apply code formatting

lib/scholar/metrics/classification.ex

@@ -1517,8 +1517,8 @@ defmodule Scholar.Metrics.Classification do
 
       iex> y_true = Nx.tensor([0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1])
       iex> y_score = Nx.tensor([0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.4, 0.5, 0.5, 0.6, 0.7, 0.7, 0.8, 0.9])
-      iex> distinct_value_indices = Scholar.Metrics.Classification1.distinct_value_indices(y_score)
-      iex> {fpr, fnr, thresholds} = Scholar.Metrics.Classification1.det_curve(y_true, y_score, distinct_value_indices)
+      iex> distinct_value_indices = Scholar.Metrics.Classification.distinct_value_indices(y_score)
+      iex> {fpr, fnr, thresholds} = Scholar.Metrics.Classification.det_curve(y_true, y_score, distinct_value_indices)
       iex> fpr
       #Nx.Tensor<
         f32[9]
@@ -1536,7 +1536,6 @@ defmodule Scholar.Metrics.Classification do
       >
   """
   defn det_curve(y_true, y_score, distinct_value_indices, weights \\ 1.0) do
-
     num_samples = Nx.axis_size(y_true, 0)
 
     weights = validate_weights(weights, num_samples, type: to_float_type(y_true))
@@ -1546,12 +1545,11 @@ defmodule Scholar.Metrics.Classification do
     {fps, tps, thresholds} =
       binary_clf_curve(y_true, y_score, distinct_value_indices, weights)
 
-    positive_count= tps[[-1]]
+    positive_count = tps[[-1]]
     negative_count = fps[[-1]]
 
     fns = positive_count - tps
 
-    {Nx.reverse(fps)/ negative_count, Nx.reverse(fns)/ positive_count, Nx.reverse(thresholds)}
+    {Nx.reverse(fps) / negative_count, Nx.reverse(fns) / positive_count, Nx.reverse(thresholds)}
   end
-
 end