sklearn.svm.SVCのカスタムカーネル関数をどのように定義できますか？

Question

私はscikit-learnを使ってPythonで株式予測システムを作ろうとしています。

import numpy as np 
import pandas as pd 
from sklearn.preprocessing import StandardScaler 
from sklearn.metrics import accuracy_score 
import matplotlib.pyplot as plt 
from sklearn import svm,preprocessing 
from sklearn.metrics import precision_recall_fscore_support 
import pandas as pd 
import time 
##import statistics 


def my_kernel(X, Y): 
    """ 
    We create a custom kernel: 

       (2 0) 
    k(X, Y) = X ( ) Y.T 
       (0 1) 
    """ 
    M = np.array([[2, 0], [0, 1.0]]) 
    return np.dot(np.dot(X, M), Y.T) 



FEATURES = ['DE Ratio', 
      'Trailing P/E', 
      'Price/Sales', 
      'Price/Book', 
      'Profit Margin', 
      'Operating Margin', 
      'Return on Assets', 
      'Return on Equity', 
      'Revenue Per Share', 
      'Market Cap', 
      'Enterprise Value', 
      'Forward P/E', 
      'PEG Ratio', 
      'Enterprise Value/Revenue', 
      'Enterprise Value/EBITDA', 
      'Revenue', 
      'Gross Profit', 
      'EBITDA', 
      'Net Income Avl to Common ', 
      'Diluted EPS', 
      'Earnings Growth', 
      'Revenue Growth', 
      'Total Cash', 
      'Total Cash Per Share', 
      'Total Debt', 
      'Current Ratio', 
      'Book Value Per Share', 
      'Cash Flow', 
      'Beta', 
      'Held by Insiders', 
      'Held by Institutions', 
      'Shares Short (as of', 
      'Short Ratio', 
      'Short % of Float', 
      'Shares Short (prior '] 

def Build_Data_Set(): 
    data_df = pd.DataFrame.from_csv("key_stats.csv") 
    data_df = data_df.reindex(np.random.permutation(data_df.index)) 
    ##print data_df 
    X = np.array(data_df[FEATURES].values) 

    y = (data_df["Status"] 
     .replace("underperform",0) 
     .replace("outperform",1) 
     .values.tolist()) 

    X = preprocessing.scale(X) 
    X = StandardScaler().fit_transform(X) 
    Z0 = np.array(data_df["stock_p_hancge"]) 
    Z1 = np.array(data_df["sp500_p_change"]) 
    return X,y,Z0,Z1 


def mykernel(X, Y,gamma=None): 

    X, Y = check_pairwise_arrays(X, Y) 
    if gamma is None: 
     gamma = 1.0/X.shape[1] 

    K = euclidean_distances(X, Y, squared=True) 
    k *= -gamma 
    np.exp(K, K) # exponentiate K in-place 
    return safe_sparse_dot(X, Y.T, dense_output=True) + k 

size = 2094 
invest_amount = 10000 
total_invests = 0 
if_market = 0 
if_strat = 0 
X, y , Z0,Z1= Build_Data_Set() 
print(len(X)) 
test_size = len(X) - size -1 

start = time.clock() 
clf = svm.SVC(kernel="mykernel") 
clf.fit(X[:size],y[:size]) 

y_pred = clf.predict(X[size+1:]) 
y_true = y[size+1:] 
time_taken = time.clock()-start 
print time_taken,"Seconds" 

for x in range(1, test_size+1): 
    if y_pred[-x] == 1: 
     invest_return = invest_amount + (invest_amount * (Z0[-x]/100)) 
     market_return = invest_amount + (invest_amount * (Z1[-x]/100)) 
     total_invests += 1 
     if_market += market_return 
     if_strat += invest_return 

print accuracy_score(y_true, y_pred) 

print precision_recall_fscore_support(y_true, y_pred, average='macro') 

print "Total Trades:", total_invests 
print "Ending with Strategy:",if_strat 
print "Ending with Market:",if_market 

compared = ((if_strat - if_market)/if_market) * 100.0 
do_nothing = total_invests * invest_amount 

avg_market = ((if_market - do_nothing)/do_nothing) * 100.0 
avg_strat = ((if_strat - do_nothing)/do_nothing) * 100.0 


print "Compared to market, we earn",str(compared)+"% more" 
print "Average investment return:", str(avg_strat)+"%" 
print "Average market return:", str(avg_market)+"%" 

事前に定義されたカーネルが動作しているが、私のカスタムカーネルのために、私はエラーを取得しています：

ValueError: 'mykernel' is not in list 

上記のコードは動作するはずのようにそれはそう公式ドキュメントによると、ここに私のコードです。

Answer 1

あなたはすなわち、kernel=パラメータだけではなく、関数名としてカーネル関数自体を渡す必要があります：むしろ

clf = svm.SVC(kernel="mykernel")