将分类器投入生产

Question

我已经使用 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: Vocabulary was not compatible or is empty！ 我想我应该从 te joblid 加载词汇，但我无法让它工作

Answer 1

只需替换：

  #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

作者：

  # 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 包含完整的管道，无需创建新的矢量化器实例。正如错误消息所说，您需要重用最初训练的矢量化器，因为从标记（字符串 ngram）到列索引的特征映射保存在矢量化器本身中。这种映射被命名为“词汇表”。