[WIP] Explained Variance

docs/api/features/explained_variance.rst

@@ -0,0 +1,54 @@
+.. -*- mode: rst -*-
+
+Explained Variance
+==================
+
+=================   =================
+Visualizer           :class:`~yellowbrick.features.explained_variance.ExplainedVariance`
+Quick Method         :func:`~yellowbrick.features.explained_variance.explained_variance`
+Models               Decomposition
+Workflow             Feature Engineering
+=================   =================
+
+.. plot::
+    :context: close-figs
+    :alt: Explained variance quick method on the credit dataset
+
+    from yellowbrick.datasets import load_credit
+    from yellowbrick.features import ExplainedVariance
+
+    # Specify the features of interest and the target
+    X, _ = load_credit()
+
+    # Instantiate the visualizer, fit and transform the data
+    oz = ExplainedVariance()
+    oz.fit_transform(X)
+    oz.show()
+
+
+Quick Method
+------------
+
+The same functionality above can be achieved with the associated quick method ``explained_variance``. This method will build the ``ExplainedVariance`` visualizer with the associated arguments, fit it, then (optionally) immediately show it.
+
+.. plot::
+    :context: close-figs
+    :alt: Explained variance quick method on the concrete dataset
+
+    from yellowbrick.datasets import load_concrete
+    from yellowbrick.features import explained_variance
+
+    # Specify the features of interest and the target
+    X, _ = load_concrete()
+
+    # Determine the optimal number of components
+    oz = explained_variance(X)
+
+
+API Reference
+-------------
+
+.. automodule:: yellowbrick.features.explained_variance
+    :members: ExplainedVariance, explained_variance
+    :undoc-members:
+    :show-inheritance:

docs/api/features/index.rst

@@ -47,6 +47,7 @@ finalizes and displays the image.
    radviz
    rankd
    pcoords
-   pca
    manifold
+   pca
+   explained_variance
    jointplot

docs/changelog.rst

@@ -3,12 +3,23 @@
 Changelog
 =========
 
+Under Development
+-----------------
+
+Major Changes:
+   - New ``ExplainedVariance`` visualizer that assists in the selection of the number of
+   components for principal component analysis. The visualizer plots both cumulative
+   and discrete explained variance against the number of components and shades in
+   percentiles of total explained variance for visual selection of the best fit.
+
+
+
 Version 1.1
 -----------
 
 * Tag: v1.1_
 * Deployed Wednesday, February 12, 2020
-* Contributors: Benjamin Bengfort, Rebecca Bilbro, Kristen McIntyre, Larry Gray, Prema Roman, Adam Morris, Shivendra Sharma, Michael Chestnut, Michael Garod, Naresh Bachwani, Piyush Gautam, Daniel Navarrete, Molly Morrison, Emma Kwiecinska, Sarthak Jain, Tony Ojeda, Edwin Schmier,  Nathan Danielsen
+* Contributors: Benjamin Bengfort, Rebecca Bilbro, Kristen McIntyre, Larry Gray, Prema Roman, Adam Morris, Shivendra Sharma, Michael Chestnut, Michael Garod, Naresh Bachwani, Piyush Gautam, Daniel Navarrete, Molly Morrison, Emma Kwiecinska, Sarthak Jain, Tony Ojeda, Edwin Schmierer, Nathan Danielsen
 
 Major Changes:
    - Quick methods (aka Oneliners), which return a fully fitted finalized visualizer object in only a single line, are now implemented for all Yellowbrick Visualizers. Test coverage has been added for all quick methods. The documentation has been updated to document and demonstrate the usage of the quick methods.

docs/index.rst

@@ -56,8 +56,9 @@ Feature Visualization
 - :doc:`api/features/rankd`: pairwise ranking of features to detect relationships
 - :doc:`api/features/pcoords`: horizontal visualization of instances
 - :doc:`Radial Visualization <api/features/radviz>`: separation of instances around a circular plot
-- :doc:`api/features/pca`: projection of instances based on principal components
 - :doc:`api/features/manifold`: high dimensional visualization with manifold learning
+- :doc:`api/features/pca`: projection of instances based on principal components
+- :doc:`api/features/explained_variance`: select number of components for PCA
 - :doc:`Joint Plots <api/features/jointplot>`: direct data visualization with feature selection
 
 Classification Visualization

tests/test_features/test_explained_variance.py

@@ -0,0 +1,43 @@
+# tests.test_features.test_explained_variance
+# Tests for the PCA explained variance visualizer
+#
+# Author:   Benjamin Bengfort
+# Created:  Mon Feb 10 19:11:46 2020 -0500
+#
+# Copyright (C) 2019 The scikit-yb developers
+# For license information, see LICENSE.txt
+#
+# ID: test_explained_variance.py [] [email protected] $
+
+"""
+Tests for the PCA explained variance visualizer
+"""
+
+##########################################################################
+## Imports
+##########################################################################
+
+from tests.base import VisualTestCase
+
+from yellowbrick.datasets import load_credit
+from yellowbrick.features.explained_variance import *
+
+
+##########################################################################
+## ExplainedVariance Tests
+##########################################################################
+
+class TextExplainedVariance(VisualTestCase):
+    """
+    Test the explained variance visualizer
+    """
+
+    def test_quick_method(self):
+        """
+        Test the explained variance quick method
+        """
+        X, _ = load_credit()
+        oz = explained_variance(X)
+
+        assert isinstance(oz, ExplainedVariance)
+        self.assert_images_similar(oz)

yellowbrick/features/__init__.py

@@ -24,6 +24,7 @@
 from .jointplot import JointPlot, JointPlotVisualizer, joint_plot
 from .pca import PCA, PCADecomposition, pca_decomposition
 from .manifold import Manifold, manifold_embedding
+from .explained_variance import ExplainedVariance, explained_variance
 
 # Alias the TargetType defined in yellowbrick.utils.target
 from yellowbrick.utils.target import TargetType