如何使用pymc3时，pm.traceplot(trace)出现问题？(语言-python)

Question

问题遇到的现象和发生背景

问题相关代码，请勿粘贴截图

import numpy as np
import pymc3 as pm
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy import optimize
import sys

def main_1():
# 设置随机数种子
np.random.seed(123)

alpha = 1
sigma = 1
beta = [1, 2.5]

N = 100

X1 = np.random.randn(N)
X2 = np.random.randn(N)

Y = alpha + beta[0] * X1 + beta[1] * X2 + np.random.randn(N) * sigma



basic_model = pm.Model()
with basic_model:
    alpha = pm.Normal('alpha', mu=0, sd=10)
    beta = pm.Normal('beta', mu=0, sd=10, shape=2)
    sigma = pm.HalfNormal('sigma', sd=1)

    mu = alpha + beta[0] * X1 + beta[1] * X2

    Y_obs = pm.Normal('Y_obs', mu=mu, sd=sigma, observed=Y)

with basic_model:
    # 用MAP获得初始点
    start = pm.find_MAP(method='BFGS')

    # 实例化采样器
    step = pm.Slice(vars=[sigma])

    # 对后验分布进行5000次采样
    trace = pm.sample(5000, step=step, start=start)

pm.traceplot(trace)

if name == 'main':
sys.exit(main_1())

运行结果及报错内容

我的解答思路和尝试过的方法

我想要达到的结果

Answer 1

使用az的plot_trace方法：
import arviz as az
...
with basic_model:
az.plot_trace(trace)
display(az.summary(trace, round_to=2))
参考文档
https://docs.pymc.io/en/v3/pymc-examples/examples/getting_started.html
参考如下可完整运行代码：

import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
from IPython.display import display
print(f"Running on PyMC3 v{pm.__version__}")
RANDOM_SEED = 8927
rng = np.random.default_rng(RANDOM_SEED)
az.style.use("arviz-darkgrid")
# True parameter values
alpha, sigma = 1, 1
beta = [1, 2.5]

# Size of dataset
size = 100

# Predictor variable
X1 = np.random.randn(size)
X2 = np.random.randn(size) * 0.2

# Simulate outcome variable
Y = alpha + beta[0] * X1 + beta[1] * X2 + rng.normal(size=size) * sigma
basic_model = pm.Model()

with basic_model:

    # Priors for unknown model parameters
    alpha = pm.Normal("alpha", mu=0, sigma=10)
    beta = pm.Normal("beta", mu=0, sigma=10, shape=2)
    sigma = pm.HalfNormal("sigma", sigma=1)

    # Expected value of outcome
    mu = alpha + beta[0] * X1 + beta[1] * X2

    # Likelihood (sampling distribution) of observations
    Y_obs = pm.Normal("Y_obs", mu=mu, sigma=sigma, observed=Y)
# By default,BFGS,method="powell"
map_estimate = pm.find_MAP(model=basic_model, method="powell")
print(map_estimate)
with basic_model:
    # draw 500 posterior samples
    trace = pm.sample(500, return_inferencedata=False)
with basic_model:
    # instantiate sampler
    step = pm.Slice()
    trace = pm.sample(5000, step=step, return_inferencedata=False)
with basic_model:
    az.plot_trace(trace)
    display(az.summary(trace, round_to=2))
plt.show()