Update

app_lib/viz.py

@@ -1,8 +1,67 @@
+import numpy as np
 import pandas as pd
 import plotly.express as px
+import plotly.graph_objects as go
 import streamlit as st
 
 
+def _viz_rank(results):
+    rejected = results["rejected"]
+    tau = results["tau"]
+    concepts = results["concepts"]
+    significance_level = results["significance_level"]
+
+    rejected_mu = rejected.mean(axis=0)
+    tau_mu = tau.mean(axis=0)
+
+    sorted_idx = np.argsort(tau_mu)[::-1]
+    sorted_tau = tau_mu[sorted_idx]
+    sorted_rejected = rejected_mu[sorted_idx]
+    sorted_concepts = [concepts[idx] for idx in sorted_idx]
+
+    rank_df = []
+    for concept, tau, rejected in zip(sorted_concepts, sorted_tau, sorted_rejected):
+        rank_df.append({"concept": concept, "tau": tau, "rejected": rejected})
+    rank_df = pd.DataFrame(rank_df)
+
+    fig = go.Figure()
+
+    fig.add_trace(
+        go.Scatter(
+            x=rank_df["rejected"],
+            y=rank_df["concept"],
+            marker=dict(size=8),
+            line=dict(color="#1f78b4", dash="dash"),
+            name="Rejection rate",
+        )
+    )
+    fig.add_trace(
+        go.Bar(
+            x=rank_df["tau"],
+            y=rank_df["concept"],
+            orientation="h",
+            marker=dict(color="#a6cee3"),
+            name="Normalized rejection time",
+        )
+    )
+    fig.add_shape(
+        type="line",
+        yref="paper",
+        line=dict(color="black", dash="dash"),
+        x0=significance_level,
+        x1=significance_level,
+        y0=0,
+        y1=1,
+        name="significance level",
+        showlegend=True,
+    )
+    fig.update_layout(yaxis_title="Rank of importance", xaxis_title="")
+
+    _, centercol, _ = st.columns([1, 4, 1])
+    with centercol:
+        st.plotly_chart(fig, use_container_width=True)
+
+
 def _viz_wealth(results):
     wealth = results["wealth"]
     concepts = results["concepts"]
@@ -27,6 +86,7 @@ def _viz_wealth(results):
         annotation_position="bottom right",
     )
     fig.update_yaxes(range=[0, 1.5 * 1 / significance_level])
+    # fig.update_layout(legend=dict(orientation="h", x=0, y=1.2))
     st.plotly_chart(fig, use_container_width=True)
 
 
@@ -40,14 +100,16 @@ def viz_results():
 
         with rank_tab:
             st.subheader("Rank of Semantic Importance")
-
             with st.expander("Details"):
                 st.write(
                     "This tab shows the rank of semantic importance of the concepts for the predictions of the model on the image. Concepts are sorted by increasing rejection time, where a shorter rejection time indicates higher importance."
                 )
+
+            if results is not None:
+                _viz_rank(results)
+
         with wealth_tab:
             st.subheader("Wealth Process of Testing Procedures")
-
             with st.expander("Details"):
                 st.write(
                     "This tab shows the average wealth process of the testing procedures for random draws of conditioning subsets with the same cardinality. The black dashed line represents the rejection threshold."