I Want To Eavalute The Time Needed To Perform Prediction Using A Trained Model

I know timeit can be used to measure the elapsed time, but I don't how to implement in my code. For example, I will evaluate the model performance as follows: scores = model.evalua

Solution 1:

Here is a basic setup:

import time
start_time = time.time()
# code
print("time - {}".format(time.time()-start_time))

You can also make use of Python's function wrappers, and make a wrapper to time your functions. eg.

import time

def getime(func):
    def func_wrapper(*args, **kwargs):
        start_time = time.time()
        func(*args, **kwargs)
        print("function {} completed in - {} seconds".format(
            func.__name__,
            time.time()-start_time))
    return func_wrapper

# ------------ test example of wrapper --------- #
@getime
def foo():
    for i in range(1000):
        for j in range(2000):
            pass

foo()

Output:

function foo completed in - 0.13300752639770508 seconds

Solution 2:

Try:

import time
...
t = time.time()
scores = model.evaluate(X_test, Y_test, verbose=0)
elapsed_time = time.time()-t

To check it 10000 times (for more accurateness), try:

def check():
    t = time.time()
    scores = model.evaluate(X_test, Y_test, verbose=0)
    elapsed_time = time.time()-t
    return elapsed_time
average_elapsed_time = [sum(check() for i in range(10000))/10000][0]