找到最好的那个参数lmbda。
from mlxtend.regressor import StackingCVRegressor from sklearn.datasets import load_boston from sklearn.svm import SVR from sklearn.linear_model import Lasso from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import cross_val_score import numpy as np RANDOM_SEED = 42 X,y = load_boston(return_X_y=True) svr = SVR(kernel=‘linear‘) lasso = Lasso() rf = RandomForestRegressor(n_estimators=5,random_state=RANDOM_SEED) # The StackingCVRegressor uses scikit-learn‘s check_cv # internally,which doesn‘t support a random seed. Thus # NumPy‘s random seed need to be specified explicitely for # deterministic behavior np.random.seed(RANDOM_SEED) stack = StackingCVRegressor(regressors=(svr,lasso,rf),Meta_regressor=lasso) print(‘5-fold cross validation scores:\n‘) for clf,label in zip([svr,rf,stack],[‘SVM‘,‘Lasso‘,‘Random Forest‘,‘StackingClassifier‘]): scores = cross_val_score(clf,X,y,cv=5) print("R^2 score: %0.2f (+/- %0.2f) [%s]" % (scores.mean(),scores.std(),label)) 5-fold cross validation scores: R^2 score: 0.45 (+/- 0.29) [SVM] R^2 score: 0.43 (+/- 0.14) [Lasso] R^2 score: 0.52 (+/- 0.28) [Random Forest] R^2 score: 0.58 (+/- 0.24) [StackingClassifier] # The StackingCVRegressor uses scikit-learn‘s check_cv # internally,cv=5,scoring=‘neg_mean_squared_error‘) print("Neg. MSE score: %0.2f (+/- %0.2f) [%s]"
from mlxtend.regressor import StackingCVRegressor from sklearn.datasets import load_boston from sklearn.linear_model import Lasso from sklearn.linear_model import Ridge from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import GridSearchCV X,y = load_boston(return_X_y=True) ridge = Ridge() lasso = Lasso() rf = RandomForestRegressor(random_state=RANDOM_SEED) # The StackingCVRegressor uses scikit-learn‘s check_cv # internally,which doesn‘t support a random seed. Thus # NumPy‘s random seed need to be specified explicitely for # deterministic behavior np.random.seed(RANDOM_SEED) stack = StackingCVRegressor(regressors=(lasso,ridge),Meta_regressor=rf,use_features_in_secondary=True) params = {‘lasso__alpha‘: [0.1,1.0,10.0],‘ridge__alpha‘: [0.1,10.0]} grid = GridSearchCV( estimator=stack,param_grid={‘lasso__alpha‘: [x/5.0 for x in range(1,10)],‘ridge__alpha‘: [x/20.0 for x in range(1,‘Meta-randomforestregressor__n_estimators‘: [10,100]},cv=5,refit=True ) grid.fit(X,y) print("Best: %f using %s" % (grid.best_score_,grid.best_params_)) #Best: 0.673590 using {‘lasso__alpha‘: 0.4,‘Meta-randomforestregressor__n_estimators‘: 10,‘ridge__alpha cv_keys = (‘mean_test_score‘,‘std_test_score‘,‘params‘) for r,_ in enumerate(grid.cv_results_[‘mean_test_score‘]): print("%0.3f +/- %0.2f %r" % (grid.cv_results_[cv_keys[0]][r],grid.cv_results_[cv_keys[1]][r] / 2.0,grid.cv_results_[cv_keys[2]][r])) if r > 10: break print(‘...‘) print(‘Best parameters: %s‘ % grid.best_params_) print(‘Accuracy: %.2f‘ % grid.best_score_)
