Entity.plot compute dask entitysets from delayed

[systemshift]

Pull Request Description

This is a first draft that should fix #1051
I will work on the failing test unit if the changes are approved.

My concern is that while this change 'solves' dask entity sets being plotted, it only works on low memory use cases. And would break when a user has large entityset and keeps calling .compute() inside a loop. My assumption is that a dask user would otherwise use pandas if they did not need to work on extremely large data.

Thoughts @rwedge?

update: might be another task to add to #901

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[rwedge]

Thanks for putting up this PR, @systemshift

It would be good to avoid the compute. Perhaps es.plot should not display the row counts for a dask entityset. That way there would be no need for a compute.

[rwedge]

The main change is fixing EntitySet.plot, an additional changelog entry is unnecessary

[systemshift]

an additional changelog entry is unnecessary

Not sure what you mean by this, should I remove the changes I made to testing section?

[rwedge]

Not sure what you mean by this, should I remove the changes I made to testing section?

Sorry, I meant remove this line from the changelog, since we already have the line added in the Fixes section.

[systemshift]

Perhaps es.plot should not display the row counts for a dask entityset.

Wouldn't this mean a dask user will lose some function features?

[rwedge]

Perhaps es.plot should not display the row counts for a dask entityset.

Wouldn't this mean a dask user will lose some function features?

It would, but this allows a dask user to generate the plot without needing a .compute(). There's currently a similar compromise when printing an EntiySet object:

Entityset: ecommerce
  Entities:
    régions [Rows: Delayed('int-aadf06bc-0566-49f6-b2ff-7dc4165c4108'), Columns: 2]
    stores [Rows: Delayed('int-3b836190-bb8b-4e7b-a9ca-265f8a24ab08'), Columns: 3]
    products [Rows: Delayed('int-ccb84bf7-0d44-4561-9961-e4928895b152'), Columns: 4]
    customers [Rows: Delayed('int-31180abd-1530-4ff7-9921-8acb542985e8'), Columns: 15]
    sessions [Rows: Delayed('int-e6c393be-30c7-4faa-abe3-7044f8b55122'), Columns: 6]
    log [Rows: Delayed('int-e186025b-a5f4-45cc-a3e4-fb1a6ffa6457'), Columns: 15]
    cohorts [Rows: Delayed('int-fecffa69-0a66-4884-ba98-829d7c5fce12'), Columns: 3]
  Relationships:
    customers.cohort -> cohorts.cohort
    customers.région_id -> régions.id
    stores.région_id -> régions.id
    sessions.customer_id -> customers.id
    log.session_id -> sessions.id
    log.product_id -> products.id

I think the shorter time creating the plot due to not needing to compute is worth the loss of information

[systemshift]

@rwedge if this looks good I will start working on the broken test units to finish it up.

[rwedge]

Looks good!

[codecov]

Codecov Report

Merging #1086 into main will decrease coverage by 0.00%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##             main    #1086      +/-   ##
==========================================
- Coverage   98.36%   98.36%   -0.01%     
==========================================
  Files         126      126              
  Lines       13272    13257      -15     
==========================================
- Hits        13055    13040      -15     
  Misses        217      217              

Impacted Files	Coverage Δ
featuretools/entityset/entityset.py	97.66% <100.00%> (+0.01%)	⬆️
...eaturetools/tests/entityset_tests/test_plotting.py	100.00% <100.00%> (ø)

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[rwedge]

Looks good