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genienlp
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Refactor code that writes predictions to file

This commit is contained in:
Sina 2021-01-04 23:07:05 -08:00
parent 4a080366df
commit a30fcba976
5 changed files with 26 additions and 17 deletions
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1
.gitignore vendored
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@ -13,6 +13,7 @@ local_scripts/*
dataset/*
generated/*
*events.out.tfevents*
*.pkl
.DS_Store
.idea/

6
genienlp/calibrate.py
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@ -230,6 +230,12 @@ class ConfidenceEstimator():
return features, labels
def estimate(self, confidences: Iterable[ConfidenceOutput]):
features, labels = self.convert_to_dataset(confidences, train=False)
dataset = xgb.DMatrix(data=features, label=labels)
confidence_scores = ConfidenceEstimator._extract_confidence_scores(self.model, dataset)
return confidence_scores
def evaluate(self, dev_features, dev_labels):
dev_dataset = xgb.DMatrix(data=dev_features, label=dev_labels)
confidence_scores = ConfidenceEstimator._extract_confidence_scores(self.model, dev_dataset)

14
genienlp/predict.py
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@ -167,9 +167,10 @@ def run(args, device):
else:
raise OSError(f'{results_file_name} already exists')
generation_outputs = generate_with_model(model, it, model.numericalizer, task, args, prediction_file_name, output_confidences=args.output_confidences, original_order=original_order)
generation_outputs = generate_with_model(model, it, model.numericalizer, task, args, output_confidences=args.output_confidences, original_order=original_order)
if args.output_confidences:
_, predictions, answers, contexts, confidences = generation_outputs
_, example_ids, predictions, answers, contexts, confidences = generation_outputs
# print('confidences = ', confidences)
import pickle
@ -177,7 +178,12 @@ def run(args, device):
pickle.dump(confidences, f, protocol=4)
else:
_, predictions, answers, contexts = generation_outputs
_, example_ids, predictions, answers, contexts = generation_outputs
# write into file
with open(prediction_file_name, 'w' + ('' if args.overwrite else 'x')) as prediction_file:
for i in range(len(example_ids)):
prediction_file.write(example_ids[i] + '\t' + '\t'.join(predictions[i]) + '\n') # write all outputs in the prediction file, separated by \t
if len(answers) > 0:
metrics_to_compute = task.metrics
@ -207,7 +213,7 @@ def run(args, device):
def parse_argv(parser):
parser.add_argument('--path', required=True)
parser.add_argument('--path', type=str, required=True, help='Folder to load the model from')
parser.add_argument('--evaluate', type=str, required=True, choices=['valid', 'test'],
help='Which dataset to do predictions for (test or dev)')
parser.add_argument('--pred_set_name', type=str, help='Name of dataset to run prediction for; will be ignored if --evaluate is test')

4
genienlp/server.py
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@ -86,7 +86,7 @@ class Server:
self.model.add_new_vocab_from_data([task])
batch = self.numericalize_examples(examples)
# it is a single batch, so wrap it in []
predictions = generate_with_model(self.model, [batch], self.numericalizer, task, self.args, prediction_file_name=None, output_predictions_only=True)
predictions = generate_with_model(self.model, [batch], self.numericalizer, task, self.args, output_predictions_only=True)
response = json.dumps({ 'id': request['id'], 'instances': [{ 'answer': p[0] } for p in predictions] })
return response + '\n'
@ -103,7 +103,7 @@ class Server:
self.model.add_new_vocab_from_data([task])
batch = self.numericalize_examples([ex])
predictions = generate_with_model(self.model, [batch], self.numericalizer, task, self.args, prediction_file_name=None, output_predictions_only=True)
predictions = generate_with_model(self.model, [batch], self.numericalizer, task, self.args, output_predictions_only=True)
response = json.dumps(dict(id=request['id'], answer=predictions[0][0]))
return response + '\n'

16
genienlp/validate.py
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@ -35,7 +35,7 @@ from collections import OrderedDict
from .metrics import compute_metrics
def generate_with_model(model, data_iterator, numericalizer, task, args, prediction_file_name=None, output_predictions_only=False, output_confidences=False, original_order=None):
def generate_with_model(model, data_iterator, numericalizer, task, args, output_predictions_only=False, output_confidences=False, original_order=None):
"""
Inputs:
original_order: List of indices. If provided, we will sort the results according to this order
@ -97,19 +97,15 @@ def generate_with_model(model, data_iterator, numericalizer, task, args, predict
# sort back to the original order
original_order, example_ids, predictions, answers, contexts, confidences = [list(a) for a in tuple(zip(*sorted(list(zip(original_order, example_ids, predictions, answers, contexts, confidences)))))]
if prediction_file_name is not None:
with open(prediction_file_name, 'w' + ('' if args.overwrite else 'x')) as prediction_file:
for i in range(len(example_ids)):
prediction_file.write(example_ids[i] + '\t' + '\t'.join(predictions[i]) + '\n') # write all outputs in the prediction file, separated by \t
# TODO calculate and return loss
loss = None
if output_predictions_only:
return predictions
# TODO calculate and return loss
loss = None
if output_confidences:
return loss, predictions, answers, contexts, confidences
return loss, example_ids, predictions, answers, contexts, confidences
else:
return loss, predictions, answers, contexts
return loss, example_ids, predictions, answers, contexts
def calculate_and_reduce_metrics(predictions, answers, metrics_to_compute, args):
@ -142,7 +138,7 @@ def validate(task, val_iter, model, numericalizer, args, num_print=10):
with torch.no_grad():
model.eval()
names = ['beam search', 'answer', 'context']
loss, predictions, answers, contexts = generate_with_model(model, val_iter, numericalizer, task, args, prediction_file_name=None)
loss, _, predictions, answers, contexts = generate_with_model(model, val_iter, numericalizer, task, args)
metrics = calculate_and_reduce_metrics(predictions, answers, task.metrics, args)
results = [predictions, answers, contexts]