reranker.train(text_seqs, da_seqs, scores, log_probs, cfg["epoch"],
cfg["valid_size"], cfg["num_ranks"],
cfg.get("only_bottom",
False), cfg.get("only_top", False),
cfg.get("min_training_passes", 5))
if cfg["show_reranker_post_training_stats"]:
test_das = get_test_das()
test_texts = get_true_sents()
final_beam_path = TEST_BEAM_SAVE_FORMAT.format(10)
if not os.path.exists(final_beam_path):
print("Creating final beams file")
models = TGEN_Model(da_embedder, text_embedder,
cfg['tgen_seq2seq_config'])
models.load_models()
scorer = get_score_function('identity', cfg, models, None, 10)
run_beam_search_with_rescorer(scorer,
models,
test_das,
10,
only_rerank_final=True,
save_final_beam_path=final_beam_path)
bleu = BLEUScore()
test_da_embs = da_embedder.get_embeddings(test_das)
final_beam = pickle.load(open(final_beam_path, 'rb+'))
all_reals = []
all_preds = []
for da_emb, beam, true in zip(test_da_embs, final_beam, test_texts):
real_scores = []
def get_scores_ordered_beam(cfg,
da_embedder,
text_embedder,
beam_save_path=None):
print("Loading Training Data")
beam_size = cfg["beam_size"]
train_texts, train_das = get_multi_reference_training_variables()
if beam_save_path is None:
beam_save_path = TRAIN_BEAM_SAVE_FORMAT.format(
beam_size, cfg["tgen_seq2seq_config"].split('.')[0].split('/')[-1])
if not os.path.exists(beam_save_path):
models = TGEN_Model(da_embedder, text_embedder,
cfg["tgen_seq2seq_config"])
models.load_models()
print("Creating test final beams")
scorer = get_score_function('identity', cfg, models, None, beam_size)
run_beam_search_with_rescorer(scorer,
models,
train_das,
beam_size,
cfg,
only_rerank_final=True,
save_final_beam_path=beam_save_path)
bleu = BLEUScore()
final_beam = pickle.load(open(beam_save_path, "rb"))
text_seqs = []
da_seqs = []
scores = []
log_probs = []
with_ref_train_flag = cfg["with_refs_train"]
num_ranks = cfg["num_ranks"]
cut_offs = get_section_cutoffs(num_ranks)
regression_vals = get_regression_vals(num_ranks, with_ref_train_flag)
if cfg["output_type"] != 'pair':
print("Cut off values:", cut_offs)
print("Regression vals:", regression_vals)
only_top = cfg.get("only_top", False)
only_bottom = cfg.get("only_bottom", False)
merge_middles = cfg["merge_middle_sections"]
if only_top:
print("Only using top value")
if merge_middles and only_top:
print("Ignoring only top since have merge_middle_sections set")
training_vals = list(zip(final_beam, train_texts, train_das))
training_vals = training_vals[:cfg.get("use_size", len(training_vals))]
for beam, real_texts, da in tqdm(training_vals):
beam_scores = []
if with_ref_train_flag:
# I am not sure how to do log probs?
text_seqs.extend(real_texts)
da_seqs.extend([da for _ in real_texts])
scores.extend([0 for _ in real_texts])
for i, path in enumerate(beam):
bleu.reset()
hyp = [
x for x in text_embedder.reverse_embedding(path[1])
if x not in [START_TOK, END_TOK, PAD_TOK]
]
bleu.append(
hyp, [x for x in real_texts if x not in [START_TOK, END_TOK]])
beam_scores.append((bleu.score(), hyp, path))
# log_probs.append(i)
for i, (score, hyp,
path) in enumerate(sorted(beam_scores, reverse=True)):
text_seqs.append([START_TOK] + hyp + [END_TOK])
da_seqs.append(da)
if cfg["output_type"] in ['bleu', 'pair']:
scores.append(score)
elif cfg["output_type"] == 'order_discrete':
scores.append(to_categorical([i], num_classes=beam_size))
elif cfg["output_type"] in [
'regression_ranker', 'regression_reranker_relative'
]:
scores.append(i / (beam_size - 1))
elif cfg["output_type"] in [
'regression_sections', 'binary_classif'
]:
val = (i / (beam_size - 1))
regression_val = get_section_value(val, cut_offs,
regression_vals,
merge_middles, only_top,
only_bottom)
scores.append(
regression_val
) # converts range from [0,1] to [-1,1] (which has mean of 0)
else:
raise ValueError("Unknown output type")
log_probs.append([path[0]])
text_seqs = np.array(
text_embedder.get_embeddings(text_seqs, pad_from_end=False))
da_seqs = np.array(da_embedder.get_embeddings(da_seqs))
if cfg["output_type"] in [
'regression_ranker', 'bleu', 'regression_reranker_relative',
'pair', 'regression_sections', 'binary_classif'
]:
# print("SCORES: ", Counter(scores))
scores = np.array(scores).reshape((-1, 1))
elif cfg["output_type"] == 'order_discrete':
scores = np.array(scores).reshape((-1, beam_size))
log_probs = np.array(log_probs)
return text_seqs, da_seqs, scores, log_probs
if cfg_path is None:
filenames = os.listdir(CONFIGS_MODEL_DIR)
filepaths = [
os.path.join(CONFIGS_MODEL_DIR, filename) for filename in filenames
]
mod_times = [(os.path.getmtime(x), i) for i, x in enumerate(filepaths)]
cfg_path = filepaths[max(mod_times)[1]]
cfg = yaml.safe_load(open(cfg_path, 'r'))
texts, das = get_training_variables()
text_embedder = TokEmbeddingSeq2SeqExtractor(texts)
da_embedder = DAEmbeddingSeq2SeqExtractor(das)
texts_mr, da_mr = get_multi_reference_training_variables()
# train_text = np.array(text_embedder.get_embeddings(texts, pad_from_end=True) + [text_embedder.empty_embedding])
# da_embs = da_embedder.get_embeddings(das) + [da_embedder.empty_embedding]
seq2seq = TGEN_Model(da_embedder, text_embedder, cfg_path)
seq2seq.load_models()
seq2seq.full_model.summary()
if "use_prop" in cfg:
da_mr = da_mr[:int(len(da_mr) * cfg['use_prop'])]
texts_mr = texts_mr[:int(len(da_mr) * cfg['use_prop'])]
seq2seq.train(da_seq=da_mr,
text_seq=texts_mr,
n_epochs=cfg["epoch"],
valid_size=cfg["valid_size"],
early_stop_point=cfg["min_epoch"],
minimum_stop_point=20,
multi_ref=True)