Implementing KMedoids in scikit-learn-extra

.gitignore

@@ -8,6 +8,9 @@ __pycache__/
 # C extensions
 *.so
 
+# Text Editors
+.vscode/
+
 # scikit-learn specific
 doc/_build/
 doc/auto_examples/
@@ -17,6 +20,7 @@ doc/datasets/generated/
 # Distribution / packaging
 
 .Python
+venv/
 env/
 build/
 develop-eggs/

benchmarks/bench_rbfsampler_fastfood.py

@@ -15,9 +15,9 @@
 Y /= Y.sum(axis=1)[:, np.newaxis]
 
 # calculate feature maps
-gamma = 10.
+gamma = 10.0
 sigma = np.sqrt(1 / (2 * gamma))
-number_of_features_to_generate = 4096*4
+number_of_features_to_generate = 4096 * 4
 
 exact_start = datetime.datetime.utcnow()
 # original rbf kernel method:
@@ -27,23 +27,24 @@
 exact_spent_time = exact_end - exact_start
 print("Timimg exact rbf: \t\t", exact_spent_time)
 
-rbf_transform = Fastfood(sigma=sigma,
-                         n_components=number_of_features_to_generate,
-                         tradeoff_mem_accuracy='mem',
-                         random_state=42)
+rbf_transform = Fastfood(
+    sigma=sigma,
+    n_components=number_of_features_to_generate,
+    tradeoff_mem_accuracy="mem",
+    random_state=42,
+)
 _ = rbf_transform.fit(X)
 fastfood_fast_vec_start = datetime.datetime.utcnow()
 # Fastfood: approximate kernel mapping
 _ = rbf_transform.transform(X)
 _ = rbf_transform.transform(Y)
 fastfood_fast_vec_end = datetime.datetime.utcnow()
-fastfood_fast_vec_spent_time = fastfood_fast_vec_end - \
-    fastfood_fast_vec_start
+fastfood_fast_vec_spent_time = fastfood_fast_vec_end - fastfood_fast_vec_start
 print("Timimg fastfood fast vectorized: \t\t", fastfood_fast_vec_spent_time)
 
-rks_rbf_transform = RBFSampler(gamma=gamma,
-                               n_components=number_of_features_to_generate,
-                               random_state=42)
+rks_rbf_transform = RBFSampler(
+    gamma=gamma, n_components=number_of_features_to_generate, random_state=42
+)
 _ = rks_rbf_transform.fit(X)
 rks_start = datetime.datetime.utcnow()
 # Random Kitchens Sinks: approximate kernel mapping

doc/api.rst

@@ -12,3 +12,13 @@ Kernel approximation
    :template: class.rst
 
    kernel_approximation.Fastfood
+
+Clustering
+====================
+
+.. autosummary::
+   :toctree: generated/
+   :template: class.rst
+
+   cluster.KMedoids
+