Skip to content

#2712: Adjust 'README.md' to correspond to #5 #2716

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 4 commits into from
Sep 15, 2016
Merged

#2712: Adjust 'README.md' to correspond to #5 #2716

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
21b85f8
#2712: README.md, mention SVRs, and fix links in intro section
jeff1evesque Sep 14, 2016
99b7934
#2712: README.md, adjust 'Data Attributes' section
jeff1evesque Sep 15, 2016
7a765d7
#2712: README.md, adjust 'Test Scripts'
jeff1evesque Sep 15, 2016
401f9d4
#2712: README.md, adjust 'Test Scripts' links
jeff1evesque Sep 15, 2016
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
53 changes: 32 additions & 21 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,11 +1,10 @@
# Machine Learning [![Build Status](https://travis-ci.org/jeff1evesque/machine-learning.svg?branch=master)](https://travis-ci.org/jeff1evesque/machine-learning)

In [machine learning](http://en.wikipedia.org/wiki/Machine_learning), support
vector machines (SVMs) are [supervised learning](http://en.wikipedia.org/wiki/
Supervised_learning) models with associated learning [algorithms](http://en.wi
kipedia.org/wiki/Algorithm) that analyze data and recognize patterns, used for
[classification](http://en.wikipedia.org/wiki/Statistical_classification) and
[regression analysis](http://en.wikipedia.org/wiki/Regression_analysis). More
vector machines (SVMs), and support vector machines (SVRs) are [supervised learning](http://en.wikipedia.org/wiki/Supervised_learning)
models with associated learning [algorithms](http://en.wikipedia.org/wiki/Algorithm)
that analyze data and recognize patterns, used for [classification](http://en.wikipedia.org/wiki/Statistical_classification)
and [regression analysis](http://en.wikipedia.org/wiki/Regression_analysis). More
generally, machine-learning deals with the construction and study of systems
that can [learn](http://en.wikipedia.org/wiki/Learning) from data, rather than
follow only explicitly programmed instructions.
Expand Down Expand Up @@ -314,8 +313,12 @@ Some additional sample files have been provided, which outline how the `data`
attribute implement should be implemented, with respect to the above `post`
implementation:

- [SVM sample datasets](https://github.com/jeff1evesque/machine-learning/blob/master/interface/static/data/json/programmatic_interface/svm)
- [SVR sample datasets](https://github.com/jeff1evesque/machine-learning/blob/master/interface/static/data/json/programmatic_interface/svr)
- [SVM datasets](https://github.com/jeff1evesque/machine-learning/blob/master/interface/static/data/json/programmatic_interface/svm)
- [dataset url](https://github.com/jeff1evesque/machine-learning/tree/master/interface/static/data/json/programmatic_interface/svm/dataset_url)
- [file upload](https://github.com/jeff1evesque/machine-learning/tree/master/interface/static/data/json/programmatic_interface/svm/file_upload)
- [SVR datasets](https://github.com/jeff1evesque/machine-learning/blob/master/interface/static/data/json/programmatic_interface/svr)
- [dataset url](https://github.com/jeff1evesque/machine-learning/tree/master/interface/static/data/json/programmatic_interface/svr/dataset_url)
- [file upload](https://github.com/jeff1evesque/machine-learning/tree/master/interface/static/data/json/programmatic_interface/svr/file_upload)

**Note:** the content of each of the above files, can substituted for the above
`data` attribute.
Expand All @@ -326,13 +329,17 @@ The following (non-exhaustive) properties define the above implemented `data`
attribute:

- `model_id`: the numeric id value, of the generated model in the nosql
datastore.
datastore
- `model_type`: corresponds to the desired model type, which can be one of
the following:
- `classification`
- `regression`
- `session_id`: the numeric id value, that represents the dataset stored in
the sql database.
- `dataset_type`: corresponds to one of the following types:
- `dataset_url`: indication that the supplied dataset will be url references
- `file_upload`: indication that the supplied dataset(s) will be defined as a
json string within the `dataset` attribute
- `session_type`: corresponds to one of the following session types:
- `data_new`
- `data_append`
Expand All @@ -346,15 +353,16 @@ The following (non-exhaustive) properties define the above implemented `data`
- `polynomial`
- `rbf`
- `sigmoid`
- `prediction_input[]`: an array of prediction input, supplied to the generated
model to compute a prediction

### Test Scripts

This project implements [unit testing](https://en.wikipedia.org/wiki/Unit_testing),
to validate logic in a consistent fashion. Currently, only high-level unit
tests have been defined within [`pytest_svm_session.py`](https://github.com/jeff1evesque/machine-learning/blob/master/test/programmatic_interface/pytest_svm_session.py),
and [`pytest_svr_session.py`](https://github.com/jeff1evesque/machine-learning/blob/master/test/programmatic_interface/pytest_svr_session.py).
These unit tests have been automated within corresponding travis [builds](https://travis-ci.org/jeff1evesque/machine-learning),
using a series of docker containers, connected via a common docker network:
to validate logic in a consistent fashion. Currently, only [high-level](https://github.com/jeff1evesque/machine-learning/tree/master/test/live_server)
unit tests have been defined. These unit tests have been automated within corresponding
travis [builds](https://travis-ci.org/jeff1evesque/machine-learning), using
a series of docker containers, connected via a common docker network:

- [`.travis.yml`](https://github.com/jeff1evesque/machine-learning/blob/e83f4222a9de11fcd839d6b3e789d63bab82e093/.travis.yml#L101-L120)
- [`default.dockerfile`](https://github.com/jeff1evesque/machine-learning/blob/master/default.dockerfile)
Expand All @@ -369,23 +377,26 @@ Current unit tests cover the following sessions:
- `model_predict`
- `model_generate`

which can be executed manually as follows:
which can be executed [manually](https://github.com/jeff1evesque/machine-learning/tree/master/test/manual)
as follows:

```bash
$ cd /path/to/machine-learning/
$ vagrant up
$ vagrant ssh
vagrant@vagrant-ubuntu-trusty-64:~$ cd /vagrant/test && py.test manual
============================================ test session starts =============================================
platform linux2 -- Python 2.7.6, pytest-2.9.2, py-1.4.31, pluggy-0.3.1
vagrant@vagrant-ubuntu-trusty-64:~$ (cd /vagrant/test && pytest manual)
================================================= test session starts ==================================================
platform linux2 -- Python 2.7.6, pytest-3.0.2, py-1.4.31, pluggy-0.3.1
rootdir: /vagrant/test/manual, inifile: pytest.ini
plugins: flask-0.10.0
collected 8 items
collected 16 items

manual/programmatic_interface/pytest_svm_session.py ....
manual/programmatic_interface/pytest_svr_session.py ....
manual/programmatic_interface/dataset_url/pytest_svm_dataset_url.py ....
manual/programmatic_interface/dataset_url/pytest_svr_dataset_url.py ....
manual/programmatic_interface/file_upload/pytest_svm_file_upload.py ....
manual/programmatic_interface/file_upload/pytest_svr_file_upload.py ....

========================================= 8 passed in 7.82 seconds ==========================================
============================================== 16 passed in 58.60 seconds ==============================================
```

**Note:** future releases (i.e. milestone [1.0](https://github.com/jeff1evesque/machine-learning/milestones/1.0)),
Expand Down