def test_perplexity_vs_ntopics(corpus, model_dir, tp):
plsi_perplexities = []
plsi_mg_perplexities = []
lda_perplexities = []
model_files = os.listdir(model_dir)
model_files = [model_file for model_file in model_files if tp in model_file]
model_full_files = [model_dir + "/" + model_file for model_file in model_files]
idxs = corpus["idxs"]
for i in range(len(model_files)):
model_fname = model_files[i]
model_fullname = model_full_files[i]
model_split = model_fname.split('_')
mtype, mk, mcorp = model_split[0], model_split[1], model_split[2]
params = dmg.load_model_params(model_fullname)
total_words = 0
for j in range(len(idxs)):
total_words += idxs[j].shape[0]
print 'total words', total_words
# TODO: perhaps clean by allowing model to be instantiated from params
if mtype == 'plsi':
beta, pi = params["beta"], params["pi"]
model = PLSIModel(int(mk), beta=beta, pi=pi)
fin_ll, mg_ll = cperplexity(model, corpus, 'plsi')
perplexity = -1 * fin_ll / total_words
perplexity_mg = -1 * mg_ll / total_words
perplexity = np.exp(perplexity)
perplexity_mg = np.exp(perplexity_mg)
plsi_perplexities.append((mk, perplexity))
plsi_mg_perplexities.append((mk, perplexity_mg))
elif mtype == 'lda':
# TODO: write LDA perplexity
alpha, beta = params["alpha"], params["beta"]
model = LDAModel(int(mk), beta=beta, alpha=alpha)
var_ll = cperplexity(model, corpus, 'lda')
perplexity = -1 * var_ll / total_words
lda_perplexities.append((mk, perplexity))
if mtype == 'plsi':
print "Marg PLSI Perplexity: {0}".format(perplexity_mg)
print "Model={0}, K={1}, Perplexity={2}".format(mtype, mk, perplexity)
# sort perplexities by num topics
plsi_perplexities = sorted(plsi_perplexities, key=lambda x: x[0])
lda_perplexities = sorted(lda_perplexities, key= lambda x: x[0])
plsi_perplexities = [list(t) for t in zip(*plsi_perplexities)]
lda_perplexities = [list(t) for t in zip(*lda_perplexities)]
# plotting
line_plsi, = plt.plot(plsi_perplexities[0], plsi_perplexities[1], 'b^')
plt.plot(plsi_perplexities[0], plsi_perplexities[1], 'k')
line_lda, = plt.plot(lda_perplexities[0], lda_perplexities[1], 'rs')
plt.plot(lda_perplexities[0], lda_perplexities[1], 'r--')
plt.title("Perplexity vs. Num Topics for Various Models")
plt.legend(handles=[line_plsi, line_lda])
plt.show()
def test_word_feature_reps(model_dir, num_words=500):
model_files = os.listdir(model_dir)
model_files = [model_dir + "/" + model_file for model_file in model_files]
for model_file in model_files:
last_part = model_file.split("/")[-1]
print "\n", last_part, 'word features'
params = dmg.load_model_params(model_file)
if 'plsi' in model_file:
word_gens = params['pi']
else:
word_gens= params["beta"]
vocab = load_vocab(last_part)
index = {v: k for (k, v) in vocab.items()}
best_words = np.argpartition(word_gens, -500, axis=0)
best_words = best_words[-500:,:]
rand_idx = np.random.choice(best_words.shape[0], num_words)
rand_words = best_words[rand_idx, :]
#rand_idx = np.random.choice(word_gens.shape[0], num_words)
#rand_words = word_gens[rand_idx, :]
tsne = TSNE(n_components=2, random_state=0)
projected = tsne.fit_transform(rand_words)
# plotting
fig = plt.figure(figsize=(18, 12))
ax = fig.add_subplot(111)
ax.plot(projected[:,0], projected[:,1],linestyle='None',marker='o',markersize=1)
for i in range(projected.shape[0]):
feat1, feat2 = projected[i, 0], projected[i, 1]
word = index[rand_idx[i]].decode('utf-8')
chars = []
for j in word:
if ord(j) < 128:
chars.append(j)
word == ''.join(chars)
print word
ax.text(feat1, feat2, word, fontsize=15)
plt.title(last_part)
plt.show()
def test_best_topic_words(model_dir, result_dir, num_words=10):
vocab_file = None
model_files = os.listdir(model_dir)
model_files = [model_dir + "/" + model_file for model_file in model_files]
for model_file in model_files:
last_part = model_file.split("/")[-1]
print "\n", last_part, 'words'
params = dmg.load_model_params(model_file)
if 'plsi' in model_file:
word_gens = params['pi']
else:
word_gens = params["beta"]
vocab = load_vocab(last_part)
vocab_size = word_gens.shape[0]
index = {v: k for (k, v) in vocab.items()}
best_words = np.argpartition(word_gens, -num_words, axis=0)
best_words = best_words[-num_words:, :].T
best_words = [[index[idx] for idx in words] for words in best_words]
best_strings = [",".join(words) for words in best_words]
best_strings = "\n".join(best_strings)
result_file = open(result_dir + "/bw_" + last_part[:-4] + ".txt", 'w')
print "\n", best_strings
result_file.write(best_strings)
result_file.close()
