noahong 0.10.0

noahong is a fork https://github.com/JDonner/NoAho

The initial objective was to reduce its memory consumption. When possible we
try to preserve the existing interface. But specialization offers new
optimization opportunities and we make no promise we will not break it at some
point, or partially repurpose the library. You might still find it useful.

Regardless, many thanks to JDonner and the other people having worked on this
project.


How does it differ from the original project:

- Trie compilation is required before use. Once compiled it cannot be modified
  anymore. It makes it easier to catch issues related to thread-safety.
- Reduce memory consumption by 50% on our dataset without affecting match
  performances.
- We support Windows.


# ORIGINAL README

Non-Overlapping Aho-Corasick Trie

Features:
- 'short' and 'long' (longest matching key) searches, both one-off and
  iteration over all non-overlapping keyword matches in some text.
- Works with both unicode and str in Python 2, and unicode in Python 3.  NOTE:
  As everything is simply single UCS4 / UTF-32 codepoints under the hood, all
  substrings and input unicode must be normalized, ie any separate modifying
  marks must be folded into each codepoint. See:
     http://stackoverflow.com/questions/16467479/normalizing-unicode
  Or, theoretically, you could put into the tree all forms of the
  keywords you expect to see in your text.
- Allows you to associate an arbitrary Python object payload with each
  keyword, and supports dict operations len(), [], and 'in' for the
  keywords (though no del or traversal).
- Does the 'compilation' (generation of Aho-Corasick failure links) of
  the trie on-demand, ie you could mix adding keywords and searching
  text, freely, but mostly it just relieves you of worrying about
  compiling.
- Can be used commercially, it's under the minimal, MIT license (if you
  somehow need a different license, ask me, I mean for it to be used).

Anti-Features:
- Will not find overlapped keywords (eg given keywords 'abc' and 'cdef', will
  not find 'cdef' in 'abcdef'. Any full Aho-Corasick implementation would give
  you both. The package 'Acora' is an alternative package for this use.  (noaho
  can be relatively easily modified to be a normal Aho-Corasick, but it wasn't
  what I personally needed.)
- Lacking overlap, find[all]_short is kind of useless.
- Lacks key iteration and deletion from the mapping (dict) protocol.
- Memory leaking untested (one run under valgrind turned up nothing, but it
  wasn't extensive).
- No /testcase/ for unicode in Python 2 (did manual test however)
  Unicode chars represented as ucs4, and, each character has its own hashtable,
  so it's relatively memory-heavy (see 'Ways to Reduce Memory Use' below).
- Requires a C++ compiler (C++98 support is enough).

Bug reports and patches welcome of course!


To build and install, use either
  pip install noaho
or
  # Python 2
  python2 setup.py install # (or ... build, and copy the .so to where you want it)
  pip install
or
  # Python 3
  python3 setup.py install # (or ... build, and copy the .so to where you want it)


API:
    from noaho import NoAho
    trie = NoAho()
'text' below applies to str and unicode in Python 2, or unicode in Python 3 (all there is)
    trie.add(key_text, optional payload)
    (key_start, key_end, key_value) = trie.find_short(text_to_search)
    (key_start, key_end, key_value) = trie.find_long(text_to_search)
    (key_start, key_end, key_value) = trie.findall_short(text_to_search)
    (key_start, key_end, key_value) = trie.findall_long(text_to_search)
    # keyword = text_to_search[key_start:key_end]
    trie['keyword] = key_value
    key_value = trie.find_long(text_to_search)
    assert len(trie)
    assert keyword in trie

Examples:
    >>> a = NoAho()
    >>> a.add('ms windows')
    >>> a.add('ms windows 2000', "this is canonical")
    >>> a.add('windows', None)
    >>> a.add('windows 2000')
    >>> a['apple'] = None
    >>> text = 'windows 2000 ms windows 2000 windows'
    >>> for k in a.findall_short(text):
    ...     print text[k[0]:k[1]]
    ...
    windows
    ms windows
    windows
    >>> for k in a.findall_long(text):
    ...     print text[k[0]:k[1]]
    ...
    windows 2000
    ms windows 2000
    windows

Mapping (dictionary) methods:
    trie = NoAho()
    trie['apple'] = apple_handling_function
    trie['orange'] = Orange()
    trie.add('banana') # payload will be None
    trie['pear'] # will give key error
    assert isinstance(trie['orange'], Orange)
    assert 'banana' in trie
    len(trie)
    # No del;
    # no iteration over keys

The 'find[all]_short' forms are named as long and awkwardly as they are,
to leave plain 'find[all]' free if overlapping matches are ever implemented.


For the fullest spec of what the code will and will not do, check out
test-noaho.py (run it with: python[3] test-noaho.py)

Untested: whether the payload handling is complete, ie that there are no
memory leaks. It should be correct though.


Regenerating the Python Wrapper:
- Needs a C++ compiler (C++98 is fine) and Cython.

You do not need to rebuild the Cython wrapper (the generated noaho.cpp), but if
you want to make changes to the module itself, there is a script:

  test-all-configurations.sh

which will, with minor configuration tweaking, rebuild and test against both
python 2 and 3. It requires you to have a Cython tarball in the top directory.
Note that the python you used to install Cython should be the same as the one
you use to do the regeneration, because the regeneration setup includes a module
Cython.Distutils, from the installation.

Cython generates python-wrapper noaho.cpp from noaho.pyx (be careful
to distinguish it from the misnamed array-aho.* (it uses hash tables),
which is the original C++ code).

Ways to Reduce Memory Use:
One of its aims is to handle Unicode, which means you have to accommodate a huge
branching factor, thus the hashtable (a full array would be out of the
question). Ways to attack memory size might be, to either force very
conservative hashtable growth, or, once the trie is complete (in 'compile', say)
go through the tree and replace the hashtables with just-the-right-size arrays -
linear scan / binary search should be fast enough if small enough, and take less
memory. If you're willing to do a linear scan at that point, you could switch to
UTF-8, too, saving quite a bit of memory. Danny Yoo's original code I think just
started out as arrays and would grow to hashtables when needed.

Also, if all you need is ASCII, you could re-define AC_CHAR_TYPE to be 'char'.
I've tried to be careful to use AC_CHAR_TYPE consistently, but you'd probably
want to go through the code to make sure if you're going to rely on this. Python
3 uses Unicode internally though and would do a lot of conversions anyway.
Otherwise, I don't trust my knowledge of Unicode enough to try to play games
with storing fewer bits.

In the Hopper:
I have a case-insensitive version (the easiest thing is just to downcase
everything you add or search for in noaho.pyx), and, one that will only yield
keywords at word boundaries, thanks to Python's unicode character classes.
(However, this second is a bit raw, and you can do it manually anyway.)