pixel214', 'pixel215', 'pixel216', 'pixel217', 'pixel218', 'pixel219', 'pixel220', 'pixel221', 'pixel222', 'pixel223', 'pixel224', 'pixel225', 'pixel226', 'pixel227', 'pixel228', 'pixel229', 'pixel230', 'pixel231', 'pixel232', 'pixel233', 'pixel234', 'pixel235', 'pixel236', 'pixel237', 'pixel238', 'pixel239', 'pixel240', 'pixel241', 'pixel242', 'pixel243', 'pixel244', 'pixel245', 'pixel246', 'pixel247', 'pixel248', 'pixel249', 'pixel250', 'pixel251', 'pixel252', 'pixel253', 'pixel254', 'pixel255', 'pixel256', 'pixel257', 'pixel258', 'pixel259', 'pixel260', 'pixel261', 'pixel262', 'pixel263', 'pixel264', 'pixel265', 'pixel266', 'pixel267', 'pixel268', 'pixel269', 'pixel270', 'pixel271', 'pixel272', 'pixel273', 'pixel274', 'pixel275', 'pixel276', 'pixel277', 'pixel278', 'pixel279', 'pixel280', 'pixel281', 'pixel282', 'pixel283', 'pixel284', 'pixel285', 'pixel286', 'pixel287', 'pixel288', 'pixel289', 'pixel290', 'pixel291', 'pixel292', 'pixel293', 'pixel294', 'pixel295', 'pixel296', 'pixel297', 'pixel298', 'pixel299', 'pixel300', 'pixel301', 'pixel302', 'pixel303', 'pixel304', 'pixel305', 'pixel306', 'pixel307', 'pixel308', 'pixel309', 'pixel310', 'pixel311', 'pixel312', 'pixel313', 'pixel314', 'pixel315', 'pixel316', 'pixel317', 'pixel318', 'pixel319', 'pixel320', 'pixel321', 'pixel322', 'pixel323', 'pixel324', 'pixel325', 'pixel326', 'pixel327', 'pixel328', 'pixel329', 'pixel330', 'pixel331', 'pixel332', 'pixel333', 'pixel334', 'pixel335', 'pixel336', 'pixel337', 'pixel338', 'pixel339', 'pixel340', 'pixel341', 'pixel342', 'pixel343', 'pixel344', 'pixel345', 'pixel346', 'pixel347', 'pixel348', 'pixel349', 'pixel350', 'pixel351', 'pixel352', 'pixel353', 'pixel354', 'pixel355', 'pixel356', 'pixel357', 'pixel358', 'pixel359', 'pixel360', 'pixel361', 'pixel362', 'pixel363', 'pixel364', 'pixel365', 'pixel366', 'pixel367', 'pixel368', 'pixel369', 'pixel370', 'pixel371', 'pixel372', 'pixel373', 'pixel374', 'pixel375', 'pixel376', 'pixel377', 'pixel378', 'pixel379', 'pixel380', 'pixel381', 'pixel382', 'pixel383', 'pixel384', 'pixel385', 'pixel386', 'pixel387', 'pixel388', 'pixel389', 'pixel390', 'pixel391', 'pixel392', 'pixel393', 'pixel394', 'pixel395', 'pixel396', 'pixel397', 'pixel398', 'pixel399', 'pixel400', 'pixel401', 'pixel402', 'pixel403', 'pixel404', 'pixel405', 'pixel406', 'pixel407', 'pixel408', 'pixel409', 'pixel410', 'pixel411', 'pixel412', 'pixel413', 'pixel414', 'pixel415', 'pixel416', 'pixel417', 'pixel418', 'pixel419', 'pixel420', 'pixel421', 'pixel422', 'pixel423', 'pixel424', 'pixel425', 'pixel426', 'pixel427', 'pixel428', 'pixel429', 'pixel430', 'pixel431', 'pixel432', 'pixel433', 'pixel434', 'pixel435', 'pixel436', 'pixel437', 'pixel438', 'pixel439', 'pixel440', 'pixel441', 'pixel442', 'pixel443', 'pixel444', 'pixel445', 'pixel446', 'pixel447', 'pixel448', 'pixel449', 'pixel450', 'pixel451', 'pixel452', 'pixel453', 'pixel454', 'pixel455', 'pixel456', 'pixel457', 'pixel458', 'pixel459', 'pixel460', 'pixel461', 'pixel462', 'pixel463', 'pixel464', 'pixel465', 'pixel466', 'pixel467', 'pixel468', 'pixel469', 'pixel470', 'pixel471', 'pixel472', 'pixel473', 'pixel474', 'pixel475', 'pixel476', 'pixel477', 'pixel478', 'pixel479', 'pixel480', 'pixel481', 'pixel482', 'pixel483', 'pixel484', 'pixel485', 'pixel486', 'pixel487', 'pixel488', 'pixel489', 'pixel490', 'pixel491', 'pixel492', 'pixel493', 'pixel494', 'pixel495', 'pixel496', 'pixel497', 'pixel498', 'pixel499', 'pixel500', 'pixel501', 'pixel502', 'pixel503', 'pixel504', 'pixel505', 'pixel506', 'pixel507', 'pixel508', 'pixel509', 'pixel510', 'pixel511', 'pixel512', 'pixel513', 'pixel514', 'pixel515', 'pixel516', 'pixel517', 'pixel518', 'pixel519', 'pixel520', 'pixel521', 'pixel522', 'pixel523', 'pixel524', 'pixel525', 'pixel526', 'pixel527', 'pixel528', 'pixel529', 'pixel530', 'pixel531', 'pixel532', 'pixel533', 'pixel534', 'pixel535', 'pixel536', 'pixel537', 'pixel538', 'pixel539', 'pixel540', 'pixel541', 'pixel542', 'pixel543', 'pixel544', 'pixel545', 'pixel546', 'pixel547', 'pixel548', 'pixel549', 'pixel550', 'pixel551', 'pixel552', 'pixel553', 'pixel554', 'pixel555', 'pixel556', 'pixel557', 'pixel558', 'pixel559', 'pixel560', 'pixel561', 'pixel562', 'pixel563', 'pixel564', 'pixel565', 'pixel566', 'pixel567', 'pixel568', 'pixel569', 'pixel570', 'pixel571', 'pixel572', 'pixel573', 'pixel574', 'pixel575', 'pixel576', 'pixel577', 'pixel578', 'pixel579', 'pixel580', 'pixel581', 'pixel582', 'pixel583', 'pixel584', 'pixel585', 'pixel586', 'pixel587', 'pixel588', 'pixel589', 'pixel590', 'pixel591', 'pixel592', 'pixel593', 'pixel594', 'pixel595', 'pixel596', 'pixel597', 'pixel598', 'pixel599', 'pixel600', 'pixel601', 'pixel602', 'pixel603', 'pixel604', 'pixel605', 'pixel606', 'pixel607', 'pixel608', 'pixel609', 'pixel610', 'pixel611', 'pixel612', 'pixel613', 'pixel614', 'pixel615', 'pixel616', 'pixel617', 'pixel618', 'pixel619', 'pixel620', 'pixel621', 'pixel622', 'pixel623', 'pixel624', 'pixel625', 'pixel626', 'pixel627', 'pixel628', 'pixel629', 'pixel630', 'pixel631', 'pixel632', 'pixel633', 'pixel634', 'pixel635', 'pixel636', 'pixel637', 'pixel638', 'pixel639', 'pixel640', 'pixel641', 'pixel642', 'pixel643', 'pixel644', 'pixel645', 'pixel646', 'pixel647', 'pixel648', 'pixel649', 'pixel650', 'pixel651', 'pixel652', 'pixel653', 'pixel654', 'pixel655', 'pixel656', 'pixel657', 'pixel658', 'pixel659', 'pixel660', 'pixel661', 'pixel662', 'pixel663', 'pixel664', 'pixel665', 'pixel666', 'pixel667', 'pixel668', 'pixel669', 'pixel670', 'pixel671', 'pixel672', 'pixel673', 'pixel674', 'pixel675', 'pixel676', 'pixel677', 'pixel678', 'pixel679', 'pixel680', 'pixel681', 'pixel682', 'pixel683', 'pixel684', 'pixel685', 'pixel686', 'pixel687', 'pixel688', 'pixel689', 'pixel690', 'pixel691', 'pixel692', 'pixel693', 'pixel694', 'pixel695', 'pixel696', 'pixel697', 'pixel698', 'pixel699', 'pixel700', 'pixel701', 'pixel702', 'pixel703', 'pixel704', 'pixel705', 'pixel706', 'pixel707', 'pixel708', 'pixel709', 'pixel710', 'pixel711', 'pixel712', 'pixel713', 'pixel714', 'pixel715', 'pixel716', 'pixel717', 'pixel718', 'pixel719', 'pixel720', 'pixel721', 'pixel722', 'pixel723', 'pixel724', 'pixel725', 'pixel726', 'pixel727', 'pixel728', 'pixel729', 'pixel730', 'pixel731', 'pixel732', 'pixel733', 'pixel734', 'pixel735', 'pixel736', 'pixel737', 'pixel738', 'pixel739', 'pixel740', 'pixel741', 'pixel742', 'pixel743', 'pixel744', 'pixel745', 'pixel746', 'pixel747', 'pixel748', 'pixel749', 'pixel750', 'pixel751', 'pixel752', 'pixel753', 'pixel754', 'pixel755', 'pixel756', 'pixel757', 'pixel758', 'pixel759', 'pixel760', 'pixel761', 'pixel762', 'pixel763', 'pixel764', 'pixel765', 'pixel766', 'pixel767', 'pixel768', 'pixel769', 'pixel770', 'pixel771', 'pixel772', 'pixel773', 'pixel774', 'pixel775', 'pixel776', 'pixel777', 'pixel778', 'pixel779', 'pixel780', 'pixel781', 'pixel782', 'pixel783']
In [10]:
# set target to factor - for multinomial classification
train[y] = train[y].asfactor()
valid[y] = valid[y].asfactor()
In [11]:
# NN with random hyperparameter search
# train many different NN models with random hyperparameters
# and select best model based on validation error
# define random grid search parameters
hyper_parameters = {'hidden': [[500, 500], [250, 250, 250, 250], [1000, 500], [500, 1000], [1000, 500, 250], [1000, 1000]],
'l1':[s/1e4 for s in range(0, 1000, 100)],
'l2':[s/1e5 for s in range(0, 1000, 100)],
'input_dropout_ratio':[s/1e2 for s in range(0, 20, 2)]}
# define search strategy
search_criteria = {'strategy':'RandomDiscrete',
'max_models':100,
'max_runtime_secs':60000}
# initialize grid search
gsearch = H2OGridSearch(H2ODeepLearningEstimator,
hyper_params=hyper_parameters,
search_criteria=search_criteria)
# execute training w/ grid search
gsearch.train(x=X,
y=y,
training_frame=train,
validation_frame=valid,
activation='RectifierWithDropout',
epochs=8000,
stopping_rounds=20,
sparse=True, # handles data w/ many zeros more efficiently
ignore_const_cols=True,
adaptive_rate=True)
# view detailed results at http://host:ip/flow/index.html
deeplearning Grid Build progress: |███████████████████████████████████████| 100%
In [12]:
# show grid search results
gsearch.show()
# select best model
mnist_model = gsearch.get_grid()[0]
# print model information
mnist_model
# hit-ratio = ((TP + TN)/(TP + TN + FP + FN)), for two-classes
hidden input_dropout_ratio l1 l2 \
0 [500, 1000] 0.02 0.0 0.004
1 [250, 250, 250, 250] 0.12 0.0 0.005
2 [250, 250, 250, 250] 0.14 0.0 0.004
3 [500, 500] 0.04 0.01 0.001
4 [1000, 1000] 0.08 0.01 0.007
5 [1000, 500] 0.1 0.05 0.003
6 [500, 500] 0.04 0.05 0.006
7 [1000, 500] 0.1 0.07 0.009
8 [1000, 1000] 0.02 0.05 0.008
9 [500, 500] 0.08 0.08 0.006
10 [1000, 500] 0.16 0.09 0.009
11 [500, 500] 0.12 0.05 0.004
12 [500, 1000] 0.14 0.07 0.006
13 [1000, 500, 250] 0.12 0.05 0.0
14 [1000, 500, 250] 0.18 0.04 0.005
15 [250, 250, 250, 250] 0.06 0.09 0.002
16 [250, 250, 250, 250] 0.08 0.05 0.004
17 [250, 250, 250, 250] 0.04 0.04 0.001
18 [1000, 1000] 0.02 0.06 0.004
model_ids \
0 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_5
1 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_10
2 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_0
3 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_6
4 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_17
5 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_13
6 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_8
7 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_3
8 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_12
9 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_7
10 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_4
11 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_15
12 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_1
13 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_14
14 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_2
15 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_16
16 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_11
17 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_9
18 Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_18
logloss
0 0.0813791957775858
1 0.10150189183448939
2 0.10219182732179348
3 0.22270926824856263
4 0.23666966039908877
5 2.2672589076179572
6 2.29792389877742
7 2.298302732871996
8 2.3010391061790467
9 2.302220573253344
10 2.302367211172909
11 2.3025250080345563
12 2.302676933140575
13 2.306021931640824
14 2.3381326424124413
15 2.349169147514861
16 2.3560493101302833
17 2.3696788315812
18 2.6079328484740665
Model Details
=============
H2ODeepLearningEstimator : Deep Learning
Model Key: Grid_DeepLearning_py_5_sid_a4f7_model_python_1527827232201_1_model_5
ModelMetricsMultinomial: deeplearning
** Reported on train data. **
MSE: 0.003027974960375359
RMSE: 0.05502703844816073
LogLoss: 0.012548114077993484
Mean Per-Class Error: 0.0030595719228275484
Confusion Matrix: Row labels: Actual class; Column labels: Predicted class
0
1
2
3
4
5
6
7
8
9
Error
Rate
951.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0 / 951
0.0
1119.0
1.0
0.0
0.0
0.0
0.0
0.0
1.0
0.0
0.0017841
2 / 1,121
0.0
0.0
967.0
1.0
2.0
0.0
0.0
2.0
1.0
0.0
0.0061665
6 / 973
0.0
0.0
0.0
990.0
0.0
0.0
0.0
3.0
0.0
1.0
0.0040241
4 / 994
0.0
1.0
0.0
0.0
925.0
0.0
0.0
0.0
1.0
2.0
0.0043057
4 / 929
0.0
0.0
1.0
1.0
0.0
892.0
0.0
0.0
1.0
0.0
0.0033520
3 / 895
3.0
0.0
0.0
0.0
0.0
1.0
1011.0
0.0
0.0
0.0
0.0039409
4 / 1,015
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1035.0
0.0
0.0
0.0
0 / 1,035
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1013.0
1.0
0.0009862
1 / 1,014
1.0
0.0
0.0
2.0
0.0
0.0
0.0
2.0
1.0
988.0
0.0060362
6 / 994
955.0
1120.0
969.0
994.0
927.0
893.0
1011.0
1042.0
1018.0
992.0
0.0030239
30 / 9,921
Top-10 Hit Ratios:
k
hit_ratio
1
0.9969761
2
0.9993952
3
0.9998992
4
1.0
5
1.0
6
1.0
7
1.0
8
1.0
9
1.0
10
1.0
ModelMetricsMultinomial: deeplearning
** Reported on validation data. **
MSE: 0.018297410138058034
RMSE: 0.13526791984080347
LogLoss: 0.0813791957775858
Mean Per-Class Error: 0.020746734154994634
Confusion Matrix: Row labels: Actual class; Column labels: Predicted class
0
1
2
3
4
5
6
7
8
9
Error
Rate
1166.0
1.0
1.0
0.0
0.0
2.0
7.0
2.0
1.0
0.0
0.0118644
14 / 1,180
0.0
1320.0
3.0
5.0
0.0
0.0
0.0
1.0
3.0
0.0
0.0090090
12 / 1,332
1.0
1.0
1205.0
4.0
3.0
0.0
3.0
9.0
5.0
0.0
0.0211210
26 / 1,231
0.0
2.0
6.0
1235.0
0.0
3.0
0.0
6.0
14.0
2.0
0.0260252
33 / 1,268
1.0
2.0
2.0
0.0
1124.0
2.0
6.0
5.0
3.0
9.0
0.0259965
30 / 1,154
2.0
1.0
1.0
14.0
1.0
1108.0
3.0
2.0
6.0
2.0
0.0280702
32 / 1,140
2.0
5.0
0.0
0.0
2.0
5.0
1139.0
0.0
5.0
0.0
0.0164076
19 / 1,158
0.0
2.0
4.0
0.0
2.0
0.0
0.0
1220.0
2.0
7.0
0.0137429
17 / 1,237
1.0
5.0
1.0
8.0
0.0
3.0
6.0
2.0
1113.0
2.0
0.0245399
28 / 1,141
2.0
2.0
0.0
3.0
6.0
6.0
0.0
12.0
5.0
1137.0
0.0306905
36 / 1,173
1175.0
1341.0
1223.0
1269.0
1138.0
1129.0
1164.0
1259.0
1157.0
1159.0
0.0205593
247 / 12,014
Top-10 Hit Ratios:
k
hit_ratio
1
0.9794406
2
0.9946728
3
0.9975029
4
0.9988347
5
0.9995838
6
0.9997503
7
0.9998335
8
0.9999167
9
0.9999167
10
0.9999999
Scoring History:
timestamp
duration
training_speed
epochs
iterations
samples
training_rmse
training_logloss
training_classification_error
validation_rmse
validation_logloss
validation_classification_error
2018-06-01 05:52:33
0.000 sec
None
0.0
0
0.0
nan
nan
nan
nan
nan
nan
2018-06-01 05:52:38
5:25:25.436
1460 obs/sec
0.1274955
1
6118.0
0.3137048
0.4365653
0.1170245
0.3145291
0.4460997
0.1158648
2018-06-01 05:53:56
5:26:44.271
1727 obs/sec
2.6926812
21
129211.0
0.1666627
0.1013986
0.0307429
0.1846801
0.1244377
0.0388713
2018-06-01 05:55:20
5:28:08.118
1874 obs/sec
5.8930313
46
282783.0
0.1312903
0.0650657
0.0191513
0.1595478
0.0988074
0.0293824
2018-06-01 05:56:41
5:29:27.546
1925 obs/sec
8.9644896
70
430170.0
0.1126705
0.0469285
0.0146155
0.1513525
0.0896927
0.0258032
---
---
---
---
---
---
---
---
---
---
---
---
---
2018-06-01 06:45:55
6:18:42.777
2142 obs/sec
128.9778685
1003
6189132.0
0.0491715
0.0095191
0.0027215
0.1344723
0.0871130
0.0207258
2018-06-01 06:47:12
6:19:59.755
2144 obs/sec
132.1793648
1028
6342759.0
0.0421763
0.0080966
0.0012096
0.1362132
0.0858401
0.0211420
2018-06-01 06:48:33
6:21:20.221
2151 obs/sec
135.8990747
1057
6521253.0
0.0463655
0.0089080
0.0021167
0.1357118
0.0866437
0.0204761
2018-06-01 06:49:46
6:22:33.912
2153 obs/sec
138.9794523
1081
6669068.0
0.0442301
0.0082408
0.0024191
0.1330820
0.0849904
0.0193940
2018-06-01 06:49:54
6:22:40.802
2153 obs/sec
138.9794523
1081
6669068.0
0.0550270
0.0125481
0.0030239
0.1352679
0.0813792
0.0205593
See the whole table with table.as_data_frame()
Variable Importances:
variable
relative_importance
scaled_importance
percentage
pixel293
1.0
1.0
0.0019564
pixel294
0.9432160
0.9432160
0.0018453
pixel349
0.9159057
0.9159057
0.0017918
pixel322
0.9067742
0.9067742
0.0017740
pixel515
0.9033036
0.9033036
0.0017672
---
---
---
---
pixel601
0.5004087
0.5004087
0.0009790
pixel574
0.4984325
0.4984325
0.0009751
pixel628
0.4927354
0.4927354
0.0009640
pixel658
0.4925828
0.4925828
0.0009637
pixel629
0.4697497
0.4697497
0.0009190
See the whole table with table.as_data_frame()
Out[12]:
In [13]:
"""
best_model = H2ODeepLearningEstimator(
activation = 'RectifierWithDropout',
hidden = [1024,1024,2048],
epochs = 8000,
l1 = 1e-5,
input_dropout_ratio = 0.2,
train_samples_per_iteration = -1,
classification_stop = -1)
"""
Out[13]:
"\n\nbest_model = H2ODeepLearningEstimator(\n activation = 'RectifierWithDropout', \n hidden = [1024,1024,2048],\n epochs = 8000, \n l1 = 1e-5, \n input_dropout_ratio = 0.2,\n train_samples_per_iteration = -1, \n classification_stop = -1)\n \n"
In [15]:
"""
# create time stamp
import re
import time
time_stamp = re.sub('[: ]', '_', time.asctime())
# score unlabeled test data
sub = mnist_model.predict(test)
# save file for submission
sub = sub['predict']
import numpy as np # create ID column
sub = h2o.H2OFrame(np.arange(1, 28001)).cbind(sub)
sub.columns = ['ImageId', 'Label']
print(sub.head())
sub_fname = '../data/submission_' + str(time_stamp) + '.csv'
h2o.download_csv(sub, sub_fname)
"""
Out[15]:
"\n# create time stamp\nimport re\nimport time\ntime_stamp = re.sub('[: ]', '_', time.asctime())\n\n# score unlabeled test data\nsub = mnist_model.predict(test)\n\n# save file for submission\nsub = sub['predict']\n\nimport numpy as np # create ID column\nsub = h2o.H2OFrame(np.arange(1, 28001)).cbind(sub) \n\nsub.columns = ['ImageId', 'Label']\n\nprint(sub.head())\n\nsub_fname = '../data/submission_' + str(time_stamp) + '.csv'\nh2o.download_csv(sub, sub_fname)\n"
In [16]:
# shutdown h2o - this will erase all your unsaved frames and models in H2O
h2o.cluster().shutdown(prompt=True)
Are you sure you want to shutdown the H2O instance running at http://127.0.0.1:54321 (Y/N)? y
H2O session _sid_a4f7 closed.
Content source: jphall663/GWU_data_mining
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