This is the notebook that Sven Anderson and I used for our “Family Weekend” class over the weekend. We began the class with two poems from an NPR article called, “Human or Machine: Can you tell who wrote these poems?”
The question of what constitutes poetic style has and will continue to produce varied and complicated answers, but, at bottom, it comes down to choice. This experiment in generating “poetry” represents one way of simulating this process of choice by randomly picking words according to their frequency in a particular poet’s corpus. (Just to note, some of the functions in this script are in a separate file (functions.py) to make it easier to read. You can access those functions and the accompanying text files here.)
Objective: We will be able to articulate specific features of poetic writing that can distinguish computer-generated poems from ones written by people.
In this class, we’ll be working with python code in this Jupyter notebook interface. What’s great about this platform is that we can easily move between formatted text (as illustrated here), code, images, and pretty much any other type of digital object.
We will use this notebook to produce a randomly-generated poem based on a particular poet’s style.
A word cloud generated from Abraham Lincoln’s *Gettysburg Address. The size of the words correspond to their frequency in the speech.*
How might we mathematically represent authorial choice?
We can start by attempting to simulate the process by which particular authors make stylistic choices – for instance, how they choose which words to use.
In programming, we produce simulations using algorithms, which are sets of rules for the computer to follow. They are basically computational recipes for turning a given input into an output. Here we create an algorithm in a function for calculating the tip for a meal.
def tipCalculator (mealTotal): ### Our "mealTotal" is our input.
return mealTotal * 0.20 + mealTotal ### We add 20% of the total to the input to get our output
## We've created the function, now we can run it:
print(tipCalculator(50.00))
60.0
Now let’s think about how we might simulate poetic word choice to produce randomly generated poetry.
# Load some functionality and some pre-computed probabilities.
import functions
import pickle
dickinsonProbs = pickle.load(open('dickinsonProbs.p', 'rb'))
print(dickinsonProbs['the'])
print(dickinsonProbs['heart'])
0.06506088945704097
0.0009419363520150709
These numbers tell us the likelihood of picking each word if we were to draw them at random from the text: the more frequent the word in the text, the greater chance of choosing it.
from random import random
def randomWord(probs):
'''Given probs, a dictionary of word probabilities, this
returns a word according to how frequently that word is found
in the dictionary.'''
rnum = random()
sumprob = 0.0
for k in probs.keys():
sumprob += probs[k]
if sumprob > rnum:
return k
return k
We can simulate this process by using a function that randomly chooses words from Dickinson’s poetry, based on her word usage.
print(randomWord(dickinsonProbs))
drum
# what happens if 10 is replaced by 20?
for i in range(10):
print(randomWord(dickinsonProbs))
since
but
them
most
few
sinks
one
repeal
the
delight
This frequency-based approach gives us a way to simulate the poet’s process of choosing words. But after we choose a random word what comes next?
dickinsonBigrams = pickle.load(open('dickinsonBigrams.p', 'rb'))
shakespeareProbs = pickle.load(open('shakeProbs.p', 'rb'))
shakespeareBigrams = pickle.load(open('shakeBigrams.p', 'rb'))
print(shakespeareBigrams['compare'])
{'thou': 0.3333333333333333, 'with': 0.16666666666666666, 'myself': 0.16666666666666666, 'thee': 0.16666666666666666, 'them': 0.16666666666666666}
We can choose the next word based on which words are likely to follow the first word, according to the poet’s usage. In Shakespeare’s Sonnets, thou is the most likely word to follow compare.
With this process of randomly choosing words likely to follow one another we can build entire poems. In the following algorithm, we combine this process with a method of making the poems rhyme by matching the last word of every odd line with a random rhyming word in every even line.
def generatePoem(probs, bigrams, lineLength, poemLength):
poemLines = []
# create poemLines from probabilities
for i in range(poemLength):
line = functions.generateFromBigrams(probs, bigrams, lineLength)
poemLines.append(line)
lineCount = 1
newLines = []
for line in poemLines:
if lineCount % 2 == 1: # an odd line sets the rhyme
rhymedLine = line
newLines.append(rhymedLine)
lineCount += 1
elif lineCount % 2 == 0: # an even line must match rhyme of
rhymingLine = line # preceding line
try:
if rhymedLine[-1] in functions.pronounce.keys():
newWord = functions.rhyme(rhymedLine[-1], 3)[0]
rhymingLine[-1] = newWord # fix the rhyme to match
newLines.append(rhymingLine)
lineCount += 1
except:
newLines.append(rhymingLine)
lineCount += 1
# Now we concatenate to a single string.
fullPoem = []
for line in newLines:
line.append('\n')
fullLine = ' '.join(word for word in line)
fullPoem.append(fullLine)
return ''.join(line for line in fullPoem)
Now we can generate our own “poems” from the word choice of either Shakespeare or Emily Dickinson.
Generate Shakespearean poetry
For this code to work, change the hashtags to numbers – the first for the number of words you want in each line and the second for the number of lines you want in the poem.
(I really like the concluding couplet of the Shakespearean poem.)
print(generatePoem(shakespeareProbs, shakespeareBigrams, 8, 14 ))
praises worse than at that i not so affined
that our time and eyes have might to give
in my bosoms ward but let me do forgive
yore those children nursed deliverd from serving with thee
which in the strength and they look what thee
from the first the morning have been mine eye
thy fair assistance in loves fire shall in ai
yet the perfumed tincture of my desire keep open
they see barren tender feeling but yet eyes alagappan
doubting the face should transport me that is kind
enforced to my deepest sense to his brief affined
and gives thee back the past i have often
not my love thy store when days when acetaminophen
Generate Dickinsonian Poery
print(generatePoem(dickinsonProbs, dickinsonBigrams, ##, ##))
for heaven she had come nor simplified her
consummate plush how cold i had the her
off for pearl then of love is finished
you lost was there came out time abolished
held but internal evidence gives us is overcome
he stayed away upon the merchant smiled branscome