Update my_pages/rashomon_effect.py

my_pages/rashomon_effect.py

@@ -3,90 +3,91 @@ import streamlit as st
 import numpy as np
 import matplotlib.pyplot as plt
 
-st.title("Rashomon Effect")
-
-# Generate synthetic data
-np.random.seed(42)
-n_points = 100
-
-# Mostly correlated data
-annual_income = np.random.normal(50, 15, n_points)  # x-axis
-credit_score = annual_income + np.random.normal(0, 10, n_points)  # y-axis
-
-# Default labels: 1 = paid back (green), 0 = default (red)
-labels = (annual_income + credit_score > 120).astype(int)
-colors = np.where(labels == 1, 'green', 'red')
-
-# --- Function to draw scatter with optional decision boundary ---
-def draw_scatter(x, y, colors, boundary=None, boundary_type=None, extra_point=None):
-    fig, ax = plt.subplots()
-
-    # Background for decision boundary
-    if boundary is not None and boundary_type:
-        x_min, x_max = ax.get_xlim()
-        y_min, y_max = ax.get_ylim()
-
-        if boundary_type == "vertical":
-            ax.axvline(boundary, color="black", linestyle="--")
-            ax.fill_betweenx([y_min, y_max], x_min, boundary, color="red", alpha=0.1)
-            ax.fill_betweenx([y_min, y_max], boundary, x_max, color="green", alpha=0.1)
-
-        elif boundary_type == "horizontal":
-            ax.axhline(boundary, color="black", linestyle="--")
-            ax.fill_between([x_min, x_max], y_min, boundary, color="red", alpha=0.1)
-            ax.fill_between([x_min, x_max], boundary, y_max, color="green", alpha=0.1)
-
-    ax.scatter(x, y, c=colors, edgecolors='black')
-
-    if extra_point:
-        ax.scatter(*extra_point["coords"], c=extra_point["color"], s=150, edgecolors="black", marker="o")
-
-    ax.set_xlabel("Annual Income")
-    ax.set_ylabel("Credit Score")
-
-    return fig
-
-# --- Layout ---
-col_top = st.container()
-col_bottom = st.columns(2)
-
-# Draw top scatter (raw data)
-with col_top:
-    fig_top = draw_scatter(annual_income, credit_score, colors)
-    st.pyplot(fig_top)
-    st.markdown("⬇️ Data split into two different models ⬇️")
-
-# Session state to store choice
-if "chosen_boundary" not in st.session_state:
-    st.session_state.chosen_boundary = None
-
-# Bottom left: vertical decision boundary
-with col_bottom[0]:
-    fig_left = draw_scatter(annual_income, credit_score, colors, boundary=55, boundary_type="vertical")
-    st.pyplot(fig_left)
-    if st.button("Choose Vertical Model"):
-        st.session_state.chosen_boundary = "vertical"
-
-# Bottom right: horizontal decision boundary
-with col_bottom[1]:
-    fig_right = draw_scatter(annual_income, credit_score, colors, boundary=55, boundary_type="horizontal")
-    st.pyplot(fig_right)
-    if st.button("Choose Horizontal Model"):
-        st.session_state.chosen_boundary = "horizontal"
-
-# After choice: add extra individual
-if st.session_state.chosen_boundary:
-    st.markdown("---")
-    st.subheader("Your chosen model in action")
-
-    if st.session_state.chosen_boundary == "vertical":
-        extra = {"coords": (40, 80), "color": "blue"}
-        fig_choice = draw_scatter(annual_income, credit_score, colors, boundary=55, boundary_type="vertical", extra_point=extra)
-        st.pyplot(fig_choice)
-        st.warning("This person has a high credit score but low income. You rejected them. Why not choose a model that would approve them?")
-
-    elif st.session_state.chosen_boundary == "horizontal":
-        extra = {"coords": (80, 40), "color": "blue"}
-        fig_choice = draw_scatter(annual_income, credit_score, colors, boundary=55, boundary_type="horizontal", extra_point=extra)
-        st.pyplot(fig_choice)
-        st.warning("This person has high income but low credit score. You rejected them. Why not choose a model that would approve them?")
+def render():
+    st.title("Rashomon Effect")
+    
+    # Generate synthetic data
+    np.random.seed(42)
+    n_points = 100
+    
+    # Mostly correlated data
+    annual_income = np.random.normal(50, 15, n_points)  # x-axis
+    credit_score = annual_income + np.random.normal(0, 10, n_points)  # y-axis
+    
+    # Default labels: 1 = paid back (green), 0 = default (red)
+    labels = (annual_income + credit_score > 120).astype(int)
+    colors = np.where(labels == 1, 'green', 'red')
+    
+    # --- Function to draw scatter with optional decision boundary ---
+    def draw_scatter(x, y, colors, boundary=None, boundary_type=None, extra_point=None):
+        fig, ax = plt.subplots()
+    
+        # Background for decision boundary
+        if boundary is not None and boundary_type:
+            x_min, x_max = ax.get_xlim()
+            y_min, y_max = ax.get_ylim()
+    
+            if boundary_type == "vertical":
+                ax.axvline(boundary, color="black", linestyle="--")
+                ax.fill_betweenx([y_min, y_max], x_min, boundary, color="red", alpha=0.1)
+                ax.fill_betweenx([y_min, y_max], boundary, x_max, color="green", alpha=0.1)
+    
+            elif boundary_type == "horizontal":
+                ax.axhline(boundary, color="black", linestyle="--")
+                ax.fill_between([x_min, x_max], y_min, boundary, color="red", alpha=0.1)
+                ax.fill_between([x_min, x_max], boundary, y_max, color="green", alpha=0.1)
+    
+        ax.scatter(x, y, c=colors, edgecolors='black')
+    
+        if extra_point:
+            ax.scatter(*extra_point["coords"], c=extra_point["color"], s=150, edgecolors="black", marker="o")
+    
+        ax.set_xlabel("Annual Income")
+        ax.set_ylabel("Credit Score")
+    
+        return fig
+    
+    # --- Layout ---
+    col_top = st.container()
+    col_bottom = st.columns(2)
+    
+    # Draw top scatter (raw data)
+    with col_top:
+        fig_top = draw_scatter(annual_income, credit_score, colors)
+        st.pyplot(fig_top)
+        st.markdown("⬇️ Data split into two different models ⬇️")
+    
+    # Session state to store choice
+    if "chosen_boundary" not in st.session_state:
+        st.session_state.chosen_boundary = None
+    
+    # Bottom left: vertical decision boundary
+    with col_bottom[0]:
+        fig_left = draw_scatter(annual_income, credit_score, colors, boundary=55, boundary_type="vertical")
+        st.pyplot(fig_left)
+        if st.button("Choose Vertical Model"):
+            st.session_state.chosen_boundary = "vertical"
+    
+    # Bottom right: horizontal decision boundary
+    with col_bottom[1]:
+        fig_right = draw_scatter(annual_income, credit_score, colors, boundary=55, boundary_type="horizontal")
+        st.pyplot(fig_right)
+        if st.button("Choose Horizontal Model"):
+            st.session_state.chosen_boundary = "horizontal"
+    
+    # After choice: add extra individual
+    if st.session_state.chosen_boundary:
+        st.markdown("---")
+        st.subheader("Your chosen model in action")
+    
+        if st.session_state.chosen_boundary == "vertical":
+            extra = {"coords": (40, 80), "color": "blue"}
+            fig_choice = draw_scatter(annual_income, credit_score, colors, boundary=55, boundary_type="vertical", extra_point=extra)
+            st.pyplot(fig_choice)
+            st.warning("This person has a high credit score but low income. You rejected them. Why not choose a model that would approve them?")
+    
+        elif st.session_state.chosen_boundary == "horizontal":
+            extra = {"coords": (80, 40), "color": "blue"}
+            fig_choice = draw_scatter(annual_income, credit_score, colors, boundary=55, boundary_type="horizontal", extra_point=extra)
+            st.pyplot(fig_choice)
+            st.warning("This person has high income but low credit score. You rejected them. Why not choose a model that would approve them?")