Pnp evaluator #5108
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Pnp evaluator #5108
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…h original implementation
…h original implementation
| std::make_pair(query[i], std::vector<PredictionResult>())); | ||
| } | ||
| predictions[query[i]].push_back(PredictionResult( | ||
| score[i * width + column], label[i], has_weight ? weight[i] : 1.0)); |
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这里是不是也可以用emplace?
predictions[query[i]].emplace_back(score[i * width + column], label[i], has_weight ? weight[i] : 1.0);
| auto query = query_t->data<int32_t>(); | ||
| const T* weight = nullptr; | ||
| auto has_weight = weight_t != nullptr; | ||
| if (has_weight) { |
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Why not if (weight_t != nullptr) and remove line 52 ?
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| auto score = score_t->data<T>(); | ||
| auto label = label_t->data<T>(); | ||
| auto query = query_t->data<int32_t>(); |
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我们要不要考虑query id有可能不是数字的情况？
即使我们只支持数字的话，这里用size_t是不是更合适？
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- 我似乎没看到我们有支持string类型tensor的design doc，所以我假定这里是原来的实现一样，只支持整数的；而且由于这里事实上是ID，所以用数字也是比较符合直觉的。之后如果有其它需要我们可以再扩展。
- 我参考了下其它要用到int类型input的op（lookup_table_op, top_k_op), 这里用
int32_t确实和它们用int64_t不一致，所以我把这里改为int64_t了。size_t是unsigned，和这里语义有区别。
| # group by query id | ||
| predictions = {} | ||
| batch_size = label.shape[0] | ||
| print "batch_size=", batch_size |
| self.check_output() | ||
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| class TestPositiveNegativePairOpAccumulateWeight(OpTest): |
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TestPositiveNegativePairOpAccumulate 和TestPositiveNegativePairOpAccumulateWeight 有比较多的重复code, 可以考虑合并下。
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This evaluator counts positive pairs, negative pairs in the ranking result.
Tested on the 1-dimension score case.
resolve #5086