Update my_pages/rashomon_effect.py

my_pages/rashomon_effect.py

@@ -10,7 +10,7 @@ def render():
         """
         <div style='text-align: center; font-size:18px; color:gray;'>
         Consider data about individuals who either paid their loans (green) or defaulted (red). <br>
-        Which model out of the two will you choose to give loan applications? <br><br>
+        Which model out of the two will you choose to give loan applications? <br>
         </div>
         """,
         unsafe_allow_html=True
@@ -32,7 +32,7 @@ def render():
     colors = ['green' if label == 1 else 'red' for label in labels]
 
     # Function to plot scatter
-    def plot_scatter(x, y, colors, title="", decision_boundary=None, boundary_type=None, highlight_point=None):
+    def plot_scatter(x, y, colors, title="", boundary_type=None, highlight_point=None):
         fig, ax = plt.subplots(figsize=(2, 2))
         ax.scatter(x, y, c=colors, alpha=0.6)
         ax.set_xlabel("Annual Income")
@@ -45,13 +45,20 @@ def render():
         # Decision boundary
         if decision_boundary is not None:
             if boundary_type == "vertical":
-                ax.axvline(decision_boundary, color='blue', linestyle='--')
-                ax.fill_betweenx(np.arange(min(y), max(y)), decision_boundary, max(x), alpha=0.1, color='green')
-                ax.fill_betweenx(np.arange(min(y), max(y)), min(x), decision_boundary, alpha=0.1, color='red')
+                ax.axvline(65, color='blue', linestyle='--')
+                ax.fill_betweenx(np.arange(min(y), max(y)), 65, max(x), alpha=0.1, color='green')
+                ax.fill_betweenx(np.arange(min(y), max(y)), min(x), 65, alpha=0.1, color='red')
             elif boundary_type == "horizontal":
-                ax.axhline(decision_boundary, color='blue', linestyle='--')
-                ax.fill_between(np.arange(min(x), max(x)), decision_boundary, max(y), alpha=0.1, color='green')
-                ax.fill_between(np.arange(min(x), max(x)), min(y), decision_boundary, alpha=0.1, color='red')
+                ax.axhline(650, color='blue', linestyle='--')
+                ax.fill_between(np.arange(min(x), max(x)), 650, max(y), alpha=0.1, color='green')
+                ax.fill_between(np.arange(min(x), max(x)), min(y), 650, alpha=0.1, color='red')
+            elif boundary_type == "slant":
+                slope = -10.677966       # From (94, 350) and (35, 980)
+                intercept = 1353.7288
+                y_line = slope * x + intercept
+                ax.plot(x, y_line, color='blue', linestyle='--')
+                ax.fill_between(x, y_line, max(y), alpha=0.1, color='green')
+                ax.fill_between(x, min(y), y_line, alpha=0.1, color='red')
 
         # Highlight specific point
         if highlight_point is not None:
@@ -69,31 +76,32 @@ def render():
     with col2:
         st.pyplot(plot_scatter(income, credit, colors, title="Original Data"))
 
-    col1, col2, col3, col4 = st.columns([1, 1, 1, 1])
+    col1, col2, col3, col4, col5 = st.columns([0.5, 1, 1, 1, 0.5])
     with col2:
-        st.pyplot(plot_scatter(income, credit, colors, title="Vertical Boundary",
-                               decision_boundary=55, boundary_type="vertical"))
-        left_selected = st.button("Choose Vertical Boundary")
+        st.pyplot(plot_scatter(income, credit, colors, boundary_type="vertical"))
+        vertical_selected = st.button("Choose Model 1")
     with col3:
-        st.pyplot(plot_scatter(income, credit, colors, title="Horizontal Boundary",
-                               decision_boundary=55, boundary_type="horizontal"))
-        right_selected = st.button("Choose Horizontal Boundary")
+        st.pyplot(plot_scatter(income, credit, colors, boundary_type="slant"))
+        slant_selected = st.button("Choose Model 2")
+    with col3:
+        st.pyplot(plot_scatter(income, credit, colors, boundary_type="horizontal"))
+        horizontal_selected = st.button("Choose Model 3")
 
-    col1, col2, col3 = st.columns([1.5, 1, 1.5])
+    # col1, col2, col3 = st.columns([1.5, 1, 1.5])
     # Show new individual based on selection
-    if left_selected:
-        new_point = (40, 80)  # High credit score, low income
-        with col2:
-            st.pyplot(plot_scatter(income, credit, colors,
-                                   title="Vertical Boundary + New Individual",
-                                   decision_boundary=55, boundary_type="vertical",
-                                   highlight_point=new_point))
-        st.warning("This individual was rejected by your chosen model. Why not choose a model that helps them?")
-    elif right_selected:
-        new_point = (80, 40)  # Low credit score, high income
-        with col2:
-            st.pyplot(plot_scatter(income, credit, colors,
-                                   title="Horizontal Boundary + New Individual",
-                                   decision_boundary=55, boundary_type="horizontal",
-                                   highlight_point=new_point))
-        st.warning("This individual was rejected by your chosen model. Why not choose a model that helps them?")
+    # if left_selected:
+    #     new_point = (40, 80)  # High credit score, low income
+    #     with col2:
+    #         st.pyplot(plot_scatter(income, credit, colors,
+    #                                title="Vertical Boundary + New Individual",
+    #                                boundary_type="vertical",
+    #                                highlight_point=new_point))
+    #     st.warning("This individual was rejected by your chosen model. Why not choose a model that helps them?")
+    # elif right_selected:
+    #     new_point = (80, 40)  # Low credit score, high income
+    #     with col2:
+    #         st.pyplot(plot_scatter(income, credit, colors,
+    #                                title="Horizontal Boundary + New Individual",
+    #                                boundary_type="horizontal",
+    #                                highlight_point=new_point))
+    #     st.warning("This individual was rejected by your chosen model. Why not choose a model that helps them?")