H2O AutoML

https://docs.h2o.ai/h2o/latest-stable/h2o-docs/
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### Install R and Start R Session

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/getting-started/getting-started.html

Instructions for downloading and installing R from CRAN, then starting an R session. The output shows the R prompt ready for commands.

```bash
# Download and install R:
# 1. Go to http://cran.r-project.org/mirrors.html.
# 2. Select your closest local mirror.
# 3. Select your operating system (Linux, OS X, or Windows).
# 4. Depending on your OS, download the appropriate file, along with any required packages.
# 5. When the download is complete, unzip the file and install.

# Start R
h2o-3 user$ r
...
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>
```

--------------------------------

### H2O startup output

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/faq/tunneling.html

Example output from starting H2O with the command above. It shows the H2O version, internal communication port, and the URL where the web UI is available.

```text
irene@mr-0x3:~/target$ java -jar h2o.jar -port 55599
04:48:58.053 main      INFO WATER: ----- H2O started -----
04:48:58.055 main      INFO WATER: Build git branch: master
04:48:58.055 main      INFO WATER: Build git hash: 64fe68c59ced5875ac6bac26a784ce210ef9f7a0
04:48:58.055 main      INFO WATER: Build git describe: 64fe68c
04:48:58.055 main      INFO WATER: Build project version: 1.7.0.99999
04:48:58.055 main      INFO WATER: Built by: 'Irene'
04:48:58.055 main      INFO WATER: Built on: 'Wed Sep  4 07:30:45 PDT 2013'
04:48:58.055 main      INFO WATER: Java availableProcessors: 4
04:48:58.059 main      INFO WATER: Java heap totalMemory: 0.47 gb
04:48:58.059 main      INFO WATER: Java heap maxMemory: 6.96 gb
04:48:58.060 main      INFO WATER: ICE root: '/tmp'
04:48:58.081 main      INFO WATER: Internal communication uses port: 55600
+                                  Listening for HTTP and REST traffic on
+                                  http://192.168.1.173:55599/
04:48:58.109 main      INFO WATER: H2O cloud name: 'irene'
04:48:58.109 main      INFO WATER: (v1.7.0.99999) 'irene' on
/192.168.1.173:55599, discovery address /230 .252.255.19:59132
04:48:58.111 main      INFO WATER: Cloud of size 1 formed [/192.168.1.173:55599]
04:48:58.247 main      INFO WATER: Log dir: '/tmp/h2ologs'
```

--------------------------------

### General GAM Example in R

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/gam.html

Initializes H2O and loads the library in R. This is the starting point for running GAM examples in R.

```R
library(h2o)
h2o.init()
```

--------------------------------

### Python quickstart: import H2O, initialize, and load Iris dataset

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/getting-started/getting-started.html

Python commands to import the H2O module, initialize a local cluster, optionally run demos, and import the Iris dataset. Includes a summary output example.

```python
# By default, this setup is open.
# Follow our security guidelines (https://docs.h2o.ai/h2o/latest-stable/h2o-docs/security.html)
# if you want to secure your installation.

# Before starting Python, run the following commands to install dependencies.
# Prepend these commands with `sudo` only if necessary:
# h2o-3 user$ [sudo] pip install -U requests
# h2o-3 user$ [sudo] pip install -U tabulate

# Start python:
# h2o-3 user$ python

# Run the following commands to import the H2O module:
>>> import h2o

# Run the following command to initialize H2O on your local machine (single-node cluster):
>>> h2o.init()

# If desired, run the GLM, GBM, or Deep Learning demo(s):
>>> h2o.demo("glm")
>>> h2o.demo("gbm")
>>> h2o.demo("deeplearning")

# Import the Iris (with headers) dataset:
>>> path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv"
>>> iris = h2o.import_file(path=path)

# View a summary of the imported dataset:
>>> iris.summary
# sepal_len    sepal_wid    petal_len    petal_wid    class
# 5.1          3.5          1.4          0.2          Iris-setosa
# 4.9          3            1.4          0.2          Iris-setosa
# 4.7          3.2          1.3          0.2          Iris-setosa
# 4.6          3.1          1.5          0.2          Iris-setosa
# 5            3.6          1.4          0.2          Iris-setosa
# 5.4          3.9          1.7          0.4          Iris-setosa
# 4.6          3.4          1.4          0.3          Iris-setosa
# 5            3.4          1.5          0.2          Iris-setosa
# 4.4          2.9          1.4          0.2          Iris-setosa
# 4.9          3.1          1.5          0.1          Iris-setosa
#
# [150 rows x 5 columns]
# <bound method H2OFrame.summary of >

```

--------------------------------

### Start H2O on Hadoop and view YARN logs command

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/logs.html

Example output from starting an H2O instance on Hadoop with the hadoop jar command. The output includes the YARN logs command to retrieve logs for the job.

```bash
jessica@mr-0x8:~/h2o-3.1.0.3008-cdh5.2$ hadoop jar h2odriver.jar -nodes 1 -mapperXmx 6g -output hdfsOutputDirName
Determining driver host interface for mapper->driver callback...
    [Possible callback IP address: 172.16.2.178]
    [Possible callback IP address: 127.0.0.1]
Using mapper->driver callback IP address and port: 172.16.2.178:52030
(You can override these with -driverif and -driverport.)
Memory Settings:
    mapreduce.map.java.opts:     -Xms1g -Xmx1g -Dlog4j.defaultInitOverride=true
    Extra memory percent:        10
    mapreduce.map.memory.mb:     1126
15/05/06 17:11:50 INFO client.RMProxy: Connecting to ResourceManager at mr-0x10.0xdata.loc/172.16.2.180:8032
15/05/06 17:11:52 INFO mapreduce.JobSubmitter: number of splits:1
15/05/06 17:11:52 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1430127035640_0075
15/05/06 17:11:52 INFO impl.YarnClientImpl: Submitted application application_1430127035640_0075
15/05/06 17:11:52 INFO mapreduce.Job: The url to track the job: http://mr-0x10.0xdata.loc:8088/proxy/application_1430127035640_0075/
Job name 'H2O_29570' submitted
JobTracker job ID is 'job_1430127035640_0075'
For YARN users, logs command is 'yarn logs -applicationId application_1430127035640_0075'
Waiting for H2O cluster to come up...
```

--------------------------------

### Create a folder for the H2O-3 repository

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/getting-started/getting-started.html

First step of the quickstart: create a directory to hold the H2O-3 repository.

```bash
user$ mkdir ~/Desktop/repos

```

--------------------------------

### Install R Dependency Packages for H2O

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/getting-started/getting-started.html

Copy and paste these commands in R to install the required dependency packages. The setup is open by default; follow the security guidelines to secure your installation.

```R
# By default, this setup is open.
# Follow our security guidelines (https://docs.h2o.ai/h2o/latest-stable/h2o-docs/security.html)
# if you want to secure your installation.

# Copy and paste the following commands in R to download dependency packages.
> pkgs <- c("methods", "statmod", "stats", "graphics", "RCurl", "jsonlite", "tools", "utils")
> for (pkg in pkgs) {if (! (pkg %in% rownames(installed.packages()))) { install.packages(pkg) }}
```

--------------------------------

### Install and Upgrade Python Packages for H2O

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/faq/python.html

Commands to install and upgrade pip, setuptools, distribute, and wheel. Includes verification of versions and a note to start a new shell after upgrading pip.

```bash
# this gets the latest setuptools
# see https://pip.pypa.io/en/latest/installing.html
wget https://bootstrap.pypa.io/ez_setup.py -O - | sudo python

# platform dependent ways of installing pip are at
# https://pip.pypa.io/en/latest/installing.html
# but the above should work on most linux platforms?

# on ubuntu
# if you already have some version of pip, you can skip this.
sudo apt-get install python-pip

# the package manager doesn't install the latest. upgrade to latest
# we're not using easy_install any more, so don't care about checking that
pip install pip --upgrade

# I've seen pip not install to the final version ..i.e. it goes to an almost
# final version first, then another upgrade gets it to the final version.
# We'll cover that, and also double check the install.

# after upgrading pip, the path name may change from /usr/bin to /usr/local/bin
# start a new shell, just to make sure you see any path changes

bash

# Also: I like double checking that the install is bulletproof by reinstalling.
# Sometimes it seems like things say they are installed, but have errors during the install. Check for no errors or stack traces.

pip install pip --upgrade --force-reinstall

# distribute should be at the most recent now. Just in case
# don't do --force-reinstall here, it causes an issue.

pip install distribute --upgrade


# Now check the versions
pip list | egrep '(distribute|pip|setuptools)'
distribute (0.7.3)
pip (7.0.3)
setuptools (17.0)


# Re-install wheel
pip install wheel --upgrade --force-reinstall
```

--------------------------------

### GET /3/InitID

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/rest-api-reference.html

Issues a new session ID. This endpoint is used to start a new session.

```APIDOC
## GET /3/InitID

### Description
Issue a new session ID.

### Method
GET

### Endpoint
/3/InitID

### Parameters
#### Input
- **InitIDV3** - Required - Input parameters for initializing a session.

### Response
#### Success Response
- **InitIDV3** - The new session ID.

```

--------------------------------

### H2O startup log output

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/faq/general-troubleshooting.html

Example output from H2O startup showing listening address and cloud formation.

```text
INFO: Listening for HTTP and REST traffic on  IP_Address/ 06-18 10:54:21.586 192.168.1.70:54323    25638  main       INFO: H2O cloud name: 'H2O_User' on IP_Address, discovery address /Discovery_Address INFO: Cloud of size 1 formed [IP_Address]
```

--------------------------------

### H2O Python package installation error traceback

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/faq/python.html

Example error output when installing H2O via pip fails with IOError: No such file or directory: '/tmp/pip-nTu3HK-build/setup.py'. This indicates a problem with the package build process.

```text
Downloading/unpacking http://h2o-release.s3.amazonaws.com/h2o/rel-shannon/12/Python/h2o-3.42.0.2-py2.py3-none-any.whl
  Downloading h2o-3.42.0.2-py2.py3-none-any.whl (43.1Mb): 43.1Mb downloaded
  Running setup.py egg_info for package from http://h2o-release.s3.amazonaws.com/h2o/rel-shannon/12/Python/h2o-3.42.0.2-py2.py3-none-any.whl
    Traceback (most recent call last):
      File "<string>", line 14, in <module>
    IOError: [Errno 2] No such file or directory: '/tmp/pip-nTu3HK-build/setup.py'
    Complete output from command python setup.py egg_info:
    Traceback (most recent call last):

  File "<string>", line 14, in <module>

IOError: [Errno 2] No such file or directory: '/tmp/pip-nTu3HK-build/setup.py'

---
Command python setup.py egg_info failed with error code 1 in /tmp/pip-nTu3HK-build

```

--------------------------------

### Run example application with run-example.sh

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/faq/sparkling-water.html

Command to run a self-contained Sparkling Water example application. The distribution provides several examples.

```bash
bin/run-example.sh ChicagoCrimeAppSmall
```

--------------------------------

### Retrieve sample datasets with Gradle

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/getting-started/getting-started.html

Run the Gradle task to download sample datasets used throughout the user guide and booklets.

```bash
h2o-3 user$ ./gradlew syncSmalldata

```

--------------------------------

### R example using start_column in h2o.coxph

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/start_column.html

R example that imports the heart dataset, sets start and stop columns, and trains a CoxPH model with h2o.coxph. The output shows the model details including coefficients and likelihood ratio test.

```R
library(h2o)
h2o.init()
# import the heart dataset
heart <- h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")

# set the predictor name and response column
x <- "age"
y <- "event"

# set the start and stop columns
start <- "start"
stop <- "stop"

# train your model
heart_coxph <- h2o.coxph(x = x, event_column = y,
                         start_column = start, stop_column = stop,
                         training_frame = heart)

# view the model details
heart_coxph
Model Details:
==============

H2OCoxPHModel: coxph
Model ID:  CoxPH_model_R_1527700369755_2
Call:
"Surv(start, stop, event) ~ age"

      coef exp(coef) se(coef)    z     p
age 0.0307    1.0312   0.0143 2.15 0.031

Likelihood ratio test = 5.17  on 1 df, p = 0.023
n = 172, number of events = 75

```

--------------------------------

### h2o.init()

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/starting-h2o.html

Initializes H2O-3 from Python. Connects to an existing H2O-3 server or starts a new one if no connection is found.

```APIDOC
## h2o.init()

### Description
Initializes H2O-3 from Python. Connects to an existing H2O-3 server or starts a new one if no connection is found.

### Method
Function call

### Signature
`h2o.init()`

### Parameters
- **url** (string) - Optional - Full URL of the server to connect to (can be used instead of `ip` + `port` + `https`).
- **ip** (string) - Optional - The IP address (or host name) of the server where H2O-3 is running.
- **port** (integer) - Optional - Port number that H2O-3 service is listening to.
- **name** (string) - Optional - Cluster name. If `None`, while connecting to an existing cluster, it will not check the cluster name. If set, then it will connect only if the target cluster name matches. If no instance is found and it decides to start a local one then this will be used as the cluster name or a random one will be generated if set to `None`.
- **https** (boolean) - Optional - Set to `True` to connect via https:// instead of http://.
- **insecure** (boolean) - Optional - When using https, setting this to `True` will disable SSL certificates verification.
- **username** (string) - Optional - The username to log in with when using basic authentication.
- **password** (string) - Optional - The password to log in with when using basic authentication.
- **cookies** (string or list) - Optional - Cookie (or list of) to add to each request.
- **proxy** (string) - Optional - The proxy server address.
- **start_h2o** (boolean) - Optional - If `False`, do not attempt to start an H2O-3 server when a connection to an existing one failed.
- **nthreads** (integer) - Optional - "Number of threads" option when launching a new H2O-3 server.
- **ice_root** (string) - Optional - The directory for temporary files for the new H2O-3 server.
- **log_dir** (string) - Optional - Directory for H2O-3 logs to be stored if a new instance is started. Ignored if connecting to an existing node.
- **log_level** (string) - Optional - The logger level for H2O-3 if a new instance is started. One of `TRACE`, `DEBUG`, `INFO`, `WARN`, `ERRR`, or `FATAL`. Default is `INFO`. Ignored if connecting to an existing node.
- **enable_assertions** (boolean) - Optional - Enable assertions in Java for the new H2O-3 server.
- **max_mem_size** (integer or string) - Optional - Maximum memory to use for the new H2O-3 server. Integer input will be evaluated as gigabytes. Other units can be specified by passing in a string (e.g. `"160M"` for 160 megabytes).
- **min_mem_size** (integer or string) - Optional - Minimum memory to use for the new H2O-3 server. Integer input will be evaluated as gigabytes. Other units can be specified by passing in a string (e.g. `"160M"` for 160 megabytes).
- **strict_version_check** (boolean) - Optional - If `True`, an error will be raised if the client and server versions don’t match.
- **ignore_config** (boolean) - Optional - Indicates whether a processing of a `.h2oconfig` file should be conducted or not. Default value is `False`.
- **extra_classpath** (list) - Optional - List of paths to libraries that should be included on the Java classpath when starting H2O-3 from Python.
- **kwargs** (dict) - Optional - (All other deprecated attributes.)
- **jvm_custom_args** (list) - Optional - User-defined arguments for the JVM H2O-3 is instantiated in. Ignored if there is an instance of H2O-3 already running and the client connects to it.
- **bind_to_localhost** (boolean) - Optional - A flag indicating whether access to the H2O-3 instance should be restricted to the local machine (default) or if it can be reached from other computers on the network.

### Request Example
```python
import h2o
h2o.init()
```

### Response
No explicit return value documented.
```

--------------------------------

### Initialize H2O-3 in Python and run demo

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/downloading.html

Optionally initialize H2O-3 in Python and run a demo to see H2O-3 at work.

```python
import h2o
h2o.init()
h2o.demo("glm")

```

--------------------------------

### PrintMojo with Graphviz: Download H2O and Generate PNG (Second Example)

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/productionizing.html

Commands to download and extract the latest stable h2o.jar, install Graphviz (via Homebrew on macOS), run PrintMojo to create a DOT file, and convert it to a PNG. Requires Graphviz to be installed.

```bash
# In another terminal window, download and extract the
# latest stable h2o.jar from http://www.h2o.ai/download/
cd ~/Downloads
unzip h2o-3.42.0.2.zip
cd h2o-3.42.0.2

# Run the PrintMojo tool from the command line.
# This requires that graphviz is installed.
brew install graphviz # example for Mac OsX if not already installed
java -cp h2o.jar hex.genmodel.tools.PrintMojo --tree 0 -i "path/to/model.zip" -o model.gv -f 20 -d 3
dot -Tpng model.gv -o model.png
open model.png
```

--------------------------------

### Initialize H2O-3 in R and run demo

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/downloading.html

Optionally initialize H2O-3 and run a demo to see H2O-3 at work.

```R
library(h2o)
localH2O = h2o.init()
demo(h2o.kmeans)

```

--------------------------------

### Download H2O zip file

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/flow.html

Downloads the H2O zip file to your current directory. By default, the setup is open; follow security guidelines to secure your installation.

```bash
curl -o h2o.zip http://download.h2o.ai/versions/h2o-3.42.0.2.zip
```

--------------------------------

### Download and run Flow from the command line

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/downloading.html

Unzip the downloaded H2O-3 ZIP file and start the H2O-3 server using Java. After running these commands, point your browser to http://localhost:54321 to open the Flow web GUI.

```bash
cd ~/Downloads
unzip h2o-3.42.0.2.zip
cd h2o-3.42.0.2
java -jar h2o.jar

```

--------------------------------

### Import file from HDFS in R (commented example)

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/faq/hadoop.html

Commented R example showing how to import a file from HDFS using h2o.importFile. The path is commented out, but the code is provided as an example.

```R
# pathToAirlines <- "http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip"
# airlines.hex <- h2o.importFile(path = pathToAirlines, destination_frame = "airlines.hex")

```

--------------------------------

### View Variable Importances in Python

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/faq/python.html

Use model.varimp(use_pandas=False) to get a list of variable importances. The example shows training a GBM model and printing the importance for the 'Month' variable.

```python
model = h2o.estimators.H2OGradientBoostingEstimator(seed=1111)
model.train(y = "IsDepDelayed", x = ["Month"],training_frame = airlines)
vi = model.varimp(use_pandas=False)
Out[26]:
[('Month', 69.27436828613281, 1.0, 1.0)]
```

--------------------------------

### Launch H2O-3 with Alluxio client library

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/getting-data-into-h2o.html

Command line to start H2O-3 with the Alluxio client library on the classpath. The library must be compatible with the HDFS API. Each node in the cluster must be launched in the same way.

```bash
java -cp alluxio-core-client-1.3.0-jar-with-dependencies.jar:build/h2o.jar water.H2OApp

```

--------------------------------

### Get AutoML model by model ID (Python)

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html

Retrieves a specific model from an AutoML run using its model ID with h2o.get_model in Python. The example uses a StackedEnsemble model ID.

```Python
# Get a specific model by model ID
m = h2o.get_model("StackedEnsemble_BestOfFamily_AutoML_20191213_174603")

```

--------------------------------

### Get AutoML model by model ID (R)

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html

Retrieves a specific model from an AutoML run using its model ID with h2o.getModel in R. The example uses a StackedEnsemble model ID.

```R
# Get a specific model by model ID
m <- h2o.getModel("StackedEnsemble_BestOfFamily_AutoML_20191213_174603")

```

--------------------------------

### init (CoxPH) - Python Example

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/init2.html

Example of using the `init` parameter in Python to train a CoxPH model with H2O.

```APIDOC
## init (CoxPH) - Python Example

### Description
This example demonstrates how to set the `init` parameter when training a CoxPH model in Python using the H2O library.

### Method
Not applicable (parameter documentation)

### Endpoint
Not applicable

### Parameters
#### init
- **type**: numeric
- **Required**: No
- **Description**: Initial value for the coefficient vector β(0). Defaults to 0.

### Request Example
```python
import h2o
from h2o.estimators.coxph import H2OCoxProportionalHazardsEstimator
h2o.init()

# import the heart dataset
heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")

# split the dataset into train and test datasets
train, test = heart.split_frame(ratios = [.8], seed=1234)

#specify the init parameter's value
init = 3

# initialize an train a CoxPH model
coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
                                           stop_column="stop",
                                           ties="breslow",
                                           init=init)
coxph.train(x="age", y="event", training_frame=heart)

# view the model details
coxph.train
```

### Response
#### Success Response
- **Model Details**: Displays the fitted CoxPH model, including coefficients, likelihood ratio test, and number of events.

#### Response Example
```
Model Details
=============
H2OCoxProportionalHazardsEstimator :  Cox Proportional Hazards
Model Key:  CoxPH_model_python_1570808496252_2

Call:
Surv(start, stop, event) ~ age

Coefficients: CoxPH Coefficients
names    coefficients    exp_coef    exp_neg_coef    se_coef    z_coef
-------  --------------  ----------  --------------  ---------  --------
age      0.030691        1.03117     0.969775        0.0142686  2.15095

Likelihood ratio test=5.160759
n=172, number of events=75

Scoring History:
    timestamp            duration    iterations    loglik
--  -------------------  ----------  ------------  --------
    2019-10-11 08:59:31  0.000 sec   0             -298.326
    2019-10-11 08:59:31  0.001 sec   1             -295.799
    2019-10-11 08:59:31  0.002 sec   2             -295.745
    2019-10-11 08:59:31  0.004 sec   3             -295.745
```
```

--------------------------------

### Helm values for basic H2O configuration

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/cloud-integration/kubernetes.html

Example configuration for the H2O Helm chart, setting node count, CPU and memory resources, and enabling an ingress. The basic install only provides minimal resources.

```yaml
h2o:
  nodeCount: 3
resources:
  cpu: 12
  memory: 32Gi
ingress:
  enabled: true
  annotations: {}
  hosts:
    - host: ""
      paths: ["/"]
  tls: []

```

--------------------------------

### Start H2O in terminal window #1

Source: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/productionizing.html

Starts the H2O server using the h2o.jar file. This must be running before building a model and downloading the POJO.

```bash
$ java -jar h2o.jar
```
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