Python Tree.fromstring方法代碼示例

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def leaves(self):
        """
        Return the leaves of the tree.

            >>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
            >>> t.leaves()
            ['the', 'dog', 'chased', 'the', 'cat']

        :return: a list containing this tree's leaves.
            The order reflects the order of the
            leaves in the tree's hierarchical structure.
        :rtype: list
        """
        leaves = []
        for child in self:
            if isinstance(child, Tree):
                leaves.extend(child.leaves())
            else:
                leaves.append(child)
        return leaves 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def subtrees(self, filter=None):
        """
        Generate all the subtrees of this tree, optionally restricted
        to trees matching the filter function.

            >>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
            >>> for s in t.subtrees(lambda t: t.height() == 2):
            ...     print(s)
            (D the)
            (N dog)
            (V chased)
            (D the)
            (N cat)

        :type filter: function
        :param filter: the function to filter all local trees
        """
        if not filter or filter(self):
            yield self
        for child in self:
            if isinstance(child, Tree):
                for subtree in child.subtrees(filter):
                    yield subtree 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def productions(self):
        """
        Generate the productions that correspond to the non-terminal nodes of the tree.
        For each subtree of the form (P: C1 C2 ... Cn) this produces a production of the
        form P -> C1 C2 ... Cn.

            >>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
            >>> t.productions()
            [S -> NP VP, NP -> D N, D -> 'the', N -> 'dog', VP -> V NP, V -> 'chased',
            NP -> D N, D -> 'the', N -> 'cat']

        :rtype: list(Production)
        """

        if not isinstance(self._label, string_types):
            raise TypeError('Productions can only be generated from trees having node labels that are strings')

        prods = [Production(Nonterminal(self._label), _child_names(self))]
        for child in self:
            if isinstance(child, Tree):
                prods += child.productions()
        return prods 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def pos(self):
        """
        Return a sequence of pos-tagged words extracted from the tree.

            >>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
            >>> t.pos()
            [('the', 'D'), ('dog', 'N'), ('chased', 'V'), ('the', 'D'), ('cat', 'N')]

        :return: a list of tuples containing leaves and pre-terminals (part-of-speech tags).
            The order reflects the order of the leaves in the tree's hierarchical structure.
        :rtype: list(tuple)
        """
        pos = []
        for child in self:
            if isinstance(child, Tree):
                pos.extend(child.pos())
            else:
                pos.append((child, self._label))
        return pos 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def main():
    args = parse_args()
    parser = RstParser()
    parser.load('../data/model')
    with gzip.open('../data/resources/bc3200.pickle.gz') as fin:
        print('Load Brown clusters for creating features ...')
        brown_clusters = pickle.load(fin)
    core_nlp = StanfordCoreNLP('http://localhost:9000')
    annotate = lambda x: core_nlp.annotate(x, properties={
        'annotators': 'tokenize,ssplit,pos,lemma,parse,depparse',
        'outputFormat': 'json',
        'ssplit.isOneSentence': True
    })
    edu_file_list = [os.path.join(args.edu_file_dir, fname) for fname in os.listdir(args.edu_file_dir) if fname.endswith('.edu.txt')]
    for edu_file in edu_file_list:
        print('Parsing {}...'.format(edu_file))
        doc = create_doc_from_edu_file(edu_file, annotate_func=annotate)
        pred_rst = parser.sr_parse(doc, brown_clusters)
        tree_str = pred_rst.get_parse()
        pprint_tree_str = Tree.fromstring(tree_str).pformat(margin=150)
        with open(os.path.join(args.output_dir, os.path.basename(edu_file) + '.parse'), 'w') as fout:
            fout.write(pprint_tree_str) 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def add_words(self, file_name):
        # Add words to the dictionary
        f_in = open(file_name, 'r')
        for line in f_in:
            if line.strip() == '':
                continue 
            data = eval(line)
            sen_tree = Tree.fromstring(data['sentence1_parse'])
            words = self.filter_words(sen_tree)
            words = ['<s>'] + words + ['</s>']
            for word in words:
                self.dictionary.add_word(word)
            sen_tree = Tree.fromstring(data['sentence2_parse'])
            words = self.filter_words(sen_tree)
            words = ['<s>'] + words + ['</s>']
            for word in words:
                self.dictionary.add_word(word)
        f_in.close() 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def main():
    infilename = os.path.join(os.path.dirname(__file__),'வெண்பாinput.txt')
    outfilename = os.path.join(os.path.dirname(__file__),'வெண்பாoutput.txt')
    data = open(infilename).read()   
    input_stream = antlr4.InputStream(data)
    lexer = வெண்பாLexer(input_stream)
    stream = CommonTokenStream(lexer)
    parser = வெண்பாParser(stream)
    tree = parser.வெண்பா()

    # print(tree.toStringTree())
    strtree = Trees.toStringTree(tree, None, parser)
    print(strtree)
    t = nltkTree.fromstring(strtree)
    # t.pretty_print()
    a = TreePrettyPrinter(t).text()
    print (a)
    # t.pprint(margin=70, indent=0, nodesep=u'', parens=u'()', quotes=False)
    # pprint(Trees.toStringTree(tree, None, parser), width=20, indent=4)
 
    with open(outfilename, 'w', encoding='utf8') as f:
        f.write( a) 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def productions(self):
        """
        Generate the productions that correspond to the non-terminal nodes of the tree.
        For each subtree of the form (P: C1 C2 ... Cn) this produces a production of the
        form P -> C1 C2 ... Cn.

            >>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
            >>> t.productions()
            [S -> NP VP, NP -> D N, D -> 'the', N -> 'dog', VP -> V NP, V -> 'chased',
            NP -> D N, D -> 'the', N -> 'cat']

        :rtype: list(Production)
        """

        if not isinstance(self._label, string_types):
            raise TypeError(
                'Productions can only be generated from trees having node labels that are strings'
            )

        prods = [Production(Nonterminal(self._label), _child_names(self))]
        for child in self:
            if isinstance(child, Tree):
                prods += child.productions()
        return prods 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def label(self):
        """
        Return the node label of the tree.

            >>> t = Tree.fromstring('(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))')
            >>> t.label()
            'S'

        :return: the node label (typically a string)
        :rtype: any
        """
        return self._label 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def set_label(self, label):
        """
        Set the node label of the tree.

            >>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
            >>> t.set_label("T")
            >>> print(t)
            (T (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))

        :param label: the node label (typically a string)
        :type label: any
        """
        self._label = label 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def flatten(self):
        """
        Return a flat version of the tree, with all non-root non-terminals removed.

            >>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
            >>> print(t.flatten())
            (S the dog chased the cat)

        :return: a tree consisting of this tree's root connected directly to
            its leaves, omitting all intervening non-terminal nodes.
        :rtype: Tree
        """
        return Tree(self.label(), self.leaves()) 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def height(self):
        """
        Return the height of the tree.

            >>> t = Tree.fromstring("(S (NP (D the) (N dog)) (VP (V chased) (NP (D the) (N cat))))")
            >>> t.height()
            5
            >>> print(t[0,0])
            (D the)
            >>> t[0,0].height()
            2

        :return: The height of this tree.  The height of a tree
            containing no children is 1; the height of a tree
            containing only leaves is 2; and the height of any other
            tree is one plus the maximum of its children's
            heights.
        :rtype: int
        """
        max_child_height = 0
        for child in self:
            if isinstance(child, Tree):
                max_child_height = max(max_child_height, child.height())
            else:
                max_child_height = max(max_child_height, 1)
        return 1 + max_child_height 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def sinica_parse(s):
    """
    Parse a Sinica Treebank string and return a tree.  Trees are represented as nested brackettings,
    as shown in the following example (X represents a Chinese character):
    S(goal:NP(Head:Nep:XX)|theme:NP(Head:Nhaa:X)|quantity:Dab:X|Head:VL2:X)#0(PERIODCATEGORY)

    :return: A tree corresponding to the string representation.
    :rtype: Tree
    :param s: The string to be converted
    :type s: str
    """
    tokens = re.split(r'([()| ])', s)
    for i in range(len(tokens)):
        if tokens[i] == '(':
            tokens[i-1], tokens[i] = tokens[i], tokens[i-1]     # pull nonterminal inside parens
        elif ':' in tokens[i]:
            fields = tokens[i].split(':')
            if len(fields) == 2:                                # non-terminal
                tokens[i] = fields[1]
            else:
                tokens[i] = "(" + fields[-2] + " " + fields[-1] + ")"
        elif tokens[i] == '|':
            tokens[i] = ''

    treebank_string = " ".join(tokens)
    return Tree.fromstring(treebank_string, remove_empty_top_bracketing=True)

#    s = re.sub(r'^#[^\s]*\s', '', s)  # remove leading identifier
#    s = re.sub(r'\w+:', '', s)       # remove role tags

#    return s

######################################################################
## Demonstration
###################################################################### 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def process(self, data_bundle: DataBundle):
        r"""
        對DataBundle中的數據進行預處理。輸入的DataSet應該至少擁有raw_words這一列，且內容類似與

        .. csv-table:: 下麵是使用SSTLoader讀取的DataSet所具備的field
            :header: "raw_words"

            "(2 (3 (3 Effective) (2 but)) (1 (1 too-tepid)..."
            "(3 (3 (2 If) (3 (2 you) (3 (2 sometimes) ..."
            "..."

        :param ~fastNLP.io.DataBundle data_bundle: 需要處理的DataBundle對象
        :return:
        """
        #  先取出subtree
        for name in list(data_bundle.datasets.keys()):
            dataset = data_bundle.get_dataset(name)
            ds = DataSet()
            use_subtree = self.subtree or (name == 'train' and self.train_tree)
            for ins in dataset:
                raw_words = ins[Const.RAW_WORD]
                tree = Tree.fromstring(raw_words)
                if use_subtree:
                    for t in tree.subtrees():
                        raw_words = " ".join(t.leaves())
                        instance = Instance(raw_words=raw_words, target=t.label())
                        ds.append(instance)
                else:
                    instance = Instance(raw_words=' '.join(tree.leaves()), target=tree.label())
                    ds.append(instance)
            data_bundle.set_dataset(ds, name)

        # 根據granularity設置tag
        data_bundle = _granularize(data_bundle, tag_map=self.tag_map)
        
        data_bundle = super().process(data_bundle)
        
        return data_bundle 

# 需要導入模塊: from nltk import Tree [as 別名]
# 或者: from nltk.Tree import fromstring [as 別名]
def _get_one(data, subtree):
        tree = Tree.fromstring(data)
        if subtree:
            return [(t.leaves(), t.label()) for t in tree.subtrees()]
        return [(tree.leaves(), tree.label())]