nokay.py

import matplotlib.pyplot as plt

train_set = [int(i) for i in ['1000', '2000', '4000', '8000', '10000', '20000']]


y1 = []  # Only molecular descriptors
y2 = []  # Mol Desc + DFTB

for i in train_set:
    try:
        f1 = open("conv/withdft/new/%s/errors_test.dat" % i, 'r')
        f2 = open("conv/withdft/new/%s/errors.dat" % i, 'r')

    except Exception as e:
        print("*******\n")
        print(e)
        print(i)
        continue
    lines1 = f1.readlines()
    a, b, c = lines1[0].split()
    y1.append(round(float(b), 3))  # Rounded upto 3 decimal places

    lines2 = f2.readlines()
    a, b, c = lines2[0].split()
    y2.append(round(float(b), 3))  # Rounded upto 3 decimal places

plt.plot(train_set, y1, 's-', label='On test data')
plt.plot(train_set, y2, 's-', label='On entire dataset')

plt.annotate('(%s, %s)' % (20000, y1[-1]), xy=(20000, y1[-1]), textcoords='data')
plt.annotate('(%s, %s)' % (10000, y1[-2]), xy=(10000, y1[-2]), textcoords='data')

plt.annotate('(%s, %s)' % (20000, y2[-1]), xy=(20000, y2[-1]), textcoords='data')
plt.annotate('(%s, %s)' % (10000, y2[-2]), xy=(10000, y2[-2]), textcoords='data')

plt.xlabel('Training size')
plt.ylabel('MAE (eV)')
plt.title('Learning Curve of simple Sequential network')
plt.legend()
plt.show()