This notebook demonstrates the use of inheritance to extend Python’s Counter class to implement Multisets, PMFs, and suites of Bayesian hypotheses.

from __future__ import print_function, division

from collections import Counter
import numpy as np


A Counter is a map from values to their frequencies. If you initialize a Counter with a string, you get a map from each letter to the number of times it appears. If two words are anagrams, they yield equal Counters, so you can use Counters to test anagrams.

def is_anagram(word1, word2):
    """Checks whether the words are anagrams.

    word1: string
    word2: string

    returns: boolean
    return Counter(word1) == Counter(word2)
is_anagram('tachymetric', 'mccarthyite')
is_anagram('banana', 'peach')

Exercise: The Counter class inherits from dict so all methods and functions that work with a dictionary will also work with a Counter.

Read the documentation of Counter, then use a Counter to find the three most common letters in the word “pneumonoultramicroscopicsilicovolcanoconiosis”.

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A Counter is a natural representation of a multiset, which is a set where the elements can appear more than once. You could use multisets for a game like Scrabble to see if a given set of tiles can be used to spell a given word.

Exercise: Write a definition for a class called Multiset that inherits from Counter and defines an additional method called is_subset, which should take self and other as parameters, where other is another Multiset.

It should check whether self is a subset of other; for multisets, that means that every element of self appears in other with at least the same frequency. For example, aa is a subset of aaab, but aabb is not.

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The following function uses Multiset.is_subset to check whether a particular word can be spelled using a particular set of tiles.

def can_spell(word, tiles):
    """Checks whether a set of tiles can spell a word.

    word: string
    tiles: string

    returns: boolean
    return Multiset(word).is_subset(Multiset(tiles))
can_spell('SYZYGY', 'AGSYYYZ')
can_spell('omelette', 'breaking a few eggs')

Optional Exercise: If you change the name of is_subset to __le__, you can use the <= operator to test whether one Multiset is a subset of another.

Probability Mass Functions

You can also extend Counter to represent a probability mass function (PMF). A PMF is a map from possible outcomes to their probabilities. The probabilities in a PMF are “normalized” if they add up to 1 (and they are all non-negative).

The following PMF class inherits from Counter and adds the following methods:

class Pmf(Counter):
    """A Counter with probabilities."""

    def normalize(self):
        """Normalizes the PMF so the probabilities add to 1."""
        total = sum(self.values())
        for key in self:
            self[key] /= total

    def __add__(self, other):
        """Adds two distributions.

        The result is the distribution of sums of outcomes from the
        two distributions.
        Note that this method is only correct if the selections from
        the two distributions are independent; that is, if the outcome
        of the first selection does not affect the probabilities of
        the outcomes for the second selection.

        other: Pmf

        returns: new Pmf
        pmf = Pmf()
        for key1, prob1 in self.items():
            for key2, prob2 in other.items():
                pmf[key1 + key2] += prob1 * prob2
        return pmf

    def render(self):
        """Returns outcomes and their probabilities, suitable for plotting."""
        return zip(*sorted(self.items()))

As an example, we can make a Pmf object that represents a 6-sided die.

d6 = Pmf([1,2,3,4,5,6])
d6.normalize() = 'one die'

Using the add operator, we can compute the distribution for the sum of two dice.

d6_twice = d6 + d6 = 'two dice'

for key, prob in d6_twice.items():
    print(key, prob)

Using sum or np.sum, we can compute the distribution for the sum of three dice.

# if we use the built-in sum we have to provide a Pmf additive identity
pmf_ident = Pmf([0])
d6_thrice = sum([d6]*3, pmf_ident)
# with np.sum, we don't need an identity
d6_thrice = np.sum([d6]*3) = 'three dice'

And then plot the results (using Pmf.render)

import matplotlib.pyplot as plt
%matplotlib inline
for die in [d6, d6_twice, d6_thrice]:
    xs, ys = die.render()
    plt.plot(xs, ys,, linewidth=3, alpha=0.5)

Exercise: Suppose you are fighting an orc who will die if he suffers 9 or more hit points of damage. You attack successfully with short sword and dagger, so you can roll a d6 and a d4 for total damage. What is the probability that you kill the orc?

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Bayesian statistics

A Suite is a Pmf that represents a set of hypotheses and their probabilities; it provides bayesian_update, which updates the probability of the hypotheses based on new data.

Suite is an abstract parent class; child classes should provide a likelihood method that evaluates the likelihood of the data under a given hypothesis. update_bayesian loops through the hypotheses, evaluates the likelihood of the data under each hypothesis, and updates the probabilities accordingly. Then it re-normalizes the PMF.

class Suite(Pmf):
    """Map from hypothesis to probability."""

    def bayesian_update(self, data):
        """Performs a Bayesian update.
        Note: called bayesian_update to avoid overriding dict.update

        data: result of a die roll
        for hypo in self:
            like = self.likelihood(data, hypo)
            self[hypo] *= like

    def print_probs(self):
        for hypo in sorted(self):
            print(hypo, self[hypo])

As an example, I’ll use Suite to solve the “Dice Problem,” from Chapter 3 of Think Bayes.

“Suppose I have a box of dice that contains a 4-sided die, a 6-sided die, an 8-sided die, a 12-sided die, and a 20-sided die. If you have ever played Dungeons & Dragons, you know what I am talking about. Suppose I select a die from the box at random, roll it, and get a 6. What is the probability that I rolled each die?”

I’ll start by defining DiceSuite, which inherits bayesian_update from Suite and provides likelihood.

data is the observed die roll, 6 in the example.

hypo is the hypothetical number of sides on the die.

If data > hypo, that means the outcome exceeds the number of sides on the die; that’s impossible, so it has probability 0.

Otherwise, the probability of any outcome is 1/hypo, where hypo is the number of sides on the die.

class DiceSuite(Suite):
    def likelihood(self, data, hypo):
        """Computes the likelihood of the data under the hypothesis.

        data: result of a die roll
        hypo: integer number of sides on the die
        if data > hypo:
            return 0
            return 1/hypo

Now we can make a DiceSuite object that represents the possible number of sides on the die. By default, all dice have the same prior probability.

Then I update the distribution with the given outcome and print the results:

dice_suite = DiceSuite([4, 6, 8, 12, 20])



As expected, the 4-sided die has been eliminated; it now has 0 probability. The 6-sided die is the most likely, but the 8-sided die is still quite possible.

Now suppose I roll the die again and get an 8. We can update the Suite again with the new data.



Now the 6-sided die has been eliminated, the 8-sided die is most likely, and there is less than a 10% chance that I am rolling a 20-sided die.

Exercise: Draw a UML class diagram that shows the relationships among all classes in this notebook, plus Counter and dict.

Exercise: Suppose you know that up to 100 sequentially-numbered raffle tickets have been sold, and you think it is equally likely that the number sold is anywhere from 1 to 100. You find a randomly discarded ticket that is number 37. What is the probability that it is the winning ticket?

Hint: Define a class named TicketSuite that inherits from Suite and provides an appropriate likelihood function. It should also define a method that computes the average probability of winning, weighted by the probability of each outcome.

Note: This exercise is pretty challenging if you are not familiar with Bayesian statistics. But if it intrigues you, consider taking Computational Bayesian Statistics.

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