# !/usr/bin/python
# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import lda
import lda.datasets
from pprint import pprint
if __name__ == "__main__":
# document-term matrix
X = lda.datasets.load_reuters()
print("type(X): {}".format(type(X)))
print("shape: {}\n".format(X.shape))
print(X[:10, :10])
# the vocab
vocab = lda.datasets.load_reuters_vocab()
print("type(vocab): {}".format(type(vocab)))
print("len(vocab): {}\n".format(len(vocab)))
print(vocab[:10])
# titles for each story
titles = lda.datasets.load_reuters_titles()
print("type(titles): {}".format(type(titles)))
print("len(titles): {}\n".format(len(titles)))
pprint(titles[:10])
print( 'LDA start ----')
topic_num = 20
model = lda.LDA(n_topics=topic_num, n_iter=500, random_state=1)
model.fit(X)
# topic-word
topic_word = model.topic_word_
print("type(topic_word): {}".format(type(topic_word)))
print("shape: {}".format(topic_word.shape))
print(vocab[:5])
print(topic_word[:, :5])
# Print Topic distribution
n = 7
for i, topic_dist in enumerate(topic_word):
topic_words = np.array(vocab)[np.argsort(topic_dist)][:-(n + 1):-1]
print('*Topic {}\n- {}'.format(i, ' '.join(topic_words)))
# Document - topic
doc_topic = model.doc_topic_
print("type(doc_topic): {}".format(type(doc_topic)))
print("shape: {}".format(doc_topic.shape))
for i in range(10):
topic_most_pr = doc_topic[i].argmax()
print(u"文档: {} 主题: {} value: {}".format(i, topic_most_pr, doc_topic[i][topic_most_pr]))
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
# Topic - word
plt.figure(figsize=(8, 9))
# f, ax = plt.subplots(5, 1, sharex=True)
for i, k in enumerate([0, 5, 9, 14, 19]):
ax = plt.subplot(5, 1, i+1)
ax.plot(topic_word[k, :], 'r-')
ax.set_xlim(-50, 4350) # [0,4258]
ax.set_ylim(0, 0.08)
ax.set_ylabel(u"概率")
ax.set_title(u"主题 {}".format(k))
plt.xlabel(u"词", fontsize=14)
plt.tight_layout()
plt.suptitle(u'主题的词分布', fontsize=18)
plt.subplots_adjust(top=0.9)
plt.show()
# Document - Topic
plt.figure(figsize=(8, 9))
# f, ax= plt.subplots(5, 1, figsize=(8, 6), sharex=True)
for i, k in enumerate([1, 3, 4, 8, 9]):
ax = plt.subplot(5, 1, i+1)
ax.stem(doc_topic[k, :], linefmt='g-', markerfmt='ro')
ax.set_xlim(-1, topic_num+1)
ax.set_ylim(0, 1)
ax.set_ylabel(u"概率")
ax.set_title(u"文档 {}".format(k))
plt.xlabel(u"主题", fontsize=14)
plt.suptitle(u'文档的主题分布', fontsize=18)
plt.tight_layout()
plt.subplots_adjust(top=0.9)
plt.show()
打印结果：