CIS 192: Lecture 13 Scientific Computing and Unit Testing

Transcription

1 CIS 192: Lecture 13 Scientific Computing and Unit Testing Lili Dworkin University of Pennsylvania

2 Scientific Computing I Python is really popular in the scientific and statistical computing world I Why? Python is slow! I So we use libraries with routines written in C/C++ I We will look at Numpy/Scipy and Matplotlib/Pylab I But first, how do we benchmark Python?

3 Cosine Similarity I Goal: find how similar two vectors are I One measure: compute angle between them I Cosine similarity of two vectors U and V : I cos(0) = 1, cos( ) = 1 cos( ) = U V kukkv k

4 Cosine Similarity def cosine_similarity(u,v): mag1, mag2, dot = 0.0, 0.0, 0.0 for a,b in zip(u,v): dot += a * b mag1 += a ** 2 mag2 += b ** 2 return dot / (math.sqrt(mag1) * math.sqrt(mag2))

5 Timeit Previously I used time.time() don t do that. Instead: >>> import timeit >>> t = timeit.timer("<statement to time>", "<setup code>") >>> t.timeit() I The second argument is usually an import that sets up a virtual environment for the statement I timeit calls the statement 1 million times and returns the total elapsed time I timing.py

6 Numpy/Scipy I Basic operations numpy_demo.py I Doing things faster numpy_timing.py

7 Numpy/Scipy I Fast integer and floating point types (numpy.int, numpy.float) I Fast arrays (numpy.array) and matrices(numpy.ndarray), and fast operations over every element I Tons of functions and algorithms, including linear algebra, Fourier transforms, etc I Scipy depends on Numpy, and gathers a lot of high level science and engineering modules together (integrate, linalg, optimize, stats...)

8 Matplotlib/Pylab I Plotting/charting library I Good alternative to Excel I Matpotlib is the whole package I matplotlib.pyplot - non-interactive plotting (scripting) I matplotlib.pylab - interactive calculations and plotting I pylab_demo.py

9 Polya s Urn

10 Doctest The doctest module searches for pieces of text that look like interactive Python sessions, and then executes those sessions to verify that they work exactly as shown.

11 Doctest >>> import doctest >>> options = (doctest.ignore_exception_detail doctest.normalize_whitespace) >>> doctest.testmod(optionflags=options)

12 Doctest How do exceptions work? >>> factorial(30.1) Traceback (most recent call last):... ValueError: n must be exact integer If we specified doctest.ignore_exception_detail, everything to the right of the colon is ignored. Example in doctest_demo.py.

13 Unit Testing I Well-written programs can be broken up into units I I I I Functions, methods Classes Modules Packages I Unit testing aims to test the functionality of all the units in your program

14 Unittest I The unittest module makes it relatively easy to write a suite of unit tests for your programs I Modeled after JUnit, a Java unit testing framework I Caveat: many ways to use it, we ll just look at one approach

15 Random Python s random module: >>> import random >>> random.random() >>> random.randint(10, 20) 18 >>> seq = [2, 4, 6, 8, 10] >>> random.shuffle(seq) >>> seq [8, 4, 10, 2, 6] >>> random.choice(seq) 4 >>> random.sample(seq, 3) [4, 2, 6]

16 Unittest import unittest import random class TestSequenceFunctions(unittest.TestCase): def setup(self): self.seq = range(10) def test_shuffle(self): def test_choice(self): def test_sample(self): if name == ' main ': unittest.main()

17 Unittest I Inherit from unittest.testcase, abaseclassfortestcase I Here we used it to define multiple test cases at once I The setup method is called before each test case is run I Any method whose name starts with test defines a test case I unittest.main() runs all test cases

18 Unittest How do we write test cases? Use TestCase.assert* methods: I self.assertequal() I self.asserttrue() I self.assertraises() Let s practice in testing.py.

19 Digression Each of the following functions takes a callable and a list of arguments to provide it: self.assertraises(valueerror, random.sample, self.seq, 20) and Thread(target=add, args=(5,6))

20 Digression Let s look at the headers : assertraises(exception, callable, *args, **kwds) vs. Thread(target=None, args=(), kwargs={})

21 Unittest I unittest distinguishes between failures and errors I Failure: The assert statement was wrong I self.assertequal(range(5), range(10)) I Error: Something is wrong with the code I print 5 + hi