【发布时间】:2021-04-20 21:11:40
【问题描述】:
我正在通过 for 循环对机器学习算法进行超调优。 但我不知道如何将其保存到 csv 或 excel 文件中,因为它显示在终端输出中...帮帮我..在这里我分享代码以供参考。
def random_search():
options = create_opts()
# kernel
kernel_opts = np.array(["rbf", "poly", "linear"]) # 3
# C
C_opts = np.array([0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000]) # 15
# C_linear_opts = np.array([0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100]) # 10
# epsilon
epsilon_opts = np.array([.01, .02, .03, .04, .05, .06, .07, .08, .09, 0.1]) # 10
# gamma
gamma_opts = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]) # 9
# n_iter = len(kernel_opts)*len(C_opts)*len(epsilon_opts)*len(gamma_opts)
for kernel in kernel_opts:
for C in C_opts:
for epsilon in epsilon_opts:
for gamma in gamma_opts:
run_svr(options.random_state, options.poly_degree, kernel=kernel, C=C, epsilon=epsilon, gamma=gamma)
【问题讨论】:
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您可以将标准输出重定向到文件,请参阅stackoverflow.com/questions/7152762/…
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run_svr() 函数的结果是什么类型的输出?我的意思是 run_svr 函数的返回类型是什么?是清单吗?一个整数?
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@ Soroosh Noorzad k-fold mean: [ 0.79958758 9.56594248 7.53763915 29.17127736] k-fold standard deviation: [0.02541773 0.54930184 0.53217536 2.34891345] Finished running SVR for compressive data with random_state=0, poly_degree=1, kernel =rbf, C=0.1, epsilon=0.04, gamma=0.2
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创建一个数据框然后使用 df.to_csv
标签: python machine-learning optimization