Area Under the Curve (AUC) calculation

auc.py

"""
References

1. Calculating area under the curve (AUC) using Riemann Sum - https://en.wikipedia.org/wiki/Riemann_sum
2. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html#sklearn.metrics.auc
 - it uses np.trapz()
    - Code: https://github.com/numpy/numpy/blob/v1.22.0/numpy/lib/function_base.py#L4686-L4797
3. Other methods implemented in TFlow
    - https://wikidocs.net/24605
    - Note: This method only works right when we provide a list x that has evenly spaced values
"""


import os
import pdb
import sklearn
import sklearn.metrics
import numpy as np
from math import pi
import matplotlib.pyplot as plt

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" 
import tensorflow as tf

# Trapezoidal Method
## Note that dx is fixed in this case so will give incorrect results for when x does not have evenly spaced values
# def integral(y, x):
#     dx = (x[-1] - x[0]) / (int(x.shape[0]) - 1)
#     print (dx)
#     return ((y[0] + y[-1])/2 + tf.reduce_sum(y[1:-1])) * dx

def integral(y, x):
    dx = x[1:]-x[:-1]
    dy = (y[:-1] + y[1:])/2
    return tf.math.reduce_sum(dx*dy)

if 0:
    a = tf.constant(0, dtype=tf.float64)
    b = tf.constant(pi/2, dtype=tf.float64)
    x = tf.linspace(a, b, 2**5+1)
elif 0:
    x = tf.constant([0.0, 0.05] + np.arange(0.1,1.0,0.05).tolist() + [1.0])
elif 1:
    x = tf.constant(np.arange(0.00001, 0.0001, 0.00001).tolist() + np.arange(0.0001, 0.001, 0.0001).tolist() + np.arange(0.001, 0.01, 0.001).tolist() + np.arange(0.01, 0.1, 0.005).tolist() + np.arange(0.1,1.0,0.05).tolist() + [1.0])

y = tf.sin(x)
result_tflow   = integral(y, x)
result_sklearn = sklearn.metrics.auc(x, y)
result_numpy   = np.trapz(y, x)

print (' - TFlow  : {:.7f}'.format(result_tflow))
print (' - Sklearn: {:.7f}'.format(result_sklearn))
print (' - Numpy  : {:.7f}'.format(result_numpy))

plt.plot(x, y)
plt.show(block=False)

pdb.set_trace()