利用scikitlearn画ROC曲线实例

这篇文章主要介绍了利用scikitlearn画ROC曲线实例，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

一个完整的数据挖掘模型，最后都要进行模型评估，对于二分类来说，AUC，ROC这两个指标用到最多，所以 利用sklearn里面相应的函数进行模块搭建。

具体实现的代码可以参照下面博友的代码，评估svm的分类指标。注意里面的一些细节需要注意，一个是调用roc_curve 方法时，指明目标标签，否则会报错。

具体是这个参数的设置pos_label ，以前在unionbigdata实习时学到的。

重点是以下的代码需要根据实际改写：

mean_tpr = 0.0 mean_fpr = np.linspace(0, 1, 100) all_tpr = [] y_target = np.r_[train_y,test_y] cv = StratifiedKFold(y_target, n_folds=6) #画ROC曲线和计算AUC fpr, tpr, thresholds = roc_curve(test_y, predict,pos_label = 2)##指定正例标签，pos_label = ###########在数之联的时候学到的，要制定正例 mean_tpr += interp(mean_fpr, fpr, tpr) #对mean_tpr在mean_fpr处进行插值，通过scipy包调用interp()函数 mean_tpr[0] = 0.0 #初始处为0 roc_auc = auc(fpr, tpr) #画图，只需要plt.plot(fpr,tpr),变量roc_auc只是记录auc的值，通过auc()函数能计算出来 plt.plot(fpr, tpr, lw=1, label='ROC %s (area = %0.3f)' % (classifier, roc_auc))

然后是博友的参考代码：

# -*- coding: utf-8 -*- """ Created on Sun Apr 19 08:57:13 2015 @author: shifeng """ print(__doc__) import numpy as np from scipy import interp import matplotlib.pyplot as plt from sklearn import svm, datasets from sklearn.metrics import roc_curve, auc from sklearn.cross_validation import StratifiedKFold ############################################################################### # Data IO and generation,导入iris数据，做数据准备 # import some data to play with iris = datasets.load_iris() X = iris.data y = iris.target X, y = X[y != 2], y[y != 2]#去掉了label为2，label只能二分，才可以。 n_samples, n_features = X.shape # Add noisy features random_state = np.random.RandomState(0) X = np.c_[X, random_state.randn(n_samples, 200 * n_features)] ############################################################################### # Classification and ROC analysis #分类，做ROC分析 # Run classifier with cross-validation and plot ROC curves #使用6折交叉验证，并且画ROC曲线 cv = StratifiedKFold(y, n_folds=6) classifier = svm.SVC(kernel='linear', probability=True, random_state=random_state)#注意这里，probability=True,需要，不然预测的时候会出现异常。另外rbf核效果更好些。 mean_tpr = 0.0 mean_fpr = np.linspace(0, 1, 100) all_tpr = [] for i, (train, test) in enumerate(cv): #通过训练数据，使用svm线性核建立模型，并对测试集进行测试，求出预测得分 probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test]) # print set(y[train]) #set([0,1]) 即label有两个类别 # print len(X[train]),len(X[test]) #训练集有84个，测试集有16个 # print "++",probas_ #predict_proba()函数输出的是测试集在lael各类别上的置信度， # #在哪个类别上的置信度高，则分为哪类 # Compute ROC curve and area the curve #通过roc_curve()函数，求出fpr和tpr，以及阈值 fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1]) mean_tpr += interp(mean_fpr, fpr, tpr) #对mean_tpr在mean_fpr处进行插值，通过scipy包调用interp()函数 mean_tpr[0] = 0.0 #初始处为0 roc_auc = auc(fpr, tpr) #画图，只需要plt.plot(fpr,tpr),变量roc_auc只是记录auc的值，通过auc()函数能计算出来 plt.plot(fpr, tpr, lw=1, label='ROC fold %d (area = %0.2f)' % (i, roc_auc)) #画对角线 plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Luck') mean_tpr /= len(cv) #在mean_fpr100个点，每个点处插值插值多次取平均 mean_tpr[-1] = 1.0 #坐标最后一个点为（1,1） mean_auc = auc(mean_fpr, mean_tpr) #计算平均AUC值 #画平均ROC曲线 #print mean_fpr,len(mean_fpr) #print mean_tpr plt.plot(mean_fpr, mean_tpr, 'k--', label='Mean ROC (area = %0.2f)' % mean_auc, lw=2) plt.xlim([-0.05, 1.05]) plt.ylim([-0.05, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Receiver operating characteristic example') plt.legend(loc="lower right") plt.show()

补充知识：批量进行One-hot-encoder且进行特征字段拼接，并完成模型训练demo

import org.apache.spark.ml.Pipeline import org.apache.spark.ml.feature.{StringIndexer, OneHotEncoder} import org.apache.spark.ml.feature.VectorAssembler import ml.dmlc.xgboost4j.scala.spark.{XGBoostEstimator, XGBoostClassificationModel} import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator} import org.apache.spark.ml.PipelineModel val data = (spark.read.format("csv") .option("sep", ",") .option("inferSchema", "true") .option("header", "true") .load("/Affairs.csv")) data.createOrReplaceTempView("res1") val affairs = "case when affairs>0 then 1 else 0 end as affairs," val df = (spark.sql("select " + affairs + "gender,age,yearsmarried,children,religIoUsness,education,occupation,rating" + " from res1 ")) val categoricals = df.dtypes.filter(_._2 == "StringType") map (_._1) val indexers = categoricals.map( c => new StringIndexer().setInputCol(c).setoutputCol(s"${c}_idx") ) val encoders = categoricals.map( c => new OneHotEncoder().setInputCol(s"${c}_idx").setoutputCol(s"${c}_enc").setDropLast(false) ) val colArray_enc = categoricals.map(x => x + "_enc") val colArray_numeric = df.dtypes.filter(_._2 != "StringType") map (_._1) val final_colArray = (colArray_numeric ++ colArray_enc).filter(!_.contains("affairs")) val vectorAssembler = new VectorAssembler().setInputCols(final_colArray).setoutputCol("features") /* val pipeline = new Pipeline().setStages(indexers ++ encoders ++ Array(vectorAssembler)) pipeline.fit(df).transform(df) */ /// // Create an XGBoost Classifier val xgb = new XGBoostEstimator(Map("num_class" -> 2, "num_rounds" -> 5, "objective" -> "binary:logistic", "booster" -> "gbtree")).setLabelCol("affairs").setFeaturesCol("features") // XGBoost paramater grid val xgbParamGrid = (new ParamGridBuilder() .addGrid(xgb.round, Array(10)) .addGrid(xgb.maxDepth, Array(10,20)) .addGrid(xgb.minChildWeight, Array(0.1)) .addGrid(xgb.gamma, Array(0.1)) .addGrid(xgb.subSample, Array(0.8)) .addGrid(xgb.colSampleByTree, Array(0.90)) .addGrid(xgb.alpha, Array(0.0)) .addGrid(xgb.lambda, Array(0.6)) .addGrid(xgb.scalePosWeight, Array(0.1)) .addGrid(xgb.eta, Array(0.4)) .addGrid(xgb.boosterType, Array("gbtree")) .addGrid(xgb.objective, Array("binary:logistic")) .build()) // Create the XGBoost pipeline val pipeline = new Pipeline().setStages(indexers ++ encoders ++ Array(vectorAssembler, xgb)) // Setup the binary classifier evaluator val evaluator = (new BinaryClassificationEvaluator() .setLabelCol("affairs") .setRawPredictionCol("prediction") .setMetricName("areaUnderROC")) // Create the Cross Validation pipeline, using XGBoost as the estimator, the // Binary Classification evaluator, and xgbParamGrid for hyperparameters val cv = (new CrossValidator() .setEstimator(pipeline) .setEvaluator(evaluator) .setEstimatorParamMaps(xgbParamGrid) .setNumFolds(3) .setSeed(0)) // Create the model by fitting the training data val xgbModel = cv.fit(df) // Test the data by scoring the model val results = xgbModel.transform(df) // Print out a copy of the parameters used by XGBoost, attention pipeline (xgbModel.bestModel.asInstanceOf[PipelineModel] .stages(5).asInstanceOf[XGBoostClassificationModel] .extractParamMap().toSeq.foreach(println)) results.select("affairs","prediction").show println("---Confusion Matrix------") results.stat.crosstab("affairs","prediction").show() // What was the overall accuracy of the model, using AUC val auc = evaluator.evaluate(results) println("----AUC--------") println("auc="+auc)

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