Code Like a Girl

Today is a Blog Action Day when all bloggers are encouraged to blog about issues that are important to them, primarily to raise awareness among their readers. You may gather from the title of this blog what issue this post will be about.

We live in interesting times. The rights that women enjoy around the world are so non-uniformly distributed. Take, for example Sweden or Norway where talking about gender division almost no longer makes sense – they have broken down and extinguished gender bias from their society. Then look at the Middle East. I find it mind-boggling that in today’s day and age a woman cannot walk in a street unsupervised by a man? What can she possibly do if she is unsupervised? Commit a crime, disgrace the family? Most likely just run away…

Anyway, the main point of this blog is not as far reaching as the problems of the Middle East. Instead I will aim closer to home. A few years ago Always ran a #LikeAGirl campaign to highlight the inherent prejudice women face on the daily basis. When participants were asked to do something “like a girl”, they would emphasize the weaker, feebler, and less confident characteristics of whatever they were asked to portrait. And it was not just the male participants. Women of all ages took part and they too interpreted “like a girl” as meaning weaker, less capable of… No one responded to the request by saying: “Like a girl? You mean just normally, like a human being that just happens to be a young female?”.

So, why is this happening? And to what extend women are responsible for this? In my view, this is happening because women and girls allow this to happen and they are very responsible for the extend of the stigma. Note that this is not a blame. This is a conclusive statement based on what I have personally experienced and observed. I too went through a period of self-doubt, questioning if Computer Science was the right career choice for a female. I also used to feel uncomfortable in university computer labs occupied primarily by guys. I rejected the idea of blending-in by trying to be more manly, but I did avoid wearing earrings or make-up in class as to not emphasize the obvious. I did talk myself out of participating in coding competitions because almost all contestants would be men. Therefore, I too am responsible for carrying the gender divide around with me in a pocket for daily use, for a long time. But, luckily, there was a point at which I stopped. Because I got tired of comparing myself and questioning my decisions. I also got tired of seeing how easily, both, men and women apply the double-standards. If you are girl, do everything like a girl. Live your whole life like a girl. Make sure you do emphasize the obvious and don’t try to blend in. “Embracing who you are” became such a cliché statement, but it concisely summaries what I am saying. Drop the gender divide yourself before you ask others to do the same. And lastly, code like a girl too. A lot.

Python gotchas

Here is the thing – I am a big fan of Python programming language. Now that there is an Intel distribution of Python, I don’t think I ever want to write in any other language again…

Having said that, Python has its moments. Most of the examples below are based on Fluent Python book by Luciano Romalho. I highly recommend it to all Python programmers.

Here are some “gotchas” I am taking about:

***********************************
*   Leaking Variables 
*   Times what? 
*   An Inside Job
*   Deeply Shallow
*   Out of Order
*   We are all Sharing  
***********************************

Leaking Variables

In Python 2.x variables created inside list comprehension are leaked, offering nasty surprise.

x = "I need you later"
ctoten = [-1, -2, -3, -4, -5, -6, -7, -8, -9, -10]
abs_ctoten = [abs(x) for x in ctoten]
print("Oh no! ", x) # prints x to be -10

Note that this problem does not exist in generator expressions (aka. genexps):

y = "abcde"
w = "see you later"
upper_y = array.array('c',(str.upper(w) for w in y))
print ("Still here: ", w) # prints w to be "see you later"

Times what?

Let’s say I need a string of 20 a’s. I can simply create it like this:

twenty_as = "a"*20

Great. I now need a list of three lists. I proceed to create it with * and end up with another surprise!

abc_list = [['a', 'b','c']]*3
print abc_list
abc_list[1][1]='x'
print abc_list  # prints ['a', 'x', 'c'], ['a', 'x', 'c'], ['a', 'x', 'c']]

This happens because the abc_list is made of references to the same [‘a’, ‘b’, ‘c’] list. The solution is to ensure that each list a separate/new copy:

abc_list = [['a', 'b','c'] for i in range(3)]

An Inside Job

Tuples are immutable and one can take an advantage of this when an immutability is required. However, if you put a mutable object inside a tuple, keep in mind that it can still be changed.

imm = (1,2)
imm[0]+=1 # will throw an exception
imm2 = (1, 2, [3, 4])
imm2[2]+=[10] # succeeds to modify the inner list and throws an exception

Deeply Shallow

You did not think I was going to write a post on Python’s dark corners without touching on deep copying, did you?
Here is a nice little trick for you to create a shallow copy with a slicing operator. It works the first time, but fails the second time when we need a deep copy instead.

list1 = [1,2,3]
list2 = list1[:] # shallow copy
list2[2] = 5

print ([(l, k) for l, k in zip(list1, list2)]) # all good

list1 = [1, 2, 3, [8,9]]
list2=list1[:]  # shallow copy again
list2[3][0] = 7

print ([(l, k) for l, k in zip(list1, list2)]) # shows that both are modified

Out of Order

Unless you are using collections.OrderedDict, the order of Python’s dicts’s keys and values cannot be relied on. This has to do which how Python’s dicts are stored in the memory. Also, dicts equality is determined on the basis of key-item pairs, and not their order in the dict. Take a look at the example below. The output of this code is implementation dependent. Finally, adding new items to dicts will likely to reorder the keys. Python’s sets also do not guarantee a particular order will be maintained. There is no “orderedset” in the standard library, but if you need one, you can find a PyPi package (e.g. orderedset).

FRUIT_CODES = [
    ("orange", 1),
    ("apple", 45),
    ("banana", 70),
    ("grapes", 81),
    ("pineapple", 86),
    ("kiwi", 52),
    ("papaya", 413),
    ("mango", 55),
    ("lemon", 62),
    ("nectarine", 910)
]

orig = copy.copy(FRUIT_CODES)
sorted1 = sorted(FRUIT_CODES, key=lambda x:x[0])
sorted2 = sorted(FRUIT_CODES, key=lambda x:x[1])

fruit_dict = dict(FRUIT_CODES)
fruit_sorted_dict1 = dict(sorted1)
fruit_sorted_dict2 = dict(sorted2)

print fruit_dict.keys() == fruit_sorted_dict1.keys() and fruit_sorted_dict1.keys() == fruit_sorted_dict2.keys() # prints False or True (implementation dependent)
print fruit_dict == fruit_sorted_dict1 and fruit_sorted_dict1 == fruit_sorted_dict2 # prints True

We are all Sharing

In Python, mutable types are passed to functions by sharing. This means that a function/method can modify the parameter, but it cannot replace it with another object. Here is a typical “gotcha” with functions being able to modify its parameters:

def plusone(my_list):
    my_list.append(1)  # can modify
    

def newlife(my_list, your_list):
    my_list=your_list  # cannot replace with a new object

first_list = [2, 3, 4]
plusone(first_list)
print first_list # prints [2, 3, 4, 1]

second_list = [5, 6, 7]
newlife(first_list, second_list)
print first_list # prints [2, 3, 4, 1]

This should give you enough “food for thought”. Happy programming everyone! 🙂

samplepy – a new Python Sampling Package

Hello my blog readers,

This post is to introduce a new Python package samplepy. This package was written to simplify sampling tasks that so often creep-up in machine learning. The package implements Importance, Rejection and Metropolis-Hastings sampling algorithms.

samplepy has a very simple API. The package can be installed with pip by simply running pip install samplepy. Once installed, you can use it to sample from any univariate distribution as following (showing rejection sampling use):

 

from samplepy import Rejection
import matplotlib.pyplot as plt
import numpy as np

# define a unimodal function to sample under
f = lambda x: 2.0*np.exp(-2.0*x)
# instantiate Rejection sampling with f and required interval
rej = Rejection(f, [0.01, 3.0])
# create a sample of 10K points
sample = rej.sample(10000, 1)  

# plot the original function and the created sample set
x = np.arange(0.01, 3.0, (3.0-0.01)/10000)
fx = f(x)

figure, axis = plt.subplots()
axis.hist(sample, normed=1, bins=40)
axis2 = axis.twinx()
axis2.plot(x, fx, 'g', label="f(x)=2.0*exp(-2*x)")
plt.legend(loc=1)
plt.show()

 

Sample from f(x)=2.0*exp(-2*x) over [0.01, 3.0]
Sample from f(x)=2.0*exp(-2*x) over [0.01, 3.0]

The three sampling method (i.e. Rejection, Importance and MH) are quite different and will achieve slightly different results for the same function. Performance is another important difference factor, with Metropolis-Hastings probably being the slowest. Let’s compare how the three sampling algorithm deliver on a bi-modal univariate function:

f(x)=exp(-x^{2})*(2+\sin(5x)+\sin(2x))

 

from samplepy import Rejection, Importance, MH
import matplotlib.pyplot as plt
import numpy as np


f = lambda x: np.exp(-1.0*x**2)*(2.0+np.sin(5.0*x)+np.sin(2.0*x))
interval = [-3.0, 3.0]
rej = Rejection(f, interval)  # instantiate Rejection sampling with f and interval
sample = rej.sample(10000, 1)    # create a sample of 10K points

x = np.arange(interval[0], interval[1], (interval[1]-interval[0])/10000)
fx = f(x)

figure, axis = plt.subplots()
axis.hist(sample, normed=1, bins=40)
axis2 = axis.twinx()
axis2.plot(x, fx, 'g', label="Rejection")
plt.legend(loc=1)
plt.show()

mh = MH(f,interval)
sample = mh.sample(20000, 100, 1)  # Make sure we have enough points in the sample!

figure, axis = plt.subplots()
axis.hist(sample, normed=1, bins=40)
axis2 = axis.twinx()
axis2.plot(x, fx, 'g', label="MH")
plt.legend(loc=1)
plt.show()

imp = Importance(f, interval)
sample = imp.sample(10000, 0.0001, 0.0010) # create a sample where essentially no extra importance is given to any quantile

figure, axis = plt.subplots()
axis.hist(sample, normed=1, bins=40)
axis2 = axis.twinx()
axis2.plot(x, fx, 'g', label="Importance")
plt.legend(loc=1)
plt.show()

rejection
mhimportance

Hopefully this gives you enough examples to get you started using samplepy!