update examples

yzhao062 · Jul 29, 2019 · 99271aa · 99271aa
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diff --git a/README.rst b/README.rst
@@ -34,7 +34,7 @@ combo: A Python Toolbox for Machine Learning Model Combination
    :alt: Downloads
 
 
------
+----
 
 
 **Build Status & Coverage & Maintainability & License**
@@ -65,7 +65,7 @@ combo: A Python Toolbox for Machine Learning Model Combination
    :alt: License
 
 
------
+----
 
 
 **combo** is a comprehensive Python toolbox for combining machine
@@ -119,9 +119,9 @@ combo is featured for:
 * `Installation <#installation>`_
 * `API Cheatsheet & Reference <#api-cheatsheet--reference>`_
 * `Proposed Algorithms <#proposed-algorithms>`_
+* `An Example of Stacking <#an-example-of-stacking>`_
 * `Quick Start for Classifier Combination <#quick-start-for-classifier-combination>`_
 * `Quick Start for Clustering Combination <#quick-start-for-clustering-combination>`_
-* `An Example of Stacking <#an-example-of-stacking>`_
 * `Development Status <#development-status>`_
 
 
@@ -158,6 +158,7 @@ Alternatively, you could clone and run setup.py file:
 * matplotlib
 * numpy>=1.13
 * numba>=0.35
+* pyod
 * scipy>=0.19.1
 * scikit_learn>=0.19.1
 
@@ -226,7 +227,58 @@ Some of the methods are tasks specific:
    :target: https://raw.githubusercontent.com/yzhao062/combo/master/examples/ALL.png
    :alt: Comparison of Selected Models
 
------
+
+----
+
+
+An Example of Stacking
+^^^^^^^^^^^^^^^^^^^^^^
+
+`"examples/stacking_example.py" <https://github.com/yzhao062/combo/blob/master/examples/stacking_example.py>`_
+demonstrates the basic API of stacking (meta ensembling).
+
+
+#. Initialize a group of classifiers as base estimators
+
+   .. code-block:: python
+
+
+       # initialize a group of classifiers
+       classifiers = [DecisionTreeClassifier(), LogisticRegression(),
+                      KNeighborsClassifier(), RandomForestClassifier(),
+                      GradientBoostingClassifier()]
+
+
+#. Initialize, fit, predict, and evaluate with Stacking
+
+   .. code-block:: python
+
+
+       from combo.models.stacking import Stacking
+
+       clf = Stacking(base_estimators=classifiers, n_folds=4, shuffle_data=False,
+                   keep_original=True, use_proba=False, random_state=random_state)
+
+       clf.fit(X_train, y_train)
+       y_test_predict = clf.predict(X_test)
+       evaluate_print('Stacking | ', y_test, y_test_predict)
+
+
+#. See a sample output of stacking_example.py
+
+   .. code-block:: python
+
+
+       Decision Tree        | Accuracy:0.9386, ROC:0.9383, F1:0.9521
+       Logistic Regression  | Accuracy:0.9649, ROC:0.9615, F1:0.973
+       K Neighbors          | Accuracy:0.9561, ROC:0.9519, F1:0.9662
+       Gradient Boosting    | Accuracy:0.9605, ROC:0.9524, F1:0.9699
+       Random Forest        | Accuracy:0.9605, ROC:0.961, F1:0.9693
+
+       Stacking             | Accuracy:0.9868, ROC:0.9841, F1:0.9899
+
+
+----
 
 
 Quick Start for Classifier Combination
@@ -278,7 +330,7 @@ demonstrates the basic API of predicting with multiple classifiers. **It is note
        Combination by median| Accuracy:0.9693, ROC:0.9677, F1:0.9763
 
 
------
+----
 
 
 Quick Start for Clustering Combination
@@ -320,57 +372,7 @@ demonstrates the basic API of combining multiple base clustering estimators.
        predicted_labels = clf.labels_
 
 
------
-
-
-An Example of Stacking
-^^^^^^^^^^^^^^^^^^^^^^
-
-`"examples/stacking_example.py" <https://github.com/yzhao062/combo/blob/master/examples/stacking_example.py>`_
-demonstrates the basic API of stacking (meta ensembling).
-
-
-#. Initialize a group of classifiers as base estimators
-
-   .. code-block:: python
-
-
-       # initialize a group of classifiers
-       classifiers = [DecisionTreeClassifier(), LogisticRegression(),
-                      KNeighborsClassifier(), RandomForestClassifier(),
-                      GradientBoostingClassifier()]
-
-
-#. Initialize, fit, predict, and evaluate with Stacking
-
-   .. code-block:: python
-
-
-       from combo.models.stacking import Stacking
-
-       clf = Stacking(base_estimators=classifiers, n_folds=4, shuffle_data=False,
-                   keep_original=True, use_proba=False, random_state=random_state)
-
-       clf.fit(X_train, y_train)
-       y_test_predict = clf.predict(X_test)
-       evaluate_print('Stacking | ', y_test, y_test_predict)
-
-
-#. See a sample output of stacking_example.py
-
-   .. code-block:: python
-
-
-       Decision Tree        | Accuracy:0.9386, ROC:0.9383, F1:0.9521
-       Logistic Regression  | Accuracy:0.9649, ROC:0.9615, F1:0.973
-       K Neighbors          | Accuracy:0.9561, ROC:0.9519, F1:0.9662
-       Gradient Boosting    | Accuracy:0.9605, ROC:0.9524, F1:0.9699
-       Random Forest        | Accuracy:0.9605, ROC:0.961, F1:0.9693
-
-       Stacking             | Accuracy:0.9868, ROC:0.9841, F1:0.9899
-
-
------
+----
 
 
 Development Status
@@ -385,8 +387,20 @@ combo is also targeted to be published in *Journal of Machine Learning Research
 `open-source software track <http://www.jmlr.org/mloss/>`_. A demo paper to
 *AAAI* or *IJCAI* may be submitted soon for progress update.
 
-**Watch & Star** to get the latest update! Also feel free to send me an email (zhaoy@cmu.edu)
-for suggestions and ideas.
+
+----
+
+
+Inclusion Criteria
+^^^^^^^^^^^^^^^^^^
+
+Similarly to `scikit-learn <https://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms>`_,
+We mainly consider well-established algorithms for inclusion.
+A rule of thumb is at least two years since publication, 50+ citations, and usefulness.
+
+However, we encourage the author(s) of newly proposed models to share and add your implementation into combo
+for boosting ML accessibility and reproducibility.
+This exception only applies if you could commit to the maintenance of your model for at least two year period.
 
 
 ----

diff --git a/docs/index.rst b/docs/index.rst
@@ -39,7 +39,7 @@ Welcome to combo's documentation!
    :alt: Downloads
 
 
------
+----
 
 
 **Build Status & Coverage & Maintainability & License**
@@ -70,7 +70,7 @@ Welcome to combo's documentation!
    :alt: License
 
 
------
+----
 
 
 **combo** is a comprehensive Python toolbox for combining machine

diff --git a/docs/install.rst b/docs/install.rst
@@ -28,5 +28,6 @@ Alternatively, you could clone and run setup.py file:
 * matplotlib
 * numpy>=1.13
 * numba>=0.35
+* pyod
 * scipy>=0.19.1
 * scikit_learn>=0.19.1