Python sklearn to draw Roc curves

Preface : Recent bioinformatics has talked about the AUC,Roc , two indicators, is doing project, requires the ROC curve,Sklearn inside has corresponding functions, so learn to learn.

Auc:

ROC:

Specific use of reference Sklearn:

Http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html

http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc_crossval.html# Example-model-selection-plot-roc-crossval-py

http://www.tuicool.com/articles/b22eYz (Bo Friends blog)

#coding: Utf-8print (__doc__) import numpy as Npfrom scipy import interpimport matplotlib.pyplot as Pltfrom sklearn import SV M, datasetsfrom sklearn.metrics import roc_curve, aucfrom sklearn.cross_validation import stratifiedkfold############# ################################################################### data IO and generation, import iris, do data preparation # import Some data to play Withiris = Datasets.load_iris () X = Iris.datay = iris.targetx, y = x[y! = 2], y[y! = 2]n_samples, N_featu res = x.shape# Add Noisy featuresrandom_state = np.random.RandomState (0) X = np.c_[x, Random_state.randn (n_samples, $ * n _features)]################################################################################ Classification and  ROC analysis# Classification, do ROC analysis # Run classifier with cross-validation and plot Roc curves# use 60 percent cross-validation, and draw roc curve CV = stratifiedkfold (y, n_folds=6) classifier = SVM. SVC (kernel= ' linear ', Probability=true, random_state=random_state) MEAN_TPR = 0.0MEAN_FPR = Np.linspace ( 0, 1, ALL_TPR =[]for I, (train, test) in enumerate (CV): #通过训练数据, establish a model using SVM linear kernel and test the test set to find the predicted score Probas_ = Classifier.fit (X[train], Y[trai N]). Predict_proba (X[test]) # Compute ROC curve and area the curve #通过roc_curve () function, find FPR and TPR, and threshold FPR, TPR, Thres Holds = Roc_curve (Y[test], probas_[:, 1]) MEAN_TPR + = Interp (MEAN_FPR, FPR, TPR) #对mean_tpr在mean_fpr处进行插值, call scipy via inte package RP () function mean_tpr[0] = 0.0 #初始处为0 ROC_AUC = AUC (FPR, TPR) #画图, only Plt.plot (FPR,TPR), variable ROC_AUC simply records the value of AUC, can be calculated by the AUC () function To 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, interpolated interpolation at each point multiple times average mean_tpr[-1] = 1.0 #坐标最后一个点为 (max) Mean_ AUC = AUC (MEAN_FPR, MEAN_TPR) #计算平均AUC值 # Draw the average ROC curve #print Mean_fpr,len (MEAN_FPR) #print mean_tprplt.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.xla Bel (' False Positive rate ') Plt.ylabel(' True Positive rate ') plt.title (' Receiver operating characteristic example ') plt.legend (loc= "lower Right") plt.show () 

Python sklearn to draw Roc curves