我試圖用自定義RBF內核實現scikit-learn中的SVM，但它顯示錯誤，該怎么辦？

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),"%")

此代碼顯示錯誤：

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  

我嘗試了不同的方法，但我無法按照我的要求格式化我的數據集，請告訴我一個方法來做到這一點。

Answer 1

我對SVC kernel論證的工作原理感到困惑。 你擁有它不僅僅是內核函數radial_basis ; 它實際上必須返回Gram矩陣 。 您可以在sklearn文檔中看到：

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

總結該部分：您有兩個選擇。

（1）將Gram矩陣直接插入fit()方法（不僅僅是普通的X_train ），並使用kernel='precomputed' ; 要么

（2）編寫一個返回Gram矩陣的函數，然后將該函數傳遞給kernel 。

這個SO有很好的例子。 適應他們所寫的內容，你可以這樣做。 我將使用第二種方法，我會保留你原來的radial_basis作為插圖。

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)