我想在自定义RBF内核的scikit-learn中实现SVM，但它显示错误，该怎么办？

Question

import pandas as pd 
import numpy as np 
from sklearn import preprocessing, svm 
from sklearn.svm import SVC 
from sklearn.model_selection import train_test_split 
import math 
import numpy.linalg as la 

df = pd.read_csv("DataWithoutHeader162.csv") 
df.columns = ['Temperature','Humidity','Windspeed','Traffic','PM 2.5'] 
#print(df.head()) 

forecast_col = 'PM 2.5' 
df['label'] = df[forecast_col].shift(1) 
df.fillna(value=-99999, inplace=True) 

X = np.array(df.drop(['label','PM 2.5'] , 1)) 
X = preprocessing.scale(X) 
df.dropna(inplace = True) 

y = np.array(df['label']) 
df.dropna(inplace = True) 

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05) 


#kernel definition 
def radial_basis(gamma=10): 
    return lambda x, y: np.exp(-gamma*la.norm(np.subtract(x, y))) 

#SupportVectorMachine with radial_basis Kernel 
clf_SVM_radial_basis = SVC(kernel = radial_basis()) 
clf_SVM_radial_basis.fit(X_train,y_train) 
confidence3 = clf_SVM_radial_basis.score(X_test,y_test) 
print("Confidence of SVM with radial_basis Kernel = ",(confidence3*100),"%") 

此代码显示错误：

Traceback (most recent call last): 
File "F:\MachineLearningPyCodes\SvmOnDelhiAqiDataPrbf.py", line 68, in 
module 
clf_SVM_radial_basis.fit(X_train,y_train) 
File "C:\Python35\lib\site-packages\sklearn\svm\base.py", line 189, in fit 
fit(X, y, sample_weight, solver_type, kernel, random_seed=seed) 
File "C:\Python35\lib\site-packages\sklearn\svm\base.py", line 230, in 
_dense_fit 
if X.shape[0] != X.shape[1]: 
IndexError: tuple index out of range 

我尝试不同的方法，但我不能给我的数据集格式化为每个需要我猜，请告诉我一个方法来做到这一点。

Answer 1

我也被SVC的kernel参数工作困惑。它不仅仅是内核函数radial_basis;它实际上必须返回克矩阵。您可以在sklearn文档中看到这一点：

http://scikit-learn.org/stable/modules/svm.html#custom-kernels

总结该节：你有两个选择。

（1）任一堵塞格拉姆矩阵直接进入fit()方法（不只是纯X_train），并使用kernel='precomputed';或者

（2）编写一个返回克拉矩阵的函数，然后您可以将该新函数传递给kernel。

这个SO有很好的例子。调整他们写的内容，你可以这样做。我将使用第二种方法，并且我会保留原始的radial_basis以供说明。

def radial_basis(x, y, gamma=10): 
    return np.exp(-gamma * la.norm(np.subtract(x, y))) 

def proxy_kernel(X, Y, K=radial_basis): 
    """Another function to return the gram_matrix, 
    which is needed in SVC's kernel or fit 
    """ 
    gram_matrix = np.zeros((X.shape[0], Y.shape[0])) 
    for i, x in enumerate(X): 
     for j, y in enumerate(Y): 
      gram_matrix[i, j] = K(x, y) 
    return gram_matrix 

clf_SVM_radial_basis = SVC(kernel=proxy_kernel) # Note that it's proxy_kernel here now 
clf_SVM_radial_basis.fit(X_train, y_train)