ValueError: Found input variables with inconsistent numbers of samples: [218, 30]

Question

我正在使用线性 SVC 进行面部识别训练，我的数据集是 870x22。我有 29 个不同的人的 30 帧，我在图像中使用 22 个简单值像素来识别面部图像，说 22 个像素是我的特征。此外，当我调用 train_test_split() 时，它会给我一个大小为 218x22 的 X_test 和大小为 218 的 y_test。一旦我训练了分类器，我就会尝试运行一张新面孔 (30x22) 矩阵的图像，它给了我错误：

ValueError: Found input variables with inconsistent numbers of samples: [218, 30]

代码如下：

import sklearn
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score, f1_score

    img_amount = 30
    target = np.asarray([1]*img_amount + [2]*img_amount + [3]*img_amount + [4]*img_amount + [5]*img_amount + [6]*img_amount + [7]*img_amount + [8]*img_amount + [9]*img_amount + [10]*img_amount + [11]*img_amount + [12]*img_amount + [13]*img_amount + [14]*img_amount + [15]*img_amount + [16]*img_amount + [17]*img_amount + [18]*img_amount + [19]*img_amount + [20]*img_amount + [21]*img_amount + [22]*img_amount + [23]*img_amount + [24]*img_amount + [25]*img_amount + [26]*img_amount + [27]*img_amount + [28]*img_amount + [29]*img_amount)   
    dataset= dataset[:, 0:22]
        
        svc_1 = SVC(kernel='linear', C=0.00005)
        X_train, X_test, y_train, y_test = train_test_split( dataset, target, test_size=0.25, random_state=0)
        
        def train(clf, X_train, X_test, y_train, y_test):
            
            clf.fit(X_train, y_train)
            print ("Accuracy on training set:")
            print (clf.score(X_train, y_train))
            print ("Accuracy on testing set:")
            print (clf.score(X_test, y_test))
            
            y_pred = clf.predict(X_test)
            
            print ("Classification Report:")
            print (metrics.classification_report(y_test, y_pred))
            print ("Confusion Matrix:")
            print (metrics.confusion_matrix(y_test, y_pred))
    
    
    
        train(svc_1, X_train, X_test, y_train, y_test)
    
    
print ("Classification Report:")
print (metrics.classification_report(y_test, new_face_img))

为了不在视觉上污染问题，我将 new_face_img 的矩阵上传到 pastebin：https://pastebin.com/uRbvv5jD

Link 数据集：Dataset

它们只是数组，可以直接传递给它们的变量

出现错误的行是我尝试预测新样本时：

predictions = svc_1.predict(new_face_img) 
print ("Classification Report:")
->>>>print (metrics.classification_report(y_test, predictions))

predictions = svc_1.predict(michael_ocluded_array) 
expected=np.ones(len(michael_ocluded_array))
print ("Confusion Matrix:")
print (metrics.confusion_matrix(expected, predictions))

Confusion Matrix: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) in 1 predictions = svc_1.predict(michael_ocluded_array) 2 print ("Confusion Matrix:") ----> 3 print (metrics.classification_report(y_test, predictions))

C:\ProgramData\Miniconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs) 70 FutureWarning) 71 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)}) ---> 72 return f(**kwargs) 73 return inner_f 74

C:\ProgramData\Miniconda3\lib\site-packages\sklearn\metrics_classification.py in classification_report(y_true, y_pred, labels, target_names, sample_weight, digits, output_dict, zero_division) 1927 """
1928 -> 1929 y_type, y_true, y_pred = _check_targets(y_true, y_pred) 1930 1931 labels_given = True

C:\ProgramData\Miniconda3\lib\site-packages\sklearn\metrics_classification.py in _check_targets(y_true, y_pred) 79 y_pred : array or indicator matrix 80 """ ---> 81 check_consistent_length(y_true, y_pred) 82 type_true = type_of_target(y_true) 83 type_pred = type_of_target(y_pred)

C:\ProgramData\Miniconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays) 253 uniques = np.unique(lengths) 254 if len(uniques) > 1: --> 255 raise ValueError("Found input variables with inconsistent numbers of" 256 " samples: %r" % [int(l) for l in lengths]) 257

ValueError: Found input variables with inconsistent numbers of samples: [218, 30]

Answer 1

这是问题所在：

predictions = svc_1.predict(new_face_image) 
print ("Confusion Matrix:")
print (metrics.confusion_matrix(y_test, predictions))

您正在预测 new_face_image 并使用您的测试数据集进行预测。

predictions = svc_1.predict(new_face_image) 
# change this to what you expect but shape=(30,)
expected=np.ones(len(new_face_image))
print ("Confusion Matrix:")
print (metrics.confusion_matrix(expected, predictions))

Edited for validation with dataset test data:

predictions = svc_1.predict(x_test) 
print ("Confusion Matrix:")
print (metrics.confusion_matrix(y_test, predictions))

ValueError: Found input variables with inconsistent numbers of samples: [218, 30]

ValueError: Found input variables with inconsistent numbers of samples: [218, 30]

python

machine-learning

scikit-learn

svm

train-test-split