apache/beam

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

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#24,902 建立於 2023年1月5日

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 (4 留言) (0 反應) (1 負責人)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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貢獻者指南

技術棧
pythontensorflow
領域
testingdata
議題類型
feature
難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。
3
預計時間有經驗貢獻者完成調查、實作、測試並準備 pull request 的粗略時間範圍。
1-3 hours
活動狀態議題目前的可參與程度：新鮮、活躍、陳舊、阻塞或等待維護者輸入。
stale
清晰度議題是否清楚說明預期改動、驗收標準和下一步。
clear
前置要求
PythonTensorFlow basicsunderstanding of sparse tensors
新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。
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.