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("stability") 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" )
def plot_brunet_metric(self, min_num_topics=10, max_num_topics=50, iterations=10): cophenetic_correlation = self.topic_model.brunet_metric(min_num_topics, max_num_topics, iterations) plt.clf() plt.plot(range(min_num_topics, max_num_topics + 1), cophenetic_correlation) plt.title("Brunet et al. metric") plt.xlabel("number of topics") plt.ylabel("cophenetic correlation coefficient") plt.savefig("output/brunet.png") save_topic_number_metrics_data( "output/brunet.tsv", range_=(min_num_topics, max_num_topics), data=cophenetic_correlation, metric_type="brunet", )
def plot_brunet_metric(self, min_num_topics=10, max_num_topics=50, iterations=10): cophenetic_correlation = self.topic_model.brunet_metric( min_num_topics, max_num_topics, iterations) plt.clf() plt.plot(range(min_num_topics, max_num_topics + 1), cophenetic_correlation) plt.title('Brunet et al. metric') plt.xlabel('number of topics') plt.ylabel('cophenetic correlation coefficient') plt.savefig('output/brunet.png') save_topic_number_metrics_data('output/brunet.tsv', range_=(min_num_topics, max_num_topics), data=cophenetic_correlation, metric_type='brunet')
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('stability') 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')