Teaching the Find Matches transform - Amazon Glue
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China.

Teaching the Find Matches transform

Each FindMatches transform must be taught what should be considered a match and what should not be considered a match. You teach your transform by adding labels to a file and uploading your choices to Amazon Glue.

You can orchestrate this labeling on the Amazon Glue console or by using the Amazon Glue machine learning API operations.

How many times should I add labels? How many labels do I need?

The answers to these questions are mostly up to you. You must evaluate whether FindMatches is delivering the level of accuracy that you need and whether you think the extra labeling effort is worth it for you. The best way to decide this is to look at the “Precision,” “Recall,” and “Area under the precision recall curve” metrics that you can generate when you choose Estimate quality on the Amazon Glue console. After you label more sets of tasks, rerun these metrics and verify whether they have improved. If, after labeling a few sets of tasks, you don't see improvement on the metric that you are focusing on, the transform quality might have reached a plateau.

Why are both true positive and true negative labels needed?

The FindMatches transform needs both positive and negative examples to learn what you think is a match. If you are labeling FindMatches-generated training data (for example, using the I do not have labels option), FindMatches tries to generate a set of “label set ids” for you. Within each task, you give the same “label” to some records and different “labels” to other records. In other words, the tasks generally are not either all the same or all different (but it's okay if a particular task is all “the same” or all “not the same”).

If you are teaching your FindMatches transform using the Upload labels from S3 option, try to include both examples of matching and nonmatching records. It's acceptable to have only one type. These labels help you build a more accurate FindMatches transform, but you still need to label some records that you generate using the Generate labeling file option.

How can I enforce that the transform matches exactly as I taught it?

The FindMatches transform learns from the labels that you provide, so it might generate records pairs that don't respect the provided labels. To enforce that the FindMatches transform respects your labels, select EnforceProvidedLabels in FindMatchesParameter.

What techniques can you use when an ML transform identifies ttems as matches that are not true matches?

You can use the following techniques:

  • Increase the precisionRecallTradeoff to a higher value. This eventually results in finding fewer matches, but it should also break up your big cluster when it reaches a high enough value.

  • Take the output rows corresponding to the incorrect results and reformat them as a labeling set (removing the match_id column and adding a labeling_set_id and label column). If necessary, break up (subdivide) into multiple labeling sets to ensure that the labeler can keep each labeling set in mind while assigning labels. Then, correctly label the matching sets and upload the label file and append it to your existing labels. This might teach your transformer enough about what it is looking for to understand the pattern.

  • (Advanced) Finally, look at that data to see if there is a pattern that you can detect that the system is not noticing. Preprocess that data using standard Amazon Glue functions to normalize the data. Highlight what you want the algorithm to learn from by separating data that you know to be differently important into their own columns. Or construct combined columns from columns whose data you know to be related.