ENH: Update KDTree, and example documentation #25482

01-vyom · 2023-01-25T23:52:38Z

Reference Issues

What does this implement/fix?

As described in the issue, the documentation for KDTree was misleading regarding which metrics are valid. Also, the examples for the KDTree were also misleading and invalid metrics. So, first, at the base params for metrics, I changed the valid_metric from kd_tree to KDTree to clearly show to use of the object method to check valid metrics. This change was done in the BinaryTree file whose document parameters are overridden by KDTree, and BallTree respectively. Second, in the example section, I changed the documentation to include that, valid metrics can be seen by checking the respective valid_metrics methods.

adam2392 · 2023-01-27T15:34:39Z

Reference Issues

Fixes #25364.

This will automatically close the GH Issue, but I think #25364 has 1 more PR to fix it fully (I think?).

adam2392 · 2023-01-27T15:36:55Z

Also, possibly another issue w/ the docs: https://output.circle-artifacts.com/output/job/a08bb01d-1bad-46c7-83ad-f6626a900e52/artifacts/0/doc/modules/generated/sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree

@jjerphan, should the KDTree.valid_metrics be hyper linkable and lead to an actual list? Or is it just intended for users to find in the source code?

jjerphan · 2023-01-27T16:26:10Z

@jjerphan, should the KDTree.valid_metrics be hyper linkable and lead to an actual list? Or is it just intended for users to find in the source code?

Yes, we must use Sphinx references to have hyperlinks.

adam2392 · 2023-01-27T17:21:49Z

Having taken a closer look at the code, it doesn't look like VALID_METRICS is documented in sphinx, so there is no link to provide. Currently, the docs suggest users to have to go to the source code of _ball_tree.pyx.VALID_METRICS

01-vyom · 2023-01-27T17:26:00Z

Reference Issues

Fixes #25364.

This will automatically close the GH Issue, but I think #25364 has 1 more PR to fix it fully (I think?).

I solved 2 parts of the issue as one, and the issue can be closed. The first was https://output.circle-artifacts.com/output/job/a08bb01d-1bad-46c7-83ad-f6626a900e52/artifacts/0/doc/modules/generated/sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree. And the second was https://output.circle-artifacts.com/output/job/a08bb01d-1bad-46c7-83ad-f6626a900e52/artifacts/0/doc/modules/neighbors.html#kdtree-and-balltree-classes where we say that one can see valid_metric for each method to get the valid metrics.

01-vyom · 2023-01-27T17:27:26Z

Having taken a closer look at the code, it doesn't look like VALID_METRICS is documented in sphinx, so there is no link to provide. Currently, the docs suggest users to have to go to the source code of _ball_tree.pyx.VALID_METRICS

Yes, it doesn't generate a hyperlink. What would you suggest should this be dealt as another PR?

jjerphan · 2023-01-27T20:02:48Z

You can define classmethods i.e. BallTree.valid_metrics and KDTree.valid_metrics which would return BallTree.VALID_METRICS and KDTree.VALID_METRICS, which you can document, making them hyperlinkable.

01-vyom · 2023-01-27T23:15:59Z

Ok. So, does that include creating a classmethod, a test case, and respective documentation? Also, then I feel that I should close this PR and create a new PR, referencing this PR, and the linked issue. Does that sound good?

jjerphan · 2023-01-28T09:00:32Z

That's correct. You can do this as part of this PR.

01-vyom · 2023-01-29T01:14:38Z

So, I have a couple of thoughts regarding this. A class variable of the BinaryTree already holds the value id's for the VALID_METRICS as the valid_metrics variable.

The KDTree static variable:

scikit-learn/sklearn/neighbors/_kd_tree.pyx

Lines 9 to 10 in 4db0492

    
           VALID_METRICS = ['EuclideanDistance', 'ManhattanDistance', 
        
                            'ChebyshevDistance', 'MinkowskiDistance']

The BallTree static variable:

scikit-learn/sklearn/neighbors/_ball_tree.pyx

Lines 9 to 10 in 4db0492

    
           'ManhattanDistance', 'ChebyshevDistance', 
        
           'MinkowskiDistance', 'WMinkowskiDistance',

The ids generated from the static variable:

scikit-learn/sklearn/neighbors/_binary_tree.pxi

Lines 765 to 766 in 4db0492

    
           from sklearn.metrics._dist_metrics import get_valid_metric_ids 
        
           VALID_METRIC_IDS = get_valid_metric_ids(VALID_METRICS)

The ids are then assigned to the class variable valid_metrics as follows:

scikit-learn/sklearn/neighbors/_binary_tree.pxi

Line 794 in 4db0492

valid_metrics = VALID_METRIC_IDS

So, my question is, does it make sense to make a method get_valid_metrics inside BinaryTree which returns valid_metrics as that would give the valid metrics from the class variable directly as follows:

def get_valid_metrics():
        """
        get_valid_metrics()

        Get valid metrics.

        Returns
        -------
        valid_metrics: list of str
            List of valid metrics.
        """
        return self.valid_metrics

I am a newbie at working with cython, please correct me if I can make any improvements/corrections here.

jjerphan · 2023-01-30T07:39:13Z

Yes, the content of scikit-learn/sklearn/neighbors/_binary_tree.pxi is included in scikit-learn/sklearn/neighbors/{_ball_tree,_kd_tree}.pyx, this is not usual.

You are on good tracks, feel free to open a PR: it will be easier then to discuss your proposals. :)

sklearn/neighbors/_binary_tree.pxi

Co-authored-by: Adam Li <adam2392@gmail.com>

doc/modules/neighbors.rst

sklearn/neighbors/_binary_tree.pxi

adam2392

This LGTM on the docs page, but not sure if adding an extra function is wanted by the sklearn team. Let's wait to see what @jjerphan and others say.

Pros:

programmatically gives the list of variables for user
Cons:
still doesn't render the full list of accepted distance metrics

Alternatively, one can use sphinx to fill in the value of a variable https://stackoverflow.com/questions/24080491/print-the-variable-value-in-a-sphinx-document.

doc/modules/neighbors.rst

sklearn/neighbors/_binary_tree.pxi

jjerphan · 2023-02-07T09:54:40Z

Merging main in to solve the issue on the CI.

jjerphan

LGTM modulo a few comments.

I think we should prefer to use "distance metric" instead of "metric" as a term (not blocking for this PR).

doc/modules/neighbors.rst

sklearn/neighbors/_binary_tree.pxi

01-vyom · 2023-02-09T00:59:07Z

Just wanted to clarify for the code snippet that it would be Obj.method i.e.

from sklearn.neighbors import KDTree, BallTree
import numpy as np
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
kdt = KDTree(X, leaf_size=30, metric='euclidean')
bt = BallTree(X, leaf_size=2)

...
...

kdt.valid_metric
<output>
bt.valid_metric
<output>

Same for BallTree

adam2392 · 2023-02-09T02:27:39Z

Just wanted to clarify for the code snippet that it would be Obj.method i.e.

from sklearn.neighbors import KDTree, BallTree
import numpy as np
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
kdt = KDTree(X, leaf_size=30, metric='euclidean')
bt = BallTree(X, leaf_size=2)

I think you prolly don't need to even pass in leaf_size, or metric. Might not even need to even do anything w/ X. Just needs a dummy X variable that is a 2D array. You just want to show the output of valid_metrics, so it is rendered and viewable on the documentation page.

Possibly something like:

dummy_X = np.zeros((1, 1))
kdt = KDTree(X)
bt = BallTree(X)

print(f'Valid list of KDTree metrics:\n {kdt.valid_metrics}')
print(f'Valid list of BallTree metrics:\n {kdt.valid_metrics}')

jjerphan

I think this can be simplified before being merged.

sklearn/neighbors/_binary_tree.pxi

doc/modules/neighbors.rst

adam2392

Rendering: https://output.circle-artifacts.com/output/job/98c29dad-21dd-4001-8bb8-fc825b54d3ab/artifacts/0/doc/modules/neighbors.html#neighbors

doc/modules/neighbors.rst

jjerphan

I think using a class method is more appropriate, even if this changes a somewhat-public class attribute access.

I am not sure if there's a better option and would wait for another maintainer's opinion.

sklearn/neighbors/_binary_tree.pxi

sklearn/neighbors/_kde.py

sklearn/neighbors/tests/test_kde.py

doc/modules/neighbors.rst

jjerphan · 2023-02-16T20:08:56Z

I will merge this PR when this looks good to @adam2392. 🙂

adam2392

LGTM!

https://output.circle-artifacts.com/output/job/827397b6-840f-4b0b-8d41-a5ba3d3b45c0/artifacts/0/doc/modules/neighbors.html

jjerphan · 2023-02-17T05:20:24Z

Thank you for this contribution, @01-vyom!

01-vyom · 2023-02-17T16:55:49Z

@adam2392 @jjerphan Thank you so much for all your input and help with this PR. Hoping to collaborate more in the future!

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Fan <thomasjpfan@gmail.com> * MAINT Parameters validation for precision_recall_fscore_support (scikit-learn#25681) Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr> * FIX Allow negative tol in SequentialFeatureSelector (scikit-learn#25664) * MAINT Replace deprecated cython conditional compilation (scikit-learn#25654) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * DOC fix formatting typo in related_projects (scikit-learn#25706) * MAINT Parameters validation for metrics.mean_absolute_percentage_error (scikit-learn#25695) * MAINT Parameters validation for metrics.precision_recall_curve (scikit-learn#25698) Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr> * MAINT Parameter Validation for metrics.precision_score (scikit-learn#25708) Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr> * CI Stablize build with random_state (scikit-learn#25701) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Remove -Wcpp warnings when compiling arrayfuncs (scikit-learn#25415) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * DOC Add scikit-learn-intelex to related projects (scikit-learn#23766) Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * ENH Support float32 in SGDClassifier and SGDRegressor (scikit-learn#25587) * FIX Raise appropriate attribute error in ensemble (scikit-learn#25668) * FIX Allow OrdinalEncoder's encoded_missing_value set to the cardinality (scikit-learn#25704) * ENH Let csr_row_norms support multi-thread (scikit-learn#25598) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> Co-authored-by: Vincent M <maladiere.vincent@yahoo.fr> * MAINT Parameter Validation for feature_selection.chi2 (scikit-learn#25719) Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr> * MAINT Parameter Validation for feature_selection.f_classif (scikit-learn#25720) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.metrics.matthews_corrcoef (scikit-learn#25712) Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr> * MAINT parameter validation for sklearn.datasets.dump_svmlight_file (scikit-learn#25726) Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr> * MAINT Clean dead code in build helpers (scikit-learn#25661) * MAINT Use newest NumPy C API in metrics._dist_metrics (scikit-learn#25702) * CI Adds permissions to workflows that use GITHUB_TOKEN (scikit-learn#25600) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * FIX Improves error message in partial_fit when early_stopping=True (scikit-learn#25694) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * DOC Makes navbar static (scikit-learn#25688) * MAINT Remove redundant sparse square euclidian distances function (scikit-learn#25731) * MAINT Use float64 for accumulators in WeightVector* (scikit-learn#25721) * API make PatchExtractor being a real scikit-learn transformer (scikit-learn#24230) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Update pyparsing.py to use bool instead of double negation (scikit-learn#25724) * API Deprecates values in partial_dependence in favor of pdp_values (scikit-learn#21809) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * API Use grid_values instead of pdp_values in partial_dependence (scikit-learn#25732) * MAINT remove np.product and inf/nan aliases in favor of canonical names (scikit-learn#25741) * MAINT Parameters validation for metrics.label_ranking_loss (scikit-learn#25742) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for metrics.coverage_error (scikit-learn#25748) * MAINT Parameters validation for metrics.dcg_score (scikit-learn#25749) * MAINT replace cnp.ndarray with memory views in _fast_dict (scikit-learn#25754) * MAINT Parameter Validation for feature_selection.f_regression (scikit-learn#25736) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for feature_selection.r_regression (scikit-learn#25734) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameter Validation for metrics.get_scorer (scikit-learn#25738) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * DOC Move allowing pandas nullable dtypes to 1.2.2 (scikit-learn#25692) * MAINT replace cnp.ndarray with memory views in sparsefuncs_fast (scikit-learn#25764) * MAINT parameter validation for sklearn.datasets.fetch_covtype (scikit-learn#25759) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Define centralized generic, but with explicit precision, types (scikit-learn#25739) * CI Disable network when SciPy requires it (scikit-learn#25743) * CI Open issue when arm wheel fails on CirrusCI (scikit-learn#25620) * ENH Speed-up expected mutual information (scikit-learn#25713) Co-authored-by: Kshitij Mathur <k.mathur68@gmail.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Omar Salman <omar.salman@arbisoft.com> * FIX add retry mechanism to handle quotechar in read_csv (scikit-learn#25511) * Merge Population Creation (#1) --------- Co-authored-by: Alex Buzenet <94121450+albuzenet@users.noreply.github.com> Co-authored-by: Alex <alex.buzenet.fr@gmail.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Adam Kania <48769688+remilvus@users.noreply.github.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com> Co-authored-by: Shady el Gewily <90049412+shadyelgewily-slimstock@users.noreply.github.com> Co-authored-by: John Pangas <swiftyxswaggy@outlook.com> Co-authored-by: Felipe Siola <fsiola@gmail.com> Co-authored-by: Felipe Breve Siola <felipe.breve-siola@klarna.com> Co-authored-by: Tim Head <betatim@gmail.com> Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com> Co-authored-by: Loïc Estève <loic.esteve@ymail.com> Co-authored-by: Anthony22-dev <122220081+Anthony22-dev@users.noreply.github.com> Co-authored-by: adossantosalfam <anthony.dos_santos_alfama@insa-rouen.fr> Co-authored-by: Xiao Yuan <yuanx749@gmail.com> Co-authored-by: Thomas J. 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ENH: Update KDTree, and example documentation

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ENH: Add valid metric function and reference doc

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This was referenced Jun 30, 2023

KDtree.valid_metrics is no longer an attribute without warning #26742

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COMPAT: ensure we pass numpy array to cKDtree and combat with sklearn 1.3.0 pysal/pointpats#107

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FIX Revert {Ball,KD}Tree.valid_metrics to public class attributes #26754

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ENH: Update KDTree, and example documentation #25482

ENH: Update KDTree, and example documentation #25482

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ENH: Update KDTree, and example documentation #25482

ENH: Update KDTree, and example documentation #25482

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