Switch all tests to nose2 (#127)

- Convert all test files to be nose2-compatible.
- Add nose2 configuration file.
- Update Gitlab CI file.
  - Use Python 3.11 instead of Python 3.9.
  - Use nose2 for testing instead of nose.
- Update README.rst.
  - Add PyPI badge.
  - Fix conda badge.

Author: desilinguist

.gitlab-ci.yml

@@ -4,21 +4,19 @@ stages:
   - test
 
 variables:
-  PYVERSION: "3.9"
-  NOSE_WITH_COV: "1"
-  NOSE_COVER_PACKAGE: "factor_analyzer"
+  PYVERSION: "3.11"
   LOGCAPTURE_LEVEL: "DEBUG"
   CODECOV_TOKEN: "034f0bb1-e590-406f-820c-4e7c41b17712"
 
 # set up the basic job
 .runtests:
   before_script:
-    - "conda create --prefix /root/factordev --channel conda-forge --file requirements.txt python=${PYVERSION} curl nose nose-cov --yes --quiet"
+    - "conda create --prefix /root/factordev --channel conda-forge --file requirements.txt python=${PYVERSION} curl nose2 coverage --yes --quiet"
     - /root/factordev/bin/pip install -e .
     - /root/factordev/bin/curl -o /root/factordev/bin/codecov https://uploader.codecov.io/latest/linux/codecov
     - chmod +x /root/factordev/bin/codecov
   script:
-    - "/root/factordev/bin/nosetests ${TESTFILES}"
+    - "/root/factordev/bin/nose2 -s tests ${TESTFILES}"
     - "/root/factordev/bin/coverage xml"
   after_script:
     - /root/factordev/bin/codecov
@@ -27,12 +25,12 @@ variables:
 testset1:
   extends: ".runtests"
   variables:
-    TESTFILES: "tests/test_expected_confirmatory_factor_analyzer.py tests/test_factor_analyzer.py"
+    TESTFILES: "test_expected_confirmatory_factor_analyzer test_factor_analyzer"
   stage: "test"
 
 # second set of test files
 testset2:
   extends: ".runtests"
   variables:
-    TESTFILES: "tests/test_expected_factor_analyzer.py tests/test_expected_rotator.py tests/test_utils.py"
+    TESTFILES: "test_expected_factor_analyzer test_expected_rotator test_utils"
   stage: "test"

README.rst

@@ -9,10 +9,14 @@ FactorAnalyzer
    :target: https://codecov.io/gh/EducationalTestingService/factor_analyzer
    :alt: Code coverage
 
-.. image:: https://anaconda.org/ets/factor_analyzer/badges/installer/conda.svg
+.. image:: https://anaconda.org/ets/factor_analyzer/badges/version.svg
    :target: https://anaconda.org/ets/factor_analyzer/
    :alt: Conda version
 
+.. image:: https://img.shields.io/pypi/v/factor_analyzer
+   :target: https://pypi.org/project/factor-analyzer/
+   :alt: PyPI version
+
 .. image:: https://img.shields.io/readthedocs/factor_analyzer/latest.svg
    :target: https://factor-analyzer.readthedocs.io/
    :alt: Docs

tests/test_expected_confirmatory_factor_analyzer.py

@@ -6,9 +6,10 @@
 :organization: Educational Testing Service
 :date: 2022-09-05
 """
+import unittest
+
 import numpy as np
 import pandas as pd
-from nose.tools import eq_, raises
 from numpy.testing import assert_array_equal
 
 from factor_analyzer.confirmatory_factor_analyzer import (
@@ -21,81 +22,69 @@
 THRESHOLD = 1.0
 
 
-def test_11_cfa():  # noqa: D103
-
-    json_name_input = "test11"
-    data_name_input = "test11"
-
-    for check in check_cfa(json_name_input, data_name_input, rel_tol=0.1):
-        assert check >= THRESHOLD
-
-
-def test_12_cfa():  # noqa: D103
-
-    json_name_input = "test12"
-    data_name_input = "test12"
-
-    for check in check_cfa(json_name_input, data_name_input, abs_tol=0.05):
-        assert check >= THRESHOLD
-
+class TestConfirmatoryFactorAnalysis(unittest.TestCase):
+    def test_11_cfa(self):  # noqa: D103
+        json_name_input = "test11"
+        data_name_input = "test11"
 
-def test_13_cfa():  # noqa: D103
+        for check in check_cfa(json_name_input, data_name_input, rel_tol=0.1):
+            self.assertGreaterEqual(check, THRESHOLD)
 
-    json_name_input = "test13"
-    data_name_input = "test13"
+    def test_12_cfa(self):  # noqa: D103
+        json_name_input = "test12"
+        data_name_input = "test12"
 
-    for check in check_cfa(
-        json_name_input,
-        data_name_input,
-        index_col=0,
-        is_cov=True,
-        n_obs=64,
-        rel_tol=0.1,
-    ):
-        assert check >= THRESHOLD
+        for check in check_cfa(json_name_input, data_name_input, abs_tol=0.05):
+            self.assertGreaterEqual(check, THRESHOLD)
 
+    def test_13_cfa(self):  # noqa: D103
+        json_name_input = "test13"
+        data_name_input = "test13"
 
-def test_14_cfa():  # noqa: D103
+        for check in check_cfa(
+            json_name_input,
+            data_name_input,
+            index_col=0,
+            is_cov=True,
+            n_obs=64,
+            rel_tol=0.1,
+        ):
+            self.assertGreaterEqual(check, THRESHOLD)
 
-    json_name_input = "test14"
-    data_name_input = "test14"
+    def test_14_cfa(self):  # noqa: D103
+        json_name_input = "test14"
+        data_name_input = "test14"
 
-    for check in check_cfa(json_name_input, data_name_input, index_col=0, rel_tol=0.1):
-        assert check >= THRESHOLD
+        for check in check_cfa(
+            json_name_input, data_name_input, index_col=0, rel_tol=0.1
+        ):
+            self.assertGreaterEqual(check, THRESHOLD)
 
+    def test_14_cfa_no_model(self):  # noqa: D103
+        X = np.array([[0, 0, 0, 0], [0, 0, 0, 0]])
 
-@raises(ValueError)
-def test_14_cfa_no_model():  # noqa: D103
+        with self.assertRaises(ValueError):
+            cfa = ConfirmatoryFactorAnalyzer("string_not_model")
+            cfa.fit(X)
 
-    X = np.array([[0, 0, 0, 0], [0, 0, 0, 0]])
+    def test_14_cfa_bad_bounds(self):  # noqa: D103
+        X = np.array([[0, 0, 0, 0], [0, 0, 0, 0]])
 
-    cfa = ConfirmatoryFactorAnalyzer("string_not_model")
-    cfa.fit(X)
+        with self.assertRaises(AssertionError):
+            cfa = ConfirmatoryFactorAnalyzer(bounds=[(0, 1)])
+            cfa.fit(X)
 
+    def test_14_cfa_cov_with_no_obs(self):  # noqa: D103
+        with self.assertRaises(ValueError):
+            ConfirmatoryFactorAnalyzer(is_cov_matrix=True)
 
-@raises(AssertionError)
-def test_14_cfa_bad_bounds():  # noqa: D103
 
-    X = np.array([[0, 0, 0, 0], [0, 0, 0, 0]])
-
-    cfa = ConfirmatoryFactorAnalyzer(bounds=[(0, 1)])
-    cfa.fit(X)
-
-
-@raises(ValueError)
-def test_14_cfa_cov_with_no_obs():  # noqa: D103
-
-    ConfirmatoryFactorAnalyzer(is_cov_matrix=True)
-
-
-class TestModelSpecificationParser:  # noqa: D101
+class TestModelSpecificationParser(unittest.TestCase):
     def test_model_spec_str(self):  # noqa: D102
-
         ms = ModelSpecification(np.array([[0, 0, 0]]), 3, 1)
-        assert str(ms).startswith("<ModelSpecification object at ")
+        self.assertTrue(str(ms).startswith("<ModelSpecification object at "))
 
     def test_model_spec_as_dict(self):  # noqa: D102
-
         loadings = np.array([[0, 0, 0]])
         n_factors = 3
         n_variables = 1
@@ -112,48 +101,52 @@ def test_model_spec_as_dict(self):  # noqa: D102
             if isinstance(value, np.ndarray):
                 assert_array_equal(new_dict[key], value)
             else:
-                eq_(new_dict[key], value)
+                self.assertEqual(new_dict[key], value)
 
     def test_model_spec_parser_from_dict_none(self):  # noqa: D102
         X = np.array([[0, 0, 0]])
         ms = ModelSpecificationParser.parse_model_specification_from_dict(X, None)
-        assert isinstance(ms, ModelSpecification)
-        eq_(ms.n_factors, 3)
-        eq_(ms.n_variables, 3)
+        self.assertTrue(isinstance(ms, ModelSpecification))
+        self.assertEqual(ms.n_factors, 3)
+        self.assertEqual(ms.n_variables, 3)
         assert_array_equal(ms.loadings, np.ones((3, 3), dtype=int))
 
-    @raises(ValueError)
     def test_model_spec_parser_from_dict_error(self):  # noqa: D102
         X = np.array([[0, 0, 0]])
-        ModelSpecificationParser.parse_model_specification_from_dict(X, "not_a_model")
+        with self.assertRaises(ValueError):
+            ModelSpecificationParser.parse_model_specification_from_dict(
+                X, "not_a_model"
+            )
 
     def test_model_spec_parser_from_array_none(self):  # noqa: D102
         X = np.array([[0, 0, 0]])
         ms = ModelSpecificationParser.parse_model_specification_from_array(X, None)
-        assert isinstance(ms, ModelSpecification)
-        eq_(ms.n_factors, 3)
-        eq_(ms.n_variables, 3)
+        self.assertTrue(isinstance(ms, ModelSpecification))
+        self.assertEqual(ms.n_factors, 3)
+        self.assertEqual(ms.n_variables, 3)
         assert_array_equal(ms.loadings, np.ones((3, 3), dtype=int))
 
     def test_model_spec_parser_from_array(self):  # noqa: D102
         X = np.array([[0, 0, 0]])
         spec = np.ones((3, 3), dtype=int)
         ms = ModelSpecificationParser.parse_model_specification_from_array(X, spec)
-        assert isinstance(ms, ModelSpecification)
-        eq_(ms.n_factors, 3)
-        eq_(ms.n_variables, 3)
+        self.assertTrue(isinstance(ms, ModelSpecification))
+        self.assertEqual(ms.n_factors, 3)
+        self.assertEqual(ms.n_variables, 3)
         assert_array_equal(ms.loadings, np.ones((3, 3), dtype=int))
 
     def test_model_spec_parser_from_frame(self):  # noqa: D102
         X = np.array([[0, 0, 0]])
         spec = pd.DataFrame(np.ones((3, 3), dtype=int))
         ms = ModelSpecificationParser.parse_model_specification_from_array(X, spec)
-        assert isinstance(ms, ModelSpecification)
-        eq_(ms.n_factors, 3)
-        eq_(ms.n_variables, 3)
+        self.assertTrue(isinstance(ms, ModelSpecification))
+        self.assertEqual(ms.n_factors, 3)
+        self.assertEqual(ms.n_variables, 3)
         assert_array_equal(ms.loadings, np.ones((3, 3), dtype=int))
 
-    @raises(ValueError)
     def test_model_spec_parser_from_array_error(self):  # noqa: D102
         X = np.array([[0, 0, 0]])
-        ModelSpecificationParser.parse_model_specification_from_array(X, "not_a_model")
+        with self.assertRaises(ValueError):
+            ModelSpecificationParser.parse_model_specification_from_array(
+                X, "not_a_model"
+            )