gazeclassifier

Decides whether given gaze points represent a saccade, fixation, or some unknown pattern

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Project description

The Python package gazeclassifier provides functions to decide if given gaze points represent a saccade, fixation, or some unknown pattern.

The package gazeclassifier is developed at Infant Cognition Laboratory at University of Tampere.

1. Install

With pip:

$ pip install gazeclassifier

Compatible with Python 2.6, 2.7, 3.3, 3.4, and 3.5.

2. Usage

Import the lib:

>>> import gazeclassifier as gaze

Define gaze points as pointlists:

>>> g1 = [[0,0], [0,0], [1,1], [2,2], [2,2]]  # a saccade
>>> g2 = [[1,1], [1,1], [1,1], [1,1], [1,1]]  # a fixation
>>> g3 = [[1,1], [2,2], [0,0], [4,4], [1,1]]  # an unknown

Create a classifier. It is already pretrained and therefore able to predict:

>>> gc = gaze.GazeClassifier()
>>> gc.predict(g1)
'saccade'
>>> gc.predict(g2)
'fixation'
>>> gc.predict(g3)
'unknown'

Internally, predict first extracts features from pointlists before classification. The features can be extracted explicitly with extract_features and extract_raw_features:

>>> gaze.extract_features(g1)
{
  'saccade_reaction': 1,
  'saccade_duration': 3,
  'saccade_mse': 0.000623,
  'fixation_mse': 0.232406
}

>>> gaze.extract_raw_features(g1)
{
  'saccade': {
    'source_points': [[0,0]],
    'saccade_points': [[0,0], [1,1], [2,2]],
    'target_points': [[2,2]],
    'mean_squared_error': 0.000623
  },
  'fixation': {
    'centroid': [[1.0,1.0]]
    'mean_squared_error': 0.232406,
  }
}

As an alternative for using the default pretrained classifier, you can train one:

>>> training_data = [g1, g2, g3]
>>> classes = ['saccade', 'fixation', 'unknown']
>>> gc = gaze.GazeClassifier()
>>> gc.fit(training_data, classes)

The trained classifier has same API as the default:

>>> gc.predict(g1)
'saccade'

3. API

3.1. gazeclassifier.predict(pointlist)

Parameters:

pointlist: a list of [x, y] points i.e. a list of lists
- OR alternatively the result dict from extract_features. This is convenient if you need access to the features and want to prevent predict to re-extract them.

Return a string which can be one of the following:

'saccade': gaze travels from point A to B and otherways stays still
'fixation': gaze has mainly stayed still
'unknown': gaze cannot be regarded as any of the above

3.2. gazeclassifier.extract_features(pointlist)

Parameters:

pointlist: a list of [x, y] points i.e. a list of lists

Return a dict that contains mean error and details for each hypothesis. The dict can be fed into classify for classification.

3.3. gazeclassifier.extract_raw_features(pointlist)

Parameters:

pointlist: a list of [x, y] points i.e. a list of lists

Return a dict that contains mean error and details for each hypothesis. The dict can be fed into predict for classification.

3.4. gazeclassifier.GazeClassifier()

A new untrained classifier.

3.5. gazeclassifier.GazeClassifier#fit(pointlists, classes)

3.6. gazeclassifier.GazeClassifier#predict(pointlist_or_features)

3.7. gazeclassifier.version

The current version string:

>>> gazeclassifier.version
'1.2.3'

4. For developers

Tips for the developers of the package.

4.1. Use Git

To develop, clone the repository from GitHub:

$ git clone https://github.com/infant-cognition-tampere/gazeclassifier-py

Make changes to files, add them to commit, and do commit:

(edit README.rst)
$ git add README.rst
$ git commit -m "Improved documentation"

List files that are not added or not committed:

$ git status

Push local commits to GitHub:

$ git push

Ignore some files by editing .gitignore:

$ nano .gitignore

4.2. Virtualenv

Manage python versions and requirements by using virtualenv:

$ virtualenv -p python3.5 gazeclassifier-py
$ cd gazeclassifier-py
$ source bin/activate
...
$ deactivate

4.3. Testing

Follow instructions to install pyenv and then either run quick tests:

$ python3.5 setup.py test

or run comprehensive tests for multiple Python versions listed in tox.ini:

$ pyenv local 2.6.9 2.7.10 3.3.6 3.4.3 3.5.0
$ eval "$(pyenv init -)"
$ pyenv rehash
$ tox

Install new pyenv environments for example by:

$ pyenv install 3.5.0

Validate README.rst at http://rst.ninjs.org/

4.4. Publishing to PyPI

Follow python packaging instructions:

Create an unpacked sdist: $ python setup.py sdist
Create a universal wheel: $ python setup.py bdist_wheel --universal
Go to PyPI and register the project by filling the package form by uploading gazeclassifier.egg-info/PKG_INFO file.
Upload the package with twine:
1. Sign the dist: $ gpg --detach-sign -a dist/gazeclassifier-1.2.3*
2. Upload: twine upload dist/gazeclassifier-1.2.3* (will ask your PyPI password)
Package published!