ValueError：类的数量必须大于1;得到1

I am trying to write an SVM following this tutorial but using my own data. https://pythonprogramming.net/preprocessing-machine-learning/?completed=/linear-svc-machine-learning-testing-data/

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/

I keep getting this error:

我一直收到这个错误:

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

My code is:

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)

My array for features which is used for X looks like this:

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]]

My array for labels used in Y looks like this:

我在Y中使用的标签数组如下所示:

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

I have only used 5 sets of data so far because I knew the program wasn't working.

到目前为止,我只使用了5组数据,因为我知道该程序无效。

I have attached pictures of the values in their csv files in case that helps.

我已经附加了他们的csv文件中的值的图片,以防有帮助。

featureVectors.csv

Labels.csv

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

打印X.shape和y.shape并显示完整错误

1 个解决方案

#1

Looks to me like the problem is this line:

在我看来问题就是这一行:

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

Since X has 5 rows, and you've set test_size to 4, X[:-test_size] only gives one row (the first one). Read up on python's slice notation, if this confuses you: Explain Python's slice notation

由于X有5行,并且您已将test_size设置为4,因此X [: - test_size]仅提供一行(第一行)。阅读python的切片表示法,如果这让您感到困惑:解释Python的切片表示法

So there is only one class in the training set ("Square" in this case). I wonder if you meant to do X[:test_size] which would give the first 4 rows. Anyway, try training on a bigger data set.

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],它会给出前4行。无论如何,尝试对更大的数据集进行培训。

I can reproduce your error with the following:

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])

#1