Add multiclass training

pixi.lock

@@ -285,6 +285,7 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/32/46/9cb0e58b2deb7f82b84065f37f3bffeb12413f947f9388e4cac22c4621ce/sortedcontainers-2.4.0-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/14/a0/bb38d3b76b8cae341dad93a2dd83ab7462e6dbcdd84d43f54ee60a8dc167/soupsieve-2.8-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/81/ec/8bdccea3ff7d557601183581340c3768b7bb7b1e32c8991f1130f0c1e2c4/spectate-1.0.1-py2.py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/30/09/cd7134f1ed5074a7d456640e7ba9a8c8e68a831837b4e7bfd9f29e5700a4/st_theme-1.2.3-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/7a/31/7d601cc639b0362a213552a838af601105591598a4b08ec80666458083d2/stopuhr-0.0.10-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/2a/38/991bbf9fa3ed3d9c8e69265fc449bdaade8131c7f0f750dbd388c3c477dc/streamlit-1.50.0-py3-none-any.whl
@@ -1301,7 +1302,7 @@ packages:
 - pypi: ./
   name: entropice
   version: 0.1.0
-  sha256: 9d3fd2f5a282082c9205df502797c350d94b3c8b588fe7d1662f5169589925a9
+  sha256: 852c87cdbd1d452fccaa6253c6ce7410dda9fa32d2f951d35441e454414acfc0
   requires_dist:
   - aiohttp>=3.12.11
   - bokeh>=3.7.3
@@ -1350,6 +1351,7 @@ packages:
   - altair[all]>=5.5.0,<6
   - h5netcdf>=1.7.3,<2
   - streamlit-folium>=0.25.3,<0.26
+  - st-theme>=1.2.3,<2
   editable: true
 - pypi: git+ssh://git@forgejo.tobiashoelzer.de:22222/tobias/entropy.git#9ca1bdf4afc4ac9b0ea29ebbc060ffecb5cffcf7
   name: entropy
@@ -4507,6 +4509,13 @@ packages:
   version: 1.0.1
   sha256: c4585194c238979f953fbf2ecf9f94c84d9d0a929432c7104e39984f52c9e718
   requires_python: '>=3.6'
+- pypi: https://files.pythonhosted.org/packages/30/09/cd7134f1ed5074a7d456640e7ba9a8c8e68a831837b4e7bfd9f29e5700a4/st_theme-1.2.3-py3-none-any.whl
+  name: st-theme
+  version: 1.2.3
+  sha256: 0a54d9817dd5f8a6d7b0d071b25ae72eacf536c63a5fb97374923938021b1389
+  requires_dist:
+  - streamlit>=1.33
+  requires_python: '>=3.8'
 - pypi: https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl
   name: stack-data
   version: 0.6.3

pyproject.toml

@@ -51,7 +51,7 @@ dependencies = [
     "geocube>=0.7.1,<0.8",
     "streamlit>=1.50.0,<2",
     "altair[all]>=5.5.0,<6",
-    "h5netcdf>=1.7.3,<2", "streamlit-folium>=0.25.3,<0.26",
+    "h5netcdf>=1.7.3,<2", "streamlit-folium>=0.25.3,<0.26", "st-theme>=1.2.3,<2",
 ]
 
 [project.scripts]

src/entropice/darts.py

@@ -67,6 +67,10 @@ def extract_darts_rts(grid: Literal["hex", "healpix"], level: int):
     grid_gdf["darts_has_coverage"] = grid_gdf[[f"darts_{year}_coverage" for year in years]].any(axis=1)
     grid_gdf["darts_has_rts"] = grid_gdf[[f"darts_{year}_rts_count" for year in years]].any(axis=1)
 
+    darts_counts_columns = [c for c in grid_gdf.columns if c.startswith("darts_") and c.endswith("_rts_count")]
+    darts_counts = grid_gdf[darts_counts_columns]
+    grid_gdf["darts_rts_count"] = darts_counts.dropna(axis=0, how="all").sum(axis=1)
+
     output_path = get_darts_rts_file(grid, level)
     grid_gdf.to_parquet(output_path)
     print(f"Saved RTS labels to {output_path}")

src/entropice/paths.py

@@ -87,9 +87,14 @@ def get_train_dataset_file(grid: Literal["hex", "healpix"], level: int) -> Path:
     return dataset_file
 
 
-def get_cv_results_dir(name: str, grid: Literal["hex", "healpix"], level: int) -> Path:
+def get_cv_results_dir(
+    name: str,
+    grid: Literal["hex", "healpix"],
+    level: int,
+    task: Literal["binary", "multi"],
+) -> Path:
     gridname = _get_gridname(grid, level)
     now = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-    results_dir = RESULTS_DIR / f"{gridname}_{name}_cv{now}_binary"
+    results_dir = RESULTS_DIR / f"{gridname}_{name}_cv{now}_{task}"
     results_dir.mkdir(parents=True, exist_ok=True)
     return results_dir

src/entropice/training.py

@@ -29,7 +29,50 @@ pretty.install()
 set_config(array_api_dispatch=True)
 
 
-def random_cv(grid: Literal["hex", "healpix"], level: int, n_iter: int = 2000, robust: bool = False):
+def create_xy_data(grid: Literal["hex", "healpix"], level: int, task: Literal["binary", "multi"] = "binary"):
+    """Create X and y data from the training dataset.
+
+    Args:
+        grid (Literal["hex", "healpix"]): The grid type to use.
+        level (int): The grid level to use.
+        task (Literal["binary", "multi"], optional): The classification task type. Defaults to "binary".
+
+    Returns:
+        Tuple[pd.DataFrame, pd.DataFrame, pd.Series, list]: The data, Features (X), labels (y), and label names.
+
+    """
+    data = get_train_dataset_file(grid=grid, level=level)
+    data = gpd.read_parquet(data)
+    data = data[data["darts_has_coverage"]]
+
+    cols_to_drop = ["cell_id", "geometry", "darts_has_rts", "darts_rts_count"]
+    cols_to_drop += [col for col in data.columns if col.startswith("darts_")]
+    X_data = data.drop(columns=cols_to_drop).dropna()
+    if task == "binary":
+        labels = ["No RTS", "RTS"]
+        y_data = data.loc[X_data.index, "darts_has_rts"]
+    else:
+        # Put into n categories (log scaled)
+        y_data = data.loc[X_data.index, "darts_rts_count"]
+        n_categories = 5
+        bins = pd.qcut(y_data, q=n_categories, duplicates="drop").unique().categories
+        # Change the first interval to start at 1 and add a category for 0
+        bins = pd.IntervalIndex.from_tuples(
+            [(-1, 0)] + [(int(interval.left), int(interval.right)) for interval in bins]
+        )
+        y_data = pd.cut(y_data, bins=bins)
+        labels = [str(v) for v in y_data.sort_values().unique()]
+        y_data = y_data.cat.codes
+    return data, X_data, y_data, labels
+
+
+def random_cv(
+    grid: Literal["hex", "healpix"],
+    level: int,
+    n_iter: int = 2000,
+    robust: bool = False,
+    task: Literal["binary", "multi"] = "binary",
+):
     """Perform random cross-validation on the training dataset.
 
     Args:
@@ -37,21 +80,17 @@ def random_cv(grid: Literal["hex", "healpix"], level: int, n_iter: int = 2000, r
         level (int): The grid level to use.
         n_iter (int, optional): Number of parameter settings that are sampled. Defaults to 2000.
         robust (bool, optional): Whether to use robust training. Defaults to False.
+        task (Literal["binary", "multi"], optional): The classification task type. Defaults to "binary".
 
     """
-    data = get_train_dataset_file(grid=grid, level=level)
-    data = gpd.read_parquet(data)
-    data = data[data["darts_has_coverage"]]
-
-    cols_to_drop = ["cell_id", "geometry", "darts_has_rts"]
-    cols_to_drop += [col for col in data.columns if col.startswith("darts_")]
-    X_data = data.drop(columns=cols_to_drop).dropna()
-    y_data = data.loc[X_data.index, "darts_has_rts"]
+    _, X_data, y_data, labels = create_xy_data(grid=grid, level=level, task=task)
+    print(f"Using {task}-class classification with {len(labels)} classes: {labels}")
+    print(f"{y_data.describe()=}")
     X = X_data.to_numpy(dtype="float32")
     y = y_data.to_numpy(dtype="int8")
     X, y = torch.asarray(X, device=0), torch.asarray(y, device=0)
     print(f"{X.shape=}, {y.shape=}")
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
     print(f"{X_train.shape=}, {X_test.shape=}, {y_train.shape=}, {y_test.shape=}")
 
     param_grid = {
@@ -62,7 +101,21 @@ def random_cv(grid: Literal["hex", "healpix"], level: int, n_iter: int = 2000, r
 
     clf = ESPAClassifier(20, 0.1, 0.1, random_state=42, robust=robust)
     cv = KFold(n_splits=5, shuffle=True, random_state=42)
+    if task == "binary":
         metrics = ["accuracy", "recall", "precision", "f1", "jaccard"]  # "roc_auc" does not work on GPU
+    else:
+        metrics = [
+            "accuracy",  # equals "f1_micro", "precision_micro", "recall_micro",
+            "f1_macro",
+            "f1_weighted",
+            "precision_macro",
+            "precision_weighted",
+            "recall_macro",
+            "recall_weighted",
+            "jaccard_micro",
+            "jaccard_macro",
+            "jaccard_weighted",
+        ]
     search = RandomizedSearchCV(
         clf,
         param_grid,
@@ -72,7 +125,7 @@ def random_cv(grid: Literal["hex", "healpix"], level: int, n_iter: int = 2000, r
         random_state=42,
         verbose=3,
         scoring=metrics,
-        refit="f1",
+        refit="f1" if task == "binary" else "f1_weighted",
     )
 
     print(f"Starting RandomizedSearchCV with {search.n_iter} candidates...")
@@ -88,10 +141,11 @@ def random_cv(grid: Literal["hex", "healpix"], level: int, n_iter: int = 2000, r
     print(f"Accuracy of the best parameters using the inner CV of the random search: {search.best_score_:.3f}")
     print(f"Accuracy on test set: {test_accuracy:.3f}")
 
-    results_dir = get_cv_results_dir("random_search", grid=grid, level=level)
+    results_dir = get_cv_results_dir("random_search", grid=grid, level=level, task=task)
 
     # Store the search settings
     settings = {
+        "task": task,
         "grid": grid,
         "level": level,
         "random_state": 42,
@@ -115,7 +169,7 @@ def random_cv(grid: Literal["hex", "healpix"], level: int, n_iter: int = 2000, r
         },
         "cv_splits": cv.get_n_splits(),
         "metrics": metrics,
-        "classes": ["No RTS", "RTS"],
+        "classes": labels,
     }
     settings_file = results_dir / "search_settings.toml"
     print(f"Storing search settings to {settings_file}")
@@ -144,7 +198,6 @@ def random_cv(grid: Literal["hex", "healpix"], level: int, n_iter: int = 2000, r
     best_estimator = search.best_estimator_
     # Annotate the state with xarray metadata
     features = X_data.columns.tolist()
-    labels = y_data.unique().tolist()
     boxes = list(range(best_estimator.K_))
     box_centers = xr.DataArray(
         best_estimator.S_.cpu().numpy(),
@@ -185,7 +238,7 @@ def random_cv(grid: Literal["hex", "healpix"], level: int, n_iter: int = 2000, r
 
     # Predict probabilities for all cells
     print("Predicting probabilities for all cells...")
-    preds = predict_proba(grid=grid, level=level, clf=best_estimator, classes=settings["classes"])
+    preds = predict_proba(grid=grid, level=level, clf=best_estimator, classes=labels)
     preds_file = results_dir / "predicted_probabilities.parquet"
     print(f"Storing predicted probabilities to {preds_file}")
     preds.to_parquet(preds_file)

src/entropice/training_analysis_dashboard.py

@@ -5,15 +5,93 @@ from pathlib import Path
 
 import altair as alt
 import geopandas as gpd
+import matplotlib.colors as mcolors
+import matplotlib.path
 import numpy as np
 import pandas as pd
 import streamlit as st
 import streamlit_folium as st_folium
 import toml
+import ultraplot as uplt
 import xarray as xr
 import xdggs
+from matplotlib.patches import PathPatch
 
 from entropice.paths import RESULTS_DIR
+from entropice.training import create_xy_data
+
+
+def generate_unified_colormap(settings: dict):
+    """Generate unified colormaps for all plotting libraries.
+
+    This function creates consistent color schemes across Matplotlib/Ultraplot,
+    Folium/Leaflet, and Altair/Vega-Lite by determining the task type and number
+    of classes from the settings, then generating appropriate colormaps for each library.
+
+    Args:
+        settings: Settings dictionary containing task type, classes, and other configuration.
+
+    Returns:
+        Tuple of (matplotlib_cmap, folium_cmap, altair_colors) where:
+        - matplotlib_cmap: matplotlib ListedColormap object
+        - folium_cmap: matplotlib ListedColormap object (for geopandas.explore)
+        - altair_colors: list of hex color strings for Altair
+
+    """
+    # Determine task type and number of classes from settings
+    task = settings.get("task", "binary")
+    n_classes = len(settings.get("classes", []))
+
+    # Check theme
+    is_dark_theme = st.context.theme.type == "dark"
+
+    # Define base colormaps for different tasks
+    if task == "binary":
+        # For binary: use a simple two-color scheme
+        if is_dark_theme:
+            base_colors = ["#1f77b4", "#ff7f0e"]  # Blue and orange for dark theme
+        else:
+            base_colors = ["#3498db", "#e74c3c"]  # Brighter blue and red for light theme
+    else:
+        # For multi-class: use a sequential colormap
+        # Use matplotlib's viridis colormap as base (better perceptual uniformity than inferno)
+        cmap = uplt.Colormap("viridis")
+        # Sample colors evenly across the colormap
+        indices = np.linspace(0.1, 0.9, n_classes)  # Avoid extreme ends
+        base_colors = [mcolors.rgb2hex(cmap(idx)[:3]) for idx in indices]
+
+    # Create matplotlib colormap (for ultraplot and geopandas)
+    matplotlib_cmap = mcolors.ListedColormap(base_colors)
+
+    # Create Folium/Leaflet colormap (geopandas.explore uses matplotlib colormaps)
+    folium_cmap = mcolors.ListedColormap(base_colors)
+
+    # Create Altair color list (Altair uses hex color strings in range)
+    altair_colors = base_colors
+
+    return matplotlib_cmap, folium_cmap, altair_colors
+
+
+def format_metric_name(metric: str) -> str:
+    """Format metric name for display.
+
+    Args:
+        metric: Raw metric name (e.g., 'f1_micro', 'precision_macro').
+
+    Returns:
+        Formatted metric name (e.g., 'F1 Micro', 'Precision Macro').
+
+    """
+    # Split by underscore and capitalize each part
+    parts = metric.split("_")
+    # Special handling for F1
+    formatted_parts = []
+    for part in parts:
+        if part.lower() == "f1":
+            formatted_parts.append("F1")
+        else:
+            formatted_parts.append(part.capitalize())
+    return " ".join(formatted_parts)
 
 
 def get_available_result_files() -> list[Path]:
@@ -185,32 +263,229 @@ def extract_common_features(model_state: xr.Dataset) -> xr.DataArray | None:
     return common_feature_array
 
 
-def _plot_prediction_map(preds: gpd.GeoDataFrame):
-    return preds.explore(column="predicted_class", cmap="Set3", legend=True, tiles="CartoDB positron")
+def _plot_prediction_map_static(preds: gpd.GeoDataFrame, matplotlib_cmap: mcolors.ListedColormap):
+    """Create a static map of predictions using ultraplot and cartopy.
+
+    Args:
+        preds: GeoDataFrame with predicted classes (intervals as strings).
+        matplotlib_cmap: Matplotlib ListedColormap object.
+
+    Returns:
+        Matplotlib figure with predictions colored by class.
+
+    """
+    # Create a copy to avoid modifying the original data
+    preds_plot = preds.copy()
+
+    # Replace the special (-1, 0] interval with "No RTS"
+    preds_plot["predicted_class"] = preds_plot["predicted_class"].replace("(-1, 0]", "No RTS")
+
+    # Sort the classes: "No RTS" first, then by the lower bound of intervals
+    def sort_key(class_str):
+        if class_str == "No RTS":
+            return -1  # Put "No RTS" first
+        # Parse interval string like "(0, 4]" or "(4, 36]"
+        try:
+            lower = float(class_str.split(",")[0].strip("([ "))
+            return lower
+        except (ValueError, IndexError):
+            return float("inf")  # Put unparseable values at the end
+
+    # Get unique classes and sort them
+    unique_classes = sorted(preds_plot["predicted_class"].unique(), key=sort_key)
+
+    # Create categorical with ordered categories
+    preds_plot["predicted_class"] = pd.Categorical(
+        preds_plot["predicted_class"], categories=unique_classes, ordered=True
+    )
+
+    # Detect theme for styling
+    theme = st.context.theme.type
+    if theme == "light":
+        figcolor = "#f8f9fa"
+        axcolor = "#ffffff"
+        oceancolor = "#e3f2fd"
+        landcolor = "#ecf0f1"
+        gridcolor = "#666666"
+        fgcolor = "#2c3e50"
+        coastcolor = "#34495e"
+    else:
+        figcolor = "#0e1117"
+        axcolor = "#0e1117"
+        oceancolor = "#1a1d29"
+        landcolor = "#262730"
+        gridcolor = "#ffffff"
+        fgcolor = "#fafafa"
+        coastcolor = "#d0d0d0"
+
+    # Create figure with North Polar Stereographic projection
+    # proj = uplt.Proj("npaeqd")
+    proj = uplt.Proj("npstere", lon_0=-45)
+    fig, ax = uplt.subplots(proj=proj, figsize=(12, 12), facecolor=figcolor)
+
+    # Put a background on the figure.
+    bgpatch = PathPatch(matplotlib.path.Path.unit_rectangle(), transform=fig.transFigure, color=figcolor, zorder=-1)
+    fig.patches.append(bgpatch)
+
+    # Apply theme-appropriate styling with enhanced aesthetics
+    ax.format(
+        boundinglat=50,
+        coast=True,
+        coastcolor=coastcolor,
+        coastlinewidth=0.8,
+        land=True,
+        landcolor=landcolor,
+        ocean=True,
+        oceancolor=oceancolor,
+        title="Predicted RTS Classes",
+        titlecolor=fgcolor,
+        titleweight="bold",
+        titlesize=14,
+        color=fgcolor,
+        gridcolor=gridcolor,
+        gridlinewidth=0.5,
+        gridlinestyle="-",
+        gridalpha=0.3,
+        facecolor=axcolor,
+        labels=True,
+        labelcolor=fgcolor,
+        latlines=10,
+        lonlines=30,
+    )
+
+    # Plot the predictions using the provided colormap with enhanced styling
+    preds_plot.to_crs(proj.proj4_init).plot(
+        ax=ax,
+        column="predicted_class",
+        cmap=matplotlib_cmap,
+        legend=True,
+        legend_kwds={
+            "loc": "lower left",
+            "frameon": True,
+            "framealpha": 0.95,
+            "edgecolor": gridcolor,
+            "facecolor": figcolor,
+            "fancybox": True,
+            "shadow": False,
+            "title": "RTS Class",
+            "title_fontsize": 11,
+            "fontsize": 9,
+            "labelspacing": 0.8,
+            "borderpad": 1.0,
+            "columnspacing": 1.0,
+        },
+        edgecolor="none",
+        linewidth=0,
+    )
+
+    # Enhance legend styling after creation
+    legend = ax.get_legend()
+    if legend:
+        legend.set_title("RTS Class", prop={"size": 11, "weight": "bold"})
+        legend.get_frame().set_linewidth(0.5)
+        legend.get_frame().set_edgecolor(gridcolor)
+        legend.get_frame().set_facecolor(figcolor)
+        legend.get_frame().set_alpha(0.95)
+        # Set text color for legend labels
+        for text in legend.get_texts():
+            text.set_color(fgcolor)
+        # Set title color
+        legend.get_title().set_color(fgcolor)
+
+    return fig
 
 
-def _plot_prediction_class_distribution(preds: gpd.GeoDataFrame):
+def _plot_prediction_map(preds: gpd.GeoDataFrame, folium_cmap: mcolors.ListedColormap):
+    """Create an interactive map of predictions with properly sorted classes.
+
+    Args:
+        preds: GeoDataFrame with predicted classes (intervals as strings).
+        folium_cmap: Matplotlib ListedColormap object (for geopandas.explore).
+
+    Returns:
+        Folium map with predictions colored by class.
+
+    """
+    # Create a copy to avoid modifying the original data
+    preds_plot = preds.copy()
+
+    # Replace the special (-1, 0] interval with "No RTS"
+    preds_plot["predicted_class"] = preds_plot["predicted_class"].replace("(-1, 0]", "No RTS")
+
+    # Sort the classes: "No RTS" first, then by the lower bound of intervals
+    def sort_key(class_str):
+        if class_str == "No RTS":
+            return -1  # Put "No RTS" first
+        # Parse interval string like "(0, 4]" or "(4, 36]"
+        try:
+            lower = float(class_str.split(",")[0].strip("([ "))
+            return lower
+        except (ValueError, IndexError):
+            return float("inf")  # Put unparseable values at the end
+
+    # Get unique classes and sort them
+    unique_classes = sorted(preds_plot["predicted_class"].unique(), key=sort_key)
+
+    # Create categorical with ordered categories
+    preds_plot["predicted_class"] = pd.Categorical(
+        preds_plot["predicted_class"], categories=unique_classes, ordered=True
+    )
+
+    # Select tiles based on theme
+    tiles = "CartoDB dark_matter" if st.context.theme.type == "dark" else "CartoDB positron"
+    return preds_plot.explore(column="predicted_class", cmap=folium_cmap, legend=True, tiles=tiles)
+
+
+def _plot_prediction_class_distribution(preds: gpd.GeoDataFrame, altair_colors: list[str]):
     """Create a bar chart showing the count of each predicted class.
 
     Args:
         preds: GeoDataFrame with predicted classes.
+        altair_colors: List of hex color strings for altair.
 
     Returns:
         Altair chart showing class distribution.
 
     """
-    df = pd.DataFrame({"predicted_class": preds["predicted_class"].to_numpy()})
+    # Create a copy and apply the same transformations as the map
+    preds_plot = preds.copy()
+    preds_plot["predicted_class"] = preds_plot["predicted_class"].replace("(-1, 0]", "No RTS")
+
+    # Sort the classes: "No RTS" first, then by the lower bound of intervals
+    def sort_key(class_str):
+        if class_str == "No RTS":
+            return -1  # Put "No RTS" first
+        # Parse interval string like "(0, 4]" or "(4, 36]"
+        try:
+            lower = float(class_str.split(",")[0].strip("([ "))
+            return lower
+        except (ValueError, IndexError):
+            return float("inf")  # Put unparseable values at the end
+
+    df = pd.DataFrame({"predicted_class": preds_plot["predicted_class"].to_numpy()})
     counts = df["predicted_class"].value_counts().reset_index()
     counts.columns = ["class", "count"]
     counts["percentage"] = (counts["count"] / counts["count"].sum() * 100).round(2)
 
+    # Sort counts by the same key
+    counts["sort_key"] = counts["class"].apply(sort_key)
+    counts = counts.sort_values("sort_key")
+
+    # Create an ordered list of classes for consistent color mapping
+    class_order = counts["class"].tolist()
+
     chart = (
         alt.Chart(counts)
         .mark_bar()
         .encode(
-            x=alt.X("class:N", title="Predicted Class"),
+            x=alt.X("class:N", title="Predicted Class", sort=class_order, axis=alt.Axis(labelAngle=0)),
             y=alt.Y("count:Q", title="Number of Cells"),
-            color=alt.Color("class:N", title="Class", legend=None),
+            color=alt.Color(
+                "class:N",
+                title="Class",
+                scale=alt.Scale(domain=class_order, range=altair_colors),
+                legend=None,
+            ),
             tooltip=[
                 alt.Tooltip("class:N", title="Class"),
                 alt.Tooltip("count:Q", title="Count"),
@@ -286,41 +561,36 @@ def _plot_k_binned(
     return chart
 
 
-def _plot_eps_binned(results: pd.DataFrame, target: str, metric: str):
+def _plot_params_binned(results: pd.DataFrame, x: str, hue: str, col: str, metric: str):
     """Plot epsilon-binned results with K parameter."""
-    assert "initial_K" in results.columns, "initial_K column not found in results."
     assert metric in results.columns, f"{metric} not found in results."
+    assert x in ["eps_cl", "eps_e", "initial_K"]
+    assert hue in ["eps_cl", "eps_e", "initial_K"]
+    assert col in ["eps_cl_binned", "eps_e_binned", "initial_K_binned"]
 
-    if target == "eps_cl":
-        hue = "eps_cl"
-        col = "eps_e_binned"
-    elif target == "eps_e":
-        hue = "eps_e"
-        col = "eps_cl_binned"
-    else:
-        raise ValueError(f"Invalid target: {target}")
-
+    assert x in results.columns, f"{x} column not found in results."
     assert hue in results.columns, f"{hue} column not found in results."
     assert col in results.columns, f"{col} column not found in results."
 
     # Prepare data
-    plot_data = results[["initial_K", metric, hue, col]].copy()
+    plot_data = results[[x, metric, hue, col]].copy()
 
     # Sort bins by their left value and convert to string with sorted categories
     plot_data = plot_data.sort_values(col)
     plot_data[col] = plot_data[col].astype(str)
     bin_order = plot_data[col].unique().tolist()
-
+    xscale = alt.Scale(type="log") if x in ["eps_cl", "eps_e"] else alt.Scale()
+    cscheme = "bluepurple" if hue == "eps_e" else "purplered" if hue == "eps_cl" else "greenblue"
     # Create the chart
     chart = (
         alt.Chart(plot_data)
         .mark_circle(size=60, opacity=0.7)
         .encode(
-            x=alt.X("initial_K:Q", title="Initial K"),
+            x=alt.X(f"{x}:Q", title=x, scale=xscale),
             y=alt.Y(f"{metric}:Q", title=metric),
-            color=alt.Color(f"{hue}:Q", scale=alt.Scale(type="log", scheme="viridis"), title=hue),
+            color=alt.Color(f"{hue}:Q", scale=alt.Scale(type="log", scheme=cscheme), title=hue),
             tooltip=[
-                "initial_K:Q",
+                f"{x}:Q",
                 alt.Tooltip(f"{metric}:Q", format=".4f"),
                 alt.Tooltip(f"{hue}:Q", format=".2e"),
                 f"{col}:N",
@@ -334,11 +604,14 @@ def _plot_eps_binned(results: pd.DataFrame, target: str, metric: str):
 
 
 def _parse_results_dir_name(results_dir: Path) -> str:
-    gridname, date = results_dir.name.split("_random_search_cv")
+    gridname, date = results_dir.name.replace("_binary", "").replace("_multi", "").split("_random_search_cv")
     gridname = gridname.lstrip("permafrost_")
     date = datetime.strptime(date, "%Y%m%d-%H%M%S")
     date = date.strftime("%Y-%m-%d %H:%M:%S")
-    return f"{gridname} ({date})"
+
+    settings = toml.load(results_dir / "search_settings.toml")["settings"]
+    task = settings.get("task", "binary")
+    return f"[{task.capitalize()}] {gridname.capitalize()} ({date})"
 
 
 def _plot_top_features(model_state: xr.Dataset, top_n: int = 10):
@@ -667,11 +940,12 @@ def _plot_box_assignments(model_state: xr.Dataset):
     return chart
 
 
-def _plot_box_assignment_bars(model_state: xr.Dataset):
+def _plot_box_assignment_bars(model_state: xr.Dataset, altair_colors: list[str]):
     """Create a bar chart showing how many boxes are assigned to each class.
 
     Args:
         model_state: The xarray Dataset containing the model state with box_assignments.
+        altair_colors: List of hex color strings for altair.
 
     Returns:
         Altair chart showing count of boxes per class.
@@ -690,14 +964,40 @@ def _plot_box_assignment_bars(model_state: xr.Dataset):
     # Count boxes per class
     counts = primary_classes.groupby("class").size().reset_index(name="count")
 
+    # Replace the special (-1, 0] interval with "No RTS" if present
+    counts["class"] = counts["class"].replace("(-1, 0]", "No RTS")
+
+    # Sort the classes: "No RTS" first, then by the lower bound of intervals
+    def sort_key(class_str):
+        if class_str == "No RTS":
+            return -1  # Put "No RTS" first
+        # Parse interval string like "(0, 4]" or "(4, 36]"
+        try:
+            lower = float(str(class_str).split(",")[0].strip("([ "))
+            return lower
+        except (ValueError, IndexError):
+            return float("inf")  # Put unparseable values at the end
+
+    # Sort counts by the same key
+    counts["sort_key"] = counts["class"].apply(sort_key)
+    counts = counts.sort_values("sort_key")
+
+    # Create an ordered list of classes for consistent color mapping
+    class_order = counts["class"].tolist()
+
     # Create bar chart
     chart = (
         alt.Chart(counts)
         .mark_bar()
         .encode(
-            x=alt.X("class:N", title="Class Label"),
+            x=alt.X("class:N", title="Class Label", sort=class_order, axis=alt.Axis(labelAngle=-45)),
             y=alt.Y("count:Q", title="Number of Boxes"),
-            color=alt.Color("class:N", title="Class", legend=None),
+            color=alt.Color(
+                "class:N",
+                title="Class",
+                scale=alt.Scale(domain=class_order, range=altair_colors),
+                legend=None,
+            ),
             tooltip=[
                 alt.Tooltip("class:N", title="Class"),
                 alt.Tooltip("count:Q", title="Number of Boxes"),
@@ -758,7 +1058,7 @@ def _plot_common_features(common_array: xr.DataArray):
     return chart
 
 
-def _plot_metric_comparison(results: pd.DataFrame, x_metric: str, y_metric: str, color_param: str):
+def _plot_metric_comparison(results: pd.DataFrame, x_metric: str, y_metric: str, color_param: str, refit_metric: str):
     """Create a scatter plot comparing two metrics with parameter-based coloring.
 
     Args:
@@ -766,6 +1066,7 @@ def _plot_metric_comparison(results: pd.DataFrame, x_metric: str, y_metric: str,
         x_metric: Name of the metric to plot on x-axis (e.g., 'precision', 'accuracy').
         y_metric: Name of the metric to plot on y-axis (e.g., 'recall', 'jaccard').
         color_param: Parameter to use for coloring ('initial_K', 'eps_cl', or 'eps_e').
+        refit_metric: The metric used for refitting (e.g., 'f1' or 'f1_macro').
 
     Returns:
         Altair chart showing the metric comparison.
@@ -781,17 +1082,17 @@ def _plot_metric_comparison(results: pd.DataFrame, x_metric: str, y_metric: str,
         alt.Chart(results)
         .mark_circle(size=60, opacity=0.7)
         .encode(
-            x=alt.X(f"mean_test_{x_metric}:Q", title=x_metric.capitalize(), scale=alt.Scale(zero=False)),
-            y=alt.Y(f"mean_test_{y_metric}:Q", title=y_metric.capitalize(), scale=alt.Scale(zero=False)),
+            x=alt.X(f"mean_test_{x_metric}:Q", title=format_metric_name(x_metric), scale=alt.Scale(zero=False)),
+            y=alt.Y(f"mean_test_{y_metric}:Q", title=format_metric_name(y_metric), scale=alt.Scale(zero=False)),
             color=alt.Color(
                 f"{color_param}:Q",
                 scale=color_scale,
                 title=color_param,
             ),
             tooltip=[
-                alt.Tooltip(f"mean_test_{x_metric}:Q", format=".4f", title=x_metric.capitalize()),
-                alt.Tooltip(f"mean_test_{y_metric}:Q", format=".4f", title=y_metric.capitalize()),
-                alt.Tooltip("mean_test_f1:Q", format=".4f", title="F1"),
+                alt.Tooltip(f"mean_test_{x_metric}:Q", format=".4f", title=format_metric_name(x_metric)),
+                alt.Tooltip(f"mean_test_{y_metric}:Q", format=".4f", title=format_metric_name(y_metric)),
+                alt.Tooltip(f"mean_test_{refit_metric}:Q", format=".4f", title=format_metric_name(refit_metric)),
                 alt.Tooltip("initial_K:Q", format=".0f", title="initial_K"),
                 alt.Tooltip("eps_cl:Q", format=".2e", title="eps_cl"),
                 alt.Tooltip("eps_e:Q", format=".2e", title="eps_e"),
@@ -807,6 +1108,148 @@ def _plot_metric_comparison(results: pd.DataFrame, x_metric: str, y_metric: str,
     return chart
 
 
+def _load_training_data(grid: str, level: int, task: str):
+    """Load training data for analysis.
+
+    Args:
+        grid: Grid type (hex or healpix).
+        level: Grid level.
+        task: Classification task type (binary or multi).
+
+    Returns:
+        Tuple of (GeoDataFrame with training data, feature DataFrame, labels Series, label names list).
+
+    """
+    # Use create_xy_data to get X and y data
+    data, x_data, y_data, labels = create_xy_data(grid=grid, level=level, task=task)
+
+    # Convert y_data to string labels for visualization
+    if task == "binary":
+        y_data_str = y_data.map({False: "No RTS", True: "RTS"})
+    else:
+        # For multi-class, reconstruct the interval labels from the codes
+        y_data_counts = data.loc[x_data.index, "darts_rts_count"]
+        n_categories = 5
+        bins = pd.qcut(y_data_counts, q=n_categories, duplicates="drop").unique().categories
+        bins = pd.IntervalIndex.from_tuples(
+            [(-1, 0)] + [(int(interval.left), int(interval.right)) for interval in bins]
+        )
+        y_data_str = pd.cut(y_data_counts, bins=bins).astype(str)
+
+    # Create a GeoDataFrame with geometry and labels
+    training_gdf = data.loc[x_data.index, ["geometry"]].copy()
+    training_gdf["target_class"] = y_data_str
+
+    return training_gdf, x_data, y_data_str, labels
+
+
+def _plot_training_data_map(training_gdf: gpd.GeoDataFrame, folium_cmap: mcolors.ListedColormap):
+    """Create an interactive map of training data with properly sorted classes.
+
+    Args:
+        training_gdf: GeoDataFrame with target classes (intervals as strings or category names).
+        folium_cmap: Matplotlib ListedColormap object (for geopandas.explore).
+
+    Returns:
+        Folium map with training data colored by class.
+
+    """
+    # Create a copy to avoid modifying the original data
+    training_plot = training_gdf.copy()
+
+    # Replace the special (-1, 0] interval with "No RTS" if present
+    training_plot["target_class"] = training_plot["target_class"].replace("(-1, 0]", "No RTS")
+
+    # Sort the classes: "No RTS" first, then by the lower bound of intervals
+    def sort_key(class_str):
+        if class_str == "No RTS":
+            return -1  # Put "No RTS" first
+        # Parse interval string like "(0, 4]" or "(4, 36]"
+        try:
+            lower = float(class_str.split(",")[0].strip("([ "))
+            return lower
+        except (ValueError, IndexError):
+            return float("inf")  # Put unparseable values at the end
+
+    # Get unique classes and sort them
+    unique_classes = sorted(training_plot["target_class"].unique(), key=sort_key)
+
+    # Create categorical with ordered categories
+    training_plot["target_class"] = pd.Categorical(
+        training_plot["target_class"], categories=unique_classes, ordered=True
+    )
+
+    # Select tiles based on theme
+    tiles = "CartoDB dark_matter" if st.context.theme.type == "dark" else "CartoDB positron"
+    return training_plot.explore(column="target_class", cmap=folium_cmap, legend=True, tiles=tiles)
+
+
+def _plot_training_class_distribution(y_data: pd.Series, altair_colors: list[str]):
+    """Create a bar chart showing the count of each class in training data.
+
+    Args:
+        y_data: Series with target classes.
+        altair_colors: List of hex color strings for altair.
+
+    Returns:
+        Altair chart showing class distribution.
+
+    """
+    # Create a copy and apply the same transformations as the map
+    y_data_plot = y_data.copy()
+    y_data_plot = y_data_plot.replace("(-1, 0]", "No RTS")
+
+    # Sort the classes: "No RTS" first, then by the lower bound of intervals
+    def sort_key(class_str):
+        if class_str == "No RTS":
+            return -1  # Put "No RTS" first
+        # Parse interval string like "(0, 4]" or "(4, 36]"
+        try:
+            lower = float(class_str.split(",")[0].strip("([ "))
+            return lower
+        except (ValueError, IndexError):
+            return float("inf")  # Put unparseable values at the end
+
+    df = pd.DataFrame({"target_class": y_data_plot.to_numpy()})
+    counts = df["target_class"].value_counts().reset_index()
+    counts.columns = ["class", "count"]
+    counts["percentage"] = (counts["count"] / counts["count"].sum() * 100).round(2)
+
+    # Sort counts by the same key
+    counts["sort_key"] = counts["class"].apply(sort_key)
+    counts = counts.sort_values("sort_key")
+
+    # Create an ordered list of classes for consistent color mapping
+    class_order = counts["class"].tolist()
+
+    chart = (
+        alt.Chart(counts)
+        .mark_bar()
+        .encode(
+            x=alt.X("class:N", title="Target Class", sort=class_order, axis=alt.Axis(labelAngle=0)),
+            y=alt.Y("count:Q", title="Number of Samples"),
+            color=alt.Color(
+                "class:N",
+                title="Class",
+                scale=alt.Scale(domain=class_order, range=altair_colors),
+                legend=None,
+            ),
+            tooltip=[
+                alt.Tooltip("class:N", title="Class"),
+                alt.Tooltip("count:Q", title="Count"),
+                alt.Tooltip("percentage:Q", format=".2f", title="Percentage (%)"),
+            ],
+        )
+        .properties(
+            width=400,
+            height=300,
+            title="Training Data Class Distribution",
+        )
+    )
+
+    return chart
+
+
 def main():
     """Run Streamlit dashboard application."""
     st.set_page_config(page_title="Training Analysis Dashboard", layout="wide")
@@ -846,7 +1289,19 @@ def main():
         common_feature_array = extract_common_features(model_state)
         predictions = gpd.read_parquet(results_dir / "predicted_probabilities.parquet").set_crs("epsg:3413")
 
+    # Determine task type and available metrics
+    task = settings.get("task", "binary")
+    available_metrics = settings.get("metrics", ["accuracy", "recall", "precision", "f1", "jaccard"])
+    refit_metric = "f1" if task == "binary" else "f1_macro"
+
+    # Generate unified colormaps once for all visualizations
+    matplotlib_cmap, folium_cmap, altair_colors = generate_unified_colormap(settings)
+
     st.sidebar.success(f"Loaded {len(results)} results")
+    st.sidebar.info(f"Task: {task.capitalize()} Classification")
+    # Dump the settings into the sidebar
+    with st.sidebar.expander("Search Settings", expanded=True):
+        st.json(settings)
 
     # Display some basic statistics first (lightweight)
     st.header("Parameter-Search Overview")
@@ -857,16 +1312,18 @@ def main():
         st.metric("Total Runs", len(results))
 
     with col2:
-        # Best model based on F1 score
-        best_f1_idx = results["mean_test_f1"].idxmax()
-        st.metric("Best Model Index (by F1)", f"#{best_f1_idx}")
+        # Best model based on refit metric
+        best_idx = results[f"mean_test_{refit_metric}"].idxmax()
+        st.metric(f"Best Model Index (by {format_metric_name(refit_metric)})", f"#{best_idx}")
 
     # Show best parameters for the best model
-    best_params = results.loc[best_f1_idx, ["initial_K", "eps_cl", "eps_e", "mean_test_f1", "std_test_f1"]]
+    best_params = results.loc[
+        best_idx, ["initial_K", "eps_cl", "eps_e", f"mean_test_{refit_metric}", f"std_test_{refit_metric}"]
+    ]
 
     with st.container(border=True):
         st.subheader(":abacus: Best Model Parameters")
-        st.caption("Parameters of retrained best model (selected by F1 score)")
+        st.caption(f"Parameters of retrained best model (selected by {format_metric_name(refit_metric)} score)")
         col1, col2, col3 = st.columns(3)
         with col1:
             st.metric("initial_K", f"{best_params['initial_K']:.0f}")
@@ -877,32 +1334,132 @@ def main():
 
         # Show all metrics for the best model in a container
         st.subheader(":bar_chart: Performance Across All Metrics")
-        st.caption("Complete performance profile of the best model (selected by F1 score)")
+        st.caption(
+            f"Complete performance profile of the best model (selected by {format_metric_name(refit_metric)} score)"
+        )
 
-        available_metrics = ["accuracy", "recall", "precision", "f1", "jaccard"]
         cols = st.columns(len(available_metrics))
 
         for idx, metric in enumerate(available_metrics):
             with cols[idx]:
-                best_score = results.loc[best_f1_idx, f"mean_test_{metric}"]
-                best_std = results.loc[best_f1_idx, f"std_test_{metric}"]
-                # Highlight F1 since that's what we optimized for
+                best_score = results.loc[best_idx, f"mean_test_{metric}"]
+                best_std = results.loc[best_idx, f"std_test_{metric}"]
+                # Highlight refit metric since that's what we optimized for
                 st.metric(
-                    f"{metric.capitalize()}",
+                    format_metric_name(metric),
                     f"{best_score:.4f}",
                     delta=f"±{best_std:.4f}",
                     help="Mean ± std across cross-validation folds",
                 )
 
     # Create tabs for different visualizations
-    tab1, tab2, tab3 = st.tabs(["Search Results", "Model State", "Inference Analysis"])
+    tab0, tab1, tab2, tab3 = st.tabs(["Training Data", "Search Results", "Model State", "Inference Analysis"])
+
+    with tab0:
+        # Training Data Analysis
+        st.header("Training Data Analysis")
+        st.markdown("Comprehensive analysis of the training dataset used for model development")
+
+        # Load training data
+        with st.spinner("Loading training data..."):
+            training_gdf, X_data, y_data, _ = _load_training_data(
+                grid=settings["grid"], level=settings["level"], task=task
+            )
+
+        # Summary statistics
+        st.subheader("Dataset Statistics")
+
+        col1, col2, col3, col4 = st.columns(4)
+
+        with col1:
+            st.metric("Total Samples", f"{len(training_gdf):,}")
+
+        with col2:
+            st.metric("Number of Features", f"{X_data.shape[1]:,}")
+
+        with col3:
+            n_classes = y_data.nunique()
+            st.metric("Number of Classes", n_classes)
+
+        with col4:
+            missing_pct = X_data.isnull().sum().sum() / (X_data.shape[0] * X_data.shape[1]) * 100
+            st.metric("Missing Values", f"{missing_pct:.2f}%")
+
+        # Class distribution visualization
+        st.subheader("Class Distribution")
+
+        with st.spinner("Generating class distribution..."):
+            class_dist_chart = _plot_training_class_distribution(y_data, altair_colors)
+            st.altair_chart(class_dist_chart, use_container_width=True)
+
+        st.markdown(
+            """
+            **Interpretation:**
+            - Shows the balance between different classes in the training dataset
+            - Class imbalance affects model learning and may require special handling
+            - Each bar represents the count of training samples for that class
+            """
+        )
+
+        # Interactive map
+        st.subheader("Interactive Training Data Map")
+        st.markdown("Explore the spatial distribution of training samples by class")
+
+        with st.spinner("Generating interactive map..."):
+            training_map = _plot_training_data_map(training_gdf, folium_cmap)
+            st_folium.st_folium(training_map, width="100%", height=600, returned_objects=[])
+
+        # Additional statistics in expander
+        with st.expander("Detailed Training Data Statistics"):
+            st.write("**Class Distribution:**")
+            class_counts = y_data.value_counts().sort_index()
+
+            # Create columns for better layout
+            n_cols = min(5, len(class_counts))
+            cols = st.columns(n_cols)
+
+            for idx, (class_label, count) in enumerate(class_counts.items()):
+                percentage = count / len(y_data) * 100
+                with cols[idx % n_cols]:
+                    st.metric(f"Class {class_label}", f"{count:,} ({percentage:.2f}%)")
+
+            # Show detailed table
+            st.write("**Detailed Class Breakdown:**")
+            class_df = pd.DataFrame(
+                {
+                    "Class": class_counts.index,
+                    "Count": class_counts.to_numpy(),
+                    "Percentage": (class_counts.to_numpy() / len(y_data) * 100).round(2),
+                }
+            )
+            st.dataframe(class_df, width="stretch", hide_index=True)
+
+            # Feature statistics
+            st.write("**Feature Statistics:**")
+            st.markdown(f"- Total number of features: **{X_data.shape[1]}**")
+            st.markdown(f"- Features with missing values: **{X_data.isnull().any().sum()}**")
+
+            # Show feature types breakdown
+            feature_types = X_data.columns.to_series().apply(lambda x: x.split("_")[0]).value_counts()
+            st.write("**Feature Type Distribution:**")
+            feature_type_df = pd.DataFrame(
+                {
+                    "Feature Type": feature_types.index,
+                    "Count": feature_types.to_numpy(),
+                }
+            )
+            st.dataframe(feature_type_df, width="stretch", hide_index=True)
 
     with tab1:
         # Metric selection - only used in this tab
-        available_metrics = ["accuracy", "recall", "precision", "f1", "jaccard"]
-        selected_metric = st.selectbox(
-            "Select Metric", options=available_metrics, help="Choose which metric to visualize"
+        metric_display_names = {metric: format_metric_name(metric) for metric in available_metrics}
+        selected_metric_display = st.selectbox(
+            "Select Metric",
+            options=[format_metric_name(m) for m in available_metrics],
+            help="Choose which metric to visualize",
         )
+        # Convert back to raw metric name
+        selected_metric = next(k for k, v in metric_display_names.items() if v == selected_metric_display)
 
         # Show best parameters
         with st.expander("Best Parameters"):
@@ -911,7 +1468,7 @@ def main():
             st.dataframe(best_params.to_frame().T, width="content")
 
         # Main plots
-        st.header(f"Visualization for {selected_metric.capitalize()}")
+        st.header(f"Visualization for {format_metric_name(selected_metric)}")
 
         # K-binned plot configuration
         @st.fragment
@@ -978,17 +1535,35 @@ def main():
             col1, col2 = st.columns(2)
 
             with col1:
-                st.subheader("K vs eps_cl")
-                with st.spinner("Generating eps_cl plot..."):
-                    chart3 = _plot_eps_binned(results_binned, "eps_cl", f"mean_test_{selected_metric}")
+                st.subheader(f"K vs {selected_metric} (binned by eps_e)")
+                with st.spinner("Generating plot..."):
+                    chart3 = _plot_params_binned(
+                        results_binned, "initial_K", "eps_cl", "eps_e_binned", f"mean_test_{selected_metric}"
+                    )
                     st.altair_chart(chart3, use_container_width=True)
 
-            with col2:
-                st.subheader("K vs eps_e")
-                with st.spinner("Generating eps_e plot..."):
-                    chart4 = _plot_eps_binned(results_binned, "eps_e", f"mean_test_{selected_metric}")
+                st.subheader(f"eps_e vs {selected_metric} (binned by K)")
+                with st.spinner("Generating plot..."):
+                    chart4 = _plot_params_binned(
+                        results_binned, "eps_e", "eps_cl", "initial_K_binned", f"mean_test_{selected_metric}"
+                    )
                     st.altair_chart(chart4, use_container_width=True)
 
+            with col2:
+                st.subheader(f"K vs {selected_metric} (binned by eps_cl)")
+                with st.spinner("Generating plot..."):
+                    chart5 = _plot_params_binned(
+                        results_binned, "initial_K", "eps_e", "eps_cl_binned", f"mean_test_{selected_metric}"
+                    )
+                    st.altair_chart(chart5, use_container_width=True)
+
+                st.subheader(f"eps_cl vs {selected_metric} (binned by K)")
+                with st.spinner("Generating plot..."):
+                    chart6 = _plot_params_binned(
+                        results_binned, "eps_cl", "eps_e", "initial_K_binned", f"mean_test_{selected_metric}"
+                    )
+                    st.altair_chart(chart6, use_container_width=True)
+
         render_k_binned_plots()
 
         # Metric comparison plots
@@ -1003,19 +1578,37 @@ def main():
                 help="Choose which parameter to use for coloring the scatter plots",
             )
 
+            # Dynamically determine which metrics to compare based on available metrics
+            if task == "binary":
+                # For binary: show recall vs precision and accuracy vs jaccard
+                comparisons = [
+                    ("precision", "recall", "Recall vs Precision"),
+                    ("accuracy", "jaccard", "Accuracy vs Jaccard"),
+                ]
+            else:
+                # For multiclass: show micro vs macro variants
+                comparisons = [
+                    ("precision_macro", "recall_macro", "Recall Macro vs Precision Macro"),
+                    ("accuracy", "jaccard_macro", "Accuracy vs Jaccard Macro"),
+                ]
+
             col1, col2 = st.columns(2)
 
             with col1:
-                st.subheader("Recall vs Precision")
-                with st.spinner("Generating Recall vs Precision plot..."):
-                    recall_precision_chart = _plot_metric_comparison(results, "precision", "recall", color_param)
-                    st.altair_chart(recall_precision_chart, use_container_width=True)
+                st.subheader(comparisons[0][2])
+                with st.spinner(f"Generating {comparisons[0][2]} plot..."):
+                    chart1 = _plot_metric_comparison(
+                        results, comparisons[0][0], comparisons[0][1], color_param, refit_metric
+                    )
+                    st.altair_chart(chart1, use_container_width=True)
 
             with col2:
-                st.subheader("Accuracy vs Jaccard")
-                with st.spinner("Generating Accuracy vs Jaccard plot..."):
-                    accuracy_jaccard_chart = _plot_metric_comparison(results, "accuracy", "jaccard", color_param)
-                    st.altair_chart(accuracy_jaccard_chart, use_container_width=True)
+                st.subheader(comparisons[1][2])
+                with st.spinner(f"Generating {comparisons[1][2]} plot..."):
+                    chart2 = _plot_metric_comparison(
+                        results, comparisons[1][0], comparisons[1][1], color_param, refit_metric
+                    )
+                    st.altair_chart(chart2, use_container_width=True)
 
         render_metric_comparisons()
 
@@ -1094,7 +1687,7 @@ def main():
 
             with col2:
                 st.markdown("### Box Count by Class")
-                box_assignment_bars = _plot_box_assignment_bars(model_state)
+                box_assignment_bars = _plot_box_assignment_bars(model_state, altair_colors)
                 st.altair_chart(box_assignment_bars, use_container_width=True)
 
         # Show statistics
@@ -1302,30 +1895,44 @@ def main():
             n_classes = predictions["predicted_class"].nunique()
             st.metric("Number of Classes", n_classes)
 
-        # Class distribution visualization
-        st.subheader("Class Distribution")
+        # Class distribution and static map side by side
+        st.subheader("Prediction Overview")
+
+        col_map, col_dist = st.columns([0.6, 0.4])
+
+        with col_map:
+            st.markdown("#### Static Prediction Map")
+            st.markdown("High-quality map with North Polar Stereographic projection")
+
+            with st.spinner("Generating static map..."):
+                static_fig = _plot_prediction_map_static(predictions, matplotlib_cmap)
+                st.pyplot(static_fig, width="stretch")
+
+            st.markdown(
+                """
+                **Map Features:**
+                - North Polar Azimuthal Equidistant projection (optimized for Arctic)
+                - Predictions colored by class using inferno colormap
+                - Includes coastlines and 50°N latitude boundary
+                """
+            )
+
+        with col_dist:
+            st.markdown("#### Class Distribution")
 
             with st.spinner("Generating class distribution..."):
-            class_dist_chart = _plot_prediction_class_distribution(predictions)
+                class_dist_chart = _plot_prediction_class_distribution(predictions, altair_colors)
                 st.altair_chart(class_dist_chart, use_container_width=True)
 
             st.markdown(
                 """
                 **Interpretation:**
-            - Shows the balance between predicted classes
-            - Class imbalance may indicate regional patterns or model bias
-            - Each bar represents the count of cells predicted for that class
+                - Balance between predicted classes
+                - Class imbalance may indicate regional patterns
+                - Each bar shows cell count per class
                 """
             )
 
-        # Interactive map
-        st.subheader("Interactive Prediction Map")
-        st.markdown("Explore predictions spatially with the interactive map below")
-
-        with st.spinner("Generating interactive map..."):
-            chart_map = _plot_prediction_map(predictions)
-            st_folium.st_folium(chart_map, width="100%", height=600, returned_objects=[])
-
             # Additional statistics in expander
             with st.expander("Detailed Prediction Statistics"):
                 st.write("**Class Distribution:**")
@@ -1351,6 +1958,17 @@ def main():
                 )
                 st.dataframe(class_df, width="stretch", hide_index=True)
 
+        # Interactive map
+        st.subheader("Interactive Prediction Map")
+        st.markdown("Explore predictions spatially with the interactive map below")
+
+        # with st.spinner("Generating interactive map..."):
+        #     chart_map = _plot_prediction_map(predictions, folium_colors)
+        #     st_folium.st_folium(chart_map, width="100%", height=600, returned_objects=[])
+        st.text("Interactive map functionality is currently disabled.")
+
+    st.balloons()
+
 
 if __name__ == "__main__":
     main()