Python Sklearn set_params 只需要 1 个参数？
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Sklearn set_params takes exactly 1 argument?
提问by Leonid
I'm trying to use SkLearn Bayes classification.
我正在尝试使用 SkLearn Bayes 分类。
gnb = GaussianNB()
gnb.set_params('sigma__0.2')
gnb.fit(np.transpose([xn, yn]), y)
But I get:
但我得到：
set_params() takes exactly 1 argument (2 given)
now I try to use this code:
现在我尝试使用此代码：
gnb = GaussianNB()
arr = np.zeros((len(labs),len(y)))
arr.fill(sigma)
gnb.set_params(sigma_ = arr)
And get:
并得到：
ValueError: Invalid parameter sigma_ for estimator GaussianNB
Is it wrong parameter name or value?
是错误的参数名称或值吗？
采纳答案by Mezgrman
set_params()takes only keyword arguments, as can be seen in the documentation. It is declared as set_params(**params).
set_params()只接受关键字参数，如文档中所示。它被声明为set_params(**params).
So, in order to make it work, you need to call it with keyword arguments only: gnb.set_params(some_param = 'sigma__0.2')
因此，为了使其工作，您只需要使用关键字参数调用它： gnb.set_params(some_param = 'sigma__0.2')
回答by Salvador Dali
It is written in documentation that the syntax is:
它写在文档中，语法是：
set_params(**params)
设置参数（**参数）
These two stars mean that you need to give keyword arguments (read about it here). So you need to do it in the form your_param = 'sigma__0.2'
这两颗星意味着您需要提供关键字参数（在此处阅读）。所以你需要在form your_param = 'sigma__0.2'
回答by Kam Sen
I just stumbled upon this, so here is a solution for multiple arguments from a dictionary:
我只是偶然发现了这一点，所以这里是字典中多个参数的解决方案：
from sklearn import svm
params_svm = {"kernel":"rbf", "C":0.1, "gamma":0.1, "class_weight":"auto"}
clf = svm.SVC()
clf.set_params(**params_svm)
回答by Pedro Baracho
The problem here is that GaussianNBhas only one parameter and that is priors.
这里的问题是它GaussianNB只有一个参数，即priors.
From the documentation
从文档
class sklearn.naive_bayes.GaussianNB(priors=None)
The sigmaparameter you are looking for is, in fact, an attribute of the class GaussianNB, and cannot be accessed by the methods set_params()and get_params().
sigma您要查找的参数实际上是 GaussianNB 类的一个属性，无法通过方法set_params()和访问get_params()。
You can manipulate sigmaand thetaattributes, by feeding some Priorsto GaussianNB or by fitting it to a specific training set.
您可以通过向 GaussianNB提供一些数据或将其拟合到特定训练集来操作sigma和theta属性Priors。
回答by nobar
sigma_is an instance attribute which is computed during training. You probably aren't intended to modify it directly.
sigma_是在训练期间计算的实例属性。您可能不打算直接修改它。
from sklearn.naive_bayes import GaussianNB
import numpy as np
X = np.array([[-1,-1],[-2,-1],[-3,-2],[1,1],[2,1],[3,2]])
y = np.array([1,1,1,2,2,2])
gnb = GaussianNB()
print gnb.sigma_
Output:
输出：
AttributeError: 'GaussianNB' object has no attribute 'sigma_'
More code:
更多代码：
gnb.fit(X,y) ## training
print gnb.sigma_
Output:
输出：
array([[ 0.66666667, 0.22222223],
[ 0.66666667, 0.22222223]])
After training, it is possible to modify the sigma_value. This might affect the results of prediction.
训练后，可以修改该sigma_值。这可能会影响预测结果。
gnb.sigma_ = np.array([[1,1],[1,1]])
