Machine Learning as a Service (MLaaS)
Install all python packages listed in requirements file. Start your Django server(Optionally using a public ip). It is advised to use virtualenv to isolate other python environments.
virtualenv venv source venv/bin/activate pip install -r requirements.txt python manage.py migrate --run-syncdb python manage.py bower install python manage.py runserver <optional IP:PORT>
Now, UI can be accessed at the specified IP:port. If nothing is specified, UI is started at http://127.0.0.1:8000
Add CSV input data
Only file format that is supported from UI is '.csv'. You can use Titanic dataset csv file in the 'tmp' folder. All column names must be visible after you add input data.
You can add any operation from the left pane and apply it on the input data. The sequence of execution is determined by the workflow graph displayed on the right pane. Create links between components to show the relationship.
a) You can connect two components by 'drag and drop' a link between them.
b) Click “Run” if you add a new component. This registers the new component and adds it to your saved workflow. Or in other words, you won't see the effect unless you click 'Run' after adding the new component.
c) You can select any component and click "Show" to view the transformed data till that stage. If you have added a new component, you have to click 'Run' to save it first and then click 'show' to view changes.
Once data is completely clean, apply ML algorithm by choosing the classifier and the target column. You have to also specify the split percentage (the ratio of 'training data size' to 'test data size').
Validations are missing in UI.
eg: Applying mathematical operations on non numerical columns Applying operations on columns that are non-existent. (Once 'Column selection' feature is applied to project lesser number of columns, un-selected columns cannot be used in any of the future operations) Applying ML algorithm on an input that has some missing entries Applying ML algorithm on an input that has non-numerical values Applying ML algorithm on a non-existent target column
Run the topology in remote storm cluster for parallelizing heavy jobs.
Error reporting is missing in UI. It will not display any result if the user creates a component with wrong parameters. Please check console for errors.
Cognitive will support real time streaming inputs like Kafka. It will have support to save the output to databases like Influx, Elastic Search etc.
Swagger API Documentation is available via
http://127.0.0.1:8000/docs after launching Django server.
To execute test, simply execute bellow commands on the root directory.
For testing Javasctipt codes, use Jasmine by executing a following command on the root directory.
After the execution of Jasmine, you can get the results from http://localhost:8888
eslint using npm:
npm install eslint
To contribute Cognitive, please fork this repository, and send the pull requests.
Basic operation workflow is written bellow.
# click fork button on top-right corner of this repository git clone https://github.com/your_github_account/cognitive.git cd cognitive git remote add upstream https://github.com/CiscoSystems/cognitive.git # change something and commit git fetch upstream git merge upstream/master git push # click pull request on your repository
If you have some problem, ask us through github issues or email. This instruction also can help you.