Simplify analysis data tables

app.py

@@ -1188,7 +1188,7 @@ def feature_figures(
 ) -> tuple[go.Figure, pd.DataFrame]:
     frame = feature_frame(dataset, models)
     if frame.empty:
-        columns = ["method", "validation_target", "validation_metric", "validation_score"]
+        columns = ["method", "validation_score"]
         return empty_figure("No results for this selection."), pd.DataFrame(columns=columns)
     score_column, target, metric, reference = feature_spec(dataset)
     validation = frame.dropna(subset=["validation_score"]).sort_values(
@@ -1218,7 +1218,7 @@ def feature_figures(
     validation_fig.update_yaxes(title="", showgrid=False)
     figure_layout(validation_fig, height=max(430, 25 * len(validation) + 145))
 
-    columns = ["method", "validation_target", "validation_metric", "validation_score"]
+    columns = ["method", "validation_score"]
     return validation_fig, round_numeric(
         frame[columns].sort_values("validation_score", ascending=False)
     )
@@ -1965,7 +1965,7 @@ def consistency_figures(
             if dataset == "mc_pacman"
             else "No latent-consistency result is available for this selection."
         )
-        columns = ["method", "latent_consistency_r2", "n_recordings", "latent_dim", "n_pairwise"]
+        columns = ["method", "latent_consistency_r2"]
         return empty_figure(message), consistency_heatmap(models), pd.DataFrame(columns=columns)
     bar = frame.sort_values(["latent_consistency_r2", "model_order"], ascending=[True, False])
     bar_fig = go.Figure(
@@ -1974,19 +1974,14 @@ def consistency_figures(
             y=bar["method"],
             orientation="h",
             marker=dict(color=CONSISTENCY_COLOR),
-            customdata=np.stack([bar["n_recordings"], bar["latent_dim"], bar["n_pairwise"]], axis=-1),
-            hovertemplate=(
-                "Method=%{y}<br>Latent-consistency R²=%{x:.4f}<br>"
-                "Recordings=%{customdata[0]:.0f}<br>Latent dimensions=%{customdata[1]:.0f}<br>"
-                "Directional pairs=%{customdata[2]:.0f}<extra></extra>"
-            ),
+            hovertemplate="Method=%{y}<br>Latent-consistency R²=%{x:.4f}<extra></extra>",
         )
     )
     bar_fig.update_layout(title="Latent consistency")
     bar_fig.update_xaxes(title="Latent-consistency R²", range=[0, 1.02])
     bar_fig.update_yaxes(title="", showgrid=False)
     figure_layout(bar_fig, height=max(400, 27 * len(bar) + 145))
-    columns = ["method", "latent_consistency_r2", "n_recordings", "latent_dim", "n_pairwise"]
+    columns = ["method", "latent_consistency_r2"]
     return bar_fig, consistency_heatmap(models), frame[columns].sort_values("latent_consistency_r2", ascending=False)
 
 
@@ -2485,11 +2480,11 @@ def update_consistency(
 def update_feature(dataset: str, models: list[str] | None):
     dataset = dataset or DATASETS[0]
     validation_fig, table = feature_figures(dataset, models)
-    _column, _target, _metric, _reference = feature_spec(dataset)
+    _column, _target, metric, _reference = feature_spec(dataset)
     if dataset == "allen_neuropixels":
         definition = (
-            "Spearman’s ρ measures association with drifting-gratings orientation "
-            "selectivity, a biological proxy."
+            "Spearman’s ρ measures association between feature-attribution values "
+            "and each unit’s drifting-gratings orientation selectivity."
         )
     elif dataset == "ratinabox":
         definition = (
@@ -2501,11 +2496,15 @@ def update_feature(dataset: str, models: list[str] | None):
             "ROC-AUC measures whether recorded neural features rank above appended "
             "synthetic controls. Chance ROC-AUC is 0.5."
         )
+    table_columns = column_defs(table.columns)
+    for column in table_columns:
+        if column["id"] == "validation_score":
+            column["name"] = metric
     return (
         definition,
         validation_fig,
         feature_heatmap(models),
-        column_defs(table.columns),
+        table_columns,
         records(table),
     )