def treat_options( opts, arg, n_arg, usage_string ) :
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global measures
global supported_measures
global main_freq_name
global join_all_contrastive
treat_options_simplest( opts, arg, n_arg, usage_string )
for ( o, a ) in opts:
if o in ( "-m", "--measures" ) :
try :
measures = []
measures = interpret_measures( a )
except ValueError as message :
error( str(message)+"\nargument must be list separated by "
"\":\" and containing the names: "+
str( supported_measures ))
elif o in ( "-o", "--original" ) :
main_freq_name = a
elif o in ( "-a", "--all" ) :
join_all_contrastive = True
if not main_freq_name :
error( "Option -o is mandatory")
def treat_options(opts, arg, n_arg, usage_string):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
@param usage_string The usage string for the current script.
"""
global attributes
treat_options_simplest(opts, arg, n_arg, usage_string)
for (o, a) in opts:
if o in ("-a", "--attributes"):
attributes = a.split(":")
for attr in attributes:
if attr not in WORD_ATTRIBUTES:
error("Unknown attribute '%s'!" % attr)
if attributes is None:
print >> sys.stderr, "The option -a <attributes> is mandatory."
usage(usage_string)
sys.exit(2)
def handle_candidate(self, candidate, info={}):
"""For each candidate, stores it in a temporary Database (so that it can be
retrieved later) and also creates a tuple containing the sorting key
feature values and the candidate ID. All the tuples are stored in a
global list, that will be sorted once all candidates are read and stored
into the temporary DB.
@param candidate The `Candidate` that is being read from the XML file.
"""
global feat_list, all_feats, feat_list_ok, feat_to_order
# First, verifies if all the features defined as sorting keys are real
# features, by matching them against the meta-features of the header. This
# is only performed once, before the first candidate is processed
if not feat_list_ok:
for feat_name in feat_list:
if feat_name not in all_feats:
error(
"%(feat)s is not a valid feature\n"
+ "Please chose features from the list below\n"
+ "%(list)s" % {"feat": feat_name, "list": "\n".join(map(lambda x: "* " + x, all_feats))}
)
feat_list_ok = True
for tp_class in candidate.tpclasses:
for feat_name in feat_list:
feat_value = candidate.get_feat_value(feat_name)
tp_value = candidate.get_tpclass_value(tp_class.name)
if feat_value != UNKNOWN_FEAT_VALUE and tp_value != UNKNOWN_FEAT_VALUE:
tuple = (float(feat_value), tp_value == "True")
feat_to_order[tp_class.name][feat_name].append(tuple)
def search_terms(self, in_text, query):
"""
"""
if DEFAULT_LANG != "en":
print("WARNING: Yahoo terms only works for English",
file=sys.stderr)
input_text = in_text.strip()
if isinstance(input_text, unicode):
input_text = input_text.encode('utf-8')
try:
url = ('http://search.yahooapis.com/ContentAnalysisService/'
'V1/termExtraction')
post_data = urllib.urlencode({
"context": input_text,
"appid": YAHOO_APPID,
"query": query,
"output": "json"
})
request = urllib2.Request(url, post_data)
response = urllib2.urlopen(request)
results = simplejson.load(response)
return results["ResultSet"]["Result"]
except Exception as err:
error("Got an error ->" + str(err) +
"\nPLEASE VERIFY YOUR INTERNET CONNECTION")
def _fallback_entity(self, entity, info={}):
"""
For each entity, stores it in a temporary Database (so that it can be
retrieved later) and also creates a tuple containing the sorting key
feature values and the entity ID. All the tuples are stored in a
global list, that will be sorted once all candidates are read and stored
into the temporary DB.
"""
global feat_list
# First, verifies if all the features defined as sorting keys are real
# features, by matching them against the meta-features of the header. This
# is only performed once, before the first entity is processed
if not self.feat_list_ok:
for feat_name in feat_list:
if feat_name not in self.all_feats:
error("\"{feat}\" is not a valid feature\n" \
"Please chose features from the list below:\n" \
"{list}".format(feat=feat_name, list="\n".join(
"* " + feat for feat in self.all_feats)))
self.feat_list_ok = True
# Store the whole entity in a temporary database
#info['parser'] = info['fileobj'] = None
self.all_entities[unicode(entity.id_number)] = (entity, info)
# Build up a tuple to be added to a list.
one_tuple = []
for feat_name in feat_list:
one_tuple.append(self.feat_value(entity, feat_name))
# The tuple will contain the sorting key values and the
# entity ID. The former are used to sort the candidates, the
# latter is used to retrieve a entity from the temporary DB
one_tuple.append(entity.id_number)
self.feat_to_order.append(tuple(one_tuple))
def treat_options(opts, arg, n_arg, usage_string):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global web_freq
treat_options_simplest(opts, arg, n_arg, usage_string)
mode = []
for (o, a) in opts:
if o in ("-y", "--yahoo"):
web_freq = YahooFreq()
mode.append("yahoo")
elif o in ("-w", "--google"):
web_freq = GoogleFreq()
mode.append("google")
if len(mode) > 1:
error("At most one option -y or -w, should be provided")
def read_distances( d_filename ) :
"""
Reads the distances between categories from a tab-separated file and
generates a list of tuples which will, once the annotation file is
read, be converted into a category x category matrix. This needs to be
done like this because, before reading the annotations file, we do not
know how many categories Nk will be used.
@param d_filename The input file name from which the data is read
@return A list of tuples containing, in the first position, category 1,
in the second position, category 2, and in the third position a float
with the distance between them.
"""
try :
d_data = open( d_filename )
distances_map = {} # Use a map to remove duplicates if present
for line in d_data.readlines() :
if len(line.strip()) > 0 : # Ignore blank lines
try :
cat1, cat2, distance = line.strip().split("\t")
key = "###SEPARATOR###".join( sorted( [ cat1, cat2 ] ) )
distances_map[ key ] = float( distance )
except ValueError :
error("ERROR reading distances, expected three values "
"separated by TAB, found:\n" + line)
return distances_map
except IOError :
error("\nERROR: Distance file \"%s\" not found" % d_filename)
def treat_options(opts, arg, n_arg, usage_string):
"""Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global reference_fname
global mwe_evaluator
global corpus_filetype_ext
global reference_filetype_ext
sentence_aligner_class = NaiveSentenceAligner
mwe_evaluator_class = ExactMatchMWEEvaluator
treat_options_simplest(opts, arg, n_arg, usage_string)
for (o, a) in opts:
if o in ("-r", "--reference"):
reference_fname = a
elif o in ("--sentence-aligner"):
sentence_aligner_class = SENTENCE_ALIGNERS[a]
elif o in ("-e", "--evaluator"):
mwe_evaluator_class = MWE_EVALUATORS[a]
elif o == "--corpus-from":
corpus_filetype_ext = a
elif o == "--reference-from":
reference_filetype_ext = a
else:
raise Exception("Bad arg: " + o)
if not reference_fname:
error("No reference file given!")
sentence_aligner = sentence_aligner_class()
mwe_evaluator = mwe_evaluator_class(sentence_aligner)
def treat_options(opts, arg, n_arg, usage_string):
"""Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global input_filetype_ext
global output_filetype_ext
global append_pos_tag
global clean_special
treat_options_simplest(opts, arg, n_arg, usage_string)
for (o, a) in opts:
if o == ("--from"):
input_filetype_ext = a
elif o == ("--to"):
output_filetype_ext = a
elif o == "--append-pos-tag":
if a in ("coarse","fine"):
append_pos_tag = a
else:
error("Expected \"coarse\" or \"fine\", found " + a)
elif o == "--clean-special":
clean_special = True
else:
raise Exception("Bad arg: " + o)
def treat_options(opts, arg, n_arg, usage_string):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
@param usage_string The usage string for the current script.
"""
global attributes
treat_options_simplest( opts, arg, n_arg, usage_string )
for (o, a) in opts:
if o in ("-a", "--attributes"):
attributes = a.split(":")
for attr in attributes:
if attr not in WORD_ATTRIBUTES:
error("Unknown attribute '%s'!" % attr)
if attributes is None:
print >>sys.stderr, "The option -a <attributes> is mandatory."
usage(usage_string)
sys.exit(2)
def _fallback_entity(self, entity, info={}):
"""
For each entity, stores it in a temporary Database (so that it can be
retrieved later) and also creates a tuple containing the sorting key
feature values and the entity ID. All the tuples are stored in a
global list, that will be sorted once all candidates are read and stored
into the temporary DB.
"""
global feat_list
# First, verifies if all the features defined as sorting keys are real
# features, by matching them against the meta-features of the header. This
# is only performed once, before the first entity is processed
if not self.feat_list_ok:
for feat_name in feat_list:
if feat_name not in self.all_feats:
error("\"{feat}\" is not a valid feature\n" \
"Please chose features from the list below:\n" \
"{list}".format(feat=feat_name, list="\n".join(
"* " + feat for feat in self.all_feats)))
self.feat_list_ok = True
# Store the whole entity in a temporary database
#info['parser'] = info['fileobj'] = None
self.all_entities[unicode(entity.id_number)] = (entity,info)
# Build up a tuple to be added to a list.
one_tuple = []
for feat_name in feat_list:
one_tuple.append(self.feat_value(entity, feat_name))
# The tuple will contain the sorting key values and the
# entity ID. The former are used to sort the candidates, the
# latter is used to retrieve a entity from the temporary DB
one_tuple.append(entity.id_number)
self.feat_to_order.append(tuple(one_tuple))
def read_distances(d_filename):
"""
Reads the distances between categories from a tab-separated file and
generates a list of tuples which will, once the annotation file is
read, be converted into a category x category matrix. This needs to be
done like this because, before reading the annotations file, we do not
know how many categories Nk will be used.
@param d_filename The input file name from which the data is read
@return A list of tuples containing, in the first position, category 1,
in the second position, category 2, and in the third position a float
with the distance between them.
"""
try:
d_data = open(d_filename)
distances_map = {} # Use a map to remove duplicates if present
for line in d_data.readlines():
if len(line.strip()) > 0: # Ignore blank lines
try:
cat1, cat2, distance = line.strip().split("\t")
key = "###SEPARATOR###".join(sorted([cat1, cat2]))
distances_map[key] = float(distance)
except ValueError:
error("ERROR reading distances, expected three values "
"separated by TAB, found:\n" + line)
return distances_map
except IOError:
error("\nERROR: Distance file \"%s\" not found" % d_filename)
def treat_options( opts, arg, n_arg, usage_string ) :
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global algoname
global lower_attr
global input_filetype_ext
global output_filetype_ext
treat_options_simplest( opts, arg, n_arg, usage_string )
for ( o, a ) in opts:
if o == "--from":
input_filetype_ext = a
elif o == "--to":
output_filetype_ext = a
elif o in ("-l","--lemmas" ) :
lower_attr = "lemma"
elif o in ("-a", "--algorithm"):
algoname = a.lower()
elif o in ("-m", "-x"):
error( "Deprecated options -x and -m. Run with -h for details" )
else:
raise Exception("Bad arg: " + o)
def treat_options(opts, arg, n_arg, usage_string):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global web_freq
treat_options_simplest(opts, arg, n_arg, usage_string)
mode = []
for ( o, a ) in opts:
if o in ( "-y", "--yahoo" ):
web_freq = YahooFreq()
mode.append("yahoo")
elif o in ( "-w", "--google" ):
web_freq = GoogleFreq()
mode.append("google")
if len(mode) > 1:
error("At most one option -y or -w, should be provided")
def treat_options( opts, arg, n_arg, usage_string ) :
"""Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global reference_fname
global mwe_evaluator
global corpus_filetype_ext
global reference_filetype_ext
sentence_aligner_class = NaiveSentenceAligner
mwe_evaluator_class = ExactMatchMWEEvaluator
treat_options_simplest(opts, arg, n_arg, usage_string)
for (o, a) in opts:
if o in ("-r", "--reference"):
reference_fname = a
elif o in ("--sentence-aligner"):
sentence_aligner_class = SENTENCE_ALIGNERS[a]
elif o in ("-e", "--evaluator"):
mwe_evaluator_class = MWE_EVALUATORS[a]
elif o == "--corpus-from":
corpus_filetype_ext = a
elif o == "--reference-from":
reference_filetype_ext = a
else:
raise Exception("Bad arg: " + o)
if not reference_fname:
error("No reference file given!")
sentence_aligner = sentence_aligner_class()
mwe_evaluator = mwe_evaluator_class(sentence_aligner)
def treat_options(opts, arg, n_arg, usage_string):
"""Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global limit
global entity_buffer
global input_filetype_ext
global output_filetype_ext
treat_options_simplest(opts, arg, n_arg, usage_string)
for ( o, a ) in opts:
if o == "--from":
input_filetype_ext = a
elif o == "--to":
output_filetype_ext = a
elif o in ("-n", "--number"):
try:
limit = int(a)
entity_buffer = [None] * limit
if limit < 0:
raise ValueError
except ValueError:
error("You must provide a positive " + \
"integer value as argument of -n option.")
else:
raise Exception("Bad arg: " + o)
def handle_candidate(self, candidate, info={}):
"""For each candidate, stores it in a temporary Database (so that it can be
retrieved later) and also creates a tuple containing the sorting key
feature values and the candidate ID. All the tuples are stored in a
global list, that will be sorted once all candidates are read and stored
into the temporary DB.
@param candidate The `Candidate` that is being read from the XML file.
"""
global feat_list, all_feats, feat_list_ok, feat_to_order
# First, verifies if all the features defined as sorting keys are real
# features, by matching them against the meta-features of the header. This
# is only performed once, before the first candidate is processed
if not feat_list_ok:
for feat_name in feat_list:
if feat_name not in all_feats:
error("%(feat)s is not a valid feature\n" + \
"Please chose features from the list below\n" + \
"%(list)s" % {"feat": feat_name,
"list": "\n".join(
map(lambda x: "* " + x, all_feats))})
feat_list_ok = True
for tp_class in candidate.tpclasses:
for feat_name in feat_list:
feat_value = candidate.get_feat_value(feat_name)
tp_value = candidate.get_tpclass_value(tp_class.name)
if feat_value != UNKNOWN_FEAT_VALUE and \
tp_value != UNKNOWN_FEAT_VALUE :
tuple = (float(feat_value), tp_value == "True")
feat_to_order[tp_class.name][feat_name].append(tuple)
def treat_options(opts, arg, n_arg, usage_string):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global measures
global supported_measures
global main_freq_name
global join_all_contrastive
treat_options_simplest(opts, arg, n_arg, usage_string)
for (o, a) in opts:
if o in ("-m", "--measures"):
try:
measures = []
measures = interpret_measures(a)
except ValueError as message:
error(
str(message) + "\nargument must be list separated by "
"\":\" and containing the names: " +
str(supported_measures))
elif o in ("-o", "--original"):
main_freq_name = a
elif o in ("-a", "--all"):
join_all_contrastive = True
if not main_freq_name:
error("Option -o is mandatory")
def treat_options( opts, arg, n_arg, usage_string ) :
"""Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global limit
global input_filetype_ext
global output_filetype_ext
treat_options_simplest(opts, arg, n_arg, usage_string)
for (o, a) in opts:
if o == "--from":
input_filetype_ext = a
elif o == "--to":
output_filetype_ext = a
elif o in ("-n", "--number"):
try:
limit = int( a )
if limit < 0:
raise ValueError
except ValueError:
error("You must provide a positive " \
"integer value as argument of -n option.")
else:
raise Exception("Bad arg")
def feat_value(self, entity, feat_name):
r"""Return value for given feature name."""
if feat_name.startswith("@"):
if feat_name == "@SURFACE":
return tuple(w.surface for w in entity)
if feat_name == "@LEMMA":
return tuple(w.lemma for w in entity)
if feat_name == "@POS":
return tuple(w.pos for w in entity)
error("Bad pseudo-feature name", feat_name=feat_name)
return entity.get_feat_value(feat_name)
def feat_value(self, entity, feat_name):
r"""Return value for given feature name."""
if feat_name.startswith("@"):
if feat_name == "@SURFACE":
return tuple(w.surface for w in entity)
if feat_name == "@LEMMA":
return tuple(w.lemma for w in entity)
if feat_name == "@POS":
return tuple(w.pos for w in entity)
error("Bad pseudo-feature name", feat_name=feat_name)
return entity.get_feat_value(feat_name)
def __init__(self):
global algoname
if algoname == "simple" :
self.handle_sentence = self.handle_sentence_simple # Redundant, kept for clarity
elif algoname == "complex" :
self.handle_sentence = self.handle_sentence_complex
elif algoname == "aggressive" :
self.handle_sentence = self.handle_sentence_aggressive # Redundant, kept for clarity
else :
error("%s is not a valid algorithm\nYou must provide a valid "+\
"algorithm (e.g. \"complex\", \"simple\")." % algoname)
def calculate_distances(distances_map, all_categories):
"""
Generates a distances matrix from the distances map and the
correspondence between nominal categories and their IDs. This function
is called just after reading the data when a distances file is provided.
@param distances_map A dictionary where the keys are strings of the form
category1###SEPARATOR###category2 and the values are the distances
between category1 and category1
@param all_categories A dictionary where the keys are the string nominal
category names and the values are the integer unique IDs of each
category
@return A simmetric matrix Nk x Nk, with the distance between categories
represented in the cells. The rows and columns are indexed with the IDs
from 0 to Nk-1, the matrix contains 0.0 in the main diagonal. The values
not specified in the distances_map are set to the maximum distance seen
in the map by default. If no distance file is provided (distance_map is
empty) the distances between each two different categories are 1.0.
"""
Nk = len(all_categories.keys())
distances_matrix = []
max_distance = 0.0
for k in range(Nk):
distances_matrix.append(Nk * [-1.0])
for key, distance in distances_map.items():
cats = key.split("###SEPARATOR###")
try:
k1, k2 = map(lambda x: all_categories[x], cats)
except KeyError:
error("Distance file incompatible with annotations\nDid not find "
"categories %s in the annotation data" % cats)
return None
if k1 == k2:
warn(
"defined distance for category and self for %s. Replacing by 0"
% cats[0])
distances_matrix[k1][k2] = distance
distances_matrix[k2][k1] = distance
if distance > max_distance:
max_distance = distance
if len(distances_map.keys()) == 0:
max_distance = 1.0
# Fill in the non-specified distances with the maximal value
for k1 in range(Nk):
distances_matrix[k1][k1] = 0.0 # Distance between categ and itself = 0
for k2 in range(Nk):
if distances_matrix[k1][k2] < 0.0: # Not specified or negative
distances_matrix[k1][k2] = max_distance
return distances_matrix
def calculate_distances( distances_map, all_categories ) :
"""
Generates a distances matrix from the distances map and the
correspondence between nominal categories and their IDs. This function
is called just after reading the data when a distances file is provided.
@param distances_map A dictionary where the keys are strings of the form
category1###SEPARATOR###category2 and the values are the distances
between category1 and category1
@param all_categories A dictionary where the keys are the string nominal
category names and the values are the integer unique IDs of each
category
@return A simmetric matrix Nk x Nk, with the distance between categories
represented in the cells. The rows and columns are indexed with the IDs
from 0 to Nk-1, the matrix contains 0.0 in the main diagonal. The values
not specified in the distances_map are set to the maximum distance seen
in the map by default. If no distance file is provided (distance_map is
empty) the distances between each two different categories are 1.0.
"""
Nk = len( all_categories.keys() )
distances_matrix = []
max_distance = 0.0
for k in range(Nk) :
distances_matrix.append( Nk * [-1.0] )
for key,distance in distances_map.items() :
cats = key.split("###SEPARATOR###")
try :
k1,k2 = map(lambda x : all_categories[x], cats )
except KeyError :
error("Distance file incompatible with annotations\nDid not find "
"categories %s in the annotation data" % cats)
return None
if k1 == k2 :
warn("defined distance for category and self for %s. Replacing by 0"
% cats[ 0 ])
distances_matrix[ k1 ][ k2 ] = distance
distances_matrix[ k2 ][ k1 ] = distance
if distance > max_distance :
max_distance = distance
if len( distances_map.keys() ) == 0 :
max_distance = 1.0
# Fill in the non-specified distances with the maximal value
for k1 in range(Nk) :
distances_matrix[k1][k1] = 0.0 # Distance between categ and itself = 0
for k2 in range(Nk) :
if distances_matrix[ k1 ][ k2 ] < 0.0 : # Not specified or negative
distances_matrix[ k1 ][ k2 ] = max_distance
return distances_matrix
def interpret_length(l, maxormin):
"""
Transform argument given to -a or -i options into integer + error checks.
@param l: A string passed as argument to -i or -a
@param maxormin: A string indicating whether this is "maximum" or "minimum"
@return: An integer corresponding to l
"""
try:
result = int(l)
if result < 0:
raise ValueError
verbose("%s length: %d" % (maxormin, result))
return result
except ValueError:
error("Argument of must be non-negative integer, got " + repr(l))
def interpret_length( l, maxormin ):
"""
Transform argument given to -a or -i options into integer + error checks.
@param l: A string passed as argument to -i or -a
@param maxormin: A string indicating whether this is "maximum" or "minimum"
@return: An integer corresponding to l
"""
try :
result = int( l )
if result < 0:
raise ValueError
verbose( "%s length: %d" % (maxormin, result) )
return result
except ValueError:
error("Argument of must be non-negative integer, got " + repr(l))
def treat_options(opts, arg, n_arg, usage_string):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global pre_gs
global ignore_pos
global gs_name
global ignore_case
global lemma_or_surface
global input_filetype_ext
global reference_filetype_ext
ref_name = None
treat_options_simplest(opts, arg, n_arg, usage_string)
for (o, a) in opts:
if o in ("-r", "--reference"):
ref_name = a
elif o in ("-g", "--ignore-pos"):
ignore_pos = True
elif o in ("-c", "--case"):
ignore_case = False
elif o in ("-L", "--lemma-or-surface"):
lemma_or_surface = True
elif o == "--input-from":
input_filetype_ext = a
elif o == "--reference-from":
reference_filetype_ext = a
else:
raise Exception("Bad arg: " + o)
# The reference list needs to be opened after all the options are read,
# since options such as -g and -c modify the way the list is represented
if ref_name:
filetype.parse([ref_name], ReferenceReaderHandler(),
reference_filetype_ext)
gs_name = re.sub(".*/", "", re.sub("\.xml", "", ref_name))
# There's no reference list... Oh oh cannot evaluate :-(
if not pre_gs:
error("You MUST provide a non-empty reference list!")
def create_patterns_file( ngram_range ) :
"""
Create an artificial list of MWE patterns in which all the parts of
the words are wildcards. Such artificial patterns match every ngram
of size n, which is exactly what we want to do with the option -n. This
may seem a weird way to extract ngrams, but it allows a single
transparent candidate extraction function, treat_sentence.
@param ngram_range String argument of the -n option.
"""
global patterns, usage_string, shortest_pattern, longest_pattern
result = interpret_ngram( ngram_range )
if result :
( shortest_pattern, longest_pattern ) = result
patterns.append(build_generic_pattern(shortest_pattern, longest_pattern))
else :
error("Invalid argument for -n.")
def treat_options( opts, arg, n_arg, usage_string ) :
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global pre_gs
global ignore_pos
global gs_name
global ignore_case
global lemma_or_surface
global input_filetype_ext
global reference_filetype_ext
ref_name = None
treat_options_simplest( opts, arg, n_arg, usage_string )
for ( o, a ) in opts:
if o in ("-r", "--reference"):
ref_name = a
elif o in ("-g", "--ignore-pos"):
ignore_pos = True
elif o in ("-c", "--case"):
ignore_case = False
elif o in ("-L", "--lemma-or-surface"):
lemma_or_surface = True
elif o == "--input-from":
input_filetype_ext = a
elif o == "--reference-from":
reference_filetype_ext = a
else:
raise Exception("Bad arg: " + o)
# The reference list needs to be opened after all the options are read,
# since options such as -g and -c modify the way the list is represented
if ref_name :
filetype.parse([ref_name], ReferenceReaderHandler(), reference_filetype_ext)
gs_name = re.sub( ".*/", "", re.sub( "\.xml", "", ref_name ) )
# There's no reference list... Oh oh cannot evaluate :-(
if not pre_gs :
error("You MUST provide a non-empty reference list!")
def create_patterns_file(ngram_range):
"""
Create an artificial list of MWE patterns in which all the parts of
the words are wildcards. Such artificial patterns match every ngram
of size n, which is exactly what we want to do with the option -n. This
may seem a weird way to extract ngrams, but it allows a single
transparent candidate extraction function, treat_sentence.
@param ngram_range String argument of the -n option.
"""
global patterns, usage_string, shortest_pattern, longest_pattern
result = interpret_ngram(ngram_range)
if result:
(shortest_pattern, longest_pattern) = result
patterns.append(
build_generic_pattern(shortest_pattern, longest_pattern))
else:
error("Invalid argument for -n.")
def handle_meta(self, meta, info={}) :
"""
Reads the `corpus_size` meta header and initializes a global counter
dictionary with zero for each corpus. This dict will contain the total
number of candidate frequencies summed up, as in the csmwe original
formulation.
@param meta The `Meta` header that is being read from the XML file.
"""
global totals_dict, main_freq_name
main_freq_valid = False
for corpus_size in meta.corpus_sizes :
totals_dict[ corpus_size.name ] = 0
if corpus_size.name == main_freq_name :
main_freq_valid = True
if not main_freq_valid :
error("main frequency must be a valid freq. name\nPossible values: " +
str( totals_dict.keys() ))
def treat_options(opts, arg, n_arg, usage_string):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global surface_instead_lemmas
global glue
global base_attr
global min_ngram
global max_ngram
global min_frequency
global ngram_counts
global selected_candidates
global use_shelve
global input_filetype_ext
treat_options_simplest(opts, arg, n_arg, usage_string)
mode = []
for (o, a) in opts:
if o in ("-s", "--surface"):
surface_instead_lemmas = True
base_attr = 'surface'
elif o in ("-f", "--freq"):
min_frequency = int(a)
elif o in ("-n", "--ngram"):
(min_ngram, max_ngram) = interpret_ngram(a)
elif o in ("-G", "--glue"):
if a == "scp":
glue = scp_glue
else:
error("Unknown glue function '%s'" % a)
elif o in ("-S", "--shelve"):
use_shelve = True
elif o == "--from":
input_filetype_ext = a
else:
raise Exception("Bad arg: " + o)
def handle_meta(self, meta, info={}):
"""
Reads the `corpus_size` meta header and initializes a global counter
dictionary with zero for each corpus. This dict will contain the total
number of candidate frequencies summed up, as in the csmwe original
formulation.
@param meta The `Meta` header that is being read from the XML file.
"""
global totals_dict, main_freq_name
main_freq_valid = False
for corpus_size in meta.corpus_sizes:
totals_dict[corpus_size.name] = 0
if corpus_size.name == main_freq_name:
main_freq_valid = True
if not main_freq_valid:
error(
"main frequency must be a valid freq. name\nPossible values: "
+ str(totals_dict.keys()))
def treat_options( opts, arg, n_arg, usage_string ) :
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global surface_instead_lemmas
global glue
global base_attr
global min_ngram
global max_ngram
global min_frequency
global ngram_counts
global selected_candidates
global use_shelve
global input_filetype_ext
treat_options_simplest( opts, arg, n_arg, usage_string )
mode = []
for ( o, a ) in opts:
if o in ("-s", "--surface") :
surface_instead_lemmas = True
base_attr = 'surface'
elif o in ("-f", "--freq") :
min_frequency = int(a)
elif o in ("-n", "--ngram") :
(min_ngram, max_ngram) = interpret_ngram(a)
elif o in ("-G", "--glue"):
if a == "scp":
glue = scp_glue
else:
error("Unknown glue function '%s'" % a)
elif o in ("-S", "--shelve"):
use_shelve = True
elif o == "--from":
input_filetype_ext = a
else:
raise Exception("Bad arg: " + o)
def treat_options( opts, arg, n_arg, usage_string ) :
"""Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global limit
treat_options_simplest( opts, arg, n_arg, usage_string )
for ( o, a ) in opts:
if o in ("-n", "--number") :
try :
limit = int( a )
if limit < 0 :
raise ValueError
except ValueError :
error("You must provide a positive integer value as argument "
"of -n option.")
def handle_candidate(self, candidate, info={}):
"""For each candidate and for each `CorpusSize` read from the `Meta`
header, generates four features that correspond to the Association
Measures described above.
@param candidate The `Candidate` that is being read from the XML file.
"""
global corpussize_dict, main_freq
joint_freq = {}
singleword_freq = {}
backed_off = False
# Convert all these integers to floats...
for freq in candidate.freqs:
joint_freq[freq.name] = (float(abs(freq.value)))
singleword_freq[freq.name] = []
if freq.value < 0:
backed_off = True
for word in candidate:
for freq in word.freqs:
singleword_freq[freq.name].append(abs(float(freq.value)))
# Little trick: negative counts indicate backed-off counts
if freq.value < 0:
backed_off = True
for freq in candidate.freqs:
corpus_name = freq.name
if not backed_off and corpus_name == "backoff":
N = corpussize_dict[main_freq]
else:
N = corpussize_dict[corpus_name]
try:
feats = calculate_ams(joint_freq[corpus_name],
singleword_freq[corpus_name], N,
corpus_name)
for feat in feats:
candidate.add_feat(feat)
except Exception:
error(
"This should never be printed. The end of the world is here"
)
self.chain.handle_candidate(candidate, info)
def treat_options(opts, arg, n_arg, usage_string):
"""Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global limit
treat_options_simplest(opts, arg, n_arg, usage_string)
for (o, a) in opts:
if o in ("-n", "--number"):
try:
limit = int(a)
if limit < 0:
raise ValueError
except ValueError:
error("You must provide a positive integer value as argument "
"of -n option.")
def treat_options( opts, arg, n_arg, usage_string ) :
"""Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global input_patterns
global input_filetype_ext
global output_filetype_ext
global match_distance
global non_overlapping
global id_order
global annotate
global only_the_matching_subpart
util.treat_options_simplest(opts, arg, n_arg, usage_string)
for (o, a) in opts:
if o == "--input-from":
input_filetype_ext = a
elif o == "--to":
output_filetype_ext = a
elif o in ("-p", "--patterns"):
input_patterns = filetype.parse_entities([a])
elif o in ("-d", "--match-distance") :
match_distance = a
elif o in ("-N", "--non-overlapping") :
non_overlapping = True
elif o == "--id-order":
id_order = a.split(":")
elif o == "--annotate":
annotate = True
elif o == "--only-matching":
only_the_matching_subpart = True
else:
raise Exception("Bad arg " + o)
if input_patterns is None:
util.error("No patterns provided. Option --patterns is mandatory!")
if only_the_matching_subpart and annotate:
util.warn("Switch --only-matching disables --annotate")
def open_index(prefix):
"""
Open the index files (valid index created by the `index.py` script).
@param prefix The string name of the index file.
"""
global freq_name, the_corpus_size
global index, suffix_array
assert prefix.endswith(".info")
prefix = prefix[:-len(".info")]
try:
verbose("Loading index files... this may take some time.")
index = Index(prefix)
index.load_metadata()
freq_name = re.sub(".*/", "", prefix)
#pdb.set_trace()
the_corpus_size = index.metadata["corpus_size"]
except IOError:
error("Error opening the index.\nTry again with another index filename")
except KeyError:
error("Error opening the index.\nTry again with another index filename")
def calculate_and_print(annotations, Ni, Nc, Nk, categ_names):
"""
Given the set of annotations read from the files, calculate the
agreement coefficients and print them in a nice way.
@param annotations The list of annotations containing one row per item,
one column per rater, and the nominal categories in the cells
@param Ni The total number of items I in the data
@param Nc The total number of raters C in the data
@param Nk The total number of categories K in the data
@param categ_names The names of the categories used to annotate, sorted
by their IDs.
"""
global calculate_pairwise
global calculate_confusion
if Ni != 0 and Nc != 0 and Nk != 0: # empty file
if calculate_pairwise:
pairwise_map = compute_pairwise_all(annotations, Ni, Nc, Nk)
for pair in pairwise_map.keys():
print("\nAgreement for pair " + pair)
(a0, S, pi, kappa, wkappa) = pairwise_map[pair]
print("ao = %f, S = %f, pi = %f, (Cohen's) kappa = %f, "
"weighted kappa = %f" % (a0, S, pi, kappa, wkappa))
print_matrix_kappa(pairwise_map, Nc)
print ( "\nNc = %(Nc)d raters\nNi = %(Ni)d items\nNk = %(Nk)d " + \
"categories\nNc x Ni = %(j)d judgements" ) %\
{"Nc": Nc, "Ni": Ni, "Nk": Nk, "j": Ni * Nc }
coeffs = compute_multi(annotations, Ni, Nc, Nk)
print("\nOverall agreement coefficients for all annotators:")
print(
"multi-ao = %f\nmulti-pi (Fleiss' kappa) = %f\nmulti-kappa = %f\n"
% coeffs)
coeffs_weighted = compute_weighted_multi(annotations, Ni, Nc, Nk)
print("Weighted agreement coefficients for all annotators:")
print("alpha = %f\nalpha-kappa = %f\n" % coeffs_weighted)
if calculate_confusion:
confusion, counters = compute_confusion(annotations, Nc)
print_matrix_confusion(confusion, categ_names, counters, Ni, Nc,
Nk)
else:
error("you probably provided an empty file")
def handle_candidate(self, candidate, info={}):
"""For each candidate and for each `CorpusSize` read from the `Meta`
header, generates four features that correspond to the Association
Measures described above.
@param candidate The `Candidate` that is being read from the XML file.
"""
global corpussize_dict, main_freq
joint_freq = {}
singleword_freq = {}
backed_off = False
# Convert all these integers to floats...
for freq in candidate.freqs :
joint_freq[ freq.name ] = ( float(abs( freq.value ) ) )
singleword_freq[ freq.name ] = []
if freq.value < 0 :
backed_off = True
for word in candidate :
for freq in word.freqs :
singleword_freq[ freq.name ].append( abs( float(freq.value) ) )
# Little trick: negative counts indicate backed-off counts
if freq.value < 0 :
backed_off = True
for freq in candidate.freqs :
corpus_name = freq.name
if not backed_off and corpus_name == "backoff" :
N = corpussize_dict[ main_freq ]
else :
N = corpussize_dict[ corpus_name ]
try :
feats = calculate_ams( joint_freq[ corpus_name ],
singleword_freq[ corpus_name ],
N, corpus_name )
for feat in feats :
candidate.add_feat( feat )
except Exception :
error( "This should never be printed. The end of the world is here")
self.chain.handle_candidate(candidate, info)
def calculate_and_print( annotations, Ni, Nc, Nk, categ_names ) :
"""
Given the set of annotations read from the files, calculate the
agreement coefficients and print them in a nice way.
@param annotations The list of annotations containing one row per item,
one column per rater, and the nominal categories in the cells
@param Ni The total number of items I in the data
@param Nc The total number of raters C in the data
@param Nk The total number of categories K in the data
@param categ_names The names of the categories used to annotate, sorted
by their IDs.
"""
global calculate_pairwise
global calculate_confusion
if Ni != 0 and Nc != 0 and Nk != 0 : # empty file
if calculate_pairwise :
pairwise_map = compute_pairwise_all( annotations, Ni, Nc, Nk)
for pair in pairwise_map.keys() :
print("\nAgreement for pair " + pair)
(a0, S, pi, kappa, wkappa) = pairwise_map[pair]
print("ao = %f, S = %f, pi = %f, (Cohen's) kappa = %f, "
"weighted kappa = %f" % (a0, S, pi, kappa, wkappa) )
print_matrix_kappa( pairwise_map, Nc )
print ( "\nNc = %(Nc)d raters\nNi = %(Ni)d items\nNk = %(Nk)d " + \
"categories\nNc x Ni = %(j)d judgements" ) %\
{"Nc": Nc, "Ni": Ni, "Nk": Nk, "j": Ni * Nc }
coeffs = compute_multi( annotations, Ni, Nc, Nk )
print("\nOverall agreement coefficients for all annotators:")
print("multi-ao = %f\nmulti-pi (Fleiss' kappa) = %f\nmulti-kappa = %f\n"
% coeffs)
coeffs_weighted = compute_weighted_multi( annotations, Ni, Nc, Nk )
print("Weighted agreement coefficients for all annotators:")
print("alpha = %f\nalpha-kappa = %f\n" % coeffs_weighted)
if calculate_confusion :
confusion, counters = compute_confusion( annotations, Nc )
print_matrix_confusion(confusion, categ_names, counters, Ni, Nc, Nk)
else :
error("you probably provided an empty file")
def open_index(prefix):
"""
Open the index files (valid index created by the `index.py` script).
@param prefix The string name of the index file.
"""
global freq_name, the_corpus_size
global index, suffix_array
assert prefix.endswith(".info")
prefix = prefix[:-len(".info")]
try:
verbose("Loading index files... this may take some time.")
index = Index(prefix)
index.load_metadata()
freq_name = re.sub(".*/", "", prefix)
#pdb.set_trace()
the_corpus_size = index.metadata["corpus_size"]
except IOError:
error(
"Error opening the index.\nTry again with another index filename")
except KeyError:
error(
"Error opening the index.\nTry again with another index filename")
def handle_candidate(self, candidate, info={}) :
"""
For each candidate and for each `CorpusSize` read from the `Meta`
header, generates features that correspond to the Contrastive
Measures described above.
@param candidate The `Candidate` that is being read from the XML file.
"""
global corpussize_dict
global totals_dict
global main_freq_name
# get the original corpus freq, store the others in contrastive corpus dict
# We use plus one smoothing to avoid dealing with zero freqs
contrast_freqs = {}
if join_all_contrastive :
contrast_freqs[ "all" ] = 1
main_freq = None
for freq in candidate.freqs :
if freq.name == main_freq_name :
main_freq = float( freq.value ) + 1
elif join_all_contrastive :
contrast_freqs[ "all" ] += float( freq.value )
else :
contrast_freqs[ freq.name ] = float( freq.value ) + 1
for contrast_name in contrast_freqs.keys() :
try :
feats = calculate_indiv( corpussize_dict[ main_freq_name ],
corpussize_dict[ contrast_name ],
main_freq,
contrast_freqs[ contrast_name ],
totals_dict[ contrast_name ],
contrast_name )
for feat in feats :
candidate.add_feat( feat )
except Exception :
error("Error in calculating the measures.")
self.chain.handle_candidate(candidate, info)
def treat_options(opts, arg, n_arg, usage_string):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global measures
global supported_measures
global main_freq
global not_normalize_mle
global input_filetype_ext
global output_filetype_ext
treat_options_simplest(opts, arg, n_arg, usage_string)
for (o, a) in opts:
if o in ("-m", "--measures"):
try:
measures = interpret_measures(a)
except ValueError as message:
error(
str(message) + "\nargument must be list separated by "
"\":\" and containing the names: " +
str(supported_measures))
elif o in ("-o", "--original"):
main_freq = a
elif o in ("-u", "--unnorm-mle"):
not_normalize_mle = True
elif o == "--from":
input_filetype_ext = a
elif o == "--to":
output_filetype_ext = a
else:
raise Exception("Bad arg: " + o)
def handle_candidate(self, candidate, info={}):
"""
For each candidate and for each `CorpusSize` read from the `Meta`
header, generates features that correspond to the Contrastive
Measures described above.
@param candidate The `Candidate` that is being read from the XML file.
"""
global corpussize_dict
global totals_dict
global main_freq_name
# get the original corpus freq, store the others in contrastive corpus dict
# We use plus one smoothing to avoid dealing with zero freqs
contrast_freqs = {}
if join_all_contrastive:
contrast_freqs["all"] = 1
main_freq = None
for freq in candidate.freqs:
if freq.name == main_freq_name:
main_freq = float(freq.value) + 1
elif join_all_contrastive:
contrast_freqs["all"] += float(freq.value)
else:
contrast_freqs[freq.name] = float(freq.value) + 1
for contrast_name in contrast_freqs.keys():
try:
feats = calculate_indiv(corpussize_dict[main_freq_name],
corpussize_dict[contrast_name],
main_freq,
contrast_freqs[contrast_name],
totals_dict[contrast_name],
contrast_name)
for feat in feats:
candidate.add_feat(feat)
except Exception:
error("Error in calculating the measures.")
self.chain.handle_candidate(candidate, info)
def treat_options(opts, arg, n_arg, usage_string):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global feat_list
global ascending
global print_precs
treat_options_simplest(opts, arg, n_arg, usage_string)
a_or_d = []
for (o, a) in opts:
if o in ("-f", "--feat"):
feat_list = treat_feat_list(a)
elif o in ("-a", "--asc"):
ascending = True
a_or_d.append("a")
elif o in ("-d", "--desc"):
ascending = False
a_or_d.append("d")
elif o in ("-p", "--precs"):
print_precs = True
elif o == "--from":
input_filetype_ext = a
else:
raise Exception("Bad arg: " + o)
if len(a_or_d) > 1:
warn("you should provide only one option, -a OR -d. Only the last one"+\
" will be considered.")
if not feat_list:
error("You MUST provide at least one feature with -f")
def search_terms( self, in_text, query ) :
"""
"""
if DEFAULT_LANG != "en" :
print("WARNING: Yahoo terms only works for English",file=sys.stderr)
input_text = in_text.strip()
if isinstance( input_text, unicode ) :
input_text = input_text.encode( 'utf-8' )
try:
url = ('http://search.yahooapis.com/ContentAnalysisService/'
'V1/termExtraction' )
post_data = urllib.urlencode( { "context": input_text,
"appid": YAHOO_APPID,
"query": query,
"output": "json" } )
request = urllib2.Request( url, post_data )
response = urllib2.urlopen(request)
results = simplejson.load(response)
return results[ "ResultSet" ][ "Result" ]
except Exception as err:
error( "Got an error ->" + str(err) +
"\nPLEASE VERIFY YOUR INTERNET CONNECTION")
def handle_meta(self, meta, info={}):
"""Treats the meta information of the file. Besides of printing the meta
header out, it also keeps track of all the meta-features. The list of
`all_feats` will be used in order to verify that all key features have a
valid meta-feature. This is important because we need to determine the
correct type of the feature value, since it might influence sorting
order (e.g. integers 1 < 2 < 10 but strings "1" < "10" < "2")
@param meta The `Meta` header that is being read from the XML file.
"""
global all_feats, usage_string, feat_to_order
for meta_feat in meta.meta_feats:
if meta_feat.feat_type in ("integer", "real"):
all_feats.append(meta_feat.name)
tp_classes_ok = False
for meta_tp in meta.meta_tpclasses:
if meta_tp.feat_type == "{True,False}":
tp_classes_ok = True
feat_to_order[meta_tp.name] = {}
for feat_name in all_feats:
feat_to_order[meta_tp.name][feat_name] = []
if not tp_classes_ok:
error("You must define a boolean TP class")
def handle_meta(self, meta, info={}):
"""Treats the meta information of the file. Besides of printing the meta
header out, it also keeps track of all the meta-features. The list of
`all_feats` will be used in order to verify that all key features have a
valid meta-feature. This is important because we need to determine the
correct type of the feature value, since it might influence sorting
order (e.g. integers 1 < 2 < 10 but strings "1" < "10" < "2")
@param meta The `Meta` header that is being read from the XML file.
"""
global all_feats, usage_string, feat_to_order
for meta_feat in meta.meta_feats:
if meta_feat.feat_type in ("integer", "real"):
all_feats.append(meta_feat.name)
tp_classes_ok = False
for meta_tp in meta.meta_tpclasses:
if meta_tp.feat_type == "{True,False}":
tp_classes_ok = True
feat_to_order[meta_tp.name] = {}
for feat_name in all_feats:
feat_to_order[meta_tp.name][feat_name] = []
if not tp_classes_ok:
error("You must define a boolean TP class")
def treat_options( opts, arg, n_arg, usage_string ) :
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global measures
global supported_measures
global main_freq
global not_normalize_mle
global input_filetype_ext
global output_filetype_ext
treat_options_simplest( opts, arg, n_arg, usage_string )
for ( o, a ) in opts:
if o in ( "-m", "--measures" ) :
try :
measures = interpret_measures( a )
except ValueError as message :
error( str(message) + "\nargument must be list separated by "
"\":\" and containing the names: " +
str( supported_measures ))
elif o in ( "-o", "--original" ) :
main_freq = a
elif o in ( "-u", "--unnorm-mle" ) :
not_normalize_mle = True
elif o == "--from":
input_filetype_ext = a
elif o == "--to":
output_filetype_ext = a
else:
raise Exception("Bad arg: " + o)
def treat_options(opts, arg, n_arg, usage_string):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global feat_list
global ascending
global print_precs
treat_options_simplest(opts, arg, n_arg, usage_string)
a_or_d = []
for (o, a) in opts:
if o in ("-f", "--feat"):
feat_list = treat_feat_list(a)
elif o in ("-a", "--asc"):
ascending = True
a_or_d.append("a")
elif o in ("-d", "--desc"):
ascending = False
a_or_d.append("d")
elif o in ("-p", "--precs"):
print_precs = True
elif o == "--from":
input_filetype_ext = a
else:
raise Exception("Bad arg: " + o)
if len(a_or_d) > 1:
warn("you should provide only one option, -a OR -d. Only the last one" + " will be considered.")
if not feat_list:
error("You MUST provide at least one feature with -f")
def treat_options(opts, arg, n_arg, usage_string):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global used_attributes
global basename
global build_entry
global use_text_format
global input_filetype_ext
treat_options_simplest(opts, arg, n_arg, usage_string)
used_attributes = ["lemma", "pos", "surface", "syn"]
for (o, a) in opts:
if o in ("-i", "--index"):
basename = a
elif o == "--from":
input_filetype_ext = a
elif o in ("-a", "--attributes"):
used_attributes = a.split(":")
elif o in ("-m", "--moses"):
use_text_format = "moses"
elif o in ("-c", "--conll"):
use_text_format = "conll"
elif o in ("-o", "--old"):
indexlib.Index.use_c_indexer(False)
if basename is None:
error("You must provide a filename for the index.\n"
"Option -i is mandatory.")
def treat_options_csv2xml( opts, arg, n_arg, usage_string ):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global SEPCHAR
global SURFACE_FLAG
for ( o , a ) in opts:
if o == "-F":
# sets a new separator character to be used when spliting a line
SEPCHAR = a
elif o == "-s":
# sets the assignment of a word to the "surface" item.
# default is set to "lemma".
SURFACE_FLAG = 1
else:
error("Option " + o + " is not a valid option")
def treat_options_csv2xml(opts, arg, n_arg, usage_string):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global SEPCHAR
global SURFACE_FLAG
for (o, a) in opts:
if o == "-F":
# sets a new separator character to be used when spliting a line
SEPCHAR = a
elif o == "-s":
# sets the assignment of a word to the "surface" item.
# default is set to "lemma".
SURFACE_FLAG = 1
else:
error("Option " + o + " is not a valid option")
def treat_options( opts, arg, n_arg, usage_string ) :
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global surface_instead_lemmas
global glue
global corpus_from_index
global base_attr
global min_ngram
global max_ngram
global min_frequency
treat_options_simplest( opts, arg, n_arg, usage_string )
mode = []
for ( o, a ) in opts:
if o in ("-s", "--surface") :
surface_instead_lemmas = True
base_attr = 'surface'
elif o in ("-f", "--freq") :
min_frequency = int(a)
elif o in ("-n", "--ngram") :
(min_ngram, max_ngram) = interpret_ngram(a)
elif o in ("-i", "--index") :
corpus_from_index = True
elif o in ("-G", "--glue"):
if a == "scp":
glue = scp_glue
else:
error("Unknown glue function '%s'" % a)
def treat_options(opts, arg, n_arg, usage_string):
"""
Callback function that handles the command line options of this script.
@param opts The options parsed by getopts. Ignored.
@param arg The argument list parsed by getopts.
@param n_arg The number of arguments expected for this script.
"""
global surface_instead_lemmas
global glue
global corpus_from_index
global base_attr
global min_ngram
global max_ngram
global min_frequency
treat_options_simplest(opts, arg, n_arg, usage_string)
mode = []
for (o, a) in opts:
if o in ("-s", "--surface"):
surface_instead_lemmas = True
base_attr = 'surface'
elif o in ("-f", "--freq"):
min_frequency = int(a)
elif o in ("-n", "--ngram"):
(min_ngram, max_ngram) = interpret_ngram(a)
elif o in ("-i", "--index"):
corpus_from_index = True
elif o in ("-G", "--glue"):
if a == "scp":
glue = scp_glue
else:
error("Unknown glue function '%s'" % a)
