Visualizers
Visualizers也是estimators（从数据中习得的对象），其主要任务是产生可对模型选择过程有更深入了解的视图。从Scikit-Learn来看，当可视化数据空间或者封装一个模型estimator时，其和转换器（transformers）相似，就像"ModelCV" (比如 RidgeCV, LassoCV )的工作原理一样。Yellowbrick的主要目标是创建一个和Scikit-Learn类似的有意义的API。其中最受欢迎的visualizers包括：
 
特征可视化
Rank Features: 对单个或者两两对应的特征进行排序以检测其相关性
Parallel Coordinates: 对实例进行水平视图
Radial Visualization: 在一个圆形视图中将实例分隔开
PCA Projection: 通过主成分将实例投射
Feature Importances: 基于它们在模型中的表现对特征进行排序
Scatter and Joint Plots: 用选择的特征对其进行可视化
分类可视化
Class Balance: 看类的分布怎样影响模型
Classification Report: 用视图的方式呈现精确率，召回率和F1值
ROC/AUC Curves: 特征曲线和ROC曲线子下的面积
Confusion Matrices: 对分类决定进行视图描述
回归可视化
Prediction Error Plot: 沿着目标区域对模型进行细分
Residuals Plot: 显示训练数据和测试数据中残差的差异
Alpha Selection: 显示不同alpha值选择对正则化的影响
聚类可视化
K-Elbow Plot: 用肘部法则或者其他指标选择k值
Silhouette Plot: 通过对轮廓系数值进行视图来选择k值
文本可视化
Term Frequency: 对词项在语料库中的分布频率进行可视化
t-SNE Corpus Visualization: 用随机邻域嵌入来投射文档

def testFunc1(savepath='Results/breast_cancer_ROCAUC.png'):
 '''
 基于癌症数据集的测试
 '''
 data=load_breast_cancer()
 X,y=data['data'],data['target']
 X_train, X_test, y_train, y_test = train_test_split(X, y)
 viz=ROCAUC(LogisticRegression())
 viz.fit(X_train, y_train)
 viz.score(X_test, y_test)
 viz.poof(outpath=savepath)

def testFunc2(savepath='Results/breast_cancer_ParallelCoordinates.png'):
 '''
 用平行坐标的方法对高维数据进行作图
 '''
 data=load_breast_cancer()
 X,y=data['data'],data['target']
 print 'X_shape: ',X.shape #X_shape: (569L, 30L)
 visualizer=ParallelCoordinates()
 visualizer.fit_transform(X,y)
 visualizer.poof(outpath=savepath)

def testFunc3(savepath='Results/breast_cancer_LR_report.png'):
 '''
 基于癌症数据集，使用逻辑回归模型来分类，绘制分类报告
 '''
 data=load_breast_cancer()
 X,y=data['data'],data['target']
 model=LogisticRegression()
 visualizer=ClassificationReport(model)
 X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42)
 visualizer.fit(X_train,y_train)
 visualizer.score(X_test,y_test)
 visualizer.poof(outpath=savepath)

def testFunc5(savepath='Results/bikeshare_Rank2D.png'):
 '''
 共享单车数据集预测
 '''
 data=pd.read_csv('bikeshare/bikeshare.csv')
 X=data[["season", "month", "hour", "holiday", "weekday", "workingday",
   "weather", "temp", "feelslike", "humidity", "windspeed"
   ]]
 y=data["riders"]
 visualizer=Rank2D(algorithm="pearson")
 visualizer.fit_transform(X)
 visualizer.poof(outpath=savepath)

def testFunc7(savepath='Results/bikeshare_LinearRegression_ResidualsPlot.png'):
 '''
 基于共享单车数据使用线性回归模型预测
 '''
 data = pd.read_csv('bikeshare/bikeshare.csv')
 X=data[["season", "month", "hour", "holiday", "weekday", "workingday",
   "weather", "temp", "feelslike", "humidity", "windspeed"]]
 y=data["riders"]
 X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3)
 visualizer=ResidualsPlot(LinearRegression())
 visualizer.fit(X_train, y_train)
 visualizer.score(X_test, y_test)
 visualizer.poof(outpath=savepath)

def testFunc8(savepath='Results/bikeshare_RidgeCV_AlphaSelection.png'):
 '''
 基于共享单车数据使用AlphaSelection
 '''
 data=pd.read_csv('bikeshare/bikeshare.csv')
 X=data[["season", "month", "hour", "holiday", "weekday", "workingday",
   "weather", "temp", "feelslike", "humidity", "windspeed"]]
 y=data["riders"]
 alphas=np.logspace(-10, 1, 200)
 visualizer=AlphaSelection(RidgeCV(alphas=alphas))
 visualizer.fit(X, y)
 visualizer.poof(outpath=savepath)

def testFunc9(savepath='Results/bikeshare_Ridge_PredictionError.png'):
 '''
 基于共享单车数据绘制预测错误图
 '''
 data=pd.read_csv('bikeshare/bikeshare.csv')
 X=data[["season", "month", "hour", "holiday", "weekday", "workingday",
   "weather", "temp", "feelslike", "humidity", "windspeed"]]
 y=data["riders"]
 X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3)
 visualizer=PredictionError(Ridge(alpha=3.181))
 visualizer.fit(X_train, y_train)
 visualizer.score(X_test, y_test)
 visualizer.poof(outpath=savepath)
blog.csdn.net/Together_CZ/article/details/86640784