Question

我有一个建立预测模型的数据框。数据被划分为训练和测试，并且我使用了Randomforest分类器。

现在，用户传递一个新数据，该数据需要通过此模型并给出结果。

它是文本数据，下面是数据框：

     Description          Category
     Rejoin this domain   Network
     Laptop crashed       Hardware
     Installation Error   Software

代码：

  ############### Feature extraction ##############
  countvec = CountVectorizer()
  counts = countvec.fit_transform(read_data['Description'])
  df = pd.DataFrame(counts.toarray())
  df.columns = countvec.get_feature_names()
  print(df)

  ########## Join with original data ##############
  df = read_data.join(df)
  a = list(df.columns.values)

  ########## Creating the dependent variable class for "Category" variable 
  ###########
  factor = pd.factorize(df['Category'])
  df.Category = factor[0]
  definitions = factor[1]
  print(df.Category.head())
  print(definitions)

  ########## Creating the dependent variable class for "Description" 
  variable ###########
  factor = pd.factorize(df['Description'])
  df.Description = factor[0]
  definitions_1 = factor[1]
  print(df.Description.head())
  print(definitions_1)

  ######### Split into Train and Test data #######################
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.80, random_state = 21)

  ############# Random forest classification model #########################
  classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 42)
  classifier.fit(X_train, y_train)

  ######### Predicting the Test set results ##############
  y_pred = classifier.predict(X_test)

  #####Reverse factorize (converting y_pred from 0s,1s and 2s to original class for "Category" ###############
  reversefactor = dict(zip(range(3),definitions))
  y_test = np.vectorize(reversefactor.get)(y_test)
  y_pred = np.vectorize(reversefactor.get)(y_pred)

  #####Reverse factorize (converting y_pred from 0s,1s and 2s to original class for "Description" ###############
  reversefactor = dict(zip(range(53),definitions_1))
  X_test = np.vectorize(reversefactor.get)(X_test)

Answer 1

如果您只想对用户数据进行预测，那么我只需加载包含用户数据的新csv（或其他格式）（确保列与原始训练数据集中的列相同，减去相关项显然是可变的），您可以提取任务的预测值：

user_df = pd.read_csv("user_data.csv")

#insert a preprocessing step if needed to make sure user_df is identical to the original dataset

new_predictions = classifier.predict(user_df)

如何接受用户输入并将其传递给预测模型

1 个答案: