Hi all,

After I run my code I get a warning as:

"RuntimeError:

An attempt has been made to start a new process before the

current process has finished its bootstrapping phase.

```
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable."
```

I wanted to know how and where I should add “if **name** == ‘**main**’:” in my code below:

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.datasets import load_diabetes

from sklearn.model_selection import train_test_split

from theano import sharednp.random.seed(9)

#Load the Data

dataset = pd.read_csv(‘PV-PCM.csv’)

X=dataset.iloc[:,[0,1,2,3,4]].values

y=dataset.iloc[:,5].valuesX_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:

# Priors for unknown model parameters`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) 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)

Thank you all.