Added ShrunkCovariance

lib/scholar/covariance/ledoit_wolf.ex

@@ -13,7 +13,7 @@ defmodule Scholar.Covariance.LedoitWolf do
   defstruct [:covariance, :shrinkage, :location]
 
   opts_schema = [
-    assume_centered: [
+    assume_centered?: [
       default: false,
       type: :boolean,
       doc: """
@@ -93,7 +93,7 @@ defmodule Scholar.Covariance.LedoitWolf do
 
       iex> key = Nx.Random.key(0)
       iex> {x, _new_key} = Nx.Random.multivariate_normal(key, Nx.tensor([0.0, 0.0, 0.0]), Nx.tensor([[3.0, 2.0, 1.0], [1.0, 2.0, 3.0], [1.3, 1.0, 2.2]]), shape: {10}, type: :f32)
-      iex> cov = Scholar.Covariance.LedoitWolf.fit(x, assume_centered: true)
+      iex> cov = Scholar.Covariance.LedoitWolf.fit(x, assume_centered?: true)
       iex> cov.covariance
       #Nx.Tensor<
         f32[3][3]
@@ -110,7 +110,7 @@ defmodule Scholar.Covariance.LedoitWolf do
   end
 
   defnp fit_n(x, opts) do
-    {x, location} = center(x, opts)
+    {x, location} = Scholar.Covariance.Utils.center(x, opts[:assume_centered?])
 
     {covariance, shrinkage} =
       ledoit_wolf(x)
@@ -122,23 +122,6 @@ defmodule Scholar.Covariance.LedoitWolf do
     }
   end
 
-  defnp center(x, opts) do
-    x =
-      case Nx.shape(x) do
-        {_} -> Nx.new_axis(x, 1)
-        _ -> x
-      end
-
-    location =
-      if opts[:assume_centered] do
-        0
-      else
-        Nx.mean(x, axes: [0])
-      end
-
-    {x - location, location}
-  end
-
   defnp ledoit_wolf(x) do
     case Nx.shape(x) do
       {_n, 1} ->
@@ -149,23 +132,6 @@ defmodule Scholar.Covariance.LedoitWolf do
     end
   end
 
-  defnp empirical_covariance(x) do
-    n = Nx.axis_size(x, 0)
-
-    covariance = Nx.dot(x, [0], x, [0]) / n
-
-    case Nx.shape(covariance) do
-      {} -> Nx.reshape(covariance, {1, 1})
-      _ -> covariance
-    end
-  end
-
-  defnp trace(x) do
-    x
-    |> Nx.take_diagonal()
-    |> Nx.sum()
-  end
-
   defnp ledoit_wolf_shrinkage(x) do
     case Nx.shape(x) do
       {_, 1} ->
@@ -182,9 +148,9 @@ defmodule Scholar.Covariance.LedoitWolf do
 
   defnp ledoit_wolf_shrinkage_complex(x) do
     {num_samples, num_features} = Nx.shape(x)
-    emp_cov = empirical_covariance(x)
+    emp_cov = Scholar.Covariance.Utils.empirical_covariance(x)
 
-    emp_cov_trace = trace(emp_cov)
+    emp_cov_trace = Scholar.Covariance.Utils.trace(emp_cov)
     mu = Nx.sum(emp_cov_trace) / num_features
 
     flatten_delta = Nx.flatten(emp_cov)

lib/scholar/covariance/shrunk_covariance.ex

@@ -0,0 +1,119 @@
+defmodule Scholar.Covariance.ShrunkCovariance do
+  @moduledoc """
+  Covariance estimator with shrinkage.
+  """
+  import Nx.Defn
+
+  @derive {Nx.Container, containers: [:covariance, :location]}
+  defstruct [:covariance, :location]
+
+  opts_schema = [
+    assume_centered?: [
+      default: false,
+      type: :boolean,
+      doc: """
+        If `true`, data will not be centered before computation.
+        Useful when working with data whose mean is almost, but not exactly
+        zero.
+        If `false`, data will be centered before computation.
+      """
+    ],
+    shrinkage: [
+      default: 0.1,
+      type: :float,
+      doc: "Coefficient in the convex combination used for the computation
+        of the shrunk estimate. Range is [0, 1]."
+    ]
+  ]
+
+  @opts_schema NimbleOptions.new!(opts_schema)
+  @doc """
+  Fit the shrunk covariance model to `x`.
+
+  ## Options
+
+  #{NimbleOptions.docs(@opts_schema)}
+
+  ## Return Values
+
+  The function returns a struct with the following parameters:
+
+    * `:covariance` - Tensor of shape `{num_features, num_features}`. Estimated covariance matrix.
+    * `:location` - Tensor of shape `{num_features,}`.
+      Estimated location, i.e. the estimated mean.
+
+  ## Examples
+
+      iex> key = Nx.Random.key(0)
+      iex> {x, _new_key} = Nx.Random.multivariate_normal(key, Nx.tensor([0.0, 0.0]), Nx.tensor([[0.8, 0.3], [0.2, 0.4]]), shape: {10}, type: :f32)
+      iex> model = Scholar.Covariance.ShrunkCovariance.fit(x)
+      iex> model.covariance
+      #Nx.Tensor<
+        f32[2][2]
+        [
+          [0.7721845507621765, 0.19141492247581482],
+          [0.19141492247581482, 0.33952537178993225]
+        ]
+      >
+      iex> model.location
+      #Nx.Tensor<
+        f32[2]
+        [0.18202415108680725, -0.09216632694005966]
+      >
+      iex> key = Nx.Random.key(0)
+      iex> {x, _new_key} = Nx.Random.multivariate_normal(key, Nx.tensor([0.0, 0.0]), Nx.tensor([[0.8, 0.3], [0.2, 0.4]]), shape: {10}, type: :f32)
+      iex> model = Scholar.Covariance.ShrunkCovariance.fit(x, shrinkage: 0.4)
+      iex> model.covariance
+      #Nx.Tensor<
+        f32[2][2]
+        [
+          [0.7000747323036194, 0.1276099532842636],
+          [0.1276099532842636, 0.41163527965545654]
+        ]
+      >
+      iex> model.location
+      #Nx.Tensor<
+        f32[2]
+        [0.18202415108680725, -0.09216632694005966]
+      >
+  """
+
+  deftransform fit(x, opts \\ []) do
+    fit_n(x, NimbleOptions.validate!(opts, @opts_schema))
+  end
+
+  defnp fit_n(x, opts) do
+    shrinkage = opts[:shrinkage]
+
+    if shrinkage < 0 or shrinkage > 1 do
+      raise ArgumentError,
+            """
+            expected :shrinkage option to be in [0, 1] range, \
+            got shrinkage: #{inspect(Nx.shape(x))}\
+            """
+    end
+
+    {x, location} = Scholar.Covariance.Utils.center(x, opts[:assume_centered?])
+
+    covariance =
+      Scholar.Covariance.Utils.empirical_covariance(x)
+      |> shrunk_covariance(shrinkage)
+
+    %__MODULE__{
+      covariance: covariance,
+      location: location
+    }
+  end
+
+  defnp shrunk_covariance(emp_cov, shrinkage) do
+    num_features = Nx.axis_size(emp_cov, 1)
+    shrunk_cov = (1.0 - shrinkage) * emp_cov
+    emp_cov_trace = Scholar.Covariance.Utils.trace(emp_cov)
+    mu = Nx.sum(emp_cov_trace) / num_features
+
+    mask = Nx.iota(Nx.shape(shrunk_cov))
+    selector = Nx.remainder(mask, num_features + 1) == 0
+
+    shrunk_cov + shrinkage * mu * selector
+  end
+end

lib/scholar/covariance/utils.ex

@@ -0,0 +1,39 @@
+defmodule Scholar.Covariance.Utils do
+  @moduledoc false
+  import Nx.Defn
+  require Nx
+
+  defn center(x, assume_centered? \\ false) do
+    x =
+      case Nx.shape(x) do
+        {_} -> Nx.new_axis(x, 1)
+        _ -> x
+      end
+
+    location =
+      if assume_centered? do
+        0
+      else
+        Nx.mean(x, axes: [0])
+      end
+
+    {x - location, location}
+  end
+
+  defn empirical_covariance(x) do
+    n = Nx.axis_size(x, 0)
+
+    covariance = Nx.dot(x, [0], x, [0]) / n
+
+    case Nx.shape(covariance) do
+      {} -> Nx.reshape(covariance, {1, 1})
+      _ -> covariance
+    end
+  end
+
+  defn trace(x) do
+    x
+    |> Nx.take_diagonal()
+    |> Nx.sum()
+  end
+end

test/scholar/covariance/ledoit_wolf_test.exs

@@ -40,7 +40,7 @@ defmodule Scholar.Covariance.LedoitWolfTest do
     )
   end
 
-  test "fit test - :assume_centered is true" do
+  test "fit test - :assume_centered? is true" do
     key = key()
 
     {x, _new_key} =
@@ -52,7 +52,7 @@ defmodule Scholar.Covariance.LedoitWolfTest do
         type: :f32
       )
 
-    model = LedoitWolf.fit(x, assume_centered: true)
+    model = LedoitWolf.fit(x, assume_centered?: true)
 
     assert_all_close(
       model.covariance,