explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2025-01-12 18:26:30 +03:00
Code Issues Packages Projects Releases Wiki Activity

Bug fixes to beam parsing. Learns small sample

Browse Source
This commit is contained in:
Matthew Honnibal 2017-08-13 09:33:39 +02:00
parent 4ae0d5e1e6
commit 12de263813
1 changed files with 61 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

81
spacy/syntax/_beam_utils.pyx
View File

@ -66,7 +66,7 @@ cdef class ParserBeam(object):
for beam in self.beams:
if beam is not None:
_cleanup(beam)
@property
def is_done(self):
return all(b.is_done for b in self.beams)
@ -80,6 +80,8 @@ cdef class ParserBeam(object):
def advance(self, scores, follow_gold=False):
cdef Beam beam
for i, beam in enumerate(self.beams):
if beam.is_done:
continue
self._set_scores(beam, scores[i])
if self.golds is not None:
self._set_costs(beam, self.golds[i], follow_gold=follow_gold)
@ -108,7 +110,22 @@ cdef class ParserBeam(object):
for j in range(beam.nr_class):
if beam.costs[i][j] >= 1:
beam.is_valid[i][j] = 0
def is_gold(StateClass state, GoldParse gold, strings):
predicted = set()
truth = set()
for i in range(gold.length):
if gold.cand_to_gold[i] is None:
continue
if state.safe_get(i).dep:
predicted.add((i, state.H(i), strings[state.safe_get(i).dep]))
else:
predicted.add((i, state.H(i), 'ROOT'))
id_, word, tag, head, dep, ner = gold.orig_annot[gold.cand_to_gold[i]]
truth.add((id_, head, dep))
return truth == predicted
def get_token_ids(states, int n_tokens):
cdef StateClass state
@ -123,11 +140,13 @@ def get_token_ids(states, int n_tokens):
c_ids += ids.shape[1]
return ids
nr_update = 0
def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
states, tokvecs, golds,
state2vec, vec2scores, drop=0., sgd=None,
losses=None, int width=4, float density=0.001):
global nr_update
nr_update += 1
pbeam = ParserBeam(moves, states, golds,
width=width, density=density)
gbeam = ParserBeam(moves, states, golds,
@ -139,8 +158,9 @@ def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
if pbeam.is_done and gbeam.is_done:
break
beam_maps.append({})
states, p_indices, g_indices = get_states(pbeam, gbeam, beam_maps[-1])
states, p_indices, g_indices = get_states(pbeam, gbeam, beam_maps[-1], nr_update)
if not states:
break
token_ids = get_token_ids(states, nr_feature)
vectors, bp_vectors = state2vec.begin_update(token_ids, drop=drop)
scores, bp_scores = vec2scores.begin_update(vectors, drop=drop)
@ -154,6 +174,16 @@ def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
for i, violn in enumerate(violns):
violn.check_crf(pbeam[i], gbeam[i])
# The non-monotonic oracle makes it difficult to ensure final costs are
# correct. Therefore do final correction
cdef Beam pred
for i, (pred, gold_parse) in enumerate(zip(pbeam, golds)):
for j in range(pred.size):
if is_gold(<StateClass>pred.at(j), gold_parse, moves.strings):
pred._states[j].loss = 0.0
elif pred._states[j].loss == 0.0:
pred._states[j].loss = 1.0
violn.check_crf(pred, gbeam[i])
histories = [(v.p_hist + v.g_hist) for v in violns]
losses = [(v.p_probs + v.g_probs) for v in violns]
@ -162,30 +192,35 @@ def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
return states_d_scores, backprops
def get_states(pbeams, gbeams, beam_map):
def get_states(pbeams, gbeams, beam_map, nr_update):
seen = {}
states = []
p_indices = []
g_indices = []
cdef Beam pbeam, gbeam
for eg_id, (pbeam, gbeam) in enumerate(zip(pbeams, gbeams)):
if pbeam.loss > 0 and pbeam.min_score > (gbeam.score + nr_update):
continue
p_indices.append([])
for j in range(pbeam.size):
key = tuple([eg_id] + pbeam.histories[j])
seen[key] = len(states)
p_indices[-1].append(len(states))
states.append(<StateClass>pbeam.at(j))
state = <StateClass>pbeam.at(j)
if not state.is_final():
key = tuple([eg_id] + pbeam.histories[j])
seen[key] = len(states)
p_indices[-1].append(len(states))
states.append(<StateClass>pbeam.at(j))
beam_map.update(seen)
g_indices.append([])
for i in range(gbeam.size):
key = tuple([eg_id] + gbeam.histories[i])
if key in seen:
g_indices[-1].append(seen[key])
else:
g_indices[-1].append(len(states))
beam_map[key] = len(states)
states.append(<StateClass>gbeam.at(i))
state = <StateClass>gbeam.at(j)
if not state.is_final():
key = tuple([eg_id] + gbeam.histories[i])
if key in seen:
g_indices[-1].append(seen[key])
else:
g_indices[-1].append(len(states))
beam_map[key] = len(states)
states.append(<StateClass>gbeam.at(i))
p_indices = [numpy.asarray(idx, dtype='i') for idx in p_indices]
g_indices = [numpy.asarray(idx, dtype='i') for idx in g_indices]
return states, p_indices, g_indices
@ -206,12 +241,18 @@ def get_gradient(nr_class, beam_maps, histories, losses):
So history is list of lists of lists of ints
"""
nr_step = len(beam_maps)
grads = [numpy.zeros((max(beam_map.values())+1, nr_class), dtype='f')
for beam_map in beam_maps]
grads = []
for beam_map in beam_maps:
if beam_map:
grads.append(numpy.zeros((max(beam_map.values())+1, nr_class), dtype='f'))
else:
grads.append(None)
for eg_id, hists in enumerate(histories):
for loss, hist in zip(losses[eg_id], hists):
key = tuple([eg_id])
for j, clas in enumerate(hist):
if grads[j] is None:
continue
i = beam_maps[j][key]
# In step j, at state i action clas
# resulted in loss