def __init__(self, id, label, edges=None, **metas):
# double heritage
whitelist.WhitelistFile.__init__(self)
PyTextMiner.__init__(self, {}, id, label, edges, **metas)
### same database as tinasoft, but temp
self.storage = self._get_storage()
### cache for corpus
self.corpus = {}
def __init__(self,
id,
content=None,
edges=None,
**metas):
if content is None:
content = id
PyTextMiner.__init__(self, content, id, id, edges, **metas)
def __init__(
self,
content,
id,
label,
edges=None,
**metas
):
PyTextMiner.__init__(self, content, id, label, edges, **metas)
if 'keyword' not in self.edges:
self.edges['keyword']={}
def getNormId(tokens):
"""
Utility returning a normalized NGram ID given a tokenlist
For external use
"""
# normalized_tokens list to produce an unique id
normalized_tokens = [NGram.normalize(word) for word in tokens]
return PyTextMiner.getId(normalized_tokens)
def addForm(self, form_tokens, form_postag=None, form_occs=1 ):
"""
increments form edges
"""
if form_postag is None:
form_postag = ["?"]
form_label = PyTextMiner.form_label( form_tokens )
self.addEdge('label', form_label, form_occs)
self.addEdge('postag', form_label, form_postag)
def __init__(self, tokenlist, id=None, label=None, edges=None, postag=None, **metas):
"""
initiate the object
normalize must be local value for pickling reasons
"""
# normlist is the normalized list of tokens
normalized_tokens = [NGram.normalize(word) for word in tokenlist]
# prepares postag
if postag is not None:
metas["postag"] = postag
else:
metas["postag"] = ["?"]
# default emtpy edges
if edges is None:
edges = { 'label': {}, 'postag' : {} }
PyTextMiner.__init__(self, normalized_tokens, id, label, edges, **metas)
# updates majors forms before returning instance
self.addForm(normalized_tokens, metas["postag"], 1)
def ngramize(ngrams, minSize, maxSize, tagTokens, filters, stemmer):
"""
common ngramizing method
returns a dict of filtered NGram instances
using the optional stopwords object to filter by ngram length
@tagTokens == [[word1 tokens], [word2 tokens], etc]
"""
# content is the list of words from tagTokens
content = tagger.TreeBankPosTagger.getContent(tagTokens)
stemmedcontent = []
for word in content:
stemmedcontent += [stemmer.stem(word)]
# tags is the list of tags from tagTokens
tags = tagger.TreeBankPosTagger.getTag(tagTokens)
for i in range(len(content)):
for n in range(minSize, maxSize + 1):
if len(content) >= i + n:
# updates document's ngrams cache
ngid = ngram.NGram.getNormId(stemmedcontent[i:n+i])
# id made from the stemmedcontent and label from the real tokens
ng = ngram.NGram(
content[i:n+i],
id = ngid,
label = PyTextMiner.form_label(content[i:n+i]),
occs = 1,
postag = tags[i:n+i]
)
if filtering.apply_filters(ng, filters) is True:
if ngid in ngrams:
ngrams[ngid].addForm( content[i:n+i], tags[i:n+i], 1 )
ngrams[ngid].updateMajorForm()
ngrams[ngid]['occs'] += ng['occs']
else:
ngrams[ngid] = ng
return ngrams
def __init__(self, name, edges=None, **metas):
# list of corpus id
content = []
if edges is not None and 'Corpus' in edges:
content = edges['Corpus'].keys()
PyTextMiner.__init__(self, content, name, name, edges=edges, **metas)
def __init__(self, id, label, edges=None, **metas):
PyTextMiner.__init__(self, {}, id, label, edges, **metas)
### same database as tinasoft, but temp
self.storage = self._get_storage()
### cache for corpus
self.corpus = {}
