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https://github.com/kaushalshetty/FeatureSelectionGA
Feature Selection using Genetic Algorithm (DEAP Framework)
https://github.com/kaushalshetty/FeatureSelectionGA
deap feature-selection genetic-algorithm machine-learning python
Last synced: about 6 hours ago
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Feature Selection using Genetic Algorithm (DEAP Framework)
- Host: GitHub
- URL: https://github.com/kaushalshetty/FeatureSelectionGA
- Owner: kaushalshetty
- License: mit
- Created: 2017-11-12T18:32:44.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2023-02-21T22:56:56.000Z (over 1 year ago)
- Last Synced: 2024-10-07T15:17:40.796Z (about 1 month ago)
- Topics: deap, feature-selection, genetic-algorithm, machine-learning, python
- Language: Python
- Size: 54.7 KB
- Stars: 362
- Watchers: 9
- Forks: 95
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-data-science-resources - FeatureSelectionGA
- awesome-data-science-resources - FeatureSelectionGA
README
# FeatureSelectionGA
[](https://github.com/kaushalshetty/FeatureSelectionGA/actions)
[](https://pypi.python.org/pypi/feature-selection-ga/)
[](https://featureselectionga.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/psf/black)
### Feature Selection using Genetic Algorithm (DEAP Framework)
Data scientists find it really difficult to choose the right features to get maximum accuracy especially if you are dealing with a lot of features. There are currenlty lots of ways to select the right features. But we will have to struggle if the feature space is really big. Genetic algorithm is one solution which searches for one of the best feature set from other features in order to attain a high accuracy.
#### Installation:
```bash
$ pip install feature-selection-ga
```
#### Documentation:
https://featureselectionga.readthedocs.io/en/latest/
#### Usage:
```python
from sklearn.datasets import make_classification
from sklearn import linear_model
from feature_selection_ga import FeatureSelectionGA, FitnessFunction
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)
model = linear_model.LogisticRegression(solver='lbfgs', multi_class='auto')
fsga = FeatureSelectionGA(model,X,y, ff_obj = FitnessFunction())
pop = fsga.generate(100)
#print(pop)
```
#### Usage (Advanced):
By default, the FeatureSelectionGA has its own fitness function class. We can also define our own
FitnessFunction class.
```python
class FitnessFunction:
def __init__(self,n_splits = 5,*args,**kwargs):
"""
Parameters
-----------
n_splits :int,
Number of splits for cv
verbose: 0 or 1
"""
self.n_splits = n_splits
def calculate_fitness(self,model,x,y):
pass
```
With this, we can design our own fitness function by defining our calculate fitness!
Consider the following example from [Vieira, Mendoca, Sousa, et al. (2013)](http://www.sciencedirect.com/science/article/pii/S1568494613001361)
`$f(X) = \alpha(1-P) + (1-\alpha) \left(1 - \dfrac{N_f}{N_t}\right)$`
Define the constructor **init** with needed parameters: alpha and N_t.
```python
class FitnessFunction:
def __init__(self,n_total_features,n_splits = 5, alpha=0.01, *args,**kwargs):
"""
Parameters
-----------
n_total_features :int
Total number of features N_t.
n_splits :int, default = 5
Number of splits for cv
alpha :float, default = 0.01
Tradeoff between the classifier performance P and size of
feature subset N_f with respect to the total number of features
N_t.
verbose: 0 or 1
"""
self.n_splits = n_splits
self.alpha = alpha
self.n_total_features = n_total_features
```
Next, we define the fitness function, the name has to be
calculate_fitness:
```python
def calculate_fitness(self,model,x,y):
alpha = self.alpha
total_features = self.n_total_features
cv_set = np.repeat(-1.,x.shape[0])
skf = StratifiedKFold(n_splits = self.n_splits)
for train_index,test_index in skf.split(x,y):
x_train,x_test = x[train_index],x[test_index]
y_train,y_test = y[train_index],y[test_index]
if x_train.shape[0] != y_train.shape[0]:
raise Exception()
model.fit(x_train,y_train)
predicted_y = model.predict(x_test)
cv_set[test_index] = predicted_y
P = accuracy_score(y, cv_set)
fitness = (alpha*(1.0 - P) + (1.0 - alpha)*(1.0 - (x.shape[1])/total_features))
return fitness
```
Example:
You may also see `example2.py`
```python
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)
# Define the model
model = linear_model.LogisticRegression(solver='lbfgs', multi_class='auto')
# Define the fitness function object
ff = FitnessFunction(n_total_features= X.shape[1], n_splits=3, alpha=0.05)
fsga = FeatureSelectionGA(model,X,y, ff_obj = ff)
pop = fsga.generate(100)
```
Example adopted from [pyswarms](https://pyswarms.readthedocs.io/en/latest/examples/usecases/feature_subset_selection.html)