pos_track5.append(pos) # can6 = [pool2[i] for i in sort_order_certain6[start:start + step]] # train6.extend(can6) # pos = Counter(labels[train6])["yes"] # pos_track6.append(pos) start = start + step print("Round #{id} passed\r".format(id=round), end="") result["begin"] = begin result["stable"] = stable result["x"] = steps result["linear_review"] = pos_track result["simple_active"] = pos_track2 result["semi_continuous_aggressive"] = pos_track3 result["continuous_active"] = pos_track4 result["aggressive_undersampling"] = pos_track5 # result["smote"] = pos_track6 result["continuous_aggressive"] = pos_track7 result["semi_continuous"] = pos_track8 result["new_continuous_aggressive"] = pos_track9 return result if __name__ == "__main__": eval(cmd())
datalist=['beck-s','farmer-d','kaminski-v','kitchen-l','lokay-m','sanders-r','williams-w3'] plt.figure() result={} for classifier in c_name: Y_100 = list() for filename in datalist: with open('./dump/'+classifier+'_'+filename+'.pickle', 'rb') as handle: result[filename] = pickle.load(handle) tmp = result[filename]['F_M'] tmp100 = np.median(tmp['100']) t = Y_100 + [tmp100] Y_100 = t line,=plt.plot(range( len(datalist) ),Y_100,label=classifier) plt.xticks(range( len(datalist) ), (datalist)) plt.yticks(np.arange(0,1.0,0.2)) plt.ylabel("F_M score") plt.xlabel("Training Folder") plt.legend(bbox_to_anchor=(0.35, 1), loc=1, ncol = 1, borderaxespad=0.) plt.savefig("../Results/comp_classifier.eps") plt.savefig("../Results/comp_classifier.png") if __name__ == '__main__': eval(cmd())