Adding PCA correlation circle graph

.appveyor.yml

@@ -13,15 +13,14 @@ install:
   - conda config --set always_yes yes --set changeps1 no
   - conda update -q conda
   - conda info -a
-  - conda create -q -n test-environment python=%PYTHON_VERSION% numpy scipy scikit-learn pandas nose
+  - conda create -q -n test-environment python=%PYTHON_VERSION% numpy scipy scikit-learn pandas pytest
   - activate test-environment
-  - pip install nose-exclude
   - pip install dlib
   - pip install imageio
   - pip install scikit-image
 
 test_script:
-  - nosetests -s -v --exclude-dir=mlxtend/plotting
+  -  PYTHONPATH='.' pytest -sv --ignore=mlxtend/plotting
 
 notifications:
 

.github/PULL_REQUEST_TEMPLATE.md

@@ -20,7 +20,7 @@ Fixes #366
 - [ ] Added a note about the modification or contribution to the `./docs/sources/CHANGELOG.md` file (if applicable)
 - [ ] Added appropriate unit test functions in the `./mlxtend/*/tests` directories (if applicable)
 - [ ] Modify documentation in the corresponding Jupyter Notebook under `mlxtend/docs/sources/` (if applicable)
-- [ ] Ran `nosetests ./mlxtend -sv` and make sure that all unit tests pass (for small modifications, it might be sufficient to only run the specific test file, e.g., `nosetests ./mlxtend/classifier/tests/test_stacking_cv_classifier.py -sv`)
+- [ ] Ran `PYTHONPATH='.' pytest ./mlxtend -sv` and make sure that all unit tests pass (for small modifications, it might be sufficient to only run the specific test file, e.g., `PYTHONPATH='.' pytest ./mlxtend/classifier/tests/test_stacking_cv_classifier.py -sv`)
 - [ ] Checked for style issues by running `flake8 ./mlxtend`
 
 

.gitignore

@@ -1,3 +1,6 @@
+# Test Suites
+.pytest_cache/
+
 # Documentation builds
 docs/site/
 docs/mlxtend.pdf

ci/.travis_install.sh

@@ -39,6 +39,7 @@ fi
 
 conda install matplotlib
 conda install jupyter
+conda install pytest
 
 if [ "${IMAGE}" = "true" ]; then
     pip install dlib
@@ -50,8 +51,6 @@ if [ "${COVERAGE}" = "true" ]; then
     conda install coveralls
 fi
 
-pip install nose-exclude
-
 python --version
 python -c "import pandas; print('pandas %s' % pandas.__version__)"
 python -c "import numpy; print('numpy %s' % numpy.__version__)"

ci/.travis_test.sh

@@ -9,21 +9,21 @@ if [[ "$TRAVIS_OS_NAME" != "osx" ]]; then
         if [[ "$COVERAGE" == "true" ]]; then
 
             if [[ "$IMAGE" == "true" ]]; then
-                nosetests -s -v --with-coverage
+                 PYTHONPATH='.' pytest -sv --with-coverage
             else
-                nosetests -s -v --with-coverage --exclude-dir=mlxtend/image
+                 PYTHONPATH='.' pytest -sv --with-coverage --ignore=mlxtend/image
             fi
 
         else
             if [[ "$IMAGE" == "true" ]]; then
-                nosetests -s -v
+                 PYTHONPATH='.' pytest -sv
             else
-                nosetests -s -v --exclude-dir=mlxtend/image
+                 PYTHONPATH='.' pytest -sv --ignore=mlxtend/image
             fi
         fi
 
 else
-    nosetests -s -v --exclude-dir=mlxtend/plotting
+     PYTHONPATH='.' pytest -sv --ignore=mlxtend/plotting
 fi
 
 

docs/_site/sources/CONTRIBUTING.md

@@ -16,9 +16,9 @@ and checking off items as you go.
 3. [ ]  Create and check out a new topic branch (please don't make modifications in the master branch)
 4. [ ]  Implement the new feature or apply the bug-fix  
 5. [ ]  Add appropriate unit test functions in `mlxtend/*/tests`
-6. [ ]  Run `nosetests ./mlxtend -sv` and make sure that all unit tests pass  
-7. [ ]  Check/improve the test coverage by running `nosetests ./mlxtend --with-coverage`
-8. [ ]  Check for style issues by running `flake8 ./mlxtend` (you may want to run `nosetests` again after you made modifications to the code)
+6. [ ]  Run `PYTHONPATH='.' pytest ./mlxtend -sv` and make sure that all unit tests pass  
+7. [ ]  Check/improve the test coverage by running `PYTHONPATH='.' pytest ./mlxtend --with-coverage`
+8. [ ]  Check for style issues by running `flake8 ./mlxtend` (you may want to run `pytest` again after you made modifications to the code)
 8. [ ]  Add a note about the modification/contribution to the `./docs/sources/changelog.md` file  
 9. [ ]  Modify documentation in the appropriate location under `mlxtend/docs/sources/`  
 10. [ ]  Push the topic branch to the server and create a pull request
@@ -164,13 +164,7 @@ Now it's time to modify existing code or to contribute new code to the project.
 Add the respective unit tests and check if they pass:
 
 ```bash
-$ nosetests -sv
-```
-
-Use the `--with-coverage` flag to ensure that all code is being covered in the unit tests:
-
-```bash
-$ nosetests --with-coverage
+$ PYTHONPATH='.' pytest -sv
 ```
 
 #### 5. Documenting changes

docs/sources/CHANGELOG.md

@@ -19,7 +19,7 @@ The CHANGELOG for the current development version is available at
 - Added an enhancement to the existing `iris_data()` such that both the UCI Repository version of the Iris dataset as well as the corrected, original
   version of the dataset can be loaded, which has a slight difference in two data points (consistent with Fisher's paper; this is also the same as in R). (via [#539](https://github.com/rasbt/mlxtend/pull/532) via [janismdhanbad](https://github.com/janismdhanbad))
 - Add optional `groups` parameter to `SequentialFeatureSelector` and `ExhaustiveFeatureSelector` `fit()` methods for forwarding to sklearn CV ([#537](https://github.com/rasbt/mlxtend/pull/537) via [arc12](https://github.com/qiaguhttps://github.com/arc12))
-
+- Adds a new `plot_pca_correlation_graph` function to the `mlxtend.plotting` submodule for plotting a PCA correlation graph. ([#544](https://github.com/rasbt/mlxtend/pull/544) via [Gabriel-Azevedo-Ferreira](https://github.com/qiaguhttps://github.com/Gabriel-Azevedo-Ferreira))
 
 ##### Changes
 

mlxtend/_base/tests/test_classifier.py

@@ -7,7 +7,7 @@
 from mlxtend._base import _BaseModel
 from mlxtend._base import _Classifier
 import numpy as np
-from mlxtend.utils import assert_raises
+import pytest
 
 
 class BlankClassifier(_BaseModel, _Classifier):
@@ -44,39 +44,40 @@ def test_check_labels_ok_2():
 def test_check_labels_not_ok_1():
     y = np.array([1, 3, 2])
     cl = BlankClassifier(print_progress=0, random_seed=1)
-    assert_raises(AttributeError,
-                  'Labels not in {(1, 2), (0, 1)}.\nFound (1, 2, 3)',
-                  cl._check_target_array,
-                  y,
-                  {(0, 1), (1, 2)})
+    with pytest.raises(AttributeError) as excinfo:
 
+        cl._check_target_array(y, {(0, 1), (1, 2)})
+        assert excinfo.value.message == ('Labels not in {(1, 2), (0, 1)}'
+                                         '.\nFound (1, 2, 3)')
 
-def test_check_labels_interger_notok():
+
+def test_check_labels_integer_notok():
     y = np.array([1., 2.], dtype=np.float64)
     cl = BlankClassifier(print_progress=0, random_seed=1)
-    assert_raises(AttributeError,
-                  'y must be an integer array.\nFound float64',
-                  cl._check_target_array,
-                  y)
+    with pytest.raises(AttributeError) as excinfo:
+        cl._check_target_array(y)
+        assert excinfo.value.message == ('y must be an integer'
+                                         ' array.\nFound float64')
 
 
 def test_check_labels_positive_notok():
     y = np.array([1, 1, -1])
     cl = BlankClassifier(print_progress=0, random_seed=1)
-    assert_raises(AttributeError,
-                  'y array must not contain negative labels.\nFound [-1  1]',
-                  cl._check_target_array,
-                  y)
+    with pytest.raises(AttributeError) as excinfo:
+        cl._check_target_array(y)
+        assert excinfo.value.message == ('y array must not '
+                                         'contain negative '
+                                         'labels.\nFound [-1  1]')
 
 
 def test_predict_fail():
     X = np.array([[1], [2], [3]])
     est = BlankClassifier(print_progress=0, random_seed=1)
     est._is_fitted = False
-    assert_raises(AttributeError,
-                  'Model is not fitted, yet.',
-                  est.predict,
-                  X)
+    with pytest.raises(AttributeError) as excinfo:
+        est.predict(X)
+        assert excinfo.value.message == ('Model is not '
+                                         'fitted, yet.')
 
 
 def test_predict_pass():
@@ -86,14 +87,11 @@ def test_predict_pass():
     est.fit(X, y)
     est.predict(X)
 
-
-def test_fit_1():
-    X = np.array([[1], [2], [3]])
-    est = BlankClassifier(print_progress=0, random_seed=1)
-    assert_raises(TypeError,
-                  "fit() missing 1 required positional argument: 'y'",
-                  est.fit,
-                  X)
+    with pytest.raises(TypeError) as excinfo:
+        est.fit(X)
+        assert excinfo.value.message == ("fit() missing 1 "
+                                         "required positional "
+                                         "argument: 'y'")
 
 
 def test_fit_2():