Add jaccard similarity metric

lib/scholar/metrics/similarity.ex

@@ -0,0 +1,74 @@
+defmodule Scholar.Metrics.Similarity do
+  @moduledoc """
+  Similarity metrics between 1-D tensors.
+  """
+
+  import Nx.Defn
+  import Scholar.Shared
+
+  @doc ~S"""
+  Calculates Jaccard similarity (also known as Jaccard similarity coefficient, or Jaccard index).
+
+  Jaccard similarity is a statistic used to measure similarities between two sets. Mathematically, the calculation
+  of Jaccard similarity is the ratio of set intersection over set union.
+
+  $$
+  J(A, B) = \frac{\mid A \cap B \mid}{\mid A \cup B \mid}
+  $$
+
+  ## Examples
+
+      iex> x = Nx.tensor([1.0, 5.0, 3.0, 6.7])
+      iex> y = Nx.tensor([5.0, 2.5, 3.1, 9.0])
+      iex> Scholar.Metrics.Similarity.jaccard(x, y)
+      #Nx.Tensor<
+        f32
+        0.1428571492433548
+      >
+
+      iex> x = Nx.tensor([1, 2, 3, 5, 7])
+      iex> y = Nx.tensor([1, 2, 4, 8, 9])
+      iex> Scholar.Metrics.Similarity.jaccard(x, y)
+      #Nx.Tensor<
+        f32
+        0.25
+      >
+
+      iex> x = Nx.tensor([1, 2])
+      iex> y = Nx.tensor([1, 2, 3])
+      iex> Scholar.Metrics.Similarity.jaccard(x, y)
+      ** (ArgumentError) expected input shapes to be equal, got {2} != {3}
+  """
+  defn jaccard(x, y) do
+    # We're requiring the same shape because usual use cases will have the same shape.
+    # The last axis could in theory be different on both sides.
+    assert_same_shape!(x, y)
+
+    x_size = unique_size(x)
+    y_size = unique_size(y)
+
+    union_size = unique_size(Nx.concatenate([x, y]))
+    intersection_size = x_size + y_size - union_size
+
+    intersection_size / union_size
+  end
+
+  defnp unique_size(%Nx.Tensor{shape: shape} = tensor) do
+    case shape do
+      {} ->
+        raise "expected input shape of at least {1}, got: {}"
+
+      {1} ->
+        1
+
+      _ ->
+        sorted = Nx.sort(tensor)
+
+        different_from_successor? = Nx.not_equal(sorted[0..-2//1], sorted[1..-1//1])
+
+        different_from_successor?
+        |> Nx.sum()
+        |> Nx.add(1)
+    end
+  end
+end

test/scholar/metrics/similarity_test.exs

@@ -0,0 +1,67 @@
+defmodule Scholar.Metrics.SimilarityTest do
+  use ExUnit.Case
+  alias Scholar.Metrics.Similarity
+  doctest Similarity
+
+  describe "jaccard/2" do
+    test "returns similarity according to sklearn jaccard_score function" do
+      x = Nx.tensor([1, 2, 3, 5, 0])
+      y = Nx.tensor([1, 30, 4, 8, 9])
+
+      assert Similarity.jaccard(x, y) == Nx.tensor(0.1111111119389534)
+    end
+
+    test "returns 100% of similarity" do
+      x = Nx.tensor([1, 2, 3])
+      y = Nx.tensor([1, 2, 3])
+
+      assert Similarity.jaccard(x, y) == Nx.tensor(1.0)
+    end
+
+    test "returns 0% of similarity" do
+      x = Nx.tensor([1, 2, 3])
+      y = Nx.tensor([4, 5, 6])
+
+      assert Similarity.jaccard(x, y) == Nx.tensor(0.0)
+    end
+
+    test "returns 20% of similarity" do
+      x = Nx.tensor([1, 2, 3])
+      y = Nx.tensor([3, 4, 5])
+
+      assert Similarity.jaccard(x, y) == Nx.tensor(0.20)
+    end
+
+    test "returns similarity when tensors have a single element" do
+      x = Nx.tensor([1])
+      y = Nx.tensor([2])
+
+      assert Similarity.jaccard(x, y) == Nx.tensor(0.0)
+    end
+
+    test "returns similarity when tensor has multiple dimensions" do
+      x = Nx.tensor([[0, 1, 1], [1, 1, 0]])
+      y = Nx.tensor([[1, 1, 1], [1, 0, 0]])
+
+      assert Similarity.jaccard(x, y) == Nx.tensor(0.5)
+    end
+
+    test "raises exception when tensors have different shapes" do
+      x = Nx.tensor([1, 2, 3, 5])
+      y = Nx.tensor([1, 30, 4, 8, 9])
+
+      assert_raise ArgumentError, "expected input shapes to be equal, got {4} != {5}", fn ->
+        Similarity.jaccard(x, y)
+      end
+    end
+
+    test "raises exception when tensors have shape zero" do
+      x = Nx.tensor(1)
+      y = Nx.tensor(1)
+
+      assert_raise RuntimeError, "expected input shape of at least {1}, got: {}", fn ->
+        Similarity.jaccard(x, y)
+      end
+    end
+  end
+end