Scikit-learn - 在測量精確度時獲取NAN值。

Question

我對樣本數據分類正面和負面情緒。我使用了下面的代碼片斷。

一切看起來都OK，直到第20行打印預期的預測。

但是，當我嘗試使用度量標準來衡量準確性時，它給了我「NAN」值。你可以請檢閱我的代碼，並幫我找出問題。

from sklearn.naive_bayes import MultinomialNB 
from sklearn.feature_extraction.text import TfidfVectorizer 
from sklearn import metrics 
import csv 

# Read in the training data. 
with open("/Users/max/train.csv", 'r') as file: 
    reviews = list(csv.reader(file)) 

with open("/Users/max/test.csv",'r') as file: 
    test_reviews = list(csv.reader(file)) 

vectorizer = TfidfVectorizer(min_df=1) 
train_features = vectorizer.fit_transform([review[0] for review in reviews]) 
test_features = vectorizer.transform([test_review[0] for test_review in test_reviews]) 

nb = MultinomialNB() 
nb.fit(train_features, [int(review[1]) for review in reviews]) 

predictions = nb.predict(test_features) 
print("prediction : {0}".format(predictions)) 

actual = [int(r[1]) for r in test_reviews] 
fpr, tpr, threshold = metrics.roc_curve(actual, predictions, pos_label=1) 
print("Multinomial naive bayes AUC: {0}".format(metrics.auc(fpr, tpr))) 

集樣本以這種格式

i like google , 1 
i dont really like microsoft , -1 

這裏是控制檯輸出你沒有在你的數據的真正積極的實例

prediction : [1 -1] 
/Library/Python/2.7/site-packages/sklearn/metrics/ranking.py:496: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless UndefinedMetricWarning) 
Multinomial naive bayes AUC: nan 

Answer 1

概率。