Switch to pytest from nose, support Python 3.10

.travis.yml

@@ -7,6 +7,7 @@ python:
   - "3.7"
   - "3.8"
   - "3.9"
+  - "3.10"
 cache: pip
 addons:
   apt:
@@ -24,7 +25,7 @@ install:
 
 # Run tests
 script:
-  - nosetests --with-coverage
+  - pytest --cov
 
 # Calculate coverage
 after_success:

README.rst

@@ -10,15 +10,12 @@
 .. image:: https://api.codacy.com/project/badge/Grade/cb19f1f1093a44fa845ebfdaf76975f6
    :alt: Codacy Badge
    :target: https://app.codacy.com/app/nicodv/kmodes?utm_source=github.com&utm_medium=referral&utm_content=nicodv/kmodes&utm_campaign=Badge_Grade_Dashboard
-.. image:: https://requires.io/github/nicodv/kmodes/requirements.svg
-     :target: https://requires.io/github/nicodv/kmodes/requirements/
-     :alt: Requirements Status
+.. image:: https://img.shields.io/pypi/dm/kmodes.svg
+    :target: https://pypi.python.org/pypi/kmodes/
+    :alt: Monthly downloads
 .. image:: https://img.shields.io/pypi/pyversions/kmodes.svg
     :target: https://pypi.python.org/pypi/kmodes/
     :alt: Supported Python versions
-.. image:: https://img.shields.io/github/stars/nicodv/kmodes.svg
-    :target: https://github.com/nicodv/kmodes/
-    :alt: Github stars
 .. image:: https://img.shields.io/pypi/l/kmodes.svg
     :target: https://github.com/nicodv/kmodes/blob/master/LICENSE
     :alt: License

kmodes/tests/test_common.py

@@ -64,8 +64,8 @@ def test_all_estimator_no_base_class():
 
 def test_all_estimators():
     for name, Estimator in all_estimators():
-        yield (_named_check(check_parameters_default_constructible, name),
-               name, Estimator())
+        return (_named_check(check_parameters_default_constructible, name),
+                name, Estimator())
 
 
 def test_non_meta_estimators():
@@ -79,4 +79,4 @@ def test_non_meta_estimators():
         estimator = Estimator()
         for check in _yield_all_checks(estimator):
             if hasattr(check, '__name__') and check.__name__ in relevant_checks:
-                yield _named_check(check, name), name, estimator
+                return _named_check(check, name), name, estimator

kmodes/tests/test_kmodes.py

@@ -6,7 +6,6 @@
 import unittest
 
 import numpy as np
-from nose.tools import assert_equal
 
 from kmodes.kmodes import KModes
 from kmodes.util.dissim import ng_dissim, jaccard_dissim_binary, jaccard_dissim_label
@@ -234,7 +233,14 @@ class TestKModes(unittest.TestCase):
     def test_pickle(self):
         obj = KModes()
         s = pickle.dumps(obj)
-        assert_equal(type(pickle.loads(s)), obj.__class__)
+        assert type(pickle.loads(s)) == obj.__class__
+
+    def test_pickle_fitted(self):
+        kmodes_huang = KModes(n_clusters=4, n_init=2, init='Huang', verbose=2,
+                              random_state=42)
+        model = kmodes_huang.fit(SOYBEAN)
+        s = pickle.dumps(model)
+        assert type(pickle.loads(s)) == model.__class__
 
     def test_kmodes_huang_soybean(self):
         kmodes_huang = KModes(n_clusters=4, n_init=2, init='Huang', verbose=2,

kmodes/tests/test_kprototypes.py

@@ -6,7 +6,6 @@
 import unittest
 
 import numpy as np
-from nose.tools import assert_equal
 
 from kmodes import kprototypes
 from kmodes.tests.test_kmodes import assert_cluster_splits_equal
@@ -39,7 +38,13 @@ class TestKProtoTypes(unittest.TestCase):
     def test_pickle(self):
         obj = kprototypes.KPrototypes()
         s = pickle.dumps(obj)
-        assert_equal(type(pickle.loads(s)), obj.__class__)
+        assert type(pickle.loads(s)) == obj.__class__
+
+    def test_pickle_fitted(self):
+        kproto = kprototypes.KPrototypes(n_clusters=4, init='Cao', verbose=2)
+        model = kproto.fit(STOCKS[:, :2], categorical=1)
+        s = pickle.dumps(model)
+        assert type(pickle.loads(s)) == model.__class__
 
     def test_kprotoypes_categoricals_stocks(self):
         # Number/index of categoricals does not make sense

kmodes/util/__init__.py

@@ -39,7 +39,7 @@ def encode_features(X, enc_map=None):
     else:
         fit = False
 
-    Xenc = np.zeros(X.shape, dtype='uint32')
+    Xenc = np.zeros(X.shape, dtype='int32')
     for ii in range(X.shape[1]):
         if fit:
             col_enc = {val: jj for jj, val in enumerate(np.unique(X[:, ii]))

kmodes/util/tests/test_dissim.py

@@ -5,7 +5,6 @@
 import unittest
 
 import numpy as np
-from nose.tools import assert_equal
 from sklearn.utils._testing import assert_array_equal
 
 from kmodes.util.dissim import matching_dissim, euclidean_dissim, ng_dissim
@@ -17,11 +16,11 @@ class TestDissimilarityMeasures(unittest.TestCase):
     def test_matching_dissim(self):
         a = np.array([[0, 1, 2, 0, 1, 2]])
         b = np.array([[0, 1, 2, 0, 1, 0]])
-        assert_equal(1, matching_dissim(a, b))
+        assert 1 == matching_dissim(a, b)
 
         a = np.array([[np.NaN, 1, 2, 0, 1, 2]])
         b = np.array([[0, 1, 2, 0, 1, 0]])
-        assert_equal(2, matching_dissim(a, b))
+        assert 2 == matching_dissim(a, b)
 
         a = np.array([['a', 'b', 'c', 'd']])
         b = np.array([['a', 'b', 'c', 'd'], ['d', 'c', 'b', 'a']])
@@ -30,7 +29,7 @@ def test_matching_dissim(self):
     def test_jaccard_dissim_binary(self):
         a = np.array([[0, 1, 1, 0, 1, 1]])
         b = np.array([[0, 1, 1, 0, 1, 0]])
-        assert_equal(0.25, jaccard_dissim_binary(a, b))
+        assert 0.25 == jaccard_dissim_binary(a, b)
 
         a = np.array([[0, 1, 1, 0, 1, 1]])
         b = np.array([[0, np.NaN, 1, 0, 1, 0]])
@@ -46,17 +45,17 @@ def test_jaccard_dissim_binary(self):
         # test for dissimilarity = 0: sets are the same
         a = np.array([[1, 1, 0, 1, 1, 0]])
         b = np.array([[1, 1, 0, 1, 1, 0]])
-        assert_equal(0, jaccard_dissim_binary(a, b))
+        assert 0 == jaccard_dissim_binary(a, b)
 
         # test for dissimilarity = 1: sets are completely different
         a = np.array([[0, 0, 1, 0, 0, 1]])
         b = np.array([[1, 1, 0, 1, 1, 0]])
-        assert_equal(1, jaccard_dissim_binary(a, b))
+        assert 1 == jaccard_dissim_binary(a, b)
 
     def test_jaccard_dissim_label(self):
         a = np.array([[0, 1, 2, 0, 1, 2]])
         b = np.array([[0, 1, 2, 0, 3, 0]])
-        assert_equal(0.25, jaccard_dissim_label(a, b))
+        assert 0.25 == jaccard_dissim_label(a, b)
 
         a = np.array([[np.NaN, 1, 2, 0, 1, 2]])
         b = np.array([[0, 1, 2, 0, 1, 0]])
@@ -66,17 +65,17 @@ def test_jaccard_dissim_label(self):
         # test for dissimilarity = 0: sets are the same
         a = np.array([[1, 2, 0, 3, 1, 0]])
         b = np.array([[1, 2, 0, 3, 1, 0]])
-        assert_equal(0, jaccard_dissim_label(a, b))
+        assert 0 == jaccard_dissim_label(a, b)
 
         # test for dissimilarity = 1: sets are completely different
         a = np.array([[1, 2, 0, 3, 1, 0]])
         b = np.array([[5, 4, 6, 7, 8, 9]])
-        assert_equal(1, jaccard_dissim_label(a, b))
+        assert 1 == jaccard_dissim_label(a, b)
 
     def test_euclidian_dissim(self):
         a = np.array([[0., 1., 2., 0., 1., 2.]])
         b = np.array([[3., 1., 3., 0., 1., 0.]])
-        assert_equal(14., euclidean_dissim(a, b))
+        assert 14. == euclidean_dissim(a, b)
 
         a = np.array([[np.NaN, 1., 2., 0., 1., 2.]])
         b = np.array([[3., 1., 3., 0., 1., 0.]])

kmodes/util/tests/test_util.py

@@ -5,7 +5,6 @@
 import unittest
 
 import numpy as np
-from nose.tools import assert_equal
 from sklearn.utils._testing import assert_array_equal
 
 from kmodes.util import get_max_value_key, encode_features, get_unique_rows, \
@@ -47,11 +46,11 @@ class TestUtils(unittest.TestCase):
 
     def test_get_max_value_key(self):
         max_key = get_max_value_key({'a': 3, 'b': 10, 'c': -1, 'd': 9.9})
-        assert_equal('b', max_key)
+        assert 'b' == max_key
 
         # Make sure minimum key is consistently selected for equal values.
         max_key = get_max_value_key({'d': 10, 'c': 10, 'b': 10, 'a': 10})
-        assert_equal('a', max_key)
+        assert 'a' == max_key
 
     def test_encode_features(self):
         X_enc, enc_map = encode_features(STOCKS_CAT)

setup.py

@@ -28,7 +28,8 @@
         'joblib>=0.11'
     ],
     extras_requires=[
-        'nose',
+        'pytest',
+        'pytest-cov',
     ],
     classifiers=['Development Status :: 3 - Alpha',
                  'Intended Audience :: Science/Research',
@@ -42,5 +43,6 @@
                  'Programming Language :: Python :: 3.7',
                  'Programming Language :: Python :: 3.8',
                  'Programming Language :: Python :: 3.9',
+                 'Programming Language :: Python :: 3.10',
                  'Topic :: Scientific/Engineering'],
 )