Create a list of random topics from ConceptNet

random_topic.py

"""Generates a list of random related concepts from ConceptNet. Concepts are
generated via a random walk through ConceptNet using ConceptNet's /assoc
endpoint, which takes in a list of concepts and ouptputs a list of related
concepts ranked by similarity to the given concepts.

Example:
| => python random_topic.py
Current topics are apocalypse and desert_island
Current topics are desert_island and real_world
Current topics are real_world and scienceless
Current topics are scienceless and cyberteaching
Current topics are cyberteaching and radiochemistry
Current topics are radiochemistry and metaprogramming
Current topics are metaprogramming and text_file
Current topics are text_file and catalogue
Current topics are catalogue and mole_cricket
Current topics are mole_cricket and scroll
Done!
Your topics are: apocalypse, desert_island, real_world, scienceless, cyberteaching, 
radiochemistry, metaprogramming, text_file, catalogue, mole_cricket, scroll, 
air_transportation
"""


import requests
import random
from operator import itemgetter

conceptnet_uri = ("http://conceptnet5.media.mit.edu/data/5.4/assoc/list/en"
                  "/{concept_one},{concept_two}?filter=/c/en&limit=1000")
current_topics = ["apocalypse", "desert_island"]
all_topics = []
num_topics = 10

if __name__ == "__main__":

    all_topics += current_topics

    for i in xrange(num_topics):

        print "Current topics are %s and %s" % (current_topics[0],
                                                current_topics[1])

        # Choose a random similarity score to decide how to similar the next
        # term should be to the two terms we passed to /assoc
        similarity_level = random.random()

        req = conceptnet_uri.format(
                concept_one=current_topics[0],
                concept_two=current_topics[1]
            )
        response = requests.get(req)
        similar_terms = sorted(response.json()["similar"], key=itemgetter(1))
        num_terms = len(similar_terms)

        for j in xrange(num_terms):
            # Choose the least dissimilar word defined by similarity_level or
            # the last word in the list, whichever is first
            if similar_terms[j][1] > random.random() or j == (num_terms - 1):
                next_topic = similar_terms[j][0].replace("/c/en/", "")
                break

        current_topics.pop(0)
        current_topics.append(next_topic)
        all_topics.append(next_topic)

    print "Done!"
    print "Your topics are: %s" % ", ".join(all_topics)