【新手福音】分享一个自己制作的机器学习工具集合

机器学习小工具集合

我！李英俊！觉得这篇文章超级有用！值得你一看！

功能： 机器学习工具集合，直接导入一个类，传参训练集，验证集就能生成报告

使用方式：

1. 需要自己传入X_train, y_train, X_test, y_test作为输入（并没有用交叉验证，因为我在用的时候考虑到了样本不均衡的问题，交叉验证无法分层采样），下面有鸢尾花和手写数字集两种样本作为输入样例。
2. 传入save_path作为输出报告和模型的路径，例如save_path='./models/'。里面包括了每个模型的输出报告（测试集，验证集都有），每个模型（10个模型加最后的集成模型，对所有数据进行了重新学习），使用joblib保存的，使用的话直接joblib.load就行。还有一个优化报告，即对四种集成学习模型进行优化后的结果。
3. 传入target_names作为优化报告里面两种分类的名字，暂时只支持二分类。

tips： 下面会附上代码和两个使用demo，也会贴上github上的链接，如果大家需要什么新的功能可以留言告诉我，最新的更新应该会在github上同步，希望大家星星我，有空的话再更新博客。求个赞不过分吧！转载请一定表明出处哟~

点我打开github地址，求关注

结果展示：

• 输出example

  

• 输出报告example

  

• 优化报告example

  

• 代码具体实现：

  #!/usr/bin/env python
  # -*- coding: UTF-8 -*-
  # coding=utf-8 
  
  """
  @author: Li Tian
  @contact: [email protected]
  @software: pycharm
  @file: ML_combines.py
  @time: 2019/9/23 8:53
  @desc: 机器学习工具集合，直接写一个类，传参训练集，验证集就能生成报告
  """
  from sklearn.metrics import f1_score
  from sklearn.model_selection import train_test_split
  from collections import OrderedDict
  
  
  class MLTools:
      """
      包含：多项式朴素贝叶斯, 高斯朴素贝叶斯, K最近邻, 逻辑回归, 支持向量机, 决策树, 随机森林, Adaboost, GBDT, xgboost
      """
      random_state = 42
  
      # 粗略 随机森林调参数值
      # 参考链接1：https://blog.csdn.net/geduo_feng/article/details/79558572
      # 参考链接2：https://blog.csdn.net/qq_35040963/article/details/88832030
      parameter_tree = {
          # 集成模型数量越小越简单
          'n_estimators': range(10, 200, 20),
          # 最大树深度越小越简单
          'max_depth': range(1, 10, 1),
          # 最小样本分割数越大越简单
          'min_samples_split': list(range(2, 10, 1))[::-1],
      }
      parameter_tree = OrderedDict(parameter_tree)
  
      def __init__(self, X_train, y_train, X_test, y_test):
          self.X_train = X_train
          self.y_train = y_train
          self.X_test = X_test
          self.y_test = y_test
  
      # Multinomial Naive Bayes Classifier / 多项式朴素贝叶斯
      def multinomial_naive_bayes_classifier(self):
          from sklearn.naive_bayes import MultinomialNB
          model = MultinomialNB(alpha=0.01)
          model.fit(self.X_train, self.y_train)
          return model, None
  
      # Gaussian Naive Bayes Classifier / 高斯朴素贝叶斯
      def gaussian_naive_bayes_classifier(self):
          from sklearn.naive_bayes import GaussianNB
          model = GaussianNB()
          model.fit(self.X_train, self.y_train)
          return model, None
  
      # KNN Classifier / K最近邻
      def knn_classifier(self):
          from sklearn.neighbors import KNeighborsClassifier
          model = KNeighborsClassifier()
          model.fit(self.X_train, self.y_train)
          return model, None
  
      # Logistic Regression Classifier / 逻辑回归
      def logistic_regression_classifier(self):
          from sklearn.linear_model import LogisticRegression
          model = LogisticRegression(penalty='l2')
          model.fit(self.X_train, self.y_train)
          return model, None
  
      # SVM Classifier / 支持向量机
      def svm_classifier(self):
          from sklearn.svm import SVC
          model = SVC(kernel='rbf', probability=True)
          model.fit(self.X_train, self.y_train)
          return model, None
  
      # Decision Tree Classifier / 决策树
      def decision_tree_classifier(self):
          from sklearn.tree import DecisionTreeClassifier
          model = DecisionTreeClassifier()
          model.fit(self.X_train, self.y_train)
          return model, None
  
      # Random Forest Classifier / 随机森林
      def random_forest_classifier(self, is_adjust=True):
          from sklearn.ensemble import RandomForestClassifier
  
          # 训练普通模型
          model = RandomForestClassifier()
          model.fit(self.X_train, self.y_train)
          test_pred = model.predict(self.X_test)
          min_score = f1_score(self.y_test, test_pred, average='macro')
          if not is_adjust:
              return model, None
  
          max_score = 0
          best_param = [None, None, None]
          for p1 in MLTools.parameter_tree['n_estimators']:
              for p2 in MLTools.parameter_tree['max_depth']:
                  for p3 in MLTools.parameter_tree['min_samples_split']:
                      test_model = RandomForestClassifier(random_state=MLTools.random_state, n_estimators=p1,
                                                          max_depth=p2, min_samples_split=p3, n_jobs=-1)
                      test_model.fit(self.X_train, self.y_train)
                      test_pred = test_model.predict(self.X_test)
                      new_score = f1_score(self.y_test, test_pred, average='macro')
  
                      # 输出检查每一个细节，可能存在不同的参数得到相同的精度值
                      # print('n_estimators=' + str(p1) + 'max_depth=' + str(p2) + 'min_samples_split=' + str(p3) + '-->' + str(new_score))
  
                      if new_score > max_score:
                          max_score = new_score
                          best_param = [p1, p2, p3]
          best_model = RandomForestClassifier(random_state=MLTools.random_state, n_estimators=best_param[0],
                                              max_depth=best_param[1], min_samples_split=best_param[2], n_jobs=-1)
          best_model.fit(self.X_train, self.y_train)
          word = '-- optimized parameters: \n'
          count = 0
          for name in MLTools.parameter_tree.keys():
              word = word + name + ' = ' + str(best_param[count]) + '\n'
              count += 1
          word = word + 'f1_macro: ' + '%.4f' % min_score + '-->' + '%.4f' % max_score + "\n"
          return best_model, word
  
      # AdaBoost Classifier / 自适应提升法
      def adaboost_classifier(self, is_adjust=True):
          from sklearn.ensemble import AdaBoostClassifier
          from sklearn.tree import DecisionTreeClassifier
          model = AdaBoostClassifier()
          model.fit(self.X_train, self.y_train)
          test_pred = model.predict(self.X_test)
          min_score = f1_score(self.y_test, test_pred, average='macro')
          if not is_adjust:
              return model, None
  
          max_score = 0
          best_param = [None, None, None]
          for p1 in MLTools.parameter_tree['n_estimators']:
              for p2 in MLTools.parameter_tree['max_depth']:
                  for p3 in MLTools.parameter_tree['min_samples_split']:
                      test_model = AdaBoostClassifier(
                          DecisionTreeClassifier(random_state=MLTools.random_state,
                                                 max_depth=p2, min_samples_split=p3),
                          random_state=MLTools.random_state, n_estimators=p1)
                      test_model.fit(self.X_train, self.y_train)
                      test_pred = test_model.predict(self.X_test)
                      new_score = f1_score(self.y_test, test_pred, average='macro')
                      if new_score > max_score:
                          max_score = new_score
                          best_param = [p1, p2, p3]
          best_model = AdaBoostClassifier(
              DecisionTreeClassifier(random_state=MLTools.random_state,
                                     max_depth=best_param[1], min_samples_split=best_param[2]),
              random_state=MLTools.random_state, n_estimators=best_param[0])
          best_model.fit(self.X_train, self.y_train)
          word = '-- optimized parameters: \n'
          count = 0
          for name in MLTools.parameter_tree.keys():
              word = word + name + ' = ' + str(best_param[count]) + '\n'
              count += 1
          word = word + 'f1_macro: ' + '%.4f' % min_score + '-->' + '%.4f' % max_score + "\n"
          return best_model, word
  
      # GBDT(Gradient Boosting Decision Tree) Classifier / 梯度提升决策树
      def gradient_boosting_classifier(self, is_adjust=True):
          from sklearn.ensemble import GradientBoostingClassifier
          model = GradientBoostingClassifier()
          model.fit(self.X_train, self.y_train)
          test_pred = model.predict(self.X_test)
          min_score = f1_score(self.y_test, test_pred, average='macro')
          if not is_adjust:
              return model, None
  
          max_score = 0
          best_param = [None, None, None]
          for p1 in MLTools.parameter_tree['n_estimators']:
              for p2 in MLTools.parameter_tree['max_depth']:
                  for p3 in MLTools.parameter_tree['min_samples_split']:
                      test_model = GradientBoostingClassifier(random_state=MLTools.random_state, n_estimators=p1,
                                                              max_depth=p2, min_samples_split=p3)
                      test_model.fit(self.X_train, self.y_train)
                      test_pred = test_model.predict(self.X_test)
                      new_score = f1_score(self.y_test, test_pred, average='macro')
                      if new_score > max_score:
                          max_score = new_score
                          best_param = [p1, p2, p3]
          best_model = GradientBoostingClassifier(random_state=MLTools.random_state, n_estimators=best_param[0],
                                                  max_depth=best_param[1], min_samples_split=best_param[2])
          best_model.fit(self.X_train, self.y_train)
          word = '-- optimized parameters: \n'
          count = 0
          for name in MLTools.parameter_tree.keys():
              word = word + name + ' = ' + str(best_param[count]) + '\n'
              count += 1
          word = word + 'f1_macro: ' + '%.4f' % min_score + '-->' + '%.4f' % max_score + "\n"
          return best_model, word
  
      # xgboost / 极端梯度提升
      def xgboost_classifier(self, is_adjust=True):
          from xgboost import XGBClassifier
          model = XGBClassifier()
          model.fit(self.X_train, self.y_train)
          test_pred = model.predict(self.X_test)
          min_score = f1_score(self.y_test, test_pred, average='macro')
          if not is_adjust:
              return model, None
  
          max_score = 0
          best_param = [0, 0, 0]
          for p1 in MLTools.parameter_tree['n_estimators']:
              for p2 in MLTools.parameter_tree['max_depth']:
                  for p3 in MLTools.parameter_tree['min_samples_split']:
                      test_model = XGBClassifier(random_state=MLTools.random_state, n_estimators=p1,
                                                 max_depth=p2, min_samples_split=p3, n_jobs=-1)
                      test_model.fit(self.X_train, self.y_train)
                      test_pred = test_model.predict(self.X_test)
                      new_score = f1_score(self.y_test, test_pred, average='macro')
                      if new_score > max_score:
                          max_score = new_score
                          best_param = [p1, p2, p3]
          best_model = XGBClassifier(random_state=MLTools.random_state, n_estimators=best_param[0],
                                     max_depth=best_param[1], min_samples_split=best_param[2], n_jobs=-1)
          best_model.fit(self.X_train, self.y_train)
          word = '-- optimized parameters: \n'
          count = 0
          for name in MLTools.parameter_tree.keys():
              word = word + name + ' = ' + str(best_param[count]) + '\n'
              count += 1
          word = word + 'f1_macro: ' + '%.4f' % min_score + '-->' + '%.4f' % max_score + "\n"
          return best_model, word
  
  
  def model_building(X_train, y_train, X_test, y_test, save_path, target_names=None, just_emsemble=False):
      """
      训练模型，并得到结果，并重新训练所有数据，保存模型
      :param save_path: 模型的保存路径
      :param target_names: 样本标签名
      :param just_emsemble: 已经有了其他模型，只对模型进行集成
      """
      from sklearn.metrics import classification_report
      import joblib
      import os
      import numpy as np
  
      if not just_emsemble:
          tool = MLTools(X_train, y_train, X_test, y_test)
          models = [tool.multinomial_naive_bayes_classifier(),
                    tool.gaussian_naive_bayes_classifier(),
                    tool.knn_classifier(),
                    tool.logistic_regression_classifier(),
                    tool.svm_classifier(),
                    tool.decision_tree_classifier(),
                    tool.random_forest_classifier(),
                    tool.adaboost_classifier(),
                    tool.gradient_boosting_classifier(),
                    tool.xgboost_classifier()]
          model_names = ['多项式朴素贝叶斯', '高斯朴素贝叶斯', 'K最近邻', '逻辑回归', '支持向量机', '决策树', '随机森林', 'Adaboost', 'GBDT', 'xgboost']
  
          # 遍历每个模型
          f = open(save_path + 'report.txt', 'w+')
          g = open(save_path + 'optimized.txt', 'w+')
  
          for count in range(len(models)):
              model, optimized = models[count]
              model_name = model_names[count]
              print(str(count + 1) + '. 正在运行：', model_name, '...')
              train_pred = model.predict(X_train)
              test_pred = model.predict(X_test)
  
              train = classification_report(y_train, train_pred, target_names=target_names)
              test = classification_report(y_test, test_pred, target_names=target_names)
  
              f.write('- ' + model_name + '\n')
              f.write('-- 【训练集】' + '\n')
              f.writelines(train)
              f.write('\n')
              f.write('-- 【测试集】' + '\n')
              f.writelines(test)
              f.write('\n')
  
              g.write('- ' + model_name + '\n')
              if optimized:
                  g.write(optimized)
              g.write('\n')
  
              model.fit(np.r_[np.array(X_train), np.array(X_test)], np.r_[np.array(y_train), np.array(y_test)])
              joblib.dump(model, os.path.join(save_path, model_name + '.plk'))
  
          f.close()
          g.close()
  
      # 开始集成模型
      from sklearn.ensemble import VotingClassifier
      f = open(save_path + 'report.txt', 'a+')
      emsemble_names = ['随机森林', 'Adaboost', 'GBDT', 'xgboost']
      emsemble_path = [os.path.join(save_path, i + '.plk') for i in emsemble_names]
      estimators = []
      for x, y in zip(emsemble_names, emsemble_path):
          estimators.append((x, joblib.load(y)))
      voting_clf = VotingClassifier(estimators, voting='soft', n_jobs=-1)
      voting_clf.fit(X_train, y_train)
  
      print('11.  正在运行：集成模型...')
      train_pred = voting_clf.predict(X_train)
      test_pred = voting_clf.predict(X_test)
  
      train = classification_report(y_train, train_pred, target_names=target_names)
      test = classification_report(y_test, test_pred, target_names=target_names)
  
      f.write('- ' + '集成模型' + '\n')
      f.write('-- 【训练集】' + '\n')
      f.writelines(train)
      f.write('\n')
      f.write('-- 【测试集】' + '\n')
      f.writelines(test)
      f.write('\n')
  
      voting_clf.fit(np.r_[np.array(X_train), np.array(X_test)], np.r_[np.array(y_train), np.array(y_test)])
      joblib.dump(voting_clf, os.path.join(save_path, '集成模型' + '.plk'))
  
      f.close()
  
  
  def example1():
      """鸢尾花数据集进行测试"""
      from sklearn.datasets import load_iris
      iris = load_iris()
      iris_data = iris['data']
      iris_target = iris['target']
      iris_names = iris['target_names']
      X_train, X_test, y_train, y_test = train_test_split(iris_data, iris_target, test_size=0.2, random_state=42)
      model_building(X_train, y_train, X_test, y_test, save_path='./models/', target_names=iris_names)
  
  
  def example2():
      """手写数据集进行测试"""
      from sklearn.datasets import load_digits
      import numpy as np
      digits = load_digits()
      digits_data = digits['images']
      digits_target = digits['target']
      digits_names = digits['target_names']
  
      shape = digits_data.shape
      X = np.array(digits_data).reshape(shape[0], shape[1] * shape[2])
      a, b = 4, 9
      index1 = digits_target == a
      index2 = digits_target == b
      X = np.r_[X[index1], X[index2]]
      y = np.r_[digits_target[index1], digits_target[index2]]
      names = [str(a), str(b)]
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      model_building(X_train, y_train, X_test, y_test, save_path='./models2/', target_names=names)
  
  
  if __name__ == '__main__':
      example1()
  

---

我的CSDN：https://blog.csdn.net/qq_21579045

我的博客园：https://www.cnblogs.com/lyjun/

我的Github：https://github.com/TinyHandsome

纸上得来终觉浅，绝知此事要躬行~

欢迎大家过来OB~

by 李英俊小朋友