将分类器带到生产中

Question

我使用joblib保存了我的分类器管道:

vec = TfidfVectorizer(sublinear_tf=True, max_df=0.5, ngram_range=(1, 3))
pac_clf = PassiveAggressiveClassifier(C=1)
vec_clf = Pipeline([('vectorizer', vec), ('pac', pac_clf)])
vec_clf.fit(X_train,y_train)
joblib.dump(vec_clf, 'class.pkl', compress=9)

现在我正在尝试在生产环境中使用它:

def classify(title):

  #load classifier and predict
  classifier = joblib.load('class.pkl')

  #vectorize/transform the new title then predict
  vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5, ngram_range=(1, 3))
  X_test = vectorizer.transform(title)
  predict = classifier.predict(X_test)
  return predict

我得到的错误是:ValueError:词汇表没有安装或是空的!我想我应该从te joblid加载词汇表,但我不能让它工作

Answer 1

只需更换:

  #load classifier and predict
  classifier = joblib.load('class.pkl')

  #vectorize/transform the new title then predict
  vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5, ngram_range=(1, 3))
  X_test = vectorizer.transform(title)
  predict = classifier.predict(X_test)
  return predict

通过:

  # load the saved pipeline that includes both the vectorizer
  # and the classifier and predict
  classifier = joblib.load('class.pkl')
  predict = classifier.predict(X_test)
  return predict

class.pkl包括完整的管道,不需要创建新的矢量化器实例.正如错误消息所示,您需要重用首先训练的矢量化器,因为从令牌(字符串ngrams)到列索引的特征映射将保存在矢量化器本身中.该映射被命名为"词汇表".