import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNetCV
import sklearn.datasets
from pprint import pprint
from sklearn.preprocessing import PolynomialFeatures, StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import RandomForestRegressor
import warnings
def not_empty(s):
return s != ''
if __name__ == "__main__":
data = sklearn.datasets.load_boston()
x = np.array(data.data)
y = np.array(data.target)
print('样本个数:%d, 特征个数:%d' % x.shape)
print(y.shape)
y = y.ravel()
x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7, random_state=0)
model = Pipeline([
('ss', StandardScaler()),
('poly', PolynomialFeatures(degree=3, include_bias=True)),
('linear', ElasticNetCV(l1_ratio=[0.1, 0.3, 0.5, 0.7, 0.99, 1], alphas=np.logspace(-3, 2, 5),
fit_intercept=False, max_iter=1e3, cv=3))
])
# model = RandomForestRegressor(n_estimators=50, criterion='mse')
print('开始建模...')
model.fit(x_train, y_train)
linear = model.get_params('linear')['linear']
print(u'超参数:', linear.alpha_)
print(u'L1 ratio:', linear.l1_ratio_)
print(u'系数:', linear.coef_.ravel())
order = y_test.argsort(axis=0)
y_test = y_test[order]
x_test = x_test[order, :]
y_pred = model.predict(x_test)
r2 = model.score(x_test, y_test)
mse = mean_squared_error(y_test, y_pred)
print('R2:', r2)
print('均方误差:', mse)
t = np.arange(len(y_pred))
mpl.rcParams['font.sans-serif'] = ['simHei']
mpl.rcParams['axes.unicode_minus'] = False
plt.figure(facecolor='w')
plt.plot(t, y_test, 'r-', lw=2, label='真实值')
plt.plot(t, y_pred, 'g-', lw=2, label='估计值')
plt.legend(loc='best')
plt.title('波士顿房价预测', fontsize=18)
plt.xlabel('样本编号', fontsize=15)
plt.ylabel('房屋价格', fontsize=15)
plt.grid()
plt.show()
超参数: 0.31622776601683794
L1 ratio: 0.99
R2: 0.7722034192609936
均方误差: 18.967596568189173
使用RandomForestRegressor
R2: 0.7986958266918252
均方误差: 16.761692973684216