[SPARK-8298] Sliding Window CrossValidator

• Type: Improvement
• Status: Resolved
• Priority: Major
• Resolution: Incomplete
• Component/s: ML
• Labels: bulk-closed
• Assignee: Unassigned
• Reporter: Justin Yip
• Created: 10/Jun/15 21:51
• Updated: 21/May/19 04:34
• Resolved: 21/May/19 04:34

Description

CrossValidator only supports k-folds. It cannot prevent the validation data from look-ahead bias. I would like to contribute a sliding-window based CrossValidator. The sliding window guarantees a clear cutoff time between the training and validation data, to prevent look-ahead bias.

Three parameters are used to govern the generation process.
1. numFold - Int
2. firstCutoffIndex - Long, the cutoff index of the training data for the first (training, validation) data pair
3. validationWindowSize - Long, index range of the validation data set duration.

Need to decide:
Whether to make the current CrossValidator more generic or implement a new SlidingWindowCrossValidator.

• Most of the logic are identical between CrossValidator and SlidingWindowValidator, except for the part where the training-validation data pairs is generated. More, if we introduce other kinds of data splitting methods, there will be lots of code redundancy if we use multiple classes.
• However, I also foresee that things will get messy to support too many splitting methods with one CrossValidator class.

Issue Links

• relates to: SPARK-8983 ML Tuning Cross-Validation Improvements
• links to: [Github] Pull Request #6757 (yipjustin)