SKlearn python库中的精度，召回率，准确度，f1_measure等度量标准得分相同，即不变

在这个项目中，我使用下面给出的不同分类器来分类手语，如knn，svm，naive bayes等，并使用f1测量，准确度，精度和使用sklearn库调用来测量他们的分数，但它们都是相同的，即个别方法f1_measure =精度=精度=召回 . 请帮忙 .

import pandas as pd
from numpy._distributor_init import NUMPY_MKL
from sklearn import svm
from sklearn.naive_bayes import GaussianNB as nb
from sklearn.neighbors import KNeighborsClassifier as knn
from sklearn.linear_model import LogisticRegression as lr
import numpy as np
import sklearn.metrics as sm

train = pd.read_csv("train60.csv")
print(train.head())
y = np.array(train.pop('label'))
x = np.array(train)/255.
print("here1")
#train = dataset.iloc[:,1:].values
test = pd.read_csv("train40.csv")
label_test=np.array(test.pop('label'))
x_ = np.array(test)/255.
print("here2")

def calc_accuracy(method,label_test,pred):
    print("accuracy score for ",method,sm.accuracy_score(label_test,pred))
    print("precision_score for ",method,sm.precision_score(label_test,pred,average='micro'))
    print("f1 score for ",method,sm.f1_score(label_test,pred,average='micro'))
    print("recall score for ",method,sm.recall_score(label_test,pred,average='micro'))


def run_svm():
    clf=svm.SVC(decision_function_shape='ovo')
    print("svm started")
    clf.fit(x,y)
    #print clf.n_layers_
    pred=clf.predict(x_)
    #print(pred)
    np.savetxt('submission_svm.csv', np.c_[range(1,len(test)+1),pred], delimiter=',', header = 'ImageId,Label', comments = '', fmt='%d')
    calc_accuracy("SVM",label_test,pred)

def run_lr():
    clf = lr()
    print("lr started")
    clf.fit(x,y)
    #print clf.n_layers_
    pred=clf.predict(x_)
    #print(pred)
    np.savetxt('submission_lr.csv', np.c_[range(1,len(test)+1),pred], delimiter=',', header = 'ImageId,Label', comments = '', fmt='%d')
    calc_accuracy("Logistic regression",label_test,pred)


def run_nb():
    clf = nb()
    print("nb started")
    clf.fit(x,y)
    #print(clf.classes_)
    #print clf.n_layers_
    pred=clf.predict(x_)
    #print(pred)
    np.savetxt('submission_nb.csv', np.c_[range(1,len(test)+1),pred], delimiter=',', header = 'ImageId,Label', comments = '', fmt='%d')
    calc_accuracy("Naive Bayes",label_test,pred)


def run_knn():
    clf=knn(n_neighbors=3)
    print("knn started")
    clf.fit(x,y)
    #print(clf.classes_)
    #print clf.n_layers_
    pred=clf.predict(x_)
    #print(pred)
    np.savetxt('submission_knn.csv', np.c_[range(1,len(test)+1),pred], delimiter=',', header = 'ImageId,Label', comments = '', fmt='%d')
    calc_accuracy("K nearest neighbours",label_test,pred)

run_svm()
run_knn()
run_nb()
run_lr()

我在shell中的输出如下：

svm started
accuracy score for  SVM 0.596358118361
precision_score for  SVM 0.596358118361
f1 score for  SVM 0.596358118361
recall score for  SVM 0.596358118361
knn started
accuracy score for  K nearest neighbours 0.656550328781
precision_score for  K nearest neighbours 0.656550328781
f1 score for  K nearest neighbours 0.656550328781
recall score for  K nearest neighbours 0.656550328781
nb started
accuracy score for  Naive Bayes 0.360647445625
precision_score for  Naive Bayes 0.360647445625
f1 score for  Naive Bayes 0.360647445625
recall score for  Naive Bayes 0.360647445625
lr started
accuracy score for  Logistic regression 0.601922104198
precision_score for  Logistic regression 0.601922104198
f1 score for  Logistic regression 0.601922104198
recall score for  Logistic regression 0.601922104198

测试和训练中的第一列也分别是1977和2995图像的手语标记，每行都有图像标签，后面是单个图像的9126像素 .

>>> y
array([ 0,  0,  0, ..., 23, 23, 23], dtype=int64)
>>> len(y)
2995
>>> x
array([[ 1.,  1.,  1., ...,  1.,  1.,  1.],
       [ 1.,  1.,  1., ...,  1.,  1.,  1.],
       [ 1.,  1.,  1., ...,  1.,  1.,  1.],
       ..., 
       [ 1.,  1.,  1., ...,  1.,  1.,  1.],
       [ 1.,  1.,  1., ...,  1.,  1.,  1.],
       [ 1.,  1.,  1., ...,  1.,  1.,  1.]])
>>> len(x)
2995
>>> test.head()
   pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \
0     255     255     255     255     255     255     255     255     255   
1     255     255     255     255     255     255     255     255     255   
2     255     255     255     255     255     255     255     255     255   
3     255     255     255     255     255     255     255     255     255   
4     255     255     255     255     255     255     255     255     255   

   pixel9    ...      pixel9206  pixel9207  pixel9208  pixel9209  pixel9210  \
0     255    ...            255        255        255        255        255   
1     255    ...            255        255        255        255        255   
2     255    ...            255        255        255        255        255   
3     255    ...            255        255        255        255        255   
4     255    ...            255        255        255        255        255   

   pixel9211  pixel9212  pixel9213  pixel9214  pixel9215  
0        255        255        255        255        255  
1        255        255        255        255        255  
2        255        255        255        255        255  
3        255        255        255        255        255  
4        255        255        255        255        255  

[5 rows x 9216 columns]