An easy and convienent way to performance test python code.
An easy and convenient way to performance test blocks of python code. Tired of writing separate scripts for your performance tests? Don't like coding in strings? Using the pyperform decorators, you can easily implement timeit tests to your functions with just one line!
Features of pyperform include:
- Quick, easy to implement in-code performance tests that run once when the function is defined
- Speed comparison of several functions.
- Validation of results between ComparisonBenchmarks
- Summary reports.
- Supports class functions as well as global functions.
- Performance tests can easily be disabled/enabled globally.
- Community-driven library of performance tests to learn from.
To install:
pip install pyperform
To use pyperform to benchmark functions, you need to add one of the following decorators:
@BenchmarkedFunction(setup=None,
classname=None,
largs=None,
kwargs=None
timeit_repeat=3,
timeit_number=1000)
@BenchmarkedClass(setup=None,
largs=None,
kwargs=None,
timeit_repeat=3,
timeit_number=1000)
@ComparisonBenchmark(group,
classname=None,
setup=None,
largs=None,
kwargs=None
validation=False,
timeit_repeat=3,
timeit_number=1000)
where largs is a list of arguments to pass to the function and kwargs is a dictionary of keyword arguments to pass to the function. The setup argument is described in the following section. All decorators have timeit_repeat and timeit_number arguments which are can be used to set the number of trials and repetitions to use with timeit. The ComparisonBenchmark has a validation flag, which when set to True, will attempt to compare the results of the functions in the group.
Imports can be added by appending the tag #! to the end of an import statement in a script. Pyperform will find all
import statements that are tagged with #! and import them into your benchmark.
For example:
from math import log #!
@BenchmarkedFunction(largs=(16,))
def log_base_2(x):
return log(x, 2)
Results in:
log_base_2 3.567 us
Alternative, you can set the tag for pyperform to search for by calling set_import_tag(tag)` with a string argument.
All decorators have a setup argument which can be either a function with no arguments, or string of code. If given a function, the body of the function is executed in the global scope. This means that objects and variables instantiated in the body of the function are accessible from within the benchmarked function.
Example:
from pyperform import BenchmarkedFunction
def _setup():
a = 10
@BenchmarkedFunction(setup=_setup, largs=(5,))
def multiply_by_a(b):
result = a * b
assert result == 50
return result
Results in:
multiply_by_a 3.445 us
Pyperform will also work on class methods, but in order to do so, we must instantiate an instance of the class.
This is done in BenchmarkedClass. Then once we have decorated the class with BenchmarkedClass, we can use
ComparisonBenchmark or BenchmarkedFunction to performance test the class's methods.
Note that when benchmarking class methods, the classname argument to ComparisonBenchmark must be provided.
This argument will hopefully be removed in the future.
In the BenchmarkedClass we instantiate a Person object and then run three benchmarked class-methods.
Two of the class-methods are ComparisonBenchmarks and will be compared with one another. To see the result, you must
call the ComparisonBenchmark.summarize() function. The third function is a duplicate of calculate_savings_method2 but
it is a BenchmarkedFunction instead. The result of BenchmarkedFunctions is printed when the script is run.
from pyperform import BenchmarkedClass, ComparisonBenchmark, BenchmarkedFunction
@BenchmarkedClass(largs=('Calvin', 24, 1000.,), kwargs={'height': '165 cm'})
class Person(object):
def __init__(self, name, age, monthly_income, height=None, *args, **kwargs):
self.name = name
self.age = age
self.height = height
self.monthly_income = monthly_income
@ComparisonBenchmark('Calculate Savings', classname="Person", timeit_number=100,
validation=True, largs=(55,), kwargs={'monthly_spending': 500})
def calculate_savings_method1(self, retirement_age, monthly_spending=0):
savings = 0
for y in range(self.age, retirement_age):
for m in range(12):
savings += self.monthly_income - monthly_spending
return savings
@ComparisonBenchmark('Calculate Savings', classname="Person", timeit_number=100,
validation=True, largs=(55,), kwargs={'monthly_spending': 500})
def calculate_savings_method2(self, retirement_age, monthly_spending=0):
yearly_income = 12 * (self.monthly_income - monthly_spending)
n_years = retirement_age - self.age
if n_years > 0:
return yearly_income * n_years
@BenchmarkedFunction(classname="Person", timeit_number=100,
largs=(55,), kwargs={'monthly_spending': 500})
def same_as_method_2(self, retirement_age, monthly_spending=0):
yearly_income = 12 * (self.monthly_income - monthly_spending)
n_years = retirement_age - self.age
if n_years > 0:
return yearly_income * n_years
You can print the summary to file or if ComparisonBenchmark.summarize() is not given an fs parameter, it will print to console.
report_file = open('report.txt', 'w')
ComparisonBenchmark.summarize(group='Calculate Savings', fs=report_file)
This results in a file report.txt that contains the ComparisonBenchmark's results:
Call statement:
instance.calculate_savings_method2(55, monthly_spending=500)
Function Name Time % of Fastest timeit_repeat timeit_number
----------------------------------------------------------------------------------------------------
Person.calculate_savings_method2 3.814 us 100.0 3 100
Person.calculate_savings_method1 65.479 us 5.8 3 100
----------------------------------------------------------------------------------------------------
Source Code:
----------------------------------------------------------------------------------------------------
def calculate_savings_method2(self, retirement_age, monthly_spending=0):
yearly_income = 12 * (self.monthly_income - monthly_spending)
n_years = retirement_age - self.age
if n_years > 0:
return yearly_income * n_years
----------------------------------------------------------------------------------------------------
def calculate_savings_method1(self, retirement_age, monthly_spending=0):
savings = 0
for y in range(self.age, retirement_age):
for m in range(12):
savings += self.monthly_income - monthly_spending
return savings
----------------------------------------------------------------------------------------------------
and printed to the screen, the results of the BenchmarkedFunction
same_as_method_2 3.788 us
ComparisonBenchmark has a optional argument validate. When validate=True, the return value of each
ComparisonBenchmark in a group is compared. If the results of the function are the not same, a ValidationError is raised.
Example:
from pyperform import ComparisonBenchmark
from math import sin #!
@ComparisonBenchmark('Group1', validation=True, largs=(100,))
def list_append(n, *args, **kwargs):
l = []
for i in xrange(1, n):
l.append(sin(i))
return l
@ComparisonBenchmark('Group1', validation=True, largs=(100,))
def list_comprehension(n, *args, **kwargs):
return 1
Output:
pyperform.ValidationError: Results of functions list_append and list_comprehension are not equivalent.
list_append: [0.8414709848078965, 0.9092974268256817, 0.1411200080598672, -0.7568024953079282, -0.9589242746631385, -0.27941549819892586, 0.6569865987187891, 0.9893582466233818, 0.4121184852417566, -0.5440211108893698, -0.9999902065507035, -0.5365729180004349, 0.4201670368266409, 0.9906073556948704, 0.6502878401571168, -0.2879033166650653, -0.9613974918795568, -0.750987246771676, 0.14987720966295234, 0.9129452507276277, 0.8366556385360561, -0.008851309290403876, -0.8462204041751706, -0.9055783620066239, -0.13235175009777303, 0.7625584504796027, 0.956375928404503, 0.27090578830786904, -0.6636338842129675, -0.9880316240928618, -0.404037645323065, 0.5514266812416906, 0.9999118601072672, 0.5290826861200238, -0.428182669496151, -0.9917788534431158, -0.6435381333569995, 0.2963685787093853, 0.9637953862840878, 0.7451131604793488, -0.158622668804709, -0.9165215479156338, -0.8317747426285983, 0.017701925105413577, 0.8509035245341184, 0.9017883476488092, 0.123573122745224, -0.7682546613236668, -0.9537526527594719, -0.26237485370392877, 0.6702291758433747, 0.9866275920404853, 0.39592515018183416, -0.5587890488516163, -0.9997551733586199, -0.5215510020869119, 0.43616475524782494, 0.9928726480845371, 0.6367380071391379, -0.3048106211022167, -0.9661177700083929, -0.7391806966492228, 0.16735570030280691, 0.9200260381967906, 0.8268286794901034, -0.026551154023966794, -0.8555199789753223, -0.8979276806892913, -0.11478481378318722, 0.7738906815578891, 0.9510546532543747, 0.25382336276203626, -0.6767719568873076, -0.9851462604682474, -0.38778163540943045, 0.5661076368981803, 0.9995201585807313, 0.5139784559875352, -0.4441126687075084, -0.9938886539233752, -0.6298879942744539, 0.31322878243308516, 0.9683644611001854, 0.7331903200732922, -0.1760756199485871, -0.9234584470040598, -0.8218178366308225, 0.03539830273366068, 0.8600694058124533, 0.8939966636005579, 0.10598751175115685, -0.7794660696158047, -0.9482821412699473, -0.24525198546765434, 0.683261714736121, 0.9835877454343449, 0.3796077390275217, -0.5733818719904229, -0.9992068341863537]
list_comprehension: 1