First analysis

pixi.lock

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+- pypi: https://files.pythonhosted.org/packages/d8/83/9a9188aad05b3fe84d3f163e3978458e95d6db0991de614501a5473fe6c7/python_fasthtml-0.12.42-py3-none-any.whl
   name: python-fasthtml
-  version: 0.12.41
+  version: 0.12.42
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+  sha256: d74742012a9696a9fcd0c5dfcaa20b39ef751d29b2e1fb119c83568f7a0c9234
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   - statsmodels>=0.12 ; extra == 'stats'
   requires_python: '>=3.8'
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+- pypi: https://files.pythonhosted.org/packages/47/d4/2fdf854bc3b9c7f55219678f812600a20a138af2dd847d99004994eada8f/sentry_sdk-2.53.0-py2.py3-none-any.whl
   name: sentry-sdk
-  version: 2.52.0
+  version: 2.53.0
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+  sha256: 46e1ed8d84355ae54406c924f6b290c3d61f4048625989a723fd622aab838899
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   - certifi
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   - protobuf>=3.20
   requires_python: '>=3.9'
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+- pypi: https://files.pythonhosted.org/packages/d3/be/e191c2a15da20530fde03564564e3e4b4220eb9d687d4014957e5c6a5e85/textual-8.0.0-py3-none-any.whl
   name: textual
-  version: 7.5.0
+  version: 8.0.0
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+  sha256: 8908f4ebe93a6b4f77ca7262197784a52162bc88b05f4ecf50ac93a92d49bb8f
   requires_dist:
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   - mdit-py-plugins
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+- pypi: https://files.pythonhosted.org/packages/48/06/a8e97165d82f866a40117f213d2f8a32f297c9c76ec9c16a1a2c680e0f59/tifffile-2026.2.16-py3-none-any.whl
   name: tifffile
-  version: 2026.1.28
+  version: 2026.2.16
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+  sha256: ea76cb4d8aa290f7f164840dfe4e244d104bd90c84d5ee1e6de6d84fd4745a48
   requires_dist:
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   - imagecodecs>=2025.11.11 ; extra == 'codecs'
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+- pypi: https://files.pythonhosted.org/packages/85/d0/4da85c2a45054bb661993c93524138ace4956cb075a7ae0c9d1deadc331b/typer-0.24.0-py3-none-any.whl
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+  version: 0.24.0
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+  sha256: 5fc435a9c8356f6160ed6e85a6301fdd6e3d8b2851da502050d1f92c5e9eddc8
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-  - click>=8.0.0
+  - click>=8.2.1
   - shellingham>=1.3.0
-  - rich>=10.11.0
+  - rich>=12.3.0
   - annotated-doc>=0.0.2
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+  requires_python: '>=3.10'
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+- pypi: https://files.pythonhosted.org/packages/a7/24/5480c20380dfd18cf33d14784096dca45a24eae6102e91d49a718d3b6855/typer_slim-0.24.0-py3-none-any.whl
   name: typer-slim
-  version: 0.23.0
+  version: 0.24.0
-  sha256: 1d693daf22d998a7b1edab8413cdcb8af07254154ce3956c1664dc11b01e2f8b
+  sha256: d5d7ee1ee2834d5020c7c616ed5e0d0f29b9a4b1dd283bdebae198ec09778d0e
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+  - typer>=0.24.0
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+- pypi: https://files.pythonhosted.org/packages/36/48/33f00ffd1c62ca4e66f91024049107b7b372fda7bbe4d20e6a492a39ff02/ultraplot-2.0.1-py3-none-any.whl
   name: ultraplot
-  version: 1.72.0
+  version: 2.0.1
-  sha256: dc276064791661018311923bb5bf3cd541b79399682e48de1b88d8556053ae2d
+  sha256: ba2f1ab04d3fea82165facf9434e43054f4b22cae101c36a8e4f0176017d4e09
   requires_dist:
   - numpy>=1.26.0
   - matplotlib>=3.9,<3.11
+  - pycirclize>=1.10.1
   - typing-extensions ; python_full_version < '3.12'
-  requires_python: '>=3.10,<3.14'
+  - jupyter ; extra == 'docs'
+  - jupytext ; extra == 'docs'
+  - lxml-html-clean ; extra == 'docs'
+  - m2r2 ; extra == 'docs'
+  - mpltern ; extra == 'docs'
+  - nbsphinx ; extra == 'docs'
+  - sphinx ; extra == 'docs'
+  - sphinx-autoapi ; extra == 'docs'
+  - sphinx-automodapi ; extra == 'docs'
+  - sphinx-copybutton ; extra == 'docs'
+  - sphinx-design ; extra == 'docs'
+  - sphinx-gallery ; extra == 'docs'
+  - shibuya ; extra == 'docs'
+  - sphinx-sitemap ; extra == 'docs'
+  - typing-extensions ; extra == 'docs'
+  requires_python: '>=3.10,<3.15'
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@@ -9949,10 +9978,10 @@ packages:
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   - backports-zstd>=1.0.0 ; python_full_version < '3.14' and extra == 'zstd'
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   - h11>=0.8
@@ -9962,7 +9991,7 @@ packages:
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-  - watchfiles>=0.13 ; extra == 'standard'
+  - watchfiles>=0.20 ; extra == 'standard'
   - websockets>=10.4 ; extra == 'standard'
   requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/15/c0/0be24758891ef825f2065cd5db8741aaddabe3e248ee6acc5e8a80f04005/uvloop-0.22.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
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+- pypi: https://files.pythonhosted.org/packages/de/91/ec9465d014cfd199c5b2083d271d31b3c2aedeae66f3d8a0712f7f54bdf3/wandb-0.25.0-py3-none-manylinux_2_28_x86_64.whl
   name: wandb
-  version: 0.24.2
+  version: 0.25.0
-  sha256: 38661c666e70d7e1f460fc0a0edab8a393eaaa5f8773c17be534961a7022779d
+  sha256: 6c4c38077836f9b7569a35b0e1dcf1f0c43616fcd936d182f475edbfea063665
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   - gitpython>=1.0.0,!=3.1.29
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@@ -10103,7 +10131,7 @@ packages:
   - orjson ; extra == 'perf'
   - sweeps>=0.2.0 ; extra == 'sweeps'
   - wandb-workspaces ; extra == 'workspaces'
-  requires_python: '>=3.8'
+  requires_python: '>=3.9'
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   name: wasabi
   version: 1.1.3

src/entropice/dashboard/plots/inference.py

@@ -0,0 +1,460 @@
+"""Plots for visualizing model inference results."""
+
+import geopandas as gpd
+import matplotlib.colors as mcolors
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+import pydeck as pdk
+
+from entropice.dashboard.utils.colors import get_cmap, get_palette, hex_to_rgb
+from entropice.dashboard.utils.geometry import fix_hex_geometry
+from entropice.utils.types import Task
+
+
+def create_inference_map(
+    predictions_gdf: gpd.GeoDataFrame,
+    task: Task,
+    make_3d_map: bool,
+) -> pdk.Deck:
+    """Create a spatial distribution map for model predictions.
+
+    Args:
+        predictions_gdf: GeoDataFrame with columns ['cell_id', 'predicted', 'geometry']
+        task: Task type (binary, count_regimes, density_regimes, count, density)
+        make_3d_map: Whether to render the map in 3D (extruded) or 2D
+
+    Returns:
+        pdk.Deck: A PyDeck map visualization of the predictions
+
+    """
+    # Subsample if too many cells for performance
+    n_cells = len(predictions_gdf)
+    if n_cells > 50000:
+        predictions_gdf = predictions_gdf.sample(n=50000, random_state=42)
+
+    # Create a copy to avoid modifying the original
+    gdf = predictions_gdf.copy()
+
+    # Convert to WGS84 for pydeck
+    if gdf.crs is not None:
+        gdf = gdf.to_crs("EPSG:4326")
+
+    # Fix antimeridian issues for hex cells
+    gdf["geometry"] = gdf["geometry"].apply(fix_hex_geometry)
+
+    # Determine if classification or regression
+    classification_tasks: list[Task] = ["binary", "count_regimes", "density_regimes"]
+    is_classification = task in classification_tasks
+
+    if is_classification:
+        # Convert to categorical if needed
+        if gdf["predicted"].dtype != "category":
+            gdf["predicted"] = pd.Categorical(gdf["predicted"])
+
+        # Get categories and assign colors
+        categories = gdf["predicted"].cat.categories.tolist()
+        n_categories = len(categories)
+        colors = get_palette(task, n_categories + 2)[1:-1]  # Avoid too light/dark colors
+
+        # Create category to color mapping
+        category_colors = {cat: colors[i] for i, cat in enumerate(categories)}
+
+        # Assign colors to each row based on predicted category
+        rgb_colors = []
+        for cat in gdf["predicted"]:
+            color = category_colors[cat]
+            rgb_colors.append(hex_to_rgb(mcolors.to_hex(color)))
+        gdf["fill_color"] = rgb_colors
+
+        # For elevation in 3D, use category index normalized
+        if n_categories > 1:
+            gdf["elevation"] = gdf["predicted"].cat.codes / (n_categories - 1)
+        else:
+            gdf["elevation"] = 0.5
+
+        # Display value is the category name
+        gdf["display_value"] = gdf["predicted"].astype(str)
+
+    else:
+        # Regression task - use continuous colormap
+        cmap = get_cmap(task)
+
+        # Get prediction values
+        values = gdf["predicted"].to_numpy()
+
+        # Normalize using percentiles to avoid outliers
+        vmin, vmax = np.nanpercentile(values, [2, 98])
+        if vmax > vmin:
+            normalized_values = (np.clip(values, vmin, vmax) - vmin) / (vmax - vmin)
+        else:
+            normalized_values = np.full_like(values, 0.5)
+
+        # Map normalized values to colors
+        colors = [cmap(val) for val in normalized_values]
+        rgb_colors = [hex_to_rgb(mcolors.to_hex(color)) for color in colors]
+        gdf["fill_color"] = rgb_colors
+
+        # Use normalized values for elevation
+        gdf["elevation"] = normalized_values
+
+        # Display value is the actual prediction (rounded)
+        gdf["display_value"] = gdf["predicted"].round(3).astype(str)
+
+    # Convert to GeoJSON format
+    geojson_data = []
+    for _, row in gdf.iterrows():
+        geojson_data.append(
+            {
+                "type": "Feature",
+                "geometry": row["geometry"].__geo_interface__,
+                "properties": {
+                    "fill_color": row["fill_color"],
+                    "elevation": row["elevation"],
+                    "predicted": row["display_value"],
+                },
+            }
+        )
+
+    # Create pydeck layer
+    layer = pdk.Layer(
+        "GeoJsonLayer",
+        geojson_data,
+        opacity=0.7,
+        stroked=True,
+        filled=True,
+        extruded=make_3d_map,
+        wireframe=False,
+        get_fill_color="properties.fill_color",
+        get_line_color=[80, 80, 80],
+        line_width_min_pixels=0.5,
+        get_elevation="properties.elevation" if make_3d_map else 0,
+        elevation_scale=500000,
+        pickable=True,
+    )
+
+    # Set initial view state
+    view_state = pdk.ViewState(
+        latitude=70,
+        longitude=0,
+        zoom=2 if not make_3d_map else 1.5,
+        pitch=0 if not make_3d_map else 45,
+    )
+
+    # Build tooltip HTML
+    tooltip_html = "<b>Predicted:</b> {predicted}"
+
+    # Create deck
+    deck = pdk.Deck(
+        layers=[layer],
+        initial_view_state=view_state,
+        tooltip={
+            "html": tooltip_html,
+            "style": {"backgroundColor": "steelblue", "color": "white"},
+        },
+        map_style="https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json",
+    )
+
+    return deck
+
+
+def create_prediction_distribution_plot(
+    predictions_gdf: gpd.GeoDataFrame,
+    task: Task,
+) -> go.Figure:
+    """Create a distribution plot for predictions.
+
+    For classification tasks, shows a bar chart of class counts.
+    For regression tasks, shows a histogram of predicted values.
+
+    Args:
+        predictions_gdf: GeoDataFrame with 'predicted' column
+        task: Task type
+
+    Returns:
+        Plotly Figure with distribution plot
+
+    """
+    classification_tasks: list[Task] = ["binary", "count_regimes", "density_regimes"]
+    is_classification = task in classification_tasks
+
+    fig = go.Figure()
+
+    if is_classification:
+        # Bar chart for classification
+        # Convert to categorical if needed
+        if predictions_gdf["predicted"].dtype != "category":
+            predictions_gdf["predicted"] = pd.Categorical(predictions_gdf["predicted"])
+
+        categories = predictions_gdf["predicted"].cat.categories.tolist()
+        counts = predictions_gdf["predicted"].value_counts().reindex(categories, fill_value=0)
+
+        # Get colors
+        n_categories = len(categories)
+        colors = get_palette(task, n_categories + 2)[1:-1]
+
+        fig.add_trace(
+            go.Bar(
+                x=categories,
+                y=counts.to_numpy(),
+                marker_color=colors,
+                text=counts.to_numpy(),
+                textposition="outside",
+                textfont={"size": 12},
+                hovertemplate="<b>%{x}</b><br>Count: %{y}<br>Percentage: %{customdata:.1%}<extra></extra>",
+                customdata=counts.to_numpy() / counts.sum(),
+            )
+        )
+
+        fig.update_layout(
+            title=f"Predicted Class Distribution ({task.replace('_', ' ').title()})",
+            xaxis_title="Predicted Class",
+            yaxis_title="Count",
+            height=500,
+            showlegend=False,
+        )
+
+        fig.update_xaxes(tickangle=-45)
+
+    else:
+        # Histogram for regression
+        values = predictions_gdf["predicted"].to_numpy()
+
+        # Remove NaN values
+        values = values[~np.isnan(values)]
+
+        # Create histogram
+        fig.add_trace(
+            go.Histogram(
+                x=values,
+                nbinsx=50,
+                marker_color=mcolors.to_hex(get_cmap(task)(0.6)),
+                opacity=0.7,
+                hovertemplate="<b>Range:</b> %{x}<br><b>Count:</b> %{y}<extra></extra>",
+            )
+        )
+
+        # Add statistical lines
+        mean_val = np.mean(values)
+        median_val = np.median(values)
+
+        fig.add_vline(
+            x=mean_val,
+            line_dash="dash",
+            line_color="red",
+            annotation_text=f"Mean: {mean_val:.3f}",
+            annotation_position="top",
+        )
+
+        fig.add_vline(
+            x=median_val,
+            line_dash="dot",
+            line_color="green",
+            annotation_text=f"Median: {median_val:.3f}",
+            annotation_position="bottom right",
+        )
+
+        fig.update_layout(
+            title=f"Predicted Value Distribution ({task.replace('_', ' ').title()})",
+            xaxis_title=f"Predicted {task.capitalize()}",
+            yaxis_title="Count",
+            height=500,
+            showlegend=False,
+        )
+
+    return fig
+
+
+def create_prediction_statistics_plot(
+    predictions_gdf: gpd.GeoDataFrame,
+    task: Task,
+) -> go.Figure:
+    """Create a plot showing key statistics about the predictions.
+
+    Args:
+        predictions_gdf: GeoDataFrame with 'predicted' column
+        task: Task type
+
+    Returns:
+        Plotly Figure with statistics
+
+    """
+    classification_tasks: list[Task] = ["binary", "count_regimes", "density_regimes"]
+    is_classification = task in classification_tasks
+
+    if is_classification:
+        # Classification statistics: class proportions
+        # Convert to categorical if needed
+        if predictions_gdf["predicted"].dtype != "category":
+            predictions_gdf["predicted"] = pd.Categorical(predictions_gdf["predicted"])
+
+        categories = predictions_gdf["predicted"].cat.categories.tolist()
+        counts = predictions_gdf["predicted"].value_counts().reindex(categories, fill_value=0)
+        proportions = counts / counts.sum()
+
+        # Get colors
+        n_categories = len(categories)
+        colors = get_palette(task, n_categories + 2)[1:-1]
+
+        fig = go.Figure()
+
+        fig.add_trace(
+            go.Bar(
+                x=categories,
+                y=proportions.to_numpy() * 100,  # Convert to percentage
+                marker_color=colors,
+                text=[f"{p:.1%}" for p in proportions.to_numpy()],
+                textposition="outside",
+                hovertemplate="<b>%{x}</b><br>Proportion: %{y:.1f}%<extra></extra>",
+            )
+        )
+
+        fig.update_layout(
+            title="Predicted Class Proportions",
+            xaxis_title="Predicted Class",
+            yaxis_title="Proportion (%)",
+            height=400,
+            showlegend=False,
+        )
+
+        fig.update_xaxes(tickangle=-45)
+
+    else:
+        # Regression statistics: box plot with quartiles
+        values = predictions_gdf["predicted"].to_numpy()
+        values = values[~np.isnan(values)]
+
+        fig = go.Figure()
+
+        fig.add_trace(
+            go.Box(
+                y=values,
+                name="Predictions",
+                marker_color=mcolors.to_hex(get_cmap(task)(0.6)),
+                boxmean="sd",  # Show mean and standard deviation
+                hovertemplate="<b>Value:</b> %{y:.3f}<extra></extra>",
+            )
+        )
+
+        # Calculate statistics
+        stats = pd.Series(values).describe()
+
+        # Add annotation with statistics
+        stats_text = (
+            f"<b>Statistics</b><br>"
+            f"Count: {int(stats['count'])}<br>"
+            f"Mean: {stats['mean']:.3f}<br>"
+            f"Std: {stats['std']:.3f}<br>"
+            f"Min: {stats['min']:.3f}<br>"
+            f"25%: {stats['25%']:.3f}<br>"
+            f"50%: {stats['50%']:.3f}<br>"
+            f"75%: {stats['75%']:.3f}<br>"
+            f"Max: {stats['max']:.3f}"
+        )
+
+        fig.add_annotation(
+            text=stats_text,
+            xref="paper",
+            yref="paper",
+            x=1.15,
+            y=0.5,
+            showarrow=False,
+            align="left",
+            bgcolor="rgba(255, 255, 255, 0.8)",
+            bordercolor="black",
+            borderwidth=1,
+        )
+
+        fig.update_layout(
+            title=f"Prediction Statistics ({task.replace('_', ' ').title()})",
+            yaxis_title=f"Predicted {task.capitalize()}",
+            height=500,
+            showlegend=False,
+            margin={"r": 200},  # Make room for stats annotation
+        )
+
+    return fig
+
+
+def create_coverage_statistics_table(
+    predictions_gdf: gpd.GeoDataFrame, task: Task, grid_gdf: gpd.GeoDataFrame | None = None
+) -> pd.DataFrame:
+    """Create a summary table of prediction coverage statistics.
+
+    Args:
+        predictions_gdf: GeoDataFrame with predictions
+        task: Task type
+        grid_gdf: Optional GeoDataFrame with cell_id and land_area columns for RTS area calculations
+
+    Returns:
+        DataFrame with coverage statistics
+
+    """
+    classification_tasks: list[Task] = ["binary", "count_regimes", "density_regimes"]
+    is_classification = task in classification_tasks
+
+    stats = {
+        "Total Cells": len(predictions_gdf),
+        "Non-null Predictions": predictions_gdf["predicted"].notna().sum(),
+        "Null Predictions": predictions_gdf["predicted"].isna().sum(),
+    }
+
+    # Calculate RTS-affected area if grid_gdf is provided
+    if grid_gdf is not None:
+        # Merge with grid to get land areas
+        merged = predictions_gdf.merge(grid_gdf[["cell_id", "land_area"]], on="cell_id", how="left")
+
+        if is_classification:
+            # Convert to categorical if needed
+            if merged["predicted"].dtype != "category":
+                merged["predicted"] = pd.Categorical(merged["predicted"])
+
+            categories = merged["predicted"].cat.categories.tolist()
+
+            # For classification: sum areas where RTS are expected (non-zero classes)
+            # Assuming first category is "no RTS" (0 or similar)
+            rts_mask = merged["predicted"].notna()
+            if len(categories) > 1:
+                # Exclude the first category (assumed to be "no RTS")
+                rts_mask = rts_mask & (merged["predicted"] != categories[0])
+
+            rts_area_km2 = merged.loc[rts_mask, "land_area"].sum() / 1e6  # Convert m² to km²
+            stats["RTS-Affected Area (km²)"] = f"{rts_area_km2:,.2f}"
+
+        elif task == "count":
+            # For count: sum areas where count > 0
+            rts_mask = (merged["predicted"].notna()) & (merged["predicted"] > 0)
+            rts_area_km2 = merged.loc[rts_mask, "land_area"].sum() / 1e6  # Convert m² to km²
+            stats["RTS-Affected Area (km²)"] = f"{rts_area_km2:,.2f}"
+
+        elif task == "density":
+            # For density: multiply predicted density by area
+            # Density is typically RTS/km², so multiply by area in km²
+            merged["rts_area"] = merged["predicted"] * merged["land_area"] / 1e6  # density * area_km2
+            total_rts_area = merged["rts_area"].sum()
+            stats["Total RTS Area (km²)"] = f"{total_rts_area:,.2f}"
+
+    if is_classification:
+        # Add class counts
+        # Convert to categorical if needed
+        if predictions_gdf["predicted"].dtype != "category":
+            predictions_gdf["predicted"] = pd.Categorical(predictions_gdf["predicted"])
+
+        categories = predictions_gdf["predicted"].cat.categories.tolist()
+        for cat in categories:
+            count = (predictions_gdf["predicted"] == cat).sum()
+            stats[f"Class '{cat}' Count"] = count
+            stats[f"Class '{cat}' Proportion"] = f"{count / len(predictions_gdf):.2%}"
+    else:
+        # Add regression statistics
+        values = predictions_gdf["predicted"].dropna()
+        if len(values) > 0:
+            stats["Mean"] = f"{values.mean():.4f}"
+            stats["Median"] = f"{values.median():.4f}"
+            stats["Std Dev"] = f"{values.std():.4f}"
+            stats["Min"] = f"{values.min():.4f}"
+            stats["Max"] = f"{values.max():.4f}"
+
+    # Convert to DataFrame
+    df = pd.DataFrame(list(stats.items()), columns=["Statistic", "Value"])
+    return df

src/entropice/dashboard/sections/inference.py

@@ -0,0 +1,106 @@
+"""Sections for the inference page."""
+
+import geopandas as gpd
+import streamlit as st
+
+from entropice.dashboard.plots.inference import (
+    create_coverage_statistics_table,
+    create_inference_map,
+    create_prediction_distribution_plot,
+    create_prediction_statistics_plot,
+)
+from entropice.utils.types import Task
+
+
+def render_inference_map_section(predictions_gdf: gpd.GeoDataFrame, task: Task) -> None:
+    """Render the inference map section.
+
+    Args:
+        predictions_gdf: GeoDataFrame with predictions and geometries
+        task: Task type
+
+    """
+    st.subheader("📍 Spatial Prediction Map")
+
+    st.markdown(
+        """
+        Interactive map showing model predictions across the Arctic region.
+        Toggle 3D mode to see predictions as elevated terrain.
+        """
+    )
+
+    # 3D toggle
+    col1, col2 = st.columns([1, 4])
+    with col1:
+        make_3d = st.checkbox("3D View", value=False, key="inference_map_3d")
+
+    with col2:
+        if make_3d:
+            st.info("🎨 Height represents prediction magnitude. Rotate and tilt the map to explore!")
+
+    # Create and display map
+    with st.spinner("Generating map..."):
+        deck = create_inference_map(predictions_gdf, task, make_3d)
+        st.pydeck_chart(deck)
+
+
+def render_prediction_statistics_section(
+    predictions_gdf: gpd.GeoDataFrame, task: Task, grid_gdf: gpd.GeoDataFrame | None = None
+) -> None:
+    """Render the prediction statistics section.
+
+    Args:
+        predictions_gdf: GeoDataFrame with predictions
+        task: Task type
+        grid_gdf: Optional GeoDataFrame with cell_id and land_area for RTS area calculations
+
+    """
+    st.subheader("📊 Prediction Statistics")
+
+    # Create two columns for statistics
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.markdown("#### Coverage Statistics")
+        # Create statistics table
+        stats_df = create_coverage_statistics_table(predictions_gdf, task, grid_gdf)
+        st.dataframe(stats_df, hide_index=True, width="stretch")
+
+    with col2:
+        st.markdown("#### Statistical Summary")
+        # Create statistics plot (box plot for regression, proportions for classification)
+        fig = create_prediction_statistics_plot(predictions_gdf, task)
+        st.plotly_chart(fig, width="stretch")
+
+
+def render_prediction_distribution_section(predictions_gdf: gpd.GeoDataFrame, task: Task) -> None:
+    """Render the prediction distribution section.
+
+    Args:
+        predictions_gdf: GeoDataFrame with predictions
+        task: Task type
+
+    """
+    classification_tasks: list[Task] = ["binary", "count_regimes", "density_regimes"]
+    is_classification = task in classification_tasks
+
+    if is_classification:
+        st.subheader("📈 Class Distribution")
+        st.markdown(
+            """
+            Distribution of predicted classes across all grid cells.
+            Shows the count and proportion of each predicted class.
+            """
+        )
+    else:
+        st.subheader("📈 Value Distribution")
+        st.markdown(
+            """
+            Distribution of predicted values across all grid cells.
+            Histogram shows the frequency of predictions in different value ranges.
+            """
+        )
+
+    # Create and display distribution plot
+    fig = create_prediction_distribution_plot(predictions_gdf, task)
+    st.plotly_chart(fig, width="stretch")

src/entropice/dashboard/views/inference_page.py

@@ -1,8 +1,15 @@
 """Inference page: Visualization of model inference results across the study region."""
 
+import geopandas as gpd
 import streamlit as st
 from stopuhr import stopwatch
 
+import entropice.utils.paths
+from entropice.dashboard.sections.inference import (
+    render_inference_map_section,
+    render_prediction_distribution_section,
+    render_prediction_statistics_section,
+)
 from entropice.dashboard.utils.loaders import TrainingResult, load_all_training_results
 
 
@@ -77,15 +84,27 @@ def render_inference_page():
     # Main content area
     # Load predictions
     with st.spinner("Loading inference results..."):
-        # Columns: Index(['cell_id', 'predicted', 'geometry'], dtype='object')
+        # Columns: ['cell_id', 'predicted', 'geometry']
         predictions_gdf = selected_result.run.predictions
         task = selected_result.run.task
+        # Reading the data takes for the largest grids ~1.7s
+        gridfile = entropice.utils.paths.get_grid_file(
+            selected_result.run.dataset.grid, selected_result.run.dataset.level
+        )
+        grid_gdf = gpd.read_parquet(gridfile, columns=["cell_id", "land_area", "geometry"])
 
-    # TODO: Implement the sections
+    # Section 1: Spatial Map
-    # Map, optionally 3D
+    render_inference_map_section(predictions_gdf, task)
-    # Some statistics about the predictions
-    # Class Distribution for classification tasks
-    # Distribution of predicted values for regression tasks
 
-    st.balloons()
+    st.divider()
+
+    # Section 2: Prediction Statistics
+    render_prediction_statistics_section(predictions_gdf, task, grid_gdf)
+
+    st.divider()
+
+    # Section 3: Distribution Analysis
+    render_prediction_distribution_section(predictions_gdf, task)
+
+    st.success("✅ Inference visualization complete!")
     stopwatch.summary()

src/entropice/experiments/feature_importance.py

@@ -11,30 +11,186 @@ from entropice.ml.dataset import DatasetEnsemble
 from entropice.ml.hpsearchcv import RunSettings as HPOCVRunSettings
 from entropice.ml.hpsearchcv import hpsearch_cv
 from entropice.utils.paths import RESULTS_DIR
-from entropice.utils.types import Grid, Model, TargetDataset, Task
+from entropice.utils.types import Grid, L2SourceDataset, Model, TargetDataset, Task
 
 cli = cyclopts.App("entropice-feature-importance")
 
-DEV = False
 
 EXPERIMENT_NAME = "feature_importance_era5-shoulder_arcticdem-v2"
-if DEV:
+
-    EXPERIMENT_NAME = "tobis-final-tests"
+all_features = None
+univariate_features = [
+    "x",
+    "y",
+    "era5_freezing_days_AMJ",
+    "era5_freezing_days_JAS",
+    "era5_freezing_days_trend_JAS",
+    "era5_freezing_degree_days_AMJ",
+    "era5_freezing_degree_days_JAS",
+    "era5_freezing_degree_days_JFM",
+    "era5_freezing_degree_days_trend_JAS",
+    "era5_freezing_degree_days_trend_JFM",
+    "era5_freezing_degree_days_trend_OND",
+    "era5_precipitation_occurrences_AMJ",
+    "era5_precipitation_occurrences_OND",
+    "era5_sf_JAS",
+    "era5_snowc_mean_trend_JAS",
+    "era5_snowfall_occurrences_JAS",
+    "era5_snowfall_occurrences_trend_JAS",
+    "era5_sshf_JFM",
+    "era5_sshf_OND",
+    "era5_t2m_avg_AMJ",
+    "era5_t2m_avg_JAS",
+    "era5_t2m_avg_JFM",
+    "era5_t2m_avg_trend_JFM",
+    "era5_t2m_avg_trend_OND",
+    "era5_t2m_max_AMJ",
+    "era5_t2m_max_JAS",
+    "era5_t2m_max_JFM",
+    "era5_t2m_max_OND",
+    "era5_t2m_max_trend_OND",
+    "era5_t2m_mean_AMJ",
+    "era5_t2m_mean_JAS",
+    "era5_t2m_mean_JFM",
+    "era5_t2m_mean_trend_JFM",
+    "era5_t2m_mean_trend_OND",
+    "era5_t2m_min_AMJ",
+    "era5_t2m_min_trend_JAS",
+    "era5_t2m_min_trend_JFM",
+    "era5_t2m_range_AMJ",
+    "era5_t2m_range_JAS",
+    "era5_t2m_skew_JAS",
+    "era5_thawing_days_AMJ",
+    "era5_thawing_days_JAS",
+    "era5_thawing_days_trend_JAS",
+    "era5_thawing_degree_days_AMJ",
+    "era5_thawing_degree_days_JAS",
+    "era5_tp_AMJ",
+    "era5_tp_JAS",
+]
+cluster_features = [
+    "cell_area",
+    "water_area",
+    "x",
+    "y",
+    "arcticdem_aspect_median",
+    "arcticdem_curvature_median",
+    "arcticdem_dem_median",
+    "arcticdem_slope_median",
+    "era5_effective_snow_depth_trend_AMJ",
+    "era5_effective_snow_depth_trend_JAS",
+    "era5_effective_snow_depth_trend_JFM",
+    "era5_effective_snow_depth_trend_OND",
+    "era5_freezing_days_AMJ",
+    "era5_freezing_days_JAS",
+    "era5_freezing_days_JFM",
+    "era5_freezing_days_OND",
+    "era5_freezing_days_trend_AMJ",
+    "era5_freezing_days_trend_OND",
+    "era5_freezing_degree_days_trend_AMJ",
+    "era5_freezing_degree_days_trend_JFM",
+    "era5_freezing_degree_days_trend_OND",
+    "era5_lblt_max_JAS",
+    "era5_lblt_max_trend_AMJ",
+    "era5_lblt_max_trend_JAS",
+    "era5_lblt_max_trend_JFM",
+    "era5_lblt_max_trend_OND",
+    "era5_precipitation_occurrences_AMJ",
+    "era5_precipitation_occurrences_JFM",
+    "era5_precipitation_occurrences_trend_AMJ",
+    "era5_precipitation_occurrences_trend_JAS",
+    "era5_precipitation_occurrences_trend_JFM",
+    "era5_precipitation_occurrences_trend_OND",
+    "era5_sf_AMJ",
+    "era5_sf_trend_AMJ",
+    "era5_sf_trend_JAS",
+    "era5_snowc_mean_JFM",
+    "era5_snowc_mean_trend_AMJ",
+    "era5_snowc_mean_trend_OND",
+    "era5_sshf_AMJ",
+    "era5_sshf_JFM",
+    "era5_sshf_trend_AMJ",
+    "era5_sshf_trend_JFM",
+    "era5_t2m_avg_trend_AMJ",
+    "era5_t2m_avg_trend_JAS",
+    "era5_t2m_max_AMJ",
+    "era5_t2m_max_trend_JAS",
+    "era5_t2m_max_trend_JFM",
+    "era5_t2m_max_trend_OND",
+    "era5_t2m_min_JFM",
+    "era5_t2m_min_trend_OND",
+    "era5_t2m_range_JFM",
+    "era5_t2m_range_trend_AMJ",
+    "era5_t2m_range_trend_JFM",
+    "era5_t2m_skew_trend_AMJ",
+    "era5_t2m_skew_trend_JAS",
+    "era5_t2m_skew_trend_JFM",
+    "era5_t2m_skew_trend_OND",
+]
 
 
 @cli.default
 def main(grid: Grid, target: TargetDataset, selection: Literal["none", "cluster", "univariate"] = "none"):
     """Feature Importance Experiment."""
+    experiment_name = EXPERIMENT_NAME
+    if selection != "none":
+        experiment_name += f"_{selection=}"
     levels = [3, 4, 5, 6] if grid == "hex" else [6, 7, 8, 9, 10]
-    if DEV:
-        levels = [3, 6] if grid == "hex" else [6, 10]
     for level in levels:
         print(f"Running feature importance experiment for {grid} grid at level {level}...")
+        members: list[L2SourceDataset] = ["ArcticDEM", "ERA5-shoulder"]
         dimension_filters = {"ArcticDEM": {"aggregations": ["median"]}}
-        if (grid == "hex" and level in [3, 4]) or (grid == "healpix" and level in [6, 7]):
+        is_era5_median_grid = (grid == "hex" and level in [3, 4]) or (grid == "healpix" and level in [6, 7])
+        is_era5_mean_grid = (grid == "hex" and level in [5]) or (grid == "healpix" and level in [8, 9])
+        if is_era5_median_grid:
             dimension_filters["ERA5-shoulder"] = {"aggregations": ["median"]}
+
+        feature_filters = {
+            "none": all_features,
+            "cluster": cluster_features,
+            "univariate": univariate_features,
+        }[selection]
+        if feature_filters is not None:
+            if not any(f.startswith("arcticdem") for f in feature_filters):
+                dimension_filters.pop("ArcticDEM", None)
+                members.remove("ArcticDEM")
+            if not any(f.startswith("era5") for f in feature_filters):
+                dimension_filters.pop("ERA5-shoulder", None)
+                members.remove("ERA5-shoulder")
+            elif is_era5_median_grid:
+
+                def _add_median(feature: str) -> str:
+                    if not feature.startswith("era5"):
+                        return feature
+                    return (
+                        feature.replace("JAS", "median_JAS")
+                        .replace("AMJ", "median_AMJ")
+                        .replace("JFM", "median_JFM")
+                        .replace("OND", "median_OND")
+                    )
+
+                feature_filters = [_add_median(f) for f in feature_filters]
+
+            elif is_era5_mean_grid:
+
+                def _add_mean(feature: str) -> str:
+                    if not feature.startswith("era5"):
+                        return feature
+                    return (
+                        feature.replace("JAS", "mean_JAS")
+                        .replace("AMJ", "mean_AMJ")
+                        .replace("JFM", "mean_JFM")
+                        .replace("OND", "mean_OND")
+                    )
+
+                feature_filters = [_add_mean(f) for f in feature_filters]
+
         dataset_ensemble = DatasetEnsemble(
-            grid=grid, level=level, members=["ArcticDEM", "ERA5-shoulder"], dimension_filters=dimension_filters
+            grid=grid,
+            level=level,
+            members=members,
+            dimension_filters=dimension_filters,
+            post_merge_filters=feature_filters,
         )
 
         for task in cast(list[Task], ["binary", "density"]):
@@ -42,11 +198,7 @@ def main(grid: Grid, target: TargetDataset, selection: Literal["none", "cluster"
 
             # AutoGluon
             time_limit = 30 * 60  # 30 minutes
-            if DEV:
-                time_limit = 2 * 60  # 2 minutes
             presets = "extreme"
-            if DEV:
-                presets = "medium"
             settings = AutoGluonRunSettings(
                 time_limit=time_limit,
                 presets=presets,
@@ -54,7 +206,7 @@ def main(grid: Grid, target: TargetDataset, selection: Literal["none", "cluster"
                 task=task,
                 target=target,
             )
-            train_autogluon(dataset_ensemble, settings, experiment=EXPERIMENT_NAME)
+            train_autogluon(dataset_ensemble, settings, experiment=experiment_name)
 
             # HPOCV
             splitter = "stratified_shuffle" if task == "binary" else "kfold"
@@ -69,10 +221,7 @@ def main(grid: Grid, target: TargetDataset, selection: Literal["none", "cluster"
                     "rf": 100,  # RF is slow, so we reduce the number of iterations
                     "knn": 40,  # kNN hpspace is small, so we reduce the number of iterations
                 }[model]
-                if DEV:
-                    n_iter = 3
                 scaler = "standard" if model in ["espa", "knn"] else "none"
-                normalize = scaler != "none"
                 settings = HPOCVRunSettings(
                     n_iter=n_iter,
                     task=task,
@@ -80,13 +229,13 @@ def main(grid: Grid, target: TargetDataset, selection: Literal["none", "cluster"
                     splitter=splitter,
                     model=model,
                     scaler=scaler,
-                    normalize=normalize,
+                    normalize=False,
                 )
-                hpsearch_cv(dataset_ensemble, settings, experiment=EXPERIMENT_NAME)
+                hpsearch_cv(dataset_ensemble, settings, experiment=experiment_name)
 
     stopwatch.summary()
     times = stopwatch.export()
-    times.to_parquet(RESULTS_DIR / EXPERIMENT_NAME / f"training_times_{target}_{grid}.parquet")
+    times.to_parquet(RESULTS_DIR / experiment_name / f"training_times_{target}_{grid}.parquet")
     print("Done.")
 
 

src/entropice/experiments/feature_importance_alphaearth.py

@@ -15,7 +15,7 @@ from entropice.utils.types import Grid, Model, TargetDataset, Task
 
 cli = cyclopts.App("entropice-feature-importance")
 
-EXPERIMENT_NAME = "feature_importance_era5-shoulder_arcticdem-v2"
+EXPERIMENT_NAME = "feature_importance_alphaearth"
 
 
 @cli.default

src/entropice/ingest/__init__.py

@@ -8,7 +8,3 @@ external sources into the Entropice system:
 - arcticdem: Terrain data from ArcticDEM
 - alphaearth: Satellite image embeddings from AlphaEarth/Google Earth Engine
 """
-
-from . import alphaearth, arcticdem, darts, era5
-
-__all__ = ["alphaearth", "arcticdem", "darts", "era5"]

src/entropice/ml/__init__.py

@@ -6,7 +6,3 @@ This package contains modules for machine learning workflows:
 - training: Model training with eSPA, XGBoost, Random Forest, KNN
 - inference: Batch prediction pipeline for trained classifiers
 """
-
-from . import autogluon, dataset, hpsearchcv, inference
-
-__all__ = ["autogluon", "dataset", "hpsearchcv", "inference", "inference"]

src/entropice/ml/dataset.py

@@ -298,6 +298,7 @@ class DatasetEnsemble:
     # ?: We can't use L2SourceDataset as types here because cyclopts can't handle Literals as dict keys
     dimension_filters: dict[str, dict[str, list]] = field(default_factory=dict)
     variable_filters: dict[str, list[str]] = field(default_factory=dict)
+    post_merge_filters: list[str] | None = None
 
     def __post_init__(self):
         # Validate filters
@@ -325,6 +326,15 @@ class DatasetEnsemble:
             valid = {"x", "y", "cell_area", "land_area", "water_area", "land_ratio"}
             assert len(filtered - valid) == 0
 
+        if self.post_merge_filters is not None:
+            assert len(self.post_merge_filters) >= 1, "Post-merge filters must be a list with one or more entries."
+            assert "geometry" not in self.post_merge_filters, "Filtering on 'geometry' is not supported."
+            assert "cell_id" not in self.post_merge_filters, "Filtering on 'cell_id' is not supported."
+            print(
+                "Warning: Post-merge filters are not checked for validity!"
+                " Please ensure that they are valid columns in the final merged dataset."
+            )
+
     def __hash__(self):
         return int(self.id(), 16)
 
@@ -349,10 +359,20 @@ class DatasetEnsemble:
     @cache
     def read_grid(self) -> gpd.GeoDataFrame:
         """Load the grid dataframe and enrich it with lat-lon information."""
+        # Define required columns that must always be included
+        required_columns = ["cell_id", "geometry"]
+
+        # Determine columns to load based on variable filters
         if "Grid" in self.variable_filters:
-            columns_to_load = self.variable_filters["Grid"] + ["cell_id", "geometry"]
+            columns_to_load = list(set(self.variable_filters["Grid"] + required_columns))
         else:
-            columns_to_load = ["cell_id", "geometry", "cell_area", "land_area", "water_area", "land_ratio", "x", "y"]
+            default_grid_columns = ["cell_area", "land_area", "water_area", "land_ratio", "x", "y"]
+            columns_to_load = required_columns + default_grid_columns
+
+        # Apply post-merge filters if specified
+        if self.post_merge_filters is not None:
+            allowed_columns = set(self.post_merge_filters + required_columns)
+            columns_to_load = list(set(columns_to_load) & allowed_columns)
 
         # Reading the data takes for the largest grids ~1.7s
         gridfile = entropice.utils.paths.get_grid_file(self.grid, self.level)
@@ -557,6 +577,10 @@ class DatasetEnsemble:
                     raise NotImplementedError(f"Member {member} not implemented.")
         with stopwatch("Joining datasets"):
             dataset = dataset.join(member_dfs)
+
+        if self.post_merge_filters is not None:
+            dataset = dataset[list(set(self.post_merge_filters) & set(dataset.columns))]
+
         print(f"Prepared dataset with {len(dataset)} samples and {len(dataset.columns)} features.")
 
         if cache_mode in ["read", "overwrite"]:

src/entropice/ml/hpsearchcv.py

@@ -266,6 +266,7 @@ def _compute_shap_explanation(model: Model, best_estimator: Pipeline, training_d
                 feature_names=training_data.feature_names,
                 output_names=training_data.target_labels,
             )
+            explain_kwargs = {}
         case "rf":
             masker = shap.maskers.Independent(data=training_data.X.as_numpy().train)
             explainer = TreeExplainer(
@@ -273,8 +274,10 @@ def _compute_shap_explanation(model: Model, best_estimator: Pipeline, training_d
                 data=masker,
                 feature_names=training_data.feature_names,
             )
+            explain_kwargs = {"check_additivity": False}  # Additivity does not hold for RF, see
         case "xgboost":
             explainer = TreeExplainer(best_estimator.named_steps["model"], feature_names=training_data.feature_names)
+            explain_kwargs = {"check_additivity": False}  # Additivity does not hold for XGBoost, see
         case _:
             raise ValueError(f"Unknown model: {model}")
 
@@ -288,7 +291,7 @@ def _compute_shap_explanation(model: Model, best_estimator: Pipeline, training_d
         samples = best_estimator.named_steps["scaler"].transform(samples)
     if "normalizer" in best_estimator.named_steps:
         samples = best_estimator.named_steps["normalizer"].transform(samples)
-    explanation = explainer(samples)
+    explanation = explainer(samples, **explain_kwargs)
     return explanation
 
 

src/entropice/spatial/__init__.py

@@ -7,7 +7,3 @@ This package contains modules for spatial data processing and grid-based operati
 - watermask: Ocean masking utilities
 - xvec: Extended vector operations for xarray
 """
-
-from . import aggregators, grids, watermask, xvec
-
-__all__ = ["aggregators", "grids", "watermask", "xvec"]

src/entropice/utils/__init__.py

@@ -5,7 +5,3 @@ This package contains utility modules used across the Entropice system:
 - paths: Centralized path management and configuration
 - codecs: Custom codecs for data serialization
 """
-
-from . import codecs, paths
-
-__all__ = ["codecs", "paths"]

src/entropice/utils/training.py

@@ -149,9 +149,44 @@ class Training:
         else:
             return None
 
+    def to_record(self) -> dict[str, Any]:
+        """Convert the training run to a dictionary record for easy creation of DataFrames."""
+        train_metrics = {f"train_{k}": v for k, v in self.get_metrics_from_split("train").items()}
+        test_metrics = {f"test_{k}": v for k, v in self.get_metrics_from_split("test").items()}
+        complete_metrics = {f"complete_{k}": v for k, v in self.get_metrics_from_split("complete").items()}
+        return {
+            "path": self.path,
+            "dataset": f"{self.dataset.grid}-{self.dataset.level}{self.dataset.members}",
+            "grid": self.dataset.grid,
+            "level": self.dataset.level,
+            "members": self.dataset.members,
+            "method": type(self.method).__name__,
+            "task": self.task,
+            "target": self.target,
+            "model_type": self.model_type,
+            "run": self,
+            **train_metrics,
+            **test_metrics,
+            **complete_metrics,
+        }
+
     def get_metrics_from_split(self, split: Literal["train", "test", "complete"]) -> dict[str, float]:
         """Get a dictionary of metric names and values for the specified split."""
-        return self.metrics[self.metrics["split"] == split].set_index("metric")["score"].to_dict()  # ty:ignore[invalid-return-type]
+        metrics_df = self.metrics[self.metrics["split"] == split].copy()
+        # If regression task and HPOCV: remove all "neg_" from the metric names
+        # If regression task and AutoML: negate all pot. neg metrics to be consistent with HPOCV
+        if self.task not in ["binary", "count_regimes", "density_regimes"]:
+            if self.method_type == "HPOCV":
+                metrics_df["metric"] = metrics_df["metric"].str.replace("neg_", "", regex=False)
+            elif self.method_type == "AutoML":
+                neg_metrics = [
+                    "root_mean_squared_error",
+                    "mean_squared_error",
+                    "mean_absolute_error",
+                    "median_absolute_error",
+                ]
+                metrics_df.loc[metrics_df["metric"].isin(neg_metrics), "score"] *= -1
+        return metrics_df.set_index("metric")["score"].to_dict()  # ty:ignore[invalid-return-type]
 
     def save(self):
         """Save the training results to the specified path."""
@@ -221,6 +256,10 @@ class Training:
         target = config["target"]
         model_type = config["model_type"]
 
+        # Legacy support for old config files
+        if "add_lonlat" in config["dataset"].keys():
+            config["dataset"].pop("add_lonlat")
+
         dataset = DatasetEnsemble(**config["dataset"])
 
         method_type = config["method_type"]