Update readme

docs/index.md

@@ -5,6 +5,7 @@ This library contains some useful scikit-learn compatible classes for feature se
 ## Features
 
 - [Recursive Feature Elimination with Cross Validation using Permutation Importance](reference/RFE.md#felimination.rfe.PermutationImportanceRFECV)
+- [Hybrid Genetic Algorithms x Feature Importance selection](/reference/genetic_algorithms/#felimination.ga.HybridImportanceGACVFeatureSelector)
 
 ## Requirements
 
@@ -22,6 +23,9 @@ pip install felimination
 
 ## Usage
 
+### Recursive Feature Elimination
+In this section it will be illustrated how to use the `PermutationImportanceRFECV` class.
+
 ```python
 from felimination.rfe import PermutationImportanceRFECV
 from sklearn.linear_model import LogisticRegression
@@ -51,7 +55,7 @@ selector.ranking_
 # array([9, 3, 8, 9, 7, 8, 5, 6, 9, 6, 8, 1, 9, 7, 8, 9, 9, 2, 4, 7])
 selector.plot()
 ```
-![example of plot](./assets/example_plot.png)
+![RFECV fit plot](./assets/rfecv_fit_plot.png)
 
 It looks like `5` is a good number of features, we can set the number of features to select to 5 without need of retraining
 
@@ -63,6 +67,69 @@ selector.support_
 #         True, False])
 ```
 
+## Genetic Algorithms
+In this section it will be illustrated how to use the `HybridImportanceGACVFeatureSelector` class.
+
+```python
+from felimination.ga import HybridImportanceGACVFeatureSelector
+from sklearn.linear_model import LogisticRegression
+from sklearn.datasets import make_classification
+import numpy as np
+
+# Create dummy dataset
+X, y = make_classification(
+    n_samples=1000,
+    n_features=20,
+    n_informative=6,
+    n_redundant=10,
+    n_clusters_per_class=1,
+    random_state=42,
+)
+
+# Initialize selector
+selector = HybridImportanceGACVFeatureSelector(
+    LogisticRegression(random_state=42),
+    random_state=42,
+    pool_size=5,
+    patience=5
+)
+
+# Run optimisation
+selector.fit(X, y)
+
+# Show selected features
+selector.support_
+#array([False,  True, False,  True,  True, False, False, False,  True,
+#       False, False, False,  True,  True,  True,  True, False,  True,
+#        True, False])
+
+# Show best solution
+selector.best_solution_
+# {'features': [1, 12, 13, 8, 17, 15, 18, 4, 3, 14],
+#  'train_scores_per_fold': [0.88625, 0.89, 0.8825, 0.8925, 0.88625],
+#  'test_scores_per_fold': [0.895, 0.885, 0.885, 0.89, 0.89],
+#  'cv_importances': [array([[ 1.09135972,  1.13502636,  1.12100231,  0.38285736,  0.28944072,
+#            0.04688614,  0.44259813,  0.09832365,  0.10190421, -0.48101593]]),
+#   array([[ 1.17345812,  1.29375208,  1.2065342 ,  0.40418709,  0.41839714,
+#            0.00447802,  0.466717  ,  0.21733829, -0.00842075, -0.50078996]]),
+#   array([[ 1.15416104,  1.18458564,  1.18083266,  0.37071253,  0.22842685,
+#            0.1087814 ,  0.44446793,  0.12740545,  0.00621562, -0.54064287]]),
+#   array([[ 1.26011643,  1.36996058,  1.30481424,  0.48183549,  0.40589887,
+#           -0.01849671,  0.45606913,  0.18330816,  0.03667055, -0.50869557]]),
+#   array([[ 1.18227123,  1.28988253,  1.2496398 ,  0.50754295,  0.38942303,
+#           -0.01725074,  0.4481891 ,  0.19472963,  0.10034316, -0.50131192]])],
+#  'mean_train_score': 0.8875,
+#  'mean_test_score': 0.889,
+#  'mean_cv_importances': array([ 1.17227331,  1.25464144,  1.21256464,  0.42942709,  0.34631732,
+#          0.02487962,  0.45160826,  0.16422104,  0.04734256, -0.50649125])}
+
+# Show progress as a plot
+selector.plot()
+```
+![GA fit plot](./assets/ga_fit_plot.png)
+
+Looks like that the optimisation process converged after 2 steps, since the best score did not improve for 5(=`patience`) consecutive steps, the optimisation process stopped early.
+
 ## License
 
 This project is licensed under the BSD 3-Clause License - see the LICENSE.md file for details