Update my_pages/ica.py

my_pages/ica.py

@@ -11,7 +11,7 @@ def render():
     add_instruction_text(
         """
         Explore the intention-convention-arbitrariness (ICA) framework.<br>
-        Use different sliders to uncover examples in the ICA triangle.
+        Use the sliders to adjust the three dimensions simultaneously.
         """
     )
 
@@ -22,42 +22,25 @@ def render():
             "Arbitrary": 0.34
         }
 
-    col1, col2 = st.columns([0.6, 0.4])
-    with col1:
-        control_choice = st.radio(
-            "Select dimension to adjust",
-            ["Intentional", "Conventional", "Arbitrary"],
-            horizontal=True,
-            label_visibility="collapsed"
-        )
-
-    # Current values
     w = st.session_state.weights
-    current_value = w[control_choice]
 
+    # --- Three sliders ---
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        i = st.slider("Intentional", 0.0, 1.0, w["Intentional"], 0.01)
     with col2:
-        new_value = st.slider(control_choice, 0.0, 1.0, current_value, 0.01, label_visibility="collapsed")
-
-    # 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]]
-
-    if total_other > 0:
-        w[others[0]] -= diff * (w[others[0]] / total_other)
-        w[others[1]] -= diff * (w[others[1]] / total_other)
-
-    w[control_choice] = new_value
-
-    # Clamp small floating point errors
-    for k in w:
-        w[k] = max(0.0, min(1.0, round(w[k], 4)))
-
-    # Normalize back to sum=1
-    total = sum(w.values())
-    if total != 0:
-        for k in w:
-            w[k] = round(w[k] / total, 4)
+        c = st.slider("Conventional", 0.0, 1.0, w["Conventional"], 0.01)
+    with col3:
+        a = st.slider("Arbitrary", 0.0, 1.0, w["Arbitrary"], 0.01)
+
+    # Normalize to sum = 1
+    total = i + c + a
+    if total > 0:
+        w["Intentional"] = round(i / total, 4)
+        w["Conventional"] = round(c / total, 4)
+        w["Arbitrary"] = round(a / total, 4)
+    else:
+        w["Intentional"] = w["Conventional"] = w["Arbitrary"] = round(1/3, 4)
 
     # Triangle vertices
     vertices = np.array([
@@ -76,7 +59,6 @@ def render():
     # Plot
     fig, ax = plt.subplots()
     ax.plot(*np.append(vertices, [vertices[0]], axis=0).T)
-    # ax.scatter(vertices[:,0], vertices[:,1], c=["blue", "green", "red"], s=100)
     ax.text(*vertices[0], "Intentional", ha="center", va="bottom", color="green", weight="heavy")
     ax.text(*vertices[1], "Conventional", ha="right", va="top", color="green", weight="heavy")
     ax.text(*vertices[2], "Arbitrary", ha="left", va="top", color="green", weight="heavy")
@@ -89,24 +71,16 @@ def render():
     ax.patch.set_alpha(0)
 
     # --- Dummy points scattered inside triangle ---
-    # (x, y, text)
     locations = [
         (0.9, 0.1, "Random Seeds", "Random Seeds are highly arbitrary, without any convention or intentionality.", "left", "bottom"),
-        (0.35, 0.06, "Neural networks for Tabular Data", "Using neural networks of some arbitrary size (hidden layers) for a setting where \
-                                                        they are not needed is highly conventional, a bit arbitrary, and has very low intentionality.", "left", "bottom"),
-        (0.4, 0.5, "Pre-trained LLM for a Complex Task", "Using a high performing LLM for a complex task is intentional, however, it also has \
-                                                           conventionality to it, as a specialized model could have worked, depending on context.\
-                                                           No arbitrariness.", "right", "bottom"),
-        (0.5, 0.7, "Best Bias Mitigation for a Particular Setup", "Choosing the most appropriate bias mitigation technique,\
-                                                                    specialized for the particular context, is highly intentional", "center", "bottom"),
-        (0.7, 0.5, "Randomly chosen Regularization Technique", "Adding regularization to improve robustness, but choosing the regularization technique randomly,\
-                                                                    creates a decision that is intentional and arbitrary, while avoiding conventionality.", "left", "bottom"),
-        (0.1, 0.1, "ReLU Activation as Default", "Choosing ReLU activation without testing what other activations might also work,\
-                                                    is a highly conventional decision.", "right", "bottom"),
+        (0.35, 0.06, "Neural networks for Tabular Data", "Using neural networks of some arbitrary size (hidden layers) for a setting where they are not needed is highly conventional, a bit arbitrary, and has very low intentionality.", "left", "bottom"),
+        (0.4, 0.5, "Pre-trained LLM for a Complex Task", "Using a high performing LLM for a complex task is intentional, however, it also has conventionality to it, as a specialized model could have worked, depending on context. No arbitrariness.", "right", "bottom"),
+        (0.5, 0.7, "Best Bias Mitigation for a Particular Setup", "Choosing the most appropriate bias mitigation technique, specialized for the particular context, is highly intentional", "center", "bottom"),
+        (0.7, 0.5, "Randomly chosen Regularization Technique", "Adding regularization to improve robustness, but choosing the regularization technique randomly, creates a decision that is intentional and arbitrary, while avoiding conventionality.", "left", "bottom"),
+        (0.1, 0.1, "ReLU Activation as Default", "Choosing ReLU activation without testing what other activations might also work, is a highly conventional decision.", "right", "bottom"),
     ]
 
-    torch_radius = 0.177  # how far the "torch" illuminates
-    
+    torch_radius = 0.177
     explanations = []
     # Illuminate nearby points
     for (x, y, label, labeltext, ha, va) in locations:
@@ -126,4 +100,4 @@ def render():
         text_to_show = ""
         for label, labeltext in explanations:
             text_to_show += "<b>" + label + ":</b> " + labeltext + "<br>"
-        add_red_text(text_to_show)
+        add_red_text(text_to_show)