Question

我正在尝试按照本教程编写SVM，但使用我自己的数据。 https://pythonprogramming.net/preprocessing-machine-learning/?completed=/linear-svc-machine-learning-testing-data/

我一直收到这个错误：

ValueError: The number of classes has to be greater than one; got 1

我的代码是：

header1 = ["Number of Sides", "Standard Deviation of Number of Sides/Perimeter",
      "Standard Deviation of the Angles", "Largest Angle"]
header2 = ["Label"]
features = header1
features1 = header2

def Build_Data_Set():

    data_df = pd.DataFrame.from_csv("featureVectors.csv")
    #data_df = data_df[:3]
    X = np.array(data_df[features].values)

    data_df2 = pd.DataFrame.from_csv("labels.csv")
    y = np.array(data_df2[features1].replace("Circle",0).replace("Triangle",1)
                 .replace("Square",2).replace("Parallelogram",3)
                 .replace("Rectangle",4).values.tolist())

    return X,y

def Analysis():

    test_size = 4
    X,y = Build_Data_Set()
    print(len(X))

    clf = svm.SVC(kernel = 'linear', C = 1.0)
    clf.fit(X[:-test_size],y[:-test_size])

    correct_count = 0

    for x in range(1, test_size+1):
            if clf.predict(X[-x])[0] == y[-x]:
                correct_count += 1

    print("Accuracy:", (correct_count/test_size) * 100.00)

我用于X的功能数组如下所示：

[[4, 0.001743713493735165, 0.6497055601752815, 90.795723552739275], 
 [4, 0.0460937435599832, 0.19764217920409227, 90.204147248752378], 
 [1, 0.001185534503063044, 0.3034913722821194, 60.348908179729023], 
 [1, 0.015455289770298222, 0.8380914254332884, 109.02120657826231], 
 [3, 0.0169961646358455, 0.2458746325894564, 136.83829993466398]]

我在Y中使用的标签数组如下所示：

 ['Square', 'Square', 'Circle', 'Circle', 'Triangle']

到目前为止，我只使用了5组数据，因为我知道程序无效。

我已经在他们的csv文件中附加了值的图片，以防万一。

featureVectors.csv

Labels.csv

Printing X.shape and y.shape and showing the full error

Answer 1

在我看来问题就是这一行：

clf.fit(X[:-test_size],y[:-test_size])

由于X有5行，并且你已将test_size设置为4，因此X [： - test_size]只给出一行（第一行）。阅读python的切片表示法，如果这让您感到困惑：Explain Python's slice notation

因此训练集中只有一个类（在这种情况下为“Square”）。我想知道你是否打算做X[:test_size]给出前4行。无论如何，尝试对更大的数据集进行培训。

我可以使用以下内容重现您的错误：

import numpy as np
from sklearn import svm
X = np.array([[4, 0.001743713493735165, 0.6497055601752815, 90.795723552739275], 
 [4, 0.0460937435599832, 0.19764217920409227, 90.204147248752378], 
 [1, 0.001185534503063044, 0.3034913722821194, 60.348908179729023], 
 [1, 0.015455289770298222, 0.8380914254332884, 109.02120657826231], 
 [3, 0.0169961646358455, 0.2458746325894564, 136.83829993466398]])

y =  np.array(['Square', 'Square', 'Circle', 'Circle', 'Triangle'])
print X.shape # (5,4)
print y.shape # (5,)

clf = svm.SVC(kernel='linear',C=1.0)

test_size = 4
clf.fit(X[:-test_size],y[:-test_size])

ValueError：类的数量必须大于1;得到1

1 个答案: