ValueError:类的数量必须大于一;得到了1 [英] ValueError: The number of classes has to be greater than one; got 1
问题描述
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 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/
我不断收到此错误:
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:
My array for features which is used for X looks like this:
[[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:
My array for labels used in Y looks like this:
['Square', 'Square', 'Circle', 'Circle', 'Triangle']
到目前为止,我只使用了5组数据,因为我知道该程序无法正常工作.
I have only used 5 sets of data so far because I knew the program wasn't working.
为方便起见,我在其csv文件中附加了值的图片.
I have attached pictures of the values in their csv files in case that helps.
推荐答案
我觉得问题出在这行:
clf.fit(X[:-test_size],y[:-test_size])
由于X具有5行,并且您已将test_size设置为4,所以X [:-test_size]仅给出一行(第一行).如果这使您感到困惑,请仔细阅读python的切片符号:解释Python的切片符号
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
因此,训练集中只有一个班级(在这种情况下为平方").我想知道您是否打算执行X[:test_size]
这将给出前4行.无论如何,请尝试在更大的数据集上进行训练.
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.
我可以通过以下方式重现您的错误:
I can reproduce your error with the following:
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])
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