Lasso1.rar_lasso_lasso分类

import pandas as pd import numpy as np import csv as csv import matplotlib import matplotlib.pyplot as plt from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV from sklearn.model_selection import cross_val_score train = pd.read_csv('高频匹配.csv') df = pd.get_dummies(train.iloc[:,1:6]) df = df.fillna(df.mean()) X_train = df y = train.高频 def rmse_cv(model): rmse= np.sqrt(-cross_val_score(model, X_train, y, scoring="neg_mean_squared_error", cv = 3)) return(rmse) #调用LassoCV函数，并进行交叉验证，默认cv=3 model_lasso = LassoCV(alphas = [0.1,1,0.001, 0.0005]).fit(X_train, y) #模型所选择的最优正则化参数alpha print(model_lasso.alpha_) #各特征列的参数值或者说权重参数，为0代表该特征被模型剔除了 print(model_lasso.coef_) #输出看模型最终选择了几个特征向量，剔除了几个特征向量 coef = pd.Series(model_lasso.coef_, index = X_train.columns) print("Lasso picked " + str(sum(coef != 0)) + " variables and eliminated the other " + str(sum(coef == 0)) + " variables") #输出所选择的最优正则化参数情况下的残差平均值，因为是3折，所以看平均值 print(rmse_cv(model_lasso).mean()) #画出特征变量的重要程度，这里面选出前3个重要，后3个不重要的举例 imp_coef = pd.concat([coef.sort_values().head(3), coef.sort_values().tail(3)]) matplotlib.rcParams['figure.figsize'] = (8.0, 10.0) imp_coef.plot(kind = "barh") plt.title("Coefficients in the Lasso Model") plt.show()