#把离散特征和连续特征拼接起来
x_vec = np.concatenate((x_vec_con, x_vec_dis), axis=1)
#对于目标进行预测
y_registered = bike_rel['registered'].values.astype(float)
y_casual = bike_rel['casual'].values.astype(float)
y = np.stack((y_registered, y_casual), axis=1)
#建立模型进行预测
from sklearn.linear_model import MultiTaskLassoCV
from sklearn.model_selection import train_test_split
from sklearn.linear_model import MultiTaskElasticNetCV
x1, x2, y1, y2 = train_test_split(x_vec, y, test_size=0.2, random_state=20)
############ Lasso
mtl = MultiTaskLassoCV(alphas=np.logspace(-3, -1, 3), cv=8, verbose=3)
mtl.fit(x1, y1)
mtl.score(x1, y1)
mtl.score(x2, y2)
############ ElasticNetCV
mte = MultiTaskElasticNetCV(l1_ratio=np.logspace(-3, -1, 3),
alphas=np.logspace(-3, -1, 3),
cv=8,
verbose=3)
mte.fit(x1, y1)
mtl.score(x1, y1)
mtl.score(x2, y2)
# 在初始化MultiTaskLassoCV类时, 提供一组备选的α值, MultiTaskLassoCV类会帮我们选择一个合适的α值.
multiTaskLassoCV = MultiTaskLassoCV(
alphas=[0.01, 0.1, 0.5, 1, 3, 5, 7, 10, 20, 100], cv=5)
# 拟合训练集
multiTaskLassoCV.fit(train_X, train_Y)
# 打印最优的α值
print "最优的alpha值: ", multiTaskLassoCV.alpha_
# 打印模型的系数
print "系数:", multiTaskLassoCV.coef_
print "截距:", multiTaskLassoCV.intercept_
print '训练集R2: ', r2_score(train_Y, multiTaskLassoCV.predict(train_X))
# 对于线性回归模型, 一般使用均方误差(Mean Squared Error,MSE)或者
# 均方根误差(Root Mean Squared Error,RMSE)在测试集上的表现来评该价模型的好坏.
test_Y_pred = multiTaskLassoCV.predict(test_X)
print "测试集得分:", multiTaskLassoCV.score(test_X, test_Y)
print "测试集MSE:", mean_squared_error(test_Y, test_Y_pred)
print "测试集RMSE:", np.sqrt(mean_squared_error(test_Y, test_Y_pred))
print "测试集R2:", r2_score(test_Y, test_Y_pred)
tss, rss, ess, r2 = xss(Y, multiTaskLassoCV.predict(X))
print "TSS(Total Sum of Squares): ", tss
print "RSS(Residual Sum of Squares): ", rss
print "ESS(Explained Sum of Squares): ", ess
print "R^2: ", r2
print "\n**********测试MultiTaskElasticNet类**********"
# 在初始化MultiTaskElasticNet类时, 指定超参数α和ρ, 默认值分别是1.0和0.5.
multiTaskElasticNet = MultiTaskElasticNet(alpha=0.01, l1_ratio=0.7)
# 拟合训练集
multiTaskElasticNet.fit(train_X, train_Y)
