Python library for tracking time and displaying progress bars
Project description
Chronometry
ProgressBar
Estimator
Estimator
is an object that estimates the running time of a single argument function.
You can use it to avoid running a script for too long.
For example, if you want to cluster a large dataset and running it might take too long,
and cost too much if you use cloud computing,
you can create a function with one argument x
which takes a sample with x
rows
and clusters it; then you can use Estimator
to estimate how long it takes to run it
on the full dataset by providing the actual number of rows to the estimate()
method.
Estimator
uses a Polynomial Linear Regression model
and gives more weight to larger numbers for the training.
Usage
from chronometry import Estimator
from time import sleep
def multiply_with_no_delay(x, y):
return (x ** 2 + 0.1 * x ** 3 + 1) * 0.00001 + y * 0.001
def multiply(x, y):
sleep_time = multiply_with_no_delay(x, y)
if sleep_time > 30:
raise
sleep(sleep_time)
if y == 6:
sleep(12)
elif 7 < y < 15:
raise Exception()
return sleep_time
estimator = Estimator(function=multiply, polynomial_degree=3, timeout=5)
# the `unit` argument chooses the unit of time to be used. By default unit='s'
estimator.auto_explore()
estimator.predict_time(x=10000, y=10000)
The above code runs for about 53 seconds and then estimates that
multiply(10000, 10000)
will take 1002371.7 seconds which is only slightly
smaller than the correct number: 1001010 seconds.
max_time
is the maximum time allowed for the estimate function to run.
If you are using Estimator
in Jupyter,
you can plot the measurements with the plot()
method (no arguments needed) which
returns a matplotlib
AxesSubplot
object and displays it at the same time.
estimator.plot('x')
estimator.plot('y')
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