使用 sklearn 获得相同的 Precision and Recall (K-NN) 值

Question

更新问题：我这样做了，但是我在精度和召回率方面得到了相同的结果，是因为我使用的是 average ='binary'?

但是当我使用 average='macro' 时，我收到此错误消息：

Test a custom review messageC:\Python27\lib\site-packages\sklearn\metrics\classification.py:976: DeprecationWarning: From version 0.18, binary input will not be handled specially when using averaged precision/recall/F-score. Please use average='binary' to report only the positive class performance.
'positive class performance.', DeprecationWarning)

这是我更新后的代码：

path = 'opinions.tsv'
data = pd.read_table(path,header=None,skiprows=1,names=['Sentiment','Review'])
X = data.Review
y = data.Sentiment
#Using CountVectorizer to convert text into tokens/features
vect = CountVectorizer(stop_words='english', ngram_range = (1,1), max_df = .80, min_df = 4)
X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=1, test_size= 0.2)
#Using training data to transform text into counts of features for each message
vect.fit(X_train)
X_train_dtm = vect.transform(X_train) 
X_test_dtm = vect.transform(X_test)




#Accuracy using KNN Model
KNN = KNeighborsClassifier(n_neighbors = 3)
KNN.fit(X_train_dtm, y_train)
y_pred = KNN.predict(X_test_dtm)
print('\nK Nearest Neighbors (NN = 3)')



#Naive Bayes Analysis
tokens_words = vect.get_feature_names()
print '\nAnalysis'
print'Accuracy Score: %f %%'% (metrics.accuracy_score(y_test,y_pred)*100)
print "Precision Score: %f%%" %  precision_score(y_test,y_pred, average='binary')
print "Recall Score: %f%%" %  recall_score(y_test,y_pred, average='binary')

通过使用上面的代码，我得到了相同的准确率和召回率值。

感谢您回答我的问题，非常感谢。

Answer 1

要计算 precision and recall 指标，您应该从 sklearn.metrics.

导入相应的方法

如文档中所述，它们的参数是真实标签和预测标签的一维数组：

from sklearn.metrics import precision_score
from sklearn.metrics import recall_score

y_true = [0, 1, 2, 0, 1, 2]
y_pred = [0, 2, 1, 0, 0, 1]

print('Calculating the metrics...')

recision_score(y_true, y_pred, average='macro')
>>> 0.22

recall_score(y_true, y_pred, average='macro')
>>> 0.33

使用 sklearn 获得相同的 Precision and Recall (K-NN) 值

Getting same value for Precision and Recall (K-NN) using sklearn

knn

python-2.7

scikit-learn