MAINT Pull apart Splitter and Partitioner in the sklearn/tree code by adam2392 · Pull Request #29458 · scikit-learn/scikit-learn

adam2392 · 2024-07-10T14:23:39Z

Reference Issues/PRs

Note: this is entirely just a moving of code, nothing changes functionally, except the abstract implementation (see below). This should hopefully be pretty easy to review since no unit-tests will change, or fail.

What does this implement/fix? Explain your changes.

Separates the concept of Splitter and Partitioner into two separate Cython files to make the code easier to read and maintain.
Adds an abstract base class called BasePartitioner to limit the number of repeated function definitions. I can remove this or add as an in-line comment as suggested by @thomasjpfan. I have it currently added, so lmk what you think.

These are the summary of the code moves:

sorting functions: _splitter.pyx -> _partitioner.pyx
partitioner classes: _splitter.pyx -> _partitioner.pyx
any moving of samples functions (e.g. shift_missing_values_to_left_if_required) moved to _partitioner.pyx
(Optional) Implementation of an abstract BasePartitioner class, so the partitioner definition is handled in this abstract class. I can remove this if people want. See: MAINT Pull apart Splitter and Partitioner in the sklearn/tree code #29458 (comment)

Any other comments?

This shouldn't introduce any performance regressions as the computational tricks used to make the code the same speed are still there:

DensePartitioner and SparsePartitioner are still decorated with @final, so they are not able to be subclassed and Cython will optimize the code
The fused type trick within _splitter.pyx is still used to define a join of the DensePartitioner and SparsePartitioner. I'm actually not 100% sure this is needed… But I think if we use the Partitioner class in _partiitioner.pxd, this may incur performance issues via vtable lookup?

I ran some benchmarks using asv on this PR branch and the one on main, and I actually don't see that many diffs:

asv run --bench RandomForestClassifierBenchmark.time_fit —verbose

Partition PR (fused type)
[100.00%] ··· ================ =========
              —                 n_jobs 
              ---------------- ————
               representation      1    
              ================ =========
                   dense        5.34±0s 
                   sparse       6.62±0s 
              ================ =========

Main 
[100.00%] ··· ================ ============
              --                  n_jobs   
              ---------------- ——————
               representation       1      
              ================ ============
                   dense        5.32±0.02s 
                   sparse        6.63±0s   
              ================ ============

Signed-off-by: Adam Li <adam2392@gmail.com>

github-actions · 2024-07-10T14:25:03Z

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Signed-off-by: Adam Li <adam2392@gmail.com>

adam2392 · 2024-07-11T00:57:51Z

Lmk if this diff is too large… I couldn't see another way to do it (e.g. incrementally), since all the functions sort of rely on each other.

I guess one thing I could do is do the "sorting" functions first, and then one partitioner at a time, but that seems kind of tedious(?)

Signed-off-by: Adam Li <adam2392@gmail.com>

sklearn/tree/_splitter.pyx

Signed-off-by: Adam Li <adam2392@gmail.com>

asv_benchmarks/asv.conf.json

benchmarks/bench_tree.py

adam2392 · 2024-07-15T13:04:54Z

What do you think of this refactoring proposal (no code functionality change).

cc: @thomasjpfan, @glemaitre

thomasjpfan · 2024-07-15T21:16:15Z

sklearn/tree/_partitioner.pyx

+cdef float32_t INFINITY_32t = np.inf
+
+
+cdef class Partitioner:


According to your opening comment, is this suppose to be BasePartitioner?

Also, is defining this base class just for defining the interface? I didn't define the interface so we can be sure there is no v-table look up. I do not think Cython would generate one, but there could be a bug in Cython that will generate it in the future, and those are hard to detect.

If we want to be safe, we can comment out the interface and let it be a comment.

According to your opening comment, is this suppose to be BasePartitioner?

Yes, I should change it back.

Also, is defining this base class just for defining the interface? I didn't define the interface so we can be sure there is no v-table look up. I do not think Cython would generate one, but there could be a bug in Cython that will generate it in the future, and those are hard to detect.

Yes it's just to define the interface, but I noticed no runtime regression. Just to confirm, your suggestion is to remove the abstract interface definition, and just leave one there as a multi-in-line comment?

Just to confirm, your suggestion is to remove the abstract interface definition, and just leave one there as a multi-in-line comment?

Yea, that was my suggestion.

Kay I have done that in e24cda9

thomasjpfan · 2024-07-15T21:21:59Z

sklearn/tree/_splitter.pyx

+# but it would have resulted in a ~10% overall tree fitting performance
+# degradation caused by the overhead frequent virtual method lookups.
+ctypedef fused Partitioner:
+    DensePartitioner


Since this is still a fused type, I think there should not be any v-table look ups.

But to be sure, can you check the C++ code in node_split_best to see if there is a v-table look up?

The generated C++ code shows:

static CYTHON_INLINE int __pyx_fuse_0__pyx_f_7sklearn_4tree_9_splitter_node_split_best(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *, struct __pyx_obj_7sklearn_4tree_12_partitioner_DensePartitioner *, struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *, struct __pyx_t_7sklearn_4tree_5_tree_ParentInfo *); /*proto*/ static CYTHON_INLINE int __pyx_fuse_1__pyx_f_7sklearn_4tree_9_splitter_node_split_best(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *, struct __pyx_obj_7sklearn_4tree_12_partitioner_SparsePartitioner *, struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *, struct __pyx_t_7sklearn_4tree_5_tree_ParentInfo *); /*proto*/ static CYTHON_INLINE int __pyx_fuse_0__pyx_f_7sklearn_4tree_9_splitter_node_split_random(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *, struct __pyx_obj_7sklearn_4tree_12_partitioner_DensePartitioner *, struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *, struct __pyx_t_7sklearn_4tree_5_tree_ParentInfo *); /*proto*/ static CYTHON_INLINE int __pyx_fuse_1__pyx_f_7sklearn_4tree_9_splitter_node_split_random(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *, struct __pyx_obj_7sklearn_4tree_12_partitioner_SparsePartitioner *, struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *, struct __pyx_t_7sklearn_4tree_5_tree_ParentInfo *); /*proto*/

so there is no v-table lookup as there is a specialized function generated for each combination of node_split_best/node_split_random and Dense/Sparse Partitioner.

For now, I reverted the BasePartitioner in e24cda9 and implemented your suggestion: #29458 (comment)

Signed-off-by: Adam Li <adam2392@gmail.com>

…into partition

Signed-off-by: Adam Li <adam2392@gmail.com>

adam2392 · 2024-07-16T14:42:57Z

Perhaps @adrinjalali you're interested in this too?

This will help the categorical splitting for Decision Trees be a bit easier to review #29437.

OmarManzoor

Thanks for the PR @adam2392. I think the overall structure looks nice. Could you resolve the conflicts and incorporate uint8_t here as well?

Signed-off-by: Adam Li <adam2392@gmail.com>

adam2392 · 2024-07-18T12:18:54Z

Thanks for the PR @adam2392. I think the overall structure looks nice. Could you resolve the conflicts and incorporate uint8_t here as well?

Thanks for the review @OmarManzoor! I have addressed these issues.

sklearn/tree/_partitioner.pyx

OmarManzoor · 2024-07-18T12:34:48Z

sklearn/tree/_partitioner.pyx

+
+
+# Sort n-element arrays pointed to by feature_values and samples, simultaneously,
+# by the values in feature_values. Algorithm: Introsort (Musser, SP&E, 1997).


Would it be feasible to move all these sorting functions towards the end of the file so that we have the common functions in one place and the classes are present together.

I left this alone as this was how it was structured in _splitter.pyx. These functions end up being after their corresponding "Splitter". I don't have strong feelings though.

Perhaps @thomasjpfan has some thoughts since he's browsing the tree code often :p

Looking it over, I like @OmarManzoor suggestion and moving the functions to be under SparsePartitioner.

Cool. Makes sense to me. Done in e34ef57

Signed-off-by: Adam Li <adam2392@gmail.com>

OmarManzoor

Otherwise LGTM

sklearn/tree/_partitioner.pxd

sklearn/tree/_splitter.pxd

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…into partition

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…cikit-learn#29458) Signed-off-by: Adam Li <adam2392@gmail.com>

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adam2392 mentioned this pull request Jul 10, 2024

MAINT, RFC Simplify the Cython code in sklearn/tree/ by splitting the "Splitter" and "Partitioner" code #29459

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adam2392 added 3 commits July 10, 2024 10:56

Include in setup

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adam2392 marked this pull request as ready for review July 10, 2024 21:39

Update benchmarking

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FEA Categorical split support for DecisionTree*, ExtraTree*, RandomForest* and `ExtraTrees* #29437

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MAINT Pull apart Splitter and Partitioner in the sklearn/tree code (s…

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sebp mentioned this pull request Dec 28, 2024

scikit-learn 1.6 changed behavior of growing trees #30554

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sercant mentioned this pull request Sep 23, 2025

Fix FEATURE_THRESHOLD initialization in trees #32259

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		# Sort n-element arrays pointed to by feature_values and samples, simultaneously,
		# by the values in feature_values. Algorithm: Introsort (Musser, SP&E, 1997).

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