Refresh BenchDash paper result dashboard

README.md

@@ -3,3 +3,21 @@ title: BenchDash
 sdk: docker
 app_port: 7860
 ---
+
+# BenchDash
+
+Interactive dashboard for the Tang Lab neural-behavioral benchmark summaries.
+
+This Space is intentionally visualization-first. It loads lightweight CSV
+exports from the benchmark repository and lets readers inspect:
+
+- clean prediction scores across models and datasets
+- robustness curves under count-noise perturbations
+- cross-recording latent consistency
+- training time and memory summaries
+- neuron-SHAP and trial-Shapley perturbation summaries
+- model runtime coverage and container assignments
+
+The Space does not train the full benchmark model suite. Full reproduction is
+container-backed, while a curated Colab companion is the right place for a
+small interactive training demo.

app.py

@@ -1,603 +1,859 @@
 from __future__ import annotations
 
-from typing import Dict, List, Tuple
-import math
+from pathlib import Path
+from typing import Iterable
 
 import numpy as np
 import pandas as pd
+import plotly.express as px
 import plotly.graph_objects as go
-from dash import Dash, Input, Output, callback, dash_table, dcc, html
-from plotly.subplots import make_subplots
-
-
-SIGFIGS = 3
-
-COLUMN_COLORS = {
-    "r2": "rgb(230, 245, 255)",
-    "rmse": "rgb(240, 255, 230)",
-    "mae": "rgb(255, 245, 230)",
-}
-
-METRIC_DIRECTION = {
-    "r2": "high_is_good",
-    "rmse": "low_is_good",
-    "mae": "low_is_good",
+from dash import Dash, Input, Output, dash_table, dcc, html
+
+
+DATA_DIR = Path(__file__).resolve().parent / "data"
+
+MODEL_INFO = {
+    "blend": ("gpu", "full_pipeline", "pytorch_gpu.sif", "stage1, stage2, comprehensive, consistency, neuron_shap"),
+    "blend_ndt": ("gpu", "full_pipeline", "pytorch_gpu.sif", "stage1, stage2, comprehensive, neuron_shap"),
+    "cebra": ("gpu", "full_pipeline", "pytorch_gpu.sif", "stage1, stage2, comprehensive, consistency, neuron_shap"),
+    "dnn": ("gpu", "comprehensive_only", "tensorflow_gpu.sif", "stage1, stage2, comprehensive, neuron_shap"),
+    "dpad": ("gpu", "full_pipeline", "tensorflow_gpu.sif", "comprehensive, consistency, neuron_shap"),
+    "gpfa": ("cpu", "full_pipeline", "sklearn_cpu.sif", "stage1, stage2, comprehensive, consistency, neuron_shap"),
+    "gru": ("gpu", "comprehensive_only", "tensorflow_gpu.sif", "stage1, stage2, comprehensive, neuron_shap"),
+    "langevinflow_ccn": ("gpu", "full_pipeline", "pytorch_gpu.sif", "stage1, stage2, comprehensive, neuron_shap"),
+    "ldns": ("gpu", "full_pipeline", "pytorch_gpu.sif", "stage1, stage2, comprehensive, consistency, neuron_shap"),
+    "lfads_torch": ("gpu", "full_pipeline", "lfads_torch.sif", "stage1, stage2, comprehensive, consistency, neuron_shap"),
+    "lstm": ("gpu", "comprehensive_only", "tensorflow_gpu.sif", "stage1, stage2, comprehensive, neuron_shap"),
+    "marble": ("gpu", "full_pipeline", "marble_h100.sif", "stage1, stage2, comprehensive, consistency, neuron_shap"),
+    "mint": ("cpu", "comprehensive_only", "mint.sif", "comprehensive, neuron_shap, robustness"),
+    "neds": ("gpu", "full_pipeline", "neds.sif", "stage1, stage2, comprehensive, neuron_shap"),
+    "neds_pretrained": ("gpu", "full_pipeline", "neds.sif", "stage1, stage2, comprehensive, neuron_shap"),
+    "neuro_behavior_conditioning": ("gpu", "full_pipeline", "pytorch_gpu.sif", "comprehensive, consistency, neuron_shap"),
+    "pca": ("cpu", "full_pipeline", "sklearn_cpu.sif", "stage1, stage2, comprehensive, consistency, neuron_shap"),
+    "rnn": ("gpu", "comprehensive_only", "tensorflow_gpu.sif", "stage1, stage2, comprehensive, neuron_shap"),
+    "smc_rnns": ("gpu", "full_pipeline", "pytorch_gpu.sif", "stage1, stage2, comprehensive, consistency, neuron_shap"),
+    "svc": ("cpu", "comprehensive_only", "sklearn_cpu.sif", "comprehensive, neuron_shap"),
+    "tndm": ("gpu", "full_pipeline", "tensorflow_gpu.sif", "stage1, stage2, comprehensive, consistency, neuron_shap"),
+    "torchdfine": ("gpu", "full_pipeline", "pytorch_gpu.sif", "comprehensive, consistency, neuron_shap"),
+    "xg": ("cpu", "comprehensive_only", "sklearn_cpu.sif", "comprehensive, neuron_shap"),
 }
 
-# Universal axis limits for better comparison across panels
-XYZ_RANGE: Tuple[float, float] = (-25.0, 25.0)
+DEFAULT_MODELS = [
+    "pca",
+    "gpfa",
+    "cebra",
+    "dpad",
+    "ldns",
+    "lfads_torch",
+    "marble",
+    "rnn",
+    "svc",
+    "xg",
+]
+
+
+def load_csv(name: str) -> pd.DataFrame:
+    path = DATA_DIR / name
+    if not path.exists():
+        return pd.DataFrame()
+    return pd.read_csv(path)
+
+
+prediction = load_csv("clean_prediction_summary.csv")
+robustness = load_csv("robustness_summary.csv")
+consistency = load_csv("consistency_summary.csv")
+scalability = load_csv("scalability_summary.csv")
+neuron_shap = load_csv("neuron_shap_summary.csv")
+trial_shapley = load_csv("trial_shapley_summary.csv")
+
+
+def present_rows(df: pd.DataFrame) -> pd.DataFrame:
+    if df.empty or "status" not in df.columns:
+        return df.copy()
+    return df[df["status"].fillna("") == "present"].copy()
+
+
+def ordered_unique(values: Iterable[object]) -> list[str]:
+    seen: set[str] = set()
+    out: list[str] = []
+    for value in values:
+        if pd.isna(value):
+            continue
+        text = str(value)
+        if text not in seen:
+            seen.add(text)
+            out.append(text)
+    return out
+
+
+def build_dataset_labels() -> dict[str, str]:
+    if prediction.empty:
+        return {}
+    pairs = (
+        prediction[["dataset", "dataset_display"]]
+        .dropna(subset=["dataset"])
+        .drop_duplicates(subset=["dataset"])
+    )
+    return {row.dataset: row.dataset_display for row in pairs.itertuples()}
 
-# Universal behavior-value range for color normalization (keeps colors comparable)
-BEHAVIOR_RANGE: Tuple[float, float] = (0.0, 1.0)
 
-# Continuous colorscale for regression target (Plotly built-in)
-CONTINUOUS_COLORSCALE = "Viridis"
+DATASET_LABELS = build_dataset_labels()
+DATASETS = ordered_unique(prediction.get("dataset", pd.Series(dtype=str)))
+MODELS = sorted(set(prediction.get("model", pd.Series(dtype=str)).dropna().astype(str)) | set(MODEL_INFO))
+DEFAULT_SELECTED_MODELS = [m for m in DEFAULT_MODELS if m in MODELS] or MODELS[:10]
 
 
-def build_toy_benchmark() -> Dict[str, pd.DataFrame]:
-    return {
-        "Dataset 1": pd.DataFrame(
-            [
-                {"model": "Model 1", "r2": 0.62, "rmse": 4.10, "mae": 3.20},
-                {"model": "Model 2", "r2": 0.71, "rmse": 3.60, "mae": 2.90},
-                {"model": "Model 3", "r2": 0.68, "rmse": 3.85, "mae": 3.05},
-            ]
-        ),
-        "Dataset 2": pd.DataFrame(
-            [
-                {"model": "Model 1", "r2": 0.55, "rmse": 4.55, "mae": 3.55},
-                {"model": "Model 2", "r2": 0.66, "rmse": 3.95, "mae": 3.10},
-                {"model": "Model 3", "r2": 0.63, "rmse": 4.05, "mae": 3.20},
-            ]
-        ),
-        "Dataset 3": pd.DataFrame(
-            [
-                {"model": "Model 1", "r2": 0.49, "rmse": 5.05, "mae": 3.95},
-                {"model": "Model 2", "r2": 0.61, "rmse": 4.25, "mae": 3.35},
-                {"model": "Model 3", "r2": 0.59, "rmse": 4.40, "mae": 3.50},
-            ]
-        ),
-    }
-
+def model_metadata_frame() -> pd.DataFrame:
+    rows = []
+    present = present_rows(prediction)
+    coverage = (
+        present.groupby("model")["dataset"]
+        .nunique()
+        .rename("present_datasets")
+        .to_dict()
+        if not present.empty
+        else {}
+    )
+    for model in MODELS:
+        accelerator, pipeline, container, stages = MODEL_INFO.get(
+            model, ("unknown", "unknown", "unknown", "")
+        )
+        rows.append(
+            {
+                "model": model,
+                "accelerator": accelerator,
+                "pipeline": pipeline,
+                "container": container,
+                "present_datasets": int(coverage.get(model, 0)),
+                "expected_datasets": len(DATASETS),
+                "stages": stages,
+            }
+        )
+    return pd.DataFrame(rows).sort_values(["accelerator", "pipeline", "model"])
 
-def compute_mock_score(
-    df: pd.DataFrame,
-    *,
-    metric_direction: Dict[str, str],
-    metric_weights: Dict[str, float] | None = None,
-    eps: float = 1e-12,
-) -> pd.Series:
-    if metric_weights is None:
-        metric_weights = {}
 
-    metrics = [m for m in metric_direction.keys() if m in df.columns]
-    if not metrics:
-        return pd.Series([0.0] * len(df), index=df.index)
+model_inventory = model_metadata_frame()
 
-    parts = []
-    weights = []
 
-    for m in metrics:
-        x = df[m].astype(float)
-        x_min = float(x.min())
-        x_max = float(x.max())
+def compact_number(value: float | int | None, *, digits: int = 3) -> str:
+    if value is None or pd.isna(value):
+        return ""
+    value = float(value)
+    if abs(value) >= 1000:
+        return f"{value:,.0f}"
+    return f"{value:.{digits}g}"
 
-        if (x_max - x_min) < eps:
-            x_norm = pd.Series([0.5] * len(df), index=df.index)
-        else:
-            x_norm = (x - x_min) / (x_max - x_min)
 
-        if metric_direction[m] == "low_is_good":
-            x_norm = 1.0 - x_norm
+def selected_models(models: list[str] | None) -> list[str]:
+    if not models:
+        return MODELS
+    return [m for m in models if m in MODELS]
 
-        w = float(metric_weights.get(m, 1.0))
-        parts.append(x_norm * w)
-        weights.append(w)
 
-    return sum(parts) / (sum(weights) + eps)
+def filter_models(df: pd.DataFrame, models: list[str] | None) -> pd.DataFrame:
+    if df.empty or "model" not in df.columns:
+        return df.copy()
+    chosen = selected_models(models)
+    return df[df["model"].astype(str).isin(chosen)].copy()
 
 
-def generate_mock_embedding(
-    dataset_name: str,
-    model_name: str,
-    *,
-    noise_level: float,
-    n_points: int = 750,
-    # Manifold geometry (at noise=0)
-    base_radius: float = 9.0,
-    base_thickness: float = 0.8,
-    # Per-panel transforms
-    transform_strength: float = 0.70,
-    shift_strength: float = 2.5,
-    warp_strength: float = 0.45,
-    # Noise injection (spike replacement)
-    max_replace_frac: float = 0.60,
-    background_scale: float = 10.0,
-) -> pd.DataFrame:
-    """
-    Continuous target embedding.
-
-    Returns columns:
-      - x, y, z
-      - behavior_value in [0,1] (regression-style target)
-
-    noise_level in [0,1]:
-      - increases thickness/noise around the manifold
-      - replaces a fraction of points with background noise spikes
-    """
-    nl = float(np.clip(noise_level, 0.0, 1.0))
-
-    seed = abs(hash((dataset_name, model_name))) % (2**32)
-    rng = np.random.default_rng(seed)
-
-    # Continuous behavior value (regression target)
-    t = rng.uniform(low=BEHAVIOR_RANGE[0], high=BEHAVIOR_RANGE[1], size=n_points)
-
-    # Base 1D manifold (a warped helix / loop) parameterized by t
-    # You can interpret t as the "behavioral state" / continuous label.
-    phase = 2.0 * np.pi * t
-    x0 = base_radius * (2.0 * t - 1.0)
-    y0 = base_radius * np.sin(phase)
-    z0 = 0.6 * base_radius * np.cos(phase)
-
-    X = np.stack([x0, y0, z0], axis=1)
-
-    # Thickness around the manifold increases with noise_level
-    thickness = base_thickness * (1.0 + 2.5 * nl)
-    X = X + thickness * rng.normal(size=(n_points, 3))
-
-    # Strong per-panel affine transform
-    angles = rng.uniform(low=-np.pi, high=np.pi, size=3)
-    cx, cy, cz = np.cos(angles)
-    sx, sy, sz = np.sin(angles)
-
-    Rx = np.array([[1, 0, 0], [0, cx, -sx], [0, sx, cx]])
-    Ry = np.array([[cy, 0, sy], [0, 1, 0], [-sy, 0, cy]])
-    Rz = np.array([[cz, -sz, 0], [sz, cz, 0], [0, 0, 1]])
-    R = Rz @ Ry @ Rx
-
-    scales = np.diag(1.0 + transform_strength * rng.normal(size=3))
-    shear = np.eye(3)
-    shear[0, 1] = 0.30 * transform_strength * rng.normal()
-    shear[0, 2] = 0.30 * transform_strength * rng.normal()
-    shear[1, 2] = 0.30 * transform_strength * rng.normal()
-
-    A = R @ scales @ shear
-    b = shift_strength * rng.normal(size=(3,))
-
-    X = X @ A.T + b
-
-    # Nonlinear warp (keeps panels visually distinct)
-    theta = warp_strength * (0.10 * X[:, 0] + 0.08 * X[:, 1])
-    ct = np.cos(theta)
-    st = np.sin(theta)
-    x_new = ct * X[:, 0] - st * X[:, 1]
-    y_new = st * X[:, 0] + ct * X[:, 1]
-    z_new = X[:, 2] + warp_strength * np.tanh(0.20 * X[:, 0]) * 2.2
-    X = np.stack([x_new, y_new, z_new], axis=1)
-
-    # Replace a fraction with background noise spikes (increases mixing)
-    replace_frac = max_replace_frac * nl
-    n_replace = int(round(replace_frac * n_points))
-    if n_replace > 0:
-        idx_replace = rng.choice(n_points, size=n_replace, replace=False)
-        noise_bg = background_scale * rng.standard_t(df=3, size=(n_replace, 3))
-        X[idx_replace] = noise_bg
-
-        # Optional: also partially destroy label structure for replaced points
-        # (push their behavior_value toward random)
-        t[idx_replace] = rng.uniform(low=BEHAVIOR_RANGE[0], high=BEHAVIOR_RANGE[1], size=n_replace)
-
-    return pd.DataFrame(
-        {
-            "x": X[:, 0].astype(float),
-            "y": X[:, 1].astype(float),
-            "z": X[:, 2].astype(float),
-            "behavior_value": t.astype(float),
-        }
+def fig_layout(fig: go.Figure, *, height: int = 420) -> go.Figure:
+    fig.update_layout(
+        height=height,
+        paper_bgcolor="white",
+        plot_bgcolor="white",
+        margin=dict(l=18, r=18, t=54, b=36),
+        font=dict(family="Inter, Arial, sans-serif", size=13, color="#1f2933"),
+        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="left", x=0),
     )
+    fig.update_xaxes(showgrid=True, gridcolor="#e8edf1", zerolinecolor="#cfd8df")
+    fig.update_yaxes(showgrid=True, gridcolor="#e8edf1", zerolinecolor="#cfd8df")
+    return fig
 
 
-def _parse_rgb(rgb: str) -> tuple[int, int, int]:
-    vals = rgb.strip().lower().replace("rgb(", "").replace(")", "").split(",")
-    return int(vals[0]), int(vals[1]), int(vals[2])
-
-
-def _darken(rgb: str, factor: float = 0.65) -> str:
-    r, g, b = _parse_rgb(rgb)
-    r = int(max(0, min(255, round(r * factor))))
-    g = int(max(0, min(255, round(g * factor))))
-    b = int(max(0, min(255, round(b * factor))))
-    return f"rgb({r},{g},{b})"
-
-
-def _blend_rgb(rgb_a: str, rgb_b: str, t: float) -> str:
-    t = max(0.0, min(1.0, float(t)))
-    ra, ga, ba = _parse_rgb(rgb_a)
-    rb, gb, bb = _parse_rgb(rgb_b)
-    r = round(ra + (rb - ra) * t)
-    g = round(ga + (gb - ga) * t)
-    b = round(ba + (bb - ba) * t)
-    return f"rgb({r},{g},{b})"
-
-
-def make_column_gradient_styles(
-    df: pd.DataFrame,
-    *,
-    id_col: str,
-    n_bins: int,
-    column_base_colors: Dict[str, str],
-    metric_direction: Dict[str, str],
-    darken_factor: float = 0.65,
-) -> List[dict]:
-    numeric_cols = [
-        c
-        for c in df.columns
-        if c != id_col and c in column_base_colors and pd.api.types.is_numeric_dtype(df[c])
-    ]
-
-    styles: List[dict] = []
-    white = "rgb(255, 255, 255)"
-
-    for col in numeric_cols:
-        col_min = float(df[col].min())
-        col_max = float(df[col].max())
-        if not (col_max > col_min):
+def empty_figure(message: str) -> go.Figure:
+    fig = go.Figure()
+    fig.add_annotation(
+        text=message,
+        x=0.5,
+        y=0.5,
+        xref="paper",
+        yref="paper",
+        showarrow=False,
+        font=dict(size=15, color="#536471"),
+    )
+    fig.update_xaxes(visible=False)
+    fig.update_yaxes(visible=False)
+    return fig_layout(fig)
+
+
+def parse_float_list(value: object) -> list[float]:
+    if pd.isna(value):
+        return []
+    out = []
+    for part in str(value).split(";"):
+        part = part.strip()
+        if not part:
             continue
+        try:
+            out.append(float(part))
+        except ValueError:
+            continue
+    return out
 
-        base = column_base_colors[col]
-        dark_base = _darken(base, factor=darken_factor)
-        direction = metric_direction.get(col, "high_is_good")
-
-        for i in range(n_bins):
-            low = col_min + (col_max - col_min) * (i / n_bins)
-            high = col_min + (col_max - col_min) * ((i + 1) / n_bins)
-
-            intensity = (i + 1) / n_bins
-            if direction == "low_is_good":
-                intensity = 1.0 - intensity
-
-            styles.append(
-                {
-                    "if": {
-                        "column_id": col,
-                        "filter_query": f"{{{col}}} >= {low} && {{{col}}} < {high}",
-                    },
-                    "backgroundColor": _blend_rgb(white, dark_base, intensity),
-                }
-            )
-
-        best_val = col_max if direction == "high_is_good" else col_min
-        worst_val = col_min if direction == "high_is_good" else col_max
 
-        styles.append(
-            {
-                "if": {"column_id": col, "filter_query": f"{{{col}}} = {best_val}"},
-                "backgroundColor": _blend_rgb(white, dark_base, 1.0),
-            }
-        )
-        styles.append(
-            {
-                "if": {"column_id": col, "filter_query": f"{{{col}}} = {worst_val}"},
-                "backgroundColor": _blend_rgb(white, dark_base, 0.0),
-            }
-        )
-
-    return styles
+def dataframe_table(
+    table_id: str,
+    *,
+    columns: list[str] | None = None,
+    data: list[dict] | None = None,
+    page_size: int = 12,
+) -> dash_table.DataTable:
+    return dash_table.DataTable(
+        id=table_id,
+        columns=[{"name": c, "id": c} for c in (columns or [])],
+        data=data or [],
+        page_size=page_size,
+        sort_action="native",
+        filter_action="native",
+        style_table={"overflowX": "auto"},
+        style_header={
+            "backgroundColor": "#f6f8fa",
+            "fontWeight": "700",
+            "border": "1px solid #d9e2e8",
+        },
+        style_cell={
+            "fontFamily": "Inter, Arial, sans-serif",
+            "fontSize": "13px",
+            "padding": "9px",
+            "textAlign": "left",
+            "minWidth": "100px",
+            "maxWidth": "280px",
+            "whiteSpace": "normal",
+            "height": "auto",
+            "border": "1px solid #e6edf2",
+        },
+    )
 
 
-def _apply_universal_scene_ranges(fig: go.Figure, *, n_scenes: int) -> None:
-    for idx in range(n_scenes):
-        scene_id = "scene" if idx == 0 else f"scene{idx + 1}"
-        fig.update_layout(
-            **{
-                scene_id: dict(
-                    xaxis=dict(title="", range=list(XYZ_RANGE)),
-                    yaxis=dict(title="", range=list(XYZ_RANGE)),
-                    zaxis=dict(title="", range=list(XYZ_RANGE)),
-                )
-            }
-        )
+def stat_card(label: str, value: str, detail: str) -> html.Div:
+    return html.Div(
+        [
+            html.Div(label, className="stat-label"),
+            html.Div(value, className="stat-value"),
+            html.Div(detail, className="stat-detail"),
+        ],
+        className="stat-card",
+    )
 
 
-def _embedding_grid_figure(
-    *,
-    titles: List[str],
-    dataset_for_each_panel: List[str],
-    model_for_each_panel: List[str],
-    noise_level: float,
-    n_cols: int = 3,
-) -> go.Figure:
-    n_panels = len(titles)
-    n_rows = int(math.ceil(n_panels / n_cols))
-
-    specs = [[{"type": "scene"} for _ in range(n_cols)] for _ in range(n_rows)]
-    fig = make_subplots(
-        rows=n_rows,
-        cols=n_cols,
-        specs=specs,
-        subplot_titles=titles,
-        horizontal_spacing=0.02,
-        vertical_spacing=0.06,
-    )
+def panel(title: str, *children, subtitle: str | None = None) -> html.Div:
+    heading = [html.H2(title)]
+    if subtitle:
+        heading.append(html.P(subtitle, className="panel-subtitle"))
+    return html.Div([html.Div(heading, className="panel-heading"), *children], className="panel")
 
-    for idx in range(n_panels):
-        r = (idx // n_cols) + 1
-        c = (idx % n_cols) + 1
-
-        ds = dataset_for_each_panel[idx]
-        md = model_for_each_panel[idx]
-        df_emb = generate_mock_embedding(ds, md, noise_level=noise_level)
-
-        # Single trace: continuous color encodes regression target
-        # Use cmin/cmax so all panels share the same color normalization.
-        show_scale = (idx == 0)
-
-        fig.add_trace(
-            go.Scatter3d(
-                x=df_emb["x"],
-                y=df_emb["y"],
-                z=df_emb["z"],
-                mode="markers",
-                showlegend=False,
-                marker=dict(
-                    size=2.5,
-                    opacity=0.75,
-                    color=df_emb["behavior_value"],
-                    colorscale=CONTINUOUS_COLORSCALE,
-                    cmin=float(BEHAVIOR_RANGE[0]),
-                    cmax=float(BEHAVIOR_RANGE[1]),
-                    showscale=show_scale,
-                    colorbar=dict(  # colorbar config supported via marker.colorbar
-                        title=dict(text="behavior"),
-                        tickformat=".2f",
-                        len=0.70,
-                    ),
-                ),
-                hovertemplate=(
-                    "behavior=%{marker.color:.3f}<br>"
-                    "x=%{x:.3f}<br>y=%{y:.3f}<br>z=%{z:.3f}<extra></extra>"
-                ),
-            ),
-            row=r,
-            col=c,
-        )
 
-    _apply_universal_scene_ranges(fig, n_scenes=n_panels)
+def code_block(text: str) -> html.Pre:
+    return html.Pre(html.Code(text), className="code-block")
 
-    fig.update_layout(
-        height=320 * n_rows,
-        margin=dict(l=10, r=10, t=40, b=10),
-    )
-    return fig
 
+present_prediction = present_rows(prediction)
+present_cells = len(present_prediction)
+total_cells = len(prediction)
+coverage_text = f"{present_cells}/{total_cells}" if total_cells else "0/0"
+coverage_pct = 100.0 * present_cells / total_cells if total_cells else 0.0
+runtime_counts = model_inventory["accelerator"].value_counts().to_dict()
 
-TOY_STORE = build_toy_benchmark()
-ALL_DATASETS = list(TOY_STORE.keys())
-ALL_MODELS = sorted({m for df in TOY_STORE.values() for m in df["model"].astype(str).tolist()})
 
-app = Dash(__name__)
+app = Dash(__name__, title="BenchDash")
 server = app.server
 
 app.layout = html.Div(
     [
-        html.H3("Neural Decoder Benchmarking"),
         html.Div(
             [
-                html.Div("Mode"),
-                dcc.RadioItems(
-                    id="mode-radio",
-                    options=[
-                        {"label": "By dataset", "value": "by_dataset"},
-                        {"label": "By model", "value": "by_model"},
+                html.Div(
+                    [
+                        html.Div("Tang Lab", className="eyebrow"),
+                        html.H1("BenchDash"),
+                        html.P(
+                            "Interactive summaries for the neural-behavioral model benchmark. "
+                            "The Space visualizes tracked result summaries; full training runs remain container-backed.",
+                            className="lede",
+                        ),
+                    ],
+                    className="hero-copy",
+                ),
+                html.Div(
+                    [
+                        stat_card("Models", str(len(MODELS)), "registered benchmark methods"),
+                        stat_card("Datasets", str(len(DATASETS)), "default paper grid"),
+                        stat_card("Clean Coverage", coverage_text, f"{coverage_pct:.1f}% present"),
+                        stat_card(
+                            "Runtime Mix",
+                            f"{runtime_counts.get('cpu', 0)} CPU / {runtime_counts.get('gpu', 0)} GPU",
+                            "from model capability metadata",
+                        ),
                     ],
-                    value="by_dataset",
-                    inline=True,
+                    className="stat-grid",
                 ),
             ],
-            style={"marginBottom": "12px"},
+            className="hero",
         ),
         html.Div(
             [
                 html.Div(
                     [
-                        html.Div("Dataset"),
+                        html.Label("Dataset"),
                         dcc.Dropdown(
-                            id="dataset-dropdown",
-                            options=[{"label": k, "value": k} for k in ALL_DATASETS],
-                            value=ALL_DATASETS[0],
+                            id="dataset-filter",
+                            options=[
+                                {"label": DATASET_LABELS.get(ds, ds), "value": ds}
+                                for ds in DATASETS
+                            ],
+                            value=DATASETS[0] if DATASETS else None,
                             clearable=False,
-                            style={"width": "100%"},
                         ),
                     ],
-                    id="dataset-dropdown-container",
-                    style={
-                        "width": "520px",
-                        "minWidth": "520px",
-                        "flexShrink": 0,
-                        "marginBottom": "12px",
-                    },
+                    className="control",
                 ),
                 html.Div(
                     [
-                        html.Div("Model"),
+                        html.Label("Models"),
                         dcc.Dropdown(
-                            id="model-dropdown",
-                            options=[{"label": m, "value": m} for m in ALL_MODELS],
-                            value=ALL_MODELS[0] if ALL_MODELS else None,
-                            clearable=False,
-                            style={"width": "100%"},
+                            id="model-filter",
+                            options=[{"label": m, "value": m} for m in MODELS],
+                            value=DEFAULT_SELECTED_MODELS,
+                            multi=True,
+                            placeholder="Clear to show all models",
                         ),
                     ],
-                    id="model-dropdown-container",
-                    style={
-                        "width": "520px",
-                        "minWidth": "520px",
-                        "flexShrink": 0,
-                        "marginBottom": "12px",
-                        "display": "none",
-                    },
+                    className="control control-wide",
                 ),
             ],
-            style={"display": "flex", "gap": "16px", "alignItems": "flex-end"},
+            className="controls",
         ),
-        dash_table.DataTable(
-            id="metrics-table",
-            columns=[],
-            data=[],
-            sort_action="native",
-            page_action="none",
-            style_table={"overflowX": "auto"},
-            style_cell={
-                "padding": "8px",
-                "fontFamily": "Arial",
-                "fontSize": "14px",
-                "textAlign": "left",
-                "minWidth": "120px",
-                "width": "120px",
-                "maxWidth": "200px",
-            },
-            style_header={"fontWeight": "600"},
-            style_data_conditional=[],
-            sort_by=[{"column_id": "score", "direction": "desc"}],
-        ),
-        html.Div(
-            [
-                html.Div("Noise level (fraction of spikes replaced / corrupted)"),
-                dcc.Slider(
-                    id="noise-slider",
-                    min=0.0,
-                    max=1.0,
-                    step=0.05,
-                    value=0.0,
-                    marks={0.0: "0", 0.25: "0.25", 0.5: "0.5", 0.75: "0.75", 1.0: "1.0"},
-                    tooltip={"placement": "bottom", "always_visible": False},
+        dcc.Tabs(
+            id="tabs",
+            value="prediction",
+            className="tabs",
+            children=[
+                dcc.Tab(
+                    label="Prediction",
+                    value="prediction",
+                    children=[
+                        panel(
+                            "Clean Prediction",
+                            dcc.Graph(id="prediction-heatmap", config={"displayModeBar": False}),
+                            dcc.Graph(id="prediction-ranking", config={"displayModeBar": False}),
+                            dataframe_table("prediction-table"),
+                            subtitle="Task score uses R2 for regression datasets and accuracy for classification datasets.",
+                        )
+                    ],
+                ),
+                dcc.Tab(
+                    label="Robustness",
+                    value="robustness",
+                    children=[
+                        panel(
+                            "Count-Noise Robustness",
+                            dcc.Graph(id="robustness-curve", config={"displayModeBar": False}),
+                            dataframe_table("robustness-table"),
+                            subtitle="Curves show score as deterministic count noise increases from the clean condition.",
+                        )
+                    ],
+                ),
+                dcc.Tab(
+                    label="Consistency",
+                    value="consistency",
+                    children=[
+                        panel(
+                            "Cross-Recording Consistency",
+                            dcc.Graph(id="consistency-bars", config={"displayModeBar": False}),
+                            dcc.Graph(id="consistency-heatmap", config={"displayModeBar": False}),
+                            dataframe_table("consistency-table"),
+                            subtitle="Scores summarize latent alignment across sessions for datasets with multiple recordings.",
+                        )
+                    ],
+                ),
+                dcc.Tab(
+                    label="Runtime",
+                    value="runtime",
+                    children=[
+                        panel(
+                            "Score, Time, and Memory",
+                            dcc.Graph(id="runtime-scatter", config={"displayModeBar": False}),
+                            dcc.Graph(id="memory-bars", config={"displayModeBar": False}),
+                            dataframe_table("runtime-table"),
+                            subtitle="Training time and memory are exported from the local benchmark artifacts.",
+                        )
+                    ],
+                ),
+                dcc.Tab(
+                    label="Attribution",
+                    value="attribution",
+                    children=[
+                        panel(
+                            "Perturbation and Attribution Summaries",
+                            dcc.Graph(id="neuron-shap-bars", config={"displayModeBar": False}),
+                            dcc.Graph(id="trial-shapley-bars", config={"displayModeBar": False}),
+                            dataframe_table("attribution-table"),
+                            subtitle="Signed SHAP and trial-Shapley summaries are shown without absolute-value ranking.",
+                        )
+                    ],
+                ),
+                dcc.Tab(
+                    label="Coverage",
+                    value="coverage",
+                    children=[
+                        panel(
+                            "Model Coverage",
+                            dcc.Graph(id="coverage-bars", config={"displayModeBar": False}),
+                            dataframe_table(
+                                "coverage-table",
+                                columns=list(model_inventory.columns),
+                                data=model_inventory.to_dict("records"),
+                                page_size=25,
+                            ),
+                            subtitle="Container labels indicate the reproducible full-suite runtime, not what this Space executes.",
+                        )
+                    ],
+                ),
+                dcc.Tab(
+                    label="Run It",
+                    value="run-it",
+                    children=[
+                        panel(
+                            "How This Is Meant To Be Used",
+                            html.Div(
+                                [
+                                    html.Div(
+                                        [
+                                            html.H3("This Space"),
+                                            html.P(
+                                                "Use BenchDash to inspect paper-result summaries, compare models, "
+                                                "check missingness, and understand runtime tradeoffs. It does not "
+                                                "train the benchmark models on Hugging Face CPU hardware."
+                                            ),
+                                        ],
+                                        className="route-card",
+                                    ),
+                                    html.Div(
+                                        [
+                                            html.H3("Colab Companion"),
+                                            html.P(
+                                                "The right notebook target is a curated subset with clean Python installs: "
+                                                "PCA, GPFA, SVC, XGBoost, CEBRA, and selected simple neural baselines."
+                                            ),
+                                        ],
+                                        className="route-card",
+                                    ),
+                                    html.Div(
+                                        [
+                                            html.H3("Full Benchmark"),
+                                            html.P(
+                                                "The full model suite should stay container-backed because several "
+                                                "upstream methods have stale pins, MATLAB Runtime requirements, or "
+                                                "CUDA/package coupling."
+                                            ),
+                                        ],
+                                        className="route-card",
+                                    ),
+                                ],
+                                className="route-grid",
+                            ),
+                            html.H3("Example container commands"),
+                            code_block(
+                                "bin/submit_local --model pca --stage comprehensive -- --dataset local_regression\n"
+                                "bin/submit_gpu --model cebra --stage comprehensive -- --dataset monkey --session sub-C_ses-CO-20151104_behavior+ecephys\n"
+                                "bin/submit_cpu --model gpfa --stage stage1 -- --dataset monkey --session sub-C_ses-CO-20151104_behavior+ecephys"
+                            ),
+                            html.H3("Canonical uploaded-data shape"),
+                            code_block(
+                                "neural:    float array, shape (N_trials, T, N_neurons)\n"
+                                "trial_ids: array, shape (N_trials,)\n"
+                                "targets:   task-dependent behavior labels or trajectories\n"
+                                "optional:  condition_ids, consistency_ids"
+                            ),
+                            subtitle="The upload/data-contract story belongs in the Colab and container path, not as the main dashboard affordance.",
+                        )
+                    ],
                 ),
             ],
-            style={"maxWidth": "1100px", "marginTop": "10px", "marginBottom": "10px"},
-        ),
-        dcc.Graph(
-            id="embeddings-grid",
-            style={"height": "800px"},
-            config={"displayModeBar": False},
         ),
     ],
-    style={"padding": "16px"},
+    className="app-shell",
 )
 
 
-@callback(
-    Output("dataset-dropdown-container", "style"),
-    Output("model-dropdown-container", "style"),
-    Input("mode-radio", "value"),
-)
-def toggle_controls(mode: str):
-    base_box = {
-        "width": "520px",
-        "minWidth": "520px",
-        "flexShrink": 0,
-        "marginBottom": "12px",
-    }
-    if mode == "by_model":
-        return {**base_box, "display": "none"}, {**base_box, "display": "block"}
-    return {**base_box, "display": "block"}, {**base_box, "display": "none"}
-
-
-@callback(
-    Output("metrics-table", "columns"),
-    Output("metrics-table", "data"),
-    Output("metrics-table", "style_data_conditional"),
-    Output("embeddings-grid", "figure"),
-    Input("mode-radio", "value"),
-    Input("dataset-dropdown", "value"),
-    Input("model-dropdown", "value"),
-    Input("noise-slider", "value"),
+@app.callback(
+    Output("prediction-heatmap", "figure"),
+    Output("prediction-ranking", "figure"),
+    Output("prediction-table", "columns"),
+    Output("prediction-table", "data"),
+    Input("dataset-filter", "value"),
+    Input("model-filter", "value"),
 )
-def update_outputs(mode: str, dataset_name: str, model_name: str, noise_level: float):
-    column_colors = dict(COLUMN_COLORS)
-    column_colors["score"] = "rgb(220, 220, 220)"
-
-    metric_direction_for_colors = dict(METRIC_DIRECTION)
-    metric_direction_for_colors["score"] = "high_is_good"
-
-    if mode == "by_model":
-        rows = []
-        for ds_name, df_ds in TOY_STORE.items():
-            df_row = df_ds[df_ds["model"] == model_name]
-            if df_row.empty:
-                continue
-            rec = df_row.iloc[0].to_dict()
-            rows.append({"dataset": ds_name, **{k: rec[k] for k in rec if k != "model"}})
-
-        df = pd.DataFrame(rows)
-        if df.empty:
-            return [], [], [], go.Figure()
-
-        df["score"] = compute_mock_score(
-            df,
-            metric_direction=METRIC_DIRECTION,
-            metric_weights={"r2": 1.0, "rmse": 1.0, "mae": 1.0},
-        )
-        df = df.sort_values("score", ascending=False).reset_index(drop=True)
-        df = df[["dataset", "score"] + [c for c in df.columns if c not in {"dataset", "score"}]]
-
-        id_col = "dataset"
-        titles = df["dataset"].tolist()
-
-        fig_grid = _embedding_grid_figure(
-            titles=titles,
-            dataset_for_each_panel=titles,
-            model_for_each_panel=[model_name] * len(titles),
-            noise_level=float(noise_level),
-            n_cols=3,
+def update_prediction(dataset: str, models: list[str] | None):
+    df_all = filter_models(prediction, models)
+    if df_all.empty:
+        cols = [{"name": c, "id": c} for c in ["model", "dataset", "status", "score"]]
+        return empty_figure("No prediction rows found."), empty_figure("No selected models."), cols, []
+
+    heat_df = df_all.copy()
+    heat_df["score"] = pd.to_numeric(heat_df["score"], errors="coerce")
+    heat_df["dataset_label"] = heat_df["dataset"].map(DATASET_LABELS).fillna(heat_df["dataset"])
+    pivot = heat_df.pivot_table(index="model", columns="dataset_label", values="score", aggfunc="first")
+    if not pivot.empty:
+        order = pivot.mean(axis=1, skipna=True).sort_values(ascending=False).index
+        pivot = pivot.loc[order]
+        dataset_order = [DATASET_LABELS.get(ds, ds) for ds in DATASETS if DATASET_LABELS.get(ds, ds) in pivot.columns]
+        pivot = pivot.reindex(columns=dataset_order)
+    text = pivot.map(lambda x: "" if pd.isna(x) else f"{x:.3f}") if not pivot.empty else pivot
+    zmin = min(0.0, float(np.nanmin(pivot.values))) if pivot.size and not np.isnan(pivot.values).all() else 0.0
+    zmax = max(1.0, float(np.nanmax(pivot.values))) if pivot.size and not np.isnan(pivot.values).all() else 1.0
+    heatmap = go.Figure(
+        go.Heatmap(
+            z=pivot.values if not pivot.empty else [[]],
+            x=list(pivot.columns),
+            y=list(pivot.index),
+            text=text.values if not pivot.empty else [[]],
+            texttemplate="%{text}",
+            colorscale=[[0.0, "#b85c38"], [0.5, "#f7f2e8"], [1.0, "#176b5a"]],
+            zmin=zmin,
+            zmax=zmax,
+            colorbar=dict(title="score"),
+            hovertemplate="model=%{y}<br>dataset=%{x}<br>score=%{z:.4f}<extra></extra>",
         )
+    )
+    heatmap.update_layout(title="Clean-score matrix")
+    fig_layout(heatmap, height=max(430, 28 * len(pivot.index) + 170))
+
+    rank_df = present_rows(df_all)
+    rank_df = rank_df[rank_df["dataset"] == dataset].copy()
+    rank_df["score"] = pd.to_numeric(rank_df["score"], errors="coerce")
+    rank_df = rank_df.dropna(subset=["score"]).sort_values("score", ascending=True)
+    if rank_df.empty:
+        ranking = empty_figure(f"No clean prediction rows for {DATASET_LABELS.get(dataset, dataset)}.")
     else:
-        df = TOY_STORE[dataset_name].copy()
-
-        df["score"] = compute_mock_score(
-            df,
-            metric_direction=METRIC_DIRECTION,
-            metric_weights={"r2": 1.0, "rmse": 1.0, "mae": 1.0},
-        )
-        df = df.sort_values("score", ascending=False).reset_index(drop=True)
-        df = df[["model", "score"] + [c for c in df.columns if c not in {"model", "score"}]]
-
-        id_col = "model"
-        titles = df["model"].tolist()
-
-        fig_grid = _embedding_grid_figure(
-            titles=titles,
-            dataset_for_each_panel=[dataset_name] * len(titles),
-            model_for_each_panel=titles,
-            noise_level=float(noise_level),
-            n_cols=3,
+        metric = ordered_unique(rank_df["metric"])[0]
+        ranking = go.Figure(
+            go.Bar(
+                x=rank_df["score"],
+                y=rank_df["model"],
+                orientation="h",
+                marker=dict(color=rank_df["score"], colorscale="Teal", colorbar=dict(title=metric)),
+                hovertemplate="model=%{y}<br>score=%{x:.4f}<extra></extra>",
+            )
         )
+        ranking.update_layout(title=f"{DATASET_LABELS.get(dataset, dataset)} ranking ({metric})")
+        ranking.update_xaxes(title=metric)
+        ranking.update_yaxes(title="")
+        fig_layout(ranking, height=max(420, 27 * len(rank_df) + 160))
+
+    table_cols = [
+        "model",
+        "dataset_display",
+        "status",
+        "metric",
+        "score",
+        "decoder",
+        "latent_dim",
+        "n_train_trials",
+        "n_test_trials",
+        "n_neurons",
+        "source_path",
+    ]
+    table_df = df_all[df_all["dataset"] == dataset].copy()
+    for col in ["score", "latent_dim", "n_train_trials", "n_test_trials", "n_neurons"]:
+        if col in table_df:
+            table_df[col] = table_df[col].map(lambda v: compact_number(v))
+    table_df = table_df[[c for c in table_cols if c in table_df.columns]]
+    return (
+        heatmap,
+        ranking,
+        [{"name": c, "id": c} for c in table_df.columns],
+        table_df.to_dict("records"),
+    )
 
-    num_cols = [c for c in df.columns if c != id_col and pd.api.types.is_numeric_dtype(df[c])]
-
-    columns = [{"name": id_col, "id": id_col}]
-    for c in df.columns:
-        if c == id_col:
-            continue
-        col_def = {"name": c, "id": c}
-        if c in num_cols:
-            col_def["type"] = "numeric"
-            col_def["format"] = {"specifier": f".{SIGFIGS}f"}
-        columns.append(col_def)
-
-    data = df.to_dict("records")
 
-    styles = make_column_gradient_styles(
+@app.callback(
+    Output("robustness-curve", "figure"),
+    Output("robustness-table", "columns"),
+    Output("robustness-table", "data"),
+    Input("dataset-filter", "value"),
+    Input("model-filter", "value"),
+)
+def update_robustness(dataset: str, models: list[str] | None):
+    df = filter_models(present_rows(robustness), models)
+    df = df[df["dataset"] == dataset].copy() if not df.empty else df
+    if df.empty:
+        cols = [{"name": c, "id": c} for c in ["model", "metric", "raw_auc", "score_at_noise0"]]
+        return empty_figure("No robustness rows for this selection."), cols, []
+
+    fig = go.Figure()
+    for row in df.sort_values("model").itertuples():
+        xs = parse_float_list(row.noise_levels)
+        ys = parse_float_list(row.scores)
+        if xs and len(xs) == len(ys):
+            fig.add_trace(
+                go.Scatter(
+                    x=xs,
+                    y=ys,
+                    mode="lines+markers",
+                    name=row.model,
+                    hovertemplate="noise=%{x:.2f}<br>score=%{y:.4f}<extra></extra>",
+                )
+            )
+    fig.update_layout(title=f"{DATASET_LABELS.get(dataset, dataset)} robustness curves")
+    fig.update_xaxes(title="noise fraction")
+    fig.update_yaxes(title=ordered_unique(df["metric"])[0] if "metric" in df else "score")
+    fig_layout(fig, height=520)
+
+    table_cols = ["model", "metric", "score_at_noise0", "score_at_max_noise", "raw_auc", "mean_score", "source_path"]
+    table_df = df[table_cols].copy()
+    for col in ["score_at_noise0", "score_at_max_noise", "raw_auc", "mean_score"]:
+        table_df[col] = table_df[col].map(lambda v: compact_number(v))
+    return fig, [{"name": c, "id": c} for c in table_df.columns], table_df.to_dict("records")
+
+
+@app.callback(
+    Output("consistency-bars", "figure"),
+    Output("consistency-heatmap", "figure"),
+    Output("consistency-table", "columns"),
+    Output("consistency-table", "data"),
+    Input("dataset-filter", "value"),
+    Input("model-filter", "value"),
+)
+def update_consistency(dataset: str, models: list[str] | None):
+    df_all = filter_models(consistency, models)
+    if "is_active_model" in df_all.columns:
+        df_all = df_all[df_all["is_active_model"].astype(str).str.lower() == "true"]
+    df = df_all[df_all["dataset"] == dataset].copy() if not df_all.empty else df_all
+    if df.empty:
+        cols = [{"name": c, "id": c} for c in ["model", "dataset", "mean_r2_procrustes"]]
+        return empty_figure("No consistency rows for this dataset."), empty_figure("No consistency rows."), cols, []
+
+    for col in ["mean_r2", "mean_r2_procrustes"]:
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+    bar_df = df.sort_values("mean_r2_procrustes", ascending=True)
+    bars = go.Figure(
+        go.Bar(
+            x=bar_df["mean_r2_procrustes"],
+            y=bar_df["model"],
+            orientation="h",
+            marker=dict(color=bar_df["mean_r2_procrustes"], colorscale="Bluyl"),
+            hovertemplate="model=%{y}<br>Procrustes R2=%{x:.4f}<extra></extra>",
+        )
+    )
+    bars.update_layout(title=f"{DATASET_LABELS.get(dataset, dataset)} cross-session alignment")
+    bars.update_xaxes(title="mean Procrustes R2")
+    fig_layout(bars, height=max(420, 27 * len(bar_df) + 150))
+
+    heat = df_all.copy()
+    heat["dataset_label"] = heat["dataset"].map(DATASET_LABELS).fillna(heat["dataset"])
+    pivot = heat.pivot_table(index="model", columns="dataset_label", values="mean_r2_procrustes", aggfunc="first")
+    if not pivot.empty:
+        pivot = pivot.loc[pivot.mean(axis=1, skipna=True).sort_values(ascending=False).index]
+    text = pivot.map(lambda x: "" if pd.isna(x) else f"{x:.2f}") if not pivot.empty else pivot
+    heatmap = go.Figure(
+        go.Heatmap(
+            z=pivot.values if not pivot.empty else [[]],
+            x=list(pivot.columns),
+            y=list(pivot.index),
+            text=text.values if not pivot.empty else [[]],
+            texttemplate="%{text}",
+            colorscale=[[0.0, "#b85c38"], [0.5, "#f7f2e8"], [1.0, "#176b5a"]],
+            colorbar=dict(title="R2"),
+            hovertemplate="model=%{y}<br>dataset=%{x}<br>R2=%{z:.4f}<extra></extra>",
+        )
+    )
+    heatmap.update_layout(title="Consistency matrix")
+    fig_layout(heatmap, height=max(410, 28 * len(pivot.index) + 160))
+
+    table_cols = [
+        "model",
+        "dataset",
+        "n_sessions",
+        "latent_dim",
+        "mean_r2",
+        "mean_r2_procrustes",
+        "n_pairwise",
+        "scoring_modes",
+        "source_path",
+    ]
+    table_df = df[[c for c in table_cols if c in df.columns]].copy()
+    for col in ["mean_r2", "mean_r2_procrustes", "latent_dim", "n_sessions", "n_pairwise"]:
+        if col in table_df:
+            table_df[col] = table_df[col].map(lambda v: compact_number(v))
+    return bars, heatmap, [{"name": c, "id": c} for c in table_df.columns], table_df.to_dict("records")
+
+
+@app.callback(
+    Output("runtime-scatter", "figure"),
+    Output("memory-bars", "figure"),
+    Output("runtime-table", "columns"),
+    Output("runtime-table", "data"),
+    Input("dataset-filter", "value"),
+    Input("model-filter", "value"),
+)
+def update_runtime(dataset: str, models: list[str] | None):
+    df = filter_models(present_rows(scalability), models)
+    df = df[df["dataset"] == dataset].copy() if not df.empty else df
+    if df.empty:
+        cols = [{"name": c, "id": c} for c in ["model", "training_time_sec", "peak_ram_gb"]]
+        return empty_figure("No runtime rows for this selection."), empty_figure("No memory rows."), cols, []
+
+    pred = present_rows(prediction)[["model", "dataset", "score", "metric"]].copy()
+    df = df.merge(pred, on=["model", "dataset"], how="left")
+    for col in ["training_time_sec", "inference_time_sec", "peak_ram_gb", "peak_vram_gb", "score"]:
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+    df["accelerator"] = df["model"].map(lambda m: MODEL_INFO.get(m, ("unknown", "", "", ""))[0])
+    df["pipeline"] = df["model"].map(lambda m: MODEL_INFO.get(m, ("", "unknown", "", ""))[1])
+
+    scatter = px.scatter(
         df,
-        id_col=id_col,
-        n_bins=12,
-        column_base_colors=column_colors,
-        metric_direction=metric_direction_for_colors,
-        darken_factor=0.65,
+        x="training_time_sec",
+        y="score",
+        size="peak_ram_gb",
+        color="accelerator",
+        symbol="pipeline",
+        hover_name="model",
+        hover_data={
+            "training_time_sec": ":.3f",
+            "inference_time_sec": ":.3f",
+            "peak_ram_gb": ":.3f",
+            "peak_vram_gb": ":.3f",
+            "score": ":.4f",
+        },
+        log_x=True,
+        title=f"{DATASET_LABELS.get(dataset, dataset)} score vs training time",
     )
-
-    return columns, data, styles, fig_grid
+    scatter.update_xaxes(title="training time (sec, log)")
+    scatter.update_yaxes(title=ordered_unique(df["metric"].dropna())[0] if df["metric"].notna().any() else "score")
+    fig_layout(scatter, height=500)
+
+    mem_df = df.sort_values("peak_ram_gb", ascending=True)
+    memory = go.Figure()
+    memory.add_trace(go.Bar(x=mem_df["peak_ram_gb"], y=mem_df["model"], orientation="h", name="RAM GB"))
+    memory.add_trace(go.Bar(x=mem_df["peak_vram_gb"], y=mem_df["model"], orientation="h", name="VRAM GB"))
+    memory.update_layout(title=f"{DATASET_LABELS.get(dataset, dataset)} peak memory", barmode="group")
+    memory.update_xaxes(title="GB")
+    fig_layout(memory, height=max(420, 29 * len(mem_df) + 160))
+
+    table_cols = [
+        "model",
+        "accelerator",
+        "score",
+        "training_time_sec",
+        "inference_time_sec",
+        "peak_ram_gb",
+        "peak_vram_gb",
+        "source_path",
+    ]
+    table_df = df[table_cols].copy()
+    for col in ["score", "training_time_sec", "inference_time_sec", "peak_ram_gb", "peak_vram_gb"]:
+        table_df[col] = table_df[col].map(lambda v: compact_number(v))
+    return scatter, memory, [{"name": c, "id": c} for c in table_df.columns], table_df.to_dict("records")
+
+
+@app.callback(
+    Output("neuron-shap-bars", "figure"),
+    Output("trial-shapley-bars", "figure"),
+    Output("attribution-table", "columns"),
+    Output("attribution-table", "data"),
+    Input("dataset-filter", "value"),
+    Input("model-filter", "value"),
+)
+def update_attribution(dataset: str, models: list[str] | None):
+    nshap = filter_models(neuron_shap, models)
+    if "is_active_model" in nshap.columns:
+        nshap = nshap[nshap["is_active_model"].astype(str).str.lower() == "true"]
+    nshap = nshap[nshap["dataset"] == dataset].copy() if not nshap.empty else nshap
+    if nshap.empty:
+        nshap_fig = empty_figure("No neuron-SHAP rows for this dataset.")
+    else:
+        nshap["auc"] = pd.to_numeric(nshap["auc"], errors="coerce")
+        bar_df = nshap.dropna(subset=["auc"]).sort_values("auc", ascending=True)
+        nshap_fig = go.Figure(
+            go.Bar(
+                x=bar_df["auc"],
+                y=bar_df["model"],
+                orientation="h",
+                marker=dict(color=bar_df["auc"], colorscale="Teal"),
+                hovertemplate="model=%{y}<br>AUC=%{x:.4f}<extra></extra>",
+            )
+        )
+        nshap_fig.update_layout(title=f"{DATASET_LABELS.get(dataset, dataset)} neuron perturbation AUC")
+        nshap_fig.update_xaxes(title="AUC")
+        fig_layout(nshap_fig, height=max(420, 27 * len(bar_df) + 150))
+
+    tshap = filter_models(trial_shapley, models)
+    if "is_active_model" in tshap.columns:
+        tshap = tshap[tshap["is_active_model"].astype(str).str.lower() == "true"]
+    tshap = tshap[tshap["dataset"] == dataset].copy() if not tshap.empty else tshap
+    if tshap.empty:
+        tshap_fig = empty_figure("No trial-Shapley rows for this dataset.")
+    else:
+        tshap["perturbation_auc"] = pd.to_numeric(tshap["perturbation_auc"], errors="coerce")
+        trial_df = tshap.dropna(subset=["perturbation_auc"]).sort_values("perturbation_auc", ascending=True)
+        tshap_fig = go.Figure(
+            go.Bar(
+                x=trial_df["perturbation_auc"],
+                y=trial_df["model"],
+                orientation="h",
+                marker=dict(color=trial_df["perturbation_auc"], colorscale="Bluyl"),
+                hovertemplate="model=%{y}<br>perturbation AUC=%{x:.4f}<extra></extra>",
+            )
+        )
+        tshap_fig.update_layout(title=f"{DATASET_LABELS.get(dataset, dataset)} trial-Shapley perturbation AUC")
+        tshap_fig.update_xaxes(title="perturbation AUC")
+        fig_layout(tshap_fig, height=max(420, 27 * len(trial_df) + 150))
+
+    table_cols = [
+        "model",
+        "dataset",
+        "metric",
+        "baseline_score",
+        "full_model_score",
+        "auc",
+        "spearman_corr",
+        "shap_mean_value",
+        "shap_fraction_positive",
+        "source_path",
+    ]
+    table_df = nshap[[c for c in table_cols if c in nshap.columns]].copy()
+    for col in table_df.columns:
+        if col not in {"model", "dataset", "metric", "source_path"}:
+            table_df[col] = table_df[col].map(lambda v: compact_number(v))
+    return nshap_fig, tshap_fig, [{"name": c, "id": c} for c in table_df.columns], table_df.to_dict("records")
+
+
+@app.callback(Output("coverage-bars", "figure"), Input("model-filter", "value"))
+def update_coverage(models: list[str] | None):
+    df = filter_models(model_inventory, models)
+    if df.empty:
+        return empty_figure("No models selected.")
+    counts = (
+        df.groupby(["accelerator", "pipeline"])
+        .size()
+        .reset_index(name="n_models")
+        .sort_values("n_models", ascending=False)
+    )
+    fig = px.bar(
+        counts,
+        x="pipeline",
+        y="n_models",
+        color="accelerator",
+        barmode="group",
+        text="n_models",
+        title="Registered model runtimes",
+    )
+    fig.update_xaxes(title="")
+    fig.update_yaxes(title="models")
+    return fig_layout(fig, height=420)
 
 
 if __name__ == "__main__":
-    app.run_server(host="0.0.0.0", port=7860)
+    app.run(host="0.0.0.0", port=7860, debug=False)

assets/styles.css

@@ -0,0 +1,198 @@
+* {
+  box-sizing: border-box;
+}
+
+body {
+  margin: 0;
+  background: #eef3f5;
+  color: #1f2933;
+  font-family: Inter, Arial, sans-serif;
+}
+
+.app-shell {
+  min-height: 100vh;
+  padding: 24px;
+}
+
+.hero {
+  display: grid;
+  grid-template-columns: minmax(280px, 1fr) minmax(360px, 0.95fr);
+  gap: 20px;
+  align-items: stretch;
+  margin: 0 auto 18px;
+  max-width: 1480px;
+}
+
+.hero-copy,
+.stat-card,
+.controls,
+.panel,
+.route-card {
+  background: #ffffff;
+  border: 1px solid #d8e2e8;
+  border-radius: 8px;
+  box-shadow: 0 1px 2px rgba(31, 41, 51, 0.04);
+}
+
+.hero-copy {
+  padding: 28px 30px;
+}
+
+.eyebrow {
+  color: #176b5a;
+  font-size: 12px;
+  font-weight: 800;
+  letter-spacing: 0;
+  margin-bottom: 8px;
+  text-transform: uppercase;
+}
+
+h1,
+h2,
+h3,
+p {
+  margin-top: 0;
+}
+
+h1 {
+  margin-bottom: 10px;
+  font-size: 46px;
+  line-height: 1.02;
+  letter-spacing: 0;
+}
+
+h2 {
+  margin-bottom: 4px;
+  font-size: 20px;
+}
+
+h3 {
+  margin-bottom: 8px;
+  font-size: 16px;
+}
+
+.lede {
+  max-width: 760px;
+  margin-bottom: 0;
+  color: #536471;
+  font-size: 17px;
+  line-height: 1.55;
+}
+
+.stat-grid {
+  display: grid;
+  grid-template-columns: repeat(2, minmax(160px, 1fr));
+  gap: 12px;
+}
+
+.stat-card {
+  padding: 20px;
+}
+
+.stat-label {
+  color: #536471;
+  font-size: 12px;
+  font-weight: 800;
+  text-transform: uppercase;
+}
+
+.stat-value {
+  margin-top: 8px;
+  color: #111827;
+  font-size: 30px;
+  font-weight: 800;
+  line-height: 1;
+}
+
+.stat-detail {
+  margin-top: 8px;
+  color: #687987;
+  font-size: 13px;
+}
+
+.controls {
+  display: grid;
+  grid-template-columns: minmax(240px, 320px) minmax(320px, 1fr);
+  gap: 18px;
+  margin: 0 auto 18px;
+  max-width: 1480px;
+  padding: 16px;
+}
+
+.control label {
+  display: block;
+  margin-bottom: 7px;
+  color: #374151;
+  font-size: 13px;
+  font-weight: 800;
+}
+
+.tabs {
+  margin: 0 auto;
+  max-width: 1480px;
+}
+
+.tab {
+  border-radius: 8px 8px 0 0;
+}
+
+.panel {
+  margin-top: 16px;
+  padding: 18px;
+}
+
+.panel-heading {
+  margin-bottom: 12px;
+}
+
+.panel-subtitle {
+  margin-bottom: 0;
+  color: #5d6e7d;
+  font-size: 14px;
+  line-height: 1.45;
+}
+
+.route-grid {
+  display: grid;
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+  gap: 14px;
+  margin-bottom: 18px;
+}
+
+.route-card {
+  padding: 18px;
+  box-shadow: none;
+}
+
+.route-card p {
+  margin-bottom: 0;
+  color: #536471;
+  line-height: 1.5;
+}
+
+.code-block {
+  overflow-x: auto;
+  margin: 10px 0 22px;
+  padding: 14px 16px;
+  background: #18202a;
+  border-radius: 8px;
+  color: #e6edf2;
+  font-size: 13px;
+  line-height: 1.5;
+}
+
+@media (max-width: 900px) {
+  .app-shell {
+    padding: 12px;
+  }
+
+  .hero,
+  .controls,
+  .route-grid {
+    grid-template-columns: 1fr;
+  }
+
+  h1 {
+    font-size: 34px;
+  }
+}

data/clean_prediction_summary.csv

@@ -0,0 +1,116 @@
+model,dataset,dataset_display,expected_session,session,status,eval_mode,decoder,metric,score,mse,n_train_trials,n_test_trials,n_neurons,latent_dim,source_path
+blend,monkey,Monkey (CO),sub-C_ses-CO-20151104_behavior+ecephys,sub-C_ses-CO-20151104_behavior+ecephys,present,sequence_regression,ridge,r2,0.9175238609313965,1.196044921875,256,63,59,,benchmark_results/blend/blend_benchmark_sub-C_ses-CO-20151104_behavior+ecephys.json
+blend,allen_neuropixels,Visual Coding,721123822,721123822,present,sequence_classification,logistic,accuracy,0.5714285714285714,,479,119,444,,benchmark_results/blend/blend_benchmark_721123822.json
+blend,speech,Speech,t12,t12,present,sequence_classification,logistic,accuracy,0.06060606060606061,,135,33,64,,benchmark_results/blend/blend_benchmark_t12.json
+blend,mc_pacman,MC Pacman,mc_pacman,mc_pacman,present,sequence_regression,ridge,r2,0.8445752859115601,6.297308921813965,290,72,128,,benchmark_results/blend/blend_benchmark_mc_pacman.json
+blend,ratinabox,RatInABox,ratinabox_nav,ratinabox_nav,present,sequence_regression,ridge,r2,0.9870438575744629,0.0009629210690036416,240,60,300,,benchmark_results/blend/blend_benchmark_ratinabox_nav.json
+blend_ndt,monkey,Monkey (CO),sub-C_ses-CO-20151104_behavior+ecephys,sub-C_ses-CO-20151104_behavior+ecephys,present,sequence_regression,ridge,r2,0.8707619309425354,1.8752440214157104,256,63,59,,benchmark_results/blend_ndt/blend_ndt_benchmark_sub-C_ses-CO-20151104_behavior+ecephys.json
+blend_ndt,allen_neuropixels,Visual Coding,721123822,721123822,present,sequence_classification,logistic,accuracy,0.8823529411764706,,479,119,444,,benchmark_results/blend_ndt/blend_ndt_benchmark_721123822.json
+blend_ndt,speech,Speech,t12,t12,present,sequence_classification,logistic,accuracy,0.7878787878787878,,135,33,64,,benchmark_results/blend_ndt/blend_ndt_benchmark_t12.json
+blend_ndt,mc_pacman,MC Pacman,mc_pacman,mc_pacman,present,sequence_regression,ridge,r2,0.7803788185119629,8.898341178894043,290,72,128,,benchmark_results/blend_ndt/blend_ndt_benchmark_mc_pacman.json
+blend_ndt,ratinabox,RatInABox,ratinabox_nav,ratinabox_nav,present,sequence_regression,ridge,r2,0.9343204498291016,0.004853242542594671,240,60,300,,benchmark_results/blend_ndt/blend_ndt_benchmark_ratinabox_nav.json
+cebra,monkey,Monkey (CO),sub-C_ses-CO-20151104_behavior+ecephys,sub-C_ses-CO-20151104_behavior+ecephys,present,sequence_regression,knn,r2,0.7898101210594177,3.051151752471924,,,59,32,benchmark_results/cebra/cebra_benchmark_sub-C_ses-CO-20151104_behavior+ecephys.json
+cebra,allen_neuropixels,Visual Coding,721123822,721123822,present,sequence_classification,knn,accuracy,0.4789915966386555,,,,444,32,benchmark_results/cebra/cebra_benchmark_721123822.json
+cebra,speech,Speech,t12,t12,present,sequence_classification,knn,accuracy,0.3939393939393939,,,,64,32,benchmark_results/cebra/cebra_benchmark_t12.json
+cebra,mc_pacman,MC Pacman,mc_pacman,mc_pacman,present,sequence_regression,knn,r2,0.8163397908210754,7.441318988800049,,,128,32,benchmark_results/cebra/cebra_benchmark_mc_pacman.json
+cebra,ratinabox,RatInABox,ratinabox_nav,,missing,,,r2,,,,,,,
+dnn,monkey,Monkey (CO),sub-C_ses-CO-20151104_behavior+ecephys,sub-C_ses-CO-20151104_behavior+ecephys,present,sequence_regression,dnn,r2,0.893627405166626,1.5450406074523926,256,63,59,,benchmark_results/dnn/dnn_benchmark_sub-C_ses-CO-20151104_behavior+ecephys.json
+dnn,allen_neuropixels,Visual Coding,721123822,721123822,present,sequence_classification,dnn,accuracy,0.09243697478991597,,479,119,444,,benchmark_results/dnn/dnn_benchmark_721123822.json
+dnn,speech,Speech,t12,t12,present,sequence_classification,dnn,accuracy,0.9090909090909091,,135,33,64,,benchmark_results/dnn/dnn_benchmark_t12.json
+dnn,mc_pacman,MC Pacman,mc_pacman,mc_pacman,present,sequence_regression,dnn,r2,0.905195951461792,3.8411543369293213,290,72,128,,benchmark_results/dnn/dnn_benchmark_mc_pacman.json
+dnn,ratinabox,RatInABox,ratinabox_nav,ratinabox_nav,present,sequence_regression,dnn,r2,0.931598961353302,0.005079955793917179,240,60,300,,benchmark_results/dnn/dnn_benchmark_ratinabox_nav.json
+dpad,monkey,Monkey (CO),sub-C_ses-CO-20151104_behavior+ecephys,sub-C_ses-CO-20151104_behavior+ecephys,present,sequence_regression,native,r2,0.9144360423088074,1.2411186695098877,,,59,16,benchmark_results/dpad/dpad_benchmark_sub-C_ses-CO-20151104_behavior+ecephys.json
+dpad,allen_neuropixels,Visual Coding,721123822,721123822,present,sequence_classification,native,accuracy,0.7394957983193278,,,,444,16,benchmark_results/dpad/dpad_benchmark_721123822.json
+dpad,speech,Speech,t12,t12,present,sequence_classification,native,accuracy,0.5151515151515151,,,,64,16,benchmark_results/dpad/dpad_benchmark_t12.json
+dpad,mc_pacman,MC Pacman,mc_pacman,mc_pacman,present,sequence_regression,native,r2,0.8990999460220337,4.088143348693848,,,128,16,benchmark_results/dpad/dpad_benchmark_mc_pacman.json
+dpad,ratinabox,RatInABox,ratinabox_nav,ratinabox_nav,present,sequence_regression,native,r2,0.9679579734802246,0.002372820395976305,,,300,16,benchmark_results/dpad/dpad_benchmark_ratinabox_nav.json
+gpfa,monkey,Monkey (CO),sub-C_ses-CO-20151104_behavior+ecephys,sub-C_ses-CO-20151104_behavior+ecephys,present,sequence_regression,ridge,r2,0.7373411655426025,3.8092288970947266,256,63,59,10,benchmark_results/gpfa/gpfa_benchmark_sub-C_ses-CO-20151104_behavior+ecephys.json
+gpfa,allen_neuropixels,Visual Coding,721123822,721123822,present,sequence_classification,logistic,accuracy,0.4957983193277311,,479,119,444,10,benchmark_results/gpfa/gpfa_benchmark_721123822.json
+gpfa,speech,Speech,t12,t12,present,sequence_classification,logistic,accuracy,0.7878787878787878,,135,33,64,10,benchmark_results/gpfa/gpfa_benchmark_t12.json
+gpfa,mc_pacman,MC Pacman,mc_pacman,mc_pacman,present,sequence_regression,ridge,r2,0.49674326181411743,20.39034080505371,290,72,128,10,benchmark_results/gpfa/gpfa_benchmark_mc_pacman.json
+gpfa,ratinabox,RatInABox,ratinabox_nav,ratinabox_nav,present,sequence_regression,ridge,r2,0.9226212501525879,0.005764497444033623,240,60,300,10,benchmark_results/gpfa/gpfa_benchmark_ratinabox_nav.json
+gru,monkey,Monkey (CO),sub-C_ses-CO-20151104_behavior+ecephys,sub-C_ses-CO-20151104_behavior+ecephys,present,sequence_regression,gru,r2,0.9182087779045105,1.1866710186004639,256,63,59,,benchmark_results/gru/gru_benchmark_sub-C_ses-CO-20151104_behavior+ecephys.json
+gru,allen_neuropixels,Visual Coding,721123822,721123822,present,sequence_classification,gru,accuracy,0.8907563025210085,,479,119,444,,benchmark_results/gru/gru_benchmark_721123822.json
+gru,speech,Speech,t12,t12,present,sequence_classification,gru,accuracy,0.7272727272727273,,135,33,64,,benchmark_results/gru/gru_benchmark_t12.json
+gru,mc_pacman,MC Pacman,mc_pacman,mc_pacman,present,sequence_regression,gru,r2,0.901454746723175,3.99273681640625,290,72,128,,benchmark_results/gru/gru_benchmark_mc_pacman.json
+gru,ratinabox,RatInABox,ratinabox_nav,ratinabox_nav,present,sequence_regression,gru,r2,0.5741032361984253,0.03174031898379326,240,60,300,,benchmark_results/gru/gru_benchmark_ratinabox_nav.json
+langevinflow_ccn,monkey,Monkey (CO),sub-C_ses-CO-20151104_behavior+ecephys,sub-C_ses-CO-20151104_behavior+ecephys,present,sequence_regression,ridge,r2,0.9345517158508301,0.9504499435424805,256,63,59,59,benchmark_results/langevinflow_ccn/langevinflow_ccn_benchmark_sub-C_ses-CO-20151104_behavior+ecephys.json
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+xg,ratinabox,RatInABox,ratinabox_nav,ratinabox_nav,present,sequence_regression,xgboost_regression,r2,0.9662970925763346,0.002489599134414616,240,60,300,,benchmark_results/xg/xg_benchmark_ratinabox_nav.json

data/consistency_summary.csv

@@ -0,0 +1,48 @@
+model,dataset,dataset_type,is_active_model,n_sessions,sessions,latent_dim,mean_r2,mean_r2_procrustes,n_pairwise,scoring_modes,normalizations,mean_n_points,mean_n_conditions,source_path
+blend,allen_neuropixels,allen_neuropixels,True,3,721123822;715093703;732592105,3,0.47170581108431525,-0.7396574667359858,6,centroid,per_session_whitening,8.0,8.0,benchmark_results/blend_cross_allen_neuropixels_consistency.json
+blend,monkey,monkey,True,4,sub-C_ses-CO-20151104_behavior+ecephys;sub-J_ses-CO-20160405_behavior+ecephys;sub-M_ses-CO-20140203_behavior+ecephys;sub-T_ses-CO-20130819_behavior+ecephys,3,0.40004813072087275,0.1649061796931052,12,position_binned,per_session_whitening,80.0,8.0,benchmark_results/blend_cross_monkey_consistency.json
+blend,ratinabox,ratinabox,True,4,ratinabox_nav;ratinabox_nav_s123;ratinabox_nav_s456;ratinabox_nav_s789,3,0.5421967694028994,0.30262956966713195,12,direct_labels,per_session_whitening,100.0,100.0,benchmark_results/blend_cross_ratinabox_consistency.json
+blend,speech,speech_threshold_crossings,True,4,t12;t15;t16;t17,3,0.5353214365267355,0.310405139107455,12,centroid,per_session_whitening,8.0,8.0,benchmark_results/blend_cross_speech_threshold_crossings_consistency.json
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data/neuron_shap_summary.csv

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data/scalability_summary.csv

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data/trial_shapley_retrain_summary.csv

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data/trial_shapley_summary.csv

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