License


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.


MLP Starter Kit for Kaggle Digit Recognizer

Imports and inits


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

Import data


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 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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 0.0078333333333333330.0036 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 0.00026666666666666660.00091666666666666660.0092833333333333330.0242833333333333340.043716666666666670.0641 0.120133333333333330.160733333333333340.174183333333333330.177433333333333330.189316666666666660.17415 0.186933333333333340.15365 0.100116666666666670.071233333333333330.0538166666666666650.0213666666666666660.0100833333333333330.0035333333333333330.0 0.0 0.0 0.0 0.0 0.0 0.00106666666666666670.0007 0.00695 0.0054833333333333330.0471 0.1384 0.26418333333333330.50661666666666670.8668 1.29008333333333321.87035 2.52995 3.20161666666666683.62555 3.721983333333333 3.39255 2.80293333333333332.0443833333333331.20211666666666670.63345 0.29616666666666670.093983333333333340.035216666666666670.0086333333333333330.0 0.0 0.0 0.0 0.00323333333333333330.00585 0.0120166666666666670.069766666666666670.212083333333333350.5461 1.15441666666666662.2159 3.63963333333333335.438 7.40705 9.69673333333333311.88308333333333313.23736666666666713.12461666666666611.8135166666666679.5436666666666676.86305 4.19365 2.27463333333333351.06103333333333350.41306666666666670.162083333333333330.0277666666666666650.00280000000000000040.0 0.0 0.00063333333333333330.0052166666666666660.01435 0.080883333333333330.41021666666666671.04323333333333322.41928333333333354.77575 8.394416666666666 13.3102666666666719.47730000000000327.03668333333333335.2121666666666741.8408 45.2525999999999944.3388333333333339.1414499999999931.32821666666665522.92393333333333414.8310166666666678.6614 4.54796666666666652.137016666666667 0.8629 0.20775 0.02965 0.0020333333333333330.0 0.0 0.0103166666666666670.06335 0.39546666666666671.4634 3.58778333333333337.22781666666666713.01711666666666621.21791666666666431.50040000000001344.2063666666666758.90385000000000673.80415 85.1027333333333 90.59978333333333 88.92393333333337 80.18124999999996 65.9428166666666949.7938333333333334.3498333333333221.51 12.3903166666666656.639116666666666 2.99271666666666650.84366666666666670.141633333333333330.0047 0.0 0.000183333333333333340.0275 0.221433333333333341.11951666666666673.2899 7.43646666666666714.25443333333333524.13775 37.2885166666666653.1153999999999970.91281666666663 89.27846666666665106.23296666666666118.5061166666667 124.09814999999995121.71133333333334112.0333333333333396.10811666666665 75.1653666666666754.03711666666665535.3124833333333320.9919 11.4424333333333345.387766666666667 1.85558333333333340.3727 0.0303 0.00078333333333333340.0197333333333333320.107666666666666660.59565 2.3124 5.92876666666666612.43605000000000222.45180000000000236.29478333333333553.8984166666666773.76471666666671 94.1030833333333 111.5794833333333 124.99991666666668132.7591166666666 135.3987 133.33865000000006126.67753333333334113.5466666666666793.74426666666666 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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%

Select best model


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]:

Best known H2O MLP for MNIST

  • Can you train it?

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"

Create Submission


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.