How to plot the average of ROC curves?
Solved by choosing a fixed set of FPRs, then using no.interp for each pair of (fpr,tpr) returned by sklearn.metrics.roc_curve to get the corresponding TPRs, then averaged all ROCs using np.mean
I am attempting to perform outlier detection and I have 15 different test sets and 3 different models (a PCA-based classifier, One Class SVM and Isolation Forest).  
For PCA-based classification, I have written my own code for generating ROC curves. I have 2 lists pcafprs and pcatprs, each of which has 15 sublists, each sublist representing the False Positive Ratios and True Positive Ratios, required to plot the ROC curve.  
For One-Class SVM and Isolation Forest, I can get the (fpr, tpr) from sklearn.metrics.roc_curve. Similar to PCA, I have ocsvmfprs and ocsvmtprs for One-Class SVM, and isoforestfprs and isoforesttprs for Isolation Forest.  
For each test set, I can iterate over the FPR and TPR lists and plot the ROC curve. The code might look like:
for i in range(len(pcafprs)):
    plt.plot(pcafprs[i], pcatprs[i]) #Plot the ROC curve
    plt.show()

For each of the 3 models, I want to be able to plot the average of all 15 ROC curves for the 15 test sets in one graph. I cannot simply do np.mean over the arrays containing the TPRs and FPRs because the FPRs returned by sklearn.metrics.roc_curve are all different points for each test set.
For PCA, I have tried using np.mean(pcatprs, axis=0) and np.mean(pcafprs, axis=0) to average out all the TPRs and FPRs so that I can plot a single graph which represents the mean of all the test sets. This works because for PCA I have generated the same number of FPRs and TPRs for each test set.
However, I am unable to control the no. of FPRs and TPRs returned for each test set by sklearn.metrics.roc_curve, and it turns out that it returns different number of values for each test set. Due to this, I cannot use np.mean to find the average ROC curve.  
tl,dr: Is there a way to plot the average of multiple lines on a graph without having the equations and only having some points that lie on the lines, where we have a different number of points available for each line?