我试图在NLTK的代码中评估混淆矩阵和ROC曲线。

Question

这是我正在处理的代码

以下是电影简评的代码。

documents = []
all_words = []
allowed_words_types = ['J']
for p in short_pos.split('\n'):
    documents.append((p,"pos"))
    words = word_tokenize(p)
    pos = nltk.pos_tag(words)
    for w in pos:
        if w[1][0] in allowed_words_types:
            all_words.append(w[0].lower())
for p in short_neg.split('\n'):
    documents.append((p,"neg"))
    words = word_tokenize(p)
    pos = nltk.pos_tag(words)
    for w in pos:
        if w[1][0] in allowed_words_types:
            all_words.append(w[0].lower())
all_words = nltk.FreqDist(all_words)
words_features = list(all_words.keys())[:5000]
def find_features(document):
    words = word_tokenize(document)
    features = {}
    for w in words_features:
        features[w] = (w in words)
    return features
featuresets = [(find_features(rev),category) for (rev,category) in documents]
random.shuffle(featuresets)
print(len(featuresets))
training_set = featuresets[:100] 
testing_set = featuresets[100:]
classifier = nltk.NaiveBayesClassifier.train(training_set)

这里我想计算混乱Mattrix和ROC

我只是找到了准确性，但我无法找到roc和混淆矩阵，这将非常有帮助，任何人都可以帮助我。感谢。

print(" Original Naive Bayes Algo accuracy percent : ",(nltk.classify.accuracy(classifier,testing_set))*100)
MNB_classifier = SklearnClassifier(MultinomialNB())
MNB_classifier.train(training_set)
print("MNB_classifier accuracy percent : ",(nltk.classify.accuracy(MNB_classifier,testing_set))*100)
voted_classifier = VoteClassifier(classifier,
                                  MNB_classifier)
def sentiment(text):
    feats = find_features(text)
    return voted_classifier.classify(feats),voted_classifier.confidence(feats)

Answer 1

您可以使用sklearn的度量标准包

获取混淆矩阵

from sklearn.metrics import confusion_matrix
confusion_matrix(y_true, y_pred)

更多信息 http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html

同样，对于ROC曲线

http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html