は、SVCスコア機能が

Question

私はこの機械学習プラットフォームを実行しようとしています動作させることはできませんし、私は次のエラーを取得する：

ValueError: X.shape[1] = 574 should be equal to 11, the number of features at training time 

マイコード：

from pylab import * 
from sklearn.svm import SVC 
from sklearn.ensemble import RandomForestClassifier 
import numpy as np 

X = list() 
Y = list() 
validationX = list() 
validationY = list() 
file = open ('C:\\Users\\User\\Desktop\\csci4113\\project1\\whitewineTraining.txt','r') 
for eachline in file: 
    strArray = eachline.split(";") 
    row = list() 
    for i in range(len(strArray) - 1): 
     row.append(float(strArray[i])) 
    X.append(row) 
    if (int(strArray[-1]) > 6): 
     Y.append(1) 
    else: 
     Y.append(0) 
file2 = open ('C:\\Users\\User\\Desktop\\csci4113\\project1\\whitewineValidation.txt', 'r') 
for eachline in file2: 
    strArray = eachline.split(";") 
    row2 = list() 
    for i in range(len(strArray) - 1): 
     row2.append(float(strArray[i])) 
    validationX.append(row2)  
    if (int(strArray[-1]) > 6): 
     validationY.append(1) 
    else: 
     validationY.append(0) 

X = np.array(X) 
print (X) 
Y = np.array(Y) 
print (Y) 
validationX = np.array(validationX) 
validationY = np.array(validationY) 

clf = svm.SVC() 
clf.fit(X,Y) 
result = clf.predict(validationX) 
clf.score(result, validationY) 

プログラムの目的fit（）コマンドからモデルを構築します。ここで、validationYの検証セットと比較し、私たちの機械学習モデルの妥当性を確認することができます。残りのコンソール出力は以下の通りです：Xは紛らわしく11x574アレイです！

[[ 7.   0.27   0.36  ..., 3.   0.45   8.8  ] 
[ 6.3   0.3   0.34  ..., 3.3   0.49   9.5  ] 
[ 8.1   0.28   0.4  ..., 3.26   0.44  10.1  ] 
..., 
[ 6.3   0.28   0.22  ..., 3.   0.33  10.6  ] 
[ 7.4   0.16   0.33  ..., 3.04   0.68  10.5  ] 
[ 8.4   0.27   0.3  ..., 2.89   0.3 
    11.46666667]] 
[0 0 0 ..., 0 1 0] 
C:\Users\User\Anaconda3\lib\site-packages\sklearn\utils\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample. 
    DeprecationWarning) 
Traceback (most recent call last): 

    File "<ipython-input-68-31c649fe24b3>", line 1, in <module> 
    runfile('C:/Users/User/Desktop/csci4113/project1/program1.py', wdir='C:/Users/User/Desktop/csci4113/project1') 

    File "C:\Users\User\Anaconda3\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 714, in runfile 
    execfile(filename, namespace) 

    File "C:\Users\User\Anaconda3\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 89, in execfile 
    exec(compile(f.read(), filename, 'exec'), namespace) 

    File "C:/Users/User/Desktop/csci4113/project1/program1.py", line 43, in <module> 
    clf.score(result, validationY) 

    File "C:\Users\User\Anaconda3\lib\site-packages\sklearn\base.py", line 310, in score 
    return accuracy_score(y, self.predict(X), sample_weight=sample_weight) 

    File "C:\Users\User\Anaconda3\lib\site-packages\sklearn\svm\base.py", line 568, in predict 
    y = super(BaseSVC, self).predict(X) 

    File "C:\Users\User\Anaconda3\lib\site-packages\sklearn\svm\base.py", line 305, in predict 
    X = self._validate_for_predict(X) 

    File "C:\Users\User\Anaconda3\lib\site-packages\sklearn\svm\base.py", line 474, in _validate_for_predict 
    (n_features, self.shape_fit_[1])) 

ValueError: X.shape[1] = 574 should be equal to 11, the number of features at training time 


runfile('C:/Users/User/Desktop/csci4113/project1/program1.py', wdir='C:/Users/User/Desktop/csci4113/project1') 
10 
[[ 7.   0.27   0.36  ..., 3.   0.45   8.8  ] 
[ 6.3   0.3   0.34  ..., 3.3   0.49   9.5  ] 
[ 8.1   0.28   0.4  ..., 3.26   0.44  10.1  ] 
..., 
[ 6.3   0.28   0.22  ..., 3.   0.33  10.6  ] 
[ 7.4   0.16   0.33  ..., 3.04   0.68  10.5  ] 
[ 8.4   0.27   0.3  ..., 2.89   0.3 
    11.46666667]] 
[0 0 0 ..., 0 1 0] 
C:\Users\User\Anaconda3\lib\site-packages\sklearn\utils\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample. 
    DeprecationWarning) 
Traceback (most recent call last): 

    File "<ipython-input-69-31c649fe24b3>", line 1, in <module> 
    runfile('C:/Users/User/Desktop/csci4113/project1/program1.py', wdir='C:/Users/User/Desktop/csci4113/project1') 

    File "C:\Users\User\Anaconda3\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 714, in runfile 
    execfile(filename, namespace) 

    File "C:\Users\User\Anaconda3\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 89, in execfile 
    exec(compile(f.read(), filename, 'exec'), namespace) 

    File "C:/Users/User/Desktop/csci4113/project1/program1.py", line 46, in <module> 
    clf.score(result, validationY) 

    File "C:\Users\User\Anaconda3\lib\site-packages\sklearn\base.py", line 310, in score 
    return accuracy_score(y, self.predict(X), sample_weight=sample_weight) 

    File "C:\Users\User\Anaconda3\lib\site-packages\sklearn\svm\base.py", line 568, in predict 
    y = super(BaseSVC, self).predict(X) 

    File "C:\Users\User\Anaconda3\lib\site-packages\sklearn\svm\base.py", line 305, in predict 
    X = self._validate_for_predict(X) 

    File "C:\Users\User\Anaconda3\lib\site-packages\sklearn\svm\base.py", line 474, in _validate_for_predict 
    (n_features, self.shape_fit_[1]))`` 

Answer 1

あなたは、単にdocumentationは明らかに

score(X, y, sample_weight=None)

X : array-like, shape = (n_samples, n_features) Test samples.

を述べ、あなたが予測を渡し、機能を獲得するために間違ったオブジェクトを渡している代わりに、これ

result = clf.predict(validationX) 
clf.score(result, validationY) 

は無効であり、そしてべきただ

0あなたには、いくつかの スコアラー、およびない 分類器を使用する場合

clf.score(validationX, validationY) 

は何をしようとしたことは罰金になり、分類器.score方法は、このようにあなたが引数として生データを渡し、自分で.predictを呼び出します。