Add :auto option for knn_algo auto detection

lib/scholar/manifold/trimap.ex

@@ -114,14 +114,18 @@ defmodule Scholar.Manifold.Trimap do
       Metric used to compute the distances.
       """
     ],
-    algorithm: [
-      type: {:in, [:nndescent, :large_vis]},
-      default: :large_vis,
+    knn_algorithm: [
+      type: {:in, [:auto, :nndescent, :large_vis, :brute]},
+      default: :auto,
       doc: ~S"""
       Algorithm used to compute the nearest neighbors. Possible values:
       * `:nndescent` - Nearest Neighbors Descent. See `Scholar.Neighbors.NNDescent` for more details.
 
       * `:large_vis` - LargeVis algorithm. See `Scholar.Neighbors.LargeVis` for more details.
+
+      * `:brute` - Brute force algorithm. See `Scholar.Neighbors.BruteKNN` for more details.
+
+      * `:auto` - Automatically selects the algorithm based on the number of points.
       """
     ]
   ]
@@ -301,37 +305,59 @@ defmodule Scholar.Manifold.Trimap do
     num_extra = min(num_inliners + 50, num_points)
 
     neighbors =
-      if Nx.axis_size(inputs, 0) <= 500 do
-        model =
-          Scholar.Neighbors.BruteKNN.fit(inputs,
-            num_neighbors: num_extra,
-            metric: opts[:metric]
-          )
-
-        {neighbors, _distances} = Scholar.Neighbors.BruteKNN.predict(model, inputs)
-        neighbors
-      else
-        case opts[:algorithm] do
-          :nndescent ->
-            nndescent =
-              Scholar.Neighbors.NNDescent.fit(inputs,
+      case opts[:knn_algorithm] do
+        :brute ->
+          model =
+            Scholar.Neighbors.BruteKNN.fit(inputs,
+              num_neighbors: num_extra,
+              metric: opts[:metric]
+            )
+
+          {neighbors, _distances} = Scholar.Neighbors.BruteKNN.predict(model, inputs)
+          neighbors
+
+        :nndescent ->
+          nndescent =
+            Scholar.Neighbors.NNDescent.fit(inputs,
+              num_neighbors: num_extra,
+              tree_init?: false,
+              metric: opts[:metric],
+              tol: 1.0e-5,
+              key: key
+            )
+
+          nndescent.nearest_neighbors
+
+        :large_vis ->
+          {neighbors, _distances} =
+            Scholar.Neighbors.LargeVis.fit(inputs,
+              num_neighbors: num_extra,
+              metric: opts[:metric],
+              key: key
+            )
+
+          neighbors
+
+        :auto ->
+          if Nx.axis_size(inputs, 0) <= 500 do
+            model =
+              Scholar.Neighbors.BruteKNN.fit(inputs,
                 num_neighbors: num_extra,
-                tree_init?: false,
-                metric: opts[:metric],
-                tol: 1.0e-5
+                metric: opts[:metric]
               )
 
-            nndescent.nearest_neighbors
-
-          :large_vis ->
+            {neighbors, _distances} = Scholar.Neighbors.BruteKNN.predict(model, inputs)
+            neighbors
+          else
             {neighbors, _distances} =
               Scholar.Neighbors.LargeVis.fit(inputs,
                 num_neighbors: num_extra,
-                metric: opts[:metric]
+                metric: opts[:metric],
+                key: key
               )
 
             neighbors
-        end
+          end
       end
 
     neighbors = Nx.concatenate([Nx.iota({num_points, 1}), neighbors], axis: 1)
@@ -436,7 +462,7 @@ defmodule Scholar.Manifold.Trimap do
   ## Examples
 
       iex> {inputs, key} = Nx.Random.uniform(Nx.Random.key(42), shape: {30, 5})
-      iex> Scholar.Manifold.Trimap.transform(inputs, num_components: 2, num_inliers: 3, num_outliers: 1, key: key, algorithm: :nndescent)
+      iex> Scholar.Manifold.Trimap.transform(inputs, num_components: 2, num_inliers: 3, num_outliers: 1, key: key, knn_algorithm: :nndescent)
   """
   deftransform transform(inputs, opts \\ []) do
     opts = NimbleOptions.validate!(opts, @opts_schema)

test/scholar/manifold/trimap_test.exs

@@ -13,8 +13,7 @@ defmodule Scholar.Manifold.TrimapTest do
           num_components: 2,
           key: key,
           num_inliers: 3,
-          num_outliers: 1,
-          algorithm: :nndescent
+          num_outliers: 1
         )
 
       expected =
@@ -41,8 +40,7 @@ defmodule Scholar.Manifold.TrimapTest do
           num_outliers: 1,
           num_random: 5,
           weight_temp: 0.1,
-          learning_rate: 0.3,
-          algorithm: :nndescent
+          learning_rate: 0.3
         )
 
       expected =
@@ -68,8 +66,7 @@ defmodule Scholar.Manifold.TrimapTest do
           num_inliers: 3,
           num_outliers: 1,
           num_iters: 100,
-          init_embedding_type: 1,
-          algorithm: :nndescent
+          init_embedding_type: 1
         )
 
       expected =
@@ -97,8 +94,7 @@ defmodule Scholar.Manifold.TrimapTest do
           num_inliers: 3,
           num_outliers: 1,
           triplets: triplets,
-          weights: weights,
-          algorithm: :nndescent
+          weights: weights
         )
 
       expected =
@@ -132,8 +128,7 @@ defmodule Scholar.Manifold.TrimapTest do
           key: key,
           num_inliers: 3,
           num_outliers: 1,
-          init_embeddings: init_embeddings,
-          algorithm: :nndescent
+          init_embeddings: init_embeddings
         )
 
       expected =
@@ -159,7 +154,7 @@ defmodule Scholar.Manifold.TrimapTest do
           num_inliers: 3,
           num_outliers: 1,
           metric: :manhattan,
-          algorithm: :nndescent
+          knn_algorithm: :nndescent
         )
 
       expected =
@@ -208,8 +203,7 @@ defmodule Scholar.Manifold.TrimapTest do
                        num_inliers: 3,
                        num_outliers: 1,
                        triplets: triplets,
-                       weights: weights,
-                       algorithm: :nndescent
+                       weights: weights
                      )
                    end
     end