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An easy-to-use algorithm timer.
We use a context-manager and with
in Python to give an convinent way to test
a specific block of code. Just see the following examples.
Note that we design this plot function here to test some algorithms' runing time and you can use it to test(and plot) the time of any block of code with minor change in source code(the TimerPloter
class, specifically)
from algotimer import Timer, TimerPloter
def fib(n):
if n <= 2:
return 1
return fib(n - 1) + fib(n - 2)
def fibMemo(n):
cache = {1: 1, 2: 1}
def rec(n):
if n not in cache:
cache[n] = rec(n - 1) + rec(n - 2)
return cache[n]
return rec(n)
if __name__ == '__main__':
with Timer('fib, 30') as t:
print('fib(30) = ', fib(30))
with Timer('fib, 35') as t:
print('fib(35) = ', fib(35))
with Timer('fibMemo, 30') as t:
print('fibMemo(30) = ', fibMemo(30))
with Timer('fibMemo, 35') as t:
print('fibMemo(35) = ', fibMemo(35))
ploter = TimerPloter()
ploter.plot()
The output:
fib(30) = 832040
fib, 30 Spends 0.217 s
fib(35) = 9227465
fib, 35 Spends 2.434 s
fibMemo(30) = 832040
fibMemo, 30 Spends 0.0 s
fibMemo(35) = 9227465
fibMemo, 35 Spends 0.0 s
And we get two files:
logging,csv
is the time data.
fib, 30, 0.217
fib, 35, 2.434
fibMemo, 30, 0.0
fibMemo, 35, 0.0
And Timer.png
, a plot of the data.
from algotimer import Timer, TimerPloter
from sklearn import datasets
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
iris = datasets.load_iris()
with Timer('GaussianNB, Train'):
gnb = GaussianNB()
clf = gnb.fit(iris.data, iris.target)
with Timer('GaussianNB, Test'):
y_pred = clf.predict(iris.data)
print("Number of mislabeled points out of a total %d points : %d"
% (iris.data.shape[0], (iris.target != y_pred).sum()))
with Timer('KNN(K=3), Train'):
neigh = KNeighborsClassifier(n_neighbors=3)
clf = neigh.fit(iris.data, iris.target)
with Timer('KNN(K=3), Test'):
y_pred = clf.predict(iris.data)
print("Number of mislabeled points out of a total %d points : %d"
% (iris.data.shape[0], (iris.target != y_pred).sum()))
with Timer('KNN(K=5), Train'):
neigh = KNeighborsClassifier(n_neighbors=5)
clf = neigh.fit(iris.data, iris.target)
with Timer('KNN(K=5), Test'):
y_pred = clf.predict(iris.data)
print("Number of mislabeled points out of a total %d points : %d"
% (iris.data.shape[0], (iris.target != y_pred).sum()))
# plot it
ploter = TimerPloter()
ploter.plot()
The output:
GaussianNB, Train Spends 0.001 s
Number of mislabeled points out of a total 150 points : 6
GaussianNB, Test Spends 0.001 s
KNN(K=3), Train Spends 0.019 s
Number of mislabeled points out of a total 150 points : 6
KNN(K=3), Test Spends 0.019 s
KNN(K=5), Train Spends 0.001 s
Number of mislabeled points out of a total 150 points : 5
KNN(K=5), Test Spends 0.01
File logging.csv
:
GaussianNB, Train, 0.001
GaussianNB, Test, 0.001
KNN(K=3), Train, 0.019
KNN(K=3), Test, 0.019
KNN(K=5), Train, 0.001
KNN(K=5), Test, 0.01
File Timer.png
FAQs
An easy-to-use algorithms timer.
We found that algo-timer demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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