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Hide features with zero importance in plot by default #413

merged 2 commits into from Feb 24, 2020


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@angela97lin angela97lin commented Feb 24, 2020

Closes #411. Introduces a new show_all_features flag which, if True, will plot all features including those with an importance equal to zero. Defaults to False.

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codecov bot commented Feb 24, 2020

Codecov Report

Merging #413 into master will increase coverage by 0.02%.
The diff coverage is 100%.

Impacted file tree graph

@@            Coverage Diff             @@
##           master     #413      +/-   ##
+ Coverage   97.37%   97.39%   +0.02%     
  Files         103      103              
  Lines        3195     3224      +29     
+ Hits         3111     3140      +29     
  Misses         84       84
Impacted Files Coverage Δ
evalml/pipelines/ 92% <100%> (+0.33%) ⬆️
...valml/tests/pipeline_tests/ 100% <100%> (ø) ⬆️

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kmax12 approved these changes Feb 24, 2020
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@kmax12 kmax12 left a comment

Looks great!

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@jeremyliweishih jeremyliweishih left a comment


@angela97lin angela97lin merged commit 82cbe09 into master Feb 24, 2020
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angela97lin added a commit that referenced this pull request Feb 25, 2020
* adding functionality and test

* changelog and adding boolean param
@angela97lin angela97lin mentioned this pull request Mar 9, 2020
angela97lin added a commit that referenced this pull request Apr 3, 2020
* creating new binary / multiclass variants of pipelines, duplicating code for now

* moving common fxns back to pipeline base

* more moving around fxns in pipelines classes

* capping xgboost

* fixing typo

* more cleanup, making predict_proba standard regardless of binary/multiclass

* renaming other_objectives to objectives

* updating score's objective parameter to calculate all objectives, not just additional

* removing self.objective for scoring

* removing objectives from pipeline initialization, adding objective as predict param

* remove xgboost cap from branch

* changelog

* capping xgboost on local branch since tests timing out

* cleaning up

* more cleanup

* reverting requirements file

* adding classification pipeline subclass, cleaning up via PR comments

* more cleanup for docstrings

* more cleanup of changelog and comments

* putting tests in subfolders and adding few more tests

* Update dependencies  (#412)

* Update latest dependencies

* Hide features with zero importance in plot by default (#413)

* adding functionality and test

* changelog and adding boolean param

* Update dependencies check: package whitelist (#417)

* Add a whitelist for update_deps check

* Remove from expected

* Update deps

* Changelog

* adding skeleton for subclasses

* fixing test and linting

* updating change from master

* fixing fixture

* cherry picked wip remove ROC and confusion matrix

* fixing merge

* fixing merge

* cleaning up

* make test use static attribute instead of instance'

* deleting needs_fitting

* updating code to use new objective classes, still broken

* updating threshold, still need to clean up tests

* comment out for now

* more cleanup

* cleaning up

* more cleanup

* still more cleanup

* fixing plot unsuccessful merging

* more cleanup but still some things to work out

* cleaning up using multiclass objectives for binary classification problems

* fixing typo with recall and cleanup

* cleaning up

* adding default

* some more cleaning up

* removing irrelevant test

* forgot to add attribute, breaking things again

* cleanup and change objective of test

* removing objective from predict

* more cleanup :d

* remove unused attribute

* cleaning up via comments

* more comments

* changelog

* order of decorators changed

* fixing copy and paste err

* update random state for binary class pipelines

* updating objective

* typo

* fixing?

* fixing imports

* fixing tests

* adding objective as parameter for predict, removing for fit

* cleaning up test

* more fixing test

* minor linting, need more to go

* more cleanup

* forgot to fix test

* more merging :x

* starting to add tuning logic to automl

* changelog

* cleaning up

* change conditional for objective split

* cleaning up docstrings

* forgot to use classificationobjective class...

* add additional cond

* adding tests

* cleanup

* removing decision function for multiclass

* updating via comments

* removing classification_objective file

* add test + more updates

* use cls instead for pep8 standards

* updating can_optimize to property

* update score

* fix tests

* minor cleanup from comments

* updating predict behavior

* add separate objective check

* fixing some merge conflicts cont

* add fraud test

* patching

* remove old test

* updating for now

* add another test

* add more tests

* adding test structure, still need to fix

* adding test

* fix iloc

* fix tests

* fix import

* fix test?

* removing can_optimize_threshold

* linting

* update docs a little

* remove accuracy

* add more doc fixes

* move binary and multi pipelines in api ref

* revert components notebook

* updating from comments

* oops, fix none set

* update docstring

* update api ref?

* addressing comments

* revert and update

* update docstring

* updating docstrings and lint

* updating unnecessary call to constructor

* pushing empty commit to refresh

Co-authored-by: Jeremy Shih <>
Co-authored-by: Dylan Sherry <>
angela97lin added a commit that referenced this pull request Apr 13, 2020
* Objectives API: Create new binary / multiclass pipeline classes and remove objectives from pipeline classes (#405)

* Objectives API: Remove ROC and confusion matrix as objectives (#422)

* Change `score` output to return one dictionary (#429)

* Create binary and multiclass objective classes  (#504)

* Update dependencies  (#412)

* Hide features with zero importance in plot by default (#413)

* Update dependencies check: package whitelist (#417)

* Add fixes necessary for docs to build for improved objectives project (#605)

* Remove calculating plot metrics from AutoML  (#615)
@angela97lin angela97lin deleted the 411_hide_zero_importance_features branch Apr 17, 2020
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Hide features with zero importance from importance plot
3 participants