How can I use "if __name__ == '__main__':" in my code below?

Questions
4

Thank you.
This problem resolved but I ran into another problem. There is an error/warning as follow:

Sampling 4 chains for 2_500 tune and 1_000 draw iterations (10_000 + 4_000 draws total) took 75 seconds.
There were 103 divergences after tuning. Increase target_accept or reparameterize.
There were 94 divergences after tuning. Increase target_accept or reparameterize.
There were 2 divergences after tuning. Increase target_accept or reparameterize.
The number of effective samples is smaller than 25% for some parameters.

It produces the plots but it does not produce any variable (Nothing!!). I don’t know what is going on here! For example, I need my “y_pred” in the code below. My CSV file includes 5 IVs and 1 DV with 203 rows of data. I just need to get y_pred with my csv file with Bayesian linear regression and I stuck for days. Can you please tell me what should I do?

from scipy import stats, optimize
import pymc3 as pm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
from sklearn import metrics

#from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from theano import shared

np.random.seed(9)

def giant_function():
#Load the Data
dataset=pd.read_csv(‘PV-PCM.csv’)
X=dataset.iloc[:,:-1].values
y=dataset.iloc[:,5].values

X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size = 0.2, random_state=42)

#Shapes
X.shape, y.shape, X_tr.shape, X_te.shape


#Preprocess data for Modeling
shA_X = shared(X_tr)


#Generate Model
linear_model = pm.Model()


with linear_model: 
    alpha = pm.Normal("alpha", mu=y_tr.mean(),sd=10)
    betas = pm.Normal("betas", mu=0, sd=1000, shape=X.shape[1])
    sigma = pm.HalfNormal("sigma", sd=100) # you could also try with a HalfCauchy that has longer/fatter tails
    mu = alpha + pm.math.dot(betas, X_tr.T)
    likelihood = pm.Normal("likelihood", mu=mu, sd=sigma, observed=y_tr)
    step = pm.NUTS()
    trace = pm.sample(1000,step,tune=2500)
    
    chain = trace[100:]
    #pm.traceplot(chain);

    #Traceplot
    pm.traceplot(trace)
    
    ppc = pm.sample_prior_predictive(samples=1000,random_seed=9)
    
    pm.plot_posterior(trace, figsize = (12, 10))
    

sns.kdeplot(y_tr, alpha=0.5, lw=4, c='b')
for i in range(100):
    sns.kdeplot(ppc['likelihood'][i], alpha=0.1, c='g')


alpha_pred = chain['alpha'].mean()
betas_pred = chain['betas'].mean(axis=0)

y_pred = alpha_pred + np.dot(betas_pred, X_tr.T)

if name == ‘main’:
giant_function()

Thank you again for taking the time.