使用遗传算法进行特征选择（DEAP 框架）_python_代码_下载

# 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)