Fix warnings

Author: josevalim

lib/scholar/cluster/gmm.ex

@@ -22,7 +22,6 @@ defmodule Scholar.Cluster.GaussianMixture do
 
   import Nx.Defn
   import Scholar.Shared
-  require Nx
 
   @derive {Nx.Container, containers: [:weights, :means, :covariances, :precisions_cholesky]}
   defstruct [:weights, :means, :covariances, :precisions_cholesky]

lib/scholar/cluster/k_means.ex

@@ -22,7 +22,6 @@ defmodule Scholar.Cluster.KMeans do
   """
   import Nx.Defn
   import Scholar.Shared
-  require Nx
 
   @derive {Nx.Container, containers: [:num_iterations, :clusters, :inertia, :labels]}
   defstruct [:num_iterations, :clusters, :inertia, :labels]

lib/scholar/cluster/optics.ex

@@ -9,7 +9,6 @@ defmodule Scholar.Cluster.OPTICS do
   method.
   """
   import Nx.Defn
-  require Nx
 
   @derive {Nx.Container, containers: [:labels, :min_samples, :max_eps, :eps, :algorithm]}
   defstruct [:labels, :min_samples, :max_eps, :eps, :algorithm]

lib/scholar/covariance/utils.ex

@@ -1,7 +1,6 @@
 defmodule Scholar.Covariance.Utils do
   @moduledoc false
   import Nx.Defn
-  require Nx
 
   defn center(x, assume_centered? \\ false) do
     x =

lib/scholar/decomposition/utils.ex

@@ -1,7 +1,6 @@
 defmodule Scholar.Decomposition.Utils do
   @moduledoc false
   import Nx.Defn
-  require Nx
 
   defn flip_svd(u, v, u_based \\ true) do
     base =

lib/scholar/linear/bayesian_ridge_regression.ex

@@ -59,7 +59,6 @@ defmodule Scholar.Linear.BayesianRidgeRegression do
   Pedregosa et al., Scikit-learn: Machine Learning in Python,
   JMLR 12, pp. 2825-2830, 2011.
   """
-  require Nx
   import Nx.Defn
   import Scholar.Shared
   alias Scholar.Linear.LinearHelpers

lib/scholar/linear/isotonic_regression.ex

@@ -274,7 +274,7 @@ defmodule Scholar.Linear.IsotonicRegression do
         }
       }
   """
-  def preprocess(model, trim_duplicates \\ true) do
+  def preprocess(%__MODULE__{} = model, trim_duplicates \\ true) do
     cutoff = Nx.to_number(model.cutoff_index)
     x = model.x_thresholds[0..cutoff]
     y = model.y_thresholds[0..cutoff]
@@ -303,16 +303,11 @@ defmodule Scholar.Linear.IsotonicRegression do
         {x, y}
       end
 
-    model = %__MODULE__{model | x_thresholds: x}
-    model = %__MODULE__{model | y_thresholds: y}
-
     %__MODULE__{
       model
-      | preprocess:
-          Scholar.Interpolation.Linear.fit(
-            model.x_thresholds,
-            model.y_thresholds
-          )
+      | x_thresholds: x,
+        y_thresholds: y,
+        preprocess: Scholar.Interpolation.Linear.fit(x, y)
     }
   end
 

lib/scholar/linear/linear_regression.ex

@@ -5,7 +5,6 @@ defmodule Scholar.Linear.LinearRegression do
   Time complexity of linear regression is $O((K^2) * (K+N))$ where $N$ is the number of samples
   and $K$ is the number of features.
   """
-  require Nx
   import Nx.Defn
   import Scholar.Shared
   alias Scholar.Linear.LinearHelpers

lib/scholar/linear/ridge_regression.ex

@@ -19,7 +19,6 @@ defmodule Scholar.Linear.RidgeRegression do
   Time complexity is $O(N^2)$ for `:cholesky` solver and $O((N^2) * (K + N))$ for `:svd` solver,
   where $N$ is the number of observations and $K$ is the number of features.
   """
-  require Nx
   import Nx.Defn
   import Scholar.Shared
   alias Scholar.Linear.LinearHelpers

lib/scholar/manifold/mds.ex

@@ -395,8 +395,8 @@ defmodule Scholar.Manifold.MDS do
     %__MODULE__{embedding: best, stress: best_stress, n_iter: best_iter}
   end
 
-  defnp special_preprocess(model) do
-    %Scholar.Linear.IsotonicRegression{
+  defnp special_preprocess(%Scholar.Linear.IsotonicRegression{} = model) do
+    %{
       model
       | preprocess:
           Scholar.Interpolation.Linear.fit(