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Updates for Existing Demos #154

Merged
merged 109 commits into from
Aug 20, 2020
Merged

Updates for Existing Demos #154

merged 109 commits into from
Aug 20, 2020

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jeff-hernandez
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@jeff-hernandez jeff-hernandez commented Jul 22, 2020

Closes #143 by updating notebook examples.

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codecov bot commented Jul 22, 2020

Codecov Report

Merging #154 into main will not change coverage.
The diff coverage is 100.00%.

@@            Coverage Diff            @@
##              main      #154   +/-   ##
=========================================
  Coverage   100.00%   100.00%           
=========================================
  Files           28        28           
  Lines         1253      1254    +1     
=========================================
+ Hits          1253      1254    +1     
Impacted Files Coverage Δ
composeml/label_times/plots.py 100.00% <100.00%> (ø)

@jeff-hernandez jeff-hernandez changed the base branch from master to main July 29, 2020 18:21
@jeff-hernandez jeff-hernandez marked this pull request as ready for review August 12, 2020 19:05
"source": [
"### Next Steps\n",
"\n",
"At this point, we have completed the machine learning application. We can revisit each step to explore and fine-tune with different parameters until the model is ready for deployment."
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@jeff-hernandez these two examples you've added are great!! Its really exciting to have evalml included here 😁 👏

I had one suggestion: you could add something at the end like this

For more information on how to work with the models produced by EvalML, take a look at the EvalML documentation.

I know you already linked to the evalml docs at the top, but it could be nice to call this out for people who skim to the bottom and are left wondering what else they can do.

"source": [
"best_pipeline = automl.best_pipeline.fit(X_train, y_train)\n",
"score = best_pipeline.score(X_holdout, y_holdout, objectives=['f1'])\n",
"dict(score)"
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@kmax12 kmax12 Aug 12, 2020

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maybe also show the top features using permutation performance or the prediction explanation functionality, just to show off evalml some more. dont forget to update evalml to right minimum version if you do!

this could be added in a future PR tho

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The graph_feature_importance doesn't render for me in jupyter lab, but I can use the data frame for feature_importance to create a plot.

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it might render fine in the docs even if the jupyter doesnt work. this is also a known issue

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It doesn't render in the docs when I build locally.

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oh well. okay

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@jeff-hernandez @kmax12 you're probably running into this: alteryx/evalml#1040

@jeff-hernandez , do you have the ipywidgets pip package installed locally? I see you're listing evalml>=0.11.2 in the requirements, which looks fine--that should include ipywidgets, and the graph should show up... perhaps you need to rerun pip install locally? Ping the team if you'd like some help with that.

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Oh also @jeff-hernandez : @freddyaboulton ran into this issue locally in jupyterlab, where he had to install the jupyter lab extension in order to get this working. If you use jupyterlab as opposed to jupyter, you'll probably also need to do this.

@jeff-hernandez jeff-hernandez changed the base branch from main to master August 18, 2020 15:02
@jeff-hernandez jeff-hernandez changed the base branch from master to main August 18, 2020 15:02
@jeff-hernandez jeff-hernandez changed the title Updates Demos Updates for Existing Demos Aug 18, 2020
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rwedge commented Aug 19, 2020

The "using label transforms" notebook now has the outputs of each cell already as opposed to letting the docs run the notebook to get the output. Is that intentional?

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@rwedge no, I'll clear the output. I want the docs to run that notebook as well.

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Looks good

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Long Runtime in Notebook Examples
4 participants