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DKPro cassis (pronunciation: [ka.sis]) provides a pure-Python implementation of the Common Analysis System (CAS)
as defined by the UIMA <https://uima.apache.org>_ framework. The CAS is a data structure representing an object to
be enriched with annotations (the co-called Subject of Analysis, short SofA).
This library enables the creation and manipulation of annotated documents (CAS objects) and their associated type systems as well as loading
and saving them in the CAS XMI XML representation <https://uima.apache.org/d/uimaj-current/ref.html#ugr.ref.xmi>
or the CAS JSON representation <https://github.com/apache/uima-uimaj-io-jsoncas#readme> in Python programs. This can ease in particular the integration of Python-based Natural Language Processing (e.g.
spacy <https://spacy.io> or NLTK <https://www.nltk.org>) and Machine Learning librarys (e.g.
scikit-learn <https://scikit-learn.org/stable/> or Keras <https://keras.io>) in UIMA-based text analysis workflows.
An example of cassis in action is the spacy recommender for INCEpTION <https://github.com/inception-project/external-recommender-spacy>,
which wraps the spacy NLP library as a web service which can be used in conjunction with the INCEpTION <https://inception-project.github.io>
text annotation platform to automatically generate annotation suggestions.
Currently supported features are:
Some features are still under development, e.g.
To install the package with :code:pip, just run
pip install dkpro-cassisExample CAS XMI and types system files can be found under :code:tests\test_files.
Reading a CAS file
**From XMI:** A CAS can be deserialized from the UIMA CAS XMI (XML 1.0) format either
by reading from a file or string using :code:`load_cas_from_xmi`.
.. code:: python
from cassis import *
with open('typesystem.xml', 'rb') as f:
typesystem = load_typesystem(f)
with open('cas.xmi', 'rb') as f:
cas = load_cas_from_xmi(f, typesystem=typesystem)
**From JSON:** The UIMA JSON CAS format is also supported and can be loaded using :code:`load_cas_from_json`.
Most UIMA JSON CAS files come with an embedded typesystem, so it is not necessary to specify one.
.. code:: python
from cassis import *
with open('cas.json', 'rb') as f:
cas = load_cas_from_json(f)
Writing a CAS fileTo XMI: A CAS can be serialized to XMI either by writing to a file or be
returned as a string using :code:cas.to_xmi().
.. code:: python
from cassis import *
# Returned as a string
xmi = cas.to_xmi()
# Written to file
cas.to_xmi("my_cas.xmi")To JSON: A CAS can also be written to JSON using :code:cas.to_json().
.. code:: python
from cassis import *
# Returned as a string
xmi = cas.to_json()
# Written to file
cas.to_json("my_cas.json")Creating a CAS
A CAS (Common Analysis System) object typically represents a (text) document. When using cassis,
you will likely most often reading existing CAS files, modify them and then
writing them out again. But you can also create CAS objects from scratch,
e.g. if you want to convert some data into a CAS object in order to create a pre-annotated text.
If you do not have a pre-defined typesystem to work with, you will have to define one.
.. code:: python
typesystem = TypeSystem()
cas = Cas(
sofa_string = "Joe waited for the train . The train was late .",
document_language = "en",
typesystem = typesystem)
print(cas.sofa_string)
print(cas.sofa_mime)
print(cas.document_language)
Adding annotationsNote: type names used below are examples only. The actual CAS files you will be
dealing with will use other names! You can get a list of the types using
:code:cas.typesystem.get_types().
Given a type system with a type :code:cassis.Token that has an :code:id and
:code:pos feature, annotations can be added in the following:
.. code:: python
from cassis import *
with open('typesystem.xml', 'rb') as f:
typesystem = load_typesystem(f)
with open('cas.xmi', 'rb') as f:
cas = load_cas_from_xmi(f, typesystem=typesystem)
Token = typesystem.get_type('cassis.Token')
tokens = [
Token(begin=0, end=3, id='0', pos='NNP'),
Token(begin=4, end=10, id='1', pos='VBD'),
Token(begin=11, end=14, id='2', pos='IN'),
Token(begin=15, end=18, id='3', pos='DT'),
Token(begin=19, end=24, id='4', pos='NN'),
Token(begin=25, end=26, id='5', pos='.'),
]
for token in tokens:
cas.add(token)Selecting annotations
.. code:: python
from cassis import *
with open('typesystem.xml', 'rb') as f:
typesystem = load_typesystem(f)
with open('cas.xmi', 'rb') as f:
cas = load_cas_from_xmi(f, typesystem=typesystem)
for sentence in cas.select('cassis.Sentence'):
for token in cas.select_covered('cassis.Token', sentence):
print(token.get_covered_text())
# Annotation values can be accessed as properties
print('Token: begin={0}, end={1}, id={2}, pos={3}'.format(token.begin, token.end, token.id, token.pos))
Getting and setting (nested) featuresIf you want to access a variable but only have its name as a string or have nested feature structures,
e.g. a feature structure with feature :code:a that has a
feature :code:b that has a feature :code:c, some of which can be :code:None, then you can use the
following:
.. code:: python
fs.get("var_name") # Or
fs["var_name"]Or in the nested case,
.. code:: python
fs.get("a.b.c")
fs["a.b.c"]If :code:a or :code:b or :code:c are :code:None, then this returns instead of
throwing an error.
Another example would be a StringList containing :code:["Foo", "Bar", "Baz"]:
.. code:: python
assert lst.get("head") == "foo"
assert lst.get("tail.head") == "bar"
assert lst.get("tail.tail.head") == "baz"
assert lst.get("tail.tail.tail.head") == None
assert lst.get("tail.tail.tail.tail.head") == NoneThe same goes for setting:
.. code:: python
# Functional
lst.set("head", "new_foo")
lst.set("tail.head", "new_bar")
lst.set("tail.tail.head", "new_baz")
assert lst.get("head") == "new_foo"
assert lst.get("tail.head") == "new_bar"
assert lst.get("tail.tail.head") == "new_baz"
# Bracket access
lst["head"] = "newer_foo"
lst["tail.head"] = "newer_bar"
lst["tail.tail.head"] = "newer_baz"
assert lst["head"] == "newer_foo"
assert lst["tail.head"] == "newer_bar"
assert lst["tail.tail.head"] == "newer_baz"Creating types and adding features
.. code:: python
from cassis import *
typesystem = TypeSystem()
parent_type = typesystem.create_type(name='example.ParentType')
typesystem.create_feature(domainType=parent_type, name='parentFeature', rangeType=TYPE_NAME_STRING)
child_type = typesystem.create_type(name='example.ChildType', supertypeName=parent_type.name)
typesystem.create_feature(domainType=child_type, name='childFeature', rangeType=TYPE_NAME_INTEGER)
annotation = child_type(parentFeature='parent', childFeature='child')
When adding new features, these changes are propagated. For example,
adding a feature to a parent type makes it available to a child type.
Therefore, the type system does not need to be frozen for consistency.
The type system can be changed even after loading, it is not frozen
like in UIMAj.
Sofa supportA Sofa represents some form of an unstructured artifact that is processed in a UIMA pipeline. It contains for instance the document text. Currently, new Sofas can be created. This is automatically done when creating a new view. Basic properties of the Sofa can be read and written:
.. code:: python
cas = Cas(
sofa_string = "Joe waited for the train . The train was late .",
document_language = "en")
print(cas.sofa_string)
print(cas.sofa_mime)
print(cas.document_language)Array support
Array feature values are not simply Python arrays, but they are wrapped in a feature structure of
a UIMA array type such as :code:`uima.cas.FSArray`.
.. code:: python
from cassis import *
from cassis.typesystem import TYPE_NAME_FS_ARRAY, TYPE_NAME_ANNOTATION
typesystem = TypeSystem()
ArrayHolder = typesystem.create_type(name='example.ArrayHolder')
typesystem.create_feature(domainType=ArrayHolder, name='array', rangeType=TYPE_NAME_FS_ARRAY)
cas = Cas(typesystem=typesystem)
Annotation = cas.typesystem.get_type(TYPE_NAME_ANNOTATION)
FSArray = cas.typesystem.get_type(TYPE_NAME_FS_ARRAY)
ann = Annotation(begin=0, end=1)
cas.add(ann1)
holder = ArrayHolder(array=FSArray(elements=[ann, ann, ann]))
cas.add(holder)
Managing viewsA view into a CAS contains a subset of feature structures and annotations. One view corresponds to exactly one Sofa. It
can also be used to query and alter information about the Sofa, e.g. the document text. Annotations added to one view
are not visible in another view. A view Views can be created and changed. A view has the same methods and attributes
as a :code:Cas .
.. code:: python
from cassis import *
with open('typesystem.xml', 'rb') as f:
typesystem = load_typesystem(f)
Token = typesystem.get_type('cassis.Token')
# This creates automatically the view `_InitialView`
cas = Cas()
cas.sofa_string = "I like cheese ."
cas.add_all([
Token(begin=0, end=1),
Token(begin=2, end=6),
Token(begin=7, end=13),
Token(begin=14, end=15)
])
print([x.get_covered_text() for x in cas.select_all()])
# Create a new view and work on it.
view = cas.create_view('testView')
view.sofa_string = "I like blackcurrant ."
view.add_all([
Token(begin=0, end=1),
Token(begin=2, end=6),
Token(begin=7, end=19),
Token(begin=20, end=21)
])
print([x.get_covered_text() for x in view.select_all()])Merging type systems
Sometimes, it is desirable to merge two type systems. With **cassis**, this can be
achieved via the :code:`merge_typesystems` function. The detailed rules of merging can be found
`here <https://uima.apache.org/d/uimaj-current/ref.html#ugr.ref.cas.typemerging>`_.
.. code:: python
from cassis import *
with open('typesystem.xml', 'rb') as f:
typesystem = load_typesystem(f)
ts = merge_typesystems([typesystem, load_dkpro_core_typesystem()])
Type checkingWhen adding annotations, no type checking is performed for simplicity reasons.
In order to check types, call the :code:cas.typecheck() method. Currently, it only
checks whether elements in uima.cas.FSArray are
adhere to the specified :code:elementType.
A CAS using the DKPro Core Type System can be created via
.. code:: python
from cassis import *
cas = Cas(typesystem=load_dkpro_core_typesystem())
for t in cas.typesystem.get_types():
print(t)If feature names clash with Python magic variables
If your type system defines a type called :code:`self` or :code:`type`, then it will be made
available as a member variable :code:`self_` or :code:`type_` on the respective type:
.. code:: python
from cassis import *
from cassis.typesystem import *
typesystem = TypeSystem()
ExampleType = typesystem.create_type(name='example.Type')
typesystem.create_feature(domainType=ExampleType, name='self', rangeType=TYPE_NAME_STRING)
typesystem.create_feature(domainType=ExampleType, name='type', rangeType=TYPE_NAME_STRING)
annotation = ExampleType(self_="Test string1", type_="Test string2")
print(annotation.self_)
print(annotation.type_)
LeniencyIf the type for a feature structure is not found in the typesystem, it will raise an exception by default.
If you want to ignore these kind of errors, you can pass :code:lenient=True to the :code:Cas constructor or
to :code:load_cas_from_xmi.
Large XMI files
If you try to parse large XMI files and get an error message like :code:`XMLSyntaxError: internal error: Huge input lookup`,
then you can disable this security check by passing :code:`trusted=True` to your calls to :code:`load_cas_from_xmi`.
Citing & Authors
----------------
If you find this repository helpful, feel free to cite
.. code:: bibtex
@software{klie2020_cassis,
author = {Jan-Christoph Klie and
Richard Eckart de Castilho},
title = {DKPro Cassis - Reading and Writing UIMA CAS Files in Python},
publisher = {Zenodo},
doi = {10.5281/zenodo.3994108},
url = {https://github.com/dkpro/dkpro-cassis}
}
Development
-----------
The required dependencies are managed by **pip**. A virtual environment
containing all needed packages for development and production can be
created and activated by
::
virtualenv venv --python=python3 --no-site-packages
source venv/bin/activate
pip install -e ".[test, dev, doc]"
The tests can be run in the current environment by invoking
::
make test
or in a clean environment via
::
tox
Release
-------
- Make sure all issues for the milestone are completed, otherwise move them to the next
- Checkout the ``main`` branch
- Bump the version in ``cassis/__version__.py`` to a stable one, e.g. ``__version__ = "0.6.0"``, commit and push, wait until the build completed. An example commit message would be ``No issue. Release 0.6.0``
- Create a tag for that version via e.g. ``git tag v0.6.0`` and push the tags via ``git push --tags``. Pushing a tag triggers the release to pypi
- Bump the version in ``cassis/__version__.py`` to the next development version, e.g. ``0.7.0-dev``, commit and push that. An example commit message would be ``No issue. Bump version after release``
- Once the build has completed and pypi accepted the new version, go to the Github release and write the changelog based on the issues in the respective milestone
- Create a new milestone for the next version