14.4管道模型
14.4.1管道模型基础
X,y = make_blobs(n_samples=200,centers=2,cluster_std=5) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=38) scaler = StandardScaler().fit(X_train) X_train_scaled = scaler.transform(X_train) X_test_scaled = scaler.transform(X_test) print("训练集形态:",X_train_scaled.shape) print("测试集形态:",X_test_scaled.shape) #原始的训练集 plt.scatter(X_train[:,0],X_train[:,1]) #经过预处理的训练集 plt.scatter(X_train_scaled[:,0],X_train_scaled[:,1],marker='^',edgecolor='k') plt.title(u"训练集 VS 处理后的训练集") plt.rcParams['font.sans-serif']=['SimHei'] plt.rcParams['axes.unicode_minus']=False plt.show() params = {'hidden_layer_sizes':[(50,),(100,),(100,100)],"alpha":[0.0001,0.001,0.01]} grid = GridSearchCV(MLPClassifier(max_iter=1600,random_state=38),param_grid=params,cv=3) grid.fit(X_train_scaled,y_train) print("模型最高得分：\n{:.2%}".format(grid.best_score_)) print("模型最高得分时的参数：\n{}".format(grid.best_params_)) #打印模型在测试集上的得分 print("测试集得分：\n{:.2%}".format(grid.score(X_test_scaled,y_test)))
输出
训练集形态: (150, 2) 测试集形态: (50, 2) 模型最高得分： 90.00% 模型最高得分时的参数： 'alpha': 0.0001, 'hidden_layer_sizes': (50,)} 测试集得分： 82.00%
14.4.2 pipeline
pipeline = Pipeline([('scaler',StandardScaler()), ('mlp',MLPClassifier(max_iter=1600,random_state=38))]) pipeline.fit(X_train,y_train) print("使用管道后的测试集得分：\n{:.2%}".format(pipeline.score(X_test,y_test))) params = {'mlp__hidden_layer_sizes':[(50,),(100,),(100,100)],"mlp__alpha":[0.0001,0.001,0.01]} grid = GridSearchCV(pipeline,param_grid=params,cv=3) grid.fit(X_train,y_train) print("交叉验证最高得分：\n{:.2%}".format(grid.best_score_)) print("模型最优参数：\n{}".format(grid.best_params_)) print("测试集得分：\n{:.2%}".format(grid.score(X_test,y_test)))
输出
使用管道后的测试集得分： 86.00% 交叉验证最高得分： 90.00% 模型最优参数： 'mlp__alpha': 0.0001, 'mlp__hidden_layer_sizes': (50,)} 测试集得分： 82.00%
输出
随机差分交叉验证法后测试数据的得分：[0.96666667 1. 0.96666667 0.93333333 0.93333333 0.96666667 1. 0.96666667 1. 0.96666667]: 随机差分交叉验证法后测试数据的平均得分：97.00%: 随机差分预测的鸢尾花为：['setosa']:
14.4.3案例
#使用管道，Pipeline()方法与make_pipeline()等同 pipeline = Pipeline([('scaler',StandardScaler()), ('mlp',MLPRegressor(max_iter=1600,hidden_layer_sizes=[1,1],random_state=6))]) pipe = make_pipeline(StandardScaler(),MLPRegressor(max_iter=1600,hidden_layer_sizes=[1,1],random_state=6)) scores = cross_val_score(pipe,X,y,cv=20) print("pipe处理后模型平均分：{:.2%}".format(float(scores.mean())))
输出
pipe处理后模型平均分：-80419.24%
pipe = make_pipeline(StandardScaler(),SelectFromModel(RandomForestRegressor(random_state=6)), MLPRegressor(max_iter=1600,hidden_layer_sizes=[1,1],random_state=6)) scores = cross_val_score(pipe,X,y,cv=20) print(“经过pipe处理后，再经过SelectFromModel处理，模型平均分：{:.2%}".format(float(scores.mean())))
输出
经过pipe处理后，再经过SelectFromModel处理，模型平均分：-56190.48%
params =[{'reg':[MLPRegressor(max_iter=1600,hidden_layer_sizes=[1,1],random_state=6)], 'scaler':[StandardScaler(),None]}, {'reg':[RandomForestRegressor(random_state=6)], 'scaler':[None]}] pipe = Pipeline([('scaler',StandardScaler()),('reg',MLPRegressor())]) grid = GridSearchCV(pipe,params,cv=6) grid.fit(X,y) print("GridSearchCV处理后，最佳模型是：{}".format(grid.best_params_)) print("GridSearchCV处理后，模型最佳得分：{:.2%}".format(grid.best_score_)) GridSearchCV处理后，最佳模型是：{'reg': RandomForestRegressor(random_state=6), 'scaler': None} GridSearchCV处理后，模型最佳得分：-12.45% params =[{'reg':[MLPRegressor(max_iter=1600,random_state=6)], 'scaler':[StandardScaler(),None], 'reg__hidden_layer_sizes':[(1),(50,),(100,),(1,1),(50,50),(100,100)]}, {'reg':[RandomForestRegressor(random_state=6)], 'scaler':[None], 'reg__n_estimators':[10,50,100]}] pipe = Pipeline([('scaler',StandardScaler()),('reg',MLPRegressor())]) grid = GridSearchCV(pipe,params,cv=6) grid.fit(X,y) print("加入参数后，最佳模型是：{}".format(grid.best_params_)) print("加入参数后，模型最佳得分：{:.2%}".format(grid.best_score_))
输出
加入参数后，最佳模型是：{'reg': RandomForestRegressor(random_state=6), 'reg__n_estimators': 100, 'scaler': None} 加入参数后，模型最佳得分：-12.45%
输出
加入参数后，最佳模型是：{'reg': RandomForestRegressor(random_state=6), 'reg__n_estimators': 100, 'scaler': None} 加入参数后，模型最佳得分：-12.45%
paradef get_better_score(): warnings.filterwarnings("ignore") n_jobs = 2 params=[{'reg':[LinearRegression()],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__n_jobs":[n_jobs]}, {'reg':[LogisticRegression()],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__n_jobs":[n_jobs]}, {'reg':[Ridge()],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__alpha":[1,0.1,0.001,0.0001]}, {'reg':[Lasso()],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__alpha":[1,0.1,0.001,0.0001]},{'reg':[ElasticNet()],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__alpha":[0.1,0.5,1,5,10],"reg__l1_ratio":[0.1,0.5,0.9]}, {'reg':[RandomForestClassifier()],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__n_estimators":[4,5,6,7],"reg__random_state":[2,3,4,5],"reg__n_jobs":[n_jobs],"reg__random_state":[range (0,200)]}, {'reg':[RandomForestRegressor()],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__n_estimators":[4,5,6,7],"reg__random_state":[2,3,4,5],"reg__n_jobs":[n_jobs],"reg__random_state":[range (0,200)]}, {'reg':[DecisionTreeClassifier()],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__max_depth":[1,3,5,7],"reg__random_state":[range (1,200)]}, {'reg':[DecisionTreeRegressor()],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__max_depth":[1,3,5,7],"reg__random_state":[range (1,200)]}, {'reg':[KNeighborsClassifier()],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__n_jobs":[n_jobs]},{'reg':[ ()],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__n_jobs":[n_jobs]}, {'reg':[BernoulliNB()],'scaler':[StandardScaler(),MinMaxScaler(),None]}, {'reg':[GaussianNB()],'scaler':[StandardScaler(),MinMaxScaler(),None]}, {'reg':[MultinomialNB()],'scaler':[MinMaxScaler()]},{'reg':[SVC(max_iter=10000)],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__kernel":["linear","rbf","sigmoid","poly"],"reg__gamma":[0.01,0.1,1,5,10],"reg__C":[1.0,3.0,5.0]}, {'reg':[SVR(max_iter=100000)],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__kernel":["linear","rbf","sigmoid","poly"],"reg__gamma":[0.01,0.1,1,5,10],"reg__C":[1.0,3.0,5.0]}, {'reg':[LinearSVC(max_iter=100000)],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__C":[1.0,3.0,5.0]}, {'reg':[LinearSVR(max_iter=100000)],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__C":[1.0,3.0,5.0]}, {'reg':[AdaBoostClassifier()],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__random_state":[range (1,200)]}, {'reg':[AdaBoostRegressor()],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__random_state":[range (1,200)]}, {'reg':[VotingClassifier(estimators=[('log_clf', LogisticRegression()),('svm_clf', SVC(probability=True)),('dt_clf', DecisionTreeClassifier(random_state=666))])],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__voting":["hard","soft"],"reg__n_jobs":[n_jobs]}, {'reg':[LinearDiscriminantAnalysis(n_components=2)],'scaler':[StandardScaler(),MinMaxScaler(),None]}, {'reg':[MLPClassifier(max_iter=100000)],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__activation":["relu","tanh","identity","logistic"],"reg__alpha":[0.0001,0.001,0.01,1],"reg__hidden_layer_sizes":[(1),(50,),(100,),(1,1),(50,50),(100,100)]}, {'reg':[MLPRegressor(max_iter=100000)],'scaler':[StandardScaler(),MinMaxScaler(),None],"reg__activation":["relu","tanh","identity","logistic"],"reg__alpha":[0.0001,0.001,0.01,1],"reg__hidden_layer_sizes":[(1),(50,),(100,),(1,1),(50,50),(100,100)]} ] stock = pd.read_csv('stock1.csv',encoding='GBK') X = stock.loc[:,'价格':'流通市值'] y = stock['涨跌幅'] pipe = Pipeline([('scaler',StandardScaler()),('reg',MLPRegressor())]) shuffle_split = ShuffleSplit(test_size=.2,train_size=.7,n_splits=10) grid = GridSearchCV(pipe,params,cv=shuffle_split) grid.fit(X,y) print("最佳模型是：{}".format(grid.best_params_)) print("模型最佳训练得分：{:.2%}".format(grid.best_score_)) print("模型最佳测试得分：{:.2%}".format(grid.score(X,y))))
输出
最佳模型是：{'reg': LinearRegression(n_jobs=2), 'reg__n_jobs': 2, 'scaler': StandardScaler()} 模型最佳训练得分：100.00% 模型最佳测试得分：100.00%
得到这个结果好让人意外，我们直接用StandardScaler()后用LinearRegression模型来拟合一下
d
ef best_stock(): stock = pd.read_csv('stock1.csv',encoding='GBK') X = stock.loc[:,'价格':'流通市值'] y = stock['涨跌幅'] X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=62) clf = LinearRegression(n_jobs=2) scaler = StandardScaler() scaler.fit(X_train) X_train_scaled = scaler.transform(X_train) X_test_scaled = scaler.transform(X_test) clf.fit(X_train_scaled,y_train) print(“模型训练得分：{:.2%}".format(clf.score(X_train_scaled,y_train))) print(“模型测试得分：{:.2%}".format(clf.score(X_test_scaled,y_test)))
输出
模型训练得分：100.00% 模型测试得分：100.00%
