def topic_purity_maximizer(tfidf, tfidf_vectorizer): ntopic_pur_list = [] for k in xrange(40,60): n_topics = k nmf, nmf_topic_dict = fit_nmf(tfidf, n_topics, tfidf_vectorizer) H, W, mean_topic_con_nmf = get_H_W(nmf, tfidf) purity_metric_l = get_metric(W, k) ntopic_pur_list.append((purity_metric_l.mean(), k)) ntopic_pur_list.sort(key = lambda x: x[0]) return ntopic_pur_list
def frob_norm(k):
'''Return the frobenius norm for a given number of topics'''
#load tfidf and vectorizer
tfidf = joblib.load('bills_tfidf_sparse.pkl')
tfidf_vectorizer = joblib.load('tfidf_vectorizer.pkl')
#run nmf
nmf, nmf_topic_dict = fit_nmf(tfidf, k, tfidf_vectorizer)
print nmf.reconstruction_err_, k
return (nmf.reconstruction_err_, k)
def run_cos_H(k):
'''Returns the mean cosine similarity between topics of the H matrix'''
tfidf = joblib.load('bills_tfidf_sparse.pkl')
tfidf_vectorizer = joblib.load('tfidf_vectorizer.pkl')
nmf, nmf_topic_dict = fit_nmf(tfidf, k, tfidf_vectorizer)
H = nmf.components_
d = pairwise_distances(H, metric='cosine')
idx = np.tril_indices(d.shape[0], k=-1)
print d[idx].mean(), k
return (d[idx].mean(), k)
def run_topic_coherence(tfidf, reverse_lookup):
average_coherence_k = []
for k in xrange(10,41,5):
print ('running for {} topics...'.format(k))
score_list = []
nmf, nmf_topic_dict = fit_nmf(tfidf, 10, tfidf_vectorizer)
for topic_words in nmf_topic_dict.values():
val = topic_coherence(tfidf, reverse_lookup, topic_words.split())
score_list.append(val)
print (val, topic_words)
average_coherence_k.append((score_list.mean(), k))
return average_coherence_k
def run_cos_W(k):
'''Returns the average intertopic cosine similarity and the corresponding k value'''
#load tfidf and vectorizer
tfidf = joblib.load('bills_tfidf_sparse.pkl')
tfidf_vectorizer = joblib.load('tfidf_vectorizer.pkl')
#run nmf
nmf, nmf_topic_dict = fit_nmf(tfidf, k, tfidf_vectorizer)
#get W matrix
W = nmf.transform(tfidf)
#get the mean cosine similarities per topic
d = get_cos_dist_W(W, k, tfidf)
print d.mean(), k
return (d.mean(), k)
plt.legend()
plt.show()
if __name__ == '__main__':
tfidf = joblib.load('bills_tfidf_sparse_full.pkl')
tfidf_vectorizer = joblib.load('tfidf_vectorizer_full.pkl')
tf = joblib.load('bills_tf_sparse.pkl')
tf_vectorizer = joblib.load('tf_vectorizer.pkl')
n_topics = 300
ntopic_pur_list = topic_purity_maximizer(tfidf, tfidf_vectorizer)
#tf = joblib.load('bills_tf_sparse.pkl')
#tf_vectorizer = joblib.load('tf_vectorizer.pkl')
W = joblib.load('W_300_full.pkl')
nmf = joblib.load('nmf_300_full.pkl')
nmf_topic_dict = joblib.load('nmf_topic_dict_300_full.pkl')
nmf, nmf_topic_dict = fit_nmf(tfidf, n_topics, tfidf_vectorizer)
H = nmf.components_
#lda, lda_topic_dict = fit_lda(tf)
mean_topic_con_nmf = get_H_W(nmf, tfidf)
reverse_lookup = {word: idx for idx, word in enumerate(np.array(tfidf_vectorizer.get_feature_names()))}
average_coherence_k = run_topic_coherence(tfidf, reverse_lookup)
avg_d_k_H_525 = parallel_run_cos_H(range(205,525,5))
avg_d_k_W_525 = parallel_run_cos_W(range(355,525,5))
frob_norm_list = parallel_frob_norm(range(30,206,5))
