btm模型 python代码
时间: 2023-06-17 20:04:02 浏览: 112
以下是用Python实现BTM模型的代码:
```python
import numpy as np
import scipy.sparse as sp
class BTM:
def __init__(self, num_topics, V):
self.num_topics = num_topics
self.V = V
self.alpha = None
self.beta = None
self.theta = None
self.phi = None
self.z = None
self.word_topic_counts = None
self.topic_counts = None
self.num_iterations = None
def fit(self, docs, num_iterations=100, alpha=0.1, beta=0.01):
self.alpha = alpha
self.beta = beta
self.num_iterations = num_iterations
# Initialize variables
M = len(docs)
self.theta = np.zeros((M, self.num_topics))
self.phi = np.zeros((self.num_topics, self.V))
self.z = []
self.word_topic_counts = sp.lil_matrix((self.V, self.num_topics))
self.topic_counts = np.zeros(self.num_topics)
# Randomly assign topics to words
for m in range(M):
doc = docs[m]
z = []
for w in doc:
topic = np.random.randint(self.num_topics)
z.append(topic)
self.word_topic_counts[w, topic] += 1
self.topic_counts[topic] += 1
self.z.append(np.array(z))
# Gibbs sampling
for i in range(self.num_iterations):
for m in range(M):
doc = docs[m]
z = self.z[m]
for n in range(len(doc)):
w = doc[n]
topic = z[n]
self.word_topic_counts[w, topic] -= 1
self.topic_counts[topic] -= 1
# Calculate posterior distribution over topics
p_z = (self.word_topic_counts[w, :] + self.beta) * \
(self.topic_counts + self.alpha) / \
(self.topic_counts.sum() + self.alpha * self.num_topics)
p_z /= p_z.sum()
# Sample new topic assignment
new_topic = np.random.choice(self.num_topics, p=p_z)
z[n] = new_topic
self.word_topic_counts[w, new_topic] += 1
self.topic_counts[new_topic] += 1
# Calculate theta and phi
for m in range(M):
self.theta[m, :] = (self.word_topic_counts[docs[m], :] + self.alpha) / \
(len(docs[m]) + self.alpha * self.num_topics)
self.phi = (self.word_topic_counts + self.beta) / \
(self.word_topic_counts.sum(axis=0) + self.beta * self.V)
def transform(self, docs):
M = len(docs)
theta = np.zeros((M, self.num_topics))
for m in range(M):
doc = docs[m]
for w in doc:
theta[m, :] += self.phi[:, w]
theta[m, :] /= len(doc)
return theta
```
代码中使用的是Gibbs采样算法,将文本集合划分为若干文档,每个文档根据BTM模型进行主题分布计算。主题分布计算完成后,可以使用transform函数将文档转换为主题分布。