RNN -> RadiusNN

elixir-nx · Sep 10, 2024 · 017e29b · 017e29b
1 parent 4dec8ba
commit 017e29b
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Showing 6 changed files with 62 additions and 62 deletions.
diff --git a/lib/scholar/cluster/dbscan.ex b/lib/scholar/cluster/dbscan.ex
@@ -98,14 +98,14 @@ defmodule Scholar.Cluster.DBSCAN do
     y_dummy = Nx.broadcast(Nx.tensor(0), {num_samples})
 
     neighbor_model =
-      Scholar.Neighbors.RNNClassifier.fit(x, y_dummy,
+      Scholar.Neighbors.RadiusNNClassifier.fit(x, y_dummy,
         num_classes: 1,
         radius: opts[:eps],
         metric: opts[:metric]
       )
 
     {_dist, indices} =
-      Scholar.Neighbors.RNNClassifier.radius_neighbors(neighbor_model, x)
+      Scholar.Neighbors.RadiusNNClassifier.radius_neighbors(neighbor_model, x)
 
     n_neighbors = Nx.sum(indices * weights, axes: [1])
     core_samples = n_neighbors >= opts[:min_samples]

diff --git a/lib/scholar/neighbors/rnn_classifier.ex b/lib/scholar/neighbors/rnn_classifier.ex
@@ -1,4 +1,4 @@
-defmodule Scholar.Neighbors.RNNClassifier do
+defmodule Scholar.Neighbors.RadiusNNClassifier do
   @moduledoc """
   The Radius Nearest Neighbors.
 
@@ -83,8 +83,8 @@ defmodule Scholar.Neighbors.RNNClassifier do
 
       iex> x = Nx.tensor([[1, 2], [2, 4], [1, 3], [2, 5]])
       iex> y = Nx.tensor([1, 0, 1, 1])
-      iex> Scholar.Neighbors.RNNClassifier.fit(x, y, num_classes: 2)
-      %Scholar.Neighbors.RNNClassifier{
+      iex> Scholar.Neighbors.RadiusNNClassifier.fit(x, y, num_classes: 2)
+      %Scholar.Neighbors.RadiusNNClassifier{
         data: Nx.tensor([
           [1, 2],
           [2, 4],
@@ -143,8 +143,8 @@ defmodule Scholar.Neighbors.RNNClassifier do
 
       iex> x = Nx.tensor([[1, 2], [2, 4], [1, 3], [2, 5]])
       iex> y = Nx.tensor([1, 0, 1, 1])
-      iex> model = Scholar.Neighbors.RNNClassifier.fit(x, y, num_classes: 2)
-      iex> Scholar.Neighbors.RNNClassifier.predict(model, Nx.tensor([[1.9, 4.3], [1.1, 2.0]]))
+      iex> model = Scholar.Neighbors.RadiusNNClassifier.fit(x, y, num_classes: 2)
+      iex> Scholar.Neighbors.RadiusNNClassifier.predict(model, Nx.tensor([[1.9, 4.3], [1.1, 2.0]]))
       #Nx.Tensor<
         s64[2]
         [0, 1]
@@ -169,8 +169,8 @@ defmodule Scholar.Neighbors.RNNClassifier do
 
       iex> x = Nx.tensor([[1, 2], [2, 4], [1, 3], [2, 5]])
       iex> y = Nx.tensor([1, 0, 1, 1])
-      iex> model = Scholar.Neighbors.RNNClassifier.fit(x, y, num_classes: 2)
-      iex> {probs, mask} = Scholar.Neighbors.RNNClassifier.predict_probability(model, Nx.tensor([[1.9, 4.3], [1.1, 2.0]]))
+      iex> model = Scholar.Neighbors.RadiusNNClassifier.fit(x, y, num_classes: 2)
+      iex> {probs, mask} = Scholar.Neighbors.RadiusNNClassifier.predict_probability(model, Nx.tensor([[1.9, 4.3], [1.1, 2.0]]))
       iex> probs
       #Nx.Tensor<
         f32[2][2]
@@ -230,8 +230,8 @@ defmodule Scholar.Neighbors.RNNClassifier do
 
       iex> x = Nx.tensor([[1, 2], [2, 4], [1, 3], [2, 5]])
       iex> y = Nx.tensor([1, 0, 1, 1])
-      iex> model = Scholar.Neighbors.RNNClassifier.fit(x, y, num_classes: 2)
-      iex> {distances, mask} = Scholar.Neighbors.RNNClassifier.radius_neighbors(model, Nx.tensor([[1.9, 4.3], [1.1, 2.0]]))
+      iex> model = Scholar.Neighbors.RadiusNNClassifier.fit(x, y, num_classes: 2)
+      iex> {distances, mask} = Scholar.Neighbors.RadiusNNClassifier.radius_neighbors(model, Nx.tensor([[1.9, 4.3], [1.1, 2.0]]))
       iex> distances
       #Nx.Tensor<
         f32[2][4]

diff --git a/lib/scholar/neighbors/rnn_regressor.ex b/lib/scholar/neighbors/rnn_regressor.ex
@@ -1,4 +1,4 @@
-defmodule Scholar.Neighbors.RNNRegressor do
+defmodule Scholar.Neighbors.RadiusNNRegressor do
   @moduledoc """
   The Radius Nearest Neighbors.
 
@@ -80,8 +80,8 @@ defmodule Scholar.Neighbors.RNNRegressor do
 
       iex> x = Nx.tensor([[1, 2], [2, 4], [1, 3], [2, 5]])
       iex> y = Nx.tensor([1, 0, 1, 1])
-      iex> Scholar.Neighbors.RNNRegressor.fit(x, y, num_classes: 2)
-      %Scholar.Neighbors.RNNRegressor{
+      iex> Scholar.Neighbors.RadiusNNRegressor.fit(x, y, num_classes: 2)
+      %Scholar.Neighbors.RadiusNNRegressor{
         data: Nx.tensor(
           [
             [1, 2],
@@ -142,8 +142,8 @@ defmodule Scholar.Neighbors.RNNRegressor do
 
       iex> x = Nx.tensor([[1, 2], [2, 4], [1, 3], [2, 5]])
       iex> y = Nx.tensor([1, 0, 1, 1])
-      iex> model = Scholar.Neighbors.RNNRegressor.fit(x, y, num_classes: 2)
-      iex> Scholar.Neighbors.RNNRegressor.predict(model, Nx.tensor([[1.9, 4.3], [1.1, 2.0]]))
+      iex> model = Scholar.Neighbors.RadiusNNRegressor.fit(x, y, num_classes: 2)
+      iex> Scholar.Neighbors.RadiusNNRegressor.predict(model, Nx.tensor([[1.9, 4.3], [1.1, 2.0]]))
       #Nx.Tensor<
         f32[2]
         [0.5, 1.0]
@@ -196,8 +196,8 @@ defmodule Scholar.Neighbors.RNNRegressor do
 
       iex> x = Nx.tensor([[1, 2], [2, 4], [1, 3], [2, 5]])
       iex> y = Nx.tensor([1, 0, 1, 1])
-      iex> model = Scholar.Neighbors.RNNRegressor.fit(x, y, num_classes: 2)
-      iex> {distances, mask} = Scholar.Neighbors.RNNRegressor.radius_neighbors(model, Nx.tensor([[1.9, 4.3], [1.1, 2.0]]))
+      iex> model = Scholar.Neighbors.RadiusNNRegressor.fit(x, y, num_classes: 2)
+      iex> {distances, mask} = Scholar.Neighbors.RadiusNNRegressor.radius_neighbors(model, Nx.tensor([[1.9, 4.3], [1.1, 2.0]]))
       iex> distances
       #Nx.Tensor<
         f32[2][4]

diff --git a/mix.exs b/mix.exs
@@ -96,8 +96,8 @@ defmodule Scholar.MixProject do
           Scholar.Neighbors.KNNRegressor,
           Scholar.Neighbors.LargeVis,
           Scholar.Neighbors.NNDescent,
-          Scholar.Neighbors.RNNClassifier,
-          Scholar.Neighbors.RNNRegressor,
+          Scholar.Neighbors.RadiusNNClassifier,
+          Scholar.Neighbors.RadiusNNRegressor,
           Scholar.Neighbors.RandomProjectionForest
         ],
         Utilities: [

diff --git a/test/scholar/neighbors/rnn_classifier_test.exs b/test/scholar/neighbors/rnn_classifier_test.exs
@@ -1,7 +1,7 @@
-defmodule Scholar.Neighbors.RNNClassifierTest do
+defmodule Scholar.Neighbors.RadiusNNClassifierTest do
   use Scholar.Case, async: true
-  alias Scholar.Neighbors.RNNClassifier
-  doctest RNNClassifier
+  alias Scholar.Neighbors.RadiusNNClassifier
+  doctest RadiusNNClassifier
 
   defp x do
     Nx.tensor([
@@ -28,7 +28,7 @@ defmodule Scholar.Neighbors.RNNClassifierTest do
 
   describe "fit" do
     test "fit with default parameters - :num_classes set to 2" do
-      model = RNNClassifier.fit(x(), y(), num_classes: 2)
+      model = RadiusNNClassifier.fit(x(), y(), num_classes: 2)
 
       assert model.weights == :uniform
       assert model.num_classes == 2
@@ -39,51 +39,51 @@ defmodule Scholar.Neighbors.RNNClassifierTest do
 
   describe "predict" do
     test "predict with default values" do
-      model = RNNClassifier.fit(x(), y(), num_classes: 2)
-      predictions = RNNClassifier.predict(model, x_pred())
+      model = RadiusNNClassifier.fit(x(), y(), num_classes: 2)
+      predictions = RadiusNNClassifier.predict(model, x_pred())
       assert predictions == Nx.tensor([-1, -1, -1, -1])
     end
 
     test "predict with radius set to 10" do
-      model = RNNClassifier.fit(x(), y(), num_classes: 2, radius: 10)
-      predictions = RNNClassifier.predict(model, x_pred())
+      model = RadiusNNClassifier.fit(x(), y(), num_classes: 2, radius: 10)
+      predictions = RadiusNNClassifier.predict(model, x_pred())
       assert predictions == Nx.tensor([1, 1, 1, 1])
     end
 
     test "predict with weights set to :distance - classification task" do
-      model = RNNClassifier.fit(x(), y(), num_classes: 2, radius: 10, weights: :distance)
+      model = RadiusNNClassifier.fit(x(), y(), num_classes: 2, radius: 10, weights: :distance)
 
-      predictions = RNNClassifier.predict(model, x_pred())
+      predictions = RadiusNNClassifier.predict(model, x_pred())
       assert predictions == Nx.tensor([1, 1, 1, 1])
     end
 
     test "predict with weights set to :distance and with specific metric" do
       model =
-        RNNClassifier.fit(x(), y(),
+        RadiusNNClassifier.fit(x(), y(),
           num_classes: 2,
           radius: 10,
           weights: :distance,
           metric: {:minkowski, 1.5}
         )
 
-      predictions = RNNClassifier.predict(model, x_pred())
+      predictions = RadiusNNClassifier.predict(model, x_pred())
       assert predictions == Nx.tensor([1, 1, 1, 1])
     end
 
     test "predict with weights set to :distance and with x_pred that contains sample with zero-distance" do
       x_pred = Nx.tensor([[3, 6, 7, 5], [1, 6, 1, 1], [3, 7, 9, 2], [5, 2, 1, 2]])
 
-      model = RNNClassifier.fit(x(), y(), num_classes: 2, radius: 10, weights: :distance)
+      model = RadiusNNClassifier.fit(x(), y(), num_classes: 2, radius: 10, weights: :distance)
 
-      predictions = RNNClassifier.predict(model, x_pred)
+      predictions = RadiusNNClassifier.predict(model, x_pred)
       assert predictions == Nx.tensor([0, 1, 1, 1])
     end
   end
 
   describe "predict_proba" do
     test "predict_proba with default values except radius set to 10" do
-      model = RNNClassifier.fit(x(), y(), num_classes: 2, radius: 10)
-      {predictions, outliers_mask} = RNNClassifier.predict_probability(model, x_pred())
+      model = RadiusNNClassifier.fit(x(), y(), num_classes: 2, radius: 10)
+      {predictions, outliers_mask} = RadiusNNClassifier.predict_probability(model, x_pred())
 
       assert_all_close(
         predictions,
@@ -94,9 +94,9 @@ defmodule Scholar.Neighbors.RNNClassifierTest do
     end
 
     test "predict_proba with weights set to :distance" do
-      model = RNNClassifier.fit(x(), y(), num_classes: 2, radius: 10, weights: :distance)
+      model = RadiusNNClassifier.fit(x(), y(), num_classes: 2, radius: 10, weights: :distance)
 
-      {predictions, outliers_mask} = RNNClassifier.predict_probability(model, x_pred())
+      {predictions, outliers_mask} = RadiusNNClassifier.predict_probability(model, x_pred())
 
       assert_all_close(
         predictions,
@@ -113,14 +113,14 @@ defmodule Scholar.Neighbors.RNNClassifierTest do
 
     test "predict_proba with weights set to :distance and with specific metric" do
       model =
-        RNNClassifier.fit(x(), y(),
+        RadiusNNClassifier.fit(x(), y(),
           num_classes: 2,
           radius: 10,
           weights: :distance,
           metric: {:minkowski, 1.5}
         )
 
-      {predictions, outliers_mask} = RNNClassifier.predict_probability(model, x_pred())
+      {predictions, outliers_mask} = RadiusNNClassifier.predict_probability(model, x_pred())
 
       assert_all_close(
         predictions,
@@ -138,9 +138,9 @@ defmodule Scholar.Neighbors.RNNClassifierTest do
     test "predict_proba with weights set to :distance and with x_pred that contains sample with zero-distance" do
       x_pred = Nx.tensor([[3, 6, 7, 5], [1, 6, 1, 1], [3, 7, 9, 2], [5, 2, 1, 2]])
 
-      model = RNNClassifier.fit(x(), y(), num_classes: 2, radius: 10, weights: :distance)
+      model = RadiusNNClassifier.fit(x(), y(), num_classes: 2, radius: 10, weights: :distance)
 
-      {predictions, outliers_mask} = RNNClassifier.predict_probability(model, x_pred)
+      {predictions, outliers_mask} = RadiusNNClassifier.predict_probability(model, x_pred)
 
       assert_all_close(
         predictions,
@@ -158,8 +158,8 @@ defmodule Scholar.Neighbors.RNNClassifierTest do
 
   describe "radius_neighbors" do
     test "radius_neighbors with default values except radius set to 10" do
-      model = RNNClassifier.fit(x(), y(), num_classes: 2, radius: 10)
-      {distances, indices} = RNNClassifier.radius_neighbors(model, x_pred())
+      model = RadiusNNClassifier.fit(x(), y(), num_classes: 2, radius: 10)
+      {distances, indices} = RadiusNNClassifier.radius_neighbors(model, x_pred())
 
       assert_all_close(
         distances,
@@ -229,13 +229,13 @@ defmodule Scholar.Neighbors.RNNClassifierTest do
 
     test "radius_neighbors with specific metric" do
       model =
-        RNNClassifier.fit(x(), y(),
+        RadiusNNClassifier.fit(x(), y(),
           num_classes: 2,
           radius: 10,
           metric: {:minkowski, 1.5}
         )
 
-      {distances, indices} = RNNClassifier.radius_neighbors(model, x_pred())
+      {distances, indices} = RadiusNNClassifier.radius_neighbors(model, x_pred())
 
       assert_all_close(
         distances,
@@ -313,7 +313,7 @@ defmodule Scholar.Neighbors.RNNClassifierTest do
                    "expected input tensor to have shape {n_samples, n_features} or {num_samples, num_samples},
              got tensor with shape: {5}",
                    fn ->
-                     RNNClassifier.fit(x, y, num_classes: 5)
+                     RadiusNNClassifier.fit(x, y, num_classes: 5)
                    end
     end
 
@@ -325,7 +325,7 @@ defmodule Scholar.Neighbors.RNNClassifierTest do
                    "expected labels to have shape {num_samples} or {num_samples, num_outputs},
             got tensor with shape: {1, 1, 5}",
                    fn ->
-                     RNNClassifier.fit(x, y, num_classes: 5)
+                     RadiusNNClassifier.fit(x, y, num_classes: 5)
                    end
     end
 
@@ -337,7 +337,7 @@ defmodule Scholar.Neighbors.RNNClassifierTest do
                    "expected labels to have the same size of the first axis as data,
       got: 6 != 5",
                    fn ->
-                     RNNClassifier.fit(x, y, num_classes: 5)
+                     RadiusNNClassifier.fit(x, y, num_classes: 5)
                    end
     end
 
@@ -348,7 +348,7 @@ defmodule Scholar.Neighbors.RNNClassifierTest do
       assert_raise NimbleOptions.ValidationError,
                    "required :num_classes option not found, received options: []",
                    fn ->
-                     RNNClassifier.fit(x, y)
+                     RadiusNNClassifier.fit(x, y)
                    end
     end
   end

diff --git a/test/scholar/neighbors/rnn_regressor_test.exs b/test/scholar/neighbors/rnn_regressor_test.exs
@@ -1,7 +1,7 @@
-defmodule Scholar.Neighbors.RNNRegressorTest do
+defmodule Scholar.Neighbors.RadiusNNRegressorTest do
   use Scholar.Case, async: true
-  alias Scholar.Neighbors.RNNRegressor
-  doctest RNNRegressor
+  alias Scholar.Neighbors.RadiusNNRegressor
+  doctest RadiusNNRegressor
 
   defp x do
     Nx.tensor([
@@ -29,26 +29,26 @@ defmodule Scholar.Neighbors.RNNRegressorTest do
   describe "predict" do
     test "predict with weights set to :distance" do
       model =
-        RNNRegressor.fit(x(), y(),
+        RadiusNNRegressor.fit(x(), y(),
           num_classes: 2,
           radius: 10,
           weights: :distance
         )
 
-      predictions = RNNRegressor.predict(model, x_pred())
+      predictions = RadiusNNRegressor.predict(model, x_pred())
       assert_all_close(predictions, Nx.tensor([0.69033845, 0.71773642, 0.68217609, 0.75918273]))
     end
 
     test "predict with weights set to :distance and with specific metric" do
       model =
-        RNNRegressor.fit(x(), y(),
+        RadiusNNRegressor.fit(x(), y(),
           num_classes: 2,
           radius: 10,
           weights: :distance,
           metric: :cosine
         )
 
-      predictions = RNNRegressor.predict(model, x_pred())
+      predictions = RadiusNNRegressor.predict(model, x_pred())
       assert_all_close(predictions, Nx.tensor([0.683947, 0.54694187, 0.59806132, 0.86398641]))
     end
 
@@ -57,14 +57,14 @@ defmodule Scholar.Neighbors.RNNRegressorTest do
         Nx.tensor([[1, 4], [0, 3], [2, 5], [0, 3], [0, 3], [1, 4], [2, 5], [0, 3], [1, 4], [2, 5]])
 
       model =
-        RNNRegressor.fit(x(), y,
+        RadiusNNRegressor.fit(x(), y,
           num_classes: 3,
           radius: 10,
           weights: :distance,
           metric: :cosine
         )
 
-      predictions = RNNRegressor.predict(model, x_pred())
+      predictions = RadiusNNRegressor.predict(model, x_pred())
 
       assert_all_close(
         predictions,
@@ -87,7 +87,7 @@ defmodule Scholar.Neighbors.RNNRegressorTest do
                    "expected input tensor to have shape {n_samples, n_features} or {num_samples, num_samples},
              got tensor with shape: {5}",
                    fn ->
-                     RNNRegressor.fit(x, y, num_classes: 5)
+                     RadiusNNRegressor.fit(x, y, num_classes: 5)
                    end
     end
 
@@ -99,7 +99,7 @@ defmodule Scholar.Neighbors.RNNRegressorTest do
                    "expected labels to have shape {num_samples} or {num_samples, num_outputs},
             got tensor with shape: {1, 1, 5}",
                    fn ->
-                     RNNRegressor.fit(x, y, num_classes: 5)
+                     RadiusNNRegressor.fit(x, y, num_classes: 5)
                    end
     end
 
@@ -111,7 +111,7 @@ defmodule Scholar.Neighbors.RNNRegressorTest do
                    "expected labels to have the same size of the first axis as data,
       got: 6 != 5",
                    fn ->
-                     RNNRegressor.fit(x, y, num_classes: 5)
+                     RadiusNNRegressor.fit(x, y, num_classes: 5)
                    end
     end
   end