常见机器学习算法回归、分类应用示例，调参；.zip

# 1. machine-learning 常见机器学习算法回归、分类应用示例，调参；包括基础的线性回归算法、集成学习、支持向量机等；调参包括网格搜索、随机搜索、贝叶斯优化、优化算法如GA优化等。 需安装依赖库： pip install scikit-opt pip install bayesian-optimization pip install scikit-learn pip install lightgbm # 2. 算法包括以下（分类、回归）： ## 2.1 线性回归算法 1）线性回归：LinearRegression 2）岭回归：Ridge 3）套索回归：Least absolute shrinkage and selection operator：Lasso 4）弹性网络：ElasticNet 5）贝叶斯回归：BayesianRidge ## 2.2 支持向量机 1） SVR ## 2.3 Nearest Neighbors 1） KNN ## 2.4 集成学习：Ensemble methods 1）DecisionTreeRegressor：CART 2）RandomForestRegressor：RF 3）BaggingRegressor 4）AdaBoostRegressor 5）ExtraTreesRegressor 6）GradientBoostingRegressor 7）StackingRegressor 8）VotingRegressor 9）lightgbm # 3. 算法调参 -------------------------------------------------------- // 网格搜索 GridSearchCV // 随机搜索 RandomizedSearchCV // 贝叶斯优化 （BayesianOptimization） // 优化算法： GA -------------------------------------------------------- // 训练集：测试集 == 80%：20% // 训练集-> K折交叉验证调参 // 选择最优参数后模型训练，测试集测试 --------------------------------------------------------- // 四种寻优方式可选择 // grid_search_cv: 网格搜索 // randomized_search_cv：随机搜索 // bayesian_optimization：贝叶斯优化 // ga_optimization：GA优化 // 指定相应方法优化run_flag， // 1-全部优化，2-网格搜索，3-随机搜索，4贝叶斯优化，5-GA优化 --------------------------------------------------------- # 4. LightGBM示例，核心算法 ```python '''==================定义优化模型====================''' class ParameterOptimization: def __init__(self,x_train,y_train,kfold,scoring='neg_mean_squared_error'): self.x_train = x_train self.y_train = y_train self.kfold = kfold self.scoring= scoring self.num_folds = num_folds '''GridSearchCV 网格搜索''' def grid_search_cv(self,model,param_grid): grid = GridSearchCV(estimator = model, param_grid = param_grid, scoring = self.scoring, cv = self.kfold,n_jobs=1) grid_result = grid.fit(X = self.x_train,y = self.y_train) print('\nGridSearchCV 网格搜索') print('最优：%s \t 使用: %s' % (grid_result.best_score_, grid_result.best_params_)) cv_results = zip(grid_result.cv_results_['mean_test_score'], grid_result.cv_results_['std_test_score'], grid_result.cv_results_['params']) for mean, std, param in cv_results: print('mean_test_score:%f \t std_test_score:(%f)\t with\t params:%r' % (mean, std, param)) return grid_result.best_params_ ''' 随机搜索 RandomizedSearchCV''' def randomized_search_cv(self,model,param_grid,n_iter_search): grid = RandomizedSearchCV(estimator = model, param_distributions = param_grid,n_iter=n_iter_search,scoring = self.scoring, cv = self.kfold, n_jobs = 1) grid_result = grid.fit(X_train,Y_train) print('\n随机搜索 RandomizedSearchCV') print('最优：%s\t 使用:%s' % (grid_result.best_score_, grid_result.best_params_)) cv_results = zip(grid_result.cv_results_['mean_test_score'], grid_result.cv_results_['std_test_score'], grid_result.cv_results_['params']) for mean, std, param in cv_results: print('mean_test_score:%f \t std_test_score:(%f)\t with\t params:%r' % (mean, std, param)) return grid_result.best_params_ '''贝叶斯优化 （BayesianOptimization）''' # 定义贝叶斯优化模型 def bayesian_model_cv(self,n_estimators,learning_rate,subsample,max_depth,min_data_in_leaf,num_leaves,max_bin): model = Pipeline([('Minmax', MinMaxScaler()), ('lightgbm', lgb.LGBMRegressor(boosting_type = 'gbdt', objective= 'regression', metric = 'rmse',n_estimators=int(n_estimators),learning_rate=learning_rate,subsample=subsample,max_depth=int(max_depth),min_data_in_leaf=int(min_data_in_leaf),num_leaves=int(num_leaves),max_bin=int(max_bin)))]) val = cross_val_score(model, self.x_train,self.y_train, scoring=self.scoring, cv=self.kfold,n_jobs=1).mean() return val # 参数开采 def bayesian_optimization(self,n_iter,param_grid): self.bay_bo = BayesianOptimization(self.bayesian_model_cv,param_grid) self.bay_bo.maximize(n_iter) print(f'explore:{self.bay_bo.max}') return self.bay_bo.max['params'] # 参数微调 def bayesian_optimization_finetune(self,param_gird): self.bay_bo.probe(params=param_gird,lazy=True) self.bay_bo.maximize(init_points=0, n_iter=10) print(f'fine_tune:{self.bay_bo.max}') return self.bay_bo.max['params'] '''GA优化''' def ga_optimization(self,x_upper, x_bound,size_pop, max_iter): ga = GA(func=function, size_pop=size_pop,max_iter=max_iter,n_dim=len(x_upper),lb=x_upper, ub=x_bound) best_x, best_y = ga.run() print(f'ga_optimization best params ：{best_x}, \t best fitness：{best_y}') Y_history = pd.DataFrame(ga.all_history_Y) Y_history.min(axis=1).cummin().plot(kind='line') plt.ylabel('fitness') plt.xlabel('iteration') plt.savefig('./fig/{}.jpg'.format('适应度曲线'), dpi=500, bbox_inches='tight') # 保存图片 plt.show() return best_x ``` # 5.完整实例 ```python import pandas as pd from pylab import * from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler,Normalizer,MinMaxScaler from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error,mean_absolute_percentage_error,explained_variance_score from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from sklearn.model_selection import GridSearchCV from sklearn.model_selection import RandomizedSearchCV from bayes_opt import BayesianOptimization # pip install bayesian-optimization import lightgbm as lgb from sklearn.pipeline import Pipeline from sko.GA import GA #pip install scikit-opt import warnings warnings.filterwarnings("ignore") font1 = {'family': 'Times New Roman','weight': 'normal','size': 13,} font2 = {'family': 'STSong','weight': 'normal','size': 13,} fontsize1=13 # 设置字体，以作图显示中文 mpl.rcParams['font.sans-serif'] = ['SimHei'] mpl.rcParams['axes.unicode_minus']=False # 设置显示属性 pd.set_option('display.max_columns',500) pd.set_option('display.max_rows',100) np.set_printoptions(suppress=True) pd.set_option('precision',3) np.set_printoptions(precision=4) def figure_plot(predict, true_value, figure_property,key_label=None): # 折线图 fig = plt.figure() ax = fig.add_subplot(111) ax.plot(predict, '-*', label='预测值') ax.plot(true_value, '-s', label='真实值') # x_ticks = ax.set_xticks([i for i in range(len(key_label))]) # x_labels = ax.set_xticklabels(key_label,rotation=45,fontdict=font1) ax.set_title(figure_property['title'], fontdict=font2) ax.set_xlabel(figure_property['X_label'], fontdict=font2) ax.set_ylabel(figure_property['Y_label'], fontdict=font2) plt.tick_params(labelsize=12) labels = ax.get_xticklabels() + ax.get_yticklabels() [label.set_fontname('Times New Roman') for label in labels] # # y_ticks=ax.set_yticks([]) # # y_labels=ax.set_yticklabels([-20+i for i in range(20)],rotation=0,fontsize=14) plt.legend(prop=font2) plt.tight_layout() plt.savefig('../fig/{}.jpg'.format(figure_property['title']), dpi=500, bbox_inches='tight') # 保存图片 plt.show() def figure_plot_1(predict, true_value, figu