Polynomial Regression Score

Author: davidrichey

lib/learn_kit/regression/linear.ex

@@ -117,7 +117,7 @@ defmodule LearnKit.Regression.Linear do
   def predict(linear = %Linear{coefficients: _}, samples) when is_list(samples) do
     {
       :ok,
-      Enum.map(samples, fn sample -> predict(linear, sample) end)
+      do_predict(linear, samples)
     }
   end
 
@@ -138,29 +138,6 @@ defmodule LearnKit.Regression.Linear do
   @spec predict(%Linear{coefficients: coefficients}, list) :: {:ok, list}
 
   def predict(%Linear{coefficients: [alpha, beta]}, sample) do
-    sample * beta + alpha
-  end
-
-  @doc """
-  Returns the coefficient of determination R^2 of the prediction
-
-  ## Parameters
-
-    - predictor: %LearnKit.Regression.Linear{}
-
-  ## Examples
-
-      iex> predictor |> LearnKit.Regression.Linear.score
-      {:ok, 0.9876543209876543}
-
-  """
-  @spec score(%Linear{factors: factors, results: results, coefficients: coefficients}) ::
-          {:ok, number}
-
-  def score(linear = %Linear{factors: _, results: _, coefficients: _}) do
-    {
-      :ok,
-      calculate_score(linear)
-    }
+    {:ok, sample * beta + alpha}
   end
 end

lib/learn_kit/regression/linear/calculations.ex

@@ -29,6 +29,13 @@ defmodule LearnKit.Regression.Linear.Calculations do
         [alpha, beta]
       end
 
+      defp do_predict(linear, samples) do
+        Enum.map(samples, fn sample ->
+          {:ok, prediction} = predict(linear, sample)
+          prediction
+        end)
+      end
+
       defp squared_error_gradient(linear, x, y) do
         error_variable = prediction_error(linear, x, y)
 

lib/learn_kit/regression/polynomial.ex

@@ -6,7 +6,9 @@ defmodule LearnKit.Regression.Polynomial do
   defstruct factors: [], results: [], coefficients: [], degree: 2
 
   alias LearnKit.Regression.Polynomial
+
   use Polynomial.Calculations
+  use LearnKit.Regression.Score
 
   @type factors :: [number]
   @type results :: [number]

lib/learn_kit/regression/score.ex

@@ -1,20 +1,47 @@
 defmodule LearnKit.Regression.Score do
   @moduledoc """
-  Module for fit functions
+  Module for scoring regression models
   """
 
   alias LearnKit.Math
 
   defmacro __using__(_opts) do
     quote do
+      @doc """
+      Returns the coefficient of determination R^2 of the prediction
+
+      ## Parameters
+
+        - predictor: %LearnKit.Regression.Linear{}
+
+      ## Examples
+
+          iex> predictor |> LearnKit.Regression.Linear.score
+          {:ok, 0.9876543209876543}
+
+      """
+      @spec score(%LearnKit.Regression.Linear{
+              factors: factors,
+              results: results,
+              coefficients: coefficients
+            }) :: {:ok, number}
+
+      def score(regression = %_{factors: _, results: _, coefficients: _}) do
+        {
+          :ok,
+          calculate_score(regression)
+        }
+      end
+
       defp calculate_score(%_{coefficients: []}, _, _), do: raise("There was no fit for model")
 
       defp calculate_score(regression = %_{coefficients: _, factors: _, results: results}) do
         1.0 - sum_of_squared_errors(regression) / total_sum_of_squares(results)
       end
 
       defp prediction_error(regression, x, y) do
-        y - predict(regression, x)
+        {:ok, prediction} = predict(regression, x)
+        y - prediction
       end
 
       defp sum_of_squared_errors(

test/learn_kit/regression/polynomial_test.exs

@@ -59,4 +59,11 @@ defmodule LearnKit.Regression.PolynomialTest do
            |> Polynomial.fit(degree: 2)
            |> Polynomial.predict([3, 5]) == {:ok, [-0.009459989001544572, 0.10916263554608596]}
   end
+
+  test "returns coefficient of determination R^2 of the prediction", state do
+    predictor = state.predictor |> Polynomial.fit()
+
+    assert {:ok, result} = predictor |> Polynomial.score()
+    assert result == 0.9614116660464942
+  end
 end