Markovify is a simple, extensible Markov chain generator. Right now, its main use is for building Markov models of large corpora of text, and generating random sentences from that. But, in theory, it could be used for other applications.

• Why Markovify?
• Installation
• Basic Usage
• Advanced Usage
• Markovify In The Wild
• Thanks

Some reasons:

• Simplicity. "Batteries included," but it's easy to override key methods.

• Models can be stored as JSON, allowing you to cache your results and save them for later.

• Text parsing and sentence generation methods are highly extensible, allowing you to set your own rules.

• Relies only on pure-Python libraries, and very few of them.

• Tested on Python 2.6, 2.7, 3.1, and 3.4.

pip install markovify

import markovify

# Get raw text as string.
with open("/path/to/my/corpus.txt") as f:
    text = f.read()

# Build the model.
text_model = markovify.Text(text)

# Print five randomly-generated sentences
for i in range(5):
    print(text_model.make_sentence())

# Print three randomly-generated sentences of no more than 140 characters
for i in range(3):
    print(text_model.make_short_sentence(140))

Notes:

• The usage examples here assume you're trying to markovify text. If you'd like to use the underlying markovify.Chain class, which is not text-specific, check out the (annotated) source code.

• Markovify works best with large, well-punctuated texts. If your text doesn't use .s to delineate sentences, put each sentence on a newline, and use the markovify.NewlineText class instead of markovify.Text class.

• By default, the make_sentence method tries, a maximum of 10 times per invocation, to make a sentence that doesn't overlap too much with the original text. If it is successful, the method returns the sentence as a string. If not, it returns None. To increase or decrease the number of attempts, use the tries keyword argument, e.g., call .make_sentence(tries=100).

• By default, markovify.Text tries to generate sentences that don't simply regurgitate chunks of the original text. The default rule is to suppress any generated sentences that exactly overlaps the original text by 15 words or 70% of the sentence's word count. You can change this rule by passing max_overlap_ratio and/or max_overlap_total to the make_sentence method.

By default, markovify.Text uses a state size of 2. But you can instantiate a model with a different state size. E.g.,:

text_model = markovify.Text(text, state_size=3)

With markovify.combine(...), you can combine two or more Markov chains. The function accepts two arguments:

• models: A list of markovify objects to combine. Can be instances of markovify.Chain or markovify.Text (or their subclasses), but all must be of the same type.
• weights: Optional. A list — the exact length of models — of ints or floats indicating how much relative emphasis to place on each source. Default: [ 1, 1, ... ].

For instance:

model_a = markovify.Text(text_a)
model_b = markovify.Text(text_b)

model_combo = markovify.combine([ model_a, model_b ], [ 1.5, 1 ])

... would combine model_a and model_b, but place 50% more weight on the connections from model_a.

The markovify.Text class is highly extensible; most methods can be overridden. For example, the following POSifiedText class uses NLTK's part-of-speech tagger to generate a Markov model that obeys sentence structure better than a naive model. (It works. But be warned: pos_tag is very slow.)

import markovify
import nltk
import re

class POSifiedText(markovify.Text):
    def word_split(self, sentence):
        words = re.split(self.word_split_pattern, sentence)
        words = [ "::".join(tag) for tag in nltk.pos_tag(words) ]
        return words

    def word_join(self, words):
        sentence = " ".join(word.split("::")[0] for word in words)
        return sentence

The most useful markovify.Text models you can override are:

• sentence_split
• sentence_join
• word_split
• word_join
• test_sentence_input
• test_sentence_output

For details on what they do, see the (annotated) source code.

• BuzzFeed's Tom Friedman Sentence Generator / @mot_namdeirf.
• UserSim, which powers /u/user_simulator bot on Reddit and generates comments based on a user's comment history.
• SubredditSimulator, which generates random Reddit submissions and comments based on a subreddit's previous activity.
  • "What is /r/SubredditSimulator?"
  • Note re. markovify
• college crapplication, a web-app that generates college application essays. [code]
• @MarkovPicard, a Twitter bot based on Star Trek: The Next Generation transcripts. [code]
• sekrits.herokuapp.com, a markovify-powered quiz that challenges you to tell the difference between "two file titles relating to matters of [Australian] national security" — one real and one fake. [code]
• Hacker News Simulator, which does what it says on the tin. [code]
• Stak Attak, a "poetic stackoverflow answer generator." [code]
• MashBOT, a markovify-powered Twitter bot attached to a printer. Presented by Helen J Burgess at Babel Toronto 2015. [code]
• The Mansfield Reporter, "a simple device which can generate new text from some of history's greatest authors [...] running on a tiny Raspberry Pi, displaying through a tft screen from Adafruit."
• twitter markov, a tool to "create markov chain ("_ebooks") accounts on Twitter."
• @Bern_Trump_Bot, "Bernie Sanders and Donald Trump driven by Markov Chains." [code]
• @RealTrumpTalk, "A bot that uses the things that @realDonaldTrump tweets to create it's own tweets." [code]
• Taylor Swift Song Generator, which does what it says. [code]
• @BOTtalks / ideasworthautomating.com. "TIM generates talks on a broad spectrum of topics, based on the texts of slightly more coherent talks given under the auspices of his more famous big brother, who shall not be named here." [code]
• Internal Security Zones, "Generative instructions for prison design & maintenance." [code]
• Miraculous Ladybot. Generates Miraculous Ladybug fanfictions and posts them on Tumblr. [code]

Have other examples? Pull requests welcome.

Many thanks to the following GitHub users for contributing code and/or ideas:

• @orf
• @deimos
• @cjmochrie
• @Jaza
• @fitnr
• @andela-mfalade
• @ntratcliff
• @schollz

Developed at BuzzFeed.