Question

我在Python中使用pymc3实现了Bayesian Probabilistic Matrix Factorization算法。我还实现了它的前驱，概率矩阵分解（PMF）。 See my previous question用于引用此处使用的数据。

我在使用NUTS采样器绘制MCMC样本时遇到问题。我使用来自PMF的MAP初始化模型参数，使用高斯随机抽取的超参数在0附近散布。但是，在为采样器设置步骤对象时，我得到PositiveDefiniteError。我已经验证了PMF的MAP估计是合理的，所以我希望它与超参数初始化的方式有关。这是PMF模型：

import pymc3 as pm
import numpy as np
import pandas as pd
import theano
import scipy as sp

data = pd.read_csv('jester-dense-subset-100x20.csv')    
n, m = data.shape
test_size = m / 10
train_size = m - test_size

train = data.copy()
train.ix[:,train_size:] = np.nan  # remove test set data
train[train.isnull()] = train.mean().mean()  # mean value imputation
train = train.values

test = data.copy()
test.ix[:,:train_size] = np.nan  # remove train set data
test = test.values    

# Low precision reflects uncertainty; prevents overfitting
alpha_u = alpha_v = 1/np.var(train)
alpha = np.ones((n,m)) * 2  # fixed precision for likelihood function
dim = 10  # dimensionality

# Specify the model.
with pm.Model() as pmf:
    pmf_U = pm.MvNormal('U', mu=0, tau=alpha_u * np.eye(dim),
                        shape=(n, dim), testval=np.random.randn(n, dim)*.01)
    pmf_V = pm.MvNormal('V', mu=0, tau=alpha_v * np.eye(dim),
                        shape=(m, dim), testval=np.random.randn(m, dim)*.01)
    pmf_R = pm.Normal('R', mu=theano.tensor.dot(pmf_U, pmf_V.T),
                      tau=alpha, observed=train)

    # Find mode of posterior using optimization
    start = pm.find_MAP(fmin=sp.optimize.fmin_powell)

这是BPMF：

n, m = data.shape
dim = 10  # dimensionality
beta_0 = 1  # scaling factor for lambdas; unclear on its use
alpha = np.ones((n,m)) * 2  # fixed precision for likelihood function

logging.info('building the BPMF model')
std = .05  # how much noise to use for model initialization
with pm.Model() as bpmf:
    # Specify user feature matrix
    lambda_u = pm.Wishart(
        'lambda_u', n=dim, V=np.eye(dim), shape=(dim, dim),
        testval=np.random.randn(dim, dim) * std)
    mu_u = pm.Normal(
        'mu_u', mu=0, tau=beta_0 * lambda_u, shape=dim,
        testval=np.random.randn(dim) * std)
    U = pm.MvNormal(
        'U', mu=mu_u, tau=lambda_u, shape=(n, dim),
        testval=np.random.randn(n, dim) * std)

    # Specify item feature matrix
    lambda_v = pm.Wishart(
        'lambda_v', n=dim, V=np.eye(dim), shape=(dim, dim),
        testval=np.random.randn(dim, dim) * std)
    mu_v = pm.Normal(
        'mu_v', mu=0, tau=beta_0 * lambda_v, shape=dim,
         testval=np.random.randn(dim) * std)
    V = pm.MvNormal(
        'V', mu=mu_v, tau=lambda_v, shape=(m, dim),
        testval=np.random.randn(m, dim) * std)

    # Specify rating likelihood function
    R = pm.Normal(
        'R', mu=theano.tensor.dot(U, V.T), tau=alpha,
        observed=train)

# `start` is the start dictionary obtained from running find_MAP for PMF.
for key in bpmf.test_point:
    if key not in start:
        start[key] = bpmf.test_point[key]

with bpmf:
    step = pm.NUTS(scaling=start)

在最后一行，我收到以下错误：

PositiveDefiniteError: Scaling is not positive definite. Simple check failed. Diagonal contains negatives. Check indexes [   0    2   ...  2206  2207  ]

据我所知，我不能将find_MAP用于具有BPMF等超级驱动程序的模型。这就是为什么我尝试使用PMF的MAP值进行初始化，PMF使用U和V上的参数的点估计而不是参数化的超级驱动程序。

Answer 1

不幸的是，Wishart分发是无效的。我最近在此处添加了一条警告：https://github.com/pymc-devs/pymc3/commit/642f63973ec9f807fb6e55a0fc4b31bdfa1f261e

请参阅此处，了解有关此棘手发布的更多讨论：https://github.com/pymc-devs/pymc3/issues/538

您可以通过修复协方差矩阵来确认这是源。如果是这种情况，我会尝试使用JKL先前分发：https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/LKJ_correlation.py

带有PyMC3的贝叶斯概率矩阵分解（BPMF）：使用`NUTS的PositiveDefiniteError

1 个答案: