Machine Learning - Revision history

MaryH: /* Installation */

2022-02-02T19:41:50Z

Installation

← Older revision		Revision as of 19:41, 2 February 2022
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	Success if response messages have "Successfully installed h2o-3.36.0.2"		Success if response messages have "Successfully installed h2o-3.36.0.2"


	Test1: From the command line type in <code>python -V</code>. It is successful if it replies with a python version like "Python 3.7.x". If the python version is 2.x then there is a problem, possibly with paths that needs to be addresssed.		Test1: From the command line type in <code>python -V</code>. It is successful if it replies with a python version like "Python 3.7.x". If the python version is 2.x then there is a problem, possibly with paths that needs to be addresssed.

MaryH: /* Installation */

2022-02-02T19:39:42Z

Installation

← Older revision		Revision as of 19:39, 2 February 2022
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	3. Go to this link http://h2o-release.s3.amazonaws.com/h2o/rel-zorn/2/index.html and navigate to the "INSTALL IN PYTHON" tab. The instructions there are also shown below.		3. Go to this link http://h2o-release.s3.amazonaws.com/h2o/rel-zorn/2/index.html and navigate to the "INSTALL IN PYTHON" tab. The instructions there are also shown below.

	Install dependencies (prepending with `sudo` if needed):		Install dependencies (prepending with `sudo` if needed):

MaryH: /* Installation */

2022-02-02T19:39:13Z

Installation

← Older revision		Revision as of 19:39, 2 February 2022
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	3. Go to this link http://h2o-release.s3.amazonaws.com/h2o/rel-zorn/2/index.html and navigate to the "INSTALL IN PYTHON" tab. The instructions there are also shown below.		3. Go to this link http://h2o-release.s3.amazonaws.com/h2o/rel-zorn/2/index.html and navigate to the "INSTALL IN PYTHON" tab. The instructions there are also shown below.
	Install dependencies:		Install dependencies (prepending with `sudo` if needed):

	pip install requests		pip install requests
	pip install tabulate		pip install tabulate
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	pip uninstall h2o # if a permission error occurs, prepend this command with `sudo`		pip uninstall h2o # if a permission error occurs, prepend this command with `sudo`

	pip install http://h2o-release.s3.amazonaws.com/h2o/rel-zorn/2/Python/h2o-3.36.0.2-py2.py3-none-any.whl		pip install http://h2o-release.s3.amazonaws.com/h2o/rel-zorn/2/Python/h2o-3.36.0.2-py2.py3-none-any.whl

MaryH: /* Installation */

2022-02-02T19:35:34Z

Installation

← Older revision		Revision as of 19:35, 2 February 2022
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	2. Install python and add python to the PATH environment variable		2. Install python and add python to the PATH environment variable

	3. Go to this link http://h2o-release.s3.amazonaws.com/h2o/rel-zorn/2/index.html and ~~make~~ navigate to the "INSTALL IN PYTHON" tab. The instructions there are also shown below.		3. Go to this link http://h2o-release.s3.amazonaws.com/h2o/rel-zorn/2/index.html and navigate to the "INSTALL IN PYTHON" tab. The instructions there are also shown below.
	Install dependencies:		Install dependencies:
	pip install requests		pip install requests

MaryH: /* Installation */

2022-02-02T19:34:26Z

Installation

← Older revision		Revision as of 19:34, 2 February 2022
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	=Installation=		=Installation=

	Prerequisites: ~~Java 8,~~ Python 3.6.x and [https://www.h2o.ai/products/h2o/ H2O.ai 3.x]		Prerequisites: Python 3.7.x and [https://www.h2o.ai/products/h2o/ H2O.ai 3.x]

	1. Download '''Python version 3.6+''' from website https://www.python.org/downloads/		1. Download '''Python version 3.7.x''' from website https://www.python.org/downloads/

	2. Install python and add python to the PATH environment variable		2. Install python and add python to the PATH environment variable

	3. Go to this link http://h2o-release.s3.amazonaws.com/h2o/rel-~~wheeler~~/4/index.html and make navigate to the "INSTALL IN PYTHON" tab. The instructions there are also shown below.		3. Go to this link http://h2o-release.s3.amazonaws.com/h2o/rel-zorn/2/index.html and make navigate to the "INSTALL IN PYTHON" tab. The instructions there are also shown below.
	Install dependencies:		Install dependencies:
	pip install ~~--user~~ requests		pip install requests
	pip install ~~--user~~ tabulate		pip install tabulate
	pip install ~~--user scikit-learn~~		pip install future
	~~pip install --user colorama~~
	~~pip install --user~~ future

	At the command line, copy and paste these commands one line at a time:		At the command line, copy and paste these commands one line at a time:

	pip uninstall h2o # if a permission error occurs, prepend this command with `sudo`		pip uninstall h2o # if a permission error occurs, prepend this command with `sudo`
	pip install ~~--user~~ http://h2o-release.s3.amazonaws.com/h2o/rel-~~wheeler~~/4/Python/h2o-3.16.0.4-py2.py3-none-any.whl		pip install http://h2o-release.s3.amazonaws.com/h2o/rel-zorn/2/Python/h2o-3.36.0.2-py2.py3-none-any.whl

	Success if response messages have "Successfully installed h2o-3.16.0.4"		Success if response messages have "Successfully installed h2o-3.36.0.2"

	Test1: From the command line type in <code>python -V</code>. It is successful if it replies with a python version like "Python 3.6.2". If the python version is 2.x then there is a problem, possibly with paths that needs to be addresssed.		Test1: From the command line type in <code>python -V</code>. It is successful if it replies with a python version like "Python 3.7.x". If the python version is 2.x then there is a problem, possibly with paths that needs to be addresssed.

	Test2: On the command line, go into python. In python:		Test2: On the command line, go into python. In python:

Masnes: /* Installation */

2020-05-19T23:36:02Z

Installation

← Older revision		Revision as of 23:36, 19 May 2020
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	3. Go to this link http://h2o-release.s3.amazonaws.com/h2o/rel-wheeler/4/index.html and make navigate to the "INSTALL IN PYTHON" tab. The instructions there are also shown below.		3. Go to this link http://h2o-release.s3.amazonaws.com/h2o/rel-wheeler/4/index.html and make navigate to the "INSTALL IN PYTHON" tab. The instructions there are also shown below.
	Install dependencies ~~(prepending with `sudo` if needed)~~:		Install dependencies:
	pip install requests		pip install --user requests
	pip install tabulate		pip install --user tabulate
	pip install scikit-learn		pip install --user scikit-learn
	pip install colorama		pip install --user colorama
	pip install future		pip install --user future

	At the command line, copy and paste these commands one line at a time:		At the command line, copy and paste these commands one line at a time:

	pip uninstall h2o		pip uninstall h2o # if a permission error occurs, prepend this command with `sudo`
	pip install http://h2o-release.s3.amazonaws.com/h2o/rel-wheeler/4/Python/h2o-3.16.0.4-py2.py3-none-any.whl		pip install --user http://h2o-release.s3.amazonaws.com/h2o/rel-wheeler/4/Python/h2o-3.16.0.4-py2.py3-none-any.whl

	Success if response messages have "Successfully installed h2o-3.16.0.4"		Success if response messages have "Successfully installed h2o-3.16.0.4"

Rdaly: /* Prediction Reports */

2018-09-12T20:52:05Z

Prediction Reports

← Older revision		Revision as of 20:52, 12 September 2018
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	=Prediction Reports=		=Prediction Reports=

	Prediction Reports save prediction markings for a scan. When you select <b>Machine Learning->GO!</b>, Globalyzer automatically generates both the Prediction Report and Machine Learning files for the scan. If you prefer, you can use the Prediction Report without Machine Learning, allowing you to save and share your prediction markings without Machine Learning making predictions. Select <b>Machine Learning->Export Prediction Report</b> to export the Prediction Report for a scan; select <b>Machine Learning->Import Prediction Report</b> to update your scan results with the saved markings.		Prediction Reports save prediction markings you made for a scan. When you select <b>Machine Learning->GO!</b>, Globalyzer automatically generates both the Prediction Report and Machine Learning files for the scan. If you prefer, you can use the Prediction Report without Machine Learning, allowing you to save and share your prediction markings without Machine Learning making predictions. Select <b>Machine Learning->Export Prediction Report</b> to export the Prediction Report for a scan; select <b>Machine Learning->Import Prediction Report</b> to update your scan results with the saved markings.

	<br>		<br>

Rdaly: /* Prediction Reports */

2018-09-12T20:16:34Z

Prediction Reports

← Older revision		Revision as of 20:16, 12 September 2018
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	<br>		<br>

	Prediction Reports are stored in the project/lingoport directory. Lite will automatically use the Prediction Report, if it exists, when scanning. The Dashboard knows to ignore issues with F and ML False (if using Machine Learning) prediction values. In this way, the Workbench and Dashboard will see the same active results.		Prediction Reports are stored in the '''<code><project>/lingoport</code>''' directory. Lite will automatically use the Prediction Report, if it exists, when scanning. The Dashboard knows to ignore issues with F and ML False (if using Machine Learning) prediction values. In this way, the Workbench and Dashboard will see the same active results.

	=Continuous Integration System=		=Continuous Integration System=

Rdaly: /* Work Flow */

2018-09-12T19:40:52Z

Work Flow

← Older revision		Revision as of 19:40, 12 September 2018
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	Here is a listing of all possible prediction values for issues:		Here is a listing of all possible prediction values for issues:

	* <code>-</code>: Issue has not been marked by a user and Machine Learning hasn't been invoked yet		* '''<code>-</code>''': Issue has not been marked by a user and Machine Learning hasn't been invoked yet
	* <code>N</code>: Projects that ran before 6.1 are marked as N if Machine Learning is not applied. It corredsponds to another version of "-"		* '''<code>N</code>''': Projects that ran before 6.1 are marked as N if Machine Learning is not applied. It corredsponds to another version of "-"
	* <code>Negative</code>: filtered issues are predicted as <code>Negative</code> and used to train Machine Learning.		* '''<code>Negative</code>''': filtered issues are predicted as <code>Negative</code> and used to train Machine Learning.
	* <code>T</code>: Marked by a user as a real issue to train Machine Learning		* '''<code>T</code>''': Marked by a user as a real issue to train Machine Learning
	* <code>F</code>: Marked by a user as a false issue to train Machine Learning		* '''<code>F</code>''': Marked by a user as a false issue to train Machine Learning
	* <code>P</code>: Marked by a user as a pending issue; used to indicate that issue has been reviewed, but undecided if real issue or not		* '''<code>P</code>''': Marked by a user as a pending issue; used to indicate that issue has been reviewed, but undecided if real issue or not
	* <code>DFP</code>: Issue marked as False Positive on Dashboard and can be ignored		* '''<code>DFP</code>''': Issue marked as False Positive on Dashboard and can be ignored
	* <code>ML True</code>: Machine learning prediction that the issue is a true issue, i.e. the issue should be refactored.		* '''<code>ML True</code>''': Machine learning prediction that the issue is a true issue, i.e. the issue should be refactored.
	* <code>ML False</code>: Machine Learning prediction that the issue is a false issue and can be ignored		* '''<code>ML False</code>''': Machine Learning prediction that the issue is a false issue and can be ignored
	* <code>ML NULL</code>: Machine Learning cannot make a prediction, so must be considered a true issue		* '''<code>ML NULL</code>''': Machine Learning cannot make a prediction, so must be considered a true issue

Rdaly: /* Work Flow */

2018-09-12T19:35:11Z

Work Flow

← Older revision		Revision as of 19:35, 12 September 2018
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	=Work Flow=		=Work Flow=

	To use Machine Learning, first create a Globalyzer project with scans in the Globalyzer Workbench. Notice that the scan results includes a ~~predicted~~ value.		To use Machine Learning, first create a Globalyzer project with scans in the Globalyzer Workbench. Notice that the scan results includes a '''Predicted''' value.

	[[File:scan_result.png\|800px]]		[[File:scan_result.png\|800px]]

	The example shows that we are in the All Scans Issues Scan View. For this view there are 4,202 issues and all are denoted as "Predicted". Since no machine learning has been done yet, all the issues have a Prediction value of '-'. The Predicted count is the sum of the following Prediction values:		The example shows that we are in the '''All Scans Issues''' Scan View. For this view there are 4,202 issues and all are denoted as "Predicted". Since no machine learning has been done yet, all the issues have a Prediction value of '-' (Unpredicted). The Predicted count is the sum of the following Prediction values:
	*-		*-
	*T		*T
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	*ML FALSE		*ML FALSE

	This can be seen in the Scan Views -> Manage Scan Views... popup. The Predictions in red are tallied in the Predicted count and the black ones are not.		This can be seen in the '''Scan Views -> Manage Scan Views...''' popup. The Predictions in red are tallied in the Predicted count and the black ones are not.

	[[File:All_scan_issues.png\|800px]]		[[File:All_scan_issues.png\|800px]]