def produce_files(**c):
# Set up the output files
outdir = c.get('outdir')
c['tag_out'] = os.path.join(outdir, 'ablation_tags.txt')
c['class_out'] = os.path.join(outdir, 'ablation_class.txt')
c['maxent_path'] = os.path.join(outdir, 'ablation-model.maxent')
c['tag_f'] = open(c.get('tag_out'), 'w', encoding='utf-8')
c['class_f'] = open(c.get('class_out'), 'w', encoding='utf-8')
c = ArgPasser(c)
xp = XamlParser(**c)
xml_files = glob.glob(os.path.join(c.get('input_dir'), c.get('pattern', default='*.xml')))
for x_f in xml_files:
xp.parse(x_f, **c)
return c
def write_gram(token, **kwargs):
# Re-cast the kwargs as an argpasser.
kwargs = ArgPasser(kwargs)
output_type = kwargs.get('type', 'classifier')
output = kwargs.get('output', sys.stdout)
posdict = kwargs.get('posdict', None)
if posdict is None:
posdict = env.load_posdict()
# Previous tag info
prev_gram = kwargs.get('prev_gram')
next_gram = kwargs.get('next_gram')
# Get heuristic alignment
aln_labels = kwargs.get('aln_labels', [])
# ===========================================================================
# Break apart the token...
# ===========================================================================
gram = token.seq
pos = token.goldlabel
# Lowercase if asked for
lower = kwargs.get('lowercase', True, bool)
gram = gram.lower() if gram else gram
# Fix the various issues with the grams.
gram = fix_gram(gram)
# ===========================================================================
# Do some cleaning on the gram....
# ===========================================================================
# Only take the first of two slashed grams
gram = re.sub('(.*)?/(.*)', r'\1', gram)
# Remove leading and trailing stuff
gram = re.sub('^(\S+)[\-=:\[\(\]\)/\*]$', r'\1', gram)
gram = re.sub('^[\-=:\[\(\]\)/\*](\S+)$', r'\1', gram)
# ===========================================================================
# Output the grams for a classifier
#
# NOTE! Only tokens that have an ASSIGNED pos tag will be written out this way!
if output_type == 'classifier' and pos:
output.write(pos)
# =======================================================================
# Get the morphemes
# =======================================================================
morphs = intent.utils.token.tokenize_string(gram, intent.utils.token.morpheme_tokenizer)
# =============================================================================
# Gram cleaning....
# =============================================================================
# Replace the characters that cause the svmlight format issues.
gram = gram.replace(':', '-')
gram = gram.replace('#', '-')
# =======================================================================
# Is there a number
# =======================================================================
if re.search('[0-9]', gram) and kwargs.get('feat_has_number', False, bool):
output.write('\thas-number:1')
# =======================================================================
# What labels is it aligned with
# =======================================================================
if kwargs.get('feat_align', False, bool):
for aln_label in aln_labels:
output.write('\taln-label-%s:1' % aln_label)
# =======================================================================
# Suffix
# =======================================================================
if kwargs.get('feat_suffix', True, bool):
output.write('\tgram-suffix-3-%s:1' % gram[-3:])
output.write('\tgram-suffix-2-%s:1' % gram[-2:])
output.write('\tgram-suffix-1-%s:1' % gram[-1:])
# =======================================================================
# Prefix
# =======================================================================
if kwargs.get('feat_prefix', True, bool):
output.write('\tgram-prefix-3-%s:1' % gram[:3])
output.write('\tgram-prefix-2-%s:1' % gram[:2])
output.write('\tgram-prefix-1-%s:1' % gram[:1])
# =======================================================================
# Number of morphs
# =======================================================================
if kwargs.get('feat_morph_num', False, bool):
output.write('\t%d-morphs:1' % len(list(morphs)))
# ===================================================================
# Previous gram
# ===================================================================
if prev_gram:
prev_gram = prev_gram.lower() if lower else prev_gram
# And then tokenize...
for token in intent.utils.token.tokenize_string(prev_gram, intent.utils.token.morpheme_tokenizer):
if kwargs.get('feat_prev_gram', True, bool):
output.write('\tprev-gram-%s:1' % fix_gram(token.seq))
# Add prev dictionary tag
if posdict and kwargs.get('feat_prev_gram_dict', True, bool) and token.seq in posdict:
prev_tags = posdict.top_n(token.seq)
output.write('\tprev-gram-dict-tag-%s:1' % prev_tags[0][0])
# Write a "**NONE**" for prev or next...
elif kwargs.get('feat_prev_gram', True, bool):
output.write('\tprev-gram-**NONE**:1')
# ===================================================================
# Next gram
# ===================================================================
if next_gram:
next_gram = next_gram.lower() if lower else next_gram
for token in intent.utils.token.tokenize_string(next_gram, intent.utils.token.morpheme_tokenizer):
# ===================================================================
# Gram itself
# ===================================================================
if kwargs.get('feat_next_gram', True, bool):
output.write('\tnext-gram-%s:1' % fix_gram(token.seq))
if posdict and kwargs.get('feat_next_gram_dict', True, bool) and token.seq in posdict:
next_tags = posdict.top_n(token.seq)
output.write('\tnext-gram-dict-tag-%s:1' % next_tags[0][0])
elif kwargs.get('feat_next_gram', True, bool):
output.write('\tnext-gram-**NONE**:1')
# =======================================================================
# Iterate through the morphs
# =======================================================================
for token in morphs:
# ===================================================================
# Just write the morph
# ===================================================================
if kwargs.get('feat_basic', True, bool):
output.write('\t%s:1' % token.seq)
# ===================================================================
# If the morph resembles a word in our dictionary, give it
# a predicted tag
# ===================================================================
if posdict and token.seq in posdict and kwargs.get('feat_dict', True, bool):
top_tags = posdict.top_n(token.seq)
# best = top_tags[0][0]
# if best != pos:
# MODULE_LOGGER.debug('%s tagged as %s not %s' % (gram, pos, best))
output.write('\ttop-dict-word-%s:1' % top_tags[0][0])
if len(top_tags) > 1:
output.write('\tnext-dict-word-%s:1' % top_tags[1][0])
output.write('\n')
# ===========================================================================
# If writing the gram out for the tagger...
# ===========================================================================
if output_type == 'tagger' and kwargs.get('tag_f'):
output.write('%s/%s ' % (gram, pos))
def do_projection(**kwargs):
"""
(Re)project the
:param aln_method: The alignment method
"""
kwargs = ArgPasser(kwargs)
aln_method = ALN_ARG_MAP[kwargs.get('aln_method', ARG_ALN_ANY)]
successes = 0
failures = 0
in_path = kwargs.get(ARG_INFILE)
with open(in_path, 'r', encoding='utf-8') as f:
PROJ_LOG.log(1000, 'Loading file "{}"...'.format(os.path.basename(in_path)))
xc = xigtxml.load(f, mode=INCREMENTAL)
for inst in xc:
success_fail_string = 'Instance {:20s} {{:10s}}{{}}'.format('"'+inst.id+'"...')
def fail(reason):
nonlocal failures, success_fail_string
success_fail_string = success_fail_string.format('FAIL', reason)
failures += 1
def success():
nonlocal successes, success_fail_string
success_fail_string = success_fail_string.format('SUCCESS', '')
successes += 1
# Query whether we want to require to use only trees
# where the alignment is 100%.
completeness_requirement = kwargs.get('completeness', default=0, t=float)
# TODO: Find better way to do this?
try:
if kwargs.get('pos', True):
project_trans_pos_to_gloss(inst, aln_method=aln_method, completeness_requirement=completeness_requirement)
project_gloss_pos_to_lang(inst, tag_method=INTENT_POS_PROJ)
if kwargs.get('ds', True):
project_pt_tier(inst, proj_aln_method=aln_method)
project_ds_tier(inst, proj_aln_method=aln_method, completeness_requirement=completeness_requirement)
except (NoNormLineException) as ntle:
fail("Bad Lines")
except (NoAlignmentProvidedError, ProjectionException) as nape:
fail("Alignment")
except (GlossLangAlignException) as glae:
fail("Gloss-Lang")
except (ProjectionIncompleteAlignment) as pia:
fail("Alignment Incomplete")
except PhraseStructureProjectionException as pspe:
fail("Projection Failed")
else:
success()
finally:
PROJ_LOG.info(success_fail_string)
inst.sort_tiers()
out_path = kwargs.get(ARG_OUTFILE)
# Try to make the folder if it doesn't already exist.
os.makedirs(os.path.dirname(out_path), exist_ok=True)
PROJ_LOG.log(1000, 'Writing new file "{}"...'.format(os.path.basename(out_path)))
with open(out_path, 'w', encoding='utf-8') as out_f:
xigtxml.dump(out_f, xc)
PROJ_LOG.log(1000, '{} instances processed, {} successful, {} failed.'.format(len(xc), successes, failures))
