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update documentation
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yzhao062 committed Jul 29, 2019
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114 changes: 95 additions & 19 deletions README.rst
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Expand Up @@ -114,15 +114,23 @@ combo is featured for:
y_test_proba = clf.predict_proba(X_test) # probability prediction
**Key Links and Resources**\ :


* `View the latest codes on Github <https://github.com/yzhao062/combo>`_
* `View the documentation & API <https://pycombo.readthedocs.io/>`_
* `View all examples <https://github.com/yzhao062/combo/tree/master/examples>`_


**Table of Contents**\ :


* `Installation <#installation>`_
* `API Cheatsheet & Reference <#api-cheatsheet--reference>`_
* `Implemented Algorithms <#implemented-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>`_
* `Example of Stacking <#example-of-stacking>`_
* `Example of Classifier Combination <#example-of-classifier-combination>`_
* `Example of Clustering Combination <#example-of-clustering-combination>`_
* `Development Status <#development-status>`_


Expand Down Expand Up @@ -220,8 +228,14 @@ Anomaly Detection Locally Selective Combination (LSCP)
----


An Example of Stacking
^^^^^^^^^^^^^^^^^^^^^^
**All implemented modes** are associated with examples, check
`"combo examples" <https://github.com/yzhao062/combo/blob/master/examples>`_
for more information.


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).
Expand All @@ -233,9 +247,11 @@ demonstrates the basic API of stacking (meta ensembling).
# initialize a group of classifiers
classifiers = [DecisionTreeClassifier(), LogisticRegression(),
KNeighborsClassifier(), RandomForestClassifier(),
GradientBoostingClassifier()]
classifiers = [DecisionTreeClassifier(random_state=random_state),
LogisticRegression(random_state=random_state),
KNeighborsClassifier(),
RandomForestClassifier(random_state=random_state),
GradientBoostingClassifier(random_state=random_state)]
#. Initialize, fit, predict, and evaluate with Stacking
Expand All @@ -255,7 +271,7 @@ demonstrates the basic API of stacking (meta ensembling).
#. See a sample output of stacking_example.py

.. code-block:: python
.. code-block:: bash
Decision Tree | Accuracy:0.9386, ROC:0.9383, F1:0.9521
Expand All @@ -270,8 +286,9 @@ demonstrates the basic API of stacking (meta ensembling).
----


Quick Start for Classifier Combination
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Example of Classifier Combination
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


`"examples/classifier_comb_example.py" <https://github.com/yzhao062/combo/blob/master/examples/classifier_comb_example.py>`_
demonstrates the basic API of predicting with multiple classifiers. **It is noted that the API across all other algorithms are consistent/similar**.
Expand All @@ -282,9 +299,11 @@ demonstrates the basic API of predicting with multiple classifiers. **It is note
# initialize a group of classifiers
classifiers = [DecisionTreeClassifier(), LogisticRegression(),
KNeighborsClassifier(), RandomForestClassifier(),
GradientBoostingClassifier()]
classifiers = [DecisionTreeClassifier(random_state=random_state),
LogisticRegression(random_state=random_state),
KNeighborsClassifier(),
RandomForestClassifier(random_state=random_state),
GradientBoostingClassifier(random_state=random_state)]
#. Initialize, fit, predict, and evaluate with a simple aggregator (average)
Expand All @@ -303,7 +322,7 @@ demonstrates the basic API of predicting with multiple classifiers. **It is note
#. See a sample output of classifier_comb_example.py

.. code-block:: python
.. code-block:: bash
Decision Tree | Accuracy:0.9386, ROC:0.9383, F1:0.9521
Expand All @@ -322,8 +341,9 @@ demonstrates the basic API of predicting with multiple classifiers. **It is note
----


Quick Start for Clustering Combination
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Example of Clustering Combination
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


`"examples/cluster_comb_example.py" <https://github.com/yzhao062/combo/blob/master/examples/cluster_comb_example.py>`_
demonstrates the basic API of combining multiple base clustering estimators.
Expand All @@ -333,8 +353,6 @@ demonstrates the basic API of combining multiple base clustering estimators.
.. code-block:: python
from combo.models.cluster_comb import ClustererEnsemble
# Initialize a set of estimators
estimators = [KMeans(n_clusters=n_clusters),
MiniBatchKMeans(n_clusters=n_clusters),
Expand All @@ -346,6 +364,7 @@ demonstrates the basic API of combining multiple base clustering estimators.
.. code-block:: python
from combo.models.cluster_comb import ClustererEnsemble
# combine by Clusterer Ensemble
clf = ClustererEnsemble(estimators, n_clusters=n_clusters)
clf.fit(X)
Expand All @@ -361,6 +380,63 @@ demonstrates the basic API of combining multiple base clustering estimators.
predicted_labels = clf.labels_
Example of Outlier Detection Combination
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


`"examples/detector_comb_example.py" <https://github.com/yzhao062/combo/blob/master/examples/detector_comb_example.py>`_
demonstrates the basic API of combining multiple base outlier detectors.

#. Initialize a group of outlier detection methods as base estimators

.. code-block:: python
# Initialize a set of estimators
detectors = [KNN(), LOF(), OCSVM()]
#. Initialize a simple averaging aggregator, fit the model, and make
the prediction.

.. code-block:: python
from combo.models.detector combination import SimpleDetectorAggregator
clf = SimpleDetectorAggregator(base_estimators=detectors)
clf_name = 'Aggregation by Averaging'
clf.fit(X_train)
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
#. Evaluate the prediction using ROC and Precision @ Rank n.

.. code-block:: python
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
#. See sample outputs on both training and test data.

.. code-block:: bash
On Training Data:
Aggregation by Averaging ROC:0.9994, precision @ rank n:0.95
On Test Data:
Aggregation by Averaging ROC:1.0, precision @ rank n:1.0
----


Expand Down
82 changes: 72 additions & 10 deletions docs/example.rst
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Expand Up @@ -2,11 +2,17 @@ Examples by Tasks
=================


**All implemented modes** are associated with examples, check
`"combo examples" <https://github.com/yzhao062/combo/blob/master/examples>`_
for more information.


----


An Example of Stacking
^^^^^^^^^^^^^^^^^^^^^^
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).
Expand Down Expand Up @@ -42,7 +48,7 @@ demonstrates the basic API of stacking (meta ensembling).
#. See a sample output of stacking_example.py

.. code-block:: python
.. code-block:: bash
Decision Tree | Accuracy:0.9386, ROC:0.9383, F1:0.9521
Expand All @@ -57,8 +63,9 @@ demonstrates the basic API of stacking (meta ensembling).
----


Quick Start for Classifier Combination
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Example of Classifier Combination
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


`"examples/classifier_comb_example.py" <https://github.com/yzhao062/combo/blob/master/examples/classifier_comb_example.py>`_
demonstrates the basic API of predicting with multiple classifiers. **It is noted that the API across all other algorithms are consistent/similar**.
Expand Down Expand Up @@ -92,7 +99,7 @@ demonstrates the basic API of predicting with multiple classifiers. **It is note
#. See a sample output of classifier_comb_example.py

.. code-block:: python
.. code-block:: bash
Decision Tree | Accuracy:0.9386, ROC:0.9383, F1:0.9521
Expand All @@ -111,8 +118,9 @@ demonstrates the basic API of predicting with multiple classifiers. **It is note
----


Quick Start for Clustering Combination
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Example of Clustering Combination
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


`"examples/cluster_comb_example.py" <https://github.com/yzhao062/combo/blob/master/examples/cluster_comb_example.py>`_
demonstrates the basic API of combining multiple base clustering estimators.
Expand All @@ -122,8 +130,6 @@ demonstrates the basic API of combining multiple base clustering estimators.
.. code-block:: python
from combo.models.cluster_comb import ClustererEnsemble
# Initialize a set of estimators
estimators = [KMeans(n_clusters=n_clusters),
MiniBatchKMeans(n_clusters=n_clusters),
Expand All @@ -135,6 +141,7 @@ demonstrates the basic API of combining multiple base clustering estimators.
.. code-block:: python
from combo.models.cluster_comb import ClustererEnsemble
# combine by Clusterer Ensemble
clf = ClustererEnsemble(estimators, n_clusters=n_clusters)
clf.fit(X)
Expand All @@ -151,4 +158,59 @@ demonstrates the basic API of combining multiple base clustering estimators.
Example of Outlier Detection Combination
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


`"examples/detector_comb_example.py" <https://github.com/yzhao062/combo/blob/master/examples/detector_comb_example.py>`_
demonstrates the basic API of combining multiple base outlier detectors.

#. Initialize a group of outlier detection methods as base estimators

.. code-block:: python
# Initialize a set of estimators
detectors = [KNN(), LOF(), OCSVM()]
#. Initialize a simple averaging aggregator, fit the model, and make
the prediction.

.. code-block:: python
from combo.models.detector combination import SimpleDetectorAggregator
clf = SimpleDetectorAggregator(base_estimators=detectors)
clf_name = 'Aggregation by Averaging'
clf.fit(X_train)
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
#. Evaluate the prediction using ROC and Precision @ Rank n.

.. code-block:: python
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
#. See sample outputs on both training and test data.

.. code-block:: bash
On Training Data:
Aggregation by Averaging ROC:0.9994, precision @ rank n:0.95
On Test Data:
Aggregation by Averaging ROC:1.0, precision @ rank n:1.0
14 changes: 11 additions & 3 deletions docs/index.rst
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@@ -1,7 +1,7 @@
.. combo documentation master file, created by
sphinx-quickstart on Tue Jul 16 15:42:55 2019.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
sphinx-quickstart on Tue Jul 16 15:42:55 2019.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.

Welcome to combo's documentation!
=================================
Expand Down Expand Up @@ -119,6 +119,14 @@ combo is featured for:
y_test_proba = clf.predict_proba(X_test) # probability prediction
**Key Links and Resources**\ :


* `View the latest codes on Github <https://github.com/yzhao062/combo>`_
* `View the documentation & API <https://pycombo.readthedocs.io/>`_
* `View all examples <https://github.com/yzhao062/combo/tree/master/examples>`_


----


Expand Down
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