fh.write("%s\t%f\n" % (x[0], x[1]))
fh.close()
if __name__ == "__main__":
topic_file = (
"/data0/projects/fuse/rdg_experimental_lab/experiments/surveyor_2013/final_experiments/code/final_topics.txt"
)
topic_fh = open(topic_file, "r")
topics = []
for line in topic_fh:
topics.append(line.strip())
dataObj = SurveyorData(
topics[0:5], "/data0/projects/fuse/rdg_experimental_lab/experiments/content_models/data/input_text/"
)
# TODO: add caching here so vectors are not computed again and again
# store vocab and data vectors
# alpha = [background, content, docspecific]
alpha = [10.0, 1.0, 5.0]
beta = [1.0, 0.1, 1.0]
tsObj = TopicSumWeave(dataObj, alpha, beta)
tsObj.runGibbs()
phi = tsObj.phi()
vocab = dataObj.get_vocab()
tsObj.write_topic("background", dict(zip(vocab, phi[0])))
def write_topic(filekey, word_probs):
fh = open("/data0/projects/fuse/rdg_experimental_lab/experiments/content_models/code/out_topics/"+filekey+".txt", "w")
for x in sorted(word_probs.iteritems(), key=operator.itemgetter(1), reverse=True):
fh.write("%s\t%f\n" % (x[0], x[1]))
fh.close()
if __name__ == "__main__":
topic_file = "/data0/projects/fuse/rdg_experimental_lab/experiments/surveyor_2013/final_experiments/code/final_topics.txt";
topic_fh = open(topic_file, "r")
topics = []
for line in topic_fh:
topics.append(line.strip())
dataObj = SurveyorData(topics[0:2], "/data0/projects/fuse/rdg_experimental_lab/experiments/content_models/data/input_text/")
# TODO: add caching here so vectors are not computed again and again
# store vocab and data vectors
vectors = dataObj.process_data()
# alpha = [background, content, docspecific]
alpha = [10.0, 1.0, 5.0]
beta = 0.1
tsObj = TopicSum(vectors, alpha, beta)
cur_phi = None
for it, phi in enumerate(tsObj.run(10)):
print "Iteration", it
print "Likelihood", tsObj.loglikelihood()
all_phi = phi
vocab = dataObj.get_vocab()
