Question

我想获得信心，但是当我尝试clf.score(X_test)时，我收到以下错误：

TypeError：score（）至少需要3个参数（给定2个）

这是我的代码：

import cv2, os
from numpy import *
import numpy as np
from PIL import Image
from sklearn.decomposition import PCA
from sklearn.decomposition import RandomizedPCA
from sklearn.naive_bayes import GaussianNB
import matplotlib.pyplot as plt
from sklearn.svm import SVC

cascadeLocation = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadeLocation)

def prepare_dataset(directory):
    paths = [os.path.join(directory, filename) for filename in os.listdir(directory)]
    images = []
    labels = []
    row = 140
    col = 140
    for image_path in paths:

        image_pil = Image.open(image_path).convert('L')

        image = np.array(image_pil, 'uint8')
        nbr = int(os.path.split(image_path)[-1].split('.')[1])
        print(nbr)
        faces = faceCascade.detectMultiScale(image)
        for (x,y,w,h) in faces:
            images.append(image[y:y+col,x:x+row])
            labels.append(nbr)
            cv2.imshow("Reading Faces ",image[y:y+col,x:x+row])
            cv2.waitKey(50)
    return images,labels, row, col

directory = 'dataset'
directory2 = 'dataset2'
images, labels, row, col = prepare_dataset(directory)

n_components = 10
cv2.destroyAllWindows()
pca = PCA(n_components=n_components, whiten=True)

param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
              'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
clf = SVC(kernel='rbf', class_weight='balanced')

testing_data = []
for i in range(len(images)):
    testing_data.append(images[i].flatten())
pca = pca.fit(testing_data)

transformed = pca.transform(testing_data)
clf.fit(transformed,labels)

image_paths = [os.path.join(directory2, filename) for filename in os.listdir(directory2)]
la=[]
for image_path in image_paths:
    pred_image_pil = Image.open(image_path).convert('L')

    pred_image = np.array(pred_image_pil, 'uint8')

    faces = faceCascade.detectMultiScale(pred_image)
    for (x,y,w,h) in faces:
        temp=np.array(pred_image[y:y+col,x:x+row]).reshape((1, -1))

        X_test = pca.transform(temp)

        id = clf.predict(X_test)
        #a=clf.score(X_test)            
        if(id==1):
            id="john"
        elif(id==2):
            id="brad"
        elif(id==3):
            id="scr"
        elif(id==4):
            id="natalie portman"
        elif(id==5):
            id="jennifer lawrence"
        elif(id==6):
            id="van diesel"
        elif(id==7):
            id="jennifer aniston"
        elif(id==8):
            id="leonardo dicaprio"    
        else :
            id="unknown"
        print(id)               
        cv2.imshow("Recognizing Face", pred_image[y: y + h, x: x + w])
        cv2.waitKey(1000)
cv2.destroyAllWindows()

我也试过clf.score(X_test,id)。在这种情况下，所有置信度都取值1.0。

Answer 1

来自documentation（强调我的）：

<强> decision_function(X)
预测样本的置信度分数 样本的置信度得分是该样本与超平面的符号距离。

您必须在代码中将a=clf.score(X_test)更改为a = clf.decision_function(X_test)。

修改

我猜你真的希望通过检查dataset2的图像是否被识别来“手动”验证你的模型。在这种情况下，您需要在代码中引入一些更改：

将他的句子放在外loop循环的最开头：

nbr2 = int(os.path.split(image_path)[-1].split('.')[1])

我假设dataset2的文件命名约定与dataset1的文件命名约定相同，否则您必须相应地重新定义nbr2。

不要将id用作变量名，因为id是Python中的保留字。您可以改为使用idx：

idx = clf.predict(X_test)

用字典替换复合if-elif-else语句（在for循环之外）：

names = {1: "john", 2: "brad", 3: "scr", 4: "natalie portman", 5: "jennifer lawrence", 6: "van diesel", 7: "jennifer aniston", 8: "leonardo dicaprio", }

并将print(id)更改为print(names.get(idx, "unknown"))。

最后，将a=clf.score(X_test)替换为a = clf.score(X_test, nbr2)。

如何使用python获取svm算法在sklearn中的信心

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