apache/beam

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

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#24,902 opened on Jan 5, 2023

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P3good first issuepythonrun-inferencetask

Description

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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Contributor guide

Tech stack
pythontensorflow
Domain
testingdata
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
clear
Prerequisites
PythonTensorFlow basicsunderstanding of sparse tensors
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
50
Research direction
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.