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Forward-merge branch-24.06 into branch-24.08 #5905

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merged 4 commits into from
Jun 3, 2024
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Commits on May 23, 2024

  1. Reduce and rename cudf.pandas integrations jobs (#5890)

    cc @vyasr
    
    Authors:
      - Dante Gama Dessavre (https://github.com/dantegd)
    
    Approvers:
      - Vyas Ramasubramani (https://github.com/vyasr)
      - Ray Douglass (https://github.com/raydouglass)
    
    URL: #5890
    dantegd authored May 23, 2024
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  2. Fix RandomForestClassifier return type (#5896)

    Closes #5637 
    
    ```
    import cuml
    from cuml.datasets import make_classification
    
    X, y = make_classification()
    
    clf = cuml.ensemble.RandomForestClassifier().fit(X,y)
    print(clf.predict(X[:5]).dtype)
    ```
    
    Result is
    
    ```
    int64
    ```
    
    Authors:
      - Jinsol Park (https://github.com/jinsolp)
    
    Approvers:
      - Dante Gama Dessavre (https://github.com/dantegd)
    
    URL: #5896
    jinsolp authored May 23, 2024
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Commits on May 29, 2024

  1. Update scikit-learn to 1.4 (#5851)

    This is an attempt to update the scikit-learn dependency from 1.2 to 1.4. Most changes are related to constructor arguments that were deprecated in 1.2 and in 1.4 have changed/been removed.
    
    A question I have is what cuml's deprecation policy is? I've gone with "two releases" for parameters where we can easily do so (deprecated in 24.06 and then remove them in 24.10). However that is only about 4 months of deprecation which could be a bit short.
    
    Some of the changes would be hard to do as a deprecation (with 1.4 there is no way to provide the "old way"), we'd have to stick with 1.3 for now. I think this is a bit of a bummer but maybe the price to pay for not keeping on top of deprecations. And it seems like there is no deprecation policy in the docs/towards users? So maybe we can play this card once now, to catch up and at the same time introduce a deprecation policy.
    
    The SHAP test needed its reference updating. I am not sure why, at least I couldn't quickly find a reason for why you'd have to do this.
    
    I am not sure how possible it would be to support a range of scikit-learn versions (say 1.2 - 1.4). Would be cool but maybe not worth the added complexity?
    
    Todo:
    * [x] add deprecation warning in AgglomerativeClustering
    * [ ] add tests for deprecations
    	* [x] RF regressor
    	* [x] RF classifier
    	* [ ] ~~LARS~~ - LARS is experimental, so no need for deprecation
    	* [x] LogisticRegression
    	* [x] OneHotEncoder
    	* [x] AgglomerativeClustering
    * [ ] think about how to combine this with #5799
    * [x] decide deprecation cycle length - copy cudf, so 24.06 -> 24.08
    * [x] update "expiry" version in the warnings
    * [x] update doc strings
    
    xref #5799
    
    Authors:
      - Tim Head (https://github.com/betatim)
      - Dante Gama Dessavre (https://github.com/dantegd)
    
    Approvers:
      - Dante Gama Dessavre (https://github.com/dantegd)
      - Jake Awe (https://github.com/AyodeAwe)
    
    URL: #5851
    betatim authored May 29, 2024
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  2. Fix building cuml with CCCL main (#5886)

    Similar to rapidsai/cudf#15552, we are testing [building RAPIDS with CCCL's main branch](NVIDIA/cccl#1667) to get ahead of any breaking changes.
    
    Authors:
      - Paul Taylor (https://github.com/trxcllnt)
      - Dante Gama Dessavre (https://github.com/dantegd)
    
    Approvers:
      - Kyle Edwards (https://github.com/KyleFromNVIDIA)
      - Dante Gama Dessavre (https://github.com/dantegd)
    
    URL: #5886
    trxcllnt authored May 29, 2024
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