apache/beam

[Task]: Create a helper function fill_in_missing for TFT Criteo tests

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#24,902 创建于 2023年1月5日

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 (4 评论) (0 反应) (1 负责人)Java (7,313 star) (4,097 fork)batch import
P3good first issuepythonrun-inferencetask

描述

What needs to happen?

This function should accept a rank 2 SparseTensor, and a default value, and return a rank 1 Tensor. It will assume the input is from a VarLenFeature and has dimensions [batch_size, 0] or [batch_size, 1] depending on the max size of the feature over the batch. It's assumed each feature has 0 or 1 values (0 for missing, 1 for present).

It will emit a Tensor which is constructed using the code

  feature = tf.sparse_to_dense(
      feature.indices, [feature.dense_shape[0], 1], feature.values,
      default_value=-1)
  feature = tf.squeeze(feature, axis=1)

Issue Priority

Priority: 3 (nice-to-have improvement)

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贡献者指南

技术栈
pythontensorflow
领域
testingdata
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
1-3 hours
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
stale
清晰度议题是否清楚说明期望改动、验收标准和下一步。
clear
前置要求
PythonTensorFlow basicsunderstanding of sparse tensors
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
50
研究方向
Implement the fill in missing helper function as described in the issue. The function should accept a rank 2 SparseTensor and a default value, and return a rank 1 Tensor. Use tf.sparse to dense with the given logic and then tf.squeeze. The function is intended for TFT Criteo tests in the Python SDK of Apache Beam. Check the existing test files for similar patterns and ensure the function is added in the appropriate test utilities module.
[Task]: Create a helper function fill_in_missing for TFT Criteo tests · apache/beam#24902 | Good First Issue