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Add test to make sure OneHotEncoder top_n works with large number of categories #552

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merged 13 commits into from Apr 8, 2020

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angela97lin
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@angela97lin angela97lin commented Mar 31, 2020

Add test to make sure OneHotEncoder top_n works with large number of categories.

@angela97lin angela97lin self-assigned this Mar 31, 2020
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codecov bot commented Mar 31, 2020

Codecov Report

Merging #552 into master will increase coverage by 0.00%.
The diff coverage is 100.00%.

Impacted file tree graph

@@           Coverage Diff           @@
##           master     #552   +/-   ##
=======================================
  Coverage   98.87%   98.87%           
=======================================
  Files         118      118           
  Lines        4439     4453   +14     
=======================================
+ Hits         4389     4403   +14     
  Misses         50       50           
Impacted Files Coverage Δ
...ts/automl_tests/test_auto_classification_search.py 100.00% <100.00%> (ø)
...alml/tests/component_tests/test_one_hot_encoder.py 100.00% <100.00%> (ø)

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@angela97lin angela97lin requested a review from dsherry Mar 31, 2020
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@dsherry dsherry left a comment

Great start! Suggestions:

  • Please have this test apply directly to OneHotEncoder instead of going through autobase/pipelines.
  • In that case, there's no need for any numerical features, just categorical.
  • Is there a way we can have more rows, like ~100k, and then ~20k categories with a few rows each, and then top_n categories with more? That way, the test can expect we get a specific set of top_n categories as output. And having more rows than categories simulates real world data more closely.
  • I just merged #530 (🎉 ), so please update to use the get_random_state util for random number generation. And then all our tests will continue to be fully deterministic!

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angela97lin commented Apr 1, 2020

@dsherry Thanks for the comments!

I had added the test initially in automl to test if the one hot encoder would slow down the entire search process but I guess you're right, it should be sufficient to just test the performance of the encoder itself. Updated!

For the time being, using random still but will update when the random state PR is merged in :)

@angela97lin angela97lin requested a review from dsherry Apr 1, 2020
@angela97lin angela97lin requested a review from dsherry Apr 2, 2020
dsherry
dsherry approved these changes Apr 8, 2020
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@dsherry dsherry left a comment

👏 awesome, thank you! 🚢

@angela97lin angela97lin merged commit 56aa951 into master Apr 8, 2020
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@dsherry dsherry deleted the 550_many_categories branch Oct 29, 2020
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2 participants