FEAT Support precomputed distance matrix for `PairwiseDistancesReductions` #29483

kyrajeep · 2024-07-15T01:41:17Z

What does this implement/fix? Explain your changes.

(1) To take in precomputed distance directly, uses 'compute' in {ArgKmin, RadiusNeighbors}{ClassMode} {32,64}.
(2) When this is received after a XOR check (X, Y or precomputed) initializes an instance of class PrecomputedDistance.
(3) This data is formatted (wrapped?) by datasets_pair.py
(4) It is passed onto the backend by using the get_for() factory method.

Any other comments?
Thank you to @jjerphan for the reviews!

Co-authors: @jjerphan @adam2392

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

This feature was implemented last year so updates need to be pulled

…ierarchy

…nto feat_precomputed

github-actions · 2024-07-15T01:42:37Z

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--- /home/runner/work/scikit-learn/scikit-learn/sklearn/metrics/tests/test_pairwise_distances_reduction.py	2025-03-10 15:07:57.108550+00:00
+++ /home/runner/work/scikit-learn/scikit-learn/sklearn/metrics/tests/test_pairwise_distances_reduction.py	2025-03-10 15:08:15.517138+00:00
@@ -37,10 +37,11 @@
     "euclidean",
     "minkowski",
     "seuclidean",
 ]
 
+
 def _get_metric_params_list(metric: str, n_features: int, seed: int = 1):
     """Return list of dummy DistanceMetric kwargs for tests."""
 
     # Distinguishing on cases not to compute unneeded datastructures.
     rng = np.random.RandomState(seed)
@@ -60,10 +61,11 @@
         return [dict(V=rng.rand(n_features))]
 
     # Case of: "euclidean", "manhattan", "chebyshev", "haversine" or any other metric.
     # In those cases, no kwargs is needed.
     return [{}]
+
 
 def assert_same_distances_for_common_neighbors(
     query_idx,
     dist_row_a,
     dist_row_b,
@@ -98,10 +100,11 @@
                 f" for common neighbor with index {idx}:"
                 f" dist_a={dist_a} vs dist_b={dist_b} (with atol={atol} and"
                 f" rtol={rtol})"
             ) from e
 
+
 def assert_precomputed(precomputed, n_samples_X, n_samples_Y):
     """
     Validates a precomputed matrix for compatibility.
 
     Parameters:
@@ -116,11 +119,13 @@
     if not isinstance(precomputed, np.ndarray):
         raise AssertionError("Input must be a numpy array.")
 
     # Check if the array has the correct data type
     if precomputed.dtype not in [np.float32, np.float64]:
-        raise AssertionError("Precomputed matrix must be of type float (float32 or float64).")
+        raise AssertionError(
+            "Precomputed matrix must be of type float (float32 or float64)."
+        )
 
     # Check if the array is empty
     if precomputed.size == 0:
         raise AssertionError("Precomputed matrix should not be empty.")
 
@@ -132,16 +137,12 @@
             f"Expected: {expected_shape}, Got: {precomputed.shape}."
         )
 
 
 def assert_no_missing_neighbors(
-    query_idx,
-    dist_row_a,
-    dist_row_b,
-    indices_row_a,
-    indices_row_b,
-    threshold):
+    query_idx, dist_row_a, dist_row_b, indices_row_a, indices_row_b, threshold
+):
     """Compare the indices of neighbors in two results sets.
 
     Any neighbor index with a distance below the precision threshold should
     match one in the other result set. We ignore the last few neighbors beyond
     the threshold as those can typically be missing due to rounding errors.
@@ -267,10 +268,11 @@
         sampled_dists = pairwise_distances(X, Y, metric=metric, **metric_kwargs)
     else:
         sampled_dists = precomputed_dists[:n_subsampled_queries].copy()
     sampled_dists.sort(axis=1)
     return sampled_dists[:, expected_n_neighbors].mean()
+
 
 def assert_compatible_radius_results(
     neighbors_dists_a,
     neighbors_dists_b,
     neighbors_indices_a,
@@ -373,70 +375,85 @@
         RadiusNeighbors,
         np.float32,
     ): partial(assert_compatible_radius_results, **FLOAT32_TOLS),
 }
 
+
 @pytest.mark.parametrize("cls", [ArgKmin, RadiusNeighbors])
 def test_precompute_all_inputs_none(cls):
     """Test that ValueError is raised when all inputs are None."""
-    with pytest.raises(ValueError, match="Either X and Y or precomputed_matrix must be provided."):
+    with pytest.raises(
+        ValueError, match="Either X and Y or precomputed_matrix must be provided."
+    ):
         cls.compute(X=None, Y=None, precomputed_matrix=None)
+
 
 @pytest.mark.parametrize("cls", [ArgKmin, RadiusNeighbors])
 def test_precompute_all_inputs_provided(cls):
     """Test that ValueError is raised when both X/Y and precomputed_matrix are provided."""
     X = np.random.rand(10, 5)
     Y = np.random.rand(10, 5)
     precomputed_matrix = np.random.rand(10, 10)
-    with pytest.raises(ValueError, match="Only one of X and Y or precomputed_matrix must be provided."):
+    with pytest.raises(
+        ValueError, match="Only one of X and Y or precomputed_matrix must be provided."
+    ):
         cls.compute(X=X, Y=Y, precomputed_matrix=precomputed_matrix)
+
 
 @pytest.mark.parametrize("cls", [ArgKmin, RadiusNeighbors])
 def test_precompute_only_y(cls):
     """Test that ValueError is raised when only Y is provided."""
     Y = np.random.rand(10, 5)
     with pytest.raises(ValueError, match="Y should not be provided without X."):
         cls.compute(X=None, Y=Y)
 
+
 @pytest.mark.parametrize("cls", [ArgKmin, RadiusNeighbors])
 def test_precompute_only_x(cls):
     """Test that ValueError is raised when only X is provided."""
     X = np.random.rand(10, 5)
     with pytest.raises(ValueError, match="X should not be provided without Y."):
         cls.compute(X=X, Y=None)
 
+
 def test_assert_precomputed():
     # Success Case: Valid precomputed matrix
     n_samples_X, n_samples_Y = 5, 5
-    
+
     # Failure Case: Not a numpy array
     with pytest.raises(AssertionError, match="Input must be a numpy array"):
         assert_precomputed([[1, 2], [3, 4]], n_samples_X, n_samples_Y)
 
     # Failure Case: Incorrect dtype
     invalid_dtype = np.random.randint(0, 10, (n_samples_X, n_samples_Y))
-    with pytest.raises(AssertionError, match="Precomputed matrix must be of type float"):
+    with pytest.raises(
+        AssertionError, match="Precomputed matrix must be of type float"
+    ):
         assert_precomputed(invalid_dtype, n_samples_X, n_samples_Y)
 
     # Failure Case: Empty array
     with pytest.raises(AssertionError, match="Precomputed matrix should not be empty"):
         assert_precomputed(np.array([]), n_samples_X, n_samples_Y)
 
     # Failure Case: Incorrect dimensions
     incorrect_shape = np.random.rand(n_samples_X, n_samples_X).astype(np.float32)
-    with pytest.raises(AssertionError, match="Incorrect dimensions for precomputed matrix"):
+    with pytest.raises(
+        AssertionError, match="Incorrect dimensions for precomputed matrix"
+    ):
         assert_precomputed(incorrect_shape, n_samples_X, n_samples_Y)
+
 
 def test_my_function_precomputed():
     X = np.array([[1, 2], [3, 4], [5, 6]])  # Small sample data
     Y = np.array([[7, 8], [9, 10]])
     D = pairwise_distances(X, Y)  # Compute distances ONCE
 
-    result_precomputed = pairwise_distances(D, metric='precomputed')
+    result_precomputed = pairwise_distances(D, metric="precomputed")
     result_computed = pairwise_distances(X, Y)
 
     np.testing.assert_allclose(result_precomputed, result_computed)
+
 
 def test_assert_compatible_argkmin_results():
     atol = 1e-7
     rtol = 0.0
     tols = dict(atol=atol, rtol=rtol)
@@ -571,11 +588,12 @@
             np.array([[2.5, 1.2, _6_1m, 6.1, _6_1p]]),
             np.array([[1, 2, 3, 4, 5]]),
             np.array([[2, 1, 4, 5, 3]]),
             **tols,
         )
-        
+
+
 @pytest.mark.parametrize("check_sorted", [True, False])
 def test_assert_compatible_radius_results(check_sorted):
     atol = 1e-7
     rtol = 0.0
     tols = dict(atol=atol, rtol=rtol)
@@ -1707,11 +1725,11 @@
     unique_Y_labels = np.unique(Y_labels)
     results_X = RadiusNeighborsClassMode.compute(
         X=X,
         Y=Y,
         radius=radius,
-        metric=metric,     
+        metric=metric,
         weights=weights,
         Y_labels=Y_labels,
         unique_Y_labels=unique_Y_labels,
         outlier_label=outlier_label,
         strategy="parallel_on_X",
would reformat /home/runner/work/scikit-learn/scikit-learn/sklearn/metrics/tests/test_pairwise_distances_reduction.py

Oh no! 💥 💔 💥
1 file would be reformatted, 920 files would be left unchanged.

`ruff`

ruff detected issues. Please run ruff check --fix --output-format=full . locally, fix the remaining issues, and push the changes. Here you can see the detected issues. Note that the installed ruff version is ruff=0.5.1.


sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:119:12: E712 Avoid equality comparisons to `False`; use `if not is_usable:` for false checks
    |
118 |         # is_usable = (X is not None and Y is not None) ^ bool(precomputed)
119 |         if is_usable == False:
    |            ^^^^^^^^^^^^^^^^^^ E712
120 |             return is_usable
121 |         # FIXME: the current Cython implementation is too slow for a large number of
    |
    = help: Replace with `not is_usable`

sklearn/metrics/tests/test_pairwise_distances_reduction.py:121:89: E501 Line too long (94 > 88)
    |
119 |     # Check if the array has the correct data type
120 |     if precomputed.dtype not in [np.float32, np.float64]:
121 |         raise AssertionError("Precomputed matrix must be of type float (float32 or float64).")
    |                                                                                         ^^^^^^ E501
122 | 
123 |     # Check if the array is empty
    |

sklearn/metrics/tests/test_pairwise_distances_reduction.py:381:89: E501 Line too long (99 > 88)
    |
379 | def test_precompute_all_inputs_none(cls):
380 |     """Test that ValueError is raised when all inputs are None."""
381 |     with pytest.raises(ValueError, match="Either X and Y or precomputed_matrix must be provided."):
    |                                                                                         ^^^^^^^^^^^ E501
382 |         cls.compute(X=None, Y=None, precomputed_matrix=None)
    |

sklearn/metrics/tests/test_pairwise_distances_reduction.py:386:89: E501 Line too long (91 > 88)
    |
384 | @pytest.mark.parametrize("cls", [ArgKmin, RadiusNeighbors])
385 | def test_precompute_all_inputs_provided(cls):
386 |     """Test that ValueError is raised when both X/Y and precomputed_matrix are provided."""
    |                                                                                         ^^^ E501
387 |     X = np.random.rand(10, 5)
388 |     Y = np.random.rand(10, 5)
    |

sklearn/metrics/tests/test_pairwise_distances_reduction.py:390:89: E501 Line too long (104 > 88)
    |
388 |     Y = np.random.rand(10, 5)
389 |     precomputed_matrix = np.random.rand(10, 10)
390 |     with pytest.raises(ValueError, match="Only one of X and Y or precomputed_matrix must be provided."):
    |                                                                                         ^^^^^^^^^^^^^^^^ E501
391 |         cls.compute(X=X, Y=Y, precomputed_matrix=precomputed_matrix)
    |

sklearn/metrics/tests/test_pairwise_distances_reduction.py:410:1: W293 [*] Blank line contains whitespace
    |
408 |     # Success Case: Valid precomputed matrix
409 |     n_samples_X, n_samples_Y = 5, 5
410 |     
    | ^^^^ W293
411 |     # Failure Case: Not a numpy array
412 |     with pytest.raises(AssertionError, match="Input must be a numpy array"):
    |
    = help: Remove whitespace from blank line

sklearn/metrics/tests/test_pairwise_distances_reduction.py:417:89: E501 Line too long (89 > 88)
    |
415 |     # Failure Case: Incorrect dtype
416 |     invalid_dtype = np.random.randint(0, 10, (n_samples_X, n_samples_Y))
417 |     with pytest.raises(AssertionError, match="Precomputed matrix must be of type float"):
    |                                                                                         ^ E501
418 |         assert_precomputed(invalid_dtype, n_samples_X, n_samples_Y)
    |

sklearn/metrics/tests/test_pairwise_distances_reduction.py:426:89: E501 Line too long (92 > 88)
    |
424 |     # Failure Case: Incorrect dimensions
425 |     incorrect_shape = np.random.rand(n_samples_X, n_samples_X).astype(np.float32)
426 |     with pytest.raises(AssertionError, match="Incorrect dimensions for precomputed matrix"):
    |                                                                                         ^^^^ E501
427 |         assert_precomputed(incorrect_shape, n_samples_X, n_samples_Y)
    |

sklearn/metrics/tests/test_pairwise_distances_reduction.py:576:1: W293 [*] Blank line contains whitespace
    |
574 |             **tols,
575 |         )
576 |         
    | ^^^^^^^^ W293
577 | @pytest.mark.parametrize("check_sorted", [True, False])
578 | def test_assert_compatible_radius_results(check_sorted):
    |
    = help: Remove whitespace from blank line

sklearn/metrics/tests/test_pairwise_distances_reduction.py:1712:23: W291 [*] Trailing whitespace
     |
1710 |         Y=Y,
1711 |         radius=radius,
1712 |         metric=metric,     
     |                       ^^^^^ W291
1713 |         weights=weights,
1714 |         Y_labels=Y_labels,
     |
     = help: Remove trailing whitespace

Found 10 errors.
[*] 3 fixable with the `--fix` option (1 hidden fix can be enabled with the `--unsafe-fixes` option).

_{Generated for commit: 22b8c02. Link to the linter CI: here}

Micky774 · 2024-07-15T02:10:20Z

Also, in the abstract methods dist and surrogate_dist of PrecomputedDistanceMatrix, the indices [i,j] are required. Does this mean that other indices in the same dataset need computation and are not precomputed?

No. Keep in mind that surrogate_dist and dist methods are for scalar distances, i.e. they take a pair of indices and produce the distance between exactly those two data vectors. Hence, for the precomputed distances, it's simply indexing into the precomputed distances array.

kyrajeep · 2024-07-15T08:41:37Z

Thank you for the clarification

kyrajeep · 2024-07-15T11:49:40Z

In class PrecomputedDistanceMatrix, I don't see the option to input the precomputed distance matrix. Should I make that option or is it stored somewhere else?

jjerphan · 2024-07-15T16:17:01Z

Hi! I don't have much time and energy to have a look at it in details but basically:

a reference to the distance matrix needs to be passed to the compute methods of the dispatchers (which subclass BaseDistancesReductionDispatcher)
this reference needs to be passed then to the constructors of the Cython implementations (which subclass BaseDistancesReduction{{name_suffix}}) similarly to X and Y and be stored in an instance of a potentially new DatasetsPair subclass.
the logic for computing chunks size and other pieces of information in BaseDistancesReduction{{name_suffix}}.__init__ also needs adaptation.
we could have another second class hierarchy for handling precomputed distance matrices, but let's start with this simpler adaptation for now.

Let me know if this is unclear. 🙂

…computed matrix

…atrix stored in datasets_pair.pyx

kyrajeep · 2024-08-03T02:14:41Z

Thank you for your comments. They make sense, are helpful, and I believe I am going in the right direction. So far I have made progress on all the points made above by @jjerphan except for creating an instance to store the precomputed matrix. I think even though there are things (referencing the precomputed matrix and syntax) that should be definitely changed a review will be helpful and probably worthwhile at this point :) Higher level review will be helpful enough as I am familiar with the class hierarchy and codebase.

I have a couple questions that are not syntax related. We have a class for precomputed that subclasses DatasetsPair. In that class we have surrogate_dist and dist functions. My current understanding is that they are both used to compute distance depending on metric, chunking strategy, etc.. I want to create a method that will take in a precomputed matrix, if provided, and another method to retrieve this matrix to be passed into the compute method. Does that sound about right?

As for creating an instance to store the precomputed matrix itself... which file should I include it in?

Feedback will be appreciated! Thank you!

jjerphan

A quick review with more detailed explanations than my previous ones.

I pushed a commit (see 3a027c7) since your local venv_sklearn was staged and committed as part of e2ef246. I would recommend updating your global .gitignore with venv* to avoid this in the future, or to use a conda environment.

sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp

sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py

sklearn/metrics/_dist_metrics.pyx.tp

Signed-off-by: Julien Jerphanion <git@jjerphan.xyz>

Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>

kyrajeep · 2024-12-07T00:23:25Z

We are nearly there! :)

Thank you for the reviews!

jjerphan

Please, run the linters and remove the unused fixture in the tests.

.gitignore

jjerphan · 2024-12-09T20:15:38Z

sklearn/metrics/_pairwise_distances_reduction/_base.pxd.tp

Please, revert changes to this file.

sklearn/metrics/_pairwise_distances_reduction/_base.pyx.tp

sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py

jjerphan · 2024-12-09T20:21:03Z

sklearn/metrics/tests/test_pairwise_distances_reduction.py

We also need tests checking that the results of PairwiseDistancesReduction obtained respectively on (X, Y) or on their precomputed distance matrix are identical.

Micky774 · 2024-12-12T19:50:56Z

Catching up on this PR. I wanted to mention that I don't know if I like the current API with precomputed as a separate argument, since usually we include it as an option for metric. I think keeping with tradition leads to a more consistent experience since e.g. you don't have a situation where both precomputed and metric are defined (which would be entirely redundant) and instead succinctly have metric='precomputed'.

To enable this, we could simply enforce an API where metric='precomputed' expects X to be the precomputed distances, and raises an error if Y is provided (in the rare case that folks accidentally use precomputed when they didn't mean to).

What do you all think?

jjerphan · 2024-12-13T10:59:01Z

Thank you for shiming in, @Micky774.

We can indeed revise the API to get them in line with the public ones.

…-learn into feat_precomputed

kyrajeep · 2025-01-22T19:07:13Z

Hi @Micky774,

I'm sorry to take a while to respond. That's a great point. I agree that including 'precomputed' in one of the options for metrics is clearer. How about making the change in a separate PR so that this PR does not get too lengthy? Thanks!

Micky774 · 2025-01-23T22:56:44Z

Hi @Micky774,

I'm sorry to take a while to respond. That's a great point. I agree that including 'precomputed' in one of the options for metrics is clearer. How about making the change in a separate PR so that this PR does not get too lengthy? Thanks!

Considering that it is a fairly key part of the API being affected by this PR, I think it is better to resolve it here. In my opinion it costs less to fix it now, than it would to start up a new PR for that change. And no worries on the delay -- I appreciate you continuing your work here!

kyrajeep · 2025-01-29T20:06:23Z

Ok, thank you :)

kyrajeep · 2025-02-25T18:40:22Z

Hi @jjerphan, @Micky774

While working on implementing the change in the API to enable 'precomputed' as one of the metrics, I realized that X = precomputed argument may lead to issues due to the dimensions being different from X. This is from the current DatasetsPair docstring:

X : {ndarray, sparse matrix} of shape (n_samples_X, n_features)
Input data.
If provided as a ndarray, it must be C-contiguous.
If provided as a sparse matrix, it must be in CSR format.
Y : {ndarray, sparse matrix} of shape (n_samples_Y, n_features)
Input data.
If provided as a ndarray, it must be C-contiguous.
If provided as a sparse matrix, it must be in CSR format.
precomputed : ndarray of shape (n_samples_X, n_samples_Y)
Precomputed distance if provided.

What do you all think?

Micky774 · 2025-02-25T18:43:05Z

Hi @jjerphan, @Micky774

While working on implementing the change in the API to enable 'precomputed' as one of the metrics, I realized that X = precomputed argument may lead to issues due to the dimensions being different from X. This is from the current DatasetsPair docstring:

X : {ndarray, sparse matrix} of shape (n_samples_X, n_features) Input data. If provided as a ndarray, it must be C-contiguous. If provided as a sparse matrix, it must be in CSR format. Y : {ndarray, sparse matrix} of shape (n_samples_Y, n_features) Input data. If provided as a ndarray, it must be C-contiguous. If provided as a sparse matrix, it must be in CSR format. precomputed : ndarray of shape (n_samples_X, n_samples_Y) Precomputed distance if provided.

What do you all think?

I'm not sure I see the problem here. If you're worried about the fact that an NxN pre-computed matrix may be a result of NxD data for arbitrary D, then I do not believe it is a problem since the responsibility lies on the user to ensure that they are entering the correct precomputed matrix for their data -- that is not something we could (or should) verify imo.

If that's not what your concern is, then I may need a bit more of an explanation :)

kyrajeep · 2025-02-26T02:30:01Z

Hi @jjerphan, @Micky774
While working on implementing the change in the API to enable 'precomputed' as one of the metrics, I realized that X = precomputed argument may lead to issues due to the dimensions being different from X. This is from the current DatasetsPair docstring:
X : {ndarray, sparse matrix} of shape (n_samples_X, n_features) Input data. If provided as a ndarray, it must be C-contiguous. If provided as a sparse matrix, it must be in CSR format. Y : {ndarray, sparse matrix} of shape (n_samples_Y, n_features) Input data. If provided as a ndarray, it must be C-contiguous. If provided as a sparse matrix, it must be in CSR format. precomputed : ndarray of shape (n_samples_X, n_samples_Y) Precomputed distance if provided.
What do you all think?

I'm not sure I see the problem here. If you're worried about the fact that an NxN pre-computed matrix may be a result of NxD data for arbitrary D, then I do not believe it is a problem since the responsibility lies on the user to ensure that they are entering the correct precomputed matrix for their data -- that is not something we could (or should) verify imo.

If that's not what your concern is, then I may need a bit more of an explanation :)

Thank you for your input, @Micky774 ! That's exactly what I meant, but shouldn't we verify the shape of the precomputed matrix to throw an error if the dimension is not correct? What do you think, @jjerphan ? :)

jjerphan · 2025-02-26T08:03:43Z

I would look at and implement the same logic as the one of estimator taking a data matrix or a pre-computed distance matrix for X.

kyrajeep · 2025-02-27T02:09:23Z

Sounds good. Thank you.

…d_matrix

Alvaro-Kothe · 2025-04-30T12:03:44Z

sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py

+                is_usable = False
+
+        # is_usable = (X is not None and Y is not None) ^ bool(precomputed)
+        if is_usable == False:


Won't is_usable be undefined if metric != "precomputed"?

Alvaro-Kothe · 2025-04-30T12:04:48Z

sklearn/metrics/tests/test_pairwise_distances_reduction.py

@@ -1622,7 +1709,7 @@ def test_radius_neighbors_classmode_strategy_consistent(outlier_label):
        X=X,
        Y=Y,
        radius=radius,
-        metric=metric,
+        metric=metric,     


Suggested change

metric=metric,

metric=metric,

adam2392 and others added 10 commits March 30, 2023 17:56

Co-authored-by: @jjerphan Add initial commit

722033c

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

Merge branch 'main' into precomputed

124a8dc

Merge branch 'main' into precomputed

cd1e146

This feature was implemented last year so updates need to be pulled

Add precomputed option in dispatcher classes

046898b

comments, questions, code to feature precomputed and maintain class h…

02b5145

…ierarchy

Merge branch 'scikit-learn:main' into feat_precomputed

d32e593

Add the precomputed option in argkmin_classmode

5351d1a

Merge remote-tracking branch 'refs/remotes/origin/feat_precomputed' i…

5c71f6d

…nto feat_precomputed

Update is_usable_for to use the dispatcher for precomputed

50b95ea

Changed the name from distance_matrix to precomputed_distance

096ab56

github-actions bot added module:metrics cython labels Jul 15, 2024

kyrajeep and others added 3 commits July 22, 2024 13:55

Start of changing BaseDistancesReduction superclass

194ac01

Add the function to return the precomputed matrix

de74336

Merge branch 'scikit-learn:main' into feat_precomputed

e7c1e52

jjerphan changed the title ~~Add the precomputed option to bypass the computation and call the precomputed distance instead.~~ FEAT Support precomputed distance matrix for PairwiseDistancesReductions Jul 23, 2024

kyrajeep and others added 4 commits July 28, 2024 08:04

Merge branch 'scikit-learn:main' into feat_precomputed

f2f31ea

Add to BaseDistanceReduction the function to take in and return a pre…

f239c24

…computed matrix

Add comments to work on the subclass of DatasetsPair

d961e3b

Alter the compute method of ArgKminClassMode to take in precomputed m…

316c72a

…atrix stored in datasets_pair.pyx

jjerphan force-pushed the feat_precomputed branch from 316c72a to e2ef246 Compare August 5, 2024 06:56

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Delete venv_sklearn

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Signed-off-by: Julien Jerphanion <git@jjerphan.xyz>

jjerphan force-pushed the feat_precomputed branch from e2ef246 to 3a027c7 Compare August 5, 2024 07:29

kyrajeep and others added 4 commits December 6, 2024 11:45

syntax fix

38e5b84

Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>

syntax fix

a97e0c5

Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>

syntax fix

4d10786

Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>

Remove type annotations

305a90a

kyrajeep added 2 commits December 6, 2024 19:26

Merge branch 'scikit-learn:main' into feat_precomputed

b7da97d

Merge branch 'scikit-learn:main' into feat_precomputed

4334074

kyrajeep mentioned this pull request Dec 9, 2024

PERF PairwiseDistancesReductions subsequent work #25888

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kyrajeep and others added 3 commits December 13, 2024 10:06

Revert change

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Revert change

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Merge branch 'feat_precomputed' of https://github.com/kyrajeep/scikit…

59eccd4

…-learn into feat_precomputed

Merge branch 'main' into feat_precomputed

c13dc12

kyrajeep added 2 commits February 25, 2025 11:03

Test the precomputed input against the actual computation for a sample

eb7bf54

Add "precomputed" as one of the options for "metric"

8d55978

kyrajeep added 4 commits March 4, 2025 12:33

Change DatasetsPair to use the metric option to indicate precomputed

46ece7a

API change to include precomputed as a metric in the base abstract class

78244a0

API change to include precomputed as one of the metrics: check X and Y

6667775

Change API to enable precomputed as a metric and take in X=precompute…

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…d_matrix

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FEAT Support precomputed distance matrix for `PairwiseDistancesReductions` #29483

FEAT Support precomputed distance matrix for `PairwiseDistancesReductions` #29483

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FEAT Support precomputed distance matrix for PairwiseDistancesReductions #29483

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FEAT Support precomputed distance matrix for PairwiseDistancesReductions #29483

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FEAT Support precomputed distance matrix for `PairwiseDistancesReductions` #29483

FEAT Support precomputed distance matrix for `PairwiseDistancesReductions` #29483

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