Refactor KMedoids

.circleci/config.yml

@@ -8,12 +8,11 @@ jobs:
       - checkout
       - run:
           command: |
-              sudo python3 -m pip install black flake8
+              sudo python3 -m pip install pre-commit
       - run:
           command: |
-              black --check examples sklearn_extra *py
-              # ensure there is no unused imports with flake8
-              flake8
+              pre-commit install
+              pre-commit run --all-files
 
 workflows:
   version: 2

.coveragerc

@@ -18,4 +18,4 @@ exclude_lines =
     if 0:
     if __name__ == .__main__.:
     if self.verbose:
-show_missing = True
+show_missing = True

.pre-commit-config.yaml

@@ -6,7 +6,7 @@ repos:
     -   id: end-of-file-fixer
     -   id: trailing-whitespace
 -   repo: https://github.com/psf/black
-    rev: 20.8b1
+    rev: 22.3.0
     hooks:
     -   id: black
 -   repo: https://gitlab.com/pycqa/flake8

doc/docs.md

@@ -5,6 +5,5 @@
 - scikit-learn(>=0.21)
 - Cython (>0.28)
 ### User Installation:
-You can install scikit-learn-extra using this command: 
+You can install scikit-learn-extra using this command:
 `pip install https://github.com/scikit-learn-contrib/scikit-learn-extra/archive/master.zip`
-

doc/index.rst

@@ -28,4 +28,3 @@ scikit-learn-extra is a Python module for machine learning that extends scikit-l
 
    contributing
    changelog
-

doc/modules/kernel_approximation.rst

@@ -6,29 +6,29 @@ Kernel map approximation for faster kernel methods
 
 .. currentmodule:: sklearn_extra.kernel_approximation
 
-Kernel methods, which are among the most flexible and influential tools in 
-machine learning with applications in virtually all areas of the field, rely 
+Kernel methods, which are among the most flexible and influential tools in
+machine learning with applications in virtually all areas of the field, rely
 on high-dimensional feature spaces in order to construct powerfull classifiers or
 regressors or clustering algorithms. The main drawback of kernel methods
 is their prohibitive computational complexity. Both spatial and temporal complexity
  is at least quadratic because we have to compute the whole kernel matrix.
 
 One of the popular way to improve the computational scalability of kernel methods is
 to approximate the feature map impicit behind the kernel method. In practice,
-this means that we will compute a low dimensional approximation of the 
+this means that we will compute a low dimensional approximation of the
 the otherwise high-dimensional embedding used to define the kernel method.
 
 :class:`Fastfood` approximates feature map of an RBF kernel by Monte Carlo approximation
 of its Fourier transform.
 
-Fastfood replaces the random matrix of Random Kitchen Sinks 
+Fastfood replaces the random matrix of Random Kitchen Sinks
 (`RBFSampler <https://scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.RBFSampler.html#sklearn.kernel_approximation.RBFSampler>`_)
 with an approximation that uses the Walsh-Hadamard transformation to gain
 significant speed and storage advantages.  The computational complexity for
 mapping a single example is O(n_components log d).  The space complexity is
-O(n_components).  
+O(n_components).
 
 See `scikit-learn User-guide <https://scikit-learn.org/stable/modules/kernel_approximation.html#kernel-approximation>`_ for more general informations on kernel approximations.
 
-See also :class:`EigenProRegressor <sklearn_extra.kernel_methods.EigenProRegressor>` and :class:`EigenProClassifier <sklearn_extra.kernel_methods.EigenProClassifier>` for another 
+See also :class:`EigenProRegressor <sklearn_extra.kernel_methods.EigenProRegressor>` and :class:`EigenProClassifier <sklearn_extra.kernel_methods.EigenProClassifier>` for another
 way to compute fast kernel methods algorithms.