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ml_dmv_model.py
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ml_dmv_model.py
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import shutil
import numpy as np
import torch
import torch.nn as nn
import eisner_for_dmv
import utils
import m_dir
# from torch_model.NN_trainer import *
class ml_dmv_model(nn.Module):
def __init__(self, pos, sentence_map, languages, language_map, data_size, options):
super(ml_dmv_model, self).__init__()
self.options = options
self.count_smoothing = options.count_smoothing
self.param_smoothing = options.param_smoothing
self.pos = pos
self.sentence_map = sentence_map
self.language_map = language_map
self.languages = languages
self.decision_pos = {}
self.to_decision = {}
self.from_decision = {}
self.id_to_pos = {}
self.em_type = options.em_type
self.trans_counter = None
self.function_mask = options.function_mask
self.use_neural = options.use_neural
self.unified_network = options.unified_network
self.initial_flag = True
self.data_size = data_size
self.cvalency = options.c_valency
self.dvalency = options.d_valency
self.sentence_predict = options.sentence_predict
self.concat_all = options.concat_all
if self.sentence_predict:
self.sentence_counter = {}
self.sentence_decision_counter = {}
self.sentence_trans_param = np.zeros((data_size, len(pos.keys()), len(pos.keys()), 2, self.cvalency))
self.sentence_decision_param = np.zeros((data_size, len(pos.keys()), 2, self.dvalency, 2))
else:
self.sentence_trans_param = None
# head_pos,child_pos,direction,child_valence,languages
self.trans_param = np.zeros((len(pos), len(pos), 2, self.cvalency, len(languages)))
# head_pos,direction,valence,decision,languages
self.decision_param = np.zeros((len(pos), 2, self.dvalency, 2, len(languages)))
self.trans_alpha = None
self.use_prior = options.use_prior
if self.function_mask:
self.function_set = set()
self.function_set.add("ADP")
self.function_set.add("AUX")
self.function_set.add("CONJ")
self.function_set.add("DET")
self.function_set.add("PART")
self.function_set.add("SCONJ")
if self.use_neural:
self.rule_samples = list()
self.decision_samples = list()
for p in pos.keys():
self.id_to_pos[self.pos[p]] = p
# KM initialization
def init_param(self, data):
root_idx = self.pos['ROOT-POS']
count_smoothing = self.count_smoothing
# pos_tag,direction,valence,decision,languages
norm_counter = np.zeros((len(self.pos), 2, self.dvalency, 2, len(self.languages)))
s_counter = 0
for sentence in data:
word_num = sentence.size - 1
change = np.zeros((word_num, 2))
lan_id = self.languages[self.language_map[s_counter]]
for i, entry in enumerate(sentence.entries):
if i == 0:
continue
pos_id = self.pos[entry.pos]
self.trans_param[root_idx, pos_id, 1, :, lan_id] += 1. / word_num
for j, m_entry in enumerate(sentence.entries):
if j == 0:
continue
child_sum = 0
for i in range(sentence.size):
if i == 0:
continue
if i == j:
continue
child_sum += 1. / abs(i - j)
if child_sum > 0:
scale = float(word_num - 1) / word_num * (1. / child_sum)
else:
scale = 0
for i, h_entry in enumerate(sentence.entries):
if i == j:
continue
if i == 0:
continue
if j < i:
dir = 0
else:
dir = 1
span = abs(i - j)
h_pos = h_entry.pos
m_pos = m_entry.pos
h_pos_id = self.pos.get(h_pos)
m_pos_id = self.pos.get(m_pos)
self.trans_param[h_pos_id, m_pos_id, dir, :, lan_id] += 1. / span * scale
change[i - 1, dir] += 1. / span * scale
self.update_decision(change, norm_counter, sentence.entries, lan_id)
s_counter += 1
self.trans_param += count_smoothing
self.decision_param += count_smoothing
es = self.first_child_update(norm_counter)
pr_first_kid = 0.9 * es
norm_counter = norm_counter * pr_first_kid.reshape(1, 1, 1, 1, len(self.languages))
self.decision_param = self.decision_param + norm_counter
self.trans_param[:, root_idx, :, :, :] = 0
# self.trans_param[root_idx, :, 0, :] = 0
trans_sum = np.sum(self.trans_param, axis=1).reshape((len(self.pos), 1, 2, self.cvalency, len(self.languages)))
decision_sum = np.sum(self.decision_param, axis=3).reshape(
(len(self.pos), 2, self.dvalency, 1, len(self.languages)))
self.trans_param = self.trans_param / trans_sum
self.decision_param = self.decision_param / decision_sum
def update_decision(self, change, norm_counter, entries, lan_id):
word_num, _ = change.shape
for i in range(word_num):
pos_id = self.pos[entries[i + 1].pos]
for dir in range(2):
if change[i, dir] > 0:
norm_counter[pos_id, dir, 0, 1, lan_id] += 1
norm_counter[pos_id, dir, 1, 1, lan_id] += -1
self.decision_param[pos_id, dir, 1, 1, lan_id] += change[i, dir]
norm_counter[pos_id, dir, 0, 0, lan_id] += -1
norm_counter[pos_id, dir, 1, 0, lan_id] += 1
self.decision_param[pos_id, dir, 0, 0, lan_id] += 1
else:
self.decision_param[pos_id, dir, 0, 0, lan_id] += 1
def first_child_update(self, norm_counter):
es = np.ones(len(self.languages))
all_param = np.copy(self.decision_param)
all_param = all_param.reshape((len(self.pos) * 2 * self.dvalency * 2, len(self.languages)))
all_norm = norm_counter.reshape((len(self.pos) * 2 * self.dvalency * 2, len(self.languages)))
ratio = {}
for l in range(len(self.languages)):
for i in range(len(all_param[:, l])):
if all_param[i, l] > 0:
ratio[l] = -all_param[i, l] / all_norm[i, l]
if all_norm[i, l] < 0 and es[l] > ratio[l]:
es[l] = ratio[l]
return es
def em_e(self, batch_pos, batch_lan, batch_sen, trans_counter, decision_counter, em_type):
batch_pos = np.array(batch_pos)
if em_type == 'viterbi':
batch_likelihood = self.run_viterbi_estep(batch_pos, batch_lan, batch_sen, trans_counter,
decision_counter)
elif em_type == 'em':
batch_likelihood, en_like = self.run_em_estep(batch_pos, batch_lan, batch_sen, trans_counter,
decision_counter)
return batch_likelihood, en_like
def run_viterbi_estep(self, batch_pos, batch_words, batch_sen, trans_counter, decision_counter, lex_counter):
batch_size = len(batch_pos)
batch_score, batch_decision_score = self.evaluate_batch_score(batch_words, batch_pos, None)
batch_score = np.array(batch_score)
batch_decision_score = np.array(batch_decision_score)
batch_score[:, :, 0, :, :, :] = -np.inf
if self.specify_splitting:
batch_score, batch_decision_score = self.mask_scores(batch_score, batch_decision_score, batch_pos)
if self.function_mask:
batch_score = self.function_to_mask(batch_score, batch_pos)
best_parse = eisner_for_dmv.batch_parse(batch_score, batch_decision_score, self.dvalency, self.cvalency)
batch_likelihood = self.update_counter(best_parse, trans_counter, decision_counter, lex_counter, batch_pos,
batch_words)
self.trans_counter = trans_counter
return batch_likelihood
def run_em_estep(self, batch_pos, batch_lan, batch_sen, trans_counter, decision_counter):
# Assign scores to each possible dependency arc
batch_score, batch_decision_score = self.evaluate_batch_score(batch_pos, batch_sen, self.language_map,
self.languages, None)
batch_score = np.array(batch_score)
batch_decision_score = np.array(batch_decision_score)
# Root can not be taken as child
batch_score[:, :, 0, :] = -np.inf
# Mask function tags
if self.function_mask:
batch_score = self.function_to_mask(batch_score, batch_pos)
batch_size, sentence_length, _, _ = batch_score.shape
inside_batch_score = batch_score.reshape(batch_size, sentence_length, sentence_length, 1, 1, self.cvalency)
inside_batch_decision_score = batch_decision_score.reshape(batch_size, sentence_length, 1, 2, self.dvalency, 2)
# Compute inside-outside table
inside_complete_table, inside_incomplete_table, sentence_prob = \
eisner_for_dmv.batch_inside(inside_batch_score, inside_batch_decision_score, self.dvalency, self.cvalency)
outside_complete_table, outside_incomplete_table = \
eisner_for_dmv.batch_outside(inside_complete_table, inside_incomplete_table, inside_batch_score,
inside_batch_decision_score, self.dvalency, self.cvalency)
# Update counters
batch_likelihood, en_like = self.update_pseudo_count(inside_incomplete_table, inside_complete_table,
sentence_prob,
outside_incomplete_table, outside_complete_table,
trans_counter,
decision_counter, batch_pos, batch_sen, batch_lan)
return batch_likelihood, en_like
def evaluate_batch_score(self, batch_pos, batch_sen, language_map, languages, eval_trans_param):
batch_size, sentence_length = batch_pos.shape
# batch,head,child,head_tag,child_tag
scores = np.zeros((batch_size, sentence_length, sentence_length, self.cvalency))
# batch,position,tag,direction,valency,decision
decision_scores = np.zeros((batch_size, sentence_length, 2, self.dvalency, 2))
scores.fill(-np.inf)
decision_scores.fill(-np.inf)
for s in range(batch_size):
sentence_id = batch_sen[s]
lan_id = languages[language_map[sentence_id]]
if self.concat_all:
lan_id = 0
for i in range(sentence_length):
pos_id = batch_pos[s][i]
if i > 0:
decision_scores[s, i, :, :, :] = np.log(self.decision_param[pos_id, :, :, :, lan_id])
else:
decision_scores[s, i, :, :, :] = 0
for j in range(sentence_length):
h_pos_id = batch_pos[s][i]
m_pos_id = batch_pos[s][j]
if j == 0:
continue
if i == j:
continue
if i > j:
dir = 0
else:
dir = 1
if eval_trans_param is None:
if self.initial_flag or not self.sentence_predict or not self.training:
scores[s, i, j, :] = np.log(self.trans_param[h_pos_id, m_pos_id, dir, :, lan_id])
else:
scores[s, i, j, :] = np.log(
self.sentence_trans_param[sentence_id, h_pos_id, m_pos_id, dir, :])
else:
scores[s, i, j, :] = np.log(eval_trans_param[sentence_id, h_pos_id, m_pos_id, dir, :])
return scores, decision_scores # scores: batch, h, c, v ;decision_scores: batch, h d v stop
def update_counter(self, best_parse, trans_counter, decision_counter, lex_counter, batch_pos, batch_words):
batch_likelihood = 0.0
for sen_id in range(len(batch_pos)):
pos_sentence = batch_pos[sen_id]
word_sentence = batch_words[sen_id]
heads = best_parse[0][sen_id]
tags = best_parse[1][sen_id]
head_valences = best_parse[2][sen_id]
valences = best_parse[3][sen_id]
for i, h in enumerate(heads):
m_tag_id = int(tags[i])
m_pos = pos_sentence[i]
if h == -1:
continue
m_dec_pos = self.to_decision[m_pos]
m_head_valence = int(head_valences[i])
m_valence = valences[i]
if self.cvalency > 1:
m_child_valence = m_head_valence
else:
m_child_valence = 0
m_word = word_sentence[i]
h = int(h)
h_pos = pos_sentence[h]
if h < i:
dir = 1
else:
dir = 0
h_tag_id = int(tags[h])
trans_counter[h_pos, m_pos, h_tag_id, m_tag_id, dir, m_child_valence] += 1.
batch_likelihood += np.log(self.trans_param[h_pos, m_pos, h_tag_id, m_tag_id, dir, m_child_valence])
if self.use_neural:
self.rule_samples.append(list([h_pos, m_pos, h_tag_id, m_tag_id, dir, m_child_valence]))
decision_counter[m_dec_pos, m_tag_id, 0, int(m_valence[0]), 0] += 1.
decision_counter[m_dec_pos, m_tag_id, 1, int(m_valence[1]), 0] += 1.
if self.use_neural:
if not self.unified_network:
self.decision_samples.append(list([m_dec_pos, m_tag_id, 0, int(m_valence[0]), 0]))
self.decision_samples.append(list([m_dec_pos, m_tag_id, 1, int(m_valence[1]), 0]))
else:
self.decision_samples.append(list([m_pos, m_tag_id, 0, int(m_valence[0]), 0]))
self.decision_samples.append(list([m_pos, m_tag_id, 1, int(m_valence[1]), 0]))
# lexicon count
if (self.use_lex):
lex_counter[m_pos, m_tag_id, m_word] += 1
batch_likelihood += np.log(self.lex_param[m_pos, m_tag_id, m_word])
if h > 0:
h_dec_pos = self.to_decision[h_pos]
decision_counter[h_dec_pos, h_tag_id, dir, m_head_valence, 1] += 1.
if self.use_neural:
if not self.unified_network:
self.decision_samples.append(list([h_dec_pos, h_tag_id, dir, m_head_valence, 1]))
else:
self.decision_samples.append(list([h_pos, h_tag_id, dir, m_head_valence, 1]))
batch_likelihood += np.log(self.decision_param[h_dec_pos, h_tag_id, dir, m_head_valence, 1])
batch_likelihood += np.log(self.decision_param[m_dec_pos, m_tag_id, 0, int(m_valence[0]), 0])
batch_likelihood += np.log(self.decision_param[m_dec_pos, m_tag_id, 1, int(m_valence[1]), 0])
return batch_likelihood
def em_m(self, trans_counter, decision_counter):
root_idx = self.pos['ROOT-POS']
trans_counter = trans_counter + self.param_smoothing
decision_counter = decision_counter + self.param_smoothing
trans_counter[:, root_idx, :, :] = 0
child_sum = np.sum(trans_counter, axis=1).reshape(len(self.pos), 1, 2, self.cvalency, len(self.languages))
decision_sum = np.sum(decision_counter, axis=3).reshape(len(self.pos), 2, self.dvalency, 1, len(self.languages))
self.trans_param = trans_counter / child_sum
self.decision_param = decision_counter / decision_sum
return
def update_pseudo_count(self, inside_incomplete_table, inside_complete_table, sentence_prob,
outside_incomplete_table, outside_complete_table, trans_counter,
decision_counter, batch_pos, batch_sen, batch_lan):
batch_likelihood = 0.0
en_like = 0.0
batch_size, sentence_length = batch_pos.shape
span_2_id, id_2_span, ijss, ikcs, ikis, kjcs, kjis, basic_span = utils.constituent_index(sentence_length, False)
for s in range(batch_size):
pos_sentence = batch_pos[s]
sentence_id = batch_sen[s]
lan_id = batch_lan[s]
one_sentence_count = []
one_sentence_decision_count = []
for h in range(sentence_length):
for m in range(sentence_length):
if m == 0:
continue
if h == m:
continue
if h > m:
dir = 0
else:
dir = 1
h_pos = pos_sentence[h]
m_pos = pos_sentence[m]
if dir == 0:
span_id = span_2_id[(m, h, dir)]
else:
span_id = span_2_id[(h, m, dir)]
# Pseudo count for one dependency arc
dep_count = inside_incomplete_table[s, span_id, :, :, :] + \
outside_incomplete_table[s, span_id, :, :, :] - sentence_prob[s]
if dir == 0:
dep_count = dep_count.swapaxes(1, 0)
if self.cvalency == 1:
trans_counter[h_pos, m_pos, dir, 0, lan_id] += np.sum(np.exp(dep_count))
else:
trans_counter[h_pos, m_pos, dir, :, lan_id] += np.exp(dep_count).reshape(self.dvalency)
# Add training samples for neural network
if self.use_neural:
for v in range(self.cvalency):
count = np.exp(dep_count).reshape(self.dvalency)[v]
self.rule_samples.append(list([h_pos, m_pos, dir, v, sentence_id, lan_id, count]))
if h > 0:
# Add count for CONTINUE decision
decision_counter[h_pos, dir, :, 1, lan_id] += np.exp(dep_count).reshape(self.dvalency)
if self.use_neural:
reshaped_count = np.exp(dep_count).reshape(self.dvalency)
for v in range(self.dvalency):
count = reshaped_count[v]
self.decision_samples.append(list([h_pos, dir, v, sentence_id, lan_id, 1, count]))
for m in range(1, sentence_length):
m_pos = pos_sentence[m]
for d in range(2):
m_span_id = span_2_id[(m, m, d)]
# Pseudo count for STOP decision
stop_count = inside_complete_table[s, m_span_id, :, :] + \
outside_complete_table[s, m_span_id, :, :] - sentence_prob[s]
decision_counter[m_pos, d, :, 0, lan_id] += np.exp(stop_count).reshape(self.dvalency)
if self.use_neural:
for v in range(self.dvalency):
count = np.exp(stop_count).reshape(self.dvalency)[v]
self.decision_samples.append(list([m_pos, d, v, sentence_id, lan_id, 0, count]))
batch_likelihood += sentence_prob[s]
if self.language_map[sentence_id] == 'en':
en_like += sentence_prob[s]
if self.sentence_predict:
self.sentence_counter[sentence_id] = one_sentence_count
self.sentence_decision_counter[sentence_id] = one_sentence_decision_count
return batch_likelihood, en_like
def find_predict_samples(self, batch_pos, batch_lan, batch_sen):
batch_size, sentence_length = batch_pos.shape
predict_rule_samples = []
rule_involved = set()
for s in range(batch_size):
pos_sentence = batch_pos[s]
lan_id = batch_lan[s]
sentence_id = batch_sen[s]
for h in range(sentence_length):
for dir in range(2):
if h == 0 and dir == 0:
continue
h_pos = pos_sentence[h]
for v in range(self.cvalency):
rule_tuple = (h_pos, dir, v, lan_id, sentence_id)
if rule_tuple not in rule_involved:
rule_involved.add(rule_tuple)
predict_rule_samples.append(list([h_pos, dir, v, lan_id, sentence_id]))
return np.array(predict_rule_samples)
def mask_scores(self, batch_scores, batch_decision_scores, batch_pos):
batch_size, sentence_length, _, _, _, _ = batch_scores.shape
score_mask = np.zeros((batch_size, sentence_length, sentence_length, self.tag_num, self.tag_num, self.cvalency))
decision_mask = np.zeros((batch_size, sentence_length, self.tag_num, 2, self.dvalency, 2))
for s in range(batch_size):
for i in range(sentence_length):
if self.id_to_pos[batch_pos[s, i]] not in self.specify_list:
decision_mask[s, i, 1:, :] = -np.inf
for j in range(sentence_length):
if self.id_to_pos[batch_pos[s, i]] not in self.specify_list and self.id_to_pos[
batch_pos[s, j]] not in self.specify_list:
score_mask[s, i, j, :, :, :] = -np.inf
score_mask[s, i, j, 0, 0, :] = 0
if self.id_to_pos[batch_pos[s, i]] not in self.specify_list and self.id_to_pos[
batch_pos[s, j]] in self.specify_list:
score_mask[s, i, j, 1:, :, :] = -np.inf
if self.id_to_pos[batch_pos[s, i]] in self.specify_list and self.id_to_pos[
batch_pos[s, j]] not in self.specify_list:
score_mask[s, i, j, :, 1:, :] = -np.inf
batch_scores = batch_scores + score_mask
batch_decision_scores = batch_decision_scores + decision_mask
return batch_scores, batch_decision_scores
def function_to_mask(self, batch_score, batch_pos):
batch_size, sentence_length, _, _ = batch_score.shape
function_score_mask = np.zeros(
(batch_size, sentence_length, sentence_length, self.cvalency))
for s in range(batch_size):
function_count = 0
for i in range(sentence_length):
pos_id = batch_pos[s, i]
pos = self.id_to_pos[pos_id]
if pos in self.function_set:
function_score_mask[s, i, :, :] = -np.inf
function_count += 1
if function_count == sentence_length - 1:
function_score_mask[s, :, :, :] = 1e-30
batch_score = batch_score + function_score_mask
return batch_score
def apply_prior(self, trans_counter, lex_counter, prior_alpha, prior_epsilon, lex_prior_alpha, lex_epsilon):
if self.trans_alpha is None:
child_mean = np.average(trans_counter, axis=(1, 3)).reshape(len(self.pos), 1, self.tag_num, 1, 2,
self.cvalency)
self.trans_alpha = -child_mean * prior_alpha
if self.tag_num > 1:
prior_epsilon = 1e-3
for h in range(len(self.pos)):
for t in range(self.tag_num):
for dir in range(2):
for c in range(self.cvalency):
dir_trans_alpha = trans_counter[h, :, t, :, dir, c] + self.trans_alpha[h, :, t, :, dir, c]
dim = self.tag_num * len(self.pos)
dir_trans_alpha = dir_trans_alpha.reshape(dim, 1)
md = m_dir.modified_dir(dim, dir_trans_alpha, prior_epsilon)
posterior_counts = md.get_mode()
trans_counter[h, :, t, :, dir, c] = posterior_counts.reshape(len(self.pos), self.tag_num)
if self.use_lex and self.tag_num > 1:
if self.lex_alpha is None:
lex_mean = np.average(lex_counter, axis=2).reshape(len(self.pos), self.tag_num, 1)
self.lex_alpha = -lex_mean * lex_prior_alpha
for p in range(len(self.pos)):
for t in range(self.tag_num):
dir_lex_alpha = lex_counter[p, t, :] + self.lex_alpha[p, t, :]
dim = len(self.vocab)
md = m_dir.modified_dir(dim, dir_lex_alpha, lex_epsilon)
posterior_counts = md.get_mode()
lex_counter[p, t, :] = posterior_counts
def save(self, fn):
tmp = fn + '.tmp'
torch.save(self.state_dict(), tmp)
shutil.move(tmp, fn)
def load(self, fn):
self.load_state_dict(torch.load(fn))