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Gap separated training and test - #3160

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ParthivNaresh merged 24 commits into
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Gap_Separated_Training_Test
Jan 5, 2022
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ParthivNaresh merged 24 commits into
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Gap_Separated_Training_Test

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Fixes #3078


After creating the pull request: in order to pass the release_notes_updated check you will need to update the "Future Release" section of docs/source/release_notes.rst to include this pull request by adding :pr:123.

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codecov Bot commented Dec 20, 2021 •

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Codecov Report

Merging #3160 (8ccd12f) into main (f3472c2) will increase coverage by 0.1%.
The diff coverage is 100.0%.

Impacted file tree graph

@@           Coverage Diff           @@
##            main   #3160     +/-   ##
=======================================
+ Coverage   99.7%   99.7%   +0.1%     
=======================================
  Files        324     324             
  Lines      31248   31302     +54     
=======================================
+ Hits       31144   31198     +54     
  Misses       104     104             
Impacted Files Coverage Δ
evalml/demos/weather.py 100.0% <100.0%> (ø)
evalml/pipelines/time_series_pipeline_base.py 100.0% <100.0%> (ø)
evalml/tests/automl_tests/test_engine_base.py 100.0% <100.0%> (ø)
evalml/tests/conftest.py 96.2% <100.0%> (ø)
evalml/tests/demo_tests/test_datasets.py 100.0% <100.0%> (ø)
.../tests/pipeline_tests/test_time_series_pipeline.py 99.9% <100.0%> (+0.1%) ⬆️

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Comment thread evalml/demos/weather.py
@@ -15,4 +17,18 @@ def load_weather():
+ evalml.__version__
)
X, y = load_data(filename, index=None, target="Temp")

@ParthivNaresh ParthivNaresh Dec 20, 2021 •

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There were two dates missing from this dataset which prevented the new implementation in this PR from working properly. Because we no longer rely on the index to determine validity of the training/test separation, the time_index column in X has to be as we expect it to be (ordered, interval has to be detected, datetime based) for the new tools in woodwork 0.10.0 to be usable.

@ParthivNaresh
ParthivNaresh marked this pull request as ready for review December 22, 2021 16:26
temporal_columns=[train_copy.ww.time_index]
)
freq = X_frequency_dict[test_copy.ww.time_index]
if freq is None:

@ParthivNaresh ParthivNaresh Dec 22, 2021 •

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This was added regarding a conversation with @freddyaboulton. He raised the valid point that we don't want AutoMLSearch to pass only to fail when a user tries and uses predict. In this case, AutoMLSearch will raise this error if the time_index does not have values that can be inferred.
I'm up for a discussion regarding this since I know it touches a lot of time series and is essentially the first time that our whole algorithm will be directly exposed to a proper interval frequency check (outside of DateTimeFormatDataCheck).

@ParthivNaresh
ParthivNaresh requested review from bchen1116, chukarsten and freddyaboulton and removed request for chukarsten and freddyaboulton December 22, 2021 16:29

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Great work here! Left some questions about test coverage that I think need to be addressed first

Comment thread evalml/tests/conftest.py
Comment thread evalml/tests/demo_tests/test_datasets.py
Comment thread evalml/tests/pipeline_tests/test_time_series_pipeline.py

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Thanks for answering my questions! This looks good to me!

@ParthivNaresh
ParthivNaresh merged commit 47d4867 into main Jan 5, 2022
@ParthivNaresh ParthivNaresh mentioned this pull request Jan 10, 2022
@freddyaboulton
freddyaboulton deleted the Gap_Separated_Training_Test branch May 13, 2022 15:34
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Make sure date_index of test set is separated by gap from the training set

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