Coverage for nltk.parse.dependencygraph: 39%

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# Natural Language Toolkit: Dependency Grammars

# Author: Jason Narad <jason.narad@gmail.com>

# Steven Bird <sb@csse.unimelb.edu.au> (modifications)

# URL: <http://www.nltk.org/>

# For license information, see LICENSE.TXT

"""

Tools for reading and writing dependency trees.

The input is assumed to be in Malt-TAB format

(http://w3.msi.vxu.se/~nivre/research/MaltXML.html).

Currently only reads the first tree in a file.

"""

from __future__ import print_function

from nltk.tree import Tree

from pprint import pformat

import re

#################################################################

# DependencyGraph Class

#################################################################

class DependencyGraph(object):

"""

A container for the nodes and labelled edges of a dependency structure.

"""

def __init__(self, tree_str=None):

"""

We place a dummy 'top' node in the first position

in the nodelist, since the root node is often assigned '0'

as its head. This also means that the indexing of the nodelist

corresponds directly to the Malt-TAB format, which starts at 1.

"""

top = {'word':None, 'deps':[], 'rel': 'TOP', 'tag': 'TOP', 'address': 0}

self.nodelist = [top]

self.root = None

self.stream = None

if tree_str:

self._parse(tree_str)

def remove_by_address(self, address):

"""

Removes the node with the given address. References

to this node in others will still exist.

"""

node_index = len(self.nodelist) - 1

while(node_index >= 0):

node = self.nodelist[node_index]

if node['address'] == address:

self.nodelist.pop(node_index)

node_index -= 1

def redirect_arcs(self, originals, redirect):

"""

Redirects arcs to any of the nodes in the originals list

to the redirect node address.

"""

for node in self.nodelist:

new_deps = []

for dep in node['deps']:

if dep in originals:

new_deps.append(redirect)

else:

new_deps.append(dep)

node['deps'] = new_deps

def add_arc(self, head_address, mod_address):

"""

Adds an arc from the node specified by head_address to the

node specified by the mod address.

"""

for node in self.nodelist:

if node['address'] == head_address and (mod_address not in node['deps']):

node['deps'].append(mod_address)

def connect_graph(self):

"""

Fully connects all non-root nodes. All nodes are set to be dependents

of the root node.

"""

for node1 in self.nodelist:

for node2 in self.nodelist:

if node1['address'] != node2['address'] and node2['rel'] != 'TOP':

node1['deps'].append(node2['address'])

# fix error and return

def get_by_address(self, node_address):

"""

Returns the node with the given address.

"""

for node in self.nodelist:

if node['address'] == node_address:

return node

print('THROW ERROR: address not found in -get_by_address-')

return -1

def contains_address(self, node_address):

"""

Returns true if the graph contains a node with the given node

address, false otherwise.

"""

for node in self.nodelist:

if node['address'] == node_address:

return True

return False

def __str__(self):

return pformat(self.nodelist)

def __repr__(self):

return "<DependencyGraph with %d nodes>" % len(self.nodelist)

@staticmethod

def load(file):

"""

:param file: a file in Malt-TAB format

"""

return DependencyGraph(open(file).read())

@staticmethod

def _normalize(line):

"""

Deal with lines in which spaces are used rather than tabs.

"""

SPC = re.compile(' +')

return re.sub(SPC, '\t', line).strip()

def left_children(self, node_index):

"""

Returns the number of left children under the node specified

by the given address.

"""

children = self.nodelist[node_index]['deps']

index = self.nodelist[node_index]['address']

return sum(1 for c in children if c < index)

def right_children(self, node_index):

"""

Returns the number of right children under the node specified

by the given address.

"""

children = self.nodelist[node_index]['deps']

index = self.nodelist[node_index]['address']

return sum(1 for c in children if c > index)

def add_node(self, node):

if not self.contains_address(node['address']):

self.nodelist.append(node)

def _parse(self, input):

lines = [DependencyGraph._normalize(line) for line in input.split('\n') if line.strip()]

temp = []

for index, line in enumerate(lines):

# print line

try:

cells = line.split('\t')

nrCells = len(cells)

if nrCells == 3:

word, tag, head = cells

rel = ''

elif nrCells == 4:

word, tag, head, rel = cells

elif nrCells == 10:

_, word, _, _, tag, _, head, rel, _, _ = cells

else:

raise ValueError('Number of tab-delimited fields (%d) not supported by CoNLL(10) or Malt-Tab(4) format' % (nrCells))

head = int(head)

self.nodelist.append({'address': index+1, 'word': word, 'tag': tag,

'head': head, 'rel': rel,

'deps': [d for (d,h) in temp if h == index+1]})

try:

self.nodelist[head]['deps'].append(index+1)

except IndexError:

temp.append((index+1, head))

except ValueError:

break

root_address = self.nodelist[0]['deps'][0]

self.root = self.nodelist[root_address]

def _word(self, node, filter=True):

w = node['word']

if filter:

if w != ',': return w

return w

def _tree(self, i):

"""

Recursive function for turning dependency graphs into

NLTK trees.

:type i: int

:param i: index of a node in ``nodelist``

:return: either a word (if the indexed node

is a leaf) or a ``Tree``.

"""

node = self.get_by_address(i)

word = node['word']

deps = node['deps']

if len(deps) == 0:

return word

else:

return Tree(word, [self._tree(j) for j in deps])

def tree(self):

"""

Starting with the ``root`` node, build a dependency tree using the NLTK

``Tree`` constructor. Dependency labels are omitted.

"""

node = self.root

word = node['word']

deps = node['deps']

return Tree(word, [self._tree(i) for i in deps])

def _hd(self, i):

try:

return self.nodelist[i]['head']

except IndexError:

return None

def _rel(self, i):

try:

return self.nodelist[i]['rel']

except IndexError:

return None

# what's the return type? Boolean or list?

def contains_cycle(self):

distances = {}

for node in self.nodelist:

for dep in node['deps']:

key = tuple([node['address'], dep]) #'%d -> %d' % (node['address'], dep)

distances[key] = 1

window = 0

for n in range(len(self.nodelist)):

new_entries = {}

for pair1 in distances:

for pair2 in distances:

if pair1[1] == pair2[0]:

key = tuple([pair1[0], pair2[1]])

new_entries[key] = distances[pair1] + distances[pair2]

for pair in new_entries:

distances[pair] = new_entries[pair]

if pair[0] == pair[1]:

print(pair[0])

path = self.get_cycle_path(self.get_by_address(pair[0]), pair[0]) #self.nodelist[pair[0]], pair[0])

return path

return False # return []?

def get_cycle_path(self, curr_node, goal_node_index):

for dep in curr_node['deps']:

if dep == goal_node_index:

return [curr_node['address']]

for dep in curr_node['deps']:

path = self.get_cycle_path(self.get_by_address(dep), goal_node_index)#self.nodelist[dep], goal_node_index)

if len(path) > 0:

path.insert(0, curr_node['address'])

return path

return []

def to_conll(self, style):

"""

The dependency graph in CoNLL format.

:param style: the style to use for the format (3, 4, 10 columns)

:type style: int

:rtype: str

"""

lines = []

for i, node in enumerate(self.nodelist[1:]):

word, tag, head, rel = node['word'], node['tag'], node['head'], node['rel']

if style == 3:

lines.append('%s\t%s\t%s\n' % (word, tag, head))

elif style == 4:

lines.append('%s\t%s\t%s\t%s\n' % (word, tag, head, rel))

elif style == 10:

lines.append('%s\t%s\t_\t%s\t%s\t_\t%s\t%s\t_\t_\n' % (i+1, word, tag, tag, head, rel))

else:

raise ValueError('Number of tab-delimited fields (%d) not supported by CoNLL(10) or Malt-Tab(4) format' % (style))

return ''.join(lines)

def nx_graph(self):

"""

Convert the data in a ``nodelist`` into a networkx

labeled directed graph.

:rtype: XDigraph

"""

nx_nodelist = list(range(1, len(self.nodelist)))

nx_edgelist = [(n, self._hd(n), self._rel(n))

for n in nx_nodelist if self._hd(n)]

self.nx_labels = {}

for n in nx_nodelist:

self.nx_labels[n] = self.nodelist[n]['word']

g = NX.XDiGraph()

g.add_nodes_from(nx_nodelist)

g.add_edges_from(nx_edgelist)

return g

def demo():

malt_demo()

conll_demo()

conll_file_demo()

cycle_finding_demo()

def malt_demo(nx=False):

"""

A demonstration of the result of reading a dependency

version of the first sentence of the Penn Treebank.

"""

dg = DependencyGraph("""Pierre NNP 2 NMOD

Vinken NNP 8 SUB

, , 2 P

61 CD 5 NMOD

years NNS 6 AMOD

old JJ 2 NMOD

, , 2 P

will MD 0 ROOT

join VB 8 VC

the DT 11 NMOD

board NN 9 OBJ

as IN 9 VMOD

a DT 15 NMOD

nonexecutive JJ 15 NMOD

director NN 12 PMOD

Nov. NNP 9 VMOD

29 CD 16 NMOD

. . 9 VMOD

""")

tree = dg.tree()

print(tree.pprint())

if nx:

#currently doesn't work

try:

import networkx as NX

import pylab as P

except ImportError:

raise

g = dg.nx_graph()

g.info()

pos = NX.spring_layout(g, dim=1)

NX.draw_networkx_nodes(g, pos, node_size=50)

#NX.draw_networkx_edges(g, pos, edge_color='k', width=8)

NX.draw_networkx_labels(g, pos, dg.nx_labels)

P.xticks([])

P.yticks([])

P.savefig('tree.png')

P.show()

def conll_demo():

"""

A demonstration of how to read a string representation of

a CoNLL format dependency tree.

"""

dg = DependencyGraph(conll_data1)

tree = dg.tree()

print(tree.pprint())

print(dg)

print(dg.to_conll(4))

def conll_file_demo():

print('Mass conll_read demo...')

graphs = [DependencyGraph(entry)

for entry in conll_data2.split('\n\n') if entry]

for graph in graphs:

tree = graph.tree()

print('\n' + tree.pprint())

def cycle_finding_demo():

dg = DependencyGraph(treebank_data)

print(dg.contains_cycle())

cyclic_dg = DependencyGraph()

top = {'word':None, 'deps':[1], 'rel': 'TOP', 'address': 0}

child1 = {'word':None, 'deps':[2], 'rel': 'NTOP', 'address': 1}

child2 = {'word':None, 'deps':[4], 'rel': 'NTOP', 'address': 2}

child3 = {'word':None, 'deps':[1], 'rel': 'NTOP', 'address': 3}

child4 = {'word':None, 'deps':[3], 'rel': 'NTOP', 'address': 4}

cyclic_dg.nodelist = [top, child1, child2, child3, child4]

cyclic_dg.root = top

print(cyclic_dg.contains_cycle())

treebank_data = """Pierre NNP 2 NMOD

Vinken NNP 8 SUB

, , 2 P

61 CD 5 NMOD

years NNS 6 AMOD

old JJ 2 NMOD

, , 2 P

will MD 0 ROOT

join VB 8 VC

the DT 11 NMOD

board NN 9 OBJ

as IN 9 VMOD

a DT 15 NMOD

nonexecutive JJ 15 NMOD

director NN 12 PMOD

Nov. NNP 9 VMOD

29 CD 16 NMOD

. . 9 VMOD

"""

conll_data1 = """

1 Ze ze Pron Pron per|3|evofmv|nom 2 su _ _

2 had heb V V trans|ovt|1of2of3|ev 0 ROOT _ _

3 met met Prep Prep voor 8 mod _ _

4 haar haar Pron Pron bez|3|ev|neut|attr 5 det _ _

5 moeder moeder N N soort|ev|neut 3 obj1 _ _

6 kunnen kan V V hulp|ott|1of2of3|mv 2 vc _ _

7 gaan ga V V hulp|inf 6 vc _ _

8 winkelen winkel V V intrans|inf 11 cnj _ _

9 , , Punc Punc komma 8 punct _ _

10 zwemmen zwem V V intrans|inf 11 cnj _ _

11 of of Conj Conj neven 7 vc _ _

12 terrassen terras N N soort|mv|neut 11 cnj _ _

13 . . Punc Punc punt 12 punct _ _

"""

conll_data2 = """1 Cathy Cathy N N eigen|ev|neut 2 su _ _

2 zag zie V V trans|ovt|1of2of3|ev 0 ROOT _ _

3 hen hen Pron Pron per|3|mv|datofacc 2 obj1 _ _

4 wild wild Adj Adj attr|stell|onverv 5 mod _ _

5 zwaaien zwaai N N soort|mv|neut 2 vc _ _

6 . . Punc Punc punt 5 punct _ _

1 Ze ze Pron Pron per|3|evofmv|nom 2 su _ _

2 had heb V V trans|ovt|1of2of3|ev 0 ROOT _ _

3 met met Prep Prep voor 8 mod _ _

4 haar haar Pron Pron bez|3|ev|neut|attr 5 det _ _

5 moeder moeder N N soort|ev|neut 3 obj1 _ _

6 kunnen kan V V hulp|ott|1of2of3|mv 2 vc _ _

7 gaan ga V V hulp|inf 6 vc _ _

8 winkelen winkel V V intrans|inf 11 cnj _ _

9 , , Punc Punc komma 8 punct _ _

10 zwemmen zwem V V intrans|inf 11 cnj _ _

11 of of Conj Conj neven 7 vc _ _

12 terrassen terras N N soort|mv|neut 11 cnj _ _

13 . . Punc Punc punt 12 punct _ _

1 Dat dat Pron Pron aanw|neut|attr 2 det _ _

2 werkwoord werkwoord N N soort|ev|neut 6 obj1 _ _

3 had heb V V hulp|ovt|1of2of3|ev 0 ROOT _ _

4 ze ze Pron Pron per|3|evofmv|nom 6 su _ _

5 zelf zelf Pron Pron aanw|neut|attr|wzelf 3 predm _ _

6 uitgevonden vind V V trans|verldw|onverv 3 vc _ _

7 . . Punc Punc punt 6 punct _ _

1 Het het Pron Pron onbep|neut|zelfst 2 su _ _

2 hoorde hoor V V trans|ovt|1of2of3|ev 0 ROOT _ _

3 bij bij Prep Prep voor 2 ld _ _

4 de de Art Art bep|zijdofmv|neut 6 det _ _

5 warme warm Adj Adj attr|stell|vervneut 6 mod _ _

6 zomerdag zomerdag N N soort|ev|neut 3 obj1 _ _

7 die die Pron Pron betr|neut|zelfst 6 mod _ _

8 ze ze Pron Pron per|3|evofmv|nom 12 su _ _

9 ginds ginds Adv Adv gew|aanw 12 mod _ _

10 achter achter Adv Adv gew|geenfunc|stell|onverv 12 svp _ _

11 had heb V V hulp|ovt|1of2of3|ev 7 body _ _

12 gelaten laat V V trans|verldw|onverv 11 vc _ _

13 . . Punc Punc punt 12 punct _ _

1 Ze ze Pron Pron per|3|evofmv|nom 2 su _ _

2 hadden heb V V trans|ovt|1of2of3|mv 0 ROOT _ _

3 languit languit Adv Adv gew|geenfunc|stell|onverv 11 mod _ _

4 naast naast Prep Prep voor 11 mod _ _

5 elkaar elkaar Pron Pron rec|neut 4 obj1 _ _

6 op op Prep Prep voor 11 ld _ _

7 de de Art Art bep|zijdofmv|neut 8 det _ _

8 strandstoelen strandstoel N N soort|mv|neut 6 obj1 _ _

9 kunnen kan V V hulp|inf 2 vc _ _

10 gaan ga V V hulp|inf 9 vc _ _

11 liggen lig V V intrans|inf 10 vc _ _

12 . . Punc Punc punt 11 punct _ _

1 Zij zij Pron Pron per|3|evofmv|nom 2 su _ _

2 zou zal V V hulp|ovt|1of2of3|ev 7 cnj _ _

3 mams mams N N soort|ev|neut 4 det _ _

4 rug rug N N soort|ev|neut 5 obj1 _ _

5 ingewreven wrijf V V trans|verldw|onverv 6 vc _ _

6 hebben heb V V hulp|inf 2 vc _ _

7 en en Conj Conj neven 0 ROOT _ _

8 mam mam V V trans|ovt|1of2of3|ev 7 cnj _ _

9 de de Art Art bep|zijdofmv|neut 10 det _ _

10 hare hare Pron Pron bez|3|ev|neut|attr 8 obj1 _ _

11 . . Punc Punc punt 10 punct _ _

1 Of of Conj Conj onder|metfin 0 ROOT _ _

2 ze ze Pron Pron per|3|evofmv|nom 3 su _ _

3 had heb V V hulp|ovt|1of2of3|ev 0 ROOT _ _

4 gewoon gewoon Adj Adj adv|stell|onverv 10 mod _ _

5 met met Prep Prep voor 10 mod _ _

6 haar haar Pron Pron bez|3|ev|neut|attr 7 det _ _

7 vriendinnen vriendin N N soort|mv|neut 5 obj1 _ _

8 rond rond Adv Adv deelv 10 svp _ _

9 kunnen kan V V hulp|inf 3 vc _ _

10 slenteren slenter V V intrans|inf 9 vc _ _

11 in in Prep Prep voor 10 mod _ _

12 de de Art Art bep|zijdofmv|neut 13 det _ _

13 buurt buurt N N soort|ev|neut 11 obj1 _ _

14 van van Prep Prep voor 13 mod _ _

15 Trafalgar_Square Trafalgar_Square MWU N_N eigen|ev|neut_eigen|ev|neut 14 obj1 _ _

16 . . Punc Punc punt 15 punct _ _

"""

if __name__ == '__main__':

demo()

Coverage for nltk.parse.dependencygraph : 39%

226 statements 88 run 138 missing 0 excluded