Welcome to Django Dandelion’s documentation!¶

Use the Dandelion API with Django

Installation¶

django-dandelion is available on https://pypi.python.org/pypi/django-dandelion/ install it simply with:

$ pip install django-dandelion

Add the entry DANDELION_TOKEN. The recommended method is to setup your production keys using environment variables. This helps to keep them more secure. Your test keys can be displayed in your code directly.

The following entry look for your DANDELION_TOKEN in your environment and, if it can’t find them,uses your test keys values instead:

DANDELION_TOKEN = os.environ.get("DANDELION_TOKEN", "<your dandelion token>")

Contents¶

Django Dandelion¶

Use the Dandelion API with Django

Documentation¶

The full documentation is at https://django-dandelion.readthedocs.io.

Quickstart¶

Install Django Dandelion:

$ pip install django-dandelion

Add django_dandelion to your INSTALLED_APPS

INSTALLED_APPS = [
    # ...
    'django_dandelion',
]

Add the entry DANDELION_TOKEN. The recommended method is to setup your production keys using environment variables. This helps to keep them more secure. Your test keys can be displayed in your code directly.

The following entry look for your DANDELION_TOKEN in your environment and, if it can’t find them,uses your test keys values instead:

DANDELION_TOKEN = os.environ.get("DANDELION_TOKEN", "<your dandelion token>")

Register on dandelion to obtain your authentication keys and enrich your application with our semantic intelligence.

You can also change the url of the host and decide whether to use the cache:

DANDELION_HOST = 'api.dandelion.eu'  # Default 'api.dandelion.eu'
DANDELION_USE_CACHE = True  # Default True

Running Tests¶

Does the code actually work?

# Add "export DANDELION_TOKEN=<your dandelion token>" to <YOURVIRTUALENV>/bin/activate
$ source <YOURVIRTUALENV>/bin/activate
$ (myenv) $ pip install tox
$ (myenv) $ tox

Quick Start¶

Install¶

django-dandelion is available on https://pypi.python.org/pypi/django-dandelion/ install it simply with:

$ pip install django-dandelion

Configure¶

Settings¶

Add django_dandelion to your INSTALLED_APPS

INSTALLED_APPS = [
    # ...
    'django_dandelion',
]

Add the entry DANDELION_TOKEN. The recommended method is to setup your production keys using environment variables. This helps to keep them more secure. Your test keys can be displayed in your code directly.

The following entry look for your DANDELION_TOKEN in your environment and, if it can’t find them,uses your test keys values instead:

DANDELION_TOKEN = os.environ.get("DANDELION_TOKEN", "<your dandelion token>")

Register on dandelion to obtain your authentication keys and enrich your application with our semantic intelligence.

You can also change the url of the host and decide whether to use the cache:

DANDELION_HOST = 'api.dandelion.eu'  # Default 'api.dandelion.eu'
DANDELION_USE_CACHE = True  # Default True

Requests¶

DataTXT¶

EntityExtraction (Documentation)

>>> from django_dandelion.datatxt import EntityExtraction
>>> entityExtraction = EntityExtraction()
>>> entityExtraction.params = 'text', u'They say Apple is better than Windows'
>>> entityExtraction.params = 'lang', u'en'
>>> entityExtraction.analyze()
{u'annotations': [{u'confidence': 0.7353,
   u'end': 14,
   u'id': 856,
   u'label': u'Apple',
   u'spot': u'Apple',
   u'start': 9,
   u'title': u'Apple Inc.',
   u'uri': u'http://en.wikipedia.org/wiki/Apple_Inc.'},
  {u'confidence': 0.6904,
   u'end': 37,
   u'id': 18890,
   u'label': u'Microsoft Windows',
   u'spot': u'Windows',
   u'start': 30,
   u'title': u'Microsoft Windows',
   u'uri': u'http://en.wikipedia.org/wiki/Microsoft_Windows'}],
 u'lang': u'en',
 u'time': 0,
 u'timestamp': u'2017-03-09T16:10:46.703'}

>>> entityExtraction = EntityExtraction()
>>> spots = entityExtraction.UserDefinedSpots()  # spots = EntityExtraction.UserDefinedSpots()
>>> obj = spots.create(data=u'{"description": "My botanical custom spots","lang": "en","list": [{"spot": "làres","topic": "Larix decidua"},{"spot": "stropèr","topic": "Salix viminalis"},{"spot": "noselèr","topic": "Corylus avellana"},{"spot": "pomèr","topic": "Malus domestica"},{"spot": "brugnèra","topic": "Prunus domestica"}]}')
>>> obj
{u'created': u'2017-03-10T11:34:45',
 u'data': {u'defaults': {u'exactMatch': False,
   u'greedy': True,
   u'namedEntity': False},
  u'description': u'My botanical custom spots',
  u'lang': u'en',
  u'list': [{u'exactMatch': False,
    u'greedy': True,
    u'namedEntity': False,
    u'spot': u'l\xe0res',
    u'topic': u'Larix decidua'},
   {u'exactMatch': False,
    u'greedy': True,
    u'namedEntity': False,
    u'spot': u'strop\xe8r',
    u'topic': u'Salix viminalis'},
   {u'exactMatch': False,
    u'greedy': True,
    u'namedEntity': False,
    u'spot': u'nosel\xe8r',
    u'topic': u'Corylus avellana'},
   {u'exactMatch': False,
    u'greedy': True,
    u'namedEntity': False,
    u'spot': u'pom\xe8r',
    u'topic': u'Malus domestica'},
   {u'exactMatch': False,
    u'greedy': True,
    u'namedEntity': False,
    u'spot': u'brugn\xe8ra',
    u'topic': u'Prunus domestica'}]},
 u'dataType': u'custom-spots',
 u'id': u'b1290d8a-4af3-4c70-85cc-1eb6a97f7725',
 u'modified': u'2017-03-10T11:34:45',
 u'timestamp': u'2017-03-09T16:10:46.703'}
>>> entityExtraction.params = 'text', u'Larix decidua, commonly called European or common larch, is a deciduous conifer although it looks like a needled evergreen in summer. It is a large tree that will grow to 60-100’ tall with a pyramidal shape, horizontal branching and drooping branchlets.'
>>> entityExtraction.params = 'custom_spots', obj.id
>>> entityExtraction.params = 'min_confidence', 0.8
>>> entityExtraction.analyze()
{u'annotations': [{u'confidence': 0.8759,
   u'end': 13,
   u'id': 1584580,
   u'label': u'European larch',
   u'spot': u'Larix decidua',
   u'start': 0,
   u'title': u'Larix decidua',
   u'uri': u'http://en.wikipedia.org/wiki/Larix_decidua'},
  {u'confidence': 0.8184,
   u'end': 55,
   u'id': 99384,
   u'label': u'Larch',
   u'spot': u'larch',
   u'start': 50,
   u'title': u'Larch',
   u'uri': u'http://en.wikipedia.org/wiki/Larch'},
  {u'confidence': 0.829,
   u'end': 71,
   u'id': 66722,
   u'label': u'Deciduous',
   u'spot': u'deciduous',
   u'start': 62,
   u'title': u'Deciduous',
   u'uri': u'http://en.wikipedia.org/wiki/Deciduous'},
  {u'confidence': 0.9061,
   u'end': 79,
   u'id': 68085,
   u'label': u'Conifer',
   u'spot': u'conifer',
   u'start': 72,
   u'title': u'Pinophyta',
   u'uri': u'http://en.wikipedia.org/wiki/Pinophyta'}],
 u'lang': u'en',
 u'langConfidence': 1.0,
 u'time': 12,
 u'timestamp': u'2017-03-09T16:10:46.703'}

TextSimilarity (Documentation)

>>> from django_dandelion.datatxt import TextSimilarity
>>> textSimilarity = TextSimilarity()
>>> textSimilarity.params = 'text1', u'Reports that the NSA eavesdropped on world leaders have "severely shaken" relations between Europe and the U.S., German Chancellor Angela Merkel said.'
>>> textSimilarity.params = 'text2', u'Germany and France are to seek talks with the US to settle a row over spying, as espionage claims continue to overshadow an EU summit in Brussels.'
>>> textSimilarity.analyze()
{u'annotations': [{u'confidence': 0.7353,
   u'end': 14,
   u'id': 856,
   u'label': u'Apple',
   u'spot': u'Apple',
   u'start': 9,
   u'title': u'Apple Inc.',
   u'uri': u'http://en.wikipedia.org/wiki/Apple_Inc.'},
  {u'confidence': 0.6904,
   u'end': 37,
   u'id': 18890,
   u'label': u'Microsoft Windows',
   u'spot': u'Windows',
   u'start': 30,
   u'title': u'Microsoft Windows',
   u'uri': u'http://en.wikipedia.org/wiki/Microsoft_Windows'}],
 u'lang': u'en',
 u'time': 0,
 u'timestamp': u'2017-03-09T16:10:46.703'}

TextClassification (Documentation)

>>> from django_dandelion.datatxt import TextClassification
>>> textClassification = TextClassification()
>>> classifier = textClassification.UserDefinedClassifiers()  # classifier = TextClassification.UserDefinedClassifiers()
>>> obj = classifier.create(data=u'{"description": "My first model for classifying news","lang": "en","categories": [{"name": "Sport","topics": {"http://en.wikipedia.org/wiki/Sport": 2.0,"http://en.wikipedia.org/wiki/Baseball": 1.0,"http://en.wikipedia.org/wiki/Basketball": 1.0,"http://en.wikipedia.org/wiki/Football": 1.0}},{"name": "Politics","topics": {"Politics": 2.0,"Politician": 1.5,"David Cameron": 1.0,"Angela Merkel": 1.0}}]}')
>>> obj
{u'created': u'2017-03-09T16:10:46.703',
 u'data': {u'categories': [{u'name': u'Sport',
    u'topics': {u'http://en.wikipedia.org/wiki/Baseball': 1.0,
     u'http://en.wikipedia.org/wiki/Basketball': 1.0,
     u'http://en.wikipedia.org/wiki/Football': 1.0,
     u'http://en.wikipedia.org/wiki/Sport': 2.0}},
   {u'name': u'Politics',
    u'topics': {u'Angela Merkel': 1.0,
     u'David Cameron': 1.0,
     u'Politician': 1.5,
     u'Politics': 2.0}}],
  u'description': u'My first model for classifying news',
  u'lang': u'en'},
 u'dataType': u'cl-model',
 u'id': u'7a5a4c4f-8e4a-484a-9f65-b3240819bcfa',
 u'modified': u'2017-03-09T16:36:02',
 u'timestamp': u'2017-03-09T16:10:46.703'}
>>> textClassification.params = 'text', u'See how the main parties are doing in the latest opinion polls on voting intention'
>>> textClassification.params = 'model', obj.id
>>> textClassification.analyze()
{u'categories': [{u'name': u'Politics', u'score': 0.34198442},
  {u'name': u'Sport', u'score': 0.10255624}],
 u'lang': u'en',
 u'time': 1,
 u'timestamp': u'2017-03-09T16:10:46.703'}

LanguageDetection (Documentation)

>>> from django_dandelion.datatxt import LanguageDetection
>>> languageDetection = LanguageDetection()
>>> languageDetection.params = 'text', u'Le nostre tre M sono: mafia, mamma, mandolino'
>>> languageDetection.analyze()
{u'detectedLangs': [{u'confidence': 1.0, u'lang': u'it'}],
 u'time': 1,
 u'timestamp': u'2017-03-09T16:10:46.703'}

SentimentAnalysis (Documentation)

>>> from django_dandelion.datatxt import SentimentAnalysis
>>> sentimentAnalysis = SentimentAnalysis()
>>> sentimentAnalysis.params = 'text', u'Grande applicazione, grande social! Viva Twitter'
>>> sentimentAnalysis.analyze()
{u'lang': u'it',
 u'langConfidence': 1.0,
 u'sentiment': {u'score': 0.85, u'type': u'positive'},
 u'time': 0,
 u'timestamp': u'2017-03-09T16:10:46.703'}

Datagraph¶

Wikisearch (Documentation)

>>> from django_dandelion.datagraph import Wikisearch
>>> wikisearch = Wikisearch()
>>> wikisearch.params = 'text', u'Duomo di Trento'
>>> wikisearch.params = 'lang', u'it'
>>> wikisearch.params = 'limit', 3
>>> wikisearch.analyze()
{u'count': 1117,
 u'entities': [{u'id': 925191,
   u'label': u'Cattedrale di San Vigilio',
   u'title': u'Cattedrale di San Vigilio',
   u'uri': u'http://it.wikipedia.org/wiki/Cattedrale_di_San_Vigilio',
   u'weight': 35.093304},
  {u'id': 730261,
   u'label': u'Duomo',
   u'title': u'Duomo',
   u'uri': u'http://it.wikipedia.org/wiki/Duomo',
   u'weight': 30.126003},
  {u'id': 100137,
   u'label': u'Trento',
   u'title': u'Trento',
   u'uri': u'http://it.wikipedia.org/wiki/Trento',
   u'weight': 23.563704}],
 u'lang': u'it',
 u'query': u'full',
 u'time': 3,
 u'timestamp': u'2017-03-09T16:10:46.703'}

Contributing¶

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Types of Contributions¶

Report Bugs¶

Report bugs at https://github.com/AlessioBazzanella/django-dandelion/issues.

If you are reporting a bug, please include:

Your operating system name and version.
Any details about your local setup that might be helpful in troubleshooting.
Detailed steps to reproduce the bug.

Fix Bugs¶

Look through the GitHub issues for bugs. Anything tagged with “bug” is open to whoever wants to implement it.

Implement Features¶

Look through the GitHub issues for features. Anything tagged with “feature” is open to whoever wants to implement it.

Write Documentation¶

Django Dandelion could always use more documentation, whether as part of the official Django Dandelion docs, in docstrings, or even on the web in blog posts, articles, and such.

Submit Feedback¶

The best way to send feedback is to file an issue at https://github.com/AlessioBazzanella/django-dandelion/issues.

If you are proposing a feature:

Explain in detail how it would work.
Keep the scope as narrow as possible, to make it easier to implement.
Remember that this is a volunteer-driven project, and that contributions are welcome :)

Get Started!¶

Ready to contribute? Here’s how to set up django-dandelion for local development.

Fork the django-dandelion repo on GitHub.

Clone your fork locally:

$ git clone git@github.com:your_name_here/django-dandelion.git

Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:
```
$ mkvirtualenv django-dandelion
$ cd django-dandelion/
$ python setup.py develop
```
Create a branch for local development:
```
$ git checkout -b name-of-your-bugfix-or-feature
```
Now you can make your changes locally.
When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:
```
$ flake8 django_dandelion tests
$ python setup.py test
$ tox
```
To get flake8 and tox, just pip install them into your virtualenv.

Commit your changes and push your branch to GitHub:

$ git add .
$ git commit -m "Your detailed description of your changes."
$ git push origin name-of-your-bugfix-or-feature

Submit a pull request through the GitHub website.

Pull Request Guidelines¶

Before you submit a pull request, check that it meets these guidelines:

The pull request should include tests.
If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
The pull request should work for Python 2.6, 2.7, and 3.3, and for PyPy. Check https://travis-ci.org/AlessioBazzanella/django-dandelion/pull_requests and make sure that the tests pass for all supported Python versions.

Tips¶

To run a subset of tests:

$ python -m unittest tests.test_django_dandelion

Credits¶

Development Lead¶

Alessio Bazzanella <alessio.bazzanella@me.com>

Contributors¶

None yet. Why not be the first?

Change Log¶

All notable changes to this project will be documented in this file.

The format is based on KeepAChangelog and this project adheres to SemanticVersioning.

[0.1.4] - 2017-06-29¶

Added¶

Added top_entities and epsilon params to EntityExtraction
Added nex.top_entities and nex.epsilon params to TextSimilarity
Added nex.top_entities, nex.min_confidence, nex.min_length, nex.social.hashtag, nex.social.mention, nex.include,

nex.extra_types, nex.country, nex.custom_spots and nex.epsilon params to TextSimilarity

[0.1.3] - 2017-03-14¶

Fixed¶

Merge extra_dict with params in BaseDandelionParamsRequest._do_request
Fixed app name in documentation to add to INSTALLED_APPS

[0.1.2] - 2017-03-10¶

Added¶

User-defined spots (EntityExtraction.UserDefinedSpots())

[0.1.1] - 2017-03-10¶

Fixed¶

PyPI version release

[0.1.0] - 2017-03-10¶

First release on PyPI.