Update app.py

app.py

@@ -7,6 +7,7 @@ from numpyro.infer import MCMC, NUTS
 from sklearn.datasets import make_regression
 from jax import random
 import streamlit as st
+import sympy as sp
 
 
 # Define the model
@@ -22,7 +23,7 @@ def run_linear_regression(X, y, alpha_prior, beta_prior, sigma_prior):
     # Run MCMC
     rng_key = random.PRNGKey(0)
     nuts_kernel = NUTS(linear_regression)
-    mcmc = MCMC(nuts_kernel, num_warmup=100, num_samples=600)
+    mcmc = MCMC(nuts_kernel, num_warmup=50, num_samples=1000)
     mcmc.run(rng_key, jnp.array(X), jnp.array(y), alpha_prior=alpha_prior, beta_prior=beta_prior, sigma_prior=sigma_prior)
 
     mcmc.print_summary()
@@ -42,56 +43,49 @@ def run_linear_regression(X, y, alpha_prior, beta_prior, sigma_prior):
     ax.legend(loc='upper left')
     st.pyplot(fig)
 
-
 # User Input
-st.write("# Bayesian Linear Regression with numpyro")
-alpha_prior_option = st.selectbox("Choose an option for alpha prior:", ["Normal", "Laplace", "Cauchy"])
-beta_prior_option = st.selectbox("Choose an option for beta prior:", ["Normal", "Laplace", "Cauchy"])
-sigma_prior_option = st.selectbox("Choose an option for sigma prior:", ["HalfNormal", "HalfCauchy"])
+st.write("# Bayesian Linear Regression")
 
-if st.button("Run Regression"):
-    alpha_prior = None
-    beta_prior = None
-    sigma_prior = None
-    
-    if alpha_prior_option == "Normal":
-        alpha_loc = st.slider("Select a mean value for alpha", -10.0, 10.0, 0.0, 0.1)
-        alpha_scale = st.slider("Select a standard deviation value for alpha", 0.0, 10.0, 1.0, 0.1)
-        alpha_prior = dist.Normal(alpha_loc, alpha_scale)
-    elif alpha_prior_option == "Laplace":
-        alpha_loc = st.slider("Select a mean value for alpha", -10.0, 10.0, 0.0, 0.1)
-        alpha_scale = st.slider("Select a scale value for alpha", 0.0, 10.0, 1.0, 0.1)
-        alpha_prior = dist.Laplace(alpha_loc, alpha_scale)
-    elif alpha_prior_option == "Cauchy":
-        alpha_loc = st.slider("Select a location value for alpha", -10.0, 10.0, 0.0, 0.1)
-        alpha_scale = st.slider("Select a scale value for alpha", 0.0, 10.0, 1.0, 0.1)
-        alpha_prior = dist.Cauchy(alpha_loc, alpha_scale)
+st.write(f"### Y = \u03B1 + \u03B2X + \u03C3")
+alpha_prior_option = st.selectbox("Choose an option for alpha prior:", ["Normal", "Laplace", "Cauchy"])
+if alpha_prior_option == "Normal":
+    alpha_loc = st.slider("Select a mean value for alpha", -10.0, 10.0, 0.0, 0.1)
+    alpha_scale = st.slider("Select a standard deviation value for alpha", 0.0, 10.0, 1.0, 0.1)
+    alpha_prior = dist.Normal(alpha_loc, alpha_scale)
+elif alpha_prior_option == "Laplace":
+    alpha_loc = st.slider("Select a mean value for alpha", -10.0, 10.0, 0.0, 0.1)
+    alpha_scale = st.slider("Select a scale value for alpha", 0.0, 10.0, 1.0, 0.1)
+    alpha_prior = dist.Laplace(alpha_loc, alpha_scale)
+elif alpha_prior_option == "Cauchy":
+    alpha_loc = st.slider("Select a location value for alpha", -10.0, 10.0, 0.0, 0.1)
+    alpha_scale = st.slider("Select a scale value for alpha", 0.0, 10.0, 1.0, 0.1)
+    alpha_prior = dist.Cauchy(alpha_loc, alpha_scale)
 
-    if beta_prior_option == "Normal":
-        beta_loc = st.slider("Select a mean value for beta", -10.0, 10.0, 0.0, 0.1)
-        beta_scale = st.slider("Select a standard deviation value for beta", 0.0, 10.0, 1.0, 0.1)
-        beta_prior = dist.Normal(beta_loc, beta_scale)
-    elif beta_prior_option == "Laplace":
-        beta_loc = st.slider("Select a mean value for beta", -10.0, 10.0, 0.0, 0.1)
-        beta_scale = st.slider("Select a scale value for beta", 0.0, 10.0, 1.0, 0.1)
-        beta_prior = dist.Laplace(beta_loc, beta_scale)
-    elif beta_prior_option == "Cauchy":
-        beta_loc = st.slider("Select a location value for beta", -10.0, 10.0, 0.0, 0.1)
-        beta_scale = st.slider("Select a scale value for beta", 0.0, 10.0, 1.0, 0.1)
-        beta_prior = dist.Cauchy(beta_loc, beta_scale)
+beta_prior_option = st.selectbox("Choose an option for beta prior:", ["Normal", "Laplace", "Cauchy"])
+if beta_prior_option == "Normal":
+    beta_loc = st.slider("Select a mean value for beta", -10.0, 10.0, 0.0, 0.1)
+    beta_scale = st.slider("Select a standard deviation value for beta", 0.0, 10.0, 1.0, 0.1)
+    beta_prior = dist.Normal(beta_loc, beta_scale)
+elif beta_prior_option == "Laplace":
+    beta_loc = st.slider("Select a mean value for beta", -10.0, 10.0, 0.0, 0.1)
+    beta_scale = st.slider("Select a scale value for beta", 0.0, 10.0, 1.0, 0.1)
+    beta_prior = dist.Laplace(beta_loc, beta_scale)
+elif beta_prior_option == "Cauchy":
+    beta_loc = st.slider("Select a location value for beta", -10.0, 10.0, 0.0, 0.1)
+    beta_scale = st.slider("Select a scale value for beta", 0.0, 10.0, 1.0, 0.1)
+    beta_prior = dist.Cauchy(beta_loc, beta_scale)
 
-    if sigma_prior_option == "HalfNormal":
-        sigma_scale = st.slider("Select a scale value for sigma", 0.0, 10.0, 1.0, 0.1)
-        sigma_prior = dist.HalfNormal(sigma_scale)
-    elif sigma_prior_option == "HalfCauchy":
-        sigma_scale = st.slider("Select a scale value for sigma", 0.0, 10.0, 1.0, 0.1)
-        sigma_prior = dist.HalfCauchy(sigma_scale)
+sigma_prior_option = st.selectbox("Choose an option for sigma prior:", ["HalfNormal", "HalfCauchy"])
+if sigma_prior_option == "HalfNormal":
+    sigma_scale = st.slider("Select a scale value for sigma", 0.0, 10.0, 1.0, 0.1)
+    sigma_prior = dist.HalfNormal(sigma_scale)
+elif sigma_prior_option == "HalfCauchy":
+    sigma_scale = st.slider("Select a scale value for sigma", 0.0, 10.0, 1.0, 0.1)
+    sigma_prior = dist.HalfCauchy(sigma_scale)
 
-    # Generate data
-    rng_key = random.PRNGKey(0)
-    X, y = make_regression(n_samples=25, n_features=1, noise=10.0, random_state=0)
-    X = X.reshape(25)
+rng_key = random.PRNGKey(0)
+X, y = make_regression(n_samples=50, n_features=1, noise=10.0, random_state=0)
+X = X.reshape(50)
 
-    # Run the regression
-    run_linear_regression(X, y, alpha_prior, beta_prior, sigma_prior)
- 
+if alpha_prior and beta_prior and sigma_prior:
+    run_linear_regression(X, y, alpha_prior, beta_prior, sigma_prior)