Updated the package to use pytest instead of nose2

.github/workflows/run_tests.yml

@@ -60,7 +60,7 @@ jobs:
 
     - name: Run tests
       run: |
-        nose2 -vvv
+        pytest
 
     - name: Coveralls
       env:

pytest.ini

@@ -0,0 +1,17 @@
+[pytest]
+testpaths = test
+python_files = test_*.py
+python_classes = Test*
+python_functions = test_*
+addopts =
+    -v
+    --strict-markers
+    --tb=short
+markers =
+    slow: marks tests as slow (deselect with '-m "not slow"')
+filterwarnings =
+    error
+    ignore::PendingDeprecationWarning:.*matrix subclass.*
+    ignore::DeprecationWarning:.*SafeConfigParser.*
+    ignore::DeprecationWarning:.*collections.*
+    ignore::DeprecationWarning:.*check_pickle.*

setup.cfg

@@ -27,3 +27,7 @@ exclude =
 profile = black
 force_single_line = true
 force_alphabetical_sort = true
+
+[coverage]
+always-on = True
+coverage = graphtools

setup.py

@@ -14,8 +14,8 @@
 ]
 
 test_requires = [
-    "nose",
-    "nose2",
+    "pytest",
+    "pytest-cov",
     "pandas",
     "coverage",
     "coveralls",
@@ -64,7 +64,6 @@
         "fast": fast_requires,
         "all": all_requires,
     },
-    test_suite="nose2.collector.collector",
     long_description=readme,
     url="https://github.com/KrishnaswamyLab/graphtools",
     download_url="https://github.com/KrishnaswamyLab/graphtools/archive/v{}.tar.gz".format(

test/load_tests/__init__.py

@@ -1,5 +1,3 @@
-from nose.tools import assert_raises_regex
-from nose.tools import assert_warns_regex
 from scipy.spatial.distance import cdist
 from scipy.spatial.distance import pdist
 from scipy.spatial.distance import squareform
@@ -8,23 +6,35 @@
 from sklearn.decomposition import TruncatedSVD
 
 import graphtools
-import nose2
 import numpy as np
 import pandas as pd
 import pygsp
+import pytest
 import re
 import scipy.sparse as sp
 import warnings
 
 
 def assert_warns_message(expected_warning, expected_message, *args, **kwargs):
     expected_regex = re.escape(expected_message)
-    return assert_warns_regex(expected_warning, expected_regex, *args, **kwargs)
+    if args:
+        # If function arguments are provided, call the function within the context
+        with pytest.warns(expected_warning, match=expected_regex):
+            return args[0](*args[1:], **kwargs)
+    else:
+        # Return the context manager to be used with 'with' statement
+        return pytest.warns(expected_warning, match=expected_regex)
 
 
 def assert_raises_message(expected_warning, expected_message, *args, **kwargs):
     expected_regex = re.escape(expected_message)
-    return assert_raises_regex(expected_warning, expected_regex, *args, **kwargs)
+    if args:
+        # If function arguments are provided, call the function within the context
+        with pytest.raises(expected_warning, match=expected_regex):
+            return args[0](*args[1:], **kwargs)
+    else:
+        # Return the context manager to be used with 'with' statement
+        return pytest.raises(expected_warning, match=expected_regex)
 
 
 def reset_warnings():

test/test_data.py

@@ -5,15 +5,14 @@
 from load_tests import build_graph
 from load_tests import data
 from load_tests import graphtools
-from load_tests import nose2
 from load_tests import np
 from load_tests import pd
 from load_tests import pdist
 from load_tests import sp
 from load_tests import squareform
-from nose.tools import assert_raises_regex
 
 import numbers
+import pytest
 import warnings
 
 try:
@@ -99,9 +98,9 @@ def test_negative_rank_threshold():
 
 
 def test_True_n_pca_large_threshold():
-    with assert_raises_regex(
+    with pytest.raises(
         ValueError,
-        r"Supplied threshold ([0-9\.]*) was greater than maximum singular value ([0-9\.]*) for the data matrix",
+        match=r"Supplied threshold ([0-9\.]*) was greater than maximum singular value ([0-9\.]*) for the data matrix",
     ):
         build_graph(data, n_pca=True, rank_threshold=np.linalg.norm(data) ** 2)
 
@@ -388,10 +387,10 @@ def test_inverse_transform_sparse_svd():
 
     # Flexible regex pattern that works across Python versions
     sparse_error_pattern = r"(Sparse data|A sparse matrix) was passed, but dense data is required\. Use (?:'.*?'|X\.toarray\(\)) to convert to a dense numpy array\."
-    with assert_raises_regex(TypeError, sparse_error_pattern):
+    with pytest.raises(TypeError, match=sparse_error_pattern):
         G.inverse_transform(sp.csr_matrix(G.data)[:, 0])
 
-    with assert_raises_regex(TypeError, sparse_error_pattern):
+    with pytest.raises(TypeError, match=sparse_error_pattern):
         G.inverse_transform(sp.csr_matrix(G.data)[:, :15])
 
     with assert_raises_message(

test/test_estimator.py

@@ -42,7 +42,7 @@ def test_estimator():
     assert E.graph is None
 
 
-@parameterized(
+@parameterized.expand(
     [
         ("precomputed", 1 - np.eye(10), "distance"),
         ("precomputed", np.eye(10), "affinity"),

test/test_exact.py

@@ -5,15 +5,15 @@
 from load_tests import build_graph
 from load_tests import data
 from load_tests import graphtools
-from load_tests import nose2
 from load_tests import np
 from load_tests import PCA
 from load_tests import pdist
 from load_tests import pygsp
 from load_tests import sp
 from load_tests import squareform
 from load_tests import TruncatedSVD
-from nose.tools import assert_warns_regex
+
+import pytest
 from scipy.sparse.csgraph import shortest_path
 
 #####################################################
@@ -98,17 +98,17 @@ def test_precomputed_nonzero_diagonal():
 
 
 def test_duplicate_data():
-    with assert_warns_regex(
+    with pytest.warns(
         RuntimeWarning,
-        r"Detected zero distance between samples ([0-9and,\s]*). Consider removing duplicates to avoid errors in downstream processing.",
+        match=r"Detected zero distance between samples ([0-9and,\s]*). Consider removing duplicates to avoid errors in downstream processing.",
     ):
         build_graph(np.vstack([data, data[:10]]), n_pca=20, decay=10, thresh=0)
 
 
 def test_many_duplicate_data():
-    with assert_warns_regex(
+    with pytest.warns(
         RuntimeWarning,
-        "Detected zero distance between ([0-9]*) pairs of samples. Consider removing duplicates to avoid errors in downstream processing.",
+        match=r"Detected zero distance between ([0-9and,\s]*) pairs of samples. Consider removing duplicates to avoid errors in downstream processing.",
     ):
         build_graph(np.vstack([data, data]), n_pca=20, decay=10, thresh=0)
 

test/test_knn.py

@@ -12,9 +12,10 @@
 from load_tests import pygsp
 from load_tests import sp
 from load_tests import TruncatedSVD
-from nose.tools import assert_raises_regex
-from nose.tools import assert_warns_regex
+
+import pytest
 from scipy.sparse.csgraph import shortest_path
+
 from scipy.spatial.distance import pdist
 from scipy.spatial.distance import squareform
 
@@ -50,17 +51,17 @@ def test_build_knn_with_sample_idx():
 
 
 def test_duplicate_data():
-    with assert_warns_regex(
+    with pytest.warns(
         RuntimeWarning,
-        r"Detected zero distance between samples ([0-9and,\s]*). Consider removing duplicates to avoid errors in downstream processing.",
+        match=r"Detected zero distance between samples ([0-9and,\s]*). Consider removing duplicates to avoid errors in downstream processing.",
     ):
         build_graph(np.vstack([data, data[:9]]), n_pca=None, decay=10, thresh=1e-4)
 
 
 def test_duplicate_data_many():
-    with assert_warns_regex(
+    with pytest.warns(
         RuntimeWarning,
-        "Detected zero distance between ([0-9]*) pairs of samples. Consider removing duplicates to avoid errors in downstream processing.",
+        match=r"Detected zero distance between ([0-9and,\s]*) pairs of samples. Consider removing duplicates to avoid errors in downstream processing.",
     ):
         build_graph(np.vstack([data, data[:21]]), n_pca=None, decay=10, thresh=1e-4)
 
@@ -305,8 +306,9 @@ def test_knnmax():
         )
         assert isinstance(G2, graphtools.graphs.kNNGraph)
         assert G.N == G2.N
-        assert np.all(G.dw == G2.dw)
-        assert (G.W - G2.W).nnz == 0
+        np.testing.assert_allclose(G.dw, G2.dw)
+        # Use allclose for sparse matrices to handle floating-point precision
+        np.testing.assert_allclose(G.W.toarray(), G2.W.toarray(), rtol=1e-7, atol=1e-10)
     finally:
         gg.NUMBA_AVAILABLE = original_numba
 

test/test_landmark.py

@@ -7,7 +7,6 @@
 from load_tests import digits
 from load_tests import generate_swiss_roll
 from load_tests import graphtools
-from load_tests import nose2
 from load_tests import np
 
 import pygsp

test/test_matrix.py

@@ -9,7 +9,7 @@
 import numpy as np
 
 
-@parameterized(
+@parameterized.expand(
     [
         (np.array,),
         (sparse.csr_matrix,),
@@ -26,13 +26,13 @@ def test_nonzero_discrete(matrix_class):
     assert not graphtools.matrix.nonzero_discrete(X, [1, 3])
 
 
-@parameterized([(0,), (1e-4,)])
+@parameterized.expand([(0,), (1e-4,)])
 def test_nonzero_discrete_knngraph(thresh):
     G = graphtools.Graph(data, n_pca=10, knn=5, decay=None, thresh=thresh)
     assert graphtools.matrix.nonzero_discrete(G.K, [0.5, 1])
 
 
-@parameterized([(0,), (1e-4,)])
+@parameterized.expand([(0,), (1e-4,)])
 def test_nonzero_discrete_decay_graph(thresh):
     G = graphtools.Graph(data, n_pca=10, knn=5, decay=15, thresh=thresh)
     assert not graphtools.matrix.nonzero_discrete(G.K, [0.5, 1])

test/test_mnn.py

@@ -8,7 +8,6 @@
 from load_tests import digits
 from load_tests import generate_swiss_roll
 from load_tests import graphtools
-from load_tests import nose2
 from load_tests import np
 from load_tests import pd
 from load_tests import pygsp

test/test_random_landmarking.py

@@ -7,7 +7,6 @@
 from load_tests import digits
 from load_tests import generate_swiss_roll
 from load_tests import graphtools
-from load_tests import nose2
 from load_tests import np
 
 import pygsp