Last active
May 25, 2020 05:16
Star
You must be signed in to star a gist
Joint Topic Models
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import random | |
import numpy as np | |
from scipy.sparse import lil_matrix | |
class JTM: | |
def __init__(self, K, alpha, beta, max_iter, verbose=0): | |
self.K=K | |
self.alpha = alpha | |
self.beta = beta | |
self.max_iter = max_iter | |
self.verbose=verbose | |
def fit(self,X,V): | |
self._X = X | |
self._T = len(X) # number of vocab types | |
self._N = len(X[0]) # number of documents | |
self._V = V # number of vocabularies for each t | |
self.Z = self._init_Z() | |
self.ndk, self.nkv = self._init_params() | |
nk = {} | |
for t in range(self._T): | |
nk[t] = self.nkv[t].sum(axis=1) | |
remained_iter = self.max_iter | |
while True: | |
if self.verbose: print remained_iter | |
for t in np.random.choice(self._T, self._T, replace=False): | |
for d in np.random.choice(self._N, self._N, replace=False): | |
for i in np.random.choice(len(self._X[t][d]), len(self._X[t][d]), replace=False): | |
k = self.Z[t][d][i] | |
v = self._X[t][d][i] | |
self.ndk[t][d][k] -= 1 | |
self.nkv[t][k][v] -= 1 | |
nk[t][k] -= 1 | |
self.Z[t][d][i] = self._sample_z(t,d,v,nk[t]) | |
self.ndk[t][d][self.Z[t][d][i]] += 1 | |
self.nkv[t][self.Z[t][d][i]][v] += 1 | |
nk[t][self.Z[t][d][i]] += 1 | |
remained_iter -= 1 | |
if remained_iter <= 0: break | |
return self | |
def _init_Z(self): | |
Z = {} | |
for t in range(self._T): | |
Z[t] = [] | |
for d in range(len(self._X[t])): | |
Z[t].append(np.random.randint(low=0,high=self.K,size=len(self._X[t][d]))) | |
return Z | |
def _init_params(self): | |
ndk = {} | |
nkv = {} | |
for t in range(self._T): | |
ndk[t] = np.zeros((self._N,self.K)) + self.alpha | |
nkv[t] = np.zeros((self.K,self._V[t])) + self.beta | |
for d in range(self._N): | |
for i in range(len(self._X[t][d])): | |
k = self.Z[t][d][i] | |
v = self._X[t][d][i] | |
ndk[t][d,k]+=1 | |
nkv[t][k,v]+=1 | |
return ndk,nkv | |
def _sample_z(self,t,d,v,nk): | |
nkv = self.nkv[t][:,v] # k-dimensional vector | |
prob = (sum([self.ndk[t][d] for t in range(self._T)])-self.alpha*(self._T-1)) * (nkv/nk) | |
prob = prob/prob.sum() | |
z = np.random.multinomial(n=1, pvals=prob).argmax() | |
return z |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment