通过sklearn中的stdin设置决策函数的权重 [英] Set the weights of decision functions through stdin in Sklearn

问题描述

有没有一种方法可以在我的脚本中将系数输入到 SVC 的 clf 中，然后应用 clf.score() 或 clf.predict() 函数进一步测试?

Is there a method that I can input the coefficients to the clf of SVC in my script, then apply clf.score() or clf.predict() function for further test?

目前我正在使用 joblib.dump(clf,'file.plk') 来保存经过训练的 clf 的所有信息.但这涉及磁盘写入/读取.如果我能定义一个带有两个数组的 clf 表示支持向量 (clf.support_vectors_)、权重 (clf.coef_code>/clf.dual_coef_) 和偏差 (clf.intercept_).

Currently I am using joblib.dump(clf,'file.plk') to save all the information of a trained clf. But this involves the disk writing/reading. It will be helpful for me if I can just define a clf with two arrays representing the support vector (clf.support_vectors_), weights (clf.coef_/clf.dual_coef_), and bias (clf.intercept_) respectively.

推荐答案

这一行 调用来自 libsvm 的预测函数.它看起来像这样(但请看一下整个函数_dense_predict):

This line calls the prediction function from libsvm. It looks like this (but please take a look at the whole function _dense_predict):

libsvm.predict(
        X, self.support_, self.support_vectors_, self.n_support_,
        self.dual_coef_, self._intercept_,
        self.probA_, self.probB_, svm_type=svm_type, kernel=kernel,
        degree=self.degree, coef0=self.coef0, gamma=self._gamma,
        cache_size=self.cache_size)

您可以使用此行并直接为其提供所有相关信息，从而获得原始预测.为此，您必须导入 libsvm from sklearn.svm import libsvm.如果您的初始拟合分类器称为 svc，那么您可以通过将所有 self 关键字替换为 svc 并保持价值.如果 svc._impl 给你 "c_svc"，那么你设置 svm_type=0.

You can use this line and give it all the relevant information directly and will obtain a raw prediction. In order to do this, you must import the libsvm from sklearn.svm import libsvm. If your initial fitted classifier is called svc, then you can obtain all the relevant information from it by replacing all the self keywords with svc and keeping the values. If svc._impl gives you "c_svc", then you set svm_type=0.

请注意，在 _dense_predict 函数的开头，您有 X = self._compute_kernel(X).如果你的数据是X，那么你需要通过做K = svc._compute_kernel(X)来转换它，并调用libsvm.predictK 作为第一个参数的函数

Note that at the beginning of the _dense_predict function you have X = self._compute_kernel(X). If your data is X, then you need to transform it by doing K = svc._compute_kernel(X), and call the libsvm.predict function with K as the first argument

评分与这一切无关.看看 sklearn.metrics，你会在那里找到例如accuracy_score，这是 SVM 中的默认分数.

Scoring is independent from all this. Take a look at sklearn.metrics, where you will find e.g. the accuracy_score, which is the default score in SVM.

这当然是一种有点次优的做事方式，但在这种特定情况下，如果不可能(我没有非常努力地检查)设置系数，则进入代码并查看它的作用并提取相关部分肯定是一种选择.

This is of course a somewhat suboptimal way of doing things, but in this specific case, if is impossible (I didn't check very hard) to set coefficients, then going into the code and seeing what it does and extracting the relevant part is surely an option.

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