GraphMaker.py

import matplotlib.pyplot as plt


perc = []
acc = []
precis = []
recall = []

perc2 = []
acc2 = []
precis2 = []
recall2 = []
for i in range(20,21,10):
    with open("test/id3/FULLTEST_out_keys{}.txt".format(i), "r", encoding='utf-8') as f:
        lines = f.readlines()
        data = [line.split(",") for line in lines]
        data = sorted(data, key=lambda x : float(x[0]))
            
        perc = [float(line[0])*100 for line in data]
        acc = [(float(line[1]))*100 for line in data]
        precis = [float(line[2])*100 for line in data]
        recall = [float(line[3])*100 for line in data]

    f1_test2 = []
    with open("BIS-CHR-ID3-tests/{}_False.txt".format(i), "r", encoding='utf-8') as f:
        lines = f.readlines()
        data = [line.split(" ") for line in lines]
        #data = sorted(data, key=lambda x : float(x[0]))
            
        perc2 = [float(line[0]) for line in data]
        acc2 = [(float(line[1]))*100 for line in data]
        precis2 = [float(line[2])*100 for line in data]
        recall2 = [float(line[3])*100 for line in data]
        f1_test2 = [float(line[4])*100 for line in data]


    f1_test = [(2*item[0]*item[1])/(item[0]+item[1]) for item in list(zip(precis, recall))]
    #f1_test2 = [(2*item[0]*item[1])/(item[0]+item[1]) for item in list(zip(precis2, recall2))]
    

    #print("TEST ==============================")
    #for entry in list(zip(perc, f1_test)):
    #    print(entry[0], round(float(entry[1]),2))
    #print("TRAIN ==============================")
    #for entry in list(zip(perc, f1_train)):
    #    print(entry[0], round(float(entry[1]),2))

    #minerr = min(acc)
    plt.plot(perc, acc, '-b', label="SYR-GKR-TOU")
    plt.plot(perc2, acc2, '-g', label="BIS-CHR")
    #plt.plot(perc, f1_test, '-r', label="F1 (SYR-GKR-TOU)")
    #plt.plot(perc2, f1_test2, '-g', label="F1 (BIS-CHR)")
    #plt.plot(perc, [min(acc) for _ in perc], '--r', label="minimum error \nachieved ({}%)".format(round(minerr,2)))

    plt.title("ID3 Classifier - {} keys\nComparison between 2 different implementations.".format(i))
    plt.xlabel("training data (% of total)")
    plt.ylabel("accuracy (%)")
    #plt.ylim([50,90])
    plt.legend() # required to show the labels
    plt.show()