allinone.py 文件源码

def get_model_list(task_name):

    model_list, name_list = [], []

    model_list.append(linear_model.LinearRegression())
    name_list.append('LR')
    #
    model_list.append(linear_model.SGDRegressor())
    name_list.append('LR_SGD')

    model_list.append(linear_model.Lasso(alpha = 1.0))
    name_list.append('Lasso')

    model_list.append(linear_model.Ridge (alpha = 1.0))
    name_list.append('Ridge')

    model_list.append(linear_model.LassoLars(alpha=.1))
    name_list.append('LassoLars')

    model_list.append(linear_model.BayesianRidge())
    name_list.append('BayesianRidge')

    model_list.append(KernelRidge(alpha=1.0))
    name_list.append('KernelRidge')

    model_list.append(gaussian_process.GaussianProcess(theta0=1e-2, thetaL=1e-4, thetaU=1e-1))
    name_list.append('GaussianProcess')

    model_list.append(KNeighborsRegressor(weights = 'uniform',n_neighbors=3))
    name_list.append('KNN_unif')

    model_list.append(KNeighborsRegressor(weights = 'distance',n_neighbors=3))
    name_list.append('KNN_dist')

    model_list.append(SVR(kernel = 'linear', C = 1, gamma = 'auto', coef0 = 0, degree = 2))
    name_list.append('SVM_linear')

    model_list.append(SVR(kernel = 'poly', C = 1, gamma = 'auto', coef0 = 0, degree = 2))
    name_list.append('SVM_poly')

    model_list.append(SVR(kernel = 'rbf', C = 1, gamma = 'auto', coef0 = 0, degree = 2))
    name_list.append('SVM_rbf')

    model_list.append(DecisionTreeRegressor())
    name_list.append('DT')

    model_list.append(RandomForestRegressor(n_estimators=100, max_depth=None,min_samples_split=2, random_state=0))
    name_list.append('RF')

    model_list.append(ExtraTreesRegressor(n_estimators=100, max_depth=None, max_features='auto', min_samples_split=2, random_state=0))
    name_list.append('ET')

    return model_list, name_list