Update my_pages/ica.py

my_pages/ica.py

@@ -3,74 +3,82 @@ import matplotlib.pyplot as plt
 import numpy as np
 
 def render():
-    st.title("Intentional / Conventional / Arbitrary Triangle")
+    st.title("ICA Triangle")
 
-    # Initialize session state for sliders
-    if "intentional" not in st.session_state:
-        st.session_state.intentional = 0.33
-        st.session_state.conventional = 0.33
-        st.session_state.arbitrary = 0.34
+    if "weights" not in st.session_state:
+        st.session_state.weights = {
+            "Intentional": 0.33,
+            "Conventional": 0.33,
+            "Arbitrary": 0.34
+        }
 
-    # Sliders
-    intentional = st.slider("Intentional", 0.0, 1.0, st.session_state.intentional, 0.01)
-    conventional = st.slider("Conventional", 0.0, 1.0, st.session_state.conventional, 0.01)
-    arbitrary = st.slider("Arbitrary", 0.0, 1.0, st.session_state.arbitrary, 0.01)
+    # Which one to adjust
+    control_choice = st.radio(
+        "Select dimension to adjust",
+        ["Intentional", "Conventional", "Arbitrary"],
+        horizontal=True
+    )
+
+    # Current values
+    w = st.session_state.weights
+    current_value = w[control_choice]
+
+    # Slider for selected one
+    new_value = st.slider(control_choice, 0.0, 1.0, current_value, 0.01)
 
-    # Normalize values so sum is always 1
-    total = intentional + conventional + arbitrary
-    if total == 0:
-        total = 1
-    intentional /= total
-    conventional /= total
-    arbitrary /= total
+    # Adjust others proportionally
+    diff = new_value - current_value
+    others = [k for k in w.keys() if k != control_choice]
+    total_other = w[others[0]] + w[others[1]]
 
-    # Store back in session state
-    st.session_state.intentional = intentional
-    st.session_state.conventional = conventional
-    st.session_state.arbitrary = arbitrary
+    if total_other > 0:
+        w[others[0]] -= diff * (w[others[0]] / total_other)
+        w[others[1]] -= diff * (w[others[1]] / total_other)
 
-    # Plot triangle
-    fig, ax = plt.subplots(figsize=(5, 5))
+    w[control_choice] = new_value
 
-    # Triangle vertices (Intentional, Conventional, Arbitrary)
-    vertices = np.array([[0.5, np.sqrt(3)/2], [0, 0], [1, 0]])
-    labels = ["Intentional", "Conventional", "Arbitrary"]
+    # Clamp small floating point errors
+    for k in w:
+        w[k] = max(0.0, min(1.0, round(w[k], 4)))
 
-    # Draw triangle edges
-    ax.plot([vertices[0][0], vertices[1][0]], [vertices[0][1], vertices[1][1]], 'k-')
-    ax.plot([vertices[1][0], vertices[2][0]], [vertices[1][1], vertices[2][1]], 'k-')
-    ax.plot([vertices[2][0], vertices[0][0]], [vertices[2][1], vertices[0][1]], 'k-')
+    # Normalize back to sum=1
+    total = sum(w.values())
+    if total != 0:
+        for k in w:
+            w[k] = round(w[k] / total, 4)
 
-    # Add vertex labels
-    for i, label in enumerate(labels):
-        ax.text(vertices[i][0], vertices[i][1] + 0.05, label, ha='center', fontsize=12, fontweight='bold')
+    # Triangle vertices
+    vertices = np.array([
+        [0.5, np.sqrt(3)/2],  # Intentional
+        [0, 0],               # Conventional
+        [1, 0]                # Arbitrary
+    ])
 
-    # Convert (intentional, conventional, arbitrary) to XY coords
+    # Point from barycentric coords
     point = (
-        conventional + 0.5 * intentional,
-        (np.sqrt(3)/2) * intentional
+        w["Intentional"] * vertices[0] +
+        w["Conventional"] * vertices[1] +
+        w["Arbitrary"] * vertices[2]
     )
 
-    # Plot point
-    ax.scatter(point[0], point[1], color='red', s=100, zorder=5)
+    # Plot
+    fig, ax = plt.subplots()
+    ax.plot(*np.append(vertices, [vertices[0]], axis=0).T, 'k-')
+    ax.scatter(vertices[:,0], vertices[:,1], c=["blue", "green", "red"], s=100)
+    ax.text(*vertices[0], "Intentional", ha="center", va="bottom")
+    ax.text(*vertices[1], "Conventional", ha="right", va="top")
+    ax.text(*vertices[2], "Arbitrary", ha="left", va="top")
+    ax.scatter(point[0], point[1], c="orange", s=200, zorder=5)
+    ax.set_aspect("equal")
+    ax.axis("off")
 
-    ax.set_aspect('equal')
-    ax.axis('off')
     st.pyplot(fig)
 
-    # Show dominant property description
-    dominant = max(
-        ("Intentional", intentional),
-        ("Conventional", conventional),
-        ("Arbitrary", arbitrary),
-        key=lambda x: x[1]
-    )[0]
-
-    descriptions = {
-        "Intentional": "Example: Carefully designed rules.",
-        "Conventional": "Example: Following industry standards.",
-        "Arbitrary": "Example: Random seeds."
-    }
-
-    st.subheader(f"Dominant property: {dominant}")
-    st.write(descriptions[dominant])
+    # Display interpretation
+    closest = max(w.items(), key=lambda x: x[1])[0]
+    if closest == "Arbitrary":
+        st.info("Example: Random seeds — a truly arbitrary decision.")
+    elif closest == "Intentional":
+        st.info("Example: Ethical constraints — a fully intentional choice.")
+    elif closest == "Conventional":
+        st.info("Example: Industry standard preprocessing — a conventional decision.")