class Spec:
# Fallback feature values.
NO_HYPHEN = 0
HAS_HYPHEN = 1
HYPHEN_CARDINALITY = 2
LOWERCASE = 0
UPPERCASE = 1
CAPITALIZED = 2
INITIAL = 3
NON_ALPHABETIC = 4
CAPITALIZATION_CARDINALITY = 5
NO_PUNCTUATION = 0
SOME_PUNCTUATION = 1
ALL_PUNCTUATION = 2
PUNCTUATION_CARDINALITY = 3
NO_QUOTE = 0
OPEN_QUOTE = 1
CLOSE_QUOTE = 2
UNKNOWN_QUOTE = 3
QUOTE_CARDINALITY = 4
NO_DIGIT = 0
SOME_DIGIT = 1
ALL_DIGIT = 2
DIGIT_CARDINALITY = 3
def __init__(self):
# Lexicon generation settings.
self.words_normalize_digits = True
self.suffixes_max_length = 3
# Action table percentile.
self.actions_percentile = 99
# Network dimensionalities.
self.lstm_hidden_dim = 256
self.ff_hidden_dim = 128
# Fixed feature dimensionalities.
self.oov_features = True
self.words_dim = 300
self.suffixes_dim = 16
self.fallback_dim = 8 # dimensionality of each fallback feature
self.roles_dim = 16
# History feature size.
self.history_limit = 5
# Frame limit for other link features.
self.frame_limit = 5
# Link feature dimensionalities.
self.link_dim_lstm = 32
self.link_dim_ff = 64
# Bins for distance from the current token to the topmost marked token.
self.distance_bins = [0, 1, 2, 3, 6, 10, 15, 20]
# Resources.
self.commons = None
self.actions = None
self.words = None
self.suffix = None
# To be determined.
self.num_actions = None
self.lstm_features = []
self.ff_fixed_features = []
self.ff_link_features = []
self.cascade = None
# Builds an action table from 'corpora'.
def _build_action_table(self, corpora):
corpora.rewind()
corpora.set_gold(True)
self.actions = Actions()
self.actions.frame_limit = self.frame_limit
for document in corpora:
assert document.size() == 0 or len(document.gold) > 0
for action in document.gold:
self.actions.add(action)
self.actions.prune(self.actions_percentile)
# Save the actions table in commons.
actions_frame = self.actions.encoded(self.commons)
# Re-read the actions table from the commons, so all frames come
# from the commons store.
self.actions = Actions()
self.actions.decode(actions_frame)
self.num_actions = self.actions.size()
print self.num_actions, "gold actions"
allowed = self.num_actions - sum(self.actions.disallowed)
print "num allowed actions:", allowed
print len(self.actions.roles), "unique roles in action table"
# Writes parts of the spec to a flow blob.
def to_flow(self, fl):
blob = fl.blob("spec")
# Separately write some fields for the convenience of the Myelin runtime.
blob.add_attr("frame_limit", self.frame_limit)
bins = [str(d) for d in self.distance_bins]
blob.add_attr("mark_distance_bins", ' '.join(bins))
# Temporarily remove fields that can't or don't need to be pickled.
fields_to_ignore = ['commons', 'actions', 'words', 'suffix', 'cascade']
cache = {}
for k, v in self.__dict__.iteritems():
if k in fields_to_ignore:
cache[k] = v
for k in fields_to_ignore:
delattr(self, k)
blob.data = pickle.dumps(self.__dict__)
# Resurrect deleted fields.
for k, v in cache.iteritems():
setattr(self, k, v)
# Reads spec from a flow.
def from_flow(self, fl):
blob = fl.blob("spec")
temp_dict = pickle.loads(blob.data)
self.__dict__.update(temp_dict)
# Read non-pickled fields.
# Read common store.
self.commons = sling.Store()
temp_file = tempfile.NamedTemporaryFile(delete=False)
filename = temp_file.name
with open(filename, "wb") as f:
f.write(fl.blob("commons").data)
temp_file.close()
self.commons.load(filename)
_ = sling.DocumentSchema(self.commons)
self.commons.freeze()
os.unlink(filename)
# Read action table from the commons.
self.actions = Actions()
self.actions.decode(self.commons["/table"])
# Read cascade specification. This is done by calling eval()
# on the class constructor. The classname is stored in the cascade frame.
frame = self.commons["/cascade"]
self.cascade = eval(frame["name"])(self.actions)
print self.cascade
# Read word lexicon.
blob = fl.blob("lexicon")
self.words = Lexicon(self.words_normalize_digits)
self.words.read(blob.data.tobytes(),
chr(int(blob.get_attr("delimiter"))))
print self.words.size(), "words read from flow's lexicon"
# Read suffix table.
self.suffix = Lexicon(self.words_normalize_digits, oov_item=None)
data = fl.blob("suffixes").data
def read_int(mview):
output = 0
shift_bits = 0
index = 0
while index < len(mview):
part = ord(mview[index])
index += 1
output |= (part & 127) << shift_bits
shift_bits += 7
if part & 128 == 0:
break
return output, mview[index:]
affix_type, data = read_int(data) # affix type
assert affix_type == 1
max_length, data = read_int(data) # max length
assert max_length == self.suffixes_max_length
num, data = read_int(data) # num affixes
for _ in xrange(num):
num_bytes, data = read_int(data)
word = data[0:num_bytes].tobytes()
self.suffix.add(word)
data = data[num_bytes:]
num_chars, data = read_int(data)
if num_chars > 0:
shorter_index, data = read_int(data)
print self.suffix.size(), "suffixes read from flow's affix table"
# Returns suffix(es) of 'word'.
def get_suffixes(self, word, unicode_chars=None):
if unicode_chars is None:
unicode_chars = list(word.decode("utf-8"))
output = []
end = min(self.suffixes_max_length, len(unicode_chars))
for start in xrange(end, 0, -1):
output.append("".join(unicode_chars[-start:]).encode('utf-8'))
output.append("") # empty suffix
return output
# Dumps suffixes in the AffixTable format (cf. sling/nlp/document/affix.cc).
def write_suffix_table(self, buf=None):
if buf is None: buf = bytearray()
# Writes 'num' in varint encoding to 'b'.
def writeint(num, b):
while True:
part = num & 127
num = num >> 7
if num > 0:
b.append(part | 128)
else:
b.append(part)
break
writeint(1, buf) # 1 = AffixTable::SUFFIX
writeint(self.suffixes_max_length, buf)
writeint(self.suffix.size(), buf)
for i in xrange(self.suffix.size()):
v = self.suffix.value(i)
assert type(v) is str, type(v)
v_unicode = v.decode('utf-8')
writeint(len(v), buf) # number of bytes
for x in v:
buf.append(x) # the bytes themselves
writeint(len(v_unicode), buf) # number of code points
if len(v_unicode) > 0:
shorter = v_unicode[1:].encode('utf-8')
shorter_idx = self.suffix.index(shorter)
assert shorter_idx is not None, (shorter, v, v_unicode)
writeint(shorter_idx, buf) # id of the shorter suffix
return buf
# Adds LSTM feature to the specification.
def add_lstm_fixed(self, name, dim, vocab, num=1):
self.lstm_features.append(
FeatureSpec(name, dim=dim, vocab=vocab, num=num))
# Adds fixed feature to the specification.
def add_ff_fixed(self, name, dim, vocab, num):
self.ff_fixed_features.append(
FeatureSpec(name, dim=dim, vocab=vocab, num=num))
# Adds recurrent link feature from the FF unit to the specification.
def add_ff_link(self, name, num):
self.ff_link_features.append(
FeatureSpec(name, dim=self.link_dim_ff, \
activation=self.ff_hidden_dim, num=num))
# Adds link feature from the LSTMs to the FF unit to the specification.
def add_lstm_link(self, name, num):
self.ff_link_features.append(
FeatureSpec(name, dim=self.link_dim_lstm, \
activation=self.lstm_hidden_dim, num=num))
# Specifies all fixed and link features.
def _specify_features(self):
# LSTM features.
self.add_lstm_fixed("word", self.words_dim, self.words.size())
if self.oov_features:
self.add_lstm_fixed(
"suffix", self.suffixes_dim, self.suffix.size(), \
self.suffixes_max_length + 1) # +1 to account for the empty affix
self.add_lstm_fixed("capitalization", self.fallback_dim,
Spec.CAPITALIZATION_CARDINALITY)
self.add_lstm_fixed("hyphen", self.fallback_dim,
Spec.HYPHEN_CARDINALITY)
self.add_lstm_fixed("punctuation", self.fallback_dim,
Spec.PUNCTUATION_CARDINALITY)
self.add_lstm_fixed("quote", self.fallback_dim,
Spec.QUOTE_CARDINALITY)
self.add_lstm_fixed("digit", self.fallback_dim,
Spec.DIGIT_CARDINALITY)
self.lstm_input_dim = sum([f.dim for f in self.lstm_features])
print "LSTM input dim", self.lstm_input_dim
assert self.lstm_input_dim > 0
# Feed forward features.
num_roles = len(self.actions.roles)
fl = self.frame_limit
if num_roles > 0:
num = 32
dim = self.roles_dim
self.add_ff_fixed("in-roles", dim, num_roles * fl, num)
self.add_ff_fixed("out-roles", dim, num_roles * fl, num)
self.add_ff_fixed("labeled-roles", dim, num_roles * fl * fl, num)
self.add_ff_fixed("unlabeled-roles", dim, fl * fl, num)
# Distance to the top of the mark stack.
self.add_ff_fixed("mark-distance", 32, len(self.distance_bins) + 1, 1)
# Link features.
self.add_ff_link("frame-creation-steps", fl)
self.add_ff_link("frame-focus-steps", fl)
self.add_lstm_link("frame-end-lr", fl)
self.add_lstm_link("frame-end-rl", fl)
self.add_ff_link("history", self.history_limit)
self.add_lstm_link("lr", 1)
self.add_lstm_link("rl", 1)
# Link features that look at the stack of marked tokens.
mark_depth = 1 # 1 = use only the top of the stack
self.add_lstm_link("mark-lr", mark_depth)
self.add_lstm_link("mark-rl", mark_depth)
self.add_ff_link("mark-step", mark_depth)
self.ff_input_dim = sum([f.dim for f in self.ff_fixed_features])
self.ff_input_dim += sum(
[f.dim * f.num for f in self.ff_link_features])
print "FF_input_dim", self.ff_input_dim
assert self.ff_input_dim > 0
# Builds the spec using the specified corpora.
def build(self, commons_path, corpora_path):
# Prepare lexical dictionaries.
self.words = Lexicon(self.words_normalize_digits)
self.suffix = Lexicon(self.words_normalize_digits, oov_item=None)
# Initialize training corpus.
corpora = Corpora(corpora_path, commons_path)
# Collect word and affix lexicons.
for document in corpora:
for token in document.tokens:
word = token.word
self.words.add(word)
for s in self.get_suffixes(word):
assert type(s) is str
self.suffix.add(s)
print "Words:", self.words.size(), "items in lexicon, including OOV"
print "Suffix:", self.suffix.size(), "items in lexicon"
# Load common store, but not freeze it yet. We will add the action table
# and cascade specification to it.
self.commons = sling.Store()
self.commons.load(commons_path)
schema = sling.DocumentSchema(self.commons)
# Prepare action table and cascade.
self._build_action_table(corpora)
self.cascade = cascade.ShiftMarkCascade(self.actions)
print self.cascade
# Save cascade specification in commons.
_ = self.cascade.as_frame(self.commons,
delegate_cell_prefix="delegate")
# Freeze the common store.
self.commons.freeze()
# Add feature specs.
self._specify_features()
# Loads embeddings for words in the lexicon.
def load_word_embeddings(self, embeddings_file):
word_embeddings = [None] * self.words.size()
f = open(embeddings_file, 'r')
# Read header.
header = f.readline().strip()
size = int(header.split()[0])
dim = int(header.split()[1])
assert dim == self.words_dim, "%r vs %r" % (dim, self.words_dim)
# Read vectors for known words.
count = 0
fmt = "f" * dim
vector_size = 4 * dim # 4 being sizeof(float)
oov = self.words.oov_index
for line in f:
tokens = line.split(" ")
word = tokens[0]
vector = list(map(float, tokens[1:]))
index = self.words.index(word)
if index != oov and word_embeddings[index] is None:
word_embeddings[index] = vector
count += 1
word_embedding_indices =\
[i for i, v in enumerate(word_embeddings) if v is not None]
word_embeddings = [v for v in word_embeddings if v is not None]
print "Loaded", count, "pre-trained embeddings from file with", size, \
"vectors. Vectors for remaining", (self.words.size() - count), \
"words will be randomly initialized."
return word_embeddings, word_embedding_indices
# Returns raw indices of LSTM features for all tokens in 'document'.
def raw_lstm_features(self, document):
output = []
chars = []
categories = []
for token in document.tokens:
decoding = list(token.word.decode("utf-8"))
chars.append(decoding)
categories.append([unicodedata.category(ch) for ch in decoding])
for f in self.lstm_features:
features = Feature()
output.append(features)
if f.name == "word":
for token in document.tokens:
features.add(self.words.index(token.word))
elif f.name == "suffix":
for index, token in enumerate(document.tokens):
suffixes = self.get_suffixes(token.word, chars[index])
ids = [self.suffix.index(s) for s in suffixes]
ids = [i for i in ids
if i is not None] # ignore unknown suffixes
features.add(ids)
elif f.name == "hyphen":
for index, token in enumerate(document.tokens):
hyphen = any(c == 'Pd' for c in categories[index])
features.add(Spec.HAS_HYPHEN if hyphen else Spec.NO_HYPHEN)
elif f.name == "capitalization":
for index, token in enumerate(document.tokens):
has_upper = any(c == 'Lu' for c in categories[index])
has_lower = any(c == 'Ll' for c in categories[index])
value = Spec.CAPITALIZED
if not has_upper and has_lower:
value = Spec.LOWERCASE
elif has_upper and not has_lower:
value = Spec.UPPERCASE
elif not has_upper and not has_lower:
value = Spec.NON_ALPHABETIC
elif index == 0 or token.brk >= 3: # 3 = SENTENCE_BREAK
value = Spec.INITIAL
features.add(value)
elif f.name == "punctuation":
for index in xrange(len(document.tokens)):
all_punct = all(c[0] == 'P' for c in categories[index])
some_punct = any(c[0] == 'P' for c in categories[index])
if all_punct:
features.add(Spec.ALL_PUNCTUATION)
elif some_punct:
features.add(Spec.SOME_PUNCTUATION)
else:
features.add(Spec.NO_PUNCTUATION)
elif f.name == "digit":
for index in xrange(len(document.tokens)):
all_digit = all(c == 'Nd' for c in categories[index])
some_digit = any(c == 'Nd' for c in categories[index])
if all_digit:
features.add(Spec.ALL_DIGIT)
elif some_digit:
features.add(Spec.SOME_DIGIT)
else:
features.add(Spec.NO_DIGIT)
elif f.name == "quote":
in_quote = False
for index in xrange(len(document.tokens)):
value = Spec.NO_QUOTE
for cat, ch in zip(categories[index], chars[index]):
if cat == 'Pi':
value = Spec.OPEN_QUOTE
elif cat == 'Pf':
value = Spec.CLOSE_QUOTE
elif cat == 'Po' and (ch == '\'' or ch == '"'):
value = Spec.UNKNOWN_QUOTE
elif cat == 'Sk' and ch == '`':
value = Spec.UNKNOWN_QUOTE
if value != Spec.NO_QUOTE:
token = document.tokens[index]
if token.word == "``":
value = Spec.OPEN_QUOTE
elif token.word == "''":
value = Spec.CLOSE_QUOTE
if value == Spec.UNKNOWN_QUOTE:
value = Spec.CLOSE_QUOTE if in_quote else Spec.OPEN_QUOTE
in_quote = not in_quote
features.add(value)
else:
raise ValueError("LSTM feature '", f.name, "' not implemented")
return output
# Returns the index of the bin corresponding to the distance of the topmost
# marked token from the current token.
def _mark_distance(self, t1, t2):
d = t2 - t1
for i, x in enumerate(self.distance_bins):
if d <= x: return i
return len(self.distance_bins)
# Returns raw indices of all fixed FF features for 'state'.
def raw_ff_fixed_features(self, feature_spec, state):
role_graph = state.role_graph()
num_roles = len(self.actions.roles)
fl = self.frame_limit
raw_features = []
if feature_spec.name == "in-roles":
for e in role_graph:
if e[2] is not None and e[2] < fl and e[2] >= 0:
raw_features.append(e[2] * num_roles + e[1])
elif feature_spec.name == "out-roles":
for e in role_graph:
raw_features.append(e[0] * num_roles + e[1])
elif feature_spec.name == "unlabeled-roles":
for e in role_graph:
if e[2] is not None and e[2] < fl and e[2] >= 0:
raw_features.append(e[2] * fl + e[0])
elif feature_spec.name == "labeled-roles":
for e in role_graph:
if e[2] is not None and e[2] < fl and e[2] >= 0:
raw_features.append(e[0] * fl * num_roles +
e[2] * num_roles + e[1])
elif feature_spec.name == "mark-distance":
if len(state.marks) > 0:
d = self._mark_distance(state.marks[-1].token, state.current)
raw_features.append(d)
else:
raise ValueError("FF feature '", feature_spec.name,
"' not implemented")
return raw_features
# Returns link features for the FF unit.
def translated_ff_link_features(self, feature_spec, state):
name = feature_spec.name
num = feature_spec.num
output = []
if name == "history":
for i in xrange(num):
output.append(None if i >= state.steps else state.steps - i -
1)
elif name in ["lr", "rl"]:
index = None
if state.current < state.end:
index = state.current - state.begin
output.append(index)
elif name in ["frame-end-lr", "frame-end-rl"]:
for i in xrange(num):
index = None
end = state.frame_end_inclusive(i)
if end != -1:
index = end - state.begin
output.append(index)
elif name == "frame-creation-steps":
for i in xrange(num):
step = state.creation_step(i)
output.append(None if step == -1 else step)
elif name == "frame-focus-steps":
for i in xrange(num):
step = state.focus_step(i)
output.append(None if step == -1 else step)
elif name in ["mark-lr", "mark-rl"]:
for i in xrange(num):
index = None
if len(state.marks) > i:
index = state.marks[-1 - i].token - state.begin
output.append(index)
elif name == "mark-step":
for i in xrange(num):
index = None
if len(state.marks) > i:
index = state.marks[-1 - i].step
output.append(index)
else:
raise ValueError("Link feature not implemented:" + name)
return output
# Debugging methods.
# Returns feature strings for FF feature indices provided in 'indices'.
# All indices are assumed to belong to a single feature whose spec is in
# 'feature_spec'.
def ff_fixed_feature_strings(self, feature_spec, indices):
limit = self.frame_limit
roles = self.actions.roles
nr = len(roles)
strings = []
if feature_spec.name == "out-roles":
strings = [
str(i / nr) + "->" + str(roles[i % nr]) for i in indices
]
elif feature_spec.name == "in-roles":
strings = [
str(roles[i % nr]) + "->" + str(i / nr) for i in indices
]
elif feature_spec.name == "unlabeled-roles":
strings = [str(i / limit) + "->" + str(i % limit) for i in indices]
elif feature_spec.name == "labeled-roles":
t = limit * nr
for i in indices:
value = str(i / t) + "->" + str(roles[(i % t) % nr])
value += "->" + str((i % t) / nr)
strings.append(value)
else:
raise ValueError(feature_spec.name + " not implemented")
return str(strings)
# Traces the seqeuence of gold actions in 'document'.
def oracle_trace(self, document):
assert len(document.gold) > 0, "No gold actions"
state = ParserState(document, self)
for gold in document.gold:
print "Taking gold action", gold
print "On state:", state
gold_index = self.actions.indices.get(gold, None)
assert gold_index is not None, "Unknown gold action: %r" % gold
assert state.is_allowed(
gold_index), "Disallowed gold action: %r" % gold
state.advance(gold)
print "Final state after", len(document.gold), "actions:", state
def _estimate_relations(cache, docs=None):
lexicon = Lexicon()
questions = {}
question_objects = Question.objects.all()
n_questions = question_objects.count()
for (i, question) in enumerate(question_objects):
print("Handling question %i/%i"%(i+1, n_questions), file=sys.stderr)
name = 'question_%i' % question.id
if name not in cache:
words = text_to_word_features(question.text)
for option in question.option_set.all():
words.extend(text_to_basewords(option.name))
for answer in question.answer_set.all():
if(answer.explanation is None): continue
words.extend(text_to_basewords(answer.explanation))
cache[name] = ' '.join(words)
else:
words = cache[name].split(' ')
lexicon.add(*words)
itemlexicon = Lexicon()
itemlexicon.add(*words)
questions[question] = itemlexicon
sessions = {}
handled = []
session_objects = Session.objects.filter(subject__contains='Hyv')
n_sessions = session_objects.count()
for (i, session) in enumerate(session_objects):
#if session.info in handled:
# continue
#handled.append(session.info)
print("Handling session %i/%i"%(i+1, n_sessions), file=sys.stderr)
# TODO: Filter by actual period
if session.plenary_session.date.year < 2007:
continue
words = get_session_word_features(session, cache, docs)
if len(words) == 0:
continue
lexicon.add(*words)
itemlexicon = Lexicon()
itemlexicon.add(*words)
sessions[session] = itemlexicon
prod = itertools.product(list(questions.keys()), list(sessions.keys()))
minr = 1
maxr = 0
relevances = []
for (question, session) in prod:
relevance = lexicon_relevance(questions[question], sessions[session],
lexicon)
if relevance < minr:
minr = relevance
if relevance > maxr:
maxr = relevance
relevances.append((relevance, question, session))
# TODO: This is not very nice, we should have a better
# ....relevancy measure instead
relevances = [((r_q_s[0] - minr) / (maxr - minr), r_q_s[1], r_q_s[2]) for r_q_s in relevances]
for (relevance, question, session) in relevances:
try:
record = QuestionSessionRelevance.objects.get(
question=question,
session=session, user=None)
except QuestionSessionRelevance.DoesNotExist:
record = QuestionSessionRelevance(relevance=relevance,
question=question,
session=session, user=None)
record.relevance = relevance
record.save()
def _estimate_relations(cache, docs=None):
lexicon = Lexicon()
questions = {}
question_objects = Question.objects.all()
n_questions = question_objects.count()
for (i, question) in enumerate(question_objects):
print >>sys.stderr, "Handling question %i/%i"%(i+1, n_questions)
name = 'question_%i' % question.id
if name not in cache:
words = text_to_word_features(question.text)
for option in question.option_set.all():
words.extend(text_to_basewords(option.name))
for answer in question.answer_set.all():
if(answer.explanation is None): continue
words.extend(text_to_basewords(answer.explanation))
cache[name] = ' '.join(words)
else:
words = cache[name].split(' ')
lexicon.add(*words)
itemlexicon = Lexicon()
itemlexicon.add(*words)
questions[question] = itemlexicon
sessions = {}
handled = []
session_objects = Session.objects.filter(subject__contains=u'Hyv')
n_sessions = session_objects.count()
for (i, session) in enumerate(session_objects):
#if session.info in handled:
# continue
#handled.append(session.info)
print >>sys.stderr, "Handling session %i/%i"%(i+1, n_sessions)
# TODO: Filter by actual period
if session.plenary_session.date.year < 2007:
continue
words = get_session_word_features(session, cache, docs)
if len(words) == 0:
continue
lexicon.add(*words)
itemlexicon = Lexicon()
itemlexicon.add(*words)
sessions[session] = itemlexicon
prod = itertools.product(questions.keys(), sessions.keys())
minr = 1
maxr = 0
relevances = []
for (question, session) in prod:
relevance = lexicon_relevance(questions[question], sessions[session],
lexicon)
if relevance < minr:
minr = relevance
if relevance > maxr:
maxr = relevance
relevances.append((relevance, question, session))
# TODO: This is not very nice, we should have a better
# ....relevancy measure instead
relevances = map(lambda (r, q, s): ((r - minr) / (maxr - minr), q, s),
relevances)
for (relevance, question, session) in relevances:
try:
record = QuestionSessionRelevance.objects.get(
question=question,
session=session, user=None)
except QuestionSessionRelevance.DoesNotExist:
record = QuestionSessionRelevance(relevance=relevance,
question=question,
session=session, user=None)
record.relevance = relevance
record.save()
