def plot_arun_metric(self, min_num_topics=10, max_num_topics=50, iterations=10):
symmetric_kl_divergence = self.topic_model.arun_metric(min_num_topics, max_num_topics, iterations)
plt.clf()
plt.plot(range(min_num_topics, max_num_topics+1), symmetric_kl_divergence)
plt.title('Arun et al. metric')
plt.xlabel('number of topics')
plt.ylabel('symmetric KL divergence')
plt.savefig('output/arun.png')
save_topic_number_metrics_data('output/arun.tsv', range_=(min_num_topics, max_num_topics),
data=symmetric_kl_divergence, metric_type='arun')
def plot_greene_metric(self, min_num_topics=10, max_num_topics=20, tao=10, step=5,
top_n_words=10):
greene_stability = self.topic_model.greene_metric(min_num_topics=min_num_topics, max_num_topics=max_num_topics,
step=step, top_n_words=top_n_words, tao=tao)
plt.clf()
plt.plot(np.arange(min_num_topics, max_num_topics+1, step), greene_stability)
plt.title('Greene et al. metric')
plt.xlabel('number of topics')
plt.ylabel('Greene metric')
plt.savefig('output/greene.png')
save_topic_number_metrics_data('output/greene.tsv', range_=(min_num_topics, max_num_topics),
data=greene_stability, metric_type='greene')