def read_tree(text, out_dict, label):
fake_file = StringIO(text)
complete_tree = treebanks.ptb_read_tree(fake_file)
if complete_tree is None:
return None
treebanks.homogenise_tree(complete_tree)
if not complete_tree.label.strip():
complete_tree.label = 'ROOT'
tree = treebanks.apply_collins_rules(complete_tree)
if tree is None:
for out in [out_dict['out'], out_dict['err']]:
print >> out, "Empty {} tree".format(label)
print >> out, complete_tree
print >> out, tree
return tree
def read_tree(text, out_dict, label):
fake_file = StringIO(text)
complete_tree = treebanks.ptb_read_tree(fake_file)
if complete_tree is None:
return None
treebanks.homogenise_tree(complete_tree)
if not complete_tree.label.strip():
complete_tree.label = 'ROOT'
tree = treebanks.apply_collins_rules(complete_tree)
if tree is None:
for out in [out_dict['out'], out_dict['err']]:
print >> out, "Empty {} tree".format(label)
print >> out, complete_tree
print >> out, tree
return tree
if out_format == 't':
print tex_start
for tree in treebanks.generate_trees(sys.stdin,
return_empty=True,
allow_empty_labels=True):
gold_tree = None
if gold_file is not None:
gold_tree = gold_file.next()
if tree is None:
print
continue
# Apply edits
if 'h' in edits:
tree = treebanks.homogenise_tree(tree)
if gold_tree is not None:
gold_tree = treebanks.homogenise_tree(gold_tree)
if 't' in edits:
treebanks.remove_traces(tree)
if gold_tree is not None:
treebanks.remove_traces(gold_tree)
if 'f' in edits:
treebanks.remove_function_tags(tree)
if gold_tree is not None:
treebanks.remove_function_tags(gold_tree)
if 'c' in edits:
treebanks.apply_collins_rules(tree)
if gold_tree is not None:
treebanks.apply_collins_rules(gold_tree)
if 'u' in edits:
test_text = test_text.strip()
gold_relaxed_text = flatten_edited_nodes(gold_text)
test_relaxed_text = flatten_edited_nodes(test_text)
print test_text
print test_relaxed_text
if len(gold_text) == 0:
mprint("No gold tree", out, 'all')
continue
elif len(test_text) == 0:
mprint("Not parsed", out, 'all')
continue
gold_complete_tree = pstree.tree_from_text(gold_text,
allow_empty_labels=True)
gold_complete_tree = treebanks.homogenise_tree(gold_complete_tree)
treebanks.ptb_cleaning(gold_complete_tree)
gold_tree = treebanks.apply_collins_rules(gold_complete_tree, False)
gold_relaxed_tree = pstree.tree_from_text(gold_relaxed_text,
allow_empty_labels=True)
gold_relaxed_tree = treebanks.homogenise_tree(gold_relaxed_tree)
treebanks.ptb_cleaning(gold_relaxed_tree)
gold_relaxed_tree = treebanks.apply_collins_rules(
gold_relaxed_tree, False)
if gold_tree is None:
mprint("Empty gold tree", out, 'all')
mprint(gold_complete_tree.__repr__(), out, 'all')
mprint(gold_tree.__repr__(), out, 'all')
continue
gold_file = treebanks.generate_trees(gold_file, allow_empty_labels=True) if out_format == 't': print tex_start for tree in treebanks.generate_trees(sys.stdin, return_empty=True, allow_empty_labels=True): gold_tree = None if gold_file is not None: gold_tree = gold_file.next() if tree is None: print continue # Apply edits if 'h' in edits: tree = treebanks.homogenise_tree(tree) if gold_tree is not None: gold_tree = treebanks.homogenise_tree(gold_tree) if 't' in edits: treebanks.remove_traces(tree) if gold_tree is not None: treebanks.remove_traces(gold_tree) if 'f' in edits: treebanks.remove_function_tags(tree) if gold_tree is not None: treebanks.remove_function_tags(gold_tree) if 'c' in edits: treebanks.apply_collins_rules(tree) if gold_tree is not None: treebanks.apply_collins_rules(gold_tree) if 'u' in edits:
mprint("End of test input", out, 'err')
break
mprint("Sentence %d:" % sent_no, out, 'all')
gold_text = gold_text.strip()
test_text = test_text.strip()
if len(gold_text) == 0:
mprint("No gold tree", out, 'all')
continue
elif len(test_text) == 0:
mprint("Not parsed", out, 'all')
continue
gold_complete_tree = pstree.tree_from_text(gold_text)
tree = treebanks.homogenise_tree(gold_complete_tree, False)
treebanks.remove_traces(tree)
treebanks.remove_function_tags(tree)
treebanks.ptb_cleaning(tree)
treebanks.remove_trivial_unaries(tree)
gold_tree = tree
if gold_tree is None:
mprint("Empty gold tree", out, 'all')
mprint(gold_complete_tree.__repr__(), out, 'all')
mprint(gold_tree.__repr__(), out, 'all')
continue
if '()' in test_text:
mprint("() test tree", out, 'all')
continue
test_complete_tree = pstree.tree_from_text(test_text)
def compute_overall_score(gold_file, test_file):
gold_in = open(gold_file).readlines()
test_in = open(test_file).readlines()
stats = {'out_evalb': [0, 0, 0], 'out_relaxed': [0, 0, 0]}
assert len(gold_in) == len(test_in)
for i in range(len(gold_in)):
print "Sent: " + str(i)
gold_text = gold_in[i]
test_text = test_in[i]
if gold_text == '' and test_text == '':
break
elif gold_text == '':
break
elif test_text == '':
break
gold_text = gold_text.strip()
test_text = test_text.strip()
if len(gold_text) == 0:
continue
elif len(test_text) == 0:
continue
gold_complete_tree = pstree.tree_from_text(gold_text,
allow_empty_labels=True)
gold_complete_tree = treebanks.homogenise_tree(gold_complete_tree)
treebanks.ptb_cleaning(gold_complete_tree)
gold_tree = gold_complete_tree
#gold_tree = treebanks.apply_collins_rules(gold_complete_tree, False)
test_complete_tree = pstree.tree_from_text(test_text,
allow_empty_labels=True)
test_complete_tree = treebanks.homogenise_tree(test_complete_tree)
treebanks.ptb_cleaning(test_complete_tree)
test_tree = test_complete_tree
#test_tree = treebanks.apply_collins_rules(test_complete_tree, False)
gold_words = gold_tree.word_yield()
test_words = test_tree.word_yield()
if len(test_words.split()) != len(gold_words.split()):
print "Sentence lengths do not match in sentence..." + str(i)
print "Gold: " + gold_words.__repr__()
print "Test: " + test_words.__repr__()
match_strict, gold_strict, test_strict, _, _ = relaxed_parse_errors.counts_for_prf(
test_tree, gold_tree)
match_relaxed, gold_relaxed, test_relaxed, _, _ = relaxed_parse_errors.relaxed_counts_for_prf(
test_tree, gold_tree)
stats['out_evalb'][0] += match_strict
stats['out_evalb'][1] += gold_strict
stats['out_evalb'][2] += test_strict
p, r, f = nlp_eval.calc_prf(match_strict, gold_strict, test_strict)
print "Eval--Strict Evalb: %.2f %.2f %.2f" % (p * 100, r * 100,
f * 100)
stats['out_relaxed'][0] += match_relaxed
stats['out_relaxed'][1] += gold_relaxed
stats['out_relaxed'][2] += test_relaxed
p, r, f = nlp_eval.calc_prf(match_relaxed, gold_relaxed, test_relaxed)
print "Eval--Relaxed Edit: %.2f %.2f %.2f" % (p * 100, r * 100,
f * 100)
match = stats['out_evalb'][0]
gold = stats['out_evalb'][1]
test = stats['out_evalb'][2]
p, r, f = nlp_eval.calc_prf(match, gold, test)
print "Overall--Standard EVALB %s: %.2f %.2f %.2f" % ('out', p * 100,
r * 100, f * 100)
match = stats['out_relaxed'][0]
gold = stats['out_relaxed'][1]
test = stats['out_relaxed'][2]
p, r, f = nlp_eval.calc_prf(match, gold, test)
print "Overall--Relaxed EDIT %s: %.2f %.2f %.2f" % ('out', p * 100,
r * 100, f * 100)