First let us discuss what exactly are AutoEncoders ?
In [26]:
# Autoendoer using H2o
#CSCI6360 H2O WORKSHOP
from IPython.display import Image,display
from IPython.core.display import HTML
import matplotlib.pyplot as plot
from h2o.estimators.deeplearning import H2ODeepLearningEstimator
from h2o.grid.grid_search import H2OGridSearch
#special thanks to wikipedia for the image
#Code available at https://github.com/CodeMaster001/CSCI6360
img = Image(url="images/autoencoder_structure.png")
display(img)
In [27]:
img_1 = Image(url="images/autoencoder_equation.png") #special thanks to wikipedia for the image
display(img_1)
In [28]:
img_1 = Image(url="images/autoencoder_network.png") #special thanks to ufld.stanford.edu for the image
display(img_1)
This is a workshop on H2O,a library that is used extensively in the production environment, widely used in healthcare and finance
Here is its OFFICIAL WEBSITE
First, lets create a seperate environment for h2o in Anaconda and performs a switch to that environment.
conda create --name h2o-py python=3.5 h2o h2o-py
As,I am currently in Mac , I would like to use a UI as I am a bit confortable with it , reducing complexity is nice !!!
What is h2o?
A library which is used for building machine learning models at ease on huge dataset.It supports mxnet, tensorflow and caffe. It is not an alternative for any of those, its just exends the backent (h2o.ai!!)
Other examples that work like h2o is keras.
We are now going to import h2o inside python
Adventages of having h2o :
1.Notable adventages variation in Stochastic Gradient descent implementation H2O SGD algorithm is executed in parallel across all cores. The training set is also distributed across all nodes. At the final an average is taken of all the values.
For more detials on Page 16.
http://docs.h2o.ai/h2o/latest-stable/h2o-docs/booklets/DeepLearningBooklet.pdf
Lets start h2o programming
In [29]:
import h2o
h2o.init() #initialize h2o cluster
#Once h2o is initialized it actually automatically sets up the spark cluster if spark is configured as a backend,
#applies same for mxnet and tensorflow
h2o.init(ip="localhost", port=54323)
Checking whether there is an H2O instance running at http://localhost:54321. connected.
Warning: Your H2O cluster version is too old (5 months and 4 days)! Please download and install the latest version from http://h2o.ai/download/
H2O cluster uptime:
1 hour 30 mins H2O cluster version:
3.10.4.8 H2O cluster version age:
5 months and 4 days !!! H2O cluster name:
H2O_from_python_prajayshetty_heoxjp H2O cluster total nodes:
1 H2O cluster free memory:
749 Mb H2O cluster total cores:
4 H2O cluster allowed cores:
4 H2O cluster status:
locked, healthy H2O connection url:
http://localhost:54321 H2O connection proxy:
None H2O internal security:
False Python version:
3.6.2 final
Checking whether there is an H2O instance running at http://localhost:54323. connected.
Warning: Your H2O cluster version is too old (5 months and 4 days)! Please download and install the latest version from http://h2o.ai/download/
H2O cluster uptime:
1 hour 27 mins H2O cluster version:
3.10.4.8 H2O cluster version age:
5 months and 4 days !!! H2O cluster name:
H2O_from_python_prajayshetty_n9bfmr H2O cluster total nodes:
1 H2O cluster free memory:
718 Mb H2O cluster total cores:
4 H2O cluster allowed cores:
4 H2O cluster status:
locked, healthy H2O connection url:
http://localhost:54323 H2O connection proxy:
None H2O internal security:
False Python version:
3.6.2 final
Once h2o is initialized it actually automatically sets up the spark cluster, if spark is configured as a backend, applies same for mxnet, tensorflow and theano backends. We will discuss shortly how to use spark. Please use Sparkling Water if you want to use H2O wth spark.
Now lets see our cluster status info.
In [30]:
h2o.cluster().show_status()
H2O cluster uptime:
1 hour 28 mins H2O cluster version:
3.10.4.8 H2O cluster version age:
5 months and 4 days !!! H2O cluster name:
H2O_from_python_prajayshetty_n9bfmr H2O cluster total nodes:
1 H2O cluster free memory:
718 Mb H2O cluster total cores:
4 H2O cluster allowed cores:
4 H2O cluster status:
locked, healthy H2O connection url:
http://localhost:54323 H2O connection proxy:
None H2O internal security:
False Python version:
3.6.2 final
Lets us import a file and see if its added to cluster
In [31]:
h2o.ls() #list files
Out[31]:
key
0
DeepLearning_model_python_1508939596978_1
1
DeepLearning_model_python_1508939596978_10
2
DeepLearning_model_python_1508939596978_13
3
DeepLearning_model_python_1508939596978_14
4
DeepLearning_model_python_1508939596978_2
5
DeepLearning_model_python_1508939596978_5
6
DeepLearning_model_python_1508939596978_6
7
DeepLearning_model_python_1508939596978_9
8
Grid_DeepLearning_ecg_discord_train.hex_model_python_1508939596978_3
9
Grid_DeepLearning_ecg_discord_train.hex_model_python_1508939596978...
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Grid_DeepLearning_ecg_discord_train.hex_model_python_1508939596978...
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Grid_DeepLearning_ecg_discord_train.hex_model_python_1508939596978...
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Grid_DeepLearning_ecg_discord_train.hex_model_python_1508939596978_4
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In [32]:
#Now lets import a file to H2o cluster
h2o.import_file("LICENSE")
Parse progress: |█████████████████████████████████████████████████████████| 100%
Apache License Version 2.0, http://www.apache.org/licenses/ TERMS AND 1. Definitions. License shall and distribution Licensor shall the copyright Legal Entity shall other entities
Out[32]:
In [34]:
h2o.ls() #first let us see if License1 file is actually present in cluster
Out[34]:
key
0
DeepLearning_model_python_1508939596978_1
1
DeepLearning_model_python_1508939596978_10
2
DeepLearning_model_python_1508939596978_13
3
DeepLearning_model_python_1508939596978_14
4
DeepLearning_model_python_1508939596978_2
5
DeepLearning_model_python_1508939596978_5
6
DeepLearning_model_python_1508939596978_6
7
DeepLearning_model_python_1508939596978_9
8
Grid_DeepLearning_ecg_discord_train.hex_model_python_1508939596978_3
9
Grid_DeepLearning_ecg_discord_train.hex_model_python_1508939596978...
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Grid_DeepLearning_ecg_discord_train.hex_model_python_1508939596978...
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374 rows × 1 columns
In [35]:
#h2o.remove("LICENSE1.hex") #REMOVE THE LICENSE FILE
h2o.remove("LICENSE")
In [ ]:
h2o.ls()
In [36]:
help(h2o.import_file)
Help on function import_file in module h2o.h2o:
import_file(path=None, destination_frame=None, parse=True, header=0, sep=None, col_names=None, col_types=None, na_strings=None, pattern=None)
Import a dataset that is already on the cluster.
The path to the data must be a valid path for each node in the H2O cluster. If some node in the H2O cluster
cannot see the file, then an exception will be thrown by the H2O cluster. Does a parallel/distributed
multi-threaded pull of the data. The main difference between this method and :func:`upload_file` is that
the latter works with local files, whereas this method imports remote files (i.e. files local to the server).
If you running H2O server on your own maching, then both methods behave the same.
:param path: path(s) specifying the location of the data to import or a path to a directory of files to import
:param destination_frame: The unique hex key assigned to the imported file. If none is given, a key will be
automatically generated.
:param parse: If True, the file should be parsed after import.
:param header: -1 means the first line is data, 0 means guess, 1 means first line is header.
:param sep: The field separator character. Values on each line of the file are separated by
this character. If not provided, the parser will automatically detect the separator.
:param col_names: A list of column names for the file.
:param col_types: A list of types or a dictionary of column names to types to specify whether columns
should be forced to a certain type upon import parsing. If a list, the types for elements that are
one will be guessed. The possible types a column may have are:
- "unknown" - this will force the column to be parsed as all NA
- "uuid" - the values in the column must be true UUID or will be parsed as NA
- "string" - force the column to be parsed as a string
- "numeric" - force the column to be parsed as numeric. H2O will handle the compression of the numeric
data in the optimal manner.
- "enum" - force the column to be parsed as a categorical column.
- "time" - force the column to be parsed as a time column. H2O will attempt to parse the following
list of date time formats: (date) "yyyy-MM-dd", "yyyy MM dd", "dd-MMM-yy", "dd MMM yy", (time)
"HH:mm:ss", "HH:mm:ss:SSS", "HH:mm:ss:SSSnnnnnn", "HH.mm.ss" "HH.mm.ss.SSS", "HH.mm.ss.SSSnnnnnn".
Times can also contain "AM" or "PM".
:param na_strings: A list of strings, or a list of lists of strings (one list per column), or a dictionary
of column names to strings which are to be interpreted as missing values.
:param pattern: Character string containing a regular expression to match file(s) in the folder if `path` is a
directory.
:returns: a new :class:`H2OFrame` instance.
:examples:
>>> # Single file import
>>> iris = import_file("h2o-3/smalldata/iris.csv")
>>> # Return all files in the folder iris/ matching the regex r"iris_.*\.csv"
>>> iris_pattern = h2o.import_file(path = "h2o-3/smalldata/iris",
... pattern = "iris_.*\.csv")
Lets load the ECG training dataset
In [37]:
train = h2o.import_file("data/ecg_discord_train.csv")
train.summary()
Parse progress: |█████████████████████████████████████████████████████████| 100%
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26 C27 C28 C29 C30 C31 C32 C33 C34 C35 C36 C37 C38 C39 C40 C41 C42 C43 C44 C45 C46 C47 C48 C49 C50 C51 C52 C53 C54 C55 C56 C57 C58 C59 C60 C61 C62 C63 C64 C65 C66 C67 C68 C69 C70 C71 C72 C73 C74 C75 C76 C77 C78 C79 C80 C81 C82 C83 C84 C85 C86 C87 C88 C89 C90 C91 C92 C93 C94 C95 C96 C97 C98 C99 C100 C101 C102 C103 C104 C105 C106 C107 C108 C109 C110 C111 C112 C113 C114 C115 C116 C117 C118 C119 C120 C121 C122 C123 C124 C125 C126 C127 C128 C129 C130 C131 C132 C133 C134 C135 C136 C137 C138 C139 C140 C141 C142 C143 C144 C145 C146 C147 C148 C149 C150 C151 C152 C153 C154 C155 C156 C157 C158 C159 C160 C161 C162 C163 C164 C165 C166 C167 C168 C169 C170 C171 C172 C173 C174 C175 C176 C177 C178 C179 C180 C181 C182 C183 C184 C185 C186 C187 C188 C189 C190 C191 C192 C193 C194 C195 C196 C197 C198 C199 C200 C201 C202 C203 C204 C205 C206 C207 C208 C209 C210 type real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real mins 2.05 2.04 2.0300000000000002 2.02 2.0300000000000002 2.04 2.05 2.09 2.14 2.22 2.35 2.5300000000000002 2.7600000000000002 3.04 3.36 3.69 4.0 4.32 4.6000000000000005 4.86 4.94 5.3100000000000005 5.51 5.65 5.75 5.73 5.65 5.59 5.54 5.51 5.47 5.43 5.39 5.39 5.33 5.29 5.26 5.25 5.23 5.2 5.17 5.16 5.16 5.13 5.11 5.11 5.1000000000000005 5.09 5.09 5.09 5.09 5.11 5.09 5.09 5.08 5.08 5.09 5.08 5.07 5.08 5.08 5.09 5.08 5.08 5.08 5.09 5.09 5.08 5.08 5.08 5.09 5.08 5.08 5.09 5.07 5.0600000000000005 5.0600000000000005 5.07 5.0600000000000005 5.05 5.04 5.05 5.05 5.0200000000000005 5.0200000000000005 5.0200000000000005 5.0200000000000005 5.0 4.99 5.0 5.0 5.0 4.98 4.98 4.98 4.97 4.97 4.97 4.96 4.96 4.96 4.95 4.95 4.96 4.95 4.95 4.94 4.96 4.94 4.94 4.94 4.94 4.93 4.94 4.93 4.94 4.93 4.94 4.92 4.93 4.93 4.91 4.92 4.92 4.91 4.92 4.92 4.92 4.9 4.92 4.91 4.91 4.91 4.9 4.9 4.9 4.87 4.88 4.9 4.8 4.47 4.1 3.73 3.36 3.0300000000000002 2.7800000000000002 2.58 2.47 2.37 2.23 2.15 2.12 2.1 2.07 2.04 2.06 2.05 2.06 2.06 2.05 2.04 2.04 2.04 2.05 2.0300000000000002 2.02 2.02 2.02 2.0 2.0100000000000002 2.02 2.04 2.04 2.02 1.99 2.0100000000000002 2.0100000000000002 2.02 2.0100000000000002 2.02 2.0 2.02 2.0 2.0 2.0 2.0300000000000002 2.0300000000000002 2.02 2.0 1.99 1.99 1.99 1.99 2.0 2.0 2.0 2.02 2.07 2.08 2.08 2.08 2.11 2.13 2.12 2.08 2.08 2.08 2.07 2.06 2.07 mean 4.841499999999999 4.9085 4.9645 5.022 5.077500000000001 5.1370000000000005 5.210000000000001 5.288 5.3685 5.4555 5.534000000000002 5.643500000000001 5.747999999999999 5.846499999999999 5.9445 6.019 6.1065 6.1770000000000005 6.2355 6.282999999999999 6.3135 6.346499999999999 6.367500000000001 6.379499999999999 6.382 6.379 6.381 6.3765 6.367000000000001 6.360500000000001 6.350499999999999 6.34 6.3260000000000005 6.314500000000001 6.297999999999999 6.2829999999999995 6.2675 6.248000000000001 6.2305 6.21 6.188000000000001 6.168 6.1450000000000005 6.12 6.0975 6.075500000000001 6.0475 6.0225 5.999 5.9765 5.954 5.927 5.901 5.876499999999999 5.8585 5.8344999999999985 5.808000000000002 5.7780000000000005 5.754000000000001 5.7235000000000005 5.697 5.6690000000000005 5.639000000000001 5.609 5.580500000000002 5.5520000000000005 5.518 5.489 5.456 5.43 5.407000000000001 5.376500000000001 5.3515 5.3294999999999995 5.3075 5.2885 5.269500000000001 5.251000000000001 5.233999999999999 5.221000000000001 5.2065 5.193500000000001 5.184 5.174000000000001 5.165000000000001 5.155500000000001 5.148499999999999 5.140000000000001 5.133000000000001 5.128 5.121499999999999 5.117 5.112 5.1045 5.103000000000001 5.095500000000001 5.095500000000001 5.09 5.089 5.083500000000001 5.077500000000001 5.0785 5.0760000000000005 5.075499999999999 5.0710000000000015 5.0705 5.067500000000001 5.063 5.062 5.0585 5.055000000000001 5.051499999999999 5.0465 5.0455 5.0424999999999995 5.039999999999999 5.037500000000001 5.037000000000001 5.032499999999999 5.0295 5.0265 5.0234999999999985 5.022499999999999 5.0155 5.015000000000001 5.0120000000000005 5.0104999999999995 5.003500000000001 5.002 5.007999999999999 5.026000000000001 5.039 5.026 5.0120000000000005 5.024 5.026999999999999 5.050000000000001 5.037 5.05 5.053999999999999 5.038000000000001 5.0225 4.996499999999998 4.9745 4.945499999999999 4.911000000000002 4.857499999999999 4.857499999999999 4.8635 4.8149999999999995 4.768999999999999 4.697500000000001 4.6195 4.544000000000001 4.482 4.4275 4.3695 4.3385 4.314000000000002 4.2555 4.171 4.106000000000001 4.0695 4.066999999999999 4.074000000000002 4.1015000000000015 4.0755 4.045499999999999 4.016500000000001 3.979500000000001 3.937 3.9139999999999997 3.893000000000001 3.893 3.9044999999999996 3.9010000000000002 3.8600000000000003 3.845500000000001 3.8390000000000004 3.8409999999999997 3.8465 3.895 3.9545 3.9974999999999996 4.035 4.101 4.156000000000001 4.229 4.3175 4.346 4.336500000000001 4.358 4.385 4.4115 4.434500000000001 4.452999999999999 4.459500000000001 4.4615 4.469 4.489 4.5135000000000005 4.557 4.6 4.652 4.7045 4.7635 4.8345 4.8965000000000005 4.9615 5.027499999999999 maxs 6.83 6.88 6.88 6.91 6.94 6.93 6.94 6.96 6.97 7.0 7.05 7.05 7.07 7.0600000000000005 7.07 7.07 7.0600000000000005 7.08 7.09 7.08 7.05 7.04 7.01 6.96 6.99 7.01 7.03 7.05 7.08 7.1000000000000005 7.09 7.08 7.09 7.07 7.03 7.04 7.03 6.99 6.99 7.0 7.01 7.01 6.98 6.95 6.94 6.98 7.01 7.03 7.05 7.0600000000000005 7.07 7.08 7.0600000000000005 7.04 7.05 7.01 6.99 6.97 6.93 6.93 6.92 6.92 6.91 6.87 6.83 6.7700000000000005 6.73 6.66 6.57 6.49 6.46 6.32 6.22 6.140000000000001 6.0600000000000005 5.98 5.89 5.8100000000000005 5.75 5.69 5.61 5.54 5.51 5.47 5.43 5.39 5.3500000000000005 5.3500000000000005 5.32 5.28 5.2700000000000005 5.2700000000000005 5.25 5.24 5.24 5.24 5.24 5.22 5.23 5.23 5.21 5.22 5.22 5.22 5.23 5.22 5.21 5.22 5.23 5.23 5.21 5.22 5.22 5.22 5.2 5.2 5.21 5.22 5.2 5.19 5.19 5.19 5.17 5.18 5.17 5.17 5.15 5.14 5.14 5.15 5.57 5.89 5.62 5.38 5.34 5.44 5.91 5.54 5.79 5.95 5.7 6.01 5.74 5.73 5.88 5.82 5.34 5.5 5.95 5.82 5.83 5.84 5.47 5.29 5.23 5.18 5.11 5.62 5.96 5.92 5.78 5.48 5.39 5.76 6.0200000000000005 6.0 5.84 5.74 5.82 5.73 5.42 5.3100000000000005 5.26 5.46 5.83 5.97 5.79 5.8500000000000005 5.88 5.92 5.97 6.0200000000000005 6.04 6.0600000000000005 6.1000000000000005 6.12 6.16 6.17 6.22 6.25 6.29 6.3 6.34 6.390000000000001 6.44 6.46 6.51 6.5600000000000005 6.6000000000000005 6.63 6.67 6.71 6.75 6.7700000000000005 6.8 6.84 6.86 6.890000000000001 6.890000000000001 6.9 sigma 1.959372281329433 1.9739281568540057 1.981638011980572 1.979007193944532 1.9672044685422596 1.9444579815735956 1.9176795188044595 1.8730740283794112 1.8077676696324725 1.734120359448165 1.6608222440453366 1.5326079424507615 1.4106049467478392 1.2829090093673017 1.155437829979341 1.0486628076796307 0.9218817997073044 0.8184651875051834 0.7228123072962631 0.6400419394153225 0.5879247624618213 0.5147025201128576 0.4728399640247714 0.44960187066477314 0.43368191108230486 0.42302046734108917 0.4216870877795525 0.4225022578575608 0.43003182256788 0.442569946895106 0.4572282051437195 0.46942293801735124 0.4886328941681661 0.5035398379682117 0.523335456471277 0.5363531829327622 0.559585137683078 0.5783333586126698 0.593778932813073 0.6090631286959028 0.6205566261960223 0.6380777052167994 0.6504209163465245 0.6639831957290232 0.6743095884329626 0.6833776408399681 0.6979886516948083 0.7044211883002356 0.7152246610607561 0.7210465712384583 0.7324968115612303 0.7401216116287914 0.7488440214230544 0.7521427286164014 0.7579839951094647 0.7500559628243884 0.7515989972263006 0.7469841117171735 0.7380657508864507 0.7271376833353612 0.7109082404331727 0.6901860923731108 0.6697752961765767 0.6440325261482083 0.6180825613391621 0.5909457184084362 0.5565268234893609 0.5262268872284133 0.49166098718099416 0.45573538028345545 0.42680082868759867 0.387913107470434 0.352185283173801 0.3220734633425643 0.29269842572356375 0.26839238753029265 0.23816126778652397 0.21530639123180567 0.19418927283506254 0.1741415032606954 0.15530191309358135 0.13712364762083226 0.12512730359622987 0.11762563272384206 0.1064004352978831 0.09939157011802548 0.09097570525897768 0.08938385583655833 0.08736493334826839 0.0812144659139259 0.07975455769850513 0.07739849039735526 0.07851248643569866 0.07653516563611357 0.07934601107607274 0.0795033928438547 0.07996545306700022 0.07953814047030311 0.08478455668712076 0.0827981057500078 0.08384666456034313 0.0826708756710035 0.08592530415853566 0.08519173421727785 0.08866851336950468 0.08469605098604972 0.08503095411610279 0.08467274463610147 0.08941917844447193 0.09068598220464462 0.08732877984647312 0.08791592874056332 0.08969392398596457 0.0879279010864789 0.08759205683766456 0.08553238844837797 0.08759205683766536 0.08609052762858663 0.08777213321106539 0.08580731167471708 0.08228800513728374 0.08437229527828587 0.08038558393930512 0.07930454291095669 0.07850578255604446 0.07571414385005872 0.0738045070653401 0.07161850687019253 0.07105224171432976 0.07702357807157063 0.1454358241540524 0.21058564953356546 0.15445660265998074 0.1095252914795675 0.12115236166431946 0.13715530958504102 0.21911304460435374 0.15413937643710995 0.20390916450326854 0.24171318366587854 0.25552010941565734 0.33240550695292576 0.35681743820969036 0.4342261719669287 0.5129940494166009 0.5706127823387953 0.5993757717738292 0.671093961575544 0.7753931091286602 0.8203561485881091 0.8522163056284169 0.8905401967936447 0.9105549204512019 0.9575648940493746 1.0134703277665102 1.0672190866674789 1.111326732002411 1.1615519337235172 1.2210797637638933 1.2554616471116988 1.2632410279660975 1.2663515766916686 1.2920056827315143 1.3355783292956684 1.3723449006411585 1.4148843173839152 1.4193604008032703 1.402754714650314 1.4020972073816311 1.38643987475685 1.3668946750322712 1.3541414059722203 1.3484575984200775 1.3502167467328696 1.3664108767901866 1.3669362592462406 1.3123582630235984 1.2823230359082898 1.2808915480612464 1.2973978004573534 1.3364811416945857 1.361707519411218 1.4125172565317565 1.442336828315699 1.447635095050218 1.4853385224640845 1.5380520831643887 1.583490149205728 1.643500356267875 1.6505450455435686 1.651042015468445 1.62502987826783 1.6321780733602245 1.6281997646739206 1.622144373482537 1.608899918381632 1.600027878046603 1.5965662990832612 1.6071023286452053 1.6187288710265029 1.639044249747876 1.6606406499965767 1.6909573744199655 1.725025095164522 1.764332213018494 1.7864998858397005 1.8250666173854413 1.8511568928929991 1.8719347576350265 1.8846691040017998 zeros 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 missing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.1 2.13 2.19 2.2800000000000002 2.44 2.62 2.8000000000000003 3.04 3.36 3.69 3.97 4.24 4.53 4.8 5.0200000000000005 5.21 5.4 5.57 5.71 5.79 5.86 5.92 5.98 6.0200000000000005 6.0600000000000005 6.08 6.140000000000001 6.18 6.22 6.2700000000000005 6.32 6.3500000000000005 6.38 6.45 6.49 6.53 6.57 6.640000000000001 6.7 6.73 6.78 6.83 6.88 6.92 6.94 6.98 7.01 7.03 7.05 7.0600000000000005 7.07 7.08 7.0600000000000005 7.04 7.03 6.99 6.94 6.88 6.83 6.7700000000000005 6.69 6.6000000000000005 6.53 6.45 6.36 6.2700000000000005 6.19 6.11 6.03 5.94 5.88 5.8100000000000005 5.75 5.68 5.62 5.61 5.54 5.49 5.45 5.42 5.38 5.34 5.3100000000000005 5.3 5.29 5.26 5.23 5.23 5.22 5.2 5.19 5.18 5.19 5.17 5.15 5.14 5.17 5.16 5.15 5.15 5.15 5.14 5.14 5.14 5.15 5.14 5.14 5.13 5.15 5.15 5.15 5.14 5.16 5.15 5.15 5.14 5.14 5.15 5.15 5.14 5.13 5.14 5.14 5.11 5.12 5.12 5.12 5.09 5.09 5.09 5.1000000000000005 5.08 5.08 5.08 5.08 5.0600000000000005 5.05 5.0600000000000005 5.07 5.05 5.03 5.03 5.04 5.03 5.01 5.01 5.0200000000000005 5.01 5.01 5.0 5.0 5.0200000000000005 5.01 4.98 5.0 5.0 5.0 4.99 5.0 5.01 5.0200000000000005 5.01 5.03 5.03 5.0200000000000005 5.0200000000000005 5.04 5.04 5.04 5.0200000000000005 5.0200000000000005 5.01 4.99 4.98 4.96 4.96 4.96 4.94 4.93 4.93 4.93 4.93 4.93 5.0200000000000005 5.2700000000000005 5.8 5.94 5.58 5.39 5.32 5.25 5.21 5.13 4.97 4.71 4.39 4.05 3.69 3.3200000000000003 3.0500000000000003 2.99 2.74 2.61 2.47 2.35 2.2600000000000002 2.2 2.15 2.1 2.08 1 2.06 2.05 2.06 2.07 2.08 2.13 2.22 2.37 2.5300000000000002 2.71 2.97 3.31 3.65 3.96 4.2700000000000005 4.37 4.82 5.04 5.24 5.43 5.59 5.7 5.76 5.83 5.9 5.94 5.97 6.04 6.08 6.12 6.16 6.22 6.26 6.3100000000000005 6.33 6.38 6.45 6.49 6.51 6.5600000000000005 6.58 6.68 6.71 6.75 6.8100000000000005 6.8500000000000005 6.890000000000001 6.91 6.96 6.99 7.01 7.01 7.03 7.04 7.05 7.01 6.99 6.97 6.92 6.86 6.8 6.75 6.67 6.57 6.5 6.47 6.33 6.25 6.15 6.09 6.01 5.93 5.84 5.78 5.72 5.65 5.58 5.53 5.5 5.44 5.38 5.36 5.33 5.3 5.25 5.24 5.23 5.21 5.18 5.17 5.17 5.15 5.14 5.12 5.12 5.12 5.11 5.09 5.11 5.11 5.09 5.08 5.09 5.09 5.08 5.08 5.09 5.09 5.1000000000000005 5.08 5.09 5.11 5.1000000000000005 5.09 5.09 5.1000000000000005 5.1000000000000005 5.08 5.09 5.11 5.09 5.08 5.08 5.08 5.09 5.08 5.07 5.07 5.07 5.08 5.05 5.05 5.0600000000000005 5.05 5.04 5.03 5.04 5.04 5.0200000000000005 5.01 5.0 5.01 5.0 4.99 4.99 4.99 4.98 4.96 4.98 4.98 4.98 4.96 4.97 4.97 4.96 4.95 4.95 4.97 4.96 4.94 4.94 4.96 4.96 4.94 4.94 4.94 4.94 4.93 4.92 4.93 4.94 4.93 4.92 4.94 4.94 4.93 4.93 4.94 4.95 4.96 4.96 4.98 4.98 4.97 4.96 4.95 5.0 5.18 5.62 5.8500000000000005 5.8100000000000005 5.33 5.25 5.16 5.08 4.93 4.73 4.49 4.2 3.89 3.62 3.38 3.17 2.92 2.64 2.45 2.33 2.23 2.13 2.08 2 2.05 2.05 2.0300000000000002 2.02 2.0300000000000002 2.04 2.08 2.14 2.2800000000000002 2.44 2.65 2.89 3.19 3.5 3.8200000000000003 4.13 4.41 4.68 4.92 5.14 5.34 5.51 5.63 5.7 5.75 5.8100000000000005 5.87 5.91 5.94 5.98 6.0 6.05 6.09 6.11 6.16 6.2 6.23 6.26 6.32 6.3500000000000005 6.38 6.42 6.47 6.5200000000000005 6.55 6.58 6.62 6.67 6.69 6.71 6.74 6.7700000000000005 6.79 6.8 6.8 6.8100000000000005 6.82 6.79 6.7700000000000005 6.75 6.71 6.66 6.6000000000000005 6.5600000000000005 6.5 6.41 6.33 6.26 6.18 6.09 6.0 5.93 5.8500000000000005 5.7700000000000005 5.69 5.63 5.58 5.5200000000000005 5.45 5.41 5.41 5.3500000000000005 5.3 5.2700000000000005 5.26 5.24 5.2 5.18 5.18 5.17 5.15 5.14 5.14 5.14 5.14 5.12 5.11 5.13 5.12 5.1000000000000005 5.1000000000000005 5.12 5.12 5.12 5.12 5.13 5.13 5.11 5.12 5.13 5.13 5.12 5.1000000000000005 5.12 5.12 5.12 5.11 5.12 5.13 5.11 5.1000000000000005 5.1000000000000005 5.12 5.11 5.08 5.08 5.09 5.08 5.07 5.07 5.07 5.07 5.0600000000000005 5.05 5.0600000000000005 5.05 5.05 5.03 5.04 5.04 5.0200000000000005 5.01 5.0200000000000005 5.0200000000000005 5.01 5.0 5.0 5.0 5.01 4.98 4.99 4.99 4.99 4.98 4.97 4.97 4.98 4.98 4.97 4.99 4.97 4.96 4.96 4.97 4.97 4.96 4.95 4.95 4.97 4.97 4.95 4.96 4.96 4.96 4.95 4.95 4.96 4.96 4.94 4.94 4.94 4.96 4.96 4.95 4.98 4.98 5.05 5.33 5.82 5.8100000000000005 5.48 5.36 5.33 5.28 5.24 5.13 4.98 4.73 4.4 4.07 3.71 3.33 3.0100000000000002 2.74 2.5500000000000003 2.5100000000000002 2.3000000000000003 2.17 2.1 2.08 3 2.07 2.04 2.0300000000000002 2.05 2.05 2.04 2.05 2.09 2.14 2.22 2.35 2.5300000000000002 2.7600000000000002 3.04 3.36 3.69 4.0 4.32 4.6000000000000005 4.86 4.94 5.3100000000000005 5.51 5.65 5.75 5.8100000000000005 5.8500000000000005 5.9 5.95 5.99 6.0200000000000005 6.0600000000000005 6.09 6.13 6.16 6.18 6.23 6.25 6.28 6.29 6.33 6.38 6.41 6.43 6.47 6.48 6.55 6.57 6.62 6.67 6.72 6.74 6.7700000000000005 6.8100000000000005 6.84 6.86 6.890000000000001 6.91 6.93 6.93 6.92 6.92 6.91 6.87 6.83 6.7700000000000005 6.73 6.66 6.57 6.49 6.46 6.32 6.22 6.140000000000001 6.0600000000000005 5.98 5.89 5.8100000000000005 5.75 5.69 5.61 5.54 5.51 5.47 5.43 5.39 5.3500000000000005 5.3500000000000005 5.32 5.28 5.2700000000000005 5.2700000000000005 5.25 5.24 5.24 5.24 5.24 5.22 5.22 5.23 5.21 5.22 5.22 5.22 5.23 5.22 5.21 5.22 5.23 5.23 5.21 5.22 5.22 5.22 5.2 5.2 5.21 5.22 5.2 5.19 5.19 5.19 5.17 5.18 5.17 5.17 5.15 5.14 5.14 5.15 5.14 5.12 5.13 5.13 5.12 5.1000000000000005 5.1000000000000005 5.1000000000000005 5.08 5.08 5.07 5.08 5.07 5.0600000000000005 5.05 5.0600000000000005 5.0600000000000005 5.05 5.03 5.03 5.04 5.03 5.0200000000000005 5.03 5.03 5.0200000000000005 5.0 5.01 5.0200000000000005 5.0 5.0 5.01 5.01 5.01 4.99 4.99 5.0 5.0 4.99 4.98 4.98 4.99 4.97 4.98 4.99 4.99 4.97 4.97 4.98 4.99 4.97 4.96 4.98 4.99 5.0 5.03 5.2 5.73 5.9 5.53 5.36 5.3 5.22 5.09 4.91 4.84 4.39 4.0600000000000005 3.75 3.5100000000000002 3.27 3.02 2.74 2.54 2.39 2.27 2.17 2.13 2.11 2.07 4 2.06 2.07 2.07 2.08 2.08 2.12 2.18 2.2800000000000002 2.43 2.66 2.73 3.2800000000000002 3.64 4.01 4.36 4.66 4.93 5.17 5.42 5.63 5.76 5.83 5.91 5.96 5.98 6.0200000000000005 6.07 6.1000000000000005 6.11 6.13 6.15 6.19 6.2 6.22 6.25 6.2700000000000005 6.34 6.37 6.41 6.46 6.49 6.51 6.55 6.6000000000000005 6.63 6.66 6.69 6.73 6.76 6.78 6.8 6.83 6.86 6.86 6.88 6.87 6.890000000000001 6.87 6.8500000000000005 6.82 6.8100000000000005 6.74 6.67 6.61 6.55 6.48 6.390000000000001 6.3100000000000005 6.23 6.140000000000001 6.04 5.95 5.88 5.8100000000000005 5.74 5.66 5.61 5.57 5.5 5.45 5.41 5.38 5.3500000000000005 5.32 5.29 5.28 5.28 5.25 5.25 5.25 5.24 5.22 5.22 5.22 5.23 5.22 5.2 5.21 5.23 5.2 5.21 5.21 5.22 5.21 5.2 5.2 5.21 5.21 5.2 5.2 5.2 5.18 5.18 5.18 5.19 5.17 5.17 5.15 5.17 5.17 5.15 5.13 5.13 5.12 5.12 5.1000000000000005 5.11 5.11 5.1000000000000005 5.08 5.08 5.08 5.07 5.0600000000000005 5.05 5.05 5.05 5.03 5.04 5.04 5.04 5.0200000000000005 5.0200000000000005 5.0200000000000005 5.0200000000000005 5.01 5.0 5.0200000000000005 5.01 4.99 4.99 4.99 5.0 4.99 4.99 4.98 5.0 4.99 4.98 4.98 4.98 4.99 4.98 4.97 4.98 4.98 4.96 4.96 4.97 4.97 4.97 4.96 4.97 4.97 4.97 4.95 4.96 4.97 4.97 4.97 5.03 5.2700000000000005 5.7700000000000005 5.9 5.55 5.46 5.3 5.21 5.09 4.92 4.69 4.41 4.08 3.8000000000000003 3.5700000000000003 3.33 3.0700000000000003 2.79 2.58 2.43 2.31 2.22 2.16 2.12 2.08 2.08 2.08 2.07 2.06 2.09 5 2.1 2.18 2.2800000000000002 2.46 2.69 2.98 3.3000000000000003 3.64 3.98 4.28 4.55 4.8100000000000005 5.0600000000000005 5.29 5.49 5.63 5.72 5.79 5.8500000000000005 5.88 5.93 5.98 6.0200000000000005 6.04 6.07 6.09 6.16 6.19 6.22 6.26 6.29 6.32 6.3500000000000005 6.41 6.44 6.48 6.5 6.55 6.59 6.61 6.63 6.67 6.69 6.7 6.72 6.74 6.7700000000000005 6.78 6.78 6.8 6.82 6.84 6.8100000000000005 6.78 6.7700000000000005 6.72 6.66 6.6000000000000005 6.5600000000000005 6.48 6.4 6.32 6.26 6.18 6.09 6.0 5.91 5.8500000000000005 5.7700000000000005 5.69 5.63 5.58 5.51 5.46 5.41 5.41 5.34 5.3100000000000005 5.29 5.28 5.26 5.23 5.21 5.21 5.21 5.19 5.19 5.2 5.21 5.2 5.18 5.19 5.21 5.2 5.19 5.18 5.2 5.19 5.19 5.19 5.2 5.2 5.2 5.19 5.2 5.19 5.18 5.17 5.19 5.19 5.17 5.16 5.16 5.16 5.15 5.15 5.15 5.16 5.14 5.11 5.12 5.13 5.11 5.09 5.09 5.1000000000000005 5.1000000000000005 5.08 5.07 5.08 5.07 5.05 5.05 5.05 5.0600000000000005 5.05 5.04 5.04 5.05 5.04 5.03 5.03 5.03 5.0200000000000005 5.0 5.01 5.01 5.0200000000000005 5.01 5.0 5.0 5.01 5.0 5.0 5.01 5.0 5.0 4.98 5.0 5.0 4.99 4.98 4.98 4.99 4.98 4.97 4.98 4.98 4.99 4.98 4.99 5.03 5.08 5.33 5.83 5.97 5.55 5.39 5.36 5.32 5.2700000000000005 5.15 5.0 4.73 4.38 4.0 3.6 3.2600000000000002 3.0 2.7600000000000002 2.6 2.46 2.31 2.21 2.15 2.12 2.12 2.09 2.08 2.08 2.08 2.11 2.13 2.21 2.35 2.5300000000000002 2.75 3.0300000000000002 3.4 3.7600000000000002 6 4.07 4.38 4.69 4.93 5.13 5.34 5.55 5.7 5.78 5.84 5.9 5.95 5.99 6.0200000000000005 6.07 6.09 6.140000000000001 6.17 6.2 6.25 6.28 6.3 6.34 6.4 6.43 6.47 6.51 6.5600000000000005 6.61 6.66 6.68 6.74 6.78 6.8100000000000005 6.84 6.890000000000001 6.94 6.97 6.99 7.0 7.01 7.01 6.98 6.95 6.93 6.88 6.82 6.74 6.68 6.58 6.49 6.4 6.33 6.24 6.17 6.08 6.0 5.92 5.83 5.75 5.67 5.62 5.57 5.51 5.46 5.46 5.4 5.36 5.33 5.32 5.3100000000000005 5.28 5.25 5.26 5.26 5.24 5.23 5.24 5.24 5.23 5.22 5.23 5.24 5.24 5.21 5.22 5.23 5.22 5.23 5.23 5.23 5.23 5.23 5.22 5.24 5.23 5.22 5.21 5.23 5.22 5.2 5.19 5.2 5.2 5.19 5.18 5.18 5.18 5.16 5.15 5.15 5.16 5.14 5.13 5.13 5.13 5.12 5.11 5.09 5.11 5.1000000000000005 5.1000000000000005 5.09 5.09 5.1000000000000005 5.07 5.0600000000000005 5.07 5.08 5.0600000000000005 5.05 5.05 5.0600000000000005 5.0600000000000005 5.05 5.05 5.0600000000000005 5.05 5.04 5.04 5.03 5.04 5.03 5.0200000000000005 5.03 5.04 5.03 5.0200000000000005 5.03 5.03 5.0200000000000005 5.0200000000000005 5.0200000000000005 5.0200000000000005 5.01 5.01 5.0200000000000005 5.0200000000000005 5.0200000000000005 5.0200000000000005 5.04 5.1000000000000005 5.2700000000000005 5.76 6.0200000000000005 6.0 5.47 5.39 5.3100000000000005 5.23 5.09 4.86 4.58 4.29 3.98 3.69 3.42 3.21 2.94 2.65 2.46 2.34 2.25 2.15 2.11 2.11 2.11 2.1 2.09 2.1 2.1 2.15 2.2 2.33 2.52 2.7600000000000002 3.06 3.42 3.81 4.17 4.48 4.76 5.0600000000000005 5.3100000000000005 5.5200000000000005 5.67 5.78 5.84 5.9 5.94 7 6.0 6.04 6.08 6.1000000000000005 6.140000000000001 6.16 6.21 6.24 6.28 6.33 6.37 6.390000000000001 6.43 6.47 6.51 6.55 6.59 6.63 6.68 6.71 6.75 6.8 6.84 6.87 6.890000000000001 6.92 6.95 6.95 6.95 6.94 6.93 6.91 6.87 6.83 6.8 6.73 6.67 6.59 6.5200000000000005 6.43 6.32 6.22 6.15 6.05 5.96 5.88 5.8 5.74 5.66 5.59 5.55 5.5 5.45 5.39 5.36 5.37 5.3100000000000005 5.28 5.2700000000000005 5.2700000000000005 5.25 5.23 5.23 5.23 5.22 5.21 5.2 5.2 5.21 5.21 5.2 5.21 5.24 5.25 5.24 5.25 5.2700000000000005 5.26 5.24 5.24 5.25 5.25 5.24 5.24 5.25 5.24 5.21 5.18 5.17 5.16 5.14 5.12 5.11 5.11 5.09 5.09 5.11 5.12 5.11 5.09 5.09 5.1000000000000005 5.08 5.08 5.07 5.09 5.07 5.05 5.04 5.05 5.05 5.04 5.03 5.03 5.04 5.0200000000000005 5.01 5.0 5.0 5.0 5.0 5.0 5.01 4.99 4.99 4.99 5.01 4.99 4.97 4.97 4.97 4.99 4.98 4.97 4.99 4.99 4.98 4.97 4.99 4.99 4.98 4.97 4.98 4.98 4.97 4.96 4.97 4.98 4.97 4.96 4.97 4.99 4.98 4.97 4.98 5.0 5.01 5.09 5.48 5.92 5.78 5.48 5.39 5.34 5.28 5.21 5.11 4.97 4.74 4.42 4.07 3.73 3.38 3.04 2.82 2.66 2.5500000000000003 2.48 2.39 2.2800000000000002 2.25 2.16 2.14 2.1 2.1 2.09 2.08 2.08 2.11 2.14 2.18 2.2600000000000002 2.4 2.58 2.7800000000000002 3.0100000000000002 3.29 3.6 3.89 4.15 4.42 4.69 4.92 5.12 5.3100000000000005 5.37 5.64 5.73 5.8 5.87 8 5.93 5.97 6.0 6.0600000000000005 6.1000000000000005 6.15 6.18 6.24 6.28 6.33 6.36 6.43 6.47 6.51 6.55 6.62 6.66 6.71 6.74 6.79 6.8100000000000005 6.87 6.91 6.95 6.99 7.01 7.03 7.05 7.08 7.1000000000000005 7.09 7.08 7.09 7.07 7.03 6.98 6.95 6.91 6.82 6.76 6.7 6.640000000000001 6.5600000000000005 6.46 6.390000000000001 6.37 6.24 6.15 6.07 6.0200000000000005 5.92 5.8500000000000005 5.78 5.73 5.68 5.62 5.5600000000000005 5.53 5.49 5.45 5.4 5.38 5.3500000000000005 5.33 5.3 5.28 5.2700000000000005 5.24 5.21 5.2 5.21 5.19 5.17 5.16 5.17 5.15 5.15 5.13 5.15 5.15 5.14 5.13 5.14 5.14 5.13 5.12 5.13 5.14 5.13 5.12 5.14 5.15 5.13 5.11 5.13 5.13 5.14 5.12 5.12 5.12 5.12 5.11 5.1000000000000005 5.12 5.12 5.1000000000000005 5.09 5.09 5.09 5.09 5.08 5.07 5.08 5.07 5.05 5.05 5.0600000000000005 5.05 5.04 5.04 5.05 5.04 5.0200000000000005 5.01 5.03 5.03 5.01 5.0 5.0200000000000005 5.0200000000000005 5.0 4.99 5.01 5.01 5.01 5.01 5.04 5.04 5.04 5.03 5.03 5.04 5.04 5.0200000000000005 5.03 5.03 5.0200000000000005 4.99 4.98 4.97 4.95 4.94 4.93 4.93 4.93 4.91 4.91 4.94 4.95 4.94 4.93 4.95 4.96 4.98 5.0600000000000005 5.39 5.84 5.74 5.44 5.3500000000000005 5.33 5.29 5.22 5.14 5.01 4.79 4.51 4.19 3.92 3.6 3.33 3.15 3.0300000000000002 2.92 2.77 2.58 2.42 2.33 2.2800000000000002 2.2 2.16 2.13 2.1 2.09 2.09 2.09 2.12 2.18 2.29 2.44 2.62 2.83 3.11 3.45 3.79 4.09 4.41 4.69 4.95 5.17 9 5.36 5.53 5.65 5.74 5.8 5.86 5.93 5.97 6.0 6.05 6.07 6.140000000000001 6.17 6.21 6.2700000000000005 6.3 6.34 6.390000000000001 6.44 6.48 6.5200000000000005 6.5600000000000005 6.62 6.67 6.7 6.75 6.8 6.8500000000000005 6.87 6.91 6.95 6.99 7.01 7.0 7.03 7.04 7.03 6.99 6.98 6.97 6.92 6.8500000000000005 6.8 6.75 6.69 6.6000000000000005 6.5200000000000005 6.45 6.37 6.28 6.19 6.11 6.04 5.95 5.89 5.82 5.76 5.68 5.6000000000000005 5.5600000000000005 5.54 5.47 5.41 5.3500000000000005 5.3500000000000005 5.32 5.28 5.25 5.23 5.22 5.19 5.17 5.16 5.15 5.14 5.11 5.12 5.12 5.1000000000000005 5.09 5.1000000000000005 5.1000000000000005 5.09 5.08 5.09 5.09 5.1000000000000005 5.08 5.08 5.1000000000000005 5.1000000000000005 5.1000000000000005 5.08 5.1000000000000005 5.11 5.09 5.09 5.09 5.1000000000000005 5.1000000000000005 5.08 5.09 5.1000000000000005 5.1000000000000005 5.09 5.09 5.1000000000000005 5.08 5.08 5.08 5.07 5.07 5.05 5.04 5.05 5.04 5.03 5.0200000000000005 5.03 5.03 5.01 5.0 5.0200000000000005 5.01 5.0 4.98 4.98 4.99 4.97 4.97 4.97 4.99 4.97 4.95 4.96 4.96 4.96 4.93 4.94 4.95 4.96 4.94 4.93 4.93 4.94 4.92 4.92 4.93 4.93 4.92 4.92 4.92 4.92 4.92 4.9 4.91 4.93 4.92 4.89 4.9 4.91 4.91 4.9 4.9 4.92 4.95 5.04 5.4 5.82 5.73 5.42 5.3100000000000005 5.26 5.22 5.16 5.0600000000000005 4.89 4.59 4.23 3.87 3.48 3.12 2.82 2.58 2.45 2.42 2.19 2.09 2.04 2.0300000000000002 2.0100000000000002 1.99 1.99 2.0 2.0 2.0 2.02 2.07 2.17 2.3000000000000003 2.5100000000000002 2.77 3.12 3.48 3.7800000000000002 4.11 4.45 4.72 4.95 5.19
Lets load the heart disease dataset
In [38]:
test = h2o.import_file("data/ecg_discord_test.csv")
Parse progress: |█████████████████████████████████████████████████████████| 100%
In [41]:
model = H2ODeepLearningEstimator(activation="RectifierWithDropout",
hidden=[32,32,32],
autoencoder=True,input_dropout_ratio=0.2,sparse=True,l1=1e-5,epochs=10)
In [42]:
model.train(x=train.names,training_frame=train,validation_frame=test)
deeplearning Model Build progress: |██████████████████████████████████████| 100%
In [43]:
model.predict(test)
deeplearning prediction progress: |███████████████████████████████████████| 100%
reconstr_C1 reconstr_C2 reconstr_C3 reconstr_C4 reconstr_C5 reconstr_C6 reconstr_C7 reconstr_C8 reconstr_C9 reconstr_C10 reconstr_C11 reconstr_C12 reconstr_C13 reconstr_C14 reconstr_C15 reconstr_C16 reconstr_C17 reconstr_C18 reconstr_C19 reconstr_C20 reconstr_C21 reconstr_C22 reconstr_C23 reconstr_C24 reconstr_C25 reconstr_C26 reconstr_C27 reconstr_C28 reconstr_C29 reconstr_C30 reconstr_C31 reconstr_C32 reconstr_C33 reconstr_C34 reconstr_C35 reconstr_C36 reconstr_C37 reconstr_C38 reconstr_C39 reconstr_C40 reconstr_C41 reconstr_C42 reconstr_C43 reconstr_C44 reconstr_C45 reconstr_C46 reconstr_C47 reconstr_C48 reconstr_C49 reconstr_C50 reconstr_C51 reconstr_C52 reconstr_C53 reconstr_C54 reconstr_C55 reconstr_C56 reconstr_C57 reconstr_C58 reconstr_C59 reconstr_C60 reconstr_C61 reconstr_C62 reconstr_C63 reconstr_C64 reconstr_C65 reconstr_C66 reconstr_C67 reconstr_C68 reconstr_C69 reconstr_C70 reconstr_C71 reconstr_C72 reconstr_C73 reconstr_C74 reconstr_C75 reconstr_C76 reconstr_C77 reconstr_C78 reconstr_C79 reconstr_C80 reconstr_C81 reconstr_C82 reconstr_C83 reconstr_C84 reconstr_C85 reconstr_C86 reconstr_C87 reconstr_C88 reconstr_C89 reconstr_C90 reconstr_C91 reconstr_C92 reconstr_C93 reconstr_C94 reconstr_C95 reconstr_C96 reconstr_C97 reconstr_C98 reconstr_C99 reconstr_C100 reconstr_C101 reconstr_C102 reconstr_C103 reconstr_C104 reconstr_C105 reconstr_C106 reconstr_C107 reconstr_C108 reconstr_C109 reconstr_C110 reconstr_C111 reconstr_C112 reconstr_C113 reconstr_C114 reconstr_C115 reconstr_C116 reconstr_C117 reconstr_C118 reconstr_C119 reconstr_C120 reconstr_C121 reconstr_C122 reconstr_C123 reconstr_C124 reconstr_C125 reconstr_C126 reconstr_C127 reconstr_C128 reconstr_C129 reconstr_C130 reconstr_C131 reconstr_C132 reconstr_C133 reconstr_C134 reconstr_C135 reconstr_C136 reconstr_C137 reconstr_C138 reconstr_C139 reconstr_C140 reconstr_C141 reconstr_C142 reconstr_C143 reconstr_C144 reconstr_C145 reconstr_C146 reconstr_C147 reconstr_C148 reconstr_C149 reconstr_C150 reconstr_C151 reconstr_C152 reconstr_C153 reconstr_C154 reconstr_C155 reconstr_C156 reconstr_C157 reconstr_C158 reconstr_C159 reconstr_C160 reconstr_C161 reconstr_C162 reconstr_C163 reconstr_C164 reconstr_C165 reconstr_C166 reconstr_C167 reconstr_C168 reconstr_C169 reconstr_C170 reconstr_C171 reconstr_C172 reconstr_C173 reconstr_C174 reconstr_C175 reconstr_C176 reconstr_C177 reconstr_C178 reconstr_C179 reconstr_C180 reconstr_C181 reconstr_C182 reconstr_C183 reconstr_C184 reconstr_C185 reconstr_C186 reconstr_C187 reconstr_C188 reconstr_C189 reconstr_C190 reconstr_C191 reconstr_C192 reconstr_C193 reconstr_C194 reconstr_C195 reconstr_C196 reconstr_C197 reconstr_C198 reconstr_C199 reconstr_C200 reconstr_C201 reconstr_C202 reconstr_C203 reconstr_C204 reconstr_C205 reconstr_C206 reconstr_C207 reconstr_C208 reconstr_C209 reconstr_C210 5.16406 5.64597 4.9645 5.022 5.0775 7.09321 6.79416 5.56963 8.60535 5.4555 5.534 5.6435 5.748 5.8465 6.19845 8.3089 6.81929 6.65079 6.25784 7.43496 6.59324 6.36248 6.78548 6.3795 6.8924 6.379 6.73357 6.3765 6.92059 6.3605 6.3505 6.78033 6.52272 6.74641 6.52982 6.283 6.73432 6.248 6.2305 6.43296 6.188 6.168 6.145 6.12 6.0975 6.09671 6.05978 6.0225 5.999 6.8269 5.954 5.927 6.25039 6.55526 5.8585 6.02996 5.91169 6.05056 5.876 5.83959 6.46412 5.83038 5.89159 5.609 5.79013 5.552 6.22948 5.489 5.89123 5.51203 5.90231 5.55249 5.3515 5.3295 5.40323 5.2885 5.28337 5.251 5.234 5.221 5.2065 5.51477 5.184 5.174 5.32308 5.1555 5.1485 5.19689 5.133 5.128 5.1215 5.17813 5.12556 5.1045 5.103 5.15352 5.13131 5.11344 5.11162 5.16719 5.0775 5.0785 5.076 5.0755 5.23167 5.12011 5.0675 5.11491 5.10837 5.0585 5.12303 5.0515 5.11123 5.19209 5.0425 5.04 5.0479 5.03887 5.03446 5.0295 5.04564 5.10628 5.09771 5.0155 5.05307 5.19951 5.13436 5.00571 5.002 5.008 5.04544 5.2448 5.026 5.08543 5.05687 5.027 5.23709 5.08355 5.27658 5.054 5.038 5.15671 5.41246 5.64835 4.9455 6.20031 4.8575 5.91606 4.8635 4.815 5.95666 5.10211 5.55025 4.544 4.482 4.4275 4.3695 4.3385 4.91542 4.2555 4.171 4.106 4.99267 4.067 4.074 4.1015 4.0755 4.0455 4.0165 4.7504 4.7244 3.914 3.893 3.893 4.31796 4.21288 4.9941 3.8455 4.65595 5.51336 6.48994 4.03858 3.9545 4.50073 4.035 4.101 4.37341 4.229 4.35697 5.54337 4.3365 4.358 4.39455 5.01457 4.4345 4.453 4.4595 4.59859 5.67599 4.489 6.41026 5.65655 4.6 4.652 6.4193 4.90117 4.8345 4.8965 5.45693 5.26236 5.1211 5.61804 4.9645 5.022 5.0775 7.04478 6.8807 5.58085 8.58279 5.4555 5.534 5.6435 5.748 5.8465 6.13784 8.28407 6.80487 6.64984 6.25402 7.43743 6.59968 6.3465 6.77275 6.3795 6.88538 6.379 6.73304 6.3765 6.91054 6.3605 6.3505 6.76539 6.54175 6.76216 6.52177 6.283 6.7448 6.248 6.2305 6.40467 6.188 6.168 6.145 6.12 6.0975 6.09357 6.05093 6.0225 5.999 6.81566 5.954 5.927 6.23918 6.58138 5.86653 6.04709 5.88308 6.03007 5.88961 5.84744 6.48021 5.82482 5.89532 5.609 5.81147 5.552 6.20834 5.489 5.89375 5.52106 5.89742 5.55524 5.3515 5.3295 5.40446 5.2885 5.28063 5.251 5.234 5.221 5.2065 5.51111 5.184 5.174 5.32801 5.1555 5.1485 5.18957 5.133 5.128 5.1215 5.17889 5.12214 5.1045 5.103 5.15625 5.1266 5.11361 5.10746 5.16508 5.0775 5.0785 5.076 5.0755 5.23435 5.11661 5.0675 5.11321 5.10847 5.0585 5.12321 5.0515 5.11147 5.19177 5.0425 5.04 5.04816 5.0408 5.0325 5.0295 5.04469 5.10383 5.10017 5.0155 5.05371 5.19877 5.13137 5.00443 5.002 5.008 5.05097 5.24145 5.026 5.08132 5.04361 5.027 5.24034 5.08613 5.27055 5.054 5.038 5.1538 5.39504 5.65297 4.9455 6.15659 4.8575 5.89386 4.8635 4.815 5.94264 5.09711 5.5545 4.544 4.482 4.4275 4.3695 4.3385 4.89268 4.2555 4.171 4.106 4.98555 4.067 4.074 4.1015 4.0755 4.0455 4.0165 4.77164 4.73682 3.914 3.893 3.893 4.31743 4.20185 4.99683 3.8455 4.64923 5.48241 6.5283 4.01235 3.9545 4.54145 4.035 4.101 4.39128 4.229 4.32122 5.56639 4.3365 4.358 4.39493 5.03824 4.4345 4.453 4.4595 4.61193 5.67887 4.489 6.38331 5.66218 4.6 4.652 6.34976 4.88267 4.8345 4.8965 5.49133 5.14971 5.1089 5.62365 4.9645 5.022 5.0775 7.05279 6.87221 5.60405 8.55383 5.4555 5.534 5.6435 5.748 5.8465 6.14247 8.25976 6.81236 6.68674 6.2479 7.41487 6.5876 6.34701 6.78135 6.3795 6.88249 6.379 6.73462 6.3765 6.92534 6.3605 6.3505 6.756 6.54168 6.76657 6.51938 6.283 6.75499 6.248 6.2305 6.4063 6.188 6.168 6.145 6.12 6.0975 6.10941 6.05915 6.0225 5.999 6.80919 5.954 5.927 6.24695 6.55981 5.87462 6.04153 5.88713 6.05481 5.89679 5.86533 6.46949 5.84306 5.88897 5.609 5.80827 5.552 6.20479 5.489 5.90605 5.51173 5.89691 5.54979 5.3515 5.3295 5.39737 5.2885 5.28627 5.251 5.234 5.221 5.2065 5.50901 5.184 5.174 5.32722 5.1555 5.1485 5.19051 5.133 5.128 5.1215 5.177 5.12206 5.1045 5.103 5.15593 5.12745 5.11411 5.10892 5.16349 5.0775 5.0785 5.076 5.0755 5.23342 5.11528 5.0675 5.11524 5.11044 5.0585 5.1227 5.0515 5.11373 5.19143 5.0425 5.04 5.04904 5.04041 5.03321 5.0295 5.0447 5.10292 5.1005 5.0155 5.05282 5.19922 5.12924 5.00535 5.002 5.008 5.04816 5.23495 5.026 5.08399 5.0435 5.027 5.24391 5.08719 5.27156 5.054 5.038 5.15754 5.39418 5.63616 4.9455 6.16994 4.8575 5.89851 4.8635 4.815 5.91367 5.08426 5.56466 4.544 4.482 4.4275 4.3695 4.3385 4.86986 4.2555 4.171 4.106 4.99471 4.067 4.074 4.1015 4.0755 4.0455 4.0165 4.7906 4.73152 3.914 3.893 3.893 4.30755 4.20636 5.01905 3.8455 4.62389 5.48097 6.53231 4.01148 3.9545 4.5288 4.035 4.101 4.40803 4.229 4.34726 5.56549 4.3365 4.358 4.42502 5.03412 4.4345 4.453 4.4595 4.61747 5.68371 4.489 6.37278 5.65825 4.6 4.652 6.34868 4.9094 4.8345 4.8965 5.49005 5.17254 5.09751 5.68243 4.9645 5.022 5.0775 7.09159 6.86111 5.64886 8.54814 5.4555 5.534 5.6435 5.748 5.8465 6.25335 8.21978 6.83593 6.75553 6.24632 7.33176 6.60633 6.36391 6.77108 6.3795 6.88262 6.379 6.7107 6.3765 6.96152 6.3605 6.3505 6.76877 6.53474 6.75837 6.50346 6.283 6.73789 6.248 6.2305 6.37378 6.188 6.168 6.145 6.12 6.0975 6.11844 6.06633 6.0225 5.999 6.82971 5.954 5.927 6.28715 6.53264 5.8585 6.04363 5.90549 6.07759 5.92472 5.9096 6.46031 5.82974 5.86949 5.609 5.78154 5.552 6.2087 5.489 5.94223 5.48179 5.88208 5.55605 5.3515 5.3295 5.39225 5.2885 5.30071 5.251 5.234 5.221 5.2065 5.51695 5.184 5.174 5.332 5.1555 5.1485 5.1906 5.133 5.128 5.1215 5.1739 5.11569 5.1045 5.103 5.15422 5.13113 5.11982 5.10534 5.16134 5.0775 5.0785 5.076 5.0755 5.23121 5.11291 5.0675 5.11754 5.11239 5.0585 5.11959 5.0515 5.11886 5.19498 5.0425 5.04 5.04153 5.03795 5.03262 5.0295 5.04583 5.10476 5.09573 5.0155 5.05039 5.20249 5.12708 5.00657 5.002 5.008 5.04001 5.24111 5.026 5.09053 5.04502 5.027 5.2604 5.08802 5.27402 5.054 5.038 5.19177 5.33914 5.64635 4.9455 6.16304 4.8575 5.9082 4.8635 4.815 5.79265 5.08004 5.58462 4.544 4.482 4.4275 4.3695 4.3385 4.86935 4.2555 4.171 4.106 5.0171 4.067 4.074 4.1015 4.0755 4.0455 4.0165 4.82246 4.71803 3.914 3.893 3.893 4.27085 4.24069 5.07502 3.8455 4.64662 5.57831 6.57973 4.05775 3.9545 4.47499 4.035 4.101 4.41105 4.229 4.35914 5.5744 4.3365 4.358 4.48175 5.10762 4.4345 4.51291 4.4595 4.55624 5.66303 4.489 6.28688 5.62567 4.6 4.652 6.37624 5.01885 4.8345 4.8965 5.38794 5.2648 5.12546 5.6702 4.9645 5.022 5.0775 7.12712 6.8462 5.6239 8.57427 5.4555 5.534 5.6435 5.748 5.8465 6.24945 8.31552 6.83634 6.71307 6.27336 7.38893 6.59004 6.4005 6.78584 6.3795 6.89348 6.379 6.73055 6.3765 6.93751 6.3605 6.3505 6.77204 6.51957 6.74527 6.54625 6.283 6.74133 6.248 6.2305 6.4036 6.188 6.168 6.145 6.12 6.0975 6.09999 6.06536 6.0225 5.999 6.83509 5.954 5.927 6.26313 6.5459 5.86549 6.0288 5.94136 6.06189 5.87504 5.86004 6.47493 5.83519 5.88532 5.609 5.78527 5.552 6.22239 5.489 5.92079 5.4885 5.91579 5.54184 5.3515 5.3295 5.41288 5.2885 5.29024 5.251 5.234 5.221 5.2065 5.51833 5.184 5.174 5.32274 5.1555 5.1485 5.19702 5.133 5.128 5.1215 5.17558 5.11924 5.1045 5.103 5.15187 5.13449 5.11665 5.1104 5.16533 5.0775 5.0785 5.076 5.0755 5.23183 5.12101 5.0675 5.11531 5.1088 5.0585 5.12537 5.0515 5.11359 5.19034 5.0425 5.04 5.04566 5.03784 5.03673 5.0295 5.04672 5.108 5.09709 5.0155 5.05204 5.20273 5.13341 5.0063 5.002 5.008 5.04851 5.25058 5.026 5.08463 5.05787 5.027 5.25152 5.08247 5.27176 5.054 5.038 5.16277 5.40968 5.64959 4.9455 6.1782 4.8575 5.95012 4.8635 4.815 5.89667 5.07245 5.61125 4.544 4.482 4.4275 4.3695 4.3385 4.87045 4.2555 4.171 4.106 4.99998 4.067 4.074 4.1015 4.0755 4.0455 4.0165 4.76944 4.71198 3.914 3.893 3.893 4.2742 4.25644 5.05049 3.8455 4.62166 5.55114 6.52305 4.03262 3.9545 4.52373 4.035 4.101 4.35304 4.229 4.38897 5.52332 4.3365 4.358 4.42923 5.08798 4.4345 4.453 4.4595 4.63203 5.6758 4.489 6.34738 5.63178 4.6 4.652 6.44019 4.90628 4.8345 4.8965 5.42337 5.33224 5.07599 5.66168 4.9645 5.022 5.0775 7.1399 6.79663 5.62586 8.51517 5.4555 5.534 5.6435 5.748 5.8465 6.16788 8.34363 6.83066 6.69342 6.27032 7.41012 6.59642 6.42672 6.82133 6.3795 6.90401 6.379 6.75386 6.3765 6.94107 6.3605 6.3505 6.78391 6.51559 6.73581 6.56833 6.283 6.7514 6.248 6.2305 6.43083 6.188 6.168 6.145 6.12 6.0975 6.10939 6.08907 6.0225 5.999 6.82781 5.954 5.927 6.23145 6.51975 5.87818 6.02301 5.95413 6.07683 5.84471 5.84767 6.45527 5.87847 5.86454 5.609 5.76505 5.552 6.22495 5.489 5.90824 5.48167 5.91243 5.53387 5.3515 5.3295 5.40269 5.2885 5.28436 5.251 5.234 5.221 5.2065 5.50461 5.184 5.174 5.32123 5.1555 5.1485 5.20044 5.133 5.128 5.1215 5.17438 5.12613 5.1045 5.103 5.15504 5.13866 5.11009 5.11123 5.16266 5.0775 5.0785 5.076 5.0755 5.22725 5.12583 5.0675 5.11661 5.11214 5.0585 5.12381 5.0515 5.11588 5.18739 5.0425 5.04 5.04779 5.037 5.03874 5.0295 5.04878 5.10669 5.09902 5.0155 5.04841 5.19942 5.13553 5.00796 5.002 5.008 5.0452 5.23703 5.026 5.0906 5.06065 5.027 5.25791 5.08339 5.27335 5.054 5.038 5.12614 5.43378 5.62638 4.9455 6.17817 4.8575 5.9799 4.8635 4.815 5.93876 5.03031 5.64793 4.544 4.482 4.4275 4.3695 4.3385 4.78881 4.2555 4.171 4.10617 4.97057 4.067 4.074 4.1015 4.0755 4.0455 4.0165 4.78656 4.69382 3.914 3.893 3.893 4.3017 4.20652 4.95481 3.8455 4.55931 5.4978 6.52102 3.96076 3.9545 4.51578 4.035 4.101 4.35525 4.229 4.38334 5.4561 4.3365 4.358 4.40213 5.06776 4.4345 4.453 4.4595 4.70953 5.65966 4.489 6.29495 5.58102 4.6 4.652 6.43417 4.94145 4.8345 4.8965 5.36153 5.33313 5.02741 5.68027 4.9645 5.022 5.0775 7.1174 6.83697 5.59567 8.43995 5.4555 5.534 5.6435 5.748 5.8465 6.08079 8.3174 6.87148 6.74621 6.2355 7.38055 6.60435 6.41557 6.83007 6.3795 6.8932 6.379 6.74194 6.3765 6.96233 6.3605 6.3505 6.77989 6.54984 6.7457 6.54966 6.283 6.78915 6.248 6.2305 6.40369 6.188 6.168 6.145 6.12 6.0975 6.13887 6.09977 6.0225 5.999 6.80998 5.954 5.927 6.23687 6.48984 5.89905 6.03511 5.90806 6.09407 5.86374 5.90525 6.43553 5.91171 5.84383 5.609 5.76825 5.552 6.19105 5.489 5.93859 5.46716 5.87059 5.52813 5.3515 5.3295 5.37841 5.2885 5.28384 5.251 5.234 5.221 5.2065 5.49012 5.184 5.174 5.32831 5.1555 5.1485 5.19407 5.133 5.128 5.1215 5.17102 5.12872 5.1045 5.103 5.15886 5.13654 5.1081 5.10799 5.15379 5.0775 5.0785 5.076 5.0755 5.2277 5.11835 5.0675 5.12264 5.11686 5.0585 5.11738 5.0515 5.12026 5.19039 5.0425 5.04 5.04589 5.037 5.03257 5.0295 5.05073 5.10296 5.09923 5.0155 5.04662 5.19787 5.12834 5.00814 5.002 5.008 5.04182 5.19671 5.026 5.10049 5.05475 5.027 5.27121 5.09518 5.27431 5.054 5.038 5.11991 5.34562 5.61926 4.9455 6.16003 4.8575 5.98934 4.8635 4.815 5.92122 4.98169 5.68899 4.544 4.482 4.4275 4.3695 4.3385 4.74934 4.2555 4.171 4.14719 4.92895 4.067 4.074 4.1015 4.0755 4.0455 4.0165 4.84448 4.63793 3.914 3.893 3.893 4.31974 4.15658 4.9368 3.8455 4.58432 5.50451 6.63951 3.895 3.9545 4.46083 4.035 4.101 4.42507 4.229 4.3175 5.50054 4.3365 4.358 4.44232 5.08784 4.4345 4.453 4.4595 4.71043 5.63512 4.489 6.23145 5.5774 4.6 4.652 6.2976 5.11664 4.8345 4.8965 5.31269 5.29564 5.00527 5.52377 4.9645 5.022 5.0775 7.10453 6.88986 5.52414 8.34128 5.4555 5.534 5.6435 5.748 5.8465 5.97355 8.40675 6.96452 6.73546 6.2355 7.38875 6.57658 6.46578 6.84989 6.3795 6.84644 6.379 6.74007 6.3765 6.93336 6.3605 6.36109 6.72661 6.58878 6.77635 6.58645 6.283 6.80289 6.248 6.2305 6.41662 6.188 6.168 6.145 6.12 6.0975 6.18572 6.05913 6.0225 6.03473 6.80387 5.954 5.927 6.24658 6.53719 5.89084 6.05571 5.93826 6.11944 5.93202 5.93172 6.43321 5.93394 5.75185 5.609 5.73745 5.552 6.20418 5.489 5.9443 5.43936 5.91898 5.53225 5.3515 5.3295 5.36447 5.2885 5.2963 5.251 5.234 5.221 5.2065 5.47997 5.184 5.174 5.32056 5.1555 5.1485 5.19674 5.133 5.128 5.1215 5.17041 5.13694 5.1045 5.103 5.15992 5.12755 5.1027 5.10573 5.15437 5.0775 5.0785 5.076 5.0755 5.22454 5.11711 5.0675 5.12185 5.11069 5.0585 5.11314 5.0515 5.11725 5.19287 5.0425 5.04 5.05579 5.037 5.03653 5.0295 5.0531 5.10017 5.10578 5.0155 5.0455 5.1946 5.1262 5.00929 5.002 5.008 5.04919 5.17258 5.026 5.09384 5.05329 5.027 5.26092 5.10604 5.23953 5.054 5.038 5.10841 5.30887 5.62608 4.9455 6.25736 4.8575 6.02065 4.8635 4.815 5.90807 4.98029 5.60548 4.544 4.482 4.4275 4.3695 4.3385 4.77262 4.2555 4.171 4.17154 4.88025 4.067 4.074 4.1015 4.0755 4.0455 4.0165 4.91785 4.61936 3.914 3.893 3.893 4.34678 4.12715 4.95319 3.8455 4.55098 5.55442 6.71948 3.93647 3.9545 4.51705 4.035 4.101 4.46886 4.229 4.3175 5.50637 4.3365 4.358 4.39573 5.0105 4.4345 4.453 4.4595 4.70441 5.62081 4.489 6.47915 5.76428 4.6 4.652 6.20428 5.12189 4.8345 4.8965 5.40562 5.327 4.94372 5.55903 4.9645 5.022 5.0775 7.11328 6.86243 5.51105 8.3367 5.4555 5.534 5.6435 5.748 5.8465 6.03683 8.40517 6.89987 6.70012 6.2355 7.41772 6.55462 6.46511 6.86384 6.3795 6.87591 6.379 6.7704 6.3765 6.92633 6.3605 6.3505 6.72897 6.56614 6.76347 6.60147 6.283 6.77804 6.248 6.2305 6.43963 6.188 6.168 6.145 6.12 6.0975 6.1709 6.07461 6.0225 6.00667 6.8158 5.954 5.927 6.22312 6.4868 5.89932 6.04225 5.93629 6.10261 5.88284 5.86884 6.42212 5.94616 5.77264 5.609 5.76096 5.552 6.21987 5.489 5.91312 5.47368 5.92995 5.51317 5.3515 5.3295 5.38607 5.2885 5.28473 5.251 5.234 5.221 5.2065 5.47906 5.184 5.174 5.31057 5.1555 5.1485 5.20904 5.133 5.128 5.1215 5.16704 5.14034 5.1045 5.103 5.15848 5.13292 5.1041 5.11124 5.16253 5.0775 5.0785 5.076 5.0755 5.22148 5.12748 5.0675 5.12158 5.10308 5.0585 5.12049 5.0515 5.11369 5.18897 5.0425 5.04 5.05555 5.037 5.04107 5.0295 5.05607 5.10132 5.10418 5.0155 5.04629 5.19267 5.12903 5.01348 5.002 5.008 5.04717 5.19766 5.026 5.09299 5.06571 5.027 5.24022 5.09387 5.25498 5.054 5.038 5.06655 5.36741 5.60311 4.9455 6.24428 4.8575 5.99245 4.8635 4.815 5.9295 5.00542 5.59079 4.544 4.482 4.4275 4.3695 4.3385 4.81798 4.2555 4.171 4.10822 4.8501 4.067 4.074 4.1015 4.0755 4.0455 4.0165 4.76411 4.57567 3.914 3.893 3.893 4.31697 4.10674 4.82077 3.8455 4.54293 5.50511 6.60053 3.895 3.9545 4.46623 4.035 4.101 4.38409 4.229 4.3175 5.44118 4.3365 4.358 4.385 4.99554 4.4345 4.453 4.4595 4.71802 5.61528 4.489 6.45823 5.72972 4.6 4.652 6.27948 5.05172 4.8345 4.8965 5.42789 5.42471 4.93638 5.57332 4.9645 5.022 5.0775 7.0927 6.88414 5.49246 8.33847 5.4555 5.534 5.6435 5.748 5.8465 6.01383 8.40299 6.88785 6.68138 6.2355 7.43231 6.55488 6.44992 6.85546 6.3795 6.87988 6.379 6.77959 6.3765 6.91933 6.3605 6.3505 6.73266 6.56942 6.75901 6.60484 6.283 6.78782 6.248 6.2305 6.43267 6.188 6.168 6.145 6.12 6.0975 6.15468 6.0731 6.0225 6.02078 6.81148 5.954 5.927 6.21296 6.48993 5.90849 6.03917 5.91666 6.07938 5.86728 5.86956 6.42762 5.94069 5.79727 5.609 5.77697 5.552 6.20348 5.489 5.9116 5.48696 5.91871 5.51075 5.3515 5.3295 5.38952 5.2885 5.27778 5.251 5.234 5.221 5.2065 5.47616 5.184 5.174 5.31143 5.1555 5.1485 5.20503 5.133 5.128 5.1215 5.16745 5.13816 5.1045 5.103 5.15897 5.13329 5.10312 5.11111 5.16226 5.0775 5.0785 5.076 5.0755 5.22366 5.12672 5.0675 5.12079 5.10335 5.0585 5.12163 5.0515 5.11132 5.18872 5.0425 5.04 5.05645 5.037 5.03817 5.0295 5.0563 5.0996 5.10444 5.0155 5.04689 5.19174 5.1296 5.01219 5.002 5.008 5.04981 5.19839 5.026 5.09178 5.06254 5.027 5.23932 5.09466 5.25468 5.054 5.038 5.05679 5.37424 5.60266 4.9455 6.2196 4.8575 5.97268 4.8635 4.815 5.96588 5.00259 5.61965 4.544 4.482 4.4275 4.3695 4.3385 4.79675 4.2555 4.171 4.106 4.83205 4.067 4.074 4.1015 4.0755 4.0455 4.0165 4.76642 4.56201 3.914 3.893 3.893 4.32514 4.09816 4.79151 3.8455 4.56459 5.4669 6.57957 3.895 3.9545 4.47991 4.035 4.101 4.39514 4.229 4.3175 5.46192 4.3365 4.358 4.385 4.98404 4.4345 4.453 4.4595 4.73489 5.62329 4.489 6.40696 5.69484 4.6 4.652 6.27143 5.03197 4.8345 4.8965 5.42538 5.36894
Out[43]:
In [44]:
model = H2ODeepLearningEstimator(activation="RectifierWithDropout",
hidden=[32,32,32],
autoencoder=False,input_dropout_ratio=0.2,sparse=True,l1=1e-5,epochs=10)
model.train(x=train.names[:-1],y=train.names[-1],training_frame=train,validation_frame=test)
deeplearning Model Build progress: |██████████████████████████████████████| 100%
In [ ]:
print(train.names[-1])
In [45]:
model_path = h2o.save_model(model = model,force = True)
In [46]:
print(model_path)
/Users/prajayshetty/Downloads/ppt/CSCI6360/workshops/DeepLearning_model_python_1508939596978_18
In [ ]:
saved_model = h2o.load_model(model_path)
In [ ]:
print(saved_model)
In [51]:
hyper_parameters = {'input_dropout_ratio':[0.1,0.2,0.5,0.7]}
h2o_gridSearch = H2OGridSearch(H2ODeepLearningEstimator(activation="RectifierWithDropout",
hidden=[50,40,30,20,10,5],
autoencoder=True,sparse=True,l1=1e-5,epochs=10),hyper_parameters)
h2o_gridSearch.train(x=train.names,training_frame=train,validation_frame=test)
deeplearning Grid Build progress: |███████████████████████████████████████| 100%
In [52]:
print(h2o_gridSearch.get_grid(sort_by="mse"))
input_dropout_ratio \
0 0.2
1 0.5
2 0.7
3 0.1
model_ids \
0 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
1 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
2 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
3 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
mse
0 1.1177247737987623
1 1.1227460791988992
2 1.1277023677794682
3 1.1294747939986314
Now tell me who is going to win ? one with greater epochs or with lower dropout or something else ?
In [50]:
hyper_parameters = {'input_dropout_ratio':[0.1,0.2,0.5,0.7],'epochs':[10,20,30,40]}
h2o_gridSearch = H2OGridSearch(H2ODeepLearningEstimator(activation="RectifierWithDropout",
hidden=[32,32,32],
autoencoder=True,sparse=True,l1=1e-5,epochs=10),hyper_parameters)
h2o_gridSearch.train(x=train.names,training_frame=train,validation_frame=test)
print(h2o_gridSearch.get_grid(sort_by="mse"))
deeplearning Grid Build progress: |███████████████████████████████████████| 100%
epochs input_dropout_ratio \
0 10.0 0.7
1 20.0 0.1
2 30.0 0.7
3 30.0 0.1
4 30.0 0.5
5 10.0 0.2
6 40.0 0.1
7 20.0 0.7
8 40.0 0.2
9 10.0 0.1
10 40.0 0.5
11 40.0 0.7
12 30.0 0.2
13 10.0 0.5
14 20.0 0.2
15 20.0 0.5
model_ids \
0 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
1 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
2 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
3 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
4 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
5 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
6 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
7 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
8 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
9 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
10 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
11 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
12 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
13 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
14 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
15 Grid_DeepLearning_ecg_discord_train4.hex_model_python_150893959697...
mse
0 1.1535938547394573
1 1.1562329316103637
2 1.1566448588666485
3 1.159861698950193
4 1.1644009813475424
5 1.1645614739950838
6 1.1655196697103989
7 1.1656905475634471
8 1.1717198498945969
9 1.1720297722474604
10 1.1754004353240988
11 1.1760561973726498
12 1.1779863170373361
13 1.1793471264297541
14 1.192625404833221
15 1.1967470032123524
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Content source: eds-uga/csci4360-fa17
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