https://github.com/shenxiangzhuang/algo-timer
An easy-to-use algorithms timer.
https://github.com/shenxiangzhuang/algo-timer
python3 timer-application
Last synced: 9 months ago
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An easy-to-use algorithms timer.
- Host: GitHub
- URL: https://github.com/shenxiangzhuang/algo-timer
- Owner: shenxiangzhuang
- License: mit
- Created: 2019-11-23T01:32:53.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-11-23T07:11:11.000Z (about 6 years ago)
- Last Synced: 2025-03-01T06:34:22.860Z (10 months ago)
- Topics: python3, timer-application
- Language: Python
- Size: 2.08 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Algorithm Timer
## Overview
An easy-to-use algorithm timer.
## Install
[algo-timer in pypi](https://pypi.org/project/algo-timer/).
You can install `algotimer` with `pip`:
>pip install algo-timer
Note we use `pandas` `matplotlib`, and `seaborn` to plot the figure, so make sure you have
installed these packages in you environment before you use `algotimer`.
## Mechanism
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)
## Eaxmples
### Fibnacci
```python
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.
### Classification
```python
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`
