This project provides a web-interface,
as well as a programmatic-api
for various machine learning algorithms. Some of it's general applications, have
been outlined within index.rst
.
Supported algorithms:
- Support Vector Machine (SVM)
- Support Vector Regression (SVR)
Please adhere to contributing.md
,
when contributing code. Pull requests that deviate from the
contributing.md
,
could be labelled
as invalid
, and closed (without merging to master). These best practices
will ensure integrity, when revisions of code, or issues need to be reviewed.
Note: support, and philantropy can be inquired, to further assist with development.
Fork this project, and remember to generate ssh keys, before cloning the repository:
- simple clone: clone the remote master branch.
- commit hash: clone the remote master branch, then checkout a specific commit hash.
- release tag: clone the remote branch, associated with the desired release tag.
Note: the puppetfile.rst
can be reviewed, to better understand why ssh keys are required.
In order to proceed with the installation for this project, two dependencies need to be installed:
- Vagrant
- Virtualbox (with extension pack)
Once the necessary dependencies have been installed, execute the following command to build the virtual environment:
cd /path/to/machine-learning/
vagrant up
Depending on the network speed, the build can take between 10-15 minutes. So,
grab a cup of coffee, and perhaps enjoy a danish while the virtual machine
builds. Remember, the application is intended to run on localhost, where the
Vagrantfile
defines the exact port-forward on the host machine.
Note: a more complete refresher on virtualization, can be found within the vagrant wiki page.
The following lines, indicate the application is accessible via localhost:8080
,
on the host machine:
...
## Create a forwarded port mapping which allows access to a specific port
## within the machine from a port on the host machine. In the example below,
## accessing "localhost:8080" will access port 80 on the guest machine.
main.vm.network 'forwarded_port', guest: 5000, host: 8080
main.vm.network 'forwarded_port', guest: 6000, host: 9090
...
Note: ssl is configured on the reverse proxy,
such that accessing http://localhost:8080
, on the host machine, will redirect to https://localhost:8080
.
Both the web-interface, and the programmatic-api, have corresponding unit tests which can be reviewed, and implemented.
The web-interface, or GUI implementation, allow users to implement the following sessions:
data_new
: store the provided dataset(s), within the implemented sql database.data_append
: append additional dataset(s), to an existing representation (from an earlierdata_new
session), within the implemented sql database.model_generate
: using previous stored dataset(s) (from an earlierdata_new
, ordata_append
session), generate a corresponding model intomodel_predict
: using a previous stored model (from an earliermodel_predict
session), from the implemented nosql datastore, along with user supplied values, generate a corresponding prediction.
When using the web-interface, it is important to ensure the csv, xml, or json file(s), representing the corresponding dataset(s), are properly formatted. Dataset(s) poorly formatted will fail to create respective json dataset representation(s). Subsequently, the dataset(s) will not succeed being stored into corresponding database tables; therefore, no model, or prediction can be made.
The following are acceptable syntax:
Note: each dependent variable value (for JSON datasets), is an array (square brackets), since each dependent variable may have multiple observations.
As mentioned earlier, the web application can be accessed after subsequent
vagrant up
command, followed by using a browser referencing localhost:8080,
on the host machine.
The programmatic-interface, or set of API, allow users to implement the following sessions:
data_new
: store the provided dataset(s), within the implemented sql database.data_append
: append additional dataset(s), to an existing representation (from an earlierdata_new
session), within the implemented sql database.model_generate
: using previous stored dataset(s) (from an earlierdata_new
, ordata_append
session), generate a corresponding model intomodel_predict
: using a previous stored model (from an earliermodel_predict
session), from the implemented nosql datastore, along with user supplied values, generate a corresponding prediction.
A post request, can be implemented in python, as follows:
import requests
endpoint = 'https://localhost:9090/load-data'
headers = {
'Authorization': 'Bearer ' + token,
'Content-Type': 'application/json'
}
requests.post(endpoint, headers=headers, data=json_string_here)
Note: more information, regarding how to obtain a valid token
, can be further
reviewed, in the /login
documentation.
Note: various data
attributes can be nested in above POST
request.
It is important to remember that the Vagrantfile
,
as denoted by the above snippet, has defined two port forwards. Specifically, on
the host, 8080
is reserved for the web-interface, while 9090
, is reserved for
the programmatic rest-api.