def test_ensemble_chooses_highest_average_confidence_3(self): subresults = [ { "span_start_probs": torch.FloatTensor([[0.0, 0.0, 0.9, 0.1]]), "span_end_probs": torch.FloatTensor([[0.0, 0.0, 0.9, 0.1]]), "best_span": torch.LongTensor([[2, 2]]), "best_span_str": "cheese", "question_tokens": ["What", "did", "Michael", "eat", "?"], "passage_tokens": ["Michael", "ate", "cheese", "."], }, { "span_start_probs": torch.FloatTensor([[0.0, 0.0, 0.9, 0.1]]), "span_end_probs": torch.FloatTensor([[0.0, 0.0, 0.9, 0.1]]), "best_span": torch.LongTensor([[2, 2]]), "best_span_str": "cheese", "question_tokens": ["What", "did", "Michael", "eat", "?"], "passage_tokens": ["Michael", "ate", "cheese", "."], }, { "span_start_probs": torch.FloatTensor([[0.9, 0.0, 0.0, 0.0]]), "span_end_probs": torch.FloatTensor([[0.9, 0.0, 0.0, 0.0]]), "best_span": torch.LongTensor([[0, 0]]), "best_span_str": "What", "question_tokens": ["What", "did", "Michael", "eat", "?"], "passage_tokens": ["Michael", "ate", "cheese", "."], }, ] numpy.testing.assert_almost_equal( ensemble(subresults).data[0].cpu().numpy(), torch.LongTensor([2, 2]).numpy() )
def test_ensemble_chooses_highest_average_confidence_2(self): subresults = [{ u"span_start_probs": torch.FloatTensor([[0.9, 0.0, 0.0, 0.0]]), u"span_end_probs": torch.FloatTensor([[0.9, 0.0, 0.0, 0.0]]), u"best_span": torch.LongTensor([[0, 0]]), u"best_span_str": u"What", u"question_tokens": [u"What", u"did", u"Michael", u"eat", u"?"], u"passage_tokens": [u"Michael", u"ate", u"cheese", u"."] }, { u"span_start_probs": torch.FloatTensor([[0.0, 0.0, 1.0, 0.0]]), u"span_end_probs": torch.FloatTensor([[0.0, 0.0, 1.0, 0.0]]), u"best_span": torch.LongTensor([[2, 2]]), u"best_span_str": u"cheese", u"question_tokens": [u"What", u"did", u"Michael", u"eat", u"?"], u"passage_tokens": [u"Michael", u"ate", u"cheese", u"."] }] numpy.testing.assert_almost_equal( ensemble(subresults).data[0].cpu().numpy(), torch.LongTensor([2, 2]).cpu().numpy())
def test_ensemble_chooses_highest_average_confidence_3(self): subresults = [ { "span_start_probs": torch.FloatTensor([[0.0, 0.0, 0.9, 0.1]]), "span_end_probs": torch.FloatTensor([[0.0, 0.0, 0.9, 0.1]]), "best_span": torch.LongTensor([[2, 2]]), "best_span_str": "cheese", "question_tokens": ["What", "did", "Michael", "eat", "?"], "passage_tokens": ["Michael", "ate", "cheese", "."] }, { "span_start_probs": torch.FloatTensor([[0.0, 0.0, 0.9, 0.1]]), "span_end_probs": torch.FloatTensor([[0.0, 0.0, 0.9, 0.1]]), "best_span": torch.LongTensor([[2, 2]]), "best_span_str": "cheese", "question_tokens": ["What", "did", "Michael", "eat", "?"], "passage_tokens": ["Michael", "ate", "cheese", "."] }, { "span_start_probs": torch.FloatTensor([[0.9, 0.0, 0.0, 0.0]]), "span_end_probs": torch.FloatTensor([[0.9, 0.0, 0.0, 0.0]]), "best_span": torch.LongTensor([[0, 0]]), "best_span_str": "What", "question_tokens": ["What", "did", "Michael", "eat", "?"], "passage_tokens": ["Michael", "ate", "cheese", "."] } ] numpy.testing.assert_almost_equal( ensemble(subresults).data[0].cpu().numpy(), torch.LongTensor([2, 2]).numpy())