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Clean up commented out code from beam parser.
This commit is contained in:
Matthew Honnibal
parent
476977ef62
commit
a1281835a8
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@ -235,143 +235,3 @@ def _cleanup(Beam beam):
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cdef hash_t _hash_state(void* _state, void* _) except 0:
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state = <StateClass>_state
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return state.c.hash()
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#
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# def _maxent_update(self, Doc doc, pred_scores, pred_hist, gold_scores, gold_hist):
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# Z = 0
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# for i, (score, history) in enumerate(zip(pred_scores, pred_hist)):
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# prob = exp(score)
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# if prob < 1e-6:
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# continue
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# stcls = StateClass.init(doc.c, doc.length)
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# self.moves.initialize_state(stcls.c)
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# for clas in history:
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# delta_loss[clas] = prob * 1/Z
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# gradient = [(input_ * prob) / Z for input_ in hidden]
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# fill_context(context, stcls.c)
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# nr_feat = model.extracter.set_features(features, context)
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# for feat in features[:nr_feat]:
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# key = (clas, feat.key)
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# counts[key] = counts.get(key, 0.0) + feat.value
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# self.moves.c[clas].do(stcls.c, self.moves.c[clas].label)
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# for key in counts:
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# counts[key] *= prob
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# Z += prob
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# gZ, g_counts = self._maxent_counts(doc, gold_scores, gold_hist)
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# for (clas, feat), value in g_counts.items():
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# self.model.update_weight(feat, clas, value / gZ)
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#
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# Z, counts = self._maxent_counts(doc, pred_scores, pred_hist)
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# for (clas, feat), value in counts.items():
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# self.model.update_weight(feat, clas, -value / (Z + gZ))
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#
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#
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# def _maxent_update(self, doc, pred_scores, pred_hist, gold_scores, gold_hist,
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# step_size=0.001):
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# cdef weight_t Z, gZ, value
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# cdef feat_t feat
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# cdef class_t clas
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# gZ, g_counts = self._maxent_counts(doc, gold_scores, gold_hist)
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# Z, counts = self._maxent_counts(doc, pred_scores, pred_hist)
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# update = {}
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# if gZ > 0:
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# for (clas, feat), value in g_counts.items():
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# update[(clas, feat)] = value / gZ
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# Z += gZ
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# for (clas, feat), value in counts.items():
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# update.setdefault((clas, feat), 0.0)
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# update[(clas, feat)] -= value / Z
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# for (clas, feat), value in update.items():
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# if value < 1000:
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# self.model.update_weight(feat, clas, step_size * value)
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#
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# def _maxent_counts(self, Doc doc, scores, history):
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# cdef Pool mem = Pool()
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# cdef atom_t[CONTEXT_SIZE] context
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# features = <FeatureC*>mem.alloc(self.model.nr_feat, sizeof(FeatureC))
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#
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# cdef StateClass stcls
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#
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# cdef class_t clas
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# cdef ParserPerceptron model = self.model
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#
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# cdef weight_t Z = 0.0
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# cdef weight_t score
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# counts = {}
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# for i, (score, history) in enumerate(zip(scores, history)):
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# prob = exp(score)
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# if prob < 1e-6:
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# continue
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# stcls = StateClass.init(doc.c, doc.length)
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# self.moves.initialize_state(stcls.c)
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# for clas in history:
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# fill_context(context, stcls.c)
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# nr_feat = model.extracter.set_features(features, context)
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# for feat in features[:nr_feat]:
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# key = (clas, feat.key)
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# counts[key] = counts.get(key, 0.0) + feat.value
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# self.moves.c[clas].do(stcls.c, self.moves.c[clas].label)
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# for key in counts:
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# counts[key] *= prob
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# Z += prob
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# return Z, counts
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#
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#
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# def _advance_beam(self, Beam beam, GoldParse gold, bint follow_gold, words):
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# cdef atom_t[CONTEXT_SIZE] context
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# cdef int i, j, cost
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# cdef bint is_valid
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# cdef const Transition* move
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#
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# for i in range(beam.size):
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# state = <StateClass>beam.at(i)
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# if not state.is_final():
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# # What the model is predicting here:
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# # We know, separately, the probability of the current state
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# # We can think of a state as a sequence of (action, score) pairs
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# # We obtain a state by doing reduce(state, [act for act, score in scores])
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# # We obtain its probability by doing sum(score for act, score in scores)
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# #
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# # So after running the forward pass, we have this output layer...
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# # The output layer has N nodes in its output, for our N moves
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# # The model asserts that:
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# #
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# # P(actions[i](state)) == score + output[i]
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# #
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# # i.e. each node holds a score that means "This is the difference
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# # in goodness that will occur if you apply this action to this state.
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# # If you apply this action, this is how I would judge the state."
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# self.model.set_scoresC(beam.scores[i], eg)
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# self.moves.set_validC(beam.is_valid[i], state)
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# if gold is not None:
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# for i in range(beam.size):
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# state = <StateClass>beam.at(i)
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# if not stcls.is_final():
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# self.moves.set_costsC(beam.costs[i], beam.is_valid[i],
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# state, gold)
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# if follow_gold:
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# for j in range(self.moves.n_moves):
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# beam.is_valid[i][j] *= beam.costs[i][j] == 0
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# beam.advance(_transition_state, _hash_state, <void*>self.moves.c)
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# beam.check_done(_check_final_state, NULL)
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#
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# def _update(self, Doc doc, g_hist, p_hist, loss):
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# pred = StateClass(doc)
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# gold = StateClass(doc)
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# for g_move, p_move in zip(g_hist, p_hist):
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# self.model(pred_eg)
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# self.model(gold_eg)
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#
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# margin = pred_eg.scores[p_move] - gold_eg.scores[g_move] + 1
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# if margin > 0:
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# gold_eg.losses[g_move] = margin
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# self.model.update(gold_eg)
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# pred_eg.losses[p_move] = -margin
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# self.model.update(pred_eg.guess)
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# self.c.moves[g_move].do(gold)
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# self.c.moves[p_move].do(pred)
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#
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#
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#
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