def print_dialogue_batch(learner: Learner,
modeldata: ModelData,
input_field,
output_field,
num_batches=1,
num_sentences=-1,
is_test=False,
num_beams=1,
smoothing_function=None,
weights=None):
weights = (1 / 3., 1 / 3., 1 / 3.) if weights is None else weights
smoothing_function = SmoothingFunction(
).method1 if smoothing_function is None else smoothing_function
predictions, targets, inputs = learner.predict_with_targs_and_inputs(
is_test=is_test, num_beams=num_beams)
blue_scores = []
for batch_num, (input, target,
prediction) in enumerate(zip(inputs, targets,
predictions)):
input = np.transpose(
input,
[1, 2, 0]) # transpose number of utterances to beams [sl, bs, nb]
inputs_str: BatchBeamTokens = modeldata.itos(input, input_field)
inputs_str: List[str] = ["\n".join(conv) for conv in inputs_str]
predictions_str: BatchBeamTokens = modeldata.itos(
prediction, output_field)
targets_str: BatchBeamTokens = modeldata.itos(target, output_field)
for index, (inp, targ, pred) in enumerate(
zip(inputs_str, targets_str, predictions_str)):
if targ[0].split() == pred[0].split()[1:]:
blue_score = 1
else:
blue_score = sentence_bleu(
[targ[0].split()],
pred[0].split()[1:],
smoothing_function=smoothing_function,
weights=weights)
print(
f'BATCH: {batch_num} SAMPLE : {index}\nINPUT:\n{"".join(inp)}\nTARGET:\n{ "".join(targ)}\nPREDICTON:\n{"".join(pred)}\nblue: {blue_score}\n\n'
)
blue_scores.append(blue_score)
if 0 < num_sentences <= index - 1:
break
if 0 < num_batches <= batch_num - 1:
break
print(
f'bleu score: mean: {np.mean(blue_scores)}, std: {np.std(blue_scores)}'
)
def print_batch(learner: Learner,
modeldata: ModelData,
input_field,
output_field,
num_batches=1,
num_sentences=-1,
is_test=False,
num_beams=1,
weights=None,
smoothing_function=None):
predictions, targets, inputs = learner.predict_with_targs_and_inputs(
is_test=is_test, num_beams=num_beams)
weights = (1 / 3., 1 / 3., 1 / 3.) if weights is None else weights
smoothing_function = SmoothingFunction(
).method1 if smoothing_function is None else smoothing_function
blue_scores = []
for batch_num, (input, target,
prediction) in enumerate(zip(inputs, targets,
predictions)):
inputs_str: BatchBeamTokens = modeldata.itos(input, input_field)
predictions_str: BatchBeamTokens = modeldata.itos(
prediction, output_field)
targets_str: BatchBeamTokens = modeldata.itos(target, output_field)
for index, (inp, targ, pred) in enumerate(
zip(inputs_str, targets_str, predictions_str)):
blue_score = sentence_bleu([targ],
pred,
smoothing_function=smoothing_function,
weights=weights)
print(
f'batch: {batch_num} sample : {index}\ninput: {" ".join(inp)}\ntarget: { " ".join(targ)}\nprediction: {" ".join(pred)}\nbleu: {blue_score}\n\n'
)
blue_scores.append(blue_score)
if 0 < num_sentences <= index - 1:
break
if 0 < num_batches <= batch_num - 1:
break
print(f'mean bleu score: {np.mean(blue_scores)}')
