Merge branch 'master' into Refactor-bias_variance_decomposition-unit-…

…tests

.github/workflows/python-package-conda.yml

@@ -11,35 +11,34 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       max-parallel: 5
-
+    env:
+      GITHUB_ACTIONS_CI: true
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python 3.9
-      uses: actions/setup-python@v2
+    - name: Set up Miniconda
+      uses: conda-incubator/setup-miniconda@v2
       with:
+        auto-update-conda: true
         python-version: 3.9
-    - name: Add conda to system path
-      run: |
-        # $CONDA is an environment variable pointing to the root of the miniconda directory
-        echo $CONDA/bin >> $GITHUB_PATH
-    - name: Install packages and test with pytest
+        channels: conda-forge
+        activate-environment: mlxtend
+        environment-file: environment.yml
+        create-env-file: true
+
+    - name: Install dependencies and run tests
+      shell: bash -l {0}
       run: |
-        conda config --add channels conda-forge
-        conda update conda -y -q
-        conda env update --file environment.yml --name base
-        pip install scikit-learn==1.1.3 
-        pip install pandas==1.3.5
         conda install tensorflow joblib pytest -y -q
         conda install imageio scikit-image -y -q
         conda install dlib -y -q
-        pip install markdown
-        pip install coverage
-        pip install -e  .
+        pip install scikit-learn==1.1.3 pandas==1.3.5 markdown coverage
+        pip install -e .
         python -c "import numpy; print('NumPy:', numpy.__version__)"
         python -c "import scipy; print('SciPy:', scipy.__version__)"
         python -c "import sklearn; print('Scikit-learn:', sklearn.__version__)"
         python -c "import pandas; print('Pandas:', pandas.__version__)"
         coverage run --source=mlxtend --branch -m pytest mlxtend
         coverage xml
+
     - name: Upload Coverage to Codecov
-      uses: codecov/codecov-action@v2
+      uses: codecov/codecov-action@v2

docs/sources/CONTRIBUTING.md

@@ -406,10 +406,10 @@ $ source activate mlxtend-testing
 
 ### 2. Installing the package from local files
 
-Test the installation by executing
+Test the installation by executing the following from within the mlxtend main directory:
 
 ```bash
-$ python setup.py install --record files.txt
+$ pip install . -e
 ```
 
 the `--record files.txt` flag will create a `files.txt` file listing the locations where these files will be installed.
@@ -430,6 +430,7 @@ $ cat files.txt | xargs rm -rf ; rm files.txt
 Next, test if `pip` is able to install the packages. First, navigate to a different directory, and from there, install the package:
 
 ```bash
+$ pip uninstall
 $ pip install mlxtend
 ```
 

docs/sources/installation.md

@@ -10,10 +10,10 @@ To install mlxtend, just execute
 pip install mlxtend  
 ```
 
-Alternatively, you download the package manually from the Python Package Index [https://pypi.python.org/pypi/mlxtend](https://pypi.python.org/pypi/mlxtend), unzip it, navigate into the package, and use the command:
+Alternatively, you download the package manually from the Python Package Index [https://pypi.python.org/pypi/mlxtend](https://pypi.python.org/pypi/mlxtend), unzip it, navigate into the package, and use the following command from inside the mlxtend folder:
 
 ```bash
-python setup.py install
+pip install .
 ```
 
 ##### Upgrading via `pip`
@@ -61,5 +61,5 @@ pip install git+git://github.com/rasbt/mlxtend.git
 Or, you can fork the GitHub repository from https://github.com/rasbt/mlxtend and install mlxtend from your local drive via
 
 ```bash
-python setup.py install
+pip install .
 ```

docs/sources/user_guide/classifier/OneRClassifier.ipynb

@@ -13,14 +13,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# OneRClassifier: One Rule (OneR) method for classfication"
+    "# OneRClassifier: One Rule (OneR) method for classification"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And implementation of the One Rule (OneR) method for classfication."
+    "And implementation of the One Rule (OneR) method for classification."
    ]
   },
   {

mlxtend/classifier/adaline.py

@@ -14,7 +14,6 @@
 
 
 class Adaline(_BaseModel, _IterativeModel, _Classifier):
-
     """ADAptive LInear NEuron classifier.
 
     Note that this implementation of Adaline expects binary class labels

mlxtend/classifier/ensemble_vote.py

@@ -19,7 +19,6 @@
 
 
 class EnsembleVoteClassifier(BaseEstimator, ClassifierMixin, TransformerMixin):
-
     """Soft Voting/Majority Rule classifier for scikit-learn estimators.
 
     Parameters

mlxtend/classifier/logistic_regression.py

@@ -14,7 +14,6 @@
 
 
 class LogisticRegression(_BaseModel, _IterativeModel, _Classifier):
-
     """Logistic regression classifier.
 
     Note that this implementation of Logistic Regression

mlxtend/classifier/multilayerperceptron.py

@@ -17,7 +17,6 @@
 class MultiLayerPerceptron(
     _BaseModel, _IterativeModel, _MultiClass, _MultiLayer, _Classifier
 ):
-
     """Multi-layer perceptron classifier with logistic sigmoid activations
 
     Parameters

mlxtend/classifier/oner.py

@@ -17,7 +17,6 @@
 
 
 class OneRClassifier(BaseEstimator, ClassifierMixin):
-
     """OneR (One Rule) Classifier.
 
     Parameters

mlxtend/classifier/perceptron.py

@@ -14,7 +14,6 @@
 
 
 class Perceptron(_BaseModel, _IterativeModel, _Classifier):
-
     """Perceptron classifier.
 
     Note that this implementation of the Perceptron expects binary class labels

mlxtend/classifier/softmax_regression.py

@@ -16,7 +16,6 @@
 
 
 class SoftmaxRegression(_BaseModel, _IterativeModel, _Classifier, _MultiClass):
-
     """Softmax regression classifier.
 
     Parameters

mlxtend/classifier/stacking_classification.py

@@ -21,7 +21,6 @@
 
 
 class StackingClassifier(_BaseXComposition, _BaseStackingClassifier, TransformerMixin):
-
     """A Stacking classifier for scikit-learn estimators for classification.
 
     Parameters

mlxtend/classifier/stacking_cv_classification.py

@@ -26,7 +26,6 @@
 class StackingCVClassifier(
     _BaseXComposition, _BaseStackingClassifier, TransformerMixin
 ):
-
     """A 'Stacking Cross-Validation' classifier for scikit-learn estimators.
 
     New in mlxtend v0.4.3

mlxtend/evaluate/tests/test_bias_variance_decomp.py

@@ -113,7 +113,12 @@ def test_mse_bagging():
     APPVEYOR = False
 
 
-@pytest.mark.skipif(TRAVIS or APPVEYOR, reason="TensorFlow dependency")
+GITHUB_ACTIONS = os.getenv("GITHUB_ACTIONS_CI", "false").lower() == "true"
+
+
+@pytest.mark.skipif(
+    TRAVIS or APPVEYOR or GITHUB_ACTIONS, reason="TensorFlow dependency"
+)
 def test_keras():
     import tensorflow as tf
 

mlxtend/evaluate/tests/test_bootstrap_point632.py

@@ -158,7 +158,12 @@ def test_scoring_proba():
     APPVEYOR = False
 
 
-@pytest.mark.skipif(TRAVIS or APPVEYOR, reason="TensorFlow dependency")
+GITHUB_ACTIONS = os.getenv("GITHUB_ACTIONS_CI", "false").lower() == "true"
+
+
+@pytest.mark.skipif(
+    TRAVIS or APPVEYOR or GITHUB_ACTIONS, reason="TensorFlow dependency"
+)
 def test_keras_fitparams():
     import tensorflow as tf
 

mlxtend/externals/pyprind/__init__.py

@@ -10,7 +10,6 @@
 PyPI: https://pypi.python.org/pypi/PyPrind
 """
 
-
 from .generator_factory import prog_bar, prog_percent
 from .progbar import ProgBar
 from .progpercent import ProgPercent

mlxtend/externals/pyprind/prog_class.py

@@ -10,7 +10,6 @@
 PyPI: https://pypi.python.org/pypi/PyPrind
 """
 
-
 import os
 import sys
 import time

mlxtend/externals/pyprind/progbar.py

@@ -10,7 +10,6 @@
 PyPI: https://pypi.python.org/pypi/PyPrind
 """
 
-
 import time
 from math import floor
 

mlxtend/feature_selection/exhaustive_feature_selector.py

@@ -26,7 +26,6 @@
 
 
 class ExhaustiveFeatureSelector(BaseEstimator, MetaEstimatorMixin):
-
     """Exhaustive Feature Selection for Classification and Regression.
        (new in v0.4.3)
 

mlxtend/feature_selection/sequential_feature_selector.py

@@ -25,7 +25,6 @@
 
 
 class SequentialFeatureSelector(_BaseXComposition, MetaEstimatorMixin):
-
     """Sequential Feature Selection for Classification and Regression.
 
     Parameters

mlxtend/plotting/plot_confusion_matrix.py

@@ -156,9 +156,11 @@ def plot_confusion_matrix(
                     s=cell_text,
                     va="center",
                     ha="center",
-                    color="white"
-                    if conf_mat[i, j] > np.max(conf_mat) * fontcolor_threshold
-                    else "black",
+                    color=(
+                        "white"
+                        if conf_mat[i, j] > np.max(conf_mat) * fontcolor_threshold
+                        else "black"
+                    ),
                 )
     if class_names is not None:
         tick_marks = np.arange(len(class_names))

mlxtend/preprocessing/mean_centering.py

@@ -10,7 +10,6 @@
 
 
 class MeanCenterer(object):
-
     """Column centering of vectors and matrices.
 
     Attributes

mlxtend/regressor/linear_regression.py

@@ -14,7 +14,6 @@
 
 
 class LinearRegression(_BaseModel, _IterativeModel, _Regressor):
-
     """Ordinary least squares linear regression.
 
     Parameters

mlxtend/regressor/stacking_regression.py

@@ -19,7 +19,6 @@
 
 
 class StackingRegressor(_BaseXComposition, RegressorMixin, TransformerMixin):
-
     """A Stacking regressor for scikit-learn estimators for regression.
 
     Parameters

mlxtend/utils/counter.py

@@ -11,7 +11,6 @@
 
 
 class Counter(object):
-
     """Class to display the progress of for-loop iterators.
 
     Parameters