Update docs on feature subset selection (#17)

The documentation was updated to include feature subset
selection. Some examples were also edited from the
pyswarms.single.gb and pyswarms.single.lb modules in
order to fix the error reported from Reddit.

The images for the Use-Case example were then placed
in the same directory as the .reST files.

Author: ljvmiranda921

docs/examples/feature_subset_selection.rst

@@ -61,12 +61,8 @@ feature subset :math:`N_f` with respect to the total number of features
 :math:`N_t`. The classifier performance can be the accuracy, F-score,
 precision, and so on.
 
-.. code:: ipython3
 
-    import sys
-    sys.path.append('../')
-
-.. code:: ipython3
+.. code-block:: python
 
     # Import modules
     import numpy as np
@@ -91,19 +87,19 @@ the distribution of the features in order to give us a qualitative
 assessment of the feature-space.
 
 For our toy dataset, we will be rigging some parameters a bit. Out of
-the 10 features, we'll have only 5 that are informative, 5 that are
+the 15 features, we'll have only 4 that are informative, 1 that are
 redundant, and 2 that are repeated. Hopefully, we get to have Binary PSO
 select those that are informative, and prune those that are redundant or
 repeated.
 
-.. code:: ipython3
+.. code-block:: python
 
     from sklearn.datasets import make_classification
     X, y = make_classification(n_samples=100, n_features=15, n_classes=3, 
                                n_informative=4, n_redundant=1, n_repeated=2, 
                                random_state=1)
 
-.. code:: ipython3
+.. code-block:: python
 
     # Plot toy dataset per feature
     df = pd.DataFrame(X)
@@ -112,10 +108,10 @@ repeated.
     sns.pairplot(df, hue='labels');
 
 
-
 .. image:: output_6_0.png
 
 
+
 As we can see, there are some features that causes the two classes to
 overlap with one another. These might be features that are better off
 unselected. On the other hand, we can see some feature combinations
@@ -136,7 +132,7 @@ of the feature subset divided by the total (that is, divided by 10), to
 return an error in the data. We'll now write our custom-objective
 function
 
-.. code:: ipython3
+.. code-block:: python
 
     from sklearn import linear_model
     
@@ -176,7 +172,7 @@ function
         
         return j
 
-.. code:: ipython3
+.. code-block:: python
 
     def f(x, alpha=0.88):
         """Higher-level method to do classification in the 
@@ -207,7 +203,7 @@ hyperparameters are also set arbitrarily. Moreso, we'll also be setting
 the distance metric as 2 (truth is, it's not really relevant because
 each particle will see one another).
 
-.. code:: ipython3
+.. code-block:: python
 
     # Initialize swarm, arbitrary
     options = {'c1': 0.5, 'c2': 0.5, 'w':0.9, 'k': 30, 'p':2}
@@ -244,7 +240,7 @@ We can then train the classifier using the positions found by running
 another instance of logistic regression. We can compare the performance
 when we're using the full set of features
 
-.. code:: ipython3
+.. code-block:: python
 
     # Create two instances of LogisticRegression
     classfier = linear_model.LogisticRegression()
@@ -265,7 +261,7 @@ when we're using the full set of features
 .. parsed-literal::
 
     Subset performance: 0.680
-    
+
 
 Another important advantage that we have is that we were able to reduce
 the features (or do dimensionality reduction) on our data. This can save
@@ -275,24 +271,14 @@ and may in fact speed up our classification.
 
 Let's plot the feature subset that we have:
 
-.. code:: ipython3
+.. code-block:: python
 
     # Plot toy dataset per feature
     df1 = pd.DataFrame(X_selected_features)
     df1['labels'] = pd.Series(y)
     
     sns.pairplot(df1, hue='labels')
 
-
-
-
-.. parsed-literal::
-
-    <seaborn.axisgrid.PairGrid at 0x1975d4da400>
-
-
-
-
 .. image:: output_17_1.png
 
 

docs/examples/usecases.rst

@@ -6,4 +6,5 @@ If you wish to check the actual Jupyter Notebooks, please go to this `link <http
 .. toctree::
 
    basic_optimization
-   train_neural_network
+   train_neural_network
+   feature_subset_selection

pyswarms/single/gb.py

@@ -37,7 +37,7 @@
     from pyswarms.utils.functions import single_obj as fx
 
     # Set-up hyperparameters
-    options = {'c1': 0.5, 'c2': 0.3, 'm':0.9}
+    options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}
 
     # Call instance of GBestPSO
     optimizer = ps.single.GBestPSO(n_particles=10, dims=2, **options)

pyswarms/single/lb.py

@@ -41,7 +41,7 @@
     from pyswarms.utils.functions import single_obj as fx
 
     # Set-up hyperparameters
-    options = {'c1': 0.5, 'c2': 0.3, 'm': 0.9, 'k': 3, 'p': 2}
+    options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9, 'k': 3, 'p': 2}
 
     # Call instance of LBestPSO with a neighbour-size of 3 determined by
     # the L2 (p=2) distance.