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"pydantic>=2.12.5,<3", + "pydantic>=2.12.5,<3", "nbformat>=5.10.4,<6", "fastcluster>=1.3.0,<2", ] [project.scripts] diff --git a/scripts/01darts.sh b/scripts/01darts.sh index 88da9bb..33dd366 100644 --- a/scripts/01darts.sh +++ b/scripts/01darts.sh @@ -1,6 +1,33 @@ #! /bin/bash +# Check if running inside the pixi environment +which darts >/dev/null 2>&1 +if [ $? -ne 0 ]; then + echo "This script must be run inside the pixi environment." + exit 1 +fi + # pixi shell +darts extract-darts-v2 --grid hex --level 3 +darts extract-darts-v2 --grid hex --level 4 +darts extract-darts-v2 --grid hex --level 5 +darts extract-darts-v2 --grid hex --level 6 +darts extract-darts-v2 --grid healpix --level 6 +darts extract-darts-v2 --grid healpix --level 7 +darts extract-darts-v2 --grid healpix --level 8 +darts extract-darts-v2 --grid healpix --level 9 +darts extract-darts-v2 --grid healpix --level 10 + +darts extract-darts-v2-aggregated --grid hex --level 3 +darts extract-darts-v2-aggregated --grid hex --level 4 +darts extract-darts-v2-aggregated --grid hex --level 5 +darts extract-darts-v2-aggregated --grid hex --level 6 +darts extract-darts-v2-aggregated --grid healpix --level 6 +darts extract-darts-v2-aggregated --grid healpix --level 7 +darts extract-darts-v2-aggregated --grid healpix --level 8 +darts extract-darts-v2-aggregated --grid healpix --level 9 +darts extract-darts-v2-aggregated --grid healpix --level 10 +exit 0 darts extract-darts-v1 --grid hex --level 3 darts extract-darts-v1 --grid hex --level 4 darts extract-darts-v1 --grid hex --level 5 @@ -22,7 +49,6 @@ darts extract-darts-v1-aggregated --grid healpix --level 8 darts extract-darts-v1-aggregated --grid healpix --level 9 darts extract-darts-v1-aggregated --grid healpix --level 10 - darts extract-darts-mllabels --grid hex --level 3 darts extract-darts-mllabels --grid hex --level 4 darts extract-darts-mllabels --grid hex --level 5 diff --git a/src/entropice/dashboard/app.py b/src/entropice/dashboard/app.py index 26503e6..7578674 100644 --- a/src/entropice/dashboard/app.py +++ b/src/entropice/dashboard/app.py @@ -16,7 +16,6 @@ import streamlit as st from entropice.dashboard.views.dataset_page import render_dataset_page from entropice.dashboard.views.experiment_analysis_page import render_experiment_analysis_page from entropice.dashboard.views.inference_page import render_inference_page -from entropice.dashboard.views.model_state_page import render_model_state_page from entropice.dashboard.views.overview_page import render_overview_page from entropice.dashboard.views.training_analysis_page import render_training_analysis_page @@ -30,14 +29,13 @@ def main(): data_page = st.Page(render_dataset_page, title="Dataset", icon="📊") training_analysis_page = st.Page(render_training_analysis_page, title="Training Results Analysis", icon="🦾") experiment_analysis_page = st.Page(render_experiment_analysis_page, title="Experiment Analysis", icon="🔬") - model_state_page = st.Page(render_model_state_page, title="Model State", icon="🧮") inference_page = st.Page(render_inference_page, title="Inference", icon="🗺️") pg = st.navigation( { "Overview": [overview_page], "Data": [data_page], - "Experiments": [training_analysis_page, experiment_analysis_page, model_state_page], + "Experiments": [training_analysis_page, experiment_analysis_page], "Inference": [inference_page], } ) diff --git a/src/entropice/dashboard/plots/experiment_comparison.py b/src/entropice/dashboard/plots/experiment_comparison.py index a2577e7..6a87948 100644 --- a/src/entropice/dashboard/plots/experiment_comparison.py +++ b/src/entropice/dashboard/plots/experiment_comparison.py @@ -24,7 +24,7 @@ def create_grid_level_comparison_plot( Args: results_df: DataFrame with experiment results including grid, level, model, and metrics metric: Metric to compare (e.g., 'f1', 'accuracy', 'r2') - split: Data split to show ('train', 'test', or 'combined') + split: Data split to show ('train', 'test', or 'complete') Returns: Plotly figure showing performance across grid levels @@ -82,7 +82,7 @@ def create_grid_level_comparison_plot( "autogluon": "star", } - # Add a combined column for hover information + # Add a complete column for hover information results_df["model_display"] = results_df["model"].str.upper() # Create box plot without individual points first diff --git a/src/entropice/dashboard/plots/inference.py b/src/entropice/dashboard/plots/inference.py deleted file mode 100644 index 26d1adc..0000000 --- a/src/entropice/dashboard/plots/inference.py +++ /dev/null @@ -1,493 +0,0 @@ -"""Plotting functions for inference result visualizations.""" - -import geopandas as gpd -import pandas as pd -import plotly.graph_objects as go -import pydeck as pdk -import streamlit as st - -from entropice.dashboard.utils.colors import get_palette -from entropice.dashboard.utils.geometry import fix_hex_geometry -from entropice.dashboard.utils.loaders import TrainingResult -from entropice.utils.types import Task - -# Canonical orderings imported from the ML pipeline -# Binary labels are defined inline in dataset.py: {False: "No RTS", True: "RTS"} -# Count/Density labels are defined in the bin_values function -BINARY_LABELS = ["No RTS", "RTS"] -COUNT_LABELS = ["None", "Very Few", "Few", "Several", "Many", "Very Many"] -DENSITY_LABELS = ["Empty", "Very Sparse", "Sparse", "Moderate", "Dense", "Very Dense"] - -CLASS_ORDERINGS: dict[Task | str, list[str]] = { - "binary": BINARY_LABELS, - "count_regimes": COUNT_LABELS, - "density_regimes": DENSITY_LABELS, - # Legacy aliases (deprecated) - "count": COUNT_LABELS, - "density": DENSITY_LABELS, -} - - -def get_ordered_classes(task: Task | str, available_classes: list[str] | None = None) -> list[str]: - """Get properly ordered class labels for a given task. - - This uses the same canonical ordering as defined in the ML dataset module, - ensuring consistency between training and inference visualizations. - - Args: - task: Task type ('binary', 'count_regimes', 'density_regimes', 'count', 'density'). - available_classes: Optional list of available classes to filter and order. - If None, returns all canonical classes for the task. - - Returns: - List of class labels in proper order. - - Examples: - >>> get_ordered_classes("binary") - ['No RTS', 'RTS'] - >>> get_ordered_classes("count_regimes", ["None", "Few", "Several"]) - ['None', 'Few', 'Several'] - - """ - canonical_order = CLASS_ORDERINGS[task] - - if available_classes is None: - return canonical_order - - # Filter canonical order to only include available classes, preserving order - return [cls for cls in canonical_order if cls in available_classes] - - -def sort_class_series(series: pd.Series, task: Task | str) -> pd.Series: - """Sort a pandas Series with class labels according to canonical ordering. - - Args: - series: Pandas Series with class labels as index. - task: Task type ('binary', 'count_regimes', 'density_regimes', 'count', 'density'). - - Returns: - Sorted Series with classes in canonical order. - - """ - available_classes = series.index.tolist() - ordered_classes = get_ordered_classes(task, available_classes) - - # Reindex to get proper order - return series.reindex(ordered_classes) - - -def render_inference_statistics(predictions_gdf: gpd.GeoDataFrame, task: str): - """Render summary statistics about inference results. - - Args: - predictions_gdf: GeoDataFrame with predictions. - task: Task type ('binary', 'count', 'density'). - - """ - st.subheader("📊 Inference Summary") - - # Get class distribution - class_counts = predictions_gdf["predicted_class"].value_counts() - - # Create metrics layout - if task == "binary": - col1, col2, col3 = st.columns(3) - - with col1: - st.metric("Total Predictions", f"{len(predictions_gdf):,}") - - with col2: - rts_count = class_counts.get("RTS", 0) - rts_pct = rts_count / len(predictions_gdf) * 100 if len(predictions_gdf) > 0 else 0 - st.metric("RTS Predictions", f"{rts_count:,} ({rts_pct:.1f}%)") - - with col3: - no_rts_count = class_counts.get("No-RTS", 0) - no_rts_pct = no_rts_count / len(predictions_gdf) * 100 if len(predictions_gdf) > 0 else 0 - st.metric("No-RTS Predictions", f"{no_rts_count:,} ({no_rts_pct:.1f}%)") - else: - col1, col2, col3 = st.columns(3) - - with col1: - st.metric("Total Predictions", f"{len(predictions_gdf):,}") - - with col2: - st.metric("Unique Classes", len(class_counts)) - - with col3: - most_common = class_counts.index[0] if len(class_counts) > 0 else "N/A" - st.metric("Most Common Class", most_common) - - -def render_class_distribution_histogram(predictions_gdf: gpd.GeoDataFrame, task: str): - """Render histogram of predicted class distribution. - - Args: - predictions_gdf: GeoDataFrame with predictions. - task: Task type ('binary', 'count_regimes', 'density_regimes', 'count', 'density'). - - """ - st.subheader("📊 Predicted Class Distribution") - - # Get class counts and order them properly - class_counts = predictions_gdf["predicted_class"].value_counts() - class_counts = sort_class_series(class_counts, task) - - # Get colors based on task - categories = class_counts.index.tolist() - colors = get_palette(task, len(categories)) - - # Create bar chart - fig = go.Figure() - - fig.add_trace( - go.Bar( - x=categories, - y=class_counts.values, - marker_color=colors, - opacity=0.9, - text=class_counts.to_numpy(), - textposition="outside", - textfont={"size": 12}, - hovertemplate="%{x}
Count: %{y:,}
Percentage: %{customdata:.1f}%", - customdata=class_counts.to_numpy() / len(predictions_gdf) * 100, - ) - ) - - fig.update_layout( - height=400, - margin={"l": 20, "r": 20, "t": 40, "b": 20}, - showlegend=False, - xaxis_title="Predicted Class", - yaxis_title="Count", - xaxis={"tickangle": -45 if len(categories) > 3 else 0}, - ) - - st.plotly_chart(fig, width="stretch") - - # Show percentages in a table - with st.expander("📋 Detailed Class Distribution", expanded=False): - distribution_df = pd.DataFrame( - { - "Class": categories, - "Count": class_counts.to_numpy(), - "Percentage": (class_counts.to_numpy() / len(predictions_gdf) * 100).round(2), - } - ) - st.dataframe(distribution_df, hide_index=True, width="stretch") - - -def render_spatial_distribution_stats(predictions_gdf: gpd.GeoDataFrame): - """Render spatial statistics about predictions. - - Args: - predictions_gdf: GeoDataFrame with predictions. - - """ - st.subheader("🌍 Spatial Coverage") - - # Calculate spatial extent - bounds = predictions_gdf.to_crs("EPSG:4326").total_bounds - - col1, col2, col3, col4 = st.columns(4) - - with col1: - st.metric("Min Latitude", f"{bounds[1]:.2f}°") - - with col2: - st.metric("Max Latitude", f"{bounds[3]:.2f}°") - - with col3: - st.metric("Min Longitude", f"{bounds[0]:.2f}°") - - with col4: - st.metric("Max Longitude", f"{bounds[2]:.2f}°") - - # Calculate total area if cell_area is available - if "cell_area" in predictions_gdf.columns: - total_area = predictions_gdf["cell_area"].sum() - st.info(f"📏 **Total Area Covered:** {total_area:,.0f} km²") - - -def _prepare_geojson_features(display_gdf_wgs84: gpd.GeoDataFrame) -> list: - """Convert GeoDataFrame to GeoJSON features for pydeck. - - Args: - display_gdf_wgs84: GeoDataFrame in WGS84 projection with required columns. - - Returns: - List of GeoJSON feature dictionaries. - - """ - geojson_data = [] - for _, row in display_gdf_wgs84.iterrows(): - feature = { - "type": "Feature", - "geometry": row["geometry"].__geo_interface__, - "properties": { - "cell_id": str(row["cell_id"]), - "predicted_class": str(row["predicted_class"]), - "fill_color": row["fill_color"], - "elevation": float(row["elevation"]), - }, - } - geojson_data.append(feature) - return geojson_data - - -@st.fragment -def render_inference_map(result: TrainingResult): - """Render 3D pydeck map showing inference results with interactive controls. - - This is a Streamlit fragment that reruns independently when users interact with the - visualization controls (color mode and opacity), without re-running the entire page. - - Args: - result: TrainingResult object containing prediction data. - - """ - st.subheader("🗺️ Inference Results Map") - - # Load predictions - preds_gdf = gpd.read_parquet(result.path / "predicted_probabilities.parquet") - - # Get settings - task = result.settings.task - grid = result.settings.grid - - # Create controls in columns - col1, col2, col3 = st.columns([2, 2, 1]) - - with col1: - # Get unique classes for filtering (properly ordered) - all_classes = get_ordered_classes(task, preds_gdf["predicted_class"].unique().tolist()) - filter_options = ["All Classes", *all_classes] - - selected_filter = st.selectbox( - "Filter by Predicted Class", - options=filter_options, - key="inference_map_filter", - ) - - with col2: - use_elevation = st.checkbox( - "Enable 3D Elevation", - value=True, - help="Show predictions with elevation (requires count/density for meaningful height)", - key="inference_map_elevation", - ) - - with col3: - opacity = st.slider( - "Opacity", - min_value=0.1, - max_value=1.0, - value=0.7, - step=0.1, - key="inference_map_opacity", - ) - - # Filter data if needed - if selected_filter != "All Classes": - display_gdf = preds_gdf[preds_gdf["predicted_class"] == selected_filter].copy() - else: - display_gdf = preds_gdf.copy() - - if len(display_gdf) == 0: - st.warning(f"No predictions found for filter: {selected_filter}") - return - - st.info(f"Displaying {len(display_gdf):,} out of {len(preds_gdf):,} total predictions") - - # Convert to WGS84 for pydeck - display_gdf_wgs84 = display_gdf.to_crs("EPSG:4326") - - # Fix antimeridian issues for hex grids - if grid == "hex": - display_gdf_wgs84["geometry"] = display_gdf_wgs84["geometry"].apply(fix_hex_geometry) - - # Assign colors based on predicted class (using canonical ordering) - # Get all possible classes for this task to ensure consistent colors - canonical_classes = get_ordered_classes(task) - colors_palette = get_palette(task, len(canonical_classes)) - - # Create color mapping for canonical classes - color_map = {cls: colors_palette[i] for i, cls in enumerate(canonical_classes)} - - # Convert hex colors to RGB - def hex_to_rgb(hex_color): - hex_color = hex_color.lstrip("#") - return [int(hex_color[i : i + 2], 16) for i in (0, 2, 4)] - - display_gdf_wgs84["fill_color"] = display_gdf_wgs84["predicted_class"].map( - {cls: hex_to_rgb(color) for cls, color in color_map.items()} - ) - - # Add elevation based on class encoding (for ordered classes) - if use_elevation and len(all_classes) > 1: - # Create a normalized elevation based on class order - class_to_elevation = {cls: i / (len(all_classes) - 1) for i, cls in enumerate(all_classes)} - display_gdf_wgs84["elevation"] = display_gdf_wgs84["predicted_class"].map(class_to_elevation) - else: - display_gdf_wgs84["elevation"] = 0.0 - - # Convert to GeoJSON format - geojson_data = _prepare_geojson_features(display_gdf_wgs84) - - # Create pydeck layer - layer = pdk.Layer( - "GeoJsonLayer", - geojson_data, - opacity=opacity, - stroked=True, - filled=True, - extruded=use_elevation, - 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 use_elevation else 0, - elevation_scale=500000, # Scale to 500km height - pickable=True, - ) - - # Set initial view state (centered on the Arctic) - view_state = pdk.ViewState( - latitude=70, - longitude=0, - zoom=2 if not use_elevation else 1.5, - pitch=0 if not use_elevation else 45, - ) - - # Create deck - deck = pdk.Deck( - layers=[layer], - initial_view_state=view_state, - tooltip={ - "html": "Cell ID: {cell_id}
Predicted Class: {predicted_class}", - "style": {"backgroundColor": "steelblue", "color": "white"}, - }, - map_style="https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json", - ) - - # Render the map - st.pydeck_chart(deck) - - # Show info about 3D visualization - if use_elevation: - st.info("💡 3D elevation represents class order. Rotate the map by holding Ctrl/Cmd and dragging.") - - # Add legend - with st.expander("Legend", expanded=True): - st.markdown("**Predicted Classes:**") - - for cls in all_classes: - color_hex = color_map[cls] - count = len(display_gdf[display_gdf["predicted_class"] == cls]) - total_count = len(preds_gdf[preds_gdf["predicted_class"] == cls]) - percentage = total_count / len(preds_gdf) * 100 if len(preds_gdf) > 0 else 0 - - # Show if currently displayed or total count - if selected_filter == "All Classes": - count_str = f"{count:,} ({percentage:.1f}%)" - else: - count_str = f"{count:,} displayed / {total_count:,} total ({percentage:.1f}%)" - - st.markdown( - f'
' - f'
' - f"{cls}: {count_str}
", - unsafe_allow_html=True, - ) - - if use_elevation and len(all_classes) > 1: - st.markdown("---") - st.markdown("**Elevation (3D):**") - st.markdown(f"Height represents class order: {all_classes[0]} (low) → {all_classes[-1]} (high)") - - -def render_class_comparison(predictions_gdf: gpd.GeoDataFrame, task: str): - """Render comparison plots between different predicted classes. - - Args: - predictions_gdf: GeoDataFrame with predictions. - task: Task type ('binary', 'count_regimes', 'density_regimes', 'count', 'density'). - - """ - st.subheader("🔍 Class Comparison") - - # Get class distribution and order properly - class_counts = predictions_gdf["predicted_class"].value_counts() - class_counts = sort_class_series(class_counts, task) - - if len(class_counts) < 2: - st.info("Need at least 2 classes for comparison.") - return - - # Create pie chart - col1, col2 = st.columns(2) - - with col1: - st.markdown("**Class Proportions") - - # Get colors matching canonical order - canonical_classes = get_ordered_classes(task) - all_colors = get_palette(task, len(canonical_classes)) - color_map = {cls: all_colors[i] for i, cls in enumerate(canonical_classes)} - - # Extract colors for available classes in proper order - colors = [color_map[cls] for cls in class_counts.index] - - fig = go.Figure( - data=[ - go.Pie( - labels=class_counts.index, - values=class_counts.values, - marker_colors=colors, - textinfo="label+percent", - textposition="auto", - hovertemplate="%{label}
Count: %{value:,}
Percentage: %{percent}", - ) - ] - ) - - fig.update_layout( - height=400, - margin={"l": 20, "r": 20, "t": 20, "b": 20}, - showlegend=True, - ) - - st.plotly_chart(fig, width="stretch") - - with col2: - st.markdown("**Cumulative Distribution") - - # Create cumulative distribution - sorted_counts = class_counts.sort_values(ascending=False) - cumulative = sorted_counts.cumsum() - cumulative_pct = cumulative / cumulative.iloc[-1] * 100 - - fig = go.Figure() - - fig.add_trace( - go.Scatter( - x=list(range(len(cumulative))), - y=cumulative_pct.to_numpy(), - mode="lines+markers", - line={"color": colors[0], "width": 3}, - marker={"size": 8}, - customdata=sorted_counts.index, - hovertemplate="%{customdata}
Cumulative: %{y:.1f}%", - ) - ) - - fig.update_layout( - height=400, - margin={"l": 20, "r": 20, "t": 20, "b": 20}, - xaxis_title="Class Rank", - yaxis_title="Cumulative Percentage", - yaxis={"range": [0, 105]}, - ) - - st.plotly_chart(fig, width="stretch") diff --git a/src/entropice/dashboard/plots/model_state.py b/src/entropice/dashboard/plots/model_state.py deleted file mode 100644 index 86d1465..0000000 --- a/src/entropice/dashboard/plots/model_state.py +++ /dev/null @@ -1,1024 +0,0 @@ -"""Plotting functions for model state visualization.""" - -import altair as alt -import pandas as pd -import xarray as xr - - -def plot_top_features(model_state: xr.Dataset, top_n: int = 10) -> alt.Chart: - """Plot the top N most important features based on feature weights. - - Args: - model_state: The xarray Dataset containing the model state. - top_n: Number of top features to display. - - Returns: - Altair chart showing the top features by importance. - - """ - # Extract feature weights - feature_weights = model_state["feature_weights"].to_pandas() - - # Sort by absolute weight and take top N - top_features = feature_weights.abs().nlargest(top_n).sort_values(ascending=True) - - # Create DataFrame for plotting with original (signed) weights - plot_data = pd.DataFrame( - { - "feature": top_features.index, - "weight": feature_weights.loc[top_features.index].to_numpy(), - "abs_weight": top_features.to_numpy(), - } - ) - - # Create horizontal bar chart - chart = ( - alt.Chart(plot_data) - .mark_bar() - .encode( - y=alt.Y("feature:N", title="Feature", sort="-x", axis=alt.Axis(labelLimit=300)), - x=alt.X("weight:Q", title="Feature Weight (scaled by number of features)"), - color=alt.condition( - alt.datum.weight > 0, - alt.value("steelblue"), # Positive weights - alt.value("coral"), # Negative weights - ), - tooltip=[ - alt.Tooltip("feature:N", title="Feature"), - alt.Tooltip("weight:Q", format=".4f", title="Weight"), - alt.Tooltip("abs_weight:Q", format=".4f", title="Absolute Weight"), - ], - ) - .properties( - width=600, - height=400, - title=f"Top {top_n} Most Important Features", - ) - ) - - return chart - - -def plot_embedding_heatmap(embedding_array: xr.DataArray) -> alt.Chart: - """Create a heatmap showing embedding feature weights across bands and years. - - Args: - embedding_array: DataArray with dimensions (agg, band, year) containing feature weights. - - Returns: - Altair chart showing the heatmap. - - """ - # Filter out "count" aggregation if present - if "agg" in embedding_array.dims: - agg_values = embedding_array.coords["agg"].values - non_count_aggs = [agg for agg in agg_values if agg != "count"] - if non_count_aggs: - embedding_array = embedding_array.sel(agg=non_count_aggs) - - # Convert to DataFrame for plotting - df = embedding_array.to_dataframe(name="weight").reset_index() - - # Create faceted heatmap - chart = ( - alt.Chart(df) - .mark_rect() - .encode( - x=alt.X("year:O", title="Year"), - y=alt.Y( - "band:O", - title="Band", - sort=alt.SortField(field="band", order="ascending"), - ), - color=alt.Color( - "weight:Q", - scale=alt.Scale(scheme="blues"), - title="Weight", - ), - tooltip=[ - alt.Tooltip("agg:N", title="Aggregation"), - alt.Tooltip("band:N", title="Band"), - alt.Tooltip("year:O", title="Year"), - alt.Tooltip("weight:Q", format=".4f", title="Weight"), - ], - ) - .properties(width=200, height=200) - .facet(facet=alt.Facet("agg:N", title="Aggregation"), columns=11) - ) - - return chart - - -def plot_embedding_aggregation_summary( - embedding_array: xr.DataArray, -) -> tuple[alt.Chart, alt.Chart, alt.Chart]: - """Create bar charts summarizing embedding weights by aggregation, band, and year. - - Args: - embedding_array: DataArray with dimensions (agg, band, year) containing feature weights. - - Returns: - Tuple of three Altair charts (by_agg, by_band, by_year). - - """ - # Filter out "count" aggregation if present - if "agg" in embedding_array.dims: - agg_values = embedding_array.coords["agg"].values - non_count_aggs = [agg for agg in agg_values if agg != "count"] - if non_count_aggs: - embedding_array = embedding_array.sel(agg=non_count_aggs) - - # Aggregate by different dimensions - by_agg = embedding_array.mean(dim=["band", "year"]).to_pandas().abs() - by_band = embedding_array.mean(dim=["agg", "year"]).to_pandas().abs() - by_year = embedding_array.mean(dim=["agg", "band"]).to_pandas().abs() - - # Create DataFrames, handling potential MultiIndex - df_agg = pd.DataFrame({"dimension": by_agg.index.astype(str), "mean_abs_weight": by_agg.values}) - df_band = pd.DataFrame({"dimension": by_band.index.astype(str), "mean_abs_weight": by_band.values}) - df_year = pd.DataFrame({"dimension": by_year.index.astype(str), "mean_abs_weight": by_year.values}) - - # Sort by weight - df_agg = df_agg.sort_values("mean_abs_weight", ascending=True) - df_band = df_band.sort_values("mean_abs_weight", ascending=True) - df_year = df_year.sort_values("mean_abs_weight", ascending=True) - - # Create charts with different colors - chart_agg = ( - alt.Chart(df_agg) - .mark_bar() - .encode( - y=alt.Y("dimension:N", title="Aggregation", sort="-x"), - x=alt.X("mean_abs_weight:Q", title="Mean Absolute Weight"), - color=alt.Color( - "mean_abs_weight:Q", - scale=alt.Scale(scheme="blues"), - legend=None, - ), - tooltip=[ - alt.Tooltip("dimension:N", title="Aggregation"), - alt.Tooltip("mean_abs_weight:Q", format=".4f", title="Mean Abs Weight"), - ], - ) - .properties(width=250, height=200, title="By Aggregation") - ) - - chart_band = ( - alt.Chart(df_band) - .mark_bar() - .encode( - y=alt.Y("dimension:N", title="Band", sort="-x"), - x=alt.X("mean_abs_weight:Q", title="Mean Absolute Weight"), - color=alt.Color( - "mean_abs_weight:Q", - scale=alt.Scale(scheme="greens"), - legend=None, - ), - tooltip=[ - alt.Tooltip("dimension:N", title="Band"), - alt.Tooltip("mean_abs_weight:Q", format=".4f", title="Mean Abs Weight"), - ], - ) - .properties(width=250, height=200, title="By Band") - ) - - chart_year = ( - alt.Chart(df_year) - .mark_bar() - .encode( - y=alt.Y("dimension:O", title="Year", sort="-x"), - x=alt.X("mean_abs_weight:Q", title="Mean Absolute Weight"), - color=alt.Color( - "mean_abs_weight:Q", - scale=alt.Scale(scheme="oranges"), - legend=None, - ), - tooltip=[ - alt.Tooltip("dimension:O", title="Year"), - alt.Tooltip("mean_abs_weight:Q", format=".4f", title="Mean Abs Weight"), - ], - ) - .properties(width=250, height=200, title="By Year") - ) - - return chart_agg, chart_band, chart_year - - -def plot_era5_heatmap(era5_array: xr.DataArray) -> alt.Chart: - """Create a heatmap showing ERA5 feature weights across variables and time/season. - - Args: - era5_array: DataArray with dimensions (variable, time) or (variable, season, year) containing feature weights. - - Returns: - Altair chart showing the heatmap. - - """ - # Convert to DataFrame for plotting - df = era5_array.to_dataframe(name="weight").reset_index() - - # Determine if this is seasonal/shoulder data (has 'season' dimension) - has_season = "season" in df.columns - - if has_season: - # For seasonal/shoulder: create faceted heatmap by season - chart = ( - alt.Chart(df) - .mark_rect() - .encode( - x=alt.X("year:N", title="Year", sort=None), - y=alt.Y("variable:N", title="Variable", sort="-color"), - color=alt.Color( - "weight:Q", - scale=alt.Scale(scheme="redblue", domainMid=0), - title="Weight", - ), - tooltip=[ - alt.Tooltip("variable:N", title="Variable"), - alt.Tooltip("season:N", title="Season"), - alt.Tooltip("year:N", title="Year"), - alt.Tooltip("weight:Q", format=".4f", title="Weight"), - ], - ) - .properties(width=200, height=300) - .facet(facet=alt.Facet("season:N", title="Season"), columns=4) - ) - else: - # For yearly: simple heatmap - chart = ( - alt.Chart(df) - .mark_rect() - .encode( - x=alt.X("time:N", title="Time", sort=None), - y=alt.Y("variable:N", title="Variable", sort="-color"), - color=alt.Color( - "weight:Q", - scale=alt.Scale(scheme="redblue", domainMid=0), - title="Weight", - ), - tooltip=[ - alt.Tooltip("variable:N", title="Variable"), - alt.Tooltip("time:N", title="Time"), - alt.Tooltip("weight:Q", format=".4f", title="Weight"), - ], - ) - .properties( - height=400, - title="ERA5 Feature Weights Heatmap", - ) - ) - - return chart - - -def plot_era5_time_heatmap(era5_array: xr.DataArray) -> alt.Chart | None: - """Create a heatmap showing ERA5 feature weights by variable and year (averaging over season). - - This is specifically for seasonal/shoulder data to show temporal trends. - - Args: - era5_array: DataArray with dimensions (variable, season, year, agg) containing feature weights. - - Returns: - Altair chart showing the variable-year heatmap. - - """ - # Check if this has season dimension - if "season" not in era5_array.dims: - return None - - # Average over season and agg to get (variable, year) - dims_to_avg = [d for d in era5_array.dims if d not in ["variable", "year"]] - by_time = era5_array.mean(dim=dims_to_avg) - - # Convert to DataFrame for plotting - df = by_time.to_dataframe(name="weight").reset_index() - - # Create heatmap - chart = ( - alt.Chart(df) - .mark_rect() - .encode( - x=alt.X("year:N", title="Year", sort=None), - y=alt.Y("variable:N", title="Variable", sort="-color"), - color=alt.Color( - "weight:Q", - scale=alt.Scale(scheme="redblue", domainMid=0), - title="Weight", - ), - tooltip=[ - alt.Tooltip("variable:N", title="Variable"), - alt.Tooltip("year:N", title="Year"), - alt.Tooltip("weight:Q", format=".4f", title="Avg Weight"), - ], - ) - .properties( - height=400, - title="ERA5 Feature Weights by Time (averaged over seasons)", - ) - ) - - return chart - - -def plot_era5_summary(era5_array: xr.DataArray) -> tuple[alt.Chart, ...]: - """Create bar charts summarizing ERA5 weights by variable and time/season. - - Args: - era5_array: DataArray with dimensions (variable, time) or (variable, season, year, agg) containing feature weights. - - Returns: - Tuple of Altair charts: - - For yearly data: (by_variable, by_time) - - For seasonal/shoulder data: (by_variable, by_season, by_year) - - """ - # Determine which dimensions to average over - dims_to_avg_for_var = [d for d in era5_array.dims if d != "variable"] - - # Check if this is seasonal/shoulder data - has_season = "season" in era5_array.dims - - # Aggregate by variable (average over all other dimensions) - by_variable = era5_array.mean(dim=dims_to_avg_for_var).to_pandas().abs() - - if has_season: - # For seasonal/shoulder: aggregate by season and year - dims_to_avg_for_season = [d for d in era5_array.dims if d != "season"] - by_season = era5_array.mean(dim=dims_to_avg_for_season).to_pandas().abs() - - dims_to_avg_for_year = [d for d in era5_array.dims if d != "year"] - by_year = era5_array.mean(dim=dims_to_avg_for_year).to_pandas().abs() - - # Create DataFrames - df_variable = pd.DataFrame( - { - "dimension": by_variable.index.astype(str), - "mean_abs_weight": by_variable.values, - } - ) - df_season = pd.DataFrame( - { - "dimension": by_season.index.astype(str), - "mean_abs_weight": by_season.values, - } - ) - df_year = pd.DataFrame({"dimension": by_year.index.astype(str), "mean_abs_weight": by_year.values}) - - # Sort by weight - df_variable = df_variable.sort_values("mean_abs_weight", ascending=True) - df_season = df_season.sort_values("mean_abs_weight", ascending=True) - df_year = df_year.sort_values("mean_abs_weight", ascending=True) - - chart_variable = ( - alt.Chart(df_variable) - .mark_bar() - .encode( - y=alt.Y( - "dimension:N", - title="Variable", - sort="-x", - axis=alt.Axis(labelLimit=300), - ), - x=alt.X("mean_abs_weight:Q", title="Mean Absolute Weight"), - color=alt.Color( - "mean_abs_weight:Q", - scale=alt.Scale(scheme="purples"), - legend=None, - ), - tooltip=[ - alt.Tooltip("dimension:N", title="Variable"), - alt.Tooltip("mean_abs_weight:Q", format=".4f", title="Mean Abs Weight"), - ], - ) - .properties(width=400, height=300, title="By Variable") - ) - - chart_season = ( - alt.Chart(df_season) - .mark_bar() - .encode( - y=alt.Y( - "dimension:N", - title="Season", - sort="-x", - axis=alt.Axis(labelLimit=200), - ), - x=alt.X("mean_abs_weight:Q", title="Mean Absolute Weight"), - color=alt.Color( - "mean_abs_weight:Q", - scale=alt.Scale(scheme="teals"), - legend=None, - ), - tooltip=[ - alt.Tooltip("dimension:N", title="Season"), - alt.Tooltip("mean_abs_weight:Q", format=".4f", title="Mean Abs Weight"), - ], - ) - .properties(width=400, height=300, title="By Season") - ) - - chart_year = ( - alt.Chart(df_year) - .mark_bar() - .encode( - y=alt.Y( - "dimension:O", - title="Year", - sort="-x", - axis=alt.Axis(labelLimit=200), - ), - x=alt.X("mean_abs_weight:Q", title="Mean Absolute Weight"), - color=alt.Color( - "mean_abs_weight:Q", - scale=alt.Scale(scheme="oranges"), - legend=None, - ), - tooltip=[ - alt.Tooltip("dimension:O", title="Year"), - alt.Tooltip("mean_abs_weight:Q", format=".4f", title="Mean Abs Weight"), - ], - ) - .properties(width=400, height=300, title="By Time") - ) - - return chart_variable, chart_season, chart_year - else: - # For yearly: aggregate by time - dims_to_avg_for_time = [d for d in era5_array.dims if d != "time"] - by_time = era5_array.mean(dim=dims_to_avg_for_time).to_pandas().abs() - - # Create DataFrames, handling potential MultiIndex - df_variable = pd.DataFrame( - { - "dimension": by_variable.index.astype(str), - "mean_abs_weight": by_variable.values, - } - ) - df_time = pd.DataFrame({"dimension": by_time.index.astype(str), "mean_abs_weight": by_time.values}) - - # Sort by weight - df_variable = df_variable.sort_values("mean_abs_weight", ascending=True) - df_time = df_time.sort_values("mean_abs_weight", ascending=True) - - # Create charts with different colors - chart_variable = ( - alt.Chart(df_variable) - .mark_bar() - .encode( - y=alt.Y( - "dimension:N", - title="Variable", - sort="-x", - axis=alt.Axis(labelLimit=300), - ), - x=alt.X("mean_abs_weight:Q", title="Mean Absolute Weight"), - color=alt.Color( - "mean_abs_weight:Q", - scale=alt.Scale(scheme="purples"), - legend=None, - ), - tooltip=[ - alt.Tooltip("dimension:N", title="Variable"), - alt.Tooltip("mean_abs_weight:Q", format=".4f", title="Mean Abs Weight"), - ], - ) - .properties(width=400, height=300, title="By Variable") - ) - - chart_time = ( - alt.Chart(df_time) - .mark_bar() - .encode( - y=alt.Y( - "dimension:N", - title="Time", - sort="-x", - axis=alt.Axis(labelLimit=200), - ), - x=alt.X("mean_abs_weight:Q", title="Mean Absolute Weight"), - color=alt.Color( - "mean_abs_weight:Q", - scale=alt.Scale(scheme="teals"), - legend=None, - ), - tooltip=[ - alt.Tooltip("dimension:N", title="Time"), - alt.Tooltip("mean_abs_weight:Q", format=".4f", title="Mean Abs Weight"), - ], - ) - .properties(width=400, height=300, title="By Time") - ) - - return chart_variable, chart_time - - -def plot_arcticdem_heatmap(arcticdem_array: xr.DataArray) -> alt.Chart: - """Create a heatmap showing ArcticDEM feature weights across variables and aggregations. - - Args: - arcticdem_array: DataArray with dimensions (variable, agg) containing feature weights. - - Returns: - Altair chart showing the heatmap. - - """ - # Convert to DataFrame for plotting - df = arcticdem_array.to_dataframe(name="weight").reset_index() - - # Create heatmap - chart = ( - alt.Chart(df) - .mark_rect() - .encode( - x=alt.X("agg:N", title="Aggregation", sort=None), - y=alt.Y("variable:N", title="Variable", sort="-color"), - color=alt.Color( - "weight:Q", - scale=alt.Scale(scheme="redblue", domainMid=0), - title="Weight", - ), - tooltip=[ - alt.Tooltip("variable:N", title="Variable"), - alt.Tooltip("agg:N", title="Aggregation"), - alt.Tooltip("weight:Q", format=".4f", title="Weight"), - ], - ) - .properties( - width=400, - height=300, - title="ArcticDEM Feature Weights Heatmap", - ) - ) - - return chart - - -def plot_arcticdem_summary( - arcticdem_array: xr.DataArray, -) -> tuple[alt.Chart, alt.Chart]: - """Create bar charts summarizing ArcticDEM weights by variable and aggregation. - - Args: - arcticdem_array: DataArray with dimensions (variable, agg) containing feature weights. - - Returns: - Tuple of two Altair charts (by_variable, by_agg). - - """ - # Aggregate by different dimensions - by_variable = arcticdem_array.mean(dim="agg").to_pandas().abs() - by_agg = arcticdem_array.mean(dim="variable").to_pandas().abs() - - # Create DataFrames, handling potential MultiIndex - df_variable = pd.DataFrame( - { - "dimension": by_variable.index.astype(str), - "mean_abs_weight": by_variable.values, - } - ) - df_agg = pd.DataFrame({"dimension": by_agg.index.astype(str), "mean_abs_weight": by_agg.values}) - - # Sort by weight - df_variable = df_variable.sort_values("mean_abs_weight", ascending=True) - df_agg = df_agg.sort_values("mean_abs_weight", ascending=True) - - # Create charts with different colors - chart_variable = ( - alt.Chart(df_variable) - .mark_bar() - .encode( - y=alt.Y( - "dimension:N", - title="Variable", - sort="-x", - axis=alt.Axis(labelLimit=300), - ), - x=alt.X("mean_abs_weight:Q", title="Mean Absolute Weight"), - color=alt.Color( - "mean_abs_weight:Q", - scale=alt.Scale(scheme="browns"), - legend=None, - ), - tooltip=[ - alt.Tooltip("dimension:N", title="Variable"), - alt.Tooltip("mean_abs_weight:Q", format=".4f", title="Mean Abs Weight"), - ], - ) - .properties(width=400, height=300, title="By Variable") - ) - - chart_agg = ( - alt.Chart(df_agg) - .mark_bar() - .encode( - y=alt.Y( - "dimension:N", - title="Aggregation", - sort="-x", - axis=alt.Axis(labelLimit=200), - ), - x=alt.X("mean_abs_weight:Q", title="Mean Absolute Weight"), - color=alt.Color( - "mean_abs_weight:Q", - scale=alt.Scale(scheme="reds"), - legend=None, - ), - tooltip=[ - alt.Tooltip("dimension:N", title="Aggregation"), - alt.Tooltip("mean_abs_weight:Q", format=".4f", title="Mean Abs Weight"), - ], - ) - .properties(width=400, height=300, title="By Aggregation") - ) - - return chart_variable, chart_agg - - -def plot_box_assignments(model_state: xr.Dataset) -> alt.Chart: - """Create a heatmap showing which boxes are assigned to which labels/classes. - - Args: - model_state: The xarray Dataset containing the model state with box_assignments. - - Returns: - Altair chart showing the box-to-label assignment heatmap. - - """ - # Extract box assignments - box_assignments = model_state["box_assignments"] - - # Convert to DataFrame for plotting - df = box_assignments.to_dataframe(name="assignment").reset_index() - - # Create heatmap - chart = ( - alt.Chart(df) - .mark_rect() - .encode( - x=alt.X("box:O", title="Box ID", axis=alt.Axis(labelAngle=0)), - y=alt.Y("class:N", title="Class Label"), - color=alt.Color( - "assignment:Q", - scale=alt.Scale(scheme="viridis"), - title="Assignment Strength", - ), - tooltip=[ - alt.Tooltip("class:N", title="Class"), - alt.Tooltip("box:O", title="Box"), - alt.Tooltip("assignment:Q", format=".4f", title="Assignment"), - ], - ) - .properties( - height=150, - title="Box-to-Label Assignments (Lambda Matrix)", - ) - ) - - return chart - - -def plot_box_assignment_bars(model_state: xr.Dataset, altair_colors: list[str]) -> alt.Chart: - """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. - - """ - # Extract box assignments - box_assignments = model_state["box_assignments"] - - # Convert to DataFrame - df = box_assignments.to_dataframe(name="assignment").reset_index() - - # For each box, find which class it's most strongly assigned to - box_to_class = df.groupby("box")["assignment"].idxmax() - primary_classes = df.loc[box_to_class, ["box", "class", "assignment"]].reset_index(drop=True) - - # 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", - sort=class_order, - axis=alt.Axis(labelAngle=-45), - ), - y=alt.Y("count:Q", title="Number of Boxes"), - 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"), - ], - ) - .properties( - width=600, - height=300, - title="Number of Boxes Assigned to Each Class (by Primary Assignment)", - ) - ) - - return chart - - -def plot_common_features(common_array: xr.DataArray) -> alt.Chart: - """Create a bar chart showing the weights of common features. - - Args: - common_array: DataArray with dimension (feature) containing feature weights. - - Returns: - Altair chart showing the common feature weights. - - """ - # Convert to DataFrame for plotting - df = common_array.to_dataframe(name="weight").reset_index() - - # Sort by absolute weight - df["abs_weight"] = df["weight"].abs() - df = df.sort_values("abs_weight", ascending=True) - - # Create bar chart - chart = ( - alt.Chart(df) - .mark_bar() - .encode( - y=alt.Y("feature:N", title="Feature", sort="-x"), - x=alt.X("weight:Q", title="Feature Weight (scaled by number of features)"), - color=alt.condition( - alt.datum.weight > 0, - alt.value("steelblue"), # Positive weights - alt.value("coral"), # Negative weights - ), - tooltip=[ - alt.Tooltip("feature:N", title="Feature"), - alt.Tooltip("weight:Q", format=".4f", title="Weight"), - alt.Tooltip("abs_weight:Q", format=".4f", title="Absolute Weight"), - ], - ) - .properties( - width=600, - height=300, - title="Common Feature Weights", - ) - ) - - return chart - - -# XGBoost-specific visualizations -def plot_xgboost_feature_importance( - model_state: xr.Dataset, importance_type: str = "gain", top_n: int = 20 -) -> alt.Chart: - """Plot XGBoost feature importance for a specific importance type. - - Args: - model_state: The xarray Dataset containing the XGBoost model state. - importance_type: Type of importance ('weight', 'gain', 'cover', 'total_gain', 'total_cover'). - top_n: Number of top features to display. - - Returns: - Altair chart showing the top features by importance. - - """ - # Get the importance array - importance_key = f"feature_importance_{importance_type}" - if importance_key not in model_state: - raise ValueError(f"Importance type '{importance_type}' not found in model state") - - importance = model_state[importance_key].to_pandas() - - # Sort and take top N - top_features = importance.nlargest(top_n).sort_values(ascending=True) - - # Create DataFrame for plotting - plot_data = pd.DataFrame({"feature": top_features.index, "importance": top_features.to_numpy()}) - - # Create bar chart - chart = ( - alt.Chart(plot_data) - .mark_bar() - .encode( - y=alt.Y("feature:N", title="Feature", sort="-x", axis=alt.Axis(labelLimit=300)), - x=alt.X( - "importance:Q", - title=f"{importance_type.replace('_', ' ').title()} Importance", - ), - color=alt.value("steelblue"), - tooltip=[ - alt.Tooltip("feature:N", title="Feature"), - alt.Tooltip("importance:Q", format=".4f", title="Importance"), - ], - ) - .properties( - width=600, - height=400, - title=f"Top {top_n} Features by {importance_type.replace('_', ' ').title()} Importance (XGBoost)", - ) - ) - - return chart - - -def plot_xgboost_importance_comparison(model_state: xr.Dataset, top_n: int = 15) -> alt.Chart: - """Compare different importance types for XGBoost side-by-side. - - Args: - model_state: The xarray Dataset containing the XGBoost model state. - top_n: Number of top features to display. - - Returns: - Altair chart comparing importance types. - - """ - # Collect all importance types - importance_types = ["weight", "gain", "cover"] - all_data = [] - - for imp_type in importance_types: - importance_key = f"feature_importance_{imp_type}" - if importance_key in model_state: - importance = model_state[importance_key].to_pandas() - # Get top features by this importance type - top_features = importance.nlargest(top_n) - for feature, value in top_features.items(): - all_data.append( - { - "feature": feature, - "importance": value, - "type": imp_type.replace("_", " ").title(), - } - ) - - df = pd.DataFrame(all_data) - - # Create faceted bar chart - chart = ( - alt.Chart(df) - .mark_bar() - .encode( - y=alt.Y("feature:N", title="Feature", sort="-x", axis=alt.Axis(labelLimit=200)), - x=alt.X("importance:Q", title="Importance Value"), - color=alt.Color("type:N", title="Importance Type", scale=alt.Scale(scheme="category10")), - tooltip=[ - alt.Tooltip("feature:N", title="Feature"), - alt.Tooltip("type:N", title="Type"), - alt.Tooltip("importance:Q", format=".4f", title="Importance"), - ], - ) - .properties(width=250, height=300) - .facet(facet=alt.Facet("type:N", title="Importance Type"), columns=3) - ) - - return chart - - -# Random Forest-specific visualizations -def plot_rf_feature_importance(model_state: xr.Dataset, top_n: int = 20) -> alt.Chart: - """Plot Random Forest feature importance (Gini importance). - - Args: - model_state: The xarray Dataset containing the Random Forest model state. - top_n: Number of top features to display. - - Returns: - Altair chart showing the top features by importance. - - """ - importance = model_state["feature_importance"].to_pandas() - - # Sort and take top N - top_features = importance.nlargest(top_n).sort_values(ascending=True) - - # Create DataFrame for plotting - plot_data = pd.DataFrame({"feature": top_features.index, "importance": top_features.to_numpy()}) - - # Create bar chart - chart = ( - alt.Chart(plot_data) - .mark_bar() - .encode( - y=alt.Y("feature:N", title="Feature", sort="-x", axis=alt.Axis(labelLimit=300)), - x=alt.X("importance:Q", title="Gini Importance"), - color=alt.value("forestgreen"), - tooltip=[ - alt.Tooltip("feature:N", title="Feature"), - alt.Tooltip("importance:Q", format=".4f", title="Importance"), - ], - ) - .properties( - width=600, - height=400, - title=f"Top {top_n} Features by Gini Importance (Random Forest)", - ) - ) - - return chart - - -def plot_rf_tree_statistics( - model_state: xr.Dataset, -) -> tuple[alt.Chart, alt.Chart, alt.Chart]: - """Plot Random Forest tree statistics. - - Args: - model_state: The xarray Dataset containing the Random Forest model state. - - Returns: - Tuple of three Altair charts (depth histogram, leaves histogram, nodes histogram). - - """ - # Extract tree statistics - depths = model_state["tree_depths"].to_pandas() - n_leaves = model_state["tree_n_leaves"].to_pandas() - n_nodes = model_state["tree_n_nodes"].to_pandas() - - # Create DataFrames - df_depths = pd.DataFrame({"value": depths.to_numpy()}) - df_leaves = pd.DataFrame({"value": n_leaves.to_numpy()}) - df_nodes = pd.DataFrame({"value": n_nodes.to_numpy()}) - - # Histogram for tree depths - chart_depths = ( - alt.Chart(df_depths) - .mark_bar() - .encode( - x=alt.X("value:Q", bin=alt.Bin(maxbins=20), title="Tree Depth"), - y=alt.Y("count()", title="Number of Trees"), - color=alt.value("steelblue"), - tooltip=[ - alt.Tooltip("count()", title="Count"), - alt.Tooltip("value:Q", bin=True, title="Depth Range"), - ], - ) - .properties(width=300, height=200, title="Distribution of Tree Depths") - ) - - # Histogram for number of leaves - chart_leaves = ( - alt.Chart(df_leaves) - .mark_bar() - .encode( - x=alt.X("value:Q", bin=alt.Bin(maxbins=20), title="Number of Leaves"), - y=alt.Y("count()", title="Number of Trees"), - color=alt.value("forestgreen"), - tooltip=[ - alt.Tooltip("count()", title="Count"), - alt.Tooltip("value:Q", bin=True, title="Leaves Range"), - ], - ) - .properties(width=300, height=200, title="Distribution of Leaf Counts") - ) - - # Histogram for number of nodes - chart_nodes = ( - alt.Chart(df_nodes) - .mark_bar() - .encode( - x=alt.X("value:Q", bin=alt.Bin(maxbins=20), title="Number of Nodes"), - y=alt.Y("count()", title="Number of Trees"), - color=alt.value("darkorange"), - tooltip=[ - alt.Tooltip("count()", title="Count"), - alt.Tooltip("value:Q", bin=True, title="Nodes Range"), - ], - ) - .properties(width=300, height=200, title="Distribution of Node Counts") - ) - - return chart_depths, chart_leaves, chart_nodes diff --git a/src/entropice/dashboard/plots/targets.py b/src/entropice/dashboard/plots/targets.py index 2948b00..3b7ca6d 100644 --- a/src/entropice/dashboard/plots/targets.py +++ b/src/entropice/dashboard/plots/targets.py @@ -220,7 +220,7 @@ def _add_regression_subplot( train_values = z_values[dataset.split == "train"] test_values = z_values[dataset.split == "test"] - # Determine bin edges based on combined data + # Determine bin edges based on complete data all_values = pd.concat([train_values, test_values]) # Use a reasonable number of bins n_bins = min(30, int(np.sqrt(len(all_values)))) diff --git a/src/entropice/dashboard/sections/cv_result.py b/src/entropice/dashboard/sections/cv_result.py index b1da3b2..1ecf5ed 100644 --- a/src/entropice/dashboard/sections/cv_result.py +++ b/src/entropice/dashboard/sections/cv_result.py @@ -19,25 +19,25 @@ def render_run_information(selected_result: TrainingResult, refit_metric): """ st.header("📋 Run Information") - grid_config = GridConfig.from_grid_level(f"{selected_result.settings.grid}{selected_result.settings.level}") # ty:ignore[invalid-argument-type] + grid_config = GridConfig.from_grid_level((selected_result.run.dataset.grid, selected_result.run.dataset.level)) col1, col2, col3, col4, col5 = st.columns(5) with col1: - st.metric("Task", selected_result.settings.task.capitalize()) + st.metric("Task", selected_result.run.task.capitalize()) with col2: - st.metric("Target", selected_result.settings.target.capitalize()) + st.metric("Target", selected_result.run.target.capitalize()) with col3: st.metric("Grid", grid_config.display_name) with col4: - st.metric("Model", selected_result.settings.model.upper()) + st.metric("Model", selected_result.run.model_type.upper()) with col5: - st.metric("Trials", len(selected_result.results)) + st.metric("Trials", selected_result.run.n_trials or "N/A") st.caption(f"**Refit Metric:** {format_metric_name(refit_metric)}") def _render_metrics(metrics: dict[str, float]): - """Render a set of metrics in a two-column layout. + """Render a set of metrics in a max-five-column layout. Args: metrics: Dictionary of metric names and their values. @@ -57,20 +57,25 @@ def render_metrics_section(selected_result: TrainingResult): selected_result: The selected TrainingResult object. """ + # Extract metrics for each split + test_metrics = selected_result.run.get_metrics_from_split("test") + train_metrics = selected_result.run.get_metrics_from_split("train") + complete_metrics = selected_result.run.get_metrics_from_split("complete") + # Test st.header("🎯 Test Set Performance") st.caption("Performance metrics on the held-out test set (best model from hyperparameter search)") - _render_metrics(selected_result.test_metrics) + _render_metrics(test_metrics) # Train st.header("🏋️‍♂️ Training Set Performance") st.caption("Performance metrics on the training set (best model from hyperparameter search)") - _render_metrics(selected_result.train_metrics) + _render_metrics(train_metrics) - # Combined / All + # Complete / All st.header("🧮 Overall Performance") st.caption("Overall performance metrics combining training and test sets") - _render_metrics(selected_result.combined_metrics) + _render_metrics(complete_metrics) @st.fragment @@ -84,7 +89,7 @@ def render_confusion_matrices(selected_result: TrainingResult): st.header("🎭 Confusion Matrices") # Check if this is a classification task - if selected_result.settings.task not in ["binary", "count_regimes", "density_regimes"]: + if selected_result.run.task not in ["binary", "count_regimes", "density_regimes"]: st.info( "📊 Confusion matrices are only available for classification tasks " "(binary, count_regimes, density_regimes)." @@ -93,11 +98,11 @@ def render_confusion_matrices(selected_result: TrainingResult): return # Check if confusion matrix data is available - if selected_result.confusion_matrix is None: + if selected_result.run.confusion_matrix is None: st.warning("⚠️ No confusion matrix data found for this training result.") return - cm = selected_result.confusion_matrix + cm = selected_result.run.confusion_matrix # Add normalization selection st.subheader("Display Options") @@ -131,11 +136,11 @@ def render_confusion_matrices(selected_result: TrainingResult): fig_train = plot_confusion_matrix(cm["train"], title="Training Set", normalize=normalize_mode) st.plotly_chart(fig_train, width="stretch") with cols[2]: - # Combined Confusion Matrix - st.subheader("Combined") + # Complete Confusion Matrix + st.subheader("Complete") st.caption("Train + Test sets") - fig_combined = plot_confusion_matrix(cm["combined"], title="Combined", normalize=normalize_mode) - st.plotly_chart(fig_combined, width="stretch") + fig_complete = plot_confusion_matrix(cm["complete"], title="Complete", normalize=normalize_mode) + st.plotly_chart(fig_complete, width="stretch") def render_cv_statistics_section(cv_stats: CVMetricStatistics, test_score: float): diff --git a/src/entropice/dashboard/sections/dataset_statistics.py b/src/entropice/dashboard/sections/dataset_statistics.py index b19305d..9113d1a 100644 --- a/src/entropice/dashboard/sections/dataset_statistics.py +++ b/src/entropice/dashboard/sections/dataset_statistics.py @@ -413,13 +413,13 @@ def _render_aggregation_selection( col_btn1, col_btn2, col_btn3, _ = st.columns([1, 1, 1, 3]) with col_btn1: - if st.button("✅ Select All", use_container_width=True): + if st.button("✅ Select All", width="content"): _set_all_aggregations(member_datasets, members_with_aggs, member_aggregations, selected=True) with col_btn2: - if st.button("📊 Median Only", use_container_width=True): + if st.button("📊 Median Only", width="content"): _set_median_only_aggregations(member_datasets, members_with_aggs, member_aggregations) with col_btn3: - if st.button("❌ Deselect All", use_container_width=True): + if st.button("❌ Deselect All", width="content"): _set_all_aggregations(member_datasets, members_with_aggs, member_aggregations, selected=False) # Render the form with checkboxes diff --git a/src/entropice/dashboard/sections/experiment_feature_importance.py b/src/entropice/dashboard/sections/experiment_feature_importance.py index 8e4f06a..dee1825 100644 --- a/src/entropice/dashboard/sections/experiment_feature_importance.py +++ b/src/entropice/dashboard/sections/experiment_feature_importance.py @@ -8,10 +8,7 @@ from entropice.dashboard.plots.experiment_comparison import ( create_feature_consistency_plot, create_feature_importance_by_grid_level, ) -from entropice.dashboard.utils.loaders import ( - AutogluonTrainingResult, - TrainingResult, -) +from entropice.dashboard.utils.loaders import TrainingResult def _extract_feature_importance_from_results( @@ -23,107 +20,25 @@ def _extract_feature_importance_from_results( training_results: List of TrainingResult objects Returns: - DataFrame with columns: feature, importance, model, grid, level, task, target + DataFrame with columns: feature, importance, stddev, model, grid, level, task, target, data_source, grid_level """ - records = [] - + fis = [] for tr in training_results: - # Load model state if available - model_state = tr.load_model_state() - if model_state is None: - continue + fi = tr.run.feature_importance.reset_index().rename(columns={"index": "feature"}) + fi["model"] = tr.run.model_type + fi["grid"] = tr.run.dataset.grid + fi["level"] = tr.run.dataset.level + fi["task"] = tr.run.task + fi["target"] = tr.run.target + fis.append(fi) - info = tr.display_info + fi = pd.concat(fis, ignore_index=True) + # Add data source categorization + fi["data_source"] = fi["feature"].apply(_categorize_feature) + fi["grid_level"] = fi["grid"] + "_" + fi["level"].astype(str) - # Extract feature importance based on available data - if "feature_importance" in model_state.data_vars: - # eSPA or similar models with direct feature importance - importance_data = model_state["feature_importance"] - for feature_idx, feature_name in enumerate(importance_data.coords["feature"].values): - importance_value = float(importance_data.isel(feature=feature_idx).values) - records.append( - { - "feature": str(feature_name), - "importance": importance_value, - "model": info.model, - "grid": info.grid, - "level": info.level, - "task": info.task, - "target": info.target, - } - ) - elif "gain" in model_state.data_vars: - # XGBoost-style feature importance - gain_data = model_state["gain"] - for feature_idx, feature_name in enumerate(gain_data.coords["feature"].values): - importance_value = float(gain_data.isel(feature=feature_idx).values) - records.append( - { - "feature": str(feature_name), - "importance": importance_value, - "model": info.model, - "grid": info.grid, - "level": info.level, - "task": info.task, - "target": info.target, - } - ) - elif "feature_importances_" in model_state.data_vars: - # Random Forest style - importance_data = model_state["feature_importances_"] - for feature_idx, feature_name in enumerate(importance_data.coords["feature"].values): - importance_value = float(importance_data.isel(feature=feature_idx).values) - records.append( - { - "feature": str(feature_name), - "importance": importance_value, - "model": info.model, - "grid": info.grid, - "level": info.level, - "task": info.task, - "target": info.target, - } - ) - - return pd.DataFrame(records) - - -def _extract_feature_importance_from_autogluon( - autogluon_results: list[AutogluonTrainingResult], -) -> pd.DataFrame: - """Extract feature importance from AutoGluon results. - - Args: - autogluon_results: List of AutogluonTrainingResult objects - - Returns: - DataFrame with columns: feature, importance, model, grid, level, task, target - - """ - records = [] - - for ag in autogluon_results: - if ag.feature_importance is None: - continue - - info = ag.display_info - - # AutoGluon feature importance is already a DataFrame with features as index - for feature_name, importance_value in ag.feature_importance["importance"].items(): - records.append( - { - "feature": str(feature_name), - "importance": float(importance_value), - "model": "autogluon", - "grid": info.grid, - "level": info.level, - "task": info.task, - "target": info.target, - } - ) - - return pd.DataFrame(records) + return fi def _categorize_feature(feature_name: str) -> str: @@ -138,46 +53,12 @@ def _categorize_feature(feature_name: str) -> str: return "General" -def _prepare_feature_importance_data( - training_results: list[TrainingResult], - autogluon_results: list[AutogluonTrainingResult], -) -> pd.DataFrame | None: - """Extract and prepare feature importance data. - - Args: - training_results: List of RandomSearchCV training results - autogluon_results: List of AutoGluon training results - - Returns: - DataFrame with feature importance data or None if no data available - - """ - fi_df_cv = _extract_feature_importance_from_results(training_results) - fi_df_ag = _extract_feature_importance_from_autogluon(autogluon_results) - - if fi_df_cv.empty and fi_df_ag.empty: - return None - - # Combine both - fi_df = pd.concat([fi_df_cv, fi_df_ag], ignore_index=True) - - # Add data source categorization - fi_df["data_source"] = fi_df["feature"].apply(_categorize_feature) - fi_df["grid_level"] = fi_df["grid"] + "_" + fi_df["level"].astype(str) - - return fi_df - - @st.fragment -def render_feature_importance_analysis( - training_results: list[TrainingResult], - autogluon_results: list[AutogluonTrainingResult], -): +def render_feature_importance_analysis(training_results: list[TrainingResult]): """Render feature importance analysis section. Args: training_results: List of RandomSearchCV training results - autogluon_results: List of AutoGluon training results """ st.header("🔍 Feature Importance Analysis") @@ -191,13 +72,13 @@ def render_feature_importance_analysis( # Extract feature importance with st.spinner("Extracting feature importance from training results..."): - fi_df = _prepare_feature_importance_data(training_results, autogluon_results) + fi = _extract_feature_importance_from_results(training_results) - if fi_df is None: + if fi is None: st.warning("No feature importance data available. Model state files may be missing.") return - st.success(f"Extracted feature importance from {len(fi_df)} feature-model combinations") + st.success(f"Extracted feature importance from {len(fi)} feature-model combinations") # Filters st.subheader("Filters") @@ -205,12 +86,12 @@ def render_feature_importance_analysis( with col1: # Task filter - available_tasks = ["All", *sorted(fi_df["task"].unique().tolist())] + available_tasks = ["All", *sorted(fi["task"].unique().tolist())] selected_task = st.selectbox("Task", options=available_tasks, index=0, key="fi_task_filter") with col2: # Target filter - available_targets = ["All", *sorted(fi_df["target"].unique().tolist())] + available_targets = ["All", *sorted(fi["target"].unique().tolist())] selected_target = st.selectbox("Target Dataset", options=available_targets, index=0, key="fi_target_filter") with col3: @@ -218,13 +99,12 @@ def render_feature_importance_analysis( top_n_features = st.number_input("Top N Features", min_value=5, max_value=50, value=15, key="top_n_features") # Apply filters - filtered_fi_df = fi_df.copy() + filtered_fi = fi.copy() if selected_task != "All": - filtered_fi_df = filtered_fi_df.loc[filtered_fi_df["task"] == selected_task] + filtered_fi = filtered_fi.loc[filtered_fi["task"] == selected_task] if selected_target != "All": - filtered_fi_df = filtered_fi_df.loc[filtered_fi_df["target"] == selected_target] - - if len(filtered_fi_df) == 0: + filtered_fi = filtered_fi.loc[filtered_fi["target"] == selected_target] + if len(filtered_fi) == 0: st.warning("No feature importance data available for the selected filters.") return @@ -232,17 +112,17 @@ def render_feature_importance_analysis( st.subheader("Top Features by Grid Level") try: - fig = create_feature_importance_by_grid_level(filtered_fi_df, top_n=top_n_features) + fig = create_feature_importance_by_grid_level(filtered_fi, top_n=top_n_features) st.plotly_chart(fig, width="stretch") except Exception as e: st.error(f"Could not create feature importance by grid level plot: {e}") # Show detailed breakdown in expander - grid_levels = sorted(filtered_fi_df["grid_level"].unique()) + grid_levels = sorted(filtered_fi["grid_level"].unique()) with st.expander("Show Detailed Breakdown by Grid Level", expanded=False): for grid_level in grid_levels: - grid_data = filtered_fi_df[filtered_fi_df["grid_level"] == grid_level] + grid_data = filtered_fi[filtered_fi["grid_level"] == grid_level] # Get top features for this grid level top_features_grid = ( @@ -271,7 +151,7 @@ def render_feature_importance_analysis( ) try: - fig = create_feature_consistency_plot(filtered_fi_df, top_n=top_n_features) + fig = create_feature_consistency_plot(filtered_fi, top_n=top_n_features) st.plotly_chart(fig, width="stretch") except Exception as e: st.error(f"Could not create feature consistency plot: {e}") @@ -280,7 +160,7 @@ def render_feature_importance_analysis( with st.expander("Show Detailed Statistics", expanded=False): # Get top features overall overall_top_features = ( - filtered_fi_df.groupby("feature")["importance"] + filtered_fi.groupby("feature")["importance"] .mean() .reset_index() .nlargest(top_n_features, "importance")["feature"] @@ -289,7 +169,7 @@ def render_feature_importance_analysis( # Calculate variance in importance across models for each feature feature_variance = ( - filtered_fi_df[filtered_fi_df["feature"].isin(overall_top_features)] + filtered_fi[filtered_fi["feature"].isin(overall_top_features)] .groupby("feature")["importance"] .agg(["mean", "std", "min", "max"]) .reset_index() @@ -299,7 +179,7 @@ def render_feature_importance_analysis( # Add data source feature_variance = feature_variance.merge( - filtered_fi_df[["feature", "data_source"]].drop_duplicates(), on="feature", how="left" + filtered_fi[["feature", "data_source"]].drop_duplicates(), on="feature", how="left" ) feature_variance.columns = ["Feature", "Mean", "Std Dev", "Min", "Max", "CV", "Data Source"] @@ -314,7 +194,7 @@ def render_feature_importance_analysis( st.subheader("Feature Importance by Data Source") try: - fig = create_data_source_importance_bars(filtered_fi_df) + fig = create_data_source_importance_bars(filtered_fi) st.plotly_chart(fig, width="stretch") except Exception as e: st.error(f"Could not create data source importance chart: {e}") @@ -322,9 +202,7 @@ def render_feature_importance_analysis( # Show detailed table in expander with st.expander("Show Data Source Statistics", expanded=False): # Aggregate importance by data source - source_importance = ( - filtered_fi_df.groupby("data_source")["importance"].agg(["sum", "mean", "count"]).reset_index() - ) + source_importance = filtered_fi.groupby("data_source")["importance"].agg(["sum", "mean", "count"]).reset_index() source_importance.columns = ["Data Source", "Total Importance", "Mean Importance", "Feature Count"] source_importance = source_importance.sort_values("Total Importance", ascending=False) diff --git a/src/entropice/dashboard/sections/experiment_grid_analysis.py b/src/entropice/dashboard/sections/experiment_grid_analysis.py index f83bdcc..cf8555c 100644 --- a/src/entropice/dashboard/sections/experiment_grid_analysis.py +++ b/src/entropice/dashboard/sections/experiment_grid_analysis.py @@ -39,7 +39,7 @@ def render_grid_level_analysis(summary_df: pd.DataFrame, available_metrics: list unique_tasks = summary_df["task"].unique() # Split selection - split = st.selectbox("Data Split", options=["test", "train", "combined"], index=0, key="grid_split") + split = st.selectbox("Data Split", options=["test", "train", "complete"], index=0, key="grid_split") # Create plots for each task for task in sorted(unique_tasks): diff --git a/src/entropice/dashboard/sections/experiment_inference_maps.py b/src/entropice/dashboard/sections/experiment_inference_maps.py index a2570f4..f10d7fc 100644 --- a/src/entropice/dashboard/sections/experiment_inference_maps.py +++ b/src/entropice/dashboard/sections/experiment_inference_maps.py @@ -34,12 +34,12 @@ def render_inference_maps_section( # Extract unique grid configurations from training results available_grid_configs = sorted( - {GridConfig.from_grid_level((tr.settings.grid, tr.settings.level)) for tr in training_results}, + {GridConfig.from_grid_level((tr.run.dataset.grid, tr.run.dataset.level)) for tr in training_results}, key=lambda gc: gc.sort_key, ) - available_tasks = sorted({tr.settings.task for tr in training_results}) - available_targets = sorted({tr.settings.target for tr in training_results}) - available_models = sorted({tr.settings.model for tr in training_results}) + available_tasks = sorted({tr.run.task for tr in training_results}) + available_targets = sorted({tr.run.target for tr in training_results}) + available_models = sorted({tr.run.model_type for tr in training_results}) # Create form for selecting parameters with st.form("inference_map_form"): @@ -92,11 +92,11 @@ def render_inference_maps_section( filtered_results = [ tr for tr in training_results - if tr.settings.grid == selected_grid - and tr.settings.level == selected_level - and tr.settings.task == selected_task - and tr.settings.target in selected_targets - and tr.settings.model in selected_models + if tr.run.dataset.grid == selected_grid + and tr.run.dataset.level == selected_level + and tr.run.task == selected_task + and tr.run.target in selected_targets + and tr.run.model_type in selected_models ] if not filtered_results: diff --git a/src/entropice/dashboard/sections/experiment_model_comparison.py b/src/entropice/dashboard/sections/experiment_model_comparison.py index f1290d2..ccb3e68 100644 --- a/src/entropice/dashboard/sections/experiment_model_comparison.py +++ b/src/entropice/dashboard/sections/experiment_model_comparison.py @@ -34,7 +34,7 @@ def render_model_comparison(summary_df: pd.DataFrame, available_metrics: list[st } # Split selection - split = st.selectbox("Data Split", options=["test", "train", "combined"], index=0, key="model_split") + split = st.selectbox("Data Split", options=["test", "train", "complete"], index=0, key="model_split") # Get unique tasks unique_tasks = summary_df["task"].unique() diff --git a/src/entropice/dashboard/sections/experiment_overview.py b/src/entropice/dashboard/sections/experiment_overview.py index 5b0e1ba..5928035 100644 --- a/src/entropice/dashboard/sections/experiment_overview.py +++ b/src/entropice/dashboard/sections/experiment_overview.py @@ -3,11 +3,7 @@ import pandas as pd import streamlit as st -from entropice.dashboard.utils.loaders import ( - AutogluonTrainingResult, - TrainingResult, - get_available_experiments, -) +from entropice.dashboard.utils.loaders import TrainingResult, get_available_experiments def render_experiment_sidebar() -> str | None: @@ -38,7 +34,6 @@ def render_experiment_sidebar() -> str | None: def render_experiment_overview( experiment_name: str, training_results: list[TrainingResult], - autogluon_results: list[AutogluonTrainingResult], summary_df: pd.DataFrame, ): """Render experiment overview section. @@ -46,7 +41,6 @@ def render_experiment_overview( Args: experiment_name: Name of the experiment training_results: List of RandomSearchCV training results - autogluon_results: List of AutoGluon training results summary_df: Summary DataFrame with all results """ @@ -56,13 +50,15 @@ def render_experiment_overview( col1, col2, col3, col4 = st.columns(4) with col1: - st.metric("Total Training Runs", len(training_results) + len(autogluon_results)) + st.metric("Total Training Runs", len(training_results)) with col2: - st.metric("RandomSearchCV Runs", len(training_results)) + hpsearch_runs = [tr for tr in training_results if tr.run.method_type == "HPOCV"] + st.metric("RandomSearchCV Runs", len(hpsearch_runs)) with col3: - st.metric("AutoGluon Runs", len(autogluon_results)) + autogluon_runs = [tr for tr in training_results if tr.run.method_type == "AutoML"] + st.metric("AutoGluon Runs", len(autogluon_runs)) with col4: unique_configs = summary_df[["grid", "level", "task", "target"]].drop_duplicates() diff --git a/src/entropice/dashboard/sections/experiment_results.py b/src/entropice/dashboard/sections/experiment_results.py index ecfb448..16e1d39 100644 --- a/src/entropice/dashboard/sections/experiment_results.py +++ b/src/entropice/dashboard/sections/experiment_results.py @@ -2,16 +2,15 @@ from datetime import datetime -import pandas as pd import streamlit as st -from entropice.dashboard.utils.loaders import AutogluonTrainingResult, TrainingResult +from entropice.dashboard.utils.loaders import TrainingResult from entropice.utils.types import ( GridConfig, ) -def render_training_results_summary(training_results: list[TrainingResult | AutogluonTrainingResult]): +def render_training_results_summary(training_results: list[TrainingResult]): """Render summary metrics for training results.""" st.header("📊 Training Results Summary") col1, col2, col3, col4 = st.columns(4) @@ -24,7 +23,7 @@ def render_training_results_summary(training_results: list[TrainingResult | Auto st.metric("Total Runs", len(training_results)) with col3: - models = {tr.settings.model for tr in training_results if hasattr(tr.settings, "model")} + models = {tr.run.model_type for tr in training_results} st.metric("Model Types", len(models)) with col4: @@ -34,15 +33,15 @@ def render_training_results_summary(training_results: list[TrainingResult | Auto @st.fragment -def render_experiment_results(training_results: list[TrainingResult | AutogluonTrainingResult]): # noqa: C901 +def render_experiment_results(training_results: list[TrainingResult]): # noqa: C901 """Render detailed experiment results table and expandable details.""" st.header("🎯 Experiment Results") # Filters experiments = sorted({tr.experiment for tr in training_results if tr.experiment}) - tasks = sorted({tr.settings.task for tr in training_results}) - models = sorted({tr.settings.model if isinstance(tr, TrainingResult) else "autogluon" for tr in training_results}) - grids = sorted({f"{tr.settings.grid}-{tr.settings.level}" for tr in training_results}) + tasks = sorted({tr.run.task for tr in training_results}) + models = sorted({tr.run.model_type for tr in training_results}) + grids = sorted({f"{tr.run.dataset.grid}-{tr.run.dataset.level}" for tr in training_results}) # Create filter columns filter_cols = st.columns(4) @@ -83,30 +82,21 @@ def render_experiment_results(training_results: list[TrainingResult | AutogluonT ) # Apply filters - filtered_results = training_results + filtered_results: list[TrainingResult] = training_results if selected_experiment != "All": filtered_results = [tr for tr in filtered_results if tr.experiment == selected_experiment] if selected_task != "All": - filtered_results = [tr for tr in filtered_results if tr.settings.task == selected_task] - if selected_model != "All" and selected_model != "autogluon": - filtered_results = [ - tr for tr in filtered_results if isinstance(tr, TrainingResult) and tr.settings.model == selected_model - ] - elif selected_model == "autogluon": - filtered_results = [tr for tr in filtered_results if isinstance(tr, AutogluonTrainingResult)] + filtered_results = [tr for tr in filtered_results if tr.run.task == selected_task] + if selected_model != "All": + filtered_results = [tr for tr in filtered_results if tr.run.model_type == selected_model] if selected_grid != "All": - filtered_results = [tr for tr in filtered_results if f"{tr.settings.grid}-{tr.settings.level}" == selected_grid] + filtered_results = [ + tr for tr in filtered_results if f"{tr.run.dataset.grid}-{tr.run.dataset.level}" == selected_grid + ] st.subheader("Results Table") - summary_df = TrainingResult.to_dataframe([tr for tr in filtered_results if isinstance(tr, TrainingResult)]) - autogluon_df = AutogluonTrainingResult.to_dataframe( - [tr for tr in filtered_results if isinstance(tr, AutogluonTrainingResult)] - ) - if len(summary_df) == 0: - summary_df = autogluon_df - elif len(autogluon_df) > 0: - summary_df = pd.concat([summary_df, autogluon_df], ignore_index=True) + summary_df = TrainingResult.to_dataframe(filtered_results) # Display with color coding for best scores st.dataframe( @@ -120,25 +110,22 @@ def render_experiment_results(training_results: list[TrainingResult | AutogluonT for tr in filtered_results: tr_info = tr.display_info display_name = tr_info.get_display_name("model_first") - model = "autogluon" if isinstance(tr, AutogluonTrainingResult) else tr.settings.model - cv_splits = tr.settings.cv_splits if hasattr(tr.settings, "cv_splits") else "N/A" + model = tr.run.model_type with st.expander(display_name): col1, col2 = st.columns([1, 2]) with col1: - grid_config = GridConfig.from_grid_level((tr.settings.grid, tr.settings.level)) + grid_config = GridConfig.from_grid_level((tr.run.dataset.grid, tr.run.dataset.level)) st.write( "**Configuration:**\n" f"- **Experiment:** {tr.experiment}\n" - f"- **Task:** {tr.settings.task}\n" - f"- **Target:** {tr.settings.target}\n" + f"- **Task:** {tr.run.task}\n" + f"- **Target:** {tr.run.target}\n" f"- **Model:** {model}\n" f"- **Grid:** {grid_config.display_name}\n" f"- **Created At:** {tr_info.timestamp.strftime('%Y-%m-%d %H:%M')}\n" - f"- **Temporal Mode:** {tr.settings.temporal_mode}\n" - f"- **Members:** {', '.join(tr.settings.members)}\n" - f"- **CV Splits:** {cv_splits}\n" - f"- **Classes:** {tr.settings.classes}\n" + f"- **Temporal Mode:** {tr.run.dataset.temporal_mode}\n" + f"- **Members:** {', '.join(tr.run.dataset.members)}\n" ) file_str = "\n**Files:**\n" @@ -155,29 +142,32 @@ def render_experiment_results(training_results: list[TrainingResult | AutogluonT file_str += f"- 📄 `{file.name}`\n" st.write(file_str) with col2: - if isinstance(tr, AutogluonTrainingResult): + if tr.run.method_type == "AutoML": st.write("**Leaderboard:**") - st.dataframe(tr.leaderboard, width="stretch", hide_index=True) + st.dataframe(tr.run.leaderboard, width="stretch", hide_index=True) else: st.write("**CV Score Summary:**") # Extract all test scores - metric_df = tr.get_metric_dataframe() + metric_df = tr.get_cv_results_dataframe() if metric_df is not None: st.dataframe(metric_df, width="stretch", hide_index=True) else: st.write("No test scores found in results.") + cv_results = tr.run.cv_results + assert cv_results is not None, "CV results should not be None for non-AutoML runs" + # Show parameter space explored - if "initial_K" in tr.results.columns: # Common parameter + if "initial_K" in cv_results.columns: # Common parameter st.write("\n**Parameter Ranges Explored:**") for param in ["initial_K", "eps_cl", "eps_e"]: - if param in tr.results.columns: - min_val = tr.results[param].min() - max_val = tr.results[param].max() - unique_vals = tr.results[param].nunique() + if param in cv_results.columns: + min_val = cv_results[param].min() + max_val = cv_results[param].max() + unique_vals = cv_results[param].nunique() st.write(f"- **{param}:** {unique_vals} values ({min_val:.2e} to {max_val:.2e})") st.write("**CV Results DataFrame:**") - st.dataframe(tr.results, width="stretch", hide_index=True) + st.dataframe(cv_results, width="stretch", hide_index=True) st.write(f"\n**Path:** `{tr.path}`") diff --git a/src/entropice/dashboard/sections/hparam_space.py b/src/entropice/dashboard/sections/hparam_space.py index 194202a..4013131 100644 --- a/src/entropice/dashboard/sections/hparam_space.py +++ b/src/entropice/dashboard/sections/hparam_space.py @@ -1,5 +1,6 @@ """Hyperparameter Space Visualization Section.""" +import pandas as pd import streamlit as st from entropice.dashboard.plots.hyperparameter_space import ( @@ -11,9 +12,10 @@ from entropice.dashboard.plots.hyperparameter_space import ( ) from entropice.dashboard.utils.formatters import format_metric_name from entropice.dashboard.utils.loaders import TrainingResult +from entropice.utils.training import HPOCV -def _render_performance_summary(results, refit_metric: str): +def _render_performance_summary(results: pd.DataFrame, refit_metric: str): """Render performance summary subsection.""" best_idx = results[f"mean_test_{refit_metric}"].idxmax() best_row = results.loc[best_idx] @@ -47,7 +49,7 @@ def _render_performance_summary(results, refit_metric: str): st.metric(param_name, formatted_value) -def _render_parameter_distributions(results, param_grid: dict | None): +def _render_parameter_distributions(results: pd.DataFrame, param_grid: dict | None): """Render parameter distributions subsection.""" st.subheader("Parameter Distributions") st.caption("Distribution of hyperparameter values explored during random search") @@ -73,7 +75,7 @@ def _render_parameter_distributions(results, param_grid: dict | None): st.plotly_chart(param_charts[param_name], width="stretch") -def _render_score_evolution(results, selected_metric: str): +def _render_score_evolution(results: pd.DataFrame, selected_metric: str): """Render score evolution subsection.""" st.subheader("Score Evolution Over Iterations") st.caption(f"How {format_metric_name(selected_metric)} evolved during the random search") @@ -85,7 +87,7 @@ def _render_score_evolution(results, selected_metric: str): st.warning(f"Score evolution not available for metric: {selected_metric}") -def _render_score_vs_parameters(results, selected_metric: str, param_grid: dict | None): +def _render_score_vs_parameters(results: pd.DataFrame, selected_metric: str, param_grid: dict | None): """Render score vs parameters subsection.""" st.subheader("Score vs Individual Parameters") st.caption(f"Relationship between {format_metric_name(selected_metric)} and each hyperparameter") @@ -110,7 +112,7 @@ def _render_score_vs_parameters(results, selected_metric: str, param_grid: dict st.plotly_chart(score_vs_param_charts[param_name], width="stretch") -def _render_parameter_correlations(results, selected_metric: str): +def _render_parameter_correlations(results: pd.DataFrame, selected_metric: str): """Render parameter correlations subsection.""" st.subheader("Parameter-Score Correlations") st.caption(f"Correlation between numeric parameters and {format_metric_name(selected_metric)}") @@ -122,7 +124,7 @@ def _render_parameter_correlations(results, selected_metric: str): st.info("No numeric parameters found for correlation analysis.") -def _render_parameter_interactions(results, selected_metric: str, param_grid: dict | None): +def _render_parameter_interactions(results: pd.DataFrame, selected_metric: str, param_grid: dict | None): """Render parameter interactions subsection.""" st.subheader("Parameter Interactions") st.caption(f"Interaction between parameter pairs and their effect on {format_metric_name(selected_metric)}") @@ -154,19 +156,24 @@ def render_hparam_space_section(selected_result: TrainingResult, selected_metric """ st.header("🧩 Hyperparameter Space Exploration") + if selected_result.run.method_type != "HPOCV": + st.warning("Hyperparameter space visualization is only available for RandomSearchCV runs.") + return + assert isinstance(selected_result.run.method, HPOCV), "Expected method to be HPOCV for HPOCV runs" - results = selected_result.results + cv_results = selected_result.run.cv_results + assert cv_results is not None, "CV results should not be None for HPOCV runs" refit_metric = selected_result._get_best_metric_name() - param_grid = selected_result.settings.param_grid + param_grid = selected_result.run.method.hpconfig - _render_performance_summary(results, refit_metric) + _render_performance_summary(cv_results, refit_metric) - _render_parameter_distributions(results, param_grid) + _render_parameter_distributions(cv_results, param_grid) - _render_score_evolution(results, selected_metric) + _render_score_evolution(cv_results, selected_metric) - _render_score_vs_parameters(results, selected_metric, param_grid) + _render_score_vs_parameters(cv_results, selected_metric, param_grid) - _render_parameter_correlations(results, selected_metric) + _render_parameter_correlations(cv_results, selected_metric) - _render_parameter_interactions(results, selected_metric, param_grid) + _render_parameter_interactions(cv_results, selected_metric, param_grid) diff --git a/src/entropice/dashboard/sections/regression_analysis.py b/src/entropice/dashboard/sections/regression_analysis.py index c819d06..47c01bb 100644 --- a/src/entropice/dashboard/sections/regression_analysis.py +++ b/src/entropice/dashboard/sections/regression_analysis.py @@ -17,7 +17,7 @@ def render_regression_analysis(selected_result: TrainingResult): st.header("📊 Regression Analysis") # Check if this is a regression task - if selected_result.settings.task in ["binary", "count_regimes", "density_regimes"]: + if selected_result.run.task in ["binary", "count_regimes", "density_regimes"]: st.info("📈 Regression analysis is only available for regression tasks (count, density).") return @@ -30,19 +30,18 @@ def render_regression_analysis(selected_result: TrainingResult): # Create DatasetEnsemble from settings with st.spinner("Loading training data to get true values..."): ensemble = DatasetEnsemble( - grid=selected_result.settings.grid, - level=selected_result.settings.level, - members=selected_result.settings.members, - temporal_mode=selected_result.settings.temporal_mode, - dimension_filters=selected_result.settings.dimension_filters, - variable_filters=selected_result.settings.variable_filters, - add_lonlat=selected_result.settings.add_lonlat, + grid=selected_result.run.dataset.grid, + level=selected_result.run.dataset.level, + members=selected_result.run.dataset.members, + temporal_mode=selected_result.run.dataset.temporal_mode, + dimension_filters=selected_result.run.dataset.dimension_filters, + variable_filters=selected_result.run.dataset.variable_filters, ) # Create training set to get true values training_set = ensemble.create_training_set( - task=selected_result.settings.task, - target=selected_result.settings.target, + task=selected_result.run.task, + target=selected_result.run.target, device="cpu", cache_mode="read", ) @@ -59,7 +58,7 @@ def render_regression_analysis(selected_result: TrainingResult): merged = predictions_df.merge(true_values, on="cell_id", how="inner") merged["split"] = split_series.reindex(merged["cell_id"]).values - # Get train, test, and combined data + # Get train, test, and complete data train_data = merged[merged["split"] == "train"] test_data = merged[merged["split"] == "test"] @@ -94,14 +93,14 @@ def render_regression_analysis(selected_result: TrainingResult): st.plotly_chart(fig_train, use_container_width=True) with cols[2]: - st.markdown("#### Combined") + st.markdown("#### Combplete") st.caption("Train + Test sets") - fig_combined = plot_regression_scatter( + fig_complete = plot_regression_scatter( merged["y"], merged["predicted"], - title="Combined", + title="Complete", ) - st.plotly_chart(fig_combined, use_container_width=True) + st.plotly_chart(fig_complete, use_container_width=True) # Display residual plots st.subheader("Residual Analysis") @@ -118,5 +117,5 @@ def render_regression_analysis(selected_result: TrainingResult): st.plotly_chart(fig_train_res, use_container_width=True) with cols[2]: - fig_combined_res = plot_residuals(merged["y"], merged["predicted"], title="Combined Residuals") - st.plotly_chart(fig_combined_res, use_container_width=True) + fig_complete_res = plot_residuals(merged["y"], merged["predicted"], title="Complete Residuals") + st.plotly_chart(fig_complete_res, use_container_width=True) diff --git a/src/entropice/dashboard/utils/loaders.py b/src/entropice/dashboard/utils/loaders.py index 72df448..73f97b7 100644 --- a/src/entropice/dashboard/utils/loaders.py +++ b/src/entropice/dashboard/utils/loaders.py @@ -3,24 +3,24 @@ import json import pickle import subprocess +from collections import defaultdict from dataclasses import dataclass from datetime import datetime from pathlib import Path +from typing import Literal import antimeridian import geopandas as gpd import pandas as pd import streamlit as st -import toml import xarray as xr from shapely.geometry import shape import entropice.spatial.grids import entropice.utils.paths from entropice.dashboard.utils.formatters import TrainingResultDisplayInfo -from entropice.ml.autogluon_training import AutoGluonTrainingSettings from entropice.ml.dataset import DatasetEnsemble, TrainingSet -from entropice.ml.randomsearch import TrainingSettings +from entropice.utils.training import Training from entropice.utils.types import GridConfig, TargetDataset, Task, all_target_datasets, all_tasks @@ -39,12 +39,7 @@ class TrainingResult: path: Path experiment: str - settings: TrainingSettings - results: pd.DataFrame - train_metrics: dict[str, float] - test_metrics: dict[str, float] - combined_metrics: dict[str, float] - confusion_matrix: xr.Dataset | None + run: Training created_at: float available_metrics: list[str] files: list[Path] @@ -52,52 +47,16 @@ class TrainingResult: @classmethod def from_path(cls, result_path: Path, experiment_name: str | None = None) -> "TrainingResult": """Load a TrainingResult from a given result directory path.""" - result_file = result_path / "search_results.parquet" - preds_file = result_path / "predicted_probabilities.parquet" - settings_file = result_path / "search_settings.toml" - metrics_file = result_path / "metrics.toml" - confusion_matrix_file = result_path / "confusion_matrix.nc" all_files = list(result_path.iterdir()) - if not result_file.exists(): - raise FileNotFoundError(f"Missing results file in {result_path}") - if not settings_file.exists(): - raise FileNotFoundError(f"Missing settings file in {result_path}") - if not preds_file.exists(): - raise FileNotFoundError(f"Missing predictions file in {result_path}") - if not metrics_file.exists(): - raise FileNotFoundError(f"Missing metrics file in {result_path}") + run = Training.load(result_path) created_at = result_path.stat().st_ctime - settings_dict = toml.load(settings_file)["settings"] - - # Handle backward compatibility: add missing fields with defaults - if "classes" not in settings_dict: - settings_dict["classes"] = None - if "param_grid" not in settings_dict: - settings_dict["param_grid"] = {} - if "cv_splits" not in settings_dict: - settings_dict["cv_splits"] = 5 - if "metrics" not in settings_dict: - settings_dict["metrics"] = [] - - settings = TrainingSettings(**settings_dict) - results = pd.read_parquet(result_file) - metrics = toml.load(metrics_file) - if not confusion_matrix_file.exists(): - confusion_matrix = None - else: - confusion_matrix = xr.open_dataset(confusion_matrix_file, engine="h5netcdf") - available_metrics = [col.replace("mean_test_", "") for col in results.columns if col.startswith("mean_test_")] + available_metrics = [str(v) for v in run.metrics["metric"].unique()] return cls( path=result_path, experiment=experiment_name or "N/A", - settings=settings, - results=results, - train_metrics=metrics["train_metrics"], - test_metrics=metrics["test_metrics"], - combined_metrics=metrics["combined_metrics"], - confusion_matrix=confusion_matrix, + run=run, created_at=created_at, available_metrics=available_metrics, files=all_files, @@ -107,11 +66,11 @@ class TrainingResult: def display_info(self) -> TrainingResultDisplayInfo: """Get display information for the training result.""" return TrainingResultDisplayInfo( - task=self.settings.task, - target=self.settings.target, - model=self.settings.model, - grid=self.settings.grid, - level=self.settings.level, + task=self.run.task, + target=self.run.target, + model=self.run.model_type, + grid=self.run.dataset.grid, + level=self.run.dataset.level, timestamp=datetime.fromtimestamp(self.created_at), ) @@ -155,18 +114,21 @@ class TrainingResult: st.error(f"Error loading predictions: {e}") return None - def get_metric_dataframe(self) -> pd.DataFrame | None: + def get_cv_results_dataframe(self) -> pd.DataFrame | None: """Get a DataFrame of available metrics for this training result.""" - metric_cols = [col for col in self.results.columns if col.startswith("mean_test_")] + results = self.run.cv_results + if results is None: + return None + metric_cols = [col for col in results.columns if col.startswith("mean_test_")] if not metric_cols: return None metric_data = [] for col in metric_cols: metric_name = col.replace("mean_test_", "").replace("neg_", "").title() - metrics = self.results[col] + metrics = results[col] # Check if the metric is negative if col.startswith("mean_test_neg_"): - task_multiplier = 1 if self.settings.task != "density" else 100 + task_multiplier = 1 if self.run.task != "density" else 100 task_multiplier *= -1 metrics = metrics * task_multiplier @@ -184,7 +146,7 @@ class TrainingResult: def _get_best_metric_name(self) -> str: """Get the primary metric name for a given task.""" - match self.settings.task: + match self.run.task: case "binary": return "f1" case "count_regimes" | "density_regimes": @@ -192,6 +154,16 @@ class TrainingResult: case _: # regression tasks return "r2" + def _get_best_score(self, split: Literal["train", "test", "complete"]) -> float: + """Get the best score for the primary metric on a given split.""" + metric = self._get_best_metric_name() + scores = self.run.metrics[(self.run.metrics["metric"] == metric) & (self.run.metrics["split"] == split)] + # Should leave a single row + assert len(scores) == 1, ( + f"Expected exactly one score for metric {metric} and split {split}, but found {len(scores)}: {scores}" + ) + return float(scores["score"].iloc[0]) + @staticmethod def to_dataframe(training_results: list["TrainingResult"]) -> pd.DataFrame: """Convert a list of TrainingResult objects to a DataFrame for display.""" @@ -199,6 +171,8 @@ class TrainingResult: for tr in training_results: info = tr.display_info best_metric_name = tr._get_best_metric_name() + best_train_score = tr._get_best_score("train") + best_test_score = tr._get_best_score("test") record = { "Experiment": tr.experiment if tr.experiment else "N/A", @@ -208,9 +182,9 @@ class TrainingResult: "Grid": GridConfig.from_grid_level((info.grid, info.level)).display_name, "Created At": info.timestamp.strftime("%Y-%m-%d %H:%M"), "Score-Metric": best_metric_name.title(), - "Best Models Score (Train-Set)": tr.train_metrics.get(best_metric_name), - "Best Models Score (Test-Set)": tr.test_metrics.get(best_metric_name), - "Trials": len(tr.results), + "Best Models Score (Train-Set)": best_train_score, + "Best Models Score (Test-Set)": best_test_score, + "Trials": tr.run.n_trials or "N/A", "Path": str(tr.path.name), } records.append(record) @@ -219,11 +193,15 @@ class TrainingResult: @staticmethod def calculate_inference_maps(training_results: list["TrainingResult"]) -> gpd.GeoDataFrame: """Calculate the mean and standard deviation of inference maps across multiple training results.""" - assert len({tr.settings.grid for tr in training_results}) == 1, "All training results must have the same grid" - assert len({tr.settings.level for tr in training_results}) == 1, "All training results must have the same level" + assert len({tr.run.dataset.grid for tr in training_results}) == 1, ( + "All training results must have the same grid" + ) + assert len({tr.run.dataset.level for tr in training_results}) == 1, ( + "All training results must have the same level" + ) - grid = training_results[0].settings.grid - level = training_results[0].settings.level + grid = training_results[0].run.dataset.grid + level = training_results[0].run.dataset.level gridfile = entropice.utils.paths.get_grid_file(grid, level) cells = gpd.read_parquet(gridfile, columns=["cell_id", "geometry"]) if grid == "hex": @@ -232,11 +210,9 @@ class TrainingResult: vals = [] for tr in training_results: - preds_file = tr.path / "predicted_probabilities.parquet" - if not preds_file.exists(): - continue - preds = pd.read_parquet(preds_file, columns=["cell_id", "predicted"]).set_index("cell_id") + preds = tr.run.predictions.set_index("cell_id")[["predicted"]] if preds["predicted"].dtype == "category": + # We can do this because the categories are ordered preds["predicted"] = preds["predicted"].cat.codes vals.append(preds) all_preds = pd.concat(vals, axis=1) @@ -257,9 +233,6 @@ def load_all_training_results() -> list[TrainingResult]: for result_path in results_dir.iterdir(): if not result_path.is_dir(): continue - # Skip AutoGluon results directory - if "autogluon" in result_path.name.lower(): - continue try: training_result = TrainingResult.from_path(result_path) training_results.append(training_result) @@ -288,155 +261,10 @@ def load_all_training_results() -> list[TrainingResult]: return training_results -@dataclass -class AutogluonTrainingResult: - """Wrapper for training result data and metadata.""" - - path: Path - experiment: str - settings: AutoGluonTrainingSettings - test_metrics: dict[str, float | dict | pd.DataFrame] - leaderboard: pd.DataFrame - feature_importance: pd.DataFrame | None - created_at: float - files: list[Path] - - @classmethod - def from_path(cls, result_path: Path, experiment_name: str | None = None) -> "AutogluonTrainingResult": - """Load an AutogluonTrainingResult from a given result directory path.""" - settings_file = result_path / "training_settings.toml" - metrics_file = result_path / "test_metrics.pickle" - leaderboard_file = result_path / "leaderboard.parquet" - feature_importance_file = result_path / "feature_importance.parquet" - all_files = list(result_path.iterdir()) - if not settings_file.exists(): - raise FileNotFoundError(f"Missing settings file in {result_path}") - if not metrics_file.exists(): - raise FileNotFoundError(f"Missing metrics file in {result_path}") - if not leaderboard_file.exists(): - raise FileNotFoundError(f"Missing leaderboard file in {result_path}") - - created_at = result_path.stat().st_ctime - settings_dict = toml.load(settings_file)["settings"] - settings = AutoGluonTrainingSettings(**settings_dict) - with open(metrics_file, "rb") as f: - metrics = pickle.load(f) - leaderboard = pd.read_parquet(leaderboard_file) - - if feature_importance_file.exists(): - feature_importance = pd.read_parquet(feature_importance_file) - else: - feature_importance = None - - return cls( - path=result_path, - experiment=experiment_name or "N/A", - settings=settings, - test_metrics=metrics, - leaderboard=leaderboard, - feature_importance=feature_importance, - created_at=created_at, - files=all_files, - ) - - @property - def test_confusion_matrix(self) -> pd.DataFrame | None: - """Get the test confusion matrix.""" - if "confusion_matrix" not in self.test_metrics: - return None - assert isinstance(self.test_metrics["confusion_matrix"], pd.DataFrame) - return self.test_metrics["confusion_matrix"] - - @property - def display_info(self) -> TrainingResultDisplayInfo: - """Get display information for the training result.""" - return TrainingResultDisplayInfo( - task=self.settings.task, - target=self.settings.target, - model="autogluon", - grid=self.settings.grid, - level=self.settings.level, - timestamp=datetime.fromtimestamp(self.created_at), - ) - - def _get_best_metric_name(self) -> str: - """Get the primary metric name for a given task.""" - match self.settings.task: - case "binary": - return "f1" - case "count_regimes" | "density_regimes": - return "f1_weighted" - case _: # regression tasks - return "root_mean_squared_error" - - @staticmethod - def to_dataframe(training_results: list["AutogluonTrainingResult"]) -> pd.DataFrame: - """Convert a list of AutogluonTrainingResult objects to a DataFrame for display.""" - records = [] - for tr in training_results: - info = tr.display_info - best_metric_name = tr._get_best_metric_name() - - record = { - "Experiment": tr.experiment if tr.experiment else "N/A", - "Task": info.task, - "Target": info.target, - "Model": info.model, - "Grid": GridConfig.from_grid_level((info.grid, info.level)).display_name, - "Created At": info.timestamp.strftime("%Y-%m-%d %H:%M"), - "Score-Metric": best_metric_name.title(), - "Best Models Score (Test-Set)": tr.test_metrics.get(best_metric_name), - "Path": str(tr.path.name), - } - records.append(record) - return pd.DataFrame.from_records(records) - - -@st.cache_data(ttl=300) # Cache for 5 minutes -def load_all_autogluon_training_results() -> list[AutogluonTrainingResult]: - """Load all training results from the results directory.""" - results_dir = entropice.utils.paths.RESULTS_DIR - training_results: list[AutogluonTrainingResult] = [] - incomplete_results: list[tuple[Path, Exception]] = [] - for result_path in results_dir.iterdir(): - if not result_path.is_dir(): - continue - # Skip AutoGluon results directory - if "autogluon" not in result_path.name.lower(): - continue - try: - training_result = AutogluonTrainingResult.from_path(result_path) - training_results.append(training_result) - except FileNotFoundError as e: - is_experiment_dir = False - for experiment_path in result_path.iterdir(): - if not experiment_path.is_dir(): - continue - try: - experiment_name = experiment_path.parent.name - training_result = AutogluonTrainingResult.from_path(experiment_path, experiment_name) - training_results.append(training_result) - is_experiment_dir = True - except FileNotFoundError as e2: - incomplete_results.append((experiment_path, e2)) - if not is_experiment_dir: - incomplete_results.append((result_path, e)) - - if len(incomplete_results) > 0: - st.warning( - f"Found {len(incomplete_results)} incomplete autogluon training results that were skipped:\n - " - + "\n - ".join(f"{p}: {e}" for p, e in incomplete_results) - ) - # Sort by creation time (most recent first) - training_results.sort(key=lambda tr: tr.created_at, reverse=True) - return training_results - - def load_training_sets(ensemble: DatasetEnsemble) -> dict[TargetDataset, dict[Task, TrainingSet]]: """Load training sets for all target-task combinations in the ensemble.""" - train_data_dict: dict[TargetDataset, dict[Task, TrainingSet]] = {} + train_data_dict: dict[TargetDataset, dict[Task, TrainingSet]] = defaultdict(dict) for target in all_target_datasets: - train_data_dict[target] = {} for task in all_tasks: train_data_dict[target][task] = ensemble.create_training_set(target=target, task=task) return train_data_dict @@ -490,10 +318,6 @@ def load_experiment_training_results(experiment_name: str) -> list[TrainingResul for result_path in experiment_dir.iterdir(): if not result_path.is_dir(): continue - # Skip AutoGluon results - if "autogluon" in result_path.name.lower(): - continue - try: training_result = TrainingResult.from_path(result_path, experiment_name) training_results.append(training_result) @@ -505,47 +329,11 @@ def load_experiment_training_results(experiment_name: str) -> list[TrainingResul return training_results -def load_experiment_autogluon_results(experiment_name: str) -> list[AutogluonTrainingResult]: - """Load all AutoGluon training results for a specific experiment. - - Args: - experiment_name: Name of the experiment directory - - Returns: - List of AutogluonTrainingResult objects for the experiment - - """ - experiment_dir = entropice.utils.paths.RESULTS_DIR / experiment_name - if not experiment_dir.exists(): - return [] - - training_results: list[AutogluonTrainingResult] = [] - for result_path in experiment_dir.iterdir(): - if not result_path.is_dir(): - continue - # Only include AutoGluon results - if "autogluon" not in result_path.name.lower(): - continue - - try: - training_result = AutogluonTrainingResult.from_path(result_path, experiment_name) - training_results.append(training_result) - except FileNotFoundError: - pass # Skip incomplete results - - # Sort by creation time (most recent first) - training_results.sort(key=lambda tr: tr.created_at, reverse=True) - return training_results - - -def create_experiment_summary_df( - training_results: list[TrainingResult], autogluon_results: list[AutogluonTrainingResult] -) -> pd.DataFrame: +def create_experiment_summary_df(training_results: list[TrainingResult]) -> pd.DataFrame: """Create a summary DataFrame for all results in an experiment. Args: training_results: List of TrainingResult objects - autogluon_results: List of AutogluonTrainingResult objects Returns: DataFrame with summary statistics for the experiment @@ -566,55 +354,26 @@ def create_experiment_summary_df( "grid": info.grid, "level": info.level, "grid_level": f"{info.grid}_{info.level}", - "train_score": tr.train_metrics.get(best_metric_name, float("nan")), - "test_score": tr.test_metrics.get(best_metric_name, float("nan")), - "combined_score": tr.combined_metrics.get(best_metric_name, float("nan")), + "train_score": tr._get_best_score("train"), + "test_score": tr._get_best_score("test"), + "complete_score": tr._get_best_score("complete"), "best_metric": best_metric_name, - "n_trials": len(tr.results), + "n_trials": tr.run.n_trials or "N/A", "created_at": tr.created_at, "path": tr.path, } # Add all train metrics - for metric, value in tr.train_metrics.items(): + for metric, value in tr.run.get_metrics_from_split("train").items(): record[f"train_{metric}"] = value # Add all test metrics - for metric, value in tr.test_metrics.items(): + for metric, value in tr.run.get_metrics_from_split("test").items(): record[f"test_{metric}"] = value - # Add all combined metrics - for metric, value in tr.combined_metrics.items(): - record[f"combined_{metric}"] = value - - records.append(record) - - # Add AutoGluon results - for ag in autogluon_results: - info = ag.display_info - best_metric_name = ag._get_best_metric_name() - - record = { - "method": "AutoGluon", - "task": info.task, - "target": info.target, - "model": "ensemble", # AutoGluon is an ensemble - "grid": info.grid, - "level": info.level, - "grid_level": f"{info.grid}_{info.level}", - "train_score": float("nan"), # AutoGluon doesn't separate train scores - "test_score": ag.test_metrics.get(best_metric_name, float("nan")), - "combined_score": float("nan"), - "best_metric": best_metric_name, - "n_trials": len(ag.leaderboard), - "created_at": ag.created_at, - "path": ag.path, - } - - # Add test metrics - for metric, value in ag.test_metrics.items(): - if isinstance(value, (int, float)): - record[f"test_{metric}"] = value + # Add all complete metrics + for metric, value in tr.run.get_metrics_from_split("complete").items(): + record[f"complete_{metric}"] = value records.append(record) diff --git a/src/entropice/dashboard/utils/stats.py b/src/entropice/dashboard/utils/stats.py index a77cdbe..083528b 100644 --- a/src/entropice/dashboard/utils/stats.py +++ b/src/entropice/dashboard/utils/stats.py @@ -278,11 +278,11 @@ def load_all_default_dataset_statistics() -> dict[GridLevel, dict[TemporalMode, dataset_stats: dict[GridLevel, dict[TemporalMode, DatasetStatistics]] = {} for grid_config in grid_configs: dataset_stats[grid_config.id] = {} - with stopwatch(f"Loading statistics for grid={grid_config.grid}, level={grid_config.level}"): - grid_gdf = entropice.spatial.grids.open(grid_config.grid, grid_config.level) # Ensure grid is registered - total_cells = len(grid_gdf) - assert total_cells > 0, "Grid must contain at least one cell." - for temporal_mode in all_temporal_modes: + grid_gdf = entropice.spatial.grids.open(grid_config.grid, grid_config.level) # Ensure grid is registered + total_cells = len(grid_gdf) + assert total_cells > 0, "Grid must contain at least one cell." + for temporal_mode in all_temporal_modes: + with stopwatch(f"Loading statistics for {grid_config.grid=}, {grid_config.level=}, {temporal_mode=}"): e = DatasetEnsemble(grid=grid_config.grid, level=grid_config.level, temporal_mode=temporal_mode) target_statistics = {} for target in all_target_datasets: @@ -437,23 +437,27 @@ class CVMetricStatistics: mean_cv_std: float | None @classmethod - def compute(cls, result: TrainingResult, metric: str) -> "CVMetricStatistics": + def compute(cls, result: TrainingResult, metric: str) -> "CVMetricStatistics | None": """Get cross-validation statistics for a metric.""" score_col = f"mean_test_{metric}" std_col = f"std_test_{metric}" - if score_col not in result.results.columns: + cv_results = result.run.cv_results + if cv_results is None: + return None + + if score_col not in cv_results.columns: raise ValueError(f"Metric {metric} not found in results.") - best_score = result.results[score_col].max() - mean_score = result.results[score_col].mean() - std_score = result.results[score_col].std() - worst_score = result.results[score_col].min() - median_score = result.results[score_col].median() + best_score = cv_results[score_col].max() + mean_score = cv_results[score_col].mean() + std_score = cv_results[score_col].std() + worst_score = cv_results[score_col].min() + median_score = cv_results[score_col].median() mean_cv_std = None - if std_col in result.results.columns: - mean_cv_std = result.results[std_col].mean() + if std_col in cv_results.columns: + mean_cv_std = cv_results[std_col].mean() return CVMetricStatistics( best_score=best_score, @@ -477,10 +481,12 @@ class ParameterSpaceSummary: unique_values: int @classmethod - def compute(cls, result: TrainingResult, param_col: str) -> "ParameterSpaceSummary": + def compute(cls, result: TrainingResult, param_col: str) -> "ParameterSpaceSummary | None": """Get cross-validation statistics for a metric.""" + if result.run.cv_results is None: + return None param_name = param_col.replace("param_", "") - param_values = result.results[param_col].dropna() + param_values = result.run.cv_results[param_col].dropna() if pd.api.types.is_numeric_dtype(param_values): return ParameterSpaceSummary( @@ -511,14 +517,19 @@ class CVResultsStatistics: parameter_summary: list[ParameterSpaceSummary] @classmethod - def compute(cls, result: TrainingResult) -> "CVResultsStatistics": + def compute(cls, result: TrainingResult) -> "CVResultsStatistics | None": """Get cross-validation statistics for a metric.""" + if result.run.cv_results is None: + return None metrics = result.available_metrics metric_stats: dict[str, CVMetricStatistics] = {} for metric in metrics: - metric_stats[metric] = CVMetricStatistics.compute(result, metric) + stats = CVMetricStatistics.compute(result, metric) + if stats is None: + continue + metric_stats[metric] = stats - param_cols = [col for col in result.results.columns if col.startswith("param_") and col != "params"] + param_cols = [col for col in result.run.cv_results.columns if col.startswith("param_") and col != "params"] summary_data = [] for param_col in param_cols: summary_data.append(ParameterSpaceSummary.compute(result, param_col)) diff --git a/src/entropice/dashboard/views/dataset_page.py b/src/entropice/dashboard/views/dataset_page.py index f4efa27..6e4f144 100644 --- a/src/entropice/dashboard/views/dataset_page.py +++ b/src/entropice/dashboard/views/dataset_page.py @@ -34,7 +34,7 @@ def render_dataset_configuration_sidebar() -> DatasetEnsemble: # Grid selection grid_options = [gc.display_name for gc in grid_configs] - grid_level_combined = st.selectbox( + grid_level_complete = st.selectbox( "Grid Configuration", options=grid_options, index=0, @@ -43,7 +43,7 @@ def render_dataset_configuration_sidebar() -> DatasetEnsemble: ) # Find the selected grid config - selected_grid_config: GridConfig = next(gc for gc in grid_configs if gc.display_name == grid_level_combined) + selected_grid_config: GridConfig = next(gc for gc in grid_configs if gc.display_name == grid_level_complete) # Temporal mode selection temporal_mode = st.selectbox( diff --git a/src/entropice/dashboard/views/experiment_analysis_page.py b/src/entropice/dashboard/views/experiment_analysis_page.py index 14af39a..a62229e 100644 --- a/src/entropice/dashboard/views/experiment_analysis_page.py +++ b/src/entropice/dashboard/views/experiment_analysis_page.py @@ -12,7 +12,6 @@ from entropice.dashboard.sections.experiment_overview import ( ) from entropice.dashboard.utils.loaders import ( create_experiment_summary_df, - load_experiment_autogluon_results, load_experiment_training_results, ) @@ -43,14 +42,13 @@ def render_experiment_analysis_page(): # Load experiment results with st.spinner(f"Loading results for experiment: {selected_experiment}..."): training_results = load_experiment_training_results(selected_experiment) - autogluon_results = load_experiment_autogluon_results(selected_experiment) - if not training_results and not autogluon_results: + if not training_results: st.warning(f"No training results found in experiment: {selected_experiment}") st.stop() # Create summary DataFrame - summary_df = create_experiment_summary_df(training_results, autogluon_results) + summary_df = create_experiment_summary_df(training_results) # Get available metrics metric_columns = [col for col in summary_df.columns if col.startswith("test_")] @@ -61,7 +59,7 @@ def render_experiment_analysis_page(): st.stop() # Render analysis sections - render_experiment_overview(selected_experiment, training_results, autogluon_results, summary_df) + render_experiment_overview(selected_experiment, training_results, summary_df) st.divider() @@ -73,7 +71,7 @@ def render_experiment_analysis_page(): st.divider() - render_feature_importance_analysis(training_results, autogluon_results) + render_feature_importance_analysis(training_results) st.divider() diff --git a/src/entropice/dashboard/views/inference_page.py b/src/entropice/dashboard/views/inference_page.py index 83ee95b..af6fd25 100644 --- a/src/entropice/dashboard/views/inference_page.py +++ b/src/entropice/dashboard/views/inference_page.py @@ -1,16 +1,8 @@ """Inference page: Visualization of model inference results across the study region.""" -import geopandas as gpd import streamlit as st from stopuhr import stopwatch -from entropice.dashboard.plots.inference import ( - render_class_comparison, - render_class_distribution_histogram, - render_inference_map, - render_inference_statistics, - render_spatial_distribution_stats, -) from entropice.dashboard.utils.loaders import TrainingResult, load_all_training_results @@ -27,7 +19,9 @@ def render_sidebar_selection(training_results: list[TrainingResult]) -> Training st.header("Select Training Run") # Create selection options with task-first naming - training_options = {tr.display_info.get_display_name("task_first"): tr for tr in training_results} + training_options: dict[str, TrainingResult] = { + tr.display_info.get_display_name("task_first"): tr for tr in training_results + } selected_name = st.selectbox( "Training Run", @@ -44,113 +38,14 @@ def render_sidebar_selection(training_results: list[TrainingResult]) -> Training # Show run information in sidebar st.subheader("Run Information") - st.markdown(f"**Task:** {selected_result.settings.task.capitalize()}") - st.markdown(f"**Model:** {selected_result.settings.model.upper()}") - st.markdown(f"**Grid:** {selected_result.settings.grid.capitalize()}") - st.markdown(f"**Level:** {selected_result.settings.level}") - st.markdown(f"**Target:** {selected_result.settings.target.replace('darts_', '')}") - + st.markdown(f"**Task:** {selected_result.run.task.capitalize()}") + st.markdown(f"**Model:** {selected_result.run.model_type.upper()}") + st.markdown(f"**Grid:** {selected_result.run.dataset.grid.capitalize()}") + st.markdown(f"**Level:** {selected_result.run.dataset.level}") + st.markdown(f"**Target:** {selected_result.run.target.replace('darts_', '')}") return selected_result -def render_run_information(selected_result: TrainingResult): - """Render training run configuration overview. - - Args: - selected_result: The selected TrainingResult object. - - """ - st.header("📋 Run Configuration") - - col1, col2, col3, col4, col5 = st.columns(5) - - with col1: - st.metric("Task", selected_result.settings.task.capitalize()) - - with col2: - st.metric("Model", selected_result.settings.model.upper()) - - with col3: - st.metric("Grid", selected_result.settings.grid.capitalize()) - - with col4: - st.metric("Level", selected_result.settings.level) - - with col5: - st.metric("Target", selected_result.settings.target.replace("darts_", "")) - - -def render_inference_statistics_section(predictions_gdf: gpd.GeoDataFrame, task: str): - """Render inference summary statistics section. - - Args: - predictions_gdf: GeoDataFrame with predictions. - task: Task type ('binary', 'count_regimes', 'density_regimes', 'count', 'density'). - - """ - st.header("📊 Inference Summary") - render_inference_statistics(predictions_gdf, task) - - -def render_spatial_coverage_section(predictions_gdf: gpd.GeoDataFrame): - """Render spatial coverage statistics section. - - Args: - predictions_gdf: GeoDataFrame with predictions. - - """ - st.header("🌍 Spatial Coverage") - render_spatial_distribution_stats(predictions_gdf) - - -def render_map_visualization_section(selected_result: TrainingResult): - """Render 3D map visualization section. - - Args: - selected_result: The selected TrainingResult object. - - """ - st.header("🗺️ Interactive Prediction Map") - st.markdown( - """ - 3D visualization of predictions across the study region. The map shows predicted - classes with color coding and spatial distribution of model outputs. - """ - ) - render_inference_map(selected_result) - - -def render_class_distribution_section(predictions_gdf: gpd.GeoDataFrame, task: str): - """Render class distribution histogram section. - - Args: - predictions_gdf: GeoDataFrame with predictions. - task: Task type ('binary', 'count_regimes', 'density_regimes', 'count', 'density'). - - """ - st.header("📈 Class Distribution") - st.markdown("Distribution of predicted classes across all inference cells.") - render_class_distribution_histogram(predictions_gdf, task) - - -def render_class_comparison_section(predictions_gdf: gpd.GeoDataFrame, task: str): - """Render class comparison analysis section. - - Args: - predictions_gdf: GeoDataFrame with predictions. - task: Task type ('binary', 'count_regimes', 'density_regimes', 'count', 'density'). - - """ - st.header("🔍 Class Comparison Analysis") - st.markdown( - """ - Detailed comparison of predicted classes showing probability distributions - and confidence metrics for different class predictions. - """ - ) - render_class_comparison(predictions_gdf, task) - - def render_inference_page(): """Render the Inference page of the dashboard.""" st.title("🗺️ Inference Results") @@ -179,45 +74,18 @@ def render_inference_page(): with st.sidebar: selected_result = render_sidebar_selection(training_results) - # Main content area - Run Information - render_run_information(selected_result) - - st.divider() - - # Check if predictions file exists - preds_file = selected_result.path / "predicted_probabilities.parquet" - if not preds_file.exists(): - st.error("No inference results found for this training run.") - st.info("Inference results are generated automatically during training.") - return - + # Main content area # Load predictions with st.spinner("Loading inference results..."): - predictions_gdf = gpd.read_parquet(preds_file) - task = selected_result.settings.task + # Columns: Index(['cell_id', 'predicted', 'geometry'], dtype='object') + predictions_gdf = selected_result.run.predictions + task = selected_result.run.task - # Inference Statistics Section - render_inference_statistics_section(predictions_gdf, task) - - st.divider() - - # Spatial Coverage Section - render_spatial_coverage_section(predictions_gdf) - - st.divider() - - # 3D Map Visualization Section - render_map_visualization_section(selected_result) - - st.divider() - - # Class Distribution Section - render_class_distribution_section(predictions_gdf, task) - - st.divider() - - # Class Comparison Section - render_class_comparison_section(predictions_gdf, task) + # TODO: Implement the sections + # Map, optionally 3D + # Some statistics about the predictions + # Class Distribution for classification tasks + # Distribution of predicted values for regression tasks st.balloons() stopwatch.summary() diff --git a/src/entropice/dashboard/views/model_state_page.py b/src/entropice/dashboard/views/model_state_page.py deleted file mode 100644 index af2e1e4..0000000 --- a/src/entropice/dashboard/views/model_state_page.py +++ /dev/null @@ -1,919 +0,0 @@ -"""Model State page: Visualization of model internal state and feature importance.""" - -import streamlit as st -import xarray as xr -from stopuhr import stopwatch - -from entropice.dashboard.plots.model_state import ( - plot_arcticdem_heatmap, - plot_arcticdem_summary, - plot_box_assignment_bars, - plot_box_assignments, - plot_common_features, - plot_embedding_aggregation_summary, - plot_embedding_heatmap, - plot_era5_heatmap, - plot_era5_summary, - plot_era5_time_heatmap, - plot_top_features, -) -from entropice.dashboard.utils.colors import generate_unified_colormap -from entropice.dashboard.utils.loaders import TrainingResult, load_all_training_results -from entropice.dashboard.utils.unsembler import ( - extract_arcticdem_features, - extract_common_features, - extract_embedding_features, - extract_era5_features, -) -from entropice.utils.types import L2SourceDataset - - -def get_members_from_settings(settings) -> list[L2SourceDataset]: - """Extract dataset members from training settings. - - Args: - settings: TrainingSettings object containing dataset configuration. - - Returns: - List of L2SourceDataset members used in training. - - """ - return settings.members - - -def render_sidebar_selection(training_results: list[TrainingResult]) -> TrainingResult: - """Render sidebar for training run selection. - - Args: - training_results: List of available TrainingResult objects. - - Returns: - Selected TrainingResult object. - - """ - st.header("Select Training Run") - - # Result selection with task-first naming - result_options = {tr.display_info.get_display_name("task_first"): tr for tr in training_results} - selected_name = st.selectbox( - "Training Run", - options=list(result_options.keys()), - index=0, - help="Choose a training result to visualize model state", - key="model_state_training_run_select", - ) - selected_result = result_options[selected_name] - - return selected_result - - -def render_model_info(model_state: xr.Dataset, model_type: str): - """Render basic model state information. - - Args: - model_state: Xarray dataset containing model state. - model_type: Type of model (espa, xgboost, rf, knn). - - """ - with st.expander("Model State Information", expanded=False): - st.write(f"**Model Type:** {model_type.upper()}") - st.write(f"**Variables:** {list(model_state.data_vars)}") - st.write(f"**Dimensions:** {dict(model_state.sizes)}") - st.write(f"**Coordinates:** {list(model_state.coords)}") - st.write(f"**Attributes:** {dict(model_state.attrs)}") - - -def render_training_data_summary(members: list[L2SourceDataset]): - """Render summary of training data sources. - - Args: - members: List of dataset members used in training. - - """ - st.header("📊 Training Data Summary") - - st.markdown( - f""" - **Dataset Members Used in Training:** {len(members)} - - The following data sources were used to train this model: - """ - ) - - # Create a nice display of members with emojis - member_display = { - "AlphaEarth": "🛰️ AlphaEarth (Satellite Embeddings)", - "ArcticDEM": "🏔️ ArcticDEM (Topography)", - "ERA5-yearly": "⛅ ERA5 Yearly (Climate)", - "ERA5-seasonal": "⛅ ERA5 Seasonal (Summer/Winter)", - "ERA5-shoulder": "⛅ ERA5 Shoulder Seasons (JFM/AMJ/JAS/OND)", - } - - cols = st.columns(min(len(members), 3)) - for idx, member in enumerate(members): - with cols[idx % 3]: - display_name = member_display.get(member, f"📁 {member}") - st.info(display_name) - - -def render_model_state_page(): - """Render the Model State page of the dashboard.""" - st.title("🔬 Model State") - st.markdown( - """ - Comprehensive visualization of the best model's internal state and feature importance. - Select a training run from the sidebar to explore model parameters, feature weights, - and data source contributions. - """ - ) - - # Load available training results - training_results = load_all_training_results() - - if not training_results: - st.warning("No training results found. Please run some training experiments first.") - st.info("Run training using: `pixi run python -m entropice.ml.training`") - return - - st.success(f"Found **{len(training_results)}** training result(s)") - - st.divider() - - # Sidebar: Training run selection - with st.sidebar: - selected_result = render_sidebar_selection(training_results) - - # Get the model type from settings - model_type = selected_result.settings.model - - # Load model state - with st.spinner("Loading model state..."): - model_state = selected_result.load_model_state() - if model_state is None: - st.error("Could not load model state for this result.") - st.info("The model state file (best_estimator_state.nc) may be missing from the training results.") - return - - # Display basic model state info - render_model_info(model_state, model_type) - - # Display dataset members summary - members = get_members_from_settings(selected_result.settings) - render_training_data_summary(members) - - st.divider() - - # Render model-specific visualizations - if model_type == "espa": - render_espa_model_state(model_state, selected_result) - elif model_type == "xgboost": - render_xgboost_model_state(model_state, selected_result) - elif model_type == "rf": - render_rf_model_state(model_state, selected_result) - elif model_type == "knn": - render_knn_model_state(model_state, selected_result) - else: - st.warning(f"Visualization for model type '{model_type}' is not yet implemented.") - - st.balloons() - stopwatch.summary() - - -def render_espa_model_state(model_state: xr.Dataset, selected_result: TrainingResult): - """Render visualizations for ESPA model. - - Args: - model_state: Xarray dataset containing ESPA model state. - selected_result: TrainingResult object containing training configuration. - - """ - # Scale feature weights by number of features - n_features = model_state.sizes["feature"] - model_state["feature_weights"] *= n_features - - # Get members used in training - members = get_members_from_settings(selected_result.settings) - - # Extract different feature types based on what was used in training - embedding_feature_array = None - if "AlphaEarth" in members: - embedding_feature_array = extract_embedding_features(model_state) - - era5_yearly_array = None - era5_seasonal_array = None - era5_shoulder_array = None - if "ERA5-yearly" in members: - era5_yearly_array = extract_era5_features(model_state, temporal_group="yearly") - if "ERA5-seasonal" in members: - era5_seasonal_array = extract_era5_features(model_state, temporal_group="seasonal") - if "ERA5-shoulder" in members: - era5_shoulder_array = extract_era5_features(model_state, temporal_group="shoulder") - - arcticdem_feature_array = None - if "ArcticDEM" in members: - arcticdem_feature_array = extract_arcticdem_features(model_state) - - common_feature_array = extract_common_features(model_state) - - # Generate unified colormaps (convert dataclass to dict) - settings_dict = {"task": selected_result.settings.task, "classes": selected_result.settings.classes} - _, _, altair_colors = generate_unified_colormap(settings_dict) - - # Feature importance section - st.header("Feature Importance") - st.markdown("The most important features based on learned feature weights from the best estimator.") - - @st.fragment - def render_feature_importance(): - # Slider to control number of features to display - top_n = st.slider( - "Number of top features to display", - min_value=5, - max_value=50, - value=10, - step=5, - help="Select how many of the most important features to visualize", - ) - - with st.spinner("Generating feature importance plot..."): - feature_chart = plot_top_features(model_state, top_n=top_n) - st.altair_chart(feature_chart, width="stretch") - - st.markdown( - """ - **Interpretation:** - - **Magnitude**: Larger absolute values indicate more important features - - **Color**: Blue bars indicate positive weights, coral bars indicate negative weights - """ - ) - - render_feature_importance() - - # Box-to-Label Assignment Visualization - st.header("Box-to-Label Assignments") - st.markdown( - """ - This visualization shows how the learned boxes (prototypes in feature space) are - assigned to different class labels. The ESPA classifier learns K boxes and assigns - them to classes through the Lambda matrix. Higher values indicate stronger assignment - of a box to a particular class. - """ - ) - - with st.spinner("Generating box assignment visualizations..."): - col1, col2 = st.columns([0.7, 0.3]) - - with col1: - st.markdown("### Assignment Heatmap") - box_assignment_heatmap = plot_box_assignments(model_state) - st.altair_chart(box_assignment_heatmap, width="stretch") - - with col2: - st.markdown("### Box Count by Class") - box_assignment_bars = plot_box_assignment_bars(model_state, altair_colors) - st.altair_chart(box_assignment_bars, width="stretch") - - # Show statistics - with st.expander("Box Assignment Statistics"): - box_assignments = model_state["box_assignments"].to_pandas() - st.write("**Assignment Matrix Statistics:**") - col1, col2, col3, col4 = st.columns(4) - with col1: - st.metric("Total Boxes", len(box_assignments.columns)) - with col2: - st.metric("Number of Classes", len(box_assignments.index)) - with col3: - st.metric("Mean Assignment", f"{box_assignments.to_numpy().mean():.4f}") - with col4: - st.metric("Max Assignment", f"{box_assignments.to_numpy().max():.4f}") - - # Show which boxes are most strongly assigned to each class - st.write("**Top Box Assignments per Class:**") - for class_label in box_assignments.index: - top_boxes = box_assignments.loc[class_label].nlargest(5) - st.write( - f"**Class {class_label}:** Boxes {', '.join(map(str, top_boxes.index.tolist()))} " - f"(strengths: {', '.join(f'{v:.3f}' for v in top_boxes.to_numpy())})" - ) - - st.markdown( - """ - **Interpretation:** - - Each box can be assigned to multiple classes with different strengths - - Boxes with higher assignment values for a class contribute more to that class's predictions - - The distribution shows how the model partitions the feature space for classification - """ - ) - - # Embedding features analysis (if present) - if embedding_feature_array is not None: - render_embedding_features(embedding_feature_array) - - # ERA5 features analysis (if present) - split by temporal group - if era5_yearly_array is not None: - render_era5_features(era5_yearly_array, temporal_group="Yearly") - - if era5_seasonal_array is not None: - render_era5_features(era5_seasonal_array, temporal_group="Seasonal") - - if era5_shoulder_array is not None: - render_era5_features(era5_shoulder_array, temporal_group="Shoulder") - - # ArcticDEM features analysis (if present) - if arcticdem_feature_array is not None: - render_arcticdem_features(arcticdem_feature_array) - - # Common features analysis (if present) - if common_feature_array is not None: - render_common_features(common_feature_array) - - -def render_xgboost_model_state(model_state: xr.Dataset, selected_result: TrainingResult): - """Render visualizations for XGBoost model. - - Args: - model_state: Xarray dataset containing XGBoost model state. - selected_result: TrainingResult object containing training configuration. - - """ - from entropice.dashboard.plots.model_state import ( - plot_xgboost_feature_importance, - plot_xgboost_importance_comparison, - ) - - st.header("🌲 XGBoost Model Analysis") - st.markdown( - f""" - XGBoost gradient boosted tree model with **{model_state.attrs.get("n_trees", "N/A")} trees**. - - **Objective:** {model_state.attrs.get("objective", "N/A")} - """ - ) - - # Feature importance with different types - st.subheader("Feature Importance Analysis") - st.markdown( - """ - XGBoost provides multiple ways to measure feature importance: - - **Weight**: Number of times a feature is used to split the data - - **Gain**: Average gain across all splits using the feature - - **Cover**: Average coverage across all splits using the feature - - **Total Gain**: Total gain across all splits - - **Total Cover**: Total coverage across all splits - """ - ) - - # Importance type selector - importance_type = st.selectbox( - "Select Importance Type", - options=["gain", "weight", "cover", "total_gain", "total_cover"], - index=0, - help="Choose which importance metric to visualize", - key="model_state_importance_type", - ) - - # Top N slider - top_n = st.slider( - "Number of top features to display", - min_value=5, - max_value=50, - value=20, - step=5, - help="Select how many of the most important features to visualize", - ) - - with st.spinner("Generating feature importance plot..."): - importance_chart = plot_xgboost_feature_importance(model_state, importance_type=importance_type, top_n=top_n) - st.altair_chart(importance_chart, width="stretch") - - # Comparison of importance types - st.subheader("Importance Type Comparison") - st.markdown("Compare the top features across different importance metrics.") - - with st.spinner("Generating importance comparison..."): - comparison_chart = plot_xgboost_importance_comparison(model_state, top_n=15) - st.altair_chart(comparison_chart, width="stretch") - - # Statistics - with st.expander("Model Statistics"): - st.write("**Overall Statistics:**") - col1, col2 = st.columns(2) - with col1: - st.metric("Number of Trees", model_state.attrs.get("n_trees", "N/A")) - with col2: - st.metric("Total Features", model_state.sizes.get("feature", "N/A")) - - # Feature source analysis - st.subheader("Feature Importance by Data Source") - st.markdown( - """ - Breakdown of feature importance by data source (AlphaEarth embeddings, ERA5 climate, - ArcticDEM topography, and common features). - """ - ) - - # Get members used in training - members = get_members_from_settings(selected_result.settings) - - # Extract features by source using the selected importance type - importance_var = f"feature_importance_{importance_type}" - - embedding_feature_array = None - if "AlphaEarth" in members: - embedding_feature_array = extract_embedding_features(model_state, importance_type=importance_var) - - era5_yearly_array = None - era5_seasonal_array = None - era5_shoulder_array = None - if "ERA5-yearly" in members: - era5_yearly_array = extract_era5_features(model_state, importance_type=importance_var, temporal_group="yearly") - if "ERA5-seasonal" in members: - era5_seasonal_array = extract_era5_features( - model_state, importance_type=importance_var, temporal_group="seasonal" - ) - if "ERA5-shoulder" in members: - era5_shoulder_array = extract_era5_features( - model_state, importance_type=importance_var, temporal_group="shoulder" - ) - - arcticdem_feature_array = None - if "ArcticDEM" in members: - arcticdem_feature_array = extract_arcticdem_features(model_state, importance_type=importance_var) - - common_feature_array = extract_common_features(model_state, importance_type=importance_var) - - # Render each source's features if present - if embedding_feature_array is not None: - render_embedding_features(embedding_feature_array) - - if era5_yearly_array is not None: - render_era5_features(era5_yearly_array, temporal_group="Yearly") - - if era5_seasonal_array is not None: - render_era5_features(era5_seasonal_array, temporal_group="Seasonal") - - if era5_shoulder_array is not None: - render_era5_features(era5_shoulder_array, temporal_group="Shoulder") - - if arcticdem_feature_array is not None: - render_arcticdem_features(arcticdem_feature_array) - - if common_feature_array is not None: - render_common_features(common_feature_array) - - -def render_rf_model_state(model_state: xr.Dataset, selected_result: TrainingResult): - """Render visualizations for Random Forest model. - - Args: - model_state: Xarray dataset containing Random Forest model state. - selected_result: TrainingResult object containing training configuration. - - """ - from entropice.dashboard.plots.model_state import plot_rf_feature_importance - - st.header("🌳 Random Forest Model Analysis") - - # Check if using cuML (which doesn't provide tree statistics) - is_cuml = "cuML" in model_state.attrs.get("description", "") - - st.markdown( - f""" - Random Forest ensemble with **{model_state.attrs.get("n_estimators", "N/A")} trees** - (max depth: {model_state.attrs.get("max_depth", "N/A")}). - """ - ) - - if is_cuml: - st.info("ℹ️ Using cuML GPU-accelerated Random Forest. Individual tree statistics are not available.") - - # Display OOB score if available - oob_score = model_state.attrs.get("oob_score") - if oob_score is not None: - st.info(f"**Out-of-Bag Score:** {oob_score:.4f}") - - # Feature importance - st.subheader("Feature Importance (Gini Importance)") - st.markdown( - """ - Random Forest uses Gini impurity to measure feature importance. Features with higher - importance values contribute more to the model's predictions. - """ - ) - - # Top N slider - top_n = st.slider( - "Number of top features to display", - min_value=5, - max_value=50, - value=20, - step=5, - help="Select how many of the most important features to visualize", - ) - - with st.spinner("Generating feature importance plot..."): - importance_chart = plot_rf_feature_importance(model_state, top_n=top_n) - st.altair_chart(importance_chart, width="stretch") - - # Tree statistics (only if available - sklearn RF has them, cuML RF doesn't) - if not is_cuml and "tree_depths" in model_state: - from entropice.dashboard.plots.model_state import plot_rf_tree_statistics - - st.subheader("Tree Structure Statistics") - st.markdown("Distribution of tree properties across the forest.") - - with st.spinner("Generating tree statistics..."): - chart_depths, chart_leaves, chart_nodes = plot_rf_tree_statistics(model_state) - col1, col2, col3 = st.columns(3) - with col1: - st.altair_chart(chart_depths, width="stretch") - with col2: - st.altair_chart(chart_leaves, width="stretch") - with col3: - st.altair_chart(chart_nodes, width="stretch") - - # Statistics - with st.expander("Forest Statistics"): - st.write("**Overall Statistics:**") - depths = model_state["tree_depths"].to_pandas() - leaves = model_state["tree_n_leaves"].to_pandas() - nodes = model_state["tree_n_nodes"].to_pandas() - - col1, col2, col3 = st.columns(3) - with col1: - st.write("**Tree Depths:**") - st.metric("Mean Depth", f"{depths.mean():.2f}") - st.metric("Max Depth", f"{depths.max()}") - st.metric("Min Depth", f"{depths.min()}") - with col2: - st.write("**Leaf Counts:**") - st.metric("Mean Leaves", f"{leaves.mean():.2f}") - st.metric("Max Leaves", f"{leaves.max()}") - st.metric("Min Leaves", f"{leaves.min()}") - with col3: - st.write("**Node Counts:**") - st.metric("Mean Nodes", f"{nodes.mean():.2f}") - st.metric("Max Nodes", f"{nodes.max()}") - st.metric("Min Nodes", f"{nodes.min()}") - - # Feature source analysis - st.subheader("Feature Importance by Data Source") - st.markdown( - """ - Breakdown of feature importance by data source (AlphaEarth embeddings, ERA5 climate, - ArcticDEM topography, and common features). - """ - ) - - # Get members used in training - members = get_members_from_settings(selected_result.settings) - - # Extract features by source - embedding_feature_array = None - if "AlphaEarth" in members: - embedding_feature_array = extract_embedding_features(model_state, importance_type="feature_importance") - - era5_yearly_array = None - era5_seasonal_array = None - era5_shoulder_array = None - if "ERA5-yearly" in members: - era5_yearly_array = extract_era5_features( - model_state, importance_type="feature_importance", temporal_group="yearly" - ) - if "ERA5-seasonal" in members: - era5_seasonal_array = extract_era5_features( - model_state, importance_type="feature_importance", temporal_group="seasonal" - ) - if "ERA5-shoulder" in members: - era5_shoulder_array = extract_era5_features( - model_state, importance_type="feature_importance", temporal_group="shoulder" - ) - - arcticdem_feature_array = None - if "ArcticDEM" in members: - arcticdem_feature_array = extract_arcticdem_features(model_state, importance_type="feature_importance") - - common_feature_array = extract_common_features(model_state, importance_type="feature_importance") - - # Render each source's features if present - if embedding_feature_array is not None: - render_embedding_features(embedding_feature_array) - - if era5_yearly_array is not None: - render_era5_features(era5_yearly_array, temporal_group="Yearly") - - if era5_seasonal_array is not None: - render_era5_features(era5_seasonal_array, temporal_group="Seasonal") - - if era5_shoulder_array is not None: - render_era5_features(era5_shoulder_array, temporal_group="Shoulder") - - if arcticdem_feature_array is not None: - render_arcticdem_features(arcticdem_feature_array) - - if common_feature_array is not None: - render_common_features(common_feature_array) - - -def render_knn_model_state(model_state: xr.Dataset, selected_result: TrainingResult): - """Render visualizations for KNN model. - - Args: - model_state: Xarray dataset containing KNN model state. - selected_result: TrainingResult object containing training configuration. - - """ - st.header("🔍 K-Nearest Neighbors Model Analysis") - st.markdown( - """ - K-Nearest Neighbors is a non-parametric, instance-based learning algorithm. - Unlike tree-based or parametric models, KNN doesn't learn feature weights or build - a model structure. Instead, it memorizes the training data and makes predictions - based on the k nearest neighbors. - """ - ) - - # Display model metadata - st.subheader("Model Configuration") - col1, col2, col3 = st.columns(3) - with col1: - st.metric("Number of Neighbors (k)", model_state.attrs.get("n_neighbors", "N/A")) - st.metric("Training Samples", model_state.attrs.get("n_samples_fit", "N/A")) - with col2: - st.metric("Weights", model_state.attrs.get("weights", "N/A")) - st.metric("Algorithm", model_state.attrs.get("algorithm", "N/A")) - with col3: - st.metric("Metric", model_state.attrs.get("metric", "N/A")) - - st.info( - """ - **Note:** KNN doesn't have traditional feature importance or model parameters to visualize. - The model's behavior depends entirely on: - - The number of neighbors (k) - - The distance metric used - - The weighting scheme for neighbors - - To understand the model better, consider visualizing the decision boundaries on a - reduced-dimensional representation of your data (e.g., using PCA or t-SNE). - """ - ) - - -# Helper functions for embedding/era5/common features -def render_embedding_features(embedding_feature_array: xr.DataArray): - """Render embedding feature visualizations. - - Args: - embedding_feature_array: DataArray containing AlphaEarth embedding feature weights. - - """ - with st.container(border=True): - st.header("🛰️ Embedding Feature Analysis") - st.markdown( - """ - Analysis of embedding features showing which aggregations, bands, and years - are most important for the model predictions. - """ - ) - - # Summary bar charts - st.markdown("### Importance by Dimension") - with st.spinner("Generating dimension summaries..."): - chart_agg, chart_band, chart_year = plot_embedding_aggregation_summary(embedding_feature_array) - col1, col2, col3 = st.columns(3) - with col1: - st.altair_chart(chart_agg, width="stretch") - with col2: - st.altair_chart(chart_band, width="stretch") - with col3: - st.altair_chart(chart_year, width="stretch") - - # Detailed heatmap - st.markdown("### Detailed Heatmap by Aggregation") - st.markdown("Shows the weight of each band-year combination for each aggregation type.") - with st.spinner("Generating heatmap..."): - heatmap_chart = plot_embedding_heatmap(embedding_feature_array) - st.altair_chart(heatmap_chart, width="stretch") - - # Statistics - with st.expander("Embedding Feature Statistics"): - st.write("**Overall Statistics:**") - n_emb_features = embedding_feature_array.size - mean_weight = float(embedding_feature_array.mean().values) - max_weight = float(embedding_feature_array.max().values) - col1, col2, col3 = st.columns(3) - with col1: - st.metric("Total Embedding Features", n_emb_features) - with col2: - st.metric("Mean Weight", f"{mean_weight:.4f}") - with col3: - st.metric("Max Weight", f"{max_weight:.4f}") - - # Show top embedding features - st.write("**Top 10 Embedding Features:**") - emb_df = embedding_feature_array.to_dataframe(name="weight").reset_index() - top_emb = emb_df.nlargest(10, "weight")[["agg", "band", "year", "weight"]] - st.dataframe(top_emb, width="stretch") - - -def render_era5_features(era5_feature_array: xr.DataArray, temporal_group: str = ""): - """Render ERA5 feature visualizations. - - Args: - era5_feature_array: ERA5 feature importance array. - temporal_group: Name of the temporal grouping (e.g., "Yearly", "Seasonal", "Shoulder"). - - """ - group_suffix = f" ({temporal_group})" if temporal_group else "" - - with st.container(border=True): - st.header(f"⛅ ERA5 Feature Analysis{group_suffix}") - temporal_suffix = f" for {temporal_group.lower()} aggregation" if temporal_group else "" - st.markdown( - f""" - Analysis of ERA5 climate features{temporal_suffix} showing which variables and time periods - are most important for the model predictions. - """ - ) - - # Summary bar charts - st.markdown("### Importance by Dimension") - with st.spinner("Generating ERA5 dimension summaries..."): - charts = plot_era5_summary(era5_feature_array) - - # Check if this is seasonal/shoulder data (returns 3 charts) or yearly (returns 2 charts) - if len(charts) == 3: - chart_variable, chart_season, chart_year = charts - col1, col2, col3 = st.columns(3) - with col1: - st.altair_chart(chart_variable, width="stretch") - with col2: - st.altair_chart(chart_season, width="stretch") - with col3: - st.altair_chart(chart_year, width="stretch") - else: - chart_variable, chart_time = charts - col1, col2 = st.columns(2) - with col1: - st.altair_chart(chart_variable, width="stretch") - with col2: - st.altair_chart(chart_time, width="stretch") - - # Detailed heatmap - st.markdown("### Detailed Heatmap") - - # Check if this is seasonal/shoulder data - has_season = "season" in era5_feature_array.dims - - if has_season: - st.markdown("Shows the weight of each variable-season-year combination.") - with st.spinner("Generating ERA5 season heatmap..."): - era5_heatmap_chart = plot_era5_heatmap(era5_feature_array) - st.altair_chart(era5_heatmap_chart, width="stretch") - - # Add time-based heatmap for seasonal/shoulder - st.markdown("### By Time Heatmap") - st.markdown("Shows temporal trends by averaging over seasons.") - with st.spinner("Generating ERA5 time heatmap..."): - era5_time_heatmap_chart = plot_era5_time_heatmap(era5_feature_array) - if era5_time_heatmap_chart is not None: - st.altair_chart(era5_time_heatmap_chart, width="stretch") - else: - st.markdown("Shows the weight of each variable-time combination.") - with st.spinner("Generating ERA5 heatmap..."): - era5_heatmap_chart = plot_era5_heatmap(era5_feature_array) - st.altair_chart(era5_heatmap_chart, width="stretch") - - # Statistics - with st.expander("ERA5 Feature Statistics"): - st.write("**Overall Statistics:**") - n_era5_features = era5_feature_array.size - mean_weight = float(era5_feature_array.mean().values) - max_weight = float(era5_feature_array.max().values) - col1, col2, col3 = st.columns(3) - with col1: - st.metric("Total ERA5 Features", n_era5_features) - with col2: - st.metric("Mean Weight", f"{mean_weight:.4f}") - with col3: - st.metric("Max Weight", f"{max_weight:.4f}") - - # Show top ERA5 features - st.write("**Top 10 ERA5 Features:**") - era5_df = era5_feature_array.to_dataframe(name="weight").reset_index() - # Get all columns except 'weight' for display - display_cols = [col for col in era5_df.columns if col != "weight"] + ["weight"] - top_era5 = era5_df.nlargest(10, "weight")[display_cols] - st.dataframe(top_era5, width="stretch") - - -def render_arcticdem_features(arcticdem_feature_array: xr.DataArray): - """Render ArcticDEM feature visualizations. - - Args: - arcticdem_feature_array: DataArray containing ArcticDEM feature weights. - - """ - with st.container(border=True): - st.header("🏔️ ArcticDEM Feature Analysis") - st.markdown( - """ - Analysis of ArcticDEM topographic features showing which terrain variables and - aggregations are most important for the model predictions. - """ - ) - - # Summary bar charts - st.markdown("### Importance by Dimension") - with st.spinner("Generating ArcticDEM dimension summaries..."): - chart_variable, chart_agg = plot_arcticdem_summary(arcticdem_feature_array) - col1, col2 = st.columns(2) - with col1: - st.altair_chart(chart_variable, width="stretch") - with col2: - st.altair_chart(chart_agg, width="stretch") - - # Detailed heatmap - st.markdown("### Detailed Heatmap") - st.markdown("Shows the weight of each variable-aggregation combination.") - with st.spinner("Generating ArcticDEM heatmap..."): - arcticdem_heatmap_chart = plot_arcticdem_heatmap(arcticdem_feature_array) - st.altair_chart(arcticdem_heatmap_chart, width="stretch") - - # Statistics - with st.expander("ArcticDEM Feature Statistics"): - st.write("**Overall Statistics:**") - n_arcticdem_features = arcticdem_feature_array.size - mean_weight = float(arcticdem_feature_array.mean().values) - max_weight = float(arcticdem_feature_array.max().values) - col1, col2, col3 = st.columns(3) - with col1: - st.metric("Total ArcticDEM Features", n_arcticdem_features) - with col2: - st.metric("Mean Weight", f"{mean_weight:.4f}") - with col3: - st.metric("Max Weight", f"{max_weight:.4f}") - - # Show top ArcticDEM features - st.write("**Top 10 ArcticDEM Features:**") - arcticdem_df = arcticdem_feature_array.to_dataframe(name="weight").reset_index() - top_arcticdem = arcticdem_df.nlargest(10, "weight")[["variable", "agg", "weight"]] - st.dataframe(top_arcticdem, width="stretch") - - -def render_common_features(common_feature_array: xr.DataArray): - """Render common feature visualizations. - - Args: - common_feature_array: DataArray containing common feature weights. - - """ - with st.container(border=True): - st.header("🗺️ Common Feature Analysis") - st.markdown( - """ - Analysis of common features including cell area, water area, land area, land ratio, - longitude, and latitude. These features provide spatial and geographic context. - """ - ) - - # Bar chart showing all common feature weights - with st.spinner("Generating common features chart..."): - common_chart = plot_common_features(common_feature_array) - st.altair_chart(common_chart, width="stretch") - - # Statistics - with st.expander("Common Feature Statistics"): - st.write("**Overall Statistics:**") - n_common_features = common_feature_array.size - mean_weight = float(common_feature_array.mean().values) - max_weight = float(common_feature_array.max().values) - min_weight = float(common_feature_array.min().values) - col1, col2, col3, col4 = st.columns(4) - with col1: - st.metric("Total Common Features", n_common_features) - with col2: - st.metric("Mean Weight", f"{mean_weight:.4f}") - with col3: - st.metric("Max Weight", f"{max_weight:.4f}") - with col4: - st.metric("Min Weight", f"{min_weight:.4f}") - - # Show all common features sorted by importance - st.write("**All Common Features (by absolute weight):**") - common_df = common_feature_array.to_dataframe(name="weight").reset_index() - common_df["abs_weight"] = common_df["weight"].abs() - common_df = common_df.sort_values("abs_weight", ascending=False) - st.dataframe(common_df[["feature", "weight", "abs_weight"]], width="stretch") - - st.markdown( - """ - **Interpretation:** - - **cell_area, water_area, land_area**: Spatial extent features that may indicate - size-related patterns - - **land_ratio**: Proportion of land vs water in each cell - - **lon, lat**: Geographic coordinates that can capture spatial trends or regional patterns - - Positive weights indicate features that increase the probability of the positive class - - Negative weights indicate features that decrease the probability of the positive class - """ - ) diff --git a/src/entropice/dashboard/views/overview_page.py b/src/entropice/dashboard/views/overview_page.py index e594cee..dded4fd 100644 --- a/src/entropice/dashboard/views/overview_page.py +++ b/src/entropice/dashboard/views/overview_page.py @@ -9,7 +9,7 @@ from entropice.dashboard.sections.experiment_results import ( render_training_results_summary, ) from entropice.dashboard.sections.storage_statistics import render_storage_statistics -from entropice.dashboard.utils.loaders import load_all_autogluon_training_results, load_all_training_results +from entropice.dashboard.utils.loaders import load_all_training_results from entropice.dashboard.utils.stats import DatasetStatistics, load_all_default_dataset_statistics @@ -27,9 +27,6 @@ def render_overview_page(): ) # Load training results training_results = load_all_training_results() - autogluon_results = load_all_autogluon_training_results() - if len(autogluon_results) > 0: - training_results.extend(autogluon_results) if not training_results: st.warning("No training results found. Please run some training experiments first.") diff --git a/src/entropice/dashboard/views/training_analysis_page.py b/src/entropice/dashboard/views/training_analysis_page.py index 4aba169..5cd0398 100644 --- a/src/entropice/dashboard/views/training_analysis_page.py +++ b/src/entropice/dashboard/views/training_analysis_page.py @@ -51,9 +51,9 @@ def render_analysis_settings_sidebar(training_results: list[TrainingResult]) -> available_metrics = selected_result.available_metrics # Try to get refit metric from settings - if selected_result.settings.task == "binary": + if selected_result.run.task == "binary": refit_metric = "f1" - elif selected_result.settings.task in ["count_regimes", "density_regimes"]: + elif selected_result.run.task in ["count_regimes", "density_regimes"]: refit_metric = "f1_weighted" else: refit_metric = "r2" @@ -121,23 +121,29 @@ def render_training_analysis_page(): st.divider() # Render confusion matrices for classification, regression analysis for regression - if selected_result.settings.task in ["binary", "count_regimes", "density_regimes"]: + if selected_result.run.task in ["binary", "count_regimes", "density_regimes"]: render_confusion_matrices(selected_result) else: render_regression_analysis(selected_result) st.divider() - render_cv_statistics_section(cv_statistics, selected_result.test_metrics.get(selected_metric, float("nan"))) + if cv_statistics is not None: + test_score = selected_result._get_best_score("test") + render_cv_statistics_section(cv_statistics, test_score) - st.divider() + st.divider() render_hparam_space_section(selected_result, selected_metric) st.divider() # List all results at the end - st.header("📄 All Training Results") - st.dataframe(selected_result.results) + if selected_result.run.method_type == "HPOCV": + st.header("📄 All Cross-Validation Results") + st.dataframe(selected_result.run.cv_results) + elif selected_result.run.method_type == "AutoML": + st.header("📄 Model Leaderboard") + st.dataframe(selected_result.run.leaderboard) st.balloons() diff --git a/src/entropice/experiments/__init__.py b/src/entropice/experiments/__init__.py new file mode 100644 index 0000000..bf50408 --- /dev/null +++ b/src/entropice/experiments/__init__.py @@ -0,0 +1 @@ +"""Experiments.""" diff --git a/src/entropice/experiments/feature_importance.py b/src/entropice/experiments/feature_importance.py index 1a47856..e397024 100644 --- a/src/entropice/experiments/feature_importance.py +++ b/src/entropice/experiments/feature_importance.py @@ -1,4 +1,6 @@ -from typing import cast +"""Feature Importance Experiment.""" + +from typing import Literal, cast import cyclopts from stopuhr import stopwatch @@ -13,17 +15,19 @@ from entropice.utils.types import Grid, Model, TargetDataset, Task cli = cyclopts.App("entropice-feature-importance") -EXPERIMENT_NAME = "feature_importance_era5-shoulder_arcticdem" -# EXPERIMENT_NAME = "tobis-final-tests" +DEV = False + +EXPERIMENT_NAME = "feature_importance_era5-shoulder_arcticdem-v2" +if DEV: + EXPERIMENT_NAME = "tobis-final-tests" @cli.default -def main( - grid: Grid, - target: TargetDataset, -): +def main(grid: Grid, target: TargetDataset, selection: Literal["none", "cluster", "univariate"] = "none"): + """Feature Importance Experiment.""" levels = [3, 4, 5, 6] if grid == "hex" else [6, 7, 8, 9, 10] - levels = [3, 6] if grid == "hex" else [6, 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}...") dimension_filters = {"ArcticDEM": {"aggregations": ["median"]}} @@ -38,9 +42,11 @@ def main( # AutoGluon time_limit = 30 * 60 # 30 minutes - # time_limit = 60 + if DEV: + time_limit = 2 * 60 # 2 minutes presets = "extreme" - # presets = "medium" + if DEV: + presets = "medium" settings = AutoGluonRunSettings( time_limit=time_limit, presets=presets, @@ -59,11 +65,12 @@ def main( print(f"\nRunning HPOCV for model {model}...") n_iter = { "espa": 300, - "xgboost": 100, - "rf": 40, - "knn": 20, + "xgboost": 300, + "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] - # n_iter = 3 + if DEV: + n_iter = 3 scaler = "standard" if model in ["espa", "knn"] else "none" normalize = scaler != "none" settings = HPOCVRunSettings( diff --git a/src/entropice/experiments/feature_importance_alphaearth.py b/src/entropice/experiments/feature_importance_alphaearth.py new file mode 100644 index 0000000..c2cea25 --- /dev/null +++ b/src/entropice/experiments/feature_importance_alphaearth.py @@ -0,0 +1,79 @@ +"""Feature Importance Experiment.""" + +from typing import cast + +import cyclopts +from stopuhr import stopwatch + +from entropice.ml.autogluon import RunSettings as AutoGluonRunSettings +from entropice.ml.autogluon import train as train_autogluon +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 + +cli = cyclopts.App("entropice-feature-importance") + +EXPERIMENT_NAME = "feature_importance_era5-shoulder_arcticdem-v2" + + +@cli.default +def main(grid: Grid, target: TargetDataset): + """Feature Importance Experiment.""" + levels = [3, 4, 5, 6] if grid == "hex" else [6, 7, 8, 9, 10] + for level in levels: + print(f"Running feature importance experiment for {grid} grid at level {level}...") + dimension_filters = {"AlphaEarth": {"agg": ["median"]}} + dataset_ensemble = DatasetEnsemble( + grid=grid, level=level, members=["AlphaEarth"], dimension_filters=dimension_filters + ) + + for task in cast(list[Task], ["binary", "density"]): + print(f"\nRunning for {task}...") + + # AutoGluon + time_limit = 30 * 60 # 30 minutes + presets = "extreme" + settings = AutoGluonRunSettings( + time_limit=time_limit, + presets=presets, + verbosity=2, + task=task, + target=target, + ) + train_autogluon(dataset_ensemble, settings, experiment=EXPERIMENT_NAME) + + # HPOCV + splitter = "stratified_shuffle" if task == "binary" else "kfold" + models: list[Model] = ["xgboost", "rf", "knn"] + if task == "binary": + models.append("espa") + for model in models: + print(f"\nRunning HPOCV for model {model}...") + n_iter = { + "espa": 300, + "xgboost": 300, + "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] + settings = HPOCVRunSettings( + n_iter=n_iter, + task=task, + target=target, + splitter=splitter, + model=model, + # AlphaEarth Embeddings are already normalized unit vectors + scaler="none", + normalize=False, + ) + 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") + print("Done.") + + +if __name__ == "__main__": + cli() diff --git a/src/entropice/experiments/feature_selection.ipynb b/src/entropice/experiments/feature_selection.ipynb new file mode 100644 index 0000000..19aacc8 --- /dev/null +++ b/src/entropice/experiments/feature_selection.ipynb @@ -0,0 +1,3348 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "39d482f1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/raid/scratch/tohoel001/repositories/entropice/.pixi/envs/default/lib/python3.13/site-packages/cupyx/jit/_interface.py:173: FutureWarning: cupyx.jit.rawkernel is experimental. The interface can change in the future.\n", + " cupy._util.experimental('cupyx.jit.rawkernel')\n" + ] + } + ], + "source": [ + "from collections import defaultdict\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from scipy.cluster import hierarchy\n", + "from scipy.spatial.distance import squareform\n", + "from scipy.stats import spearmanr\n", + "from sklearn.feature_selection import (\n", + " VarianceThreshold,\n", + " f_classif,\n", + " f_regression,\n", + " mutual_info_classif,\n", + " mutual_info_regression,\n", + ")\n", + "from sklearn.preprocessing import (\n", + " Normalizer,\n", + " StandardScaler,\n", + ")\n", + "\n", + "from entropice.ml.dataset import DatasetEnsemble\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "70a62775", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            \n",
+       "            "
+      ],
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mIPython.core.display.HTML\u001b[0m\u001b[39m object\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading grid took 1.94s\n",
+      "Getting target labels (with task=density) took 2.09s\n",
+      "Loading features from cache took 0.03s\n",
+      "Row norms (should be close to 1): 1.0 1.0 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "grid = \"hex\"\n",
+    "level = 6\n",
+    "task = \"density\"\n",
+    "dimension_filters = {\"ArcticDEM\": {\"aggregations\": [\"median\"]}}\n",
+    "if (grid == \"hex\" and level in [3, 4]) or (grid == \"healpix\" and level in [6, 7]):\n",
+    "    dimension_filters[\"ERA5-shoulder\"] = {\"aggregations\": [\"median\"]}\n",
+    "dataset_ensemble = DatasetEnsemble(\n",
+    "    grid=grid, level=level, members=[\"ArcticDEM\", \"ERA5-shoulder\"], dimension_filters=dimension_filters\n",
+    ")\n",
+    "training_data = dataset_ensemble.create_training_set(task, \"darts_mllabels\")\n",
+    "\n",
+    "X_train = training_data.X.train\n",
+    "y_train = training_data.y.train\n",
+    "\n",
+    "X_train = StandardScaler().fit_transform(X_train)\n",
+    "X_train = Normalizer().fit_transform(X_train)\n",
+    "# Check if rows are unit vectors\n",
+    "row_norms = np.linalg.norm(X_train, axis=1)\n",
+    "print(\n",
+    "    \"Row norms (should be close to 1):\", row_norms.max().round(2), row_norms.min().round(2), row_norms.mean().round(2)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "23d54807",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            \n",
+       "            "
+      ],
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mIPython.core.display.HTML\u001b[0m\u001b[39m object\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top_features=array(['era5_t2m_avg_trend_JAS', 'era5_sshf_OND', 'era5_t2m_skew_AMJ',\n",
+      "       'era5_snowc_mean_trend_AMJ', 'era5_freezing_degree_days_trend_JFM',\n",
+      "       'era5_sf_trend_JAS', 'era5_t2m_avg_trend_JFM', 'era5_sf_trend_OND',\n",
+      "       'era5_freezing_days_OND', 'era5_thawing_days_OND',\n",
+      "       'era5_snowfall_occurrences_JFM', 'era5_t2m_avg_OND',\n",
+      "       'era5_t2m_max_trend_OND', 'era5_precipitation_occurrences_OND',\n",
+      "       'era5_t2m_max_AMJ', 'era5_sf_JAS', 'era5_freezing_degree_days_JAS',\n",
+      "       'era5_thawing_days_trend_JFM', 'era5_freezing_days_trend_JFM',\n",
+      "       'era5_effective_snow_depth_trend_AMJ',\n",
+      "       'era5_precipitation_occurrences_AMJ', 'era5_sshf_JFM',\n",
+      "       'era5_t2m_avg_trend_OND', 'era5_tp_AMJ', 'era5_t2m_max_JFM',\n",
+      "       'era5_t2m_max_JAS', 'era5_snowfall_occurrences_JAS',\n",
+      "       'era5_t2m_skew_JFM', 'era5_tp_JAS',\n",
+      "       'era5_freezing_degree_days_JFM', 'era5_t2m_mean_trend_OND',\n",
+      "       'era5_freezing_degree_days_trend_JAS', 'era5_t2m_mean_JFM',\n",
+      "       'era5_snowfall_occurrences_trend_JAS', 'era5_t2m_min_AMJ',\n",
+      "       'era5_snowc_mean_trend_JAS', 'era5_t2m_max_OND',\n",
+      "       'era5_t2m_skew_JAS', 'era5_t2m_mean_trend_JFM',\n",
+      "       'era5_thawing_days_JAS', 'era5_t2m_range_JAS',\n",
+      "       'era5_freezing_days_JAS', 'era5_t2m_avg_JFM',\n",
+      "       'era5_t2m_min_trend_JAS', 'era5_t2m_min_trend_JFM',\n",
+      "       'era5_thawing_days_trend_JAS', 'era5_freezing_days_trend_JAS',\n",
+      "       'era5_t2m_mean_JAS', 'era5_freezing_degree_days_trend_OND',\n",
+      "       'era5_freezing_days_AMJ', 'era5_thawing_days_AMJ',\n",
+      "       'era5_freezing_degree_days_AMJ', 'era5_t2m_range_AMJ',\n",
+      "       'era5_t2m_avg_AMJ', 'era5_thawing_degree_days_JAS', 'x',\n",
+      "       'era5_t2m_avg_JAS', 'era5_t2m_mean_AMJ',\n",
+      "       'era5_thawing_degree_days_AMJ', 'y'], dtype='\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            \n",
+       "            "
+      ],
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mIPython.core.display.HTML\u001b[0m\u001b[39m object\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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+       "
FeatureMIF-testVarianceSourceVariableSeasonScore
0cell_area0.2886200.0065130.006654GeneralCell Area0.159318
1land_area0.3509960.0591730.005008GeneralLand Area0.218591
2water_area0.1554410.2067970.003999GeneralWater Area0.181522
3land_ratio0.1859040.1957790.003874GeneralLand Ratio0.190856
4x0.8953580.0199010.008507GeneralX0.679752
...........................
174era5_tp_OND0.4583530.1162010.002354ERA5TpOND0.308114
175era5_tp_trend_AMJ0.2962950.0734980.006428ERA5TpAMJ Trend0.192545
176era5_tp_trend_JAS0.4475290.0152660.005601ERA5TpJAS Trend0.262412
177era5_tp_trend_JFM0.1813510.0079290.001945ERA5TpJFM Trend0.098802
178era5_tp_trend_OND0.2743160.0063980.003441ERA5TpOND Trend0.150859
\n",
+       "
179 rows × 8 columns
\n",
+       "
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+      ],
+      "text/plain": [
+       "\n",
+       "               Feature        MI    F-test  Variance   Source    Variable  \\\n",
+       "\u001b[1;36m0\u001b[0m            cell_area  \u001b[1;36m0.288620\u001b[0m  \u001b[1;36m0.006513\u001b[0m  \u001b[1;36m0.006654\u001b[0m  General   Cell Area   \n",
+       "\u001b[1;36m1\u001b[0m            land_area  \u001b[1;36m0.350996\u001b[0m  \u001b[1;36m0.059173\u001b[0m  \u001b[1;36m0.005008\u001b[0m  General   Land Area   \n",
+       "\u001b[1;36m2\u001b[0m           water_area  \u001b[1;36m0.155441\u001b[0m  \u001b[1;36m0.206797\u001b[0m  \u001b[1;36m0.003999\u001b[0m  General  Water Area   \n",
+       "\u001b[1;36m3\u001b[0m           land_ratio  \u001b[1;36m0.185904\u001b[0m  \u001b[1;36m0.195779\u001b[0m  \u001b[1;36m0.003874\u001b[0m  General  Land Ratio   \n",
+       "\u001b[1;36m4\u001b[0m                    x  \u001b[1;36m0.895358\u001b[0m  \u001b[1;36m0.019901\u001b[0m  \u001b[1;36m0.008507\u001b[0m  General           X   \n",
+       "..                 \u001b[33m...\u001b[0m       \u001b[33m...\u001b[0m       \u001b[33m...\u001b[0m       \u001b[33m...\u001b[0m      \u001b[33m...\u001b[0m         \u001b[33m...\u001b[0m   \n",
+       "\u001b[1;36m174\u001b[0m        era5_tp_OND  \u001b[1;36m0.458353\u001b[0m  \u001b[1;36m0.116201\u001b[0m  \u001b[1;36m0.002354\u001b[0m     ERA5          Tp   \n",
+       "\u001b[1;36m175\u001b[0m  era5_tp_trend_AMJ  \u001b[1;36m0.296295\u001b[0m  \u001b[1;36m0.073498\u001b[0m  \u001b[1;36m0.006428\u001b[0m     ERA5         Tp    \n",
+       "\u001b[1;36m176\u001b[0m  era5_tp_trend_JAS  \u001b[1;36m0.447529\u001b[0m  \u001b[1;36m0.015266\u001b[0m  \u001b[1;36m0.005601\u001b[0m     ERA5         Tp    \n",
+       "\u001b[1;36m177\u001b[0m  era5_tp_trend_JFM  \u001b[1;36m0.181351\u001b[0m  \u001b[1;36m0.007929\u001b[0m  \u001b[1;36m0.001945\u001b[0m     ERA5         Tp    \n",
+       "\u001b[1;36m178\u001b[0m  era5_tp_trend_OND  \u001b[1;36m0.274316\u001b[0m  \u001b[1;36m0.006398\u001b[0m  \u001b[1;36m0.003441\u001b[0m     ERA5         Tp    \n",
+       "\n",
+       "        Season     Score  \n",
+       "\u001b[1;36m0\u001b[0m               \u001b[1;36m0.159318\u001b[0m  \n",
+       "\u001b[1;36m1\u001b[0m               \u001b[1;36m0.218591\u001b[0m  \n",
+       "\u001b[1;36m2\u001b[0m               \u001b[1;36m0.181522\u001b[0m  \n",
+       "\u001b[1;36m3\u001b[0m               \u001b[1;36m0.190856\u001b[0m  \n",
+       "\u001b[1;36m4\u001b[0m               \u001b[1;36m0.679752\u001b[0m  \n",
+       "..         \u001b[33m...\u001b[0m       \u001b[33m...\u001b[0m  \n",
+       "\u001b[1;36m174\u001b[0m        OND  \u001b[1;36m0.308114\u001b[0m  \n",
+       "\u001b[1;36m175\u001b[0m  AMJ Trend  \u001b[1;36m0.192545\u001b[0m  \n",
+       "\u001b[1;36m176\u001b[0m  JAS Trend  \u001b[1;36m0.262412\u001b[0m  \n",
+       "\u001b[1;36m177\u001b[0m  JFM Trend  \u001b[1;36m0.098802\u001b[0m  \n",
+       "\u001b[1;36m178\u001b[0m  OND Trend  \u001b[1;36m0.150859\u001b[0m  \n",
+       "\n",
+       "\u001b[1m[\u001b[0m\u001b[1;36m179\u001b[0m rows x \u001b[1;36m8\u001b[0m columns\u001b[1m]\u001b[0m"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Parse the variables of the feature name\n",
+    "# A feature name looks like this: \"source_variable_season\"\n",
+    "def parse_feature_name(feature_name):\n",
+    "    parts = feature_name.split(\"_\")\n",
+    "    match parts[0]:\n",
+    "        case \"arcticdem\":\n",
+    "            source = \"ArcticDEM\"\n",
+    "            variable = \" \".join(parts[1:]).replace(\"median\", \"\").title()\n",
+    "            season = \"\"\n",
+    "        case \"era5\":\n",
+    "            source = \"ERA5\"\n",
+    "            variable = \" \".join(parts[1:-1]).replace(\"trend\", \"\").title()\n",
+    "            season = parts[-1]\n",
+    "            if \"trend\" in feature_name:\n",
+    "                season += \" Trend\"\n",
+    "        case _:\n",
+    "            return \"General\", feature_name.replace(\"_\", \" \").title(), \"\"\n",
+    "    return source, variable, season\n",
+    "\n",
+    "\n",
+    "# Make a dataframe of the feature names, their scores and their parsed components\n",
+    "feature_similarities = pd.DataFrame({\"Feature\": feature_names, \"MI\": mi, \"F-test\": f, \"Variance\": var})\n",
+    "feature_similarities[[\"Source\", \"Variable\", \"Season\"]] = (\n",
+    "    feature_similarities[\"Feature\"].apply(parse_feature_name).apply(pd.Series)\n",
+    ")\n",
+    "feature_similarities[\"Score\"] = combined_score\n",
+    "feature_similarities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "177395e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            \n",
+       "            "
+      ],
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mIPython.core.display.HTML\u001b[0m\u001b[39m object\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size \u001b[0m\u001b[1;36m1133.\u001b[0m\u001b[39m12x1000 with \u001b[0m\u001b[1;36m20\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(style=\"whitegrid\")\n",
+    "g = sns.PairGrid(\n",
+    "    feature_similarities.sort_index(ascending=False),\n",
+    "    vars=[\"MI\", \"F-test\", \"Variance\", \"Score\"],\n",
+    "    hue=\"Source\",\n",
+    "    palette=\"Set2\",\n",
+    ")\n",
+    "g.map_upper(sns.scatterplot)\n",
+    "g.map_lower(sns.kdeplot, fill=True, alpha=0.5)\n",
+    "g.map_diag(sns.histplot, kde=True)\n",
+    "g.add_legend()\n",
+    "plt.suptitle(\"Feature importance based on variance, MI and F-test\", y=1.02)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "60aa4192",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
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+          "colorway": [
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+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "white",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "#C8D4E3"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "white",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "#EBF0F8",
+            "linecolor": "#EBF0F8",
+            "ticks": ""
+           },
+           "bgcolor": "white",
+           "radialaxis": {
+            "gridcolor": "#EBF0F8",
+            "linecolor": "#EBF0F8",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "white",
+            "gridcolor": "#DFE8F3",
+            "gridwidth": 2,
+            "linecolor": "#EBF0F8",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "#EBF0F8"
+           },
+           "yaxis": {
+            "backgroundcolor": "white",
+            "gridcolor": "#DFE8F3",
+            "gridwidth": 2,
+            "linecolor": "#EBF0F8",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "#EBF0F8"
+           },
+           "zaxis": {
+            "backgroundcolor": "white",
+            "gridcolor": "#DFE8F3",
+            "gridwidth": 2,
+            "linecolor": "#EBF0F8",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "#EBF0F8"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "#DFE8F3",
+            "linecolor": "#A2B1C6",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "#DFE8F3",
+            "linecolor": "#A2B1C6",
+            "ticks": ""
+           },
+           "bgcolor": "white",
+           "caxis": {
+            "gridcolor": "#DFE8F3",
+            "linecolor": "#A2B1C6",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "#EBF0F8",
+           "linecolor": "#EBF0F8",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "#EBF0F8",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "#EBF0F8",
+           "linecolor": "#EBF0F8",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "#EBF0F8",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Feature importance based on Mututal Information and F-test"
+        },
+        "width": 900,
+        "xaxis": {
+         "title": {
+          "text": "Mutual Information"
+         }
+        },
+        "yaxis": {
+         "title": {
+          "text": "F-test p-value"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.graph_objects as go\n",
+    "\n",
+    "tplt = \"%{text}
MI: %{x:.3f}
F-test: %{y:.3f}
F-test: %{y:.3f}
Variance: %{marker.color:.3f}\"\n",
+    "fig = go.Figure(\n",
+    "    data=go.Scatter(\n",
+    "        x=mi,\n",
+    "        y=f,\n",
+    "        mode=\"markers\",\n",
+    "        text=feature_names,\n",
+    "        marker={\n",
+    "            \"size\": 8,\n",
+    "            \"color\": combined_score,\n",
+    "            \"colorscale\": \"Viridis\",\n",
+    "            \"showscale\": True,\n",
+    "            \"colorbar\": {\"title\": \"Combined Score\"},\n",
+    "            \"opacity\": 0.7,\n",
+    "            \"line\": {\"width\": 0.5, \"color\": \"white\"},\n",
+    "        },\n",
+    "        hovertemplate=(\n",
+    "            \"%{text}
MI: %{x:.3f}
F-test: %{y:.3f}
Combined: %{marker.color:.3f}}\"\n",
+    "        ),\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    title=\"Feature importance based on Mututal Information and F-test\",\n",
+    "    xaxis_title=\"Mutual Information\",\n",
+    "    yaxis_title=\"F-test p-value\",\n",
+    "    hovermode=\"closest\",\n",
+    "    height=600,\n",
+    "    width=900,\n",
+    "    template=\"plotly_white\",\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d93aa493",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            \n",
+       "            "
+      ],
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mIPython.core.display.HTML\u001b[0m\u001b[39m object\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mAxes:\u001b[0m\u001b[39m \u001b[0m\u001b[33mxlabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Score'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mylabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Source'\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 64\u001b[0m\u001b[1;36m0x480\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.swarmplot(data=feature_similarities, x=\"Score\", y=\"Source\", hue=\"Season\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "893a7e15",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            \n",
+       "            "
+      ],
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mIPython.core.display.HTML\u001b[0m\u001b[39m object\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mseaborn.axisgrid.FacetGrid\u001b[0m\u001b[39m object at \u001b[0m\u001b[1;36m0x7f8b4c1b9e80\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 1137x1000 with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(data=feature_similarities, x=\"Score\", y=\"Variable\", hue=\"Season\", height=10, aspect=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f7c3c43d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            \n",
+       "            "
+      ],
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mIPython.core.display.HTML\u001b[0m\u001b[39m object\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[1;36m46\u001b[0m"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(feature_similarities[feature_similarities[\"Score\"] > 0.4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5a025f30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            \n",
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+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mIPython.core.display.HTML\u001b[0m\u001b[39m object\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+     "metadata": {},
+     "output_type": "display_data"
+    },
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+       "
FeatureMIF-testVarianceSourceVariableSeasonScore
5y1.0000000.0533770.007329GeneralY1.000000
40era5_freezing_degree_days_trend_JAS0.6652101.0000000.005918ERA5Freezing Degree DaysJAS Trend1.000000
108era5_t2m_avg_JAS0.8988310.7361190.005625ERA5T2M AvgJAS0.836609
164era5_thawing_degree_days_JAS0.8883720.7278570.005626ERA5Thawing Degree DaysJAS0.825705
163era5_thawing_degree_days_AMJ0.9378530.4492690.006484ERA5Thawing Degree DaysAMJ0.814997
124era5_t2m_mean_JAS0.8004830.7477950.005750ERA5T2M MeanJAS0.775680
123era5_t2m_mean_AMJ0.9062580.4473500.006278ERA5T2M MeanAMJ0.772390
42era5_freezing_degree_days_trend_OND0.8279920.6395380.006551ERA5Freezing Degree DaysOND Trend0.750997
160era5_thawing_days_trend_JAS0.7672530.7039770.005935ERA5Thawing DaysJAS Trend0.737515
32era5_freezing_days_trend_JAS0.7672530.7039770.005935ERA5Freezing DaysJAS Trend0.737515
136era5_t2m_min_trend_JAS0.7253230.7444400.007912ERA5T2M MinJAS Trend0.735054
107era5_t2m_avg_AMJ0.8773510.4211840.006320ERA5T2M AvgAMJ0.733558
35era5_freezing_degree_days_AMJ0.8329200.4802560.006077ERA5Freezing Degree DaysAMJ0.705316
139era5_t2m_range_AMJ0.8411320.4263160.007174ERA5T2M RangeAMJ0.698106
88era5_snowc_mean_trend_JAS0.6912550.6981090.004649ERA5Snowc MeanJAS Trend0.694701
4x0.8953580.0199010.008507GeneralX0.679752
155era5_thawing_days_AMJ0.8284040.3621410.006062ERA5Thawing DaysAMJ0.669162
27era5_freezing_days_AMJ0.8284040.3621410.006062ERA5Freezing DaysAMJ0.669162
140era5_t2m_range_JAS0.7070270.6189880.006164ERA5T2M RangeJAS0.665895
137era5_t2m_min_trend_JFM0.7402880.5445400.006993ERA5T2M MinJFM Trend0.656069
129era5_t2m_mean_trend_JFM0.6970340.4804830.006620ERA5T2M MeanJFM Trend0.603268
115era5_t2m_max_AMJ0.5518590.6431810.005506ERA5T2M MaxAMJ0.600118
28era5_freezing_days_JAS0.7072490.4499540.003554ERA5Freezing DaysJAS0.598719
156era5_thawing_days_JAS0.7052910.4499540.003554ERA5Thawing DaysJAS0.597379
116era5_t2m_max_JAS0.6142450.5784660.004727ERA5T2M MaxJAS0.596752
118era5_t2m_max_OND0.6964210.4209450.005769ERA5T2M MaxOND0.580728
113era5_t2m_avg_trend_JFM0.5281890.5475350.006607ERA5T2M AvgJFM Trend0.537964
131era5_t2m_min_AMJ0.6863650.2868990.005750ERA5T2M MinAMJ0.527080
109era5_t2m_avg_JFM0.7198940.1954490.005759ERA5T2M AvgJFM0.525279
92era5_snowfall_occurrences_JAS0.6181770.3847000.005357ERA5Snowfall OccurrencesJAS0.515298
171era5_tp_AMJ0.5962690.4089360.004515ERA5TpAMJ0.511501
117era5_t2m_max_JFM0.6131280.3766540.006591ERA5T2M MaxJFM0.508925
96era5_snowfall_occurrences_trend_JAS0.6848690.2041020.005778ERA5Snowfall OccurrencesJAS Trend0.499189
172era5_tp_JAS0.6319090.3117140.004316ERA5TpJAS0.496659
130era5_t2m_mean_trend_OND0.6626790.2327040.007430ERA5T2M MeanOND Trend0.491251
59era5_precipitation_occurrences_AMJ0.5713890.3915430.007040ERA5Precipitation OccurrencesAMJ0.489323
41era5_freezing_degree_days_trend_JFM0.5224290.4482130.006358ERA5Freezing Degree DaysJFM Trend0.486661
125era5_t2m_mean_JFM0.6714910.1966850.005783ERA5T2M MeanJFM0.486292
148era5_t2m_skew_JAS0.6964670.1024750.009218ERA5T2M SkewJAS0.478053
101era5_sshf_JFM0.5739200.3380540.007279ERA5SshfJFM0.468924
37era5_freezing_degree_days_JFM0.6497170.1935810.005620ERA5Freezing Degree DaysJFM0.468517
36era5_freezing_degree_days_JAS0.5627960.3336400.002855ERA5Freezing Degree DaysJAS0.460245
122era5_t2m_max_trend_OND0.5485240.3367540.008155ERA5T2M MaxOND Trend0.452789
114era5_t2m_avg_trend_OND0.5904250.2495920.007352ERA5T2M AvgOND Trend0.445610
76era5_sf_JAS0.5607540.2202980.004595ERA5SfJAS0.414782
102era5_sshf_OND0.4994820.3091130.005727ERA5SshfOND0.411951
\n",
+       "
"
+      ],
+      "text/plain": [
+       "\n",
+       "                                 Feature        MI    F-test  Variance  \\\n",
+       "\u001b[1;36m5\u001b[0m                                      y  \u001b[1;36m1.000000\u001b[0m  \u001b[1;36m0.053377\u001b[0m  \u001b[1;36m0.007329\u001b[0m   \n",
+       "\u001b[1;36m40\u001b[0m   era5_freezing_degree_days_trend_JAS  \u001b[1;36m0.665210\u001b[0m  \u001b[1;36m1.000000\u001b[0m  \u001b[1;36m0.005918\u001b[0m   \n",
+       "\u001b[1;36m108\u001b[0m                     era5_t2m_avg_JAS  \u001b[1;36m0.898831\u001b[0m  \u001b[1;36m0.736119\u001b[0m  \u001b[1;36m0.005625\u001b[0m   \n",
+       "\u001b[1;36m164\u001b[0m         era5_thawing_degree_days_JAS  \u001b[1;36m0.888372\u001b[0m  \u001b[1;36m0.727857\u001b[0m  \u001b[1;36m0.005626\u001b[0m   \n",
+       "\u001b[1;36m163\u001b[0m         era5_thawing_degree_days_AMJ  \u001b[1;36m0.937853\u001b[0m  \u001b[1;36m0.449269\u001b[0m  \u001b[1;36m0.006484\u001b[0m   \n",
+       "\u001b[1;36m124\u001b[0m                    era5_t2m_mean_JAS  \u001b[1;36m0.800483\u001b[0m  \u001b[1;36m0.747795\u001b[0m  \u001b[1;36m0.005750\u001b[0m   \n",
+       "\u001b[1;36m123\u001b[0m                    era5_t2m_mean_AMJ  \u001b[1;36m0.906258\u001b[0m  \u001b[1;36m0.447350\u001b[0m  \u001b[1;36m0.006278\u001b[0m   \n",
+       "\u001b[1;36m42\u001b[0m   era5_freezing_degree_days_trend_OND  \u001b[1;36m0.827992\u001b[0m  \u001b[1;36m0.639538\u001b[0m  \u001b[1;36m0.006551\u001b[0m   \n",
+       "\u001b[1;36m160\u001b[0m          era5_thawing_days_trend_JAS  \u001b[1;36m0.767253\u001b[0m  \u001b[1;36m0.703977\u001b[0m  \u001b[1;36m0.005935\u001b[0m   \n",
+       "\u001b[1;36m32\u001b[0m          era5_freezing_days_trend_JAS  \u001b[1;36m0.767253\u001b[0m  \u001b[1;36m0.703977\u001b[0m  \u001b[1;36m0.005935\u001b[0m   \n",
+       "\u001b[1;36m136\u001b[0m               era5_t2m_min_trend_JAS  \u001b[1;36m0.725323\u001b[0m  \u001b[1;36m0.744440\u001b[0m  \u001b[1;36m0.007912\u001b[0m   \n",
+       "\u001b[1;36m107\u001b[0m                     era5_t2m_avg_AMJ  \u001b[1;36m0.877351\u001b[0m  \u001b[1;36m0.421184\u001b[0m  \u001b[1;36m0.006320\u001b[0m   \n",
+       "\u001b[1;36m35\u001b[0m         era5_freezing_degree_days_AMJ  \u001b[1;36m0.832920\u001b[0m  \u001b[1;36m0.480256\u001b[0m  \u001b[1;36m0.006077\u001b[0m   \n",
+       "\u001b[1;36m139\u001b[0m                   era5_t2m_range_AMJ  \u001b[1;36m0.841132\u001b[0m  \u001b[1;36m0.426316\u001b[0m  \u001b[1;36m0.007174\u001b[0m   \n",
+       "\u001b[1;36m88\u001b[0m             era5_snowc_mean_trend_JAS  \u001b[1;36m0.691255\u001b[0m  \u001b[1;36m0.698109\u001b[0m  \u001b[1;36m0.004649\u001b[0m   \n",
+       "\u001b[1;36m4\u001b[0m                                      x  \u001b[1;36m0.895358\u001b[0m  \u001b[1;36m0.019901\u001b[0m  \u001b[1;36m0.008507\u001b[0m   \n",
+       "\u001b[1;36m155\u001b[0m                era5_thawing_days_AMJ  \u001b[1;36m0.828404\u001b[0m  \u001b[1;36m0.362141\u001b[0m  \u001b[1;36m0.006062\u001b[0m   \n",
+       "\u001b[1;36m27\u001b[0m                era5_freezing_days_AMJ  \u001b[1;36m0.828404\u001b[0m  \u001b[1;36m0.362141\u001b[0m  \u001b[1;36m0.006062\u001b[0m   \n",
+       "\u001b[1;36m140\u001b[0m                   era5_t2m_range_JAS  \u001b[1;36m0.707027\u001b[0m  \u001b[1;36m0.618988\u001b[0m  \u001b[1;36m0.006164\u001b[0m   \n",
+       "\u001b[1;36m137\u001b[0m               era5_t2m_min_trend_JFM  \u001b[1;36m0.740288\u001b[0m  \u001b[1;36m0.544540\u001b[0m  \u001b[1;36m0.006993\u001b[0m   \n",
+       "\u001b[1;36m129\u001b[0m              era5_t2m_mean_trend_JFM  \u001b[1;36m0.697034\u001b[0m  \u001b[1;36m0.480483\u001b[0m  \u001b[1;36m0.006620\u001b[0m   \n",
+       "\u001b[1;36m115\u001b[0m                     era5_t2m_max_AMJ  \u001b[1;36m0.551859\u001b[0m  \u001b[1;36m0.643181\u001b[0m  \u001b[1;36m0.005506\u001b[0m   \n",
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+       "\u001b[1;36m118\u001b[0m                     era5_t2m_max_OND  \u001b[1;36m0.696421\u001b[0m  \u001b[1;36m0.420945\u001b[0m  \u001b[1;36m0.005769\u001b[0m   \n",
+       "\u001b[1;36m113\u001b[0m               era5_t2m_avg_trend_JFM  \u001b[1;36m0.528189\u001b[0m  \u001b[1;36m0.547535\u001b[0m  \u001b[1;36m0.006607\u001b[0m   \n",
+       "\u001b[1;36m131\u001b[0m                     era5_t2m_min_AMJ  \u001b[1;36m0.686365\u001b[0m  \u001b[1;36m0.286899\u001b[0m  \u001b[1;36m0.005750\u001b[0m   \n",
+       "\u001b[1;36m109\u001b[0m                     era5_t2m_avg_JFM  \u001b[1;36m0.719894\u001b[0m  \u001b[1;36m0.195449\u001b[0m  \u001b[1;36m0.005759\u001b[0m   \n",
+       "\u001b[1;36m92\u001b[0m         era5_snowfall_occurrences_JAS  \u001b[1;36m0.618177\u001b[0m  \u001b[1;36m0.384700\u001b[0m  \u001b[1;36m0.005357\u001b[0m   \n",
+       "\u001b[1;36m171\u001b[0m                          era5_tp_AMJ  \u001b[1;36m0.596269\u001b[0m  \u001b[1;36m0.408936\u001b[0m  \u001b[1;36m0.004515\u001b[0m   \n",
+       "\u001b[1;36m117\u001b[0m                     era5_t2m_max_JFM  \u001b[1;36m0.613128\u001b[0m  \u001b[1;36m0.376654\u001b[0m  \u001b[1;36m0.006591\u001b[0m   \n",
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+       "\u001b[1;36m172\u001b[0m                          era5_tp_JAS  \u001b[1;36m0.631909\u001b[0m  \u001b[1;36m0.311714\u001b[0m  \u001b[1;36m0.004316\u001b[0m   \n",
+       "\u001b[1;36m130\u001b[0m              era5_t2m_mean_trend_OND  \u001b[1;36m0.662679\u001b[0m  \u001b[1;36m0.232704\u001b[0m  \u001b[1;36m0.007430\u001b[0m   \n",
+       "\u001b[1;36m59\u001b[0m    era5_precipitation_occurrences_AMJ  \u001b[1;36m0.571389\u001b[0m  \u001b[1;36m0.391543\u001b[0m  \u001b[1;36m0.007040\u001b[0m   \n",
+       "\u001b[1;36m41\u001b[0m   era5_freezing_degree_days_trend_JFM  \u001b[1;36m0.522429\u001b[0m  \u001b[1;36m0.448213\u001b[0m  \u001b[1;36m0.006358\u001b[0m   \n",
+       "\u001b[1;36m125\u001b[0m                    era5_t2m_mean_JFM  \u001b[1;36m0.671491\u001b[0m  \u001b[1;36m0.196685\u001b[0m  \u001b[1;36m0.005783\u001b[0m   \n",
+       "\u001b[1;36m148\u001b[0m                    era5_t2m_skew_JAS  \u001b[1;36m0.696467\u001b[0m  \u001b[1;36m0.102475\u001b[0m  \u001b[1;36m0.009218\u001b[0m   \n",
+       "\u001b[1;36m101\u001b[0m                        era5_sshf_JFM  \u001b[1;36m0.573920\u001b[0m  \u001b[1;36m0.338054\u001b[0m  \u001b[1;36m0.007279\u001b[0m   \n",
+       "\u001b[1;36m37\u001b[0m         era5_freezing_degree_days_JFM  \u001b[1;36m0.649717\u001b[0m  \u001b[1;36m0.193581\u001b[0m  \u001b[1;36m0.005620\u001b[0m   \n",
+       "\u001b[1;36m36\u001b[0m         era5_freezing_degree_days_JAS  \u001b[1;36m0.562796\u001b[0m  \u001b[1;36m0.333640\u001b[0m  \u001b[1;36m0.002855\u001b[0m   \n",
+       "\u001b[1;36m122\u001b[0m               era5_t2m_max_trend_OND  \u001b[1;36m0.548524\u001b[0m  \u001b[1;36m0.336754\u001b[0m  \u001b[1;36m0.008155\u001b[0m   \n",
+       "\u001b[1;36m114\u001b[0m               era5_t2m_avg_trend_OND  \u001b[1;36m0.590425\u001b[0m  \u001b[1;36m0.249592\u001b[0m  \u001b[1;36m0.007352\u001b[0m   \n",
+       "\u001b[1;36m76\u001b[0m                           era5_sf_JAS  \u001b[1;36m0.560754\u001b[0m  \u001b[1;36m0.220298\u001b[0m  \u001b[1;36m0.004595\u001b[0m   \n",
+       "\u001b[1;36m102\u001b[0m                        era5_sshf_OND  \u001b[1;36m0.499482\u001b[0m  \u001b[1;36m0.309113\u001b[0m  \u001b[1;36m0.005727\u001b[0m   \n",
+       "\n",
+       "      Source                   Variable     Season     Score  \n",
+       "\u001b[1;36m5\u001b[0m    General                          Y             \u001b[1;36m1.000000\u001b[0m  \n",
+       "\u001b[1;36m40\u001b[0m      ERA5      Freezing Degree Days   JAS Trend  \u001b[1;36m1.000000\u001b[0m  \n",
+       "\u001b[1;36m108\u001b[0m     ERA5                    T2M Avg        JAS  \u001b[1;36m0.836609\u001b[0m  \n",
+       "\u001b[1;36m164\u001b[0m     ERA5        Thawing Degree Days        JAS  \u001b[1;36m0.825705\u001b[0m  \n",
+       "\u001b[1;36m163\u001b[0m     ERA5        Thawing Degree Days        AMJ  \u001b[1;36m0.814997\u001b[0m  \n",
+       "\u001b[1;36m124\u001b[0m     ERA5                   T2M Mean        JAS  \u001b[1;36m0.775680\u001b[0m  \n",
+       "\u001b[1;36m123\u001b[0m     ERA5                   T2M Mean        AMJ  \u001b[1;36m0.772390\u001b[0m  \n",
+       "\u001b[1;36m42\u001b[0m      ERA5      Freezing Degree Days   OND Trend  \u001b[1;36m0.750997\u001b[0m  \n",
+       "\u001b[1;36m160\u001b[0m     ERA5              Thawing Days   JAS Trend  \u001b[1;36m0.737515\u001b[0m  \n",
+       "\u001b[1;36m32\u001b[0m      ERA5             Freezing Days   JAS Trend  \u001b[1;36m0.737515\u001b[0m  \n",
+       "\u001b[1;36m136\u001b[0m     ERA5                   T2M Min   JAS Trend  \u001b[1;36m0.735054\u001b[0m  \n",
+       "\u001b[1;36m107\u001b[0m     ERA5                    T2M Avg        AMJ  \u001b[1;36m0.733558\u001b[0m  \n",
+       "\u001b[1;36m35\u001b[0m      ERA5       Freezing Degree Days        AMJ  \u001b[1;36m0.705316\u001b[0m  \n",
+       "\u001b[1;36m139\u001b[0m     ERA5                  T2M Range        AMJ  \u001b[1;36m0.698106\u001b[0m  \n",
+       "\u001b[1;36m88\u001b[0m      ERA5                Snowc Mean   JAS Trend  \u001b[1;36m0.694701\u001b[0m  \n",
+       "\u001b[1;36m4\u001b[0m    General                          X             \u001b[1;36m0.679752\u001b[0m  \n",
+       "\u001b[1;36m155\u001b[0m     ERA5               Thawing Days        AMJ  \u001b[1;36m0.669162\u001b[0m  \n",
+       "\u001b[1;36m27\u001b[0m      ERA5              Freezing Days        AMJ  \u001b[1;36m0.669162\u001b[0m  \n",
+       "\u001b[1;36m140\u001b[0m     ERA5                  T2M Range        JAS  \u001b[1;36m0.665895\u001b[0m  \n",
+       "\u001b[1;36m137\u001b[0m     ERA5                   T2M Min   JFM Trend  \u001b[1;36m0.656069\u001b[0m  \n",
+       "\u001b[1;36m129\u001b[0m     ERA5                  T2M Mean   JFM Trend  \u001b[1;36m0.603268\u001b[0m  \n",
+       "\u001b[1;36m115\u001b[0m     ERA5                    T2M Max        AMJ  \u001b[1;36m0.600118\u001b[0m  \n",
+       "\u001b[1;36m28\u001b[0m      ERA5              Freezing Days        JAS  \u001b[1;36m0.598719\u001b[0m  \n",
+       "\u001b[1;36m156\u001b[0m     ERA5               Thawing Days        JAS  \u001b[1;36m0.597379\u001b[0m  \n",
+       "\u001b[1;36m116\u001b[0m     ERA5                    T2M Max        JAS  \u001b[1;36m0.596752\u001b[0m  \n",
+       "\u001b[1;36m118\u001b[0m     ERA5                    T2M Max        OND  \u001b[1;36m0.580728\u001b[0m  \n",
+       "\u001b[1;36m113\u001b[0m     ERA5                   T2M Avg   JFM Trend  \u001b[1;36m0.537964\u001b[0m  \n",
+       "\u001b[1;36m131\u001b[0m     ERA5                    T2M Min        AMJ  \u001b[1;36m0.527080\u001b[0m  \n",
+       "\u001b[1;36m109\u001b[0m     ERA5                    T2M Avg        JFM  \u001b[1;36m0.525279\u001b[0m  \n",
+       "\u001b[1;36m92\u001b[0m      ERA5       Snowfall Occurrences        JAS  \u001b[1;36m0.515298\u001b[0m  \n",
+       "\u001b[1;36m171\u001b[0m     ERA5                         Tp        AMJ  \u001b[1;36m0.511501\u001b[0m  \n",
+       "\u001b[1;36m117\u001b[0m     ERA5                    T2M Max        JFM  \u001b[1;36m0.508925\u001b[0m  \n",
+       "\u001b[1;36m96\u001b[0m      ERA5      Snowfall Occurrences   JAS Trend  \u001b[1;36m0.499189\u001b[0m  \n",
+       "\u001b[1;36m172\u001b[0m     ERA5                         Tp        JAS  \u001b[1;36m0.496659\u001b[0m  \n",
+       "\u001b[1;36m130\u001b[0m     ERA5                  T2M Mean   OND Trend  \u001b[1;36m0.491251\u001b[0m  \n",
+       "\u001b[1;36m59\u001b[0m      ERA5  Precipitation Occurrences        AMJ  \u001b[1;36m0.489323\u001b[0m  \n",
+       "\u001b[1;36m41\u001b[0m      ERA5      Freezing Degree Days   JFM Trend  \u001b[1;36m0.486661\u001b[0m  \n",
+       "\u001b[1;36m125\u001b[0m     ERA5                   T2M Mean        JFM  \u001b[1;36m0.486292\u001b[0m  \n",
+       "\u001b[1;36m148\u001b[0m     ERA5                   T2M Skew        JAS  \u001b[1;36m0.478053\u001b[0m  \n",
+       "\u001b[1;36m101\u001b[0m     ERA5                       Sshf        JFM  \u001b[1;36m0.468924\u001b[0m  \n",
+       "\u001b[1;36m37\u001b[0m      ERA5       Freezing Degree Days        JFM  \u001b[1;36m0.468517\u001b[0m  \n",
+       "\u001b[1;36m36\u001b[0m      ERA5       Freezing Degree Days        JAS  \u001b[1;36m0.460245\u001b[0m  \n",
+       "\u001b[1;36m122\u001b[0m     ERA5                   T2M Max   OND Trend  \u001b[1;36m0.452789\u001b[0m  \n",
+       "\u001b[1;36m114\u001b[0m     ERA5                   T2M Avg   OND Trend  \u001b[1;36m0.445610\u001b[0m  \n",
+       "\u001b[1;36m76\u001b[0m      ERA5                         Sf        JAS  \u001b[1;36m0.414782\u001b[0m  \n",
+       "\u001b[1;36m102\u001b[0m     ERA5                       Sshf        OND  \u001b[1;36m0.411951\u001b[0m  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_similarities[feature_similarities[\"Score\"] > 0.4].sort_values(\"Score\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6d061187",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
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+     },
+     "metadata": {},
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+    {
+     "data": {
+      "text/html": [
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+     "data": {
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+     "data": {
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+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 480\u001b[0m\u001b[1;36m0x5200\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m5\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corr = spearmanr(X_train).correlation\n",
+    "\n",
+    "# Ensure the correlation matrix is symmetric\n",
+    "corr = (corr + corr.T) / 2\n",
+    "np.fill_diagonal(corr, 1)\n",
+    "\n",
+    "corr_df = pd.DataFrame(corr, index=training_data.feature_names, columns=training_data.feature_names)\n",
+    "\n",
+    "\n",
+    "parsed_feature_names = [parse_feature_name(name) for name in training_data.feature_names]\n",
+    "feature_info_df = pd.DataFrame(\n",
+    "    parsed_feature_names, columns=[\"Source\", \"Variable\", \"Season\"], index=training_data.feature_names\n",
+    ")\n",
+    "source_pal = sns.husl_palette(3, s=0.45)\n",
+    "source_lut = dict(zip(feature_info_df[\"Source\"].unique(), source_pal))\n",
+    "variable_pal = sns.husl_palette(feature_info_df[\"Season\"].nunique(), s=0.45)\n",
+    "variable_lut = dict(zip(feature_info_df[\"Season\"].unique(), variable_pal))\n",
+    "row_colors = feature_info_df[\"Source\"].map(source_lut)\n",
+    "col_colors = feature_info_df[\"Season\"].map(variable_lut)\n",
+    "\n",
+    "g = sns.clustermap(\n",
+    "    corr_df,\n",
+    "    row_colors=row_colors,\n",
+    "    col_colors=col_colors,\n",
+    "    dendrogram_ratio=(0.1, 0.2),\n",
+    "    cbar_pos=(0.02, 0.32, 0.03, 0.2),\n",
+    "    center=0,\n",
+    "    cmap=\"vlag\",\n",
+    "    linewidths=0.9,\n",
+    "    figsize=(48, 52),\n",
+    ")\n",
+    "g.ax_row_dendrogram.remove()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5ad120e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            \n",
+       "            "
+      ],
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mIPython.core.display.HTML\u001b[0m\u001b[39m object\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We convert the correlation matrix to a distance matrix before performing\n",
+    "# hierarchical clustering using Ward's linkage.\n",
+    "distance_matrix = 1 - np.abs(corr)\n",
+    "dist_linkage = hierarchy.ward(squareform(distance_matrix))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "68d5c2d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            \n",
+       "            "
+      ],
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mIPython.core.display.HTML\u001b[0m\u001b[39m object\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 130\u001b[0m\u001b[1;36m0x700\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m2\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Show how the feature-selection threshold effects the absolute correlation\n",
+    "# (min, mean, max, 1std) between the selected features\n",
+    "thresholds = np.linspace(0, 3, 20)\n",
+    "max_abs_correlations = []\n",
+    "median_abs_correlations = []\n",
+    "q75_abs_correlations = []\n",
+    "q25_abs_correlations = []\n",
+    "q95_abs_correlations = []\n",
+    "q05_abs_correlations = []\n",
+    "num_selected_features = []\n",
+    "\n",
+    "for threshold in thresholds:\n",
+    "    cluster_ids = hierarchy.fcluster(dist_linkage, threshold, criterion=\"distance\")\n",
+    "    cluster_id_to_feature_ids = defaultdict(list)\n",
+    "    for idx, cluster_id in enumerate(cluster_ids):\n",
+    "        cluster_id_to_feature_ids[cluster_id].append(idx)\n",
+    "    selected_features = [v[0] for v in cluster_id_to_feature_ids.values()]\n",
+    "    num_selected_features.append(len(selected_features))\n",
+    "    selected_corr = corr[np.ix_(selected_features, selected_features)]\n",
+    "    abs_corr_values = np.abs(selected_corr - np.eye(len(selected_features)))\n",
+    "    max_abs_correlation = np.max(abs_corr_values)\n",
+    "    max_abs_correlations.append(max_abs_correlation)\n",
+    "    median_abs_correlation = np.median(abs_corr_values)\n",
+    "    median_abs_correlations.append(median_abs_correlation)\n",
+    "    q75_abs_correlation = np.quantile(abs_corr_values, 0.75)\n",
+    "    q75_abs_correlations.append(q75_abs_correlation)\n",
+    "    q25_abs_correlation = np.quantile(abs_corr_values, 0.25)\n",
+    "    q25_abs_correlations.append(q25_abs_correlation)\n",
+    "    q95_abs_correlation = np.quantile(abs_corr_values, 0.95)\n",
+    "    q95_abs_correlations.append(q95_abs_correlation)\n",
+    "    q05_abs_correlation = np.quantile(abs_corr_values, 0.05)\n",
+    "    q05_abs_correlations.append(q05_abs_correlation)\n",
+    "\n",
+    "fig, ax1 = plt.subplots(figsize=(13, 7))\n",
+    "\n",
+    "# Left y-axis: Absolute correlation metrics\n",
+    "ax1.plot(\n",
+    "    thresholds,\n",
+    "    median_abs_correlations,\n",
+    "    label=\"Median Abs Correlation\",\n",
+    "    linewidth=3,\n",
+    "    color=\"#1f77b4\",\n",
+    "    zorder=3,\n",
+    ")\n",
+    "ax1.fill_between(\n",
+    "    thresholds,\n",
+    "    np.array(q25_abs_correlations),\n",
+    "    np.array(q75_abs_correlations),\n",
+    "    alpha=0.2,\n",
+    "    color=\"#1f77b4\",\n",
+    "    label=\"25th-75th Percentile\",\n",
+    "    zorder=2,\n",
+    ")\n",
+    "ax1.fill_between(\n",
+    "    thresholds,\n",
+    "    np.array(q05_abs_correlations),\n",
+    "    np.array(q95_abs_correlations),\n",
+    "    alpha=0.1,\n",
+    "    color=\"#1f77b4\",\n",
+    "    label=\"5th-95th Percentile\",\n",
+    "    zorder=1,\n",
+    ")\n",
+    "\n",
+    "# Plot max as dashed lines (min is always 0)\n",
+    "ax1.plot(\n",
+    "    thresholds,\n",
+    "    max_abs_correlations,\n",
+    "    label=\"Max Abs Correlation\",\n",
+    "    linewidth=2,\n",
+    "    linestyle=\"--\",\n",
+    "    color=\"#1f77b4\",\n",
+    "    alpha=0.7,\n",
+    "    zorder=1,\n",
+    ")\n",
+    "\n",
+    "ax1.set_xlabel(\"Clustering Threshold\", fontsize=12, fontweight=\"bold\")\n",
+    "ax1.set_ylabel(\"Absolute Correlation\", fontsize=12, fontweight=\"bold\", color=\"#1f77b4\")\n",
+    "ax1.tick_params(axis=\"y\", labelcolor=\"#1f77b4\")\n",
+    "ax1.grid(True, alpha=0.3, linestyle=\"--\")\n",
+    "ax1.set_ylim(bottom=0)\n",
+    "\n",
+    "# Right y-axis: Number of selected features\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.plot(\n",
+    "    thresholds,\n",
+    "    num_selected_features,\n",
+    "    label=\"Number of Selected Features\",\n",
+    "    linewidth=2.5,\n",
+    "    color=\"#C41E3A\",\n",
+    "    marker=\"o\",\n",
+    "    markersize=6,\n",
+    "    alpha=0.6,\n",
+    ")\n",
+    "ax2.grid(False)\n",
+    "ax2.set_ylabel(\"Number of Selected Features\", fontsize=12, fontweight=\"bold\", color=\"#C41E3A\")\n",
+    "ax2.tick_params(axis=\"y\", labelcolor=\"#C41E3A\")\n",
+    "\n",
+    "# Combine legends\n",
+    "lines1, labels1 = ax1.get_legend_handles_labels()\n",
+    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
+    "legend = ax1.legend(lines1 + lines2, labels1 + labels2, fontsize=11, loc=\"upper right\", framealpha=0.95, fancybox=True)\n",
+    "legend.get_frame().set_zorder(4)\n",
+    "\n",
+    "plt.title(\n",
+    "    \"Effect of Clustering Threshold on Feature Correlation and Count\",\n",
+    "    fontsize=14,\n",
+    "    fontweight=\"bold\",\n",
+    "    pad=20,\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c3d1000b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            \n",
+       "            "
+      ],
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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",
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 180\u001b[0m\u001b[1;36m0x1900\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m5\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cluster_ids = hierarchy.fcluster(dist_linkage, 0.5, criterion=\"distance\")\n",
+    "# Get the feature names of the clusters\n",
+    "cluster_id_to_feature_ids = defaultdict(list)\n",
+    "for idx, cluster_id in enumerate(cluster_ids):\n",
+    "    cluster_id_to_feature_ids[cluster_id].append(idx)\n",
+    "selected_features = [v[0] for v in cluster_id_to_feature_ids.values()]\n",
+    "X_train = pd.DataFrame(X_train, columns=feature_names)\n",
+    "selected_features_names = X_train.columns[selected_features]\n",
+    "\n",
+    "X_train_selected = X_train[selected_features_names]\n",
+    "corr_selected = spearmanr(X_train_selected).correlation\n",
+    "# Ensure the correlation matrix is symmetric\n",
+    "corr_selected = (corr_selected + corr_selected.T) / 2\n",
+    "np.fill_diagonal(corr_selected, 1)\n",
+    "corr_selected_df = pd.DataFrame(corr_selected, index=selected_features_names, columns=selected_features_names)\n",
+    "\n",
+    "parsed_feature_names = [parse_feature_name(name) for name in selected_features_names]\n",
+    "feature_info_df = pd.DataFrame(\n",
+    "    parsed_feature_names, columns=[\"Source\", \"Variable\", \"Season\"], index=selected_features_names\n",
+    ")\n",
+    "source_pal = sns.husl_palette(3, s=0.45)\n",
+    "source_lut = dict(zip(feature_info_df[\"Source\"].unique(), source_pal))\n",
+    "variable_pal = sns.husl_palette(feature_info_df[\"Season\"].nunique(), s=0.45)\n",
+    "variable_lut = dict(zip(feature_info_df[\"Season\"].unique(), variable_pal))\n",
+    "row_colors = feature_info_df[\"Source\"].map(source_lut)\n",
+    "col_colors = feature_info_df[\"Season\"].map(variable_lut)\n",
+    "\n",
+    "g = sns.clustermap(\n",
+    "    corr_selected_df,\n",
+    "    row_colors=row_colors,\n",
+    "    col_colors=col_colors,\n",
+    "    dendrogram_ratio=(0.1, 0.2),\n",
+    "    cbar_pos=(0.02, 0.32, 0.03, 0.2),\n",
+    "    center=0,\n",
+    "    cmap=\"vlag\",\n",
+    "    linewidths=0.75,\n",
+    "    figsize=(18, 19),\n",
+    ")\n",
+    "g.ax_row_dendrogram.remove()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a887dcb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "default",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/entropice/ingest/darts.py b/src/entropice/ingest/darts.py
index f7d973f..92847da 100644
--- a/src/entropice/ingest/darts.py
+++ b/src/entropice/ingest/darts.py
@@ -18,6 +18,7 @@ from entropice.spatial import grids
 from entropice.utils.paths import (
     DARTS_MLLABELS_DIR,
     DARTS_V1_DIR,
+    DARTS_V2_DIR,
     get_darts_file,
 )
 from entropice.utils.types import Grid
@@ -25,6 +26,8 @@ from entropice.utils.types import Grid
 traceback.install()
 pretty.install()
 
+darts_v2_l2_file = DARTS_V2_DIR / "all_prediction_segments_ensemble5.parquet"
+darts_v2_l2_cov_file = DARTS_V2_DIR / "all_prediction_extent_ensemble5.parquet"
 darts_v1_l2_file = DARTS_V1_DIR / "DARTS_NitzeEtAl_v1-2_features_2018-2023_level2.parquet"
 darts_v1_l2_cov_file = DARTS_V1_DIR / "DARTS_NitzeEtAl_v1-2_coverage_2018-2023_level2.parquet"
 darts_v1_corrections = DARTS_V1_DIR / "negative_correction.geojson"
@@ -131,6 +134,83 @@ def _process_rts_yearly_grid(
     return darts
 
 
+@cli.command()
+def extract_darts_v2(grid: Grid, level: int):
+    """Extract RTS labels from DARTS-v2 Level-2 dataset.
+
+    Creates a Darts-v1 xarray Dataset on the specified grid and level.
+    The Dataset contains the following variables:
+    - count: Number of RTS in the cell
+    - area_km2: Total area of RTS in the cell (in km^2)
+    - covered_area_km2: Area of the cell covered by DARTS (in km^2)
+    - coverage: Fraction of the cell covered by DARTS
+    - density: Density of RTS area per covered area (area_km2 / covered_area_km2)
+    Since the DARTS-v1 Level-2 dataset contains yearly data, all variables are indexed by year as well.
+    Thus each variable has dimensions (cell_ids, year).
+
+    Args:
+        grid (Grid): The grid type to use.
+        level (int): The grid level to use.
+
+    """
+    with stopwatch("Load data"):
+        grid_gdf, cell_areas = _load_grid(grid, level)
+        darts_l2 = gpd.read_parquet(darts_v2_l2_file).to_crs(grid_gdf.crs)
+        darts_cov_l2 = gpd.read_parquet(darts_v2_l2_cov_file).to_crs(grid_gdf.crs)
+        # Need to filter small noise pixels (I do not know where they are coming from)
+        darts_cov_l2 = darts_cov_l2[darts_cov_l2.geometry.area > 1e9]
+
+    with stopwatch("Assign RTS to grid"):
+        grid_l2 = grid_gdf.overlay(darts_l2, how="intersection")
+        grid_cov_l2 = grid_gdf.overlay(darts_cov_l2, how="intersection")
+
+    darts = _process_rts_yearly_grid(grid_l2, grid_cov_l2, cell_areas)
+    darts = _convert_xdggs(darts, grid, level)
+    output_path = get_darts_file(grid, level, version="v2")
+    with stopwatch(f"Writing Darts v2 to {output_path}"):
+        darts.to_zarr(output_path, consolidated=False, mode="w")
+
+
+@cli.command()
+def extract_darts_v2_aggregated(grid: Grid, level: int):
+    """Extract RTS labels from DARTS-v2 Level-3 dataset.
+
+    Creates a Darts-v2 xarray Dataset on the specified grid and level.
+    The Dataset contains the following variables:
+    - count: Number of RTS in the cell
+    - area_km2: Total area of RTS in the cell (in km^2)
+    - covered_area_km2: Area of the cell covered by DARTS (in km^2)
+    - coverage: Fraction of the cell covered by DARTS
+    - density: Density of RTS area per covered area (area_km2 / covered_area_km2)
+    Since the DARTS-v2 Level-2 dataset contains yearly data, the data is dissolved then exploded to obtain Level-3 data.
+    Thus each variable has only the dimension (cell_ids).
+
+    Args:
+        grid (Grid): The grid type to use.
+        level (int): The grid level to use.
+
+    """
+    with stopwatch("Load data"):
+        grid_gdf, cell_areas = _load_grid(grid, level)
+        darts_l2 = gpd.read_parquet(darts_v2_l2_file).to_crs(grid_gdf.crs)
+        darts_cov_l2 = gpd.read_parquet(darts_v2_l2_cov_file).to_crs(grid_gdf.crs)
+        # Need to filter small noise pixels (I do not know where they are coming from)
+        darts_cov_l2 = darts_cov_l2[darts_cov_l2.geometry.area > 1e9]
+        # Remove overlapping labels by dissolving
+        darts_l2 = darts_l2[["geometry"]].dissolve().explode()
+        darts_cov_l2 = darts_cov_l2[["geometry"]].dissolve().explode()
+
+    with stopwatch("Extract RTS labels"):
+        grid_l3 = grid_gdf.overlay(darts_l2, how="intersection")
+        grid_cov_l3 = grid_gdf.overlay(darts_cov_l2, how="intersection")
+
+    darts = _process_rts_grid(grid_l3, grid_cov_l3, cell_areas)
+    darts = _convert_xdggs(darts, grid, level)
+    output_path = get_darts_file(grid, level, version="v2-l3")
+    with stopwatch(f"Writing Darts v2 l3 to {output_path}"):
+        darts.to_zarr(output_path, consolidated=False, mode="w")
+
+
 @cli.command()
 def extract_darts_v1(grid: Grid, level: int):
     """Extract RTS labels from DARTS-v1 Level-2 dataset.
@@ -176,7 +256,6 @@ def extract_darts_v1(grid: Grid, level: int):
         grid_cov_l2 = grid_gdf.overlay(darts_cov_l2.to_crs(grid_gdf.crs), how="intersection")
 
     darts = _process_rts_yearly_grid(grid_l2, grid_cov_l2, cell_areas)
-
     darts = _convert_xdggs(darts, grid, level)
     output_path = get_darts_file(grid, level, version="v1")
     with stopwatch(f"Writing Darts v1 to {output_path}"):
@@ -206,10 +285,18 @@ def extract_darts_v1_aggregated(grid: Grid, level: int):
         darts_l2 = gpd.read_parquet(darts_v1_l2_file)
         darts_cov_l2 = gpd.read_parquet(darts_v1_l2_cov_file)
         grid_gdf, cell_areas = _load_grid(grid, level)
+        corrections = gpd.read_file(darts_v1_corrections).to_crs(darts_l2.crs)
         # Remove overlapping labels by dissolving
         darts_l2 = darts_l2[["geometry"]].dissolve().explode()
         darts_cov_l2 = darts_cov_l2[["geometry"]].dissolve().explode()
 
+    with stopwatch("Apply corrections"):
+        # The correction file is just an area of sure negatives
+        # Thus, we first need to remove all RTS labels that intersect with the correction area,
+        darts_l2 = gpd.overlay(darts_l2, corrections, how="difference")
+        # then we need to add the correction area as coverage to the coverage file.
+        darts_cov_l2 = gpd.overlay(darts_cov_l2, corrections, how="union")
+
     with stopwatch("Extract RTS labels"):
         grid_l3 = grid_gdf.overlay(darts_l2.to_crs(grid_gdf.crs), how="intersection")
         grid_cov_l3 = grid_gdf.overlay(darts_cov_l2.to_crs(grid_gdf.crs), how="intersection")
diff --git a/src/entropice/ml/__init__.py b/src/entropice/ml/__init__.py
index 15a46f2..a1d8c83 100644
--- a/src/entropice/ml/__init__.py
+++ b/src/entropice/ml/__init__.py
@@ -7,6 +7,6 @@ This package contains modules for machine learning workflows:
 - inference: Batch prediction pipeline for trained classifiers
 """
 
-from . import dataset, inference, randomsearch
+from . import autogluon, dataset, hpsearchcv, inference
 
-__all__ = ["dataset", "inference", "randomsearch"]
+__all__ = ["autogluon", "dataset", "hpsearchcv", "inference", "inference"]
diff --git a/src/entropice/ml/autogluon.py b/src/entropice/ml/autogluon.py
index 2ea10c0..824411f 100644
--- a/src/entropice/ml/autogluon.py
+++ b/src/entropice/ml/autogluon.py
@@ -49,7 +49,7 @@ def _compute_metrics_and_confusion_matrix(  # noqa: C901
     complete_scores = predictor.evaluate(complete_data, display=True, detailed_report=True)
     m = []
     cm = {}
-    for dataset, scores in zip(["train", "test", "complete"], [train_scores, test_scores, complete_scores]):
+    for split, scores in zip(["train", "test", "complete"], [train_scores, test_scores, complete_scores]):
         for metric, score in scores.items():
             if metric == "confusion_matrix":
                 score = cast(pd.DataFrame, score)
@@ -58,24 +58,24 @@ def _compute_metrics_and_confusion_matrix(  # noqa: C901
                     dims=("y_true", "y_pred"),
                     coords={"y_true": score.index.tolist(), "y_pred": score.columns.tolist()},
                 )
-                cm[dataset] = confusion_matrix
+                cm[split] = confusion_matrix
             elif metric == "classification_report":
                 score = cast(dict[str, dict[str, float]], score)
                 score.pop("accuracy")  # Accuracy is already included as a separate metric
                 macro_avg = score.pop("macro avg")
                 for macro_avg_metric, macro_avg_score in macro_avg.items():
                     metric_name = f"macro_avg_{macro_avg_metric}"
-                    m.append({"dataset": dataset, "metric": metric_name, "score": macro_avg_score})
+                    m.append({"split": split, "metric": metric_name, "score": macro_avg_score})
                 weighted_avg = score.pop("weighted avg")
                 for weighted_avg_metric, weighted_avg_score in weighted_avg.items():
                     metric_name = f"weighted_avg_{weighted_avg_metric}"
-                    m.append({"dataset": dataset, "metric": metric_name, "score": weighted_avg_score})
+                    m.append({"split": split, "metric": metric_name, "score": weighted_avg_score})
                 for class_name, class_scores in score.items():
                     class_name = class_name.replace(" ", "-")
                     for class_metric, class_score in class_scores.items():
-                        m.append({"dataset": dataset, "metric": f"{class_name}_{class_metric}", "score": class_score})
+                        m.append({"split": split, "metric": f"{class_name}_{class_metric}", "score": class_score})
             else:  # Scalar metric
-                m.append({"dataset": dataset, "metric": metric, "score": score})
+                m.append({"split": split, "metric": metric, "score": score})
     if len(cm) == 0:
         return pd.DataFrame(m), None
     elif len(cm) == 3:
@@ -100,8 +100,8 @@ def _compute_shap_explanation(
         output_names=target_labels,
     )
     samples = test_data.drop(columns=["label"])
-    if len(samples) > 200:
-        samples = samples.sample(n=200, random_state=42)
+    if len(samples) > 100:
+        samples = samples.sample(n=100, random_state=42)
     explanation = explainer(samples)
     return explanation
 
@@ -161,10 +161,12 @@ def train(
     feature_importance = predictor.feature_importance(test_data)
     metrics, confusion_matrix = _compute_metrics_and_confusion_matrix(predictor, train_data, test_data, complete_data)
 
-    with stopwatch("Explaining model predictions with SHAP..."):
-        explanation = _compute_shap_explanation(
-            predictor, train_data, test_data, training_data.feature_names, training_data.target_labels
-        )
+    # ?: GPU inference is not yet implemented in AutoGluon, hence SHAP computation takes ages for large model ensembles,
+    # as they are present in the higher quality presets. Disabling SHAP for now...
+    # with stopwatch("Explaining model predictions with SHAP..."):
+    #     explanation = _compute_shap_explanation(
+    #         predictor, train_data, test_data, training_data.feature_names, training_data.target_labels
+    #     )
 
     print("Predicting probabilities for all cells...")
     preds = predict_proba(dataset_ensemble, model=predictor, task=settings.task)
@@ -176,12 +178,11 @@ def train(
         method=AutoML(time_budget=settings.time_limit, preset=settings.presets, hpo=False),
         task=settings.task,
         target=settings.target,
-        training_set=training_data,
         model=predictor,
         model_type="autogluon",
         metrics=metrics,
         feature_importance=feature_importance,
-        shap_explanation=explanation,
+        shap_explanation=None,
         predictions=preds,
         confusion_matrix=confusion_matrix,
         cv_results=None,
diff --git a/src/entropice/ml/autogluon_training.py b/src/entropice/ml/autogluon_training.py
deleted file mode 100644
index 939992f..0000000
--- a/src/entropice/ml/autogluon_training.py
+++ /dev/null
@@ -1,208 +0,0 @@
-"""DePRECATED!!! Training with AutoGluon TabularPredictor for automated ML."""
-
-import pickle
-from dataclasses import asdict, dataclass
-
-import cyclopts
-import pandas as pd
-import toml
-from autogluon.tabular import TabularDataset, TabularPredictor
-from rich import pretty, traceback
-from sklearn import set_config
-from stopuhr import stopwatch
-
-from entropice.ml.dataset import DatasetEnsemble
-from entropice.utils.paths import get_training_results_dir
-from entropice.utils.types import TargetDataset, Task
-
-traceback.install()
-pretty.install()
-
-set_config(array_api_dispatch=False)
-
-cli = cyclopts.App("entropice-autogluon")
-
-
-@cyclopts.Parameter("*")
-@dataclass(frozen=True, kw_only=True)
-class AutoGluonSettings:
-    """AutoGluon training settings."""
-
-    task: Task = "binary"
-    target: TargetDataset = "darts_v1"
-    time_limit: int = 3600  # Time limit in seconds (1 hour default)
-    presets: str = "best"  # AutoGluon preset: 'best', 'high', 'good', 'medium'
-    eval_metric: str | None = None  # Evaluation metric, None for auto-detect
-    num_bag_folds: int = 5  # Number of folds for bagging
-    num_bag_sets: int = 1  # Number of bagging sets
-    num_stack_levels: int = 1  # Number of stacking levels
-    num_gpus: int = 1  # Number of GPUs to use
-    verbosity: int = 2  # Verbosity level (0-4)
-
-
-@dataclass(frozen=True, kw_only=True)
-class AutoGluonTrainingSettings(DatasetEnsemble, AutoGluonSettings):
-    """Combined settings for AutoGluon training."""
-
-    classes: list[str] | None = None
-    problem_type: str = "binary"
-
-
-def _determine_problem_type_and_metric(task: Task) -> tuple[str, str]:
-    """Determine AutoGluon problem type and appropriate evaluation metric.
-
-    Args:
-        task: The training task type
-
-    Returns:
-        Tuple of (problem_type, eval_metric)
-
-    """
-    if task == "binary":
-        return ("binary", "balanced_accuracy")  # Good for imbalanced datasets
-    elif task in ["count_regimes", "density_regimes"]:
-        return ("multiclass", "f1_weighted")  # Weighted F1 for multiclass
-    elif task in ["count", "density"]:
-        return ("regression", "mean_absolute_error")
-    else:
-        raise ValueError(f"Unknown task: {task}")
-
-
-@cli.default
-def autogluon_train(
-    dataset_ensemble: DatasetEnsemble,
-    settings: AutoGluonSettings = AutoGluonSettings(),
-    experiment: str | None = None,
-):
-    """Train models using AutoGluon TabularPredictor.
-
-    Args:
-        dataset_ensemble: Dataset ensemble configuration
-        settings: AutoGluon training settings
-        experiment: Optional experiment name for organizing results
-
-    """
-    training_data = dataset_ensemble.create_training_set(task=settings.task, target=settings.target)
-
-    # Convert to AutoGluon TabularDataset
-    train_data: pd.DataFrame = TabularDataset(training_data.to_dataframe("train"))  # ty:ignore[invalid-assignment]
-    test_data: pd.DataFrame = TabularDataset(training_data.to_dataframe("test"))  # ty:ignore[invalid-assignment]
-
-    print(f"\nTraining data: {len(train_data)} samples")
-    print(f"Test data: {len(test_data)} samples")
-    print(f"Features: {len(training_data.feature_names)}")
-    print(f"Classes: {training_data.target_labels}")
-
-    # Determine problem type and metric
-    problem_type, default_metric = _determine_problem_type_and_metric(settings.task)
-    eval_metric = settings.eval_metric or default_metric
-
-    print(f"\n🎯 Problem type: {problem_type}")
-    print(f"📈 Evaluation metric: {eval_metric}")
-
-    # Create results directory
-    results_dir = get_training_results_dir(
-        experiment=experiment,
-        grid=dataset_ensemble.grid,
-        level=dataset_ensemble.level,
-        task=settings.task,
-        target=settings.target,
-        name="autogluon",
-    )
-    print(f"\n💾 Results directory: {results_dir}")
-
-    # Initialize TabularPredictor
-    print(f"\n🚀 Initializing AutoGluon TabularPredictor (preset='{settings.presets}')...")
-    predictor = TabularPredictor(
-        label="label",
-        problem_type=problem_type,
-        eval_metric=eval_metric,
-        path=str(results_dir / "models"),
-        verbosity=settings.verbosity,
-    )
-
-    # Train models
-    print(f"\n⚡ Training models (time_limit={settings.time_limit}s, num_gpus={settings.num_gpus})...")
-    with stopwatch("AutoGluon training"):
-        predictor.fit(
-            train_data=train_data,
-            time_limit=settings.time_limit,
-            presets=settings.presets,
-            num_bag_folds=settings.num_bag_folds,
-            num_bag_sets=settings.num_bag_sets,
-            num_stack_levels=settings.num_stack_levels,
-            num_gpus=settings.num_gpus,
-            ag_args_fit={"num_gpus": settings.num_gpus} if settings.num_gpus > 0 else None,
-        )
-
-    # Evaluate on test data
-    print("\n📊 Evaluating on test data...")
-    test_score = predictor.evaluate(test_data, silent=True, detailed_report=True)
-    print(f"Test {eval_metric}: {test_score[eval_metric]:.4f}")
-
-    # Get leaderboard
-    print("\n🏆 Model Leaderboard:")
-    leaderboard = predictor.leaderboard(test_data, silent=True)
-    print(leaderboard[["model", "score_test", "score_val", "pred_time_test", "fit_time"]].head(10))
-
-    # Save leaderboard
-    leaderboard_file = results_dir / "leaderboard.parquet"
-    print(f"\n💾 Saving leaderboard to {leaderboard_file}")
-    leaderboard.to_parquet(leaderboard_file)
-
-    # Get feature importance
-    print("\n🔍 Computing feature importance...")
-    with stopwatch("Feature importance"):
-        try:
-            # Compute feature importance with reduced repeats
-            feature_importance = predictor.feature_importance(
-                test_data,
-                num_shuffle_sets=3,
-                subsample_size=min(500, len(test_data)),  # Further subsample if needed
-            )
-            fi_file = results_dir / "feature_importance.parquet"
-            print(f"💾 Saving feature importance to {fi_file}")
-            feature_importance.to_parquet(fi_file)
-        except Exception as e:
-            print(f"⚠️  Could not compute feature importance: {e}")
-
-    # Save training settings
-    print("\n💾 Saving training settings...")
-    combined_settings = AutoGluonTrainingSettings(
-        **asdict(settings),
-        **asdict(dataset_ensemble),
-        classes=training_data.target_labels,
-        problem_type=problem_type,
-    )
-    settings_file = results_dir / "training_settings.toml"
-    with open(settings_file, "w") as f:
-        toml.dump({"settings": asdict(combined_settings)}, f)
-
-    # Save test metrics
-    # We need to use pickle here, because the confusion matrix is stored as a dataframe
-    # This only matters for classification tasks
-    test_metrics_file = results_dir / "test_metrics.pickle"
-    print(f"💾 Saving test metrics to {test_metrics_file}")
-    with open(test_metrics_file, "wb") as f:
-        pickle.dump(test_score, f, protocol=pickle.HIGHEST_PROTOCOL)
-
-    # Save the predictor
-    predictor_file = results_dir / "tabular_predictor.pkl"
-    print(f"💾 Saving TabularPredictor to {predictor_file}")
-    predictor.save()
-
-    # Print summary
-    print("\n" + "=" * 80)
-    print("✅ AutoGluon Training Complete!")
-    print("=" * 80)
-    print(f"\n📂 Results saved to: {results_dir}")
-    print(f"🏆 Best model: {predictor.model_best}")
-    print(f"📈 Test {eval_metric}: {test_score[eval_metric]:.4f}")
-    print(f"⏱️  Total models trained: {len(leaderboard)}")
-
-    stopwatch.summary()
-    print("\nDone! 🎉")
-
-
-if __name__ == "__main__":
-    cli()
diff --git a/src/entropice/ml/dataset.py b/src/entropice/ml/dataset.py
index d6b547e..881cca2 100644
--- a/src/entropice/ml/dataset.py
+++ b/src/entropice/ml/dataset.py
@@ -53,14 +53,40 @@ def _collapse_to_dataframe(ds: xr.Dataset | xr.DataArray) -> pd.DataFrame:
     use_dummy = collapsed.shape[0] == 0
     if use_dummy:
         collapsed.loc[tuple(range(len(collapsed.index.names)))] = np.nan
-    pivcols = set(collapsed.index.names) - {"cell_ids"}
+    cols = cast(list[str], list(collapsed.index.names))
+    pivcols = sorted(set(cols) - {"cell_ids"})
     collapsed = collapsed.pivot_table(index="cell_ids", columns=pivcols)
     collapsed.columns = ["_".join(map(str, v)) for v in collapsed.columns]
     if use_dummy:
         collapsed = collapsed.dropna(how="all")
+    expected_cols = _get_expected_collapsed_columns(ds)
+    missing_cols = set(expected_cols) - set(collapsed.columns)
+    if missing_cols:
+        raise ValueError(
+            f"Collapsed dataframe is missing expected columns: {missing_cols=} {collapsed.columns=} {expected_cols=}"
+        )
     return collapsed
 
 
+def _get_expected_collapsed_columns(ds: xr.Dataset | xr.DataArray) -> list[str]:
+    dims = sorted(set(ds.dims) - {"cell_ids"})
+    dims_product = list(product(*[ds.coords[dim].to_numpy() for dim in dims]))
+    expected_cols = []
+    if isinstance(ds, xr.Dataset):
+        variables = list(ds.data_vars)
+        for var in variables:
+            for dims_values in dims_product:
+                agg = "_".join(dims_values)
+                expected_cols.append(f"{var}_{agg}")
+    else:
+        assert ds.name is not None, "DataArray must have a name to determine expected columns"
+        for dims_values in dims_product:
+            agg = "_".join(dims_values)
+            expected_cols.append(f"{ds.name}_{agg}")
+
+    return expected_cols
+
+
 def _cell_ids_hash(cell_ids: pd.Series) -> str:
     sorted_ids = np.sort(cell_ids.to_numpy())
     return hashlib.blake2b(sorted_ids.tobytes(), digest_size=8).hexdigest()
@@ -130,8 +156,8 @@ class SplittedArrays[ArrayType: (torch.Tensor, np.ndarray, cp.ndarray)]:
     test: ArrayType
 
     @cached_property
-    def combined(self) -> ArrayType:
-        """Combined train and test arrays."""
+    def complete(self) -> ArrayType:
+        """Complete train and test arrays."""
         if isinstance(self.train, torch.Tensor) and isinstance(self.test, torch.Tensor):
             return torch.cat([self.train, self.test], dim=0)  # ty:ignore[invalid-return-type]
         elif isinstance(self.train, cp.ndarray) and isinstance(self.test, cp.ndarray):
@@ -272,7 +298,6 @@ 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)
-    add_lonlat: bool = True
 
     def __post_init__(self):
         # Validate filters
@@ -295,6 +320,10 @@ class DatasetEnsemble:
                         f"Invalid dimension filter for {dim=}: {values}"
                         " Dimension filter values must be a list with one or more entries."
                     )
+        if "Grid" in self.variable_filters.keys():
+            filtered = set(self.variable_filters["Grid"])
+            valid = {"x", "y", "cell_area", "land_area", "water_area", "land_ratio"}
+            assert len(filtered - valid) == 0
 
     def __hash__(self):
         return int(self.id(), 16)
@@ -320,12 +349,10 @@ class DatasetEnsemble:
     @cache
     def read_grid(self) -> gpd.GeoDataFrame:
         """Load the grid dataframe and enrich it with lat-lon information."""
-        columns_to_load = ["cell_id", "geometry", "cell_area", "land_area", "water_area", "land_ratio"]
-        # The name add_lonlat has legacy reasons and should be add_location
-        # If add_location is true, keep the x and y
-        # For future reworks: "lat" and "lon" are also available columns
-        if self.add_lonlat:
-            columns_to_load.extend(["x", "y"])
+        if "Grid" in self.variable_filters:
+            columns_to_load = self.variable_filters["Grid"] + ["cell_id", "geometry"]
+        else:
+            columns_to_load = ["cell_id", "geometry", "cell_area", "land_area", "water_area", "land_ratio", "x", "y"]
 
         # Reading the data takes for the largest grids ~1.7s
         gridfile = entropice.utils.paths.get_grid_file(self.grid, self.level)
@@ -468,7 +495,7 @@ class DatasetEnsemble:
             ds = unstack_era5_time(ds, era5_agg)
 
         # Apply the temporal mode
-        if isinstance(self.temporal_mode, int):
+        if isinstance(self.temporal_mode, int) and member != "ArcticDEM":
             ds = ds.sel(year=self.temporal_mode, drop=True)
 
         # Actually read data into memory
diff --git a/src/entropice/ml/explainations.py b/src/entropice/ml/explainations.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/entropice/ml/hpsearchcv.py b/src/entropice/ml/hpsearchcv.py
index 0572342..ca16738 100644
--- a/src/entropice/ml/hpsearchcv.py
+++ b/src/entropice/ml/hpsearchcv.py
@@ -79,13 +79,16 @@ class RunSettings:
         """
         return "torch" if self.model == "espa" else "cuda"
 
-    def build_pipeline(self, model_hpo_config: ModelHPOConfig) -> Pipeline:  # noqa: C901
+    @property
+    def hpo_config(self) -> ModelHPOConfig:
+        """Get the hyperparameter optimization configuration for the selected model and task."""
+        return get_model_hpo_config(self.model, self.task)
+
+    def build_pipeline(self, model_hpo_config: ModelHPOConfig) -> Pipeline:
         """Build a scikit-learn Pipeline based on the settings."""
         # Add a feature scaler / normalization step if specified, but assert that it's only used for non-Tree models
         if self.model in ["rf", "xgboost"]:
             assert self.scaler == "none", f"Scaler {self.scaler} is not viable with model {self.model}"
-        elif self.scaler == "none":
-            assert self.scaler != "none", f"No scaler specified for model {self.model}, which is not viable."
 
         match self.scaler:
             case "standard":
@@ -159,9 +162,9 @@ def _compute_metrics(y: SplittedArrays, y_pred: SplittedArrays, metrics: list[st
     m = []
     for metric in metrics:
         metric_fn = metric_functions[metric]
-        for split in ["train", "test", "combined"]:
+        for split in ["train", "test", "complete"]:
             value = metric_fn(getattr(y, split), getattr(y_pred, split))
-            m.append({"metric": metric, "split": split, "value": value})
+            m.append({"metric": metric, "split": split, "score": value})
     return pd.DataFrame(m)
 
 
@@ -174,9 +177,9 @@ def _compute_confusion_matrices(
         {
             "test": (("true_label", "predicted_label"), confusion_matrix(y.test, y_pred.test, labels=codes)),
             "train": (("true_label", "predicted_label"), confusion_matrix(y.train, y_pred.train, labels=codes)),
-            "combined": (
+            "complete": (
                 ("true_label", "predicted_label"),
-                confusion_matrix(y.combined, y_pred.combined, labels=codes),
+                confusion_matrix(y.complete, y_pred.complete, labels=codes),
             ),
         },
         coords={"true_label": labels, "predicted_label": labels},
@@ -235,9 +238,9 @@ def _compute_feature_importance(model: Model, best_estimator: Pipeline, training
     return feature_importances
 
 
-def _compute_shap_explanation(model: Model, best_estimator: Pipeline, training_data: TrainingSet) -> Explanation:
+def _compute_shap_explanation(model: Model, best_estimator: Pipeline, training_data: TrainingSet) -> Explanation:  # noqa: C901
     match model:
-        case "espa" | "knn" | "rf":  # CUML models do not yet work with TreeExplainer...
+        case "espa" | "knn":
             train_transformed = training_data.X.as_numpy().train
             if "scaler" in best_estimator.named_steps:
                 train_transformed = best_estimator.named_steps["scaler"].transform(train_transformed)
@@ -263,15 +266,23 @@ def _compute_shap_explanation(model: Model, best_estimator: Pipeline, training_d
                 feature_names=training_data.feature_names,
                 output_names=training_data.target_labels,
             )
+        case "rf":
+            masker = shap.maskers.Independent(data=training_data.X.as_numpy().train)
+            explainer = TreeExplainer(
+                best_estimator.named_steps["model"].as_sklearn(),
+                data=masker,
+                feature_names=training_data.feature_names,
+            )
         case "xgboost":
             explainer = TreeExplainer(best_estimator.named_steps["model"], feature_names=training_data.feature_names)
         case _:
             raise ValueError(f"Unknown model: {model}")
 
     samples = training_data.X.as_numpy().test
-    if len(samples) > 200:
+    nsamples = 2 * samples.shape[1] + 2048
+    if len(samples) > nsamples:
         rng = np.random.default_rng(seed=42)
-        sample_indices = rng.choice(len(samples), size=200, replace=False)
+        sample_indices = rng.choice(len(samples), size=nsamples, replace=False)
         samples = samples[sample_indices]
     if "scaler" in best_estimator.named_steps:
         samples = best_estimator.named_steps["scaler"].transform(samples)
@@ -314,7 +325,7 @@ def hpsearch_cv(
         task=settings.task, target=settings.target, device=settings.device
     )
 
-    model_hpo_config = get_model_hpo_config(settings.model, settings.task)
+    model_hpo_config = settings.hpo_config
     print(f"Using model: {settings.model} with parameters: {model_hpo_config.hp_config}")
 
     metrics, refit = get_metrics(settings.task)
@@ -324,8 +335,8 @@ def hpsearch_cv(
     print(f"Pipeline steps: {pipeline.named_steps}")
 
     hp_search = settings.build_search(pipeline, model_hpo_config, metrics, refit)
-    print(f"Starting hyperparameter search with {settings.n_iter} iterations...")
-    with stopwatch(f"RandomizedSearchCV fitting for {settings.n_iter} candidates"):
+    print(f"Starting hyperparameter search for {settings.model} with {settings.n_iter} iterations...")
+    with stopwatch(f"RandomizedSearchCV fitting of {settings.model} for {settings.n_iter} candidates"):
         fit_params = {f"model__{k}": v for k, v in model_hpo_config.fit_params.items()}
         hp_search.fit(
             training_data.X.train,
@@ -379,7 +390,6 @@ def hpsearch_cv(
         ),
         task=settings.task,
         target=settings.target,
-        training_set=training_data,
         model=best_estimator,
         model_type=settings.model,
         metrics=metrics,
diff --git a/src/entropice/ml/inference.py b/src/entropice/ml/inference.py
index 09c1c21..5cc87bf 100644
--- a/src/entropice/ml/inference.py
+++ b/src/entropice/ml/inference.py
@@ -85,7 +85,7 @@ def predict_proba(
     grid_gdf = e.read_grid()
     for batch in e.create_inference_df(batch_size=batch_size):
         # Filter rows containing NaN values
-        batch = batch.dropna(axis=0, how="any")
+        batch = batch.dropna(axis="index", how="any")
 
         # Skip empty batches (all rows had NaN values)
         if len(batch) == 0:
@@ -96,7 +96,13 @@ def predict_proba(
 
         if isinstance(model, TabularPredictor):
             print(f"Predicting batch of size {len(batch)} ({type(batch)}) with AutoGluon TabularPredictor...")
-            batch_preds = model.predict(batch)
+            try:
+                batch_preds = model.predict(batch)
+            except Exception as ex:
+                print("Something went wrong")
+                print(batch)
+                print(batch.columns)
+                raise ex
             print(f"Batch predictions type: {type(batch_preds)}, shape: {batch_preds.shape}")
 
             assert isinstance(batch_preds, pd.DataFrame | pd.Series), (
diff --git a/src/entropice/ml/models.py b/src/entropice/ml/models.py
index 4dd2492..09f7767 100644
--- a/src/entropice/ml/models.py
+++ b/src/entropice/ml/models.py
@@ -60,7 +60,13 @@ def get_search_space(hp_config: HPConfig) -> dict[str, list | rv_continuous_froz
             f"Unknown distribution type for {key}: {dist['distribution']}"
         )
         distfn = getattr(scipy.stats, dist["distribution"])
-        search_space[key] = distfn(dist["low"], dist["high"])
+        # Add edge-case for uniform distribution, as low-high is different there
+        # https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.uniform.html#scipy.stats.uniform
+        # Using the parameters loc and scale, one obtains the uniform distribution on [loc, loc + scale].
+        if dist["distribution"] == "uniform":
+            search_space[key] = distfn(loc=dist["low"], scale=dist["high"] - dist["low"])
+        else:
+            search_space[key] = distfn(dist["low"], dist["high"])
     return search_space
 
 
@@ -188,7 +194,7 @@ def get_model_hpo_config(model: str, task: Task, **model_kwargs) -> ModelHPOConf
             clf = RandomForestClassifier(split_criterion="entropy", **model_kwargs)
             return ModelHPOConfig(clf, rf_hpconfig)
         case ("rf", "regressor"):
-            reg = RandomForestRegressor(split_criterion="variance", **model_kwargs)
+            reg = RandomForestRegressor(split_criterion="poisson", **model_kwargs)
             return ModelHPOConfig(reg, rf_hpconfig)
         case ("knn", "classifier"):
             clf = KNeighborsClassifier(**model_kwargs)
diff --git a/src/entropice/ml/randomsearch.py b/src/entropice/ml/randomsearch.py
deleted file mode 100644
index 2b1d5ba..0000000
--- a/src/entropice/ml/randomsearch.py
+++ /dev/null
@@ -1,241 +0,0 @@
-"""DEPRECATED!!! Training of classification models training."""
-
-import pickle
-from dataclasses import asdict, dataclass
-from pathlib import Path
-
-import cyclopts
-import numpy as np
-import pandas as pd
-import toml
-import xarray as xr
-from rich import pretty, traceback
-from sklearn import set_config
-from sklearn.metrics import (
-    confusion_matrix,
-)
-from sklearn.model_selection import KFold, RandomizedSearchCV
-from stopuhr import stopwatch
-
-from entropice.ml.dataset import DatasetEnsemble, SplittedArrays
-from entropice.ml.inference import predict_proba
-from entropice.ml.models import (
-    extract_espa_feature_importance,
-    extract_espa_state,
-    extract_rf_feature_importance,
-    extract_xgboost_feature_importance,
-    get_model_hpo_config,
-)
-from entropice.utils.metrics import get_metrics, metric_functions
-from entropice.utils.paths import get_training_results_dir
-from entropice.utils.types import Model, TargetDataset, Task
-
-traceback.install()
-pretty.install()
-
-
-cli = cyclopts.App("entropice-training", config=cyclopts.config.Toml("training-config.toml"))  # ty:ignore[invalid-argument-type]
-
-
-@cyclopts.Parameter("*")
-@dataclass(frozen=True, kw_only=True)
-class RunSettings:
-    """Cross-validation settings for model training."""
-
-    n_iter: int = 2000
-    task: Task = "binary"
-    target: TargetDataset = "darts_v1"
-    model: Model = "espa"
-
-
-@dataclass(frozen=True, kw_only=True)
-class TrainingSettings(DatasetEnsemble, RunSettings):
-    """Helper Wrapper to store combined training and dataset ensemble settings."""
-
-    param_grid: dict
-    cv_splits: int
-    metrics: list[str]
-    classes: list[str] | None
-
-
-@cli.default
-def random_cv(
-    dataset_ensemble: DatasetEnsemble,
-    settings: RunSettings = RunSettings(),
-    experiment: str | None = None,
-) -> Path:
-    """Perform random cross-validation on the training dataset.
-
-    Args:
-        dataset_ensemble (DatasetEnsemble): The dataset ensemble configuration.
-        settings (RunSettings): The cross-validation settings.
-        experiment (str | None): Optional experiment name for results directory.
-
-    """
-    # Since we use cuml and xgboost libraries, we can only enable array API for ESPA
-    use_array_api = settings.model != "xgboost"
-    device = "torch" if settings.model == "espa" else "cuda"
-    set_config(array_api_dispatch=use_array_api)
-
-    print("Creating training data...")
-    training_data = dataset_ensemble.create_training_set(task=settings.task, target=settings.target, device=device)
-    model_hpo_config = get_model_hpo_config(settings.model, settings.task)
-    print(f"Using model: {settings.model} with parameters: {model_hpo_config.hp_config}")
-    cv = KFold(n_splits=5, shuffle=True, random_state=42)
-    metrics, refit = get_metrics(settings.task)
-    search = RandomizedSearchCV(
-        model_hpo_config.model,
-        model_hpo_config.search_space,
-        n_iter=settings.n_iter,
-        n_jobs=1,
-        cv=cv,
-        random_state=42,
-        verbose=10,
-        scoring=metrics,
-        refit=refit,
-    )
-
-    print(f"Starting RandomizedSearchCV with {search.n_iter} candidates...")
-    with stopwatch(f"RandomizedSearchCV fitting for {search.n_iter} candidates"):
-        search.fit(
-            training_data.X.train,
-            # XGBoost returns it's labels as numpy arrays instead of cupy arrays
-            # Thus, for the scoring to work, we need to convert them back to numpy
-            training_data.y.as_numpy().train if settings.model == "xgboost" else training_data.y.train,
-            **model_hpo_config.fit_params,
-        )
-
-    print("Best parameters combination found:")
-    best_estimator = search.best_estimator_
-    best_parameters = best_estimator.get_params()
-    for param_name in sorted(model_hpo_config.hp_config.keys()):
-        print(f"{param_name}: {best_parameters[param_name]}")
-
-    test_score = search.score(
-        training_data.X.test,
-        training_data.y.as_numpy().test if settings.model == "xgboost" else training_data.y.test,
-    )
-    print(
-        f"{refit.replace('_', ' ').capitalize()} of the best parameters using the inner CV"
-        f" of the random search: {search.best_score_:.3f}"
-    )
-    print(f"{refit.replace('_', ' ').capitalize()} on test set: {test_score:.3f}")
-
-    results_dir = get_training_results_dir(
-        experiment=experiment,
-        name="random_search",
-        grid=dataset_ensemble.grid,
-        level=dataset_ensemble.level,
-        task=settings.task,
-        target=settings.target,
-        model_type=settings.model,
-    )
-
-    # Store the search settings
-    combined_settings = TrainingSettings(
-        **asdict(settings),
-        **asdict(dataset_ensemble),
-        param_grid=model_hpo_config.hp_config,
-        cv_splits=cv.get_n_splits(),
-        metrics=metrics,
-        classes=training_data.target_labels,
-    )
-    settings_file = results_dir / "search_settings.toml"
-    print(f"Storing search settings to {settings_file}")
-    with open(settings_file, "w") as f:
-        toml.dump({"settings": asdict(combined_settings)}, f)
-
-    # Store the best estimator model
-    best_model_file = results_dir / "best_estimator_model.pkl"
-    print(f"Storing best estimator model to {best_model_file}")
-    with open(best_model_file, "wb") as f:
-        pickle.dump(best_estimator, f, protocol=pickle.HIGHEST_PROTOCOL)
-
-    # Store the search results
-    results = pd.DataFrame(search.cv_results_)
-    # Parse the params into individual columns
-    params = pd.json_normalize(results["params"])  # ty:ignore[invalid-argument-type]
-    # Concatenate the params columns with the original DataFrame
-    results = pd.concat([results.drop(columns=["params"]), params], axis=1)
-    results_file = results_dir / "search_results.parquet"
-    print(f"Storing CV results to {results_file}")
-    results.to_parquet(results_file)
-
-    # Compute predictions on the all sets and move them to numpy for metric computations
-    y_pred = SplittedArrays(
-        train=best_estimator.predict(training_data.X.train),
-        test=best_estimator.predict(training_data.X.test),
-    ).as_numpy()
-
-    # Compute and StoreMetrics
-    y = training_data.y.as_numpy()
-    test_metrics = {metric: metric_functions[metric](y.test, y_pred.test) for metric in metrics}
-    train_metrics = {metric: metric_functions[metric](y.train, y_pred.train) for metric in metrics}
-    combined_metrics = {metric: metric_functions[metric](y.combined, y_pred.combined) for metric in metrics}
-    all_metrics = {
-        "test_metrics": test_metrics,
-        "train_metrics": train_metrics,
-        "combined_metrics": combined_metrics,
-    }
-    test_metrics_file = results_dir / "metrics.toml"
-    print(f"Storing test metrics to {test_metrics_file}")
-    with open(test_metrics_file, "w") as f:
-        toml.dump(all_metrics, f)
-
-    # Make confusion matrices for classification taasks
-    if settings.task in ["binary", "count_regimes", "density_regimes"]:
-        codes = np.array(training_data.target_codes)
-        cm = xr.Dataset(
-            {
-                "test": (("true_label", "predicted_label"), confusion_matrix(y.test, y_pred.test, labels=codes)),
-                "train": (("true_label", "predicted_label"), confusion_matrix(y.train, y_pred.train, labels=codes)),
-                "combined": (
-                    ("true_label", "predicted_label"),
-                    confusion_matrix(y.combined, y_pred.combined, labels=codes),
-                ),
-            },
-            coords={"true_label": training_data.target_labels, "predicted_label": training_data.target_labels},
-        )
-        # Store the confusion matrices
-        cm_file = results_dir / "confusion_matrix.nc"
-        print(f"Storing confusion matrices to {cm_file}")
-        cm.to_netcdf(cm_file, engine="h5netcdf")
-
-    # Get the inner state of the best estimator
-    if settings.model == "espa":
-        state = extract_espa_state(best_estimator, training_data)
-        state_file = results_dir / "best_estimator_state.nc"
-        print(f"Storing best estimator state to {state_file}")
-        state.to_netcdf(state_file, engine="h5netcdf")
-        fi = extract_espa_feature_importance(best_estimator, training_data)
-        fi_file = results_dir / "best_estimator_feature_importance.parquet"
-        print(f"Storing best estimator feature importance to {fi_file}")
-        fi.to_parquet(fi_file)
-
-    elif settings.model == "xgboost":
-        fi = extract_xgboost_feature_importance(best_estimator, training_data)
-        fi_file = results_dir / "best_estimator_feature_importance.parquet"
-        print(f"Storing best estimator feature importance to {fi_file}")
-        fi.to_parquet(fi_file)
-
-    elif settings.model == "rf":
-        fi = extract_rf_feature_importance(best_estimator, training_data)
-        fi_file = results_dir / "best_estimator_feature_importance.parquet"
-        print(f"Storing best estimator feature importance to {fi_file}")
-        fi.to_parquet(fi_file)
-
-    # Predict probabilities for all cells
-    print("Predicting probabilities for all cells...")
-    preds = predict_proba(dataset_ensemble, model=best_estimator, task=settings.task, device=device)
-    print(f"Predicted probabilities DataFrame with {len(preds)} entries.")
-    preds_file = results_dir / "predicted_probabilities.parquet"
-    print(f"Storing predicted probabilities to {preds_file}")
-    preds.to_parquet(preds_file)
-
-    stopwatch.summary()
-    print("Done.")
-    return results_dir
-
-
-if __name__ == "__main__":
-    cli()
diff --git a/src/entropice/utils/training.py b/src/entropice/utils/training.py
index 46b33ee..bc22cb7 100644
--- a/src/entropice/utils/training.py
+++ b/src/entropice/utils/training.py
@@ -1,3 +1,5 @@
+"""Training utilities for Entropice."""
+
 import pickle
 from dataclasses import asdict, dataclass
 from functools import cached_property
@@ -47,7 +49,7 @@ def move_data_to_device(data: ndarray, device: Literal["torch", "cuda", "cpu"])
 
 
 @dataclass
-class HPOCV:
+class HPOCV:  # noqa: D101
     method: HPSearch
     splitter: Splitter
     scaler: Scaler
@@ -55,13 +57,13 @@ class HPOCV:
     n_iter: int
     hpconfig: HPConfig
 
-    @property
-    def search_space(self):
+    @cached_property
+    def search_space(self):  # noqa: D102
         return get_search_space(self.hpconfig)
 
 
 @dataclass
-class AutoML:
+class AutoML:  # noqa: D101
     time_budget: int
     preset: str
     hpo: bool
@@ -80,7 +82,7 @@ class Training:
 
     f(dataset, method) -> (model, metrics)
 
-    Metrics refer to a simple dataframe in long format, with the columns: "metric", "split", "value".
+    Metrics refer to a simple dataframe in long format, with the columns: "metric", "split", "score".
     Split is either "train", "test" or "complete".
     """
 
@@ -89,12 +91,11 @@ class Training:
     method: HPOCV | AutoML
     task: Task
     target: TargetDataset
-    training_set: TrainingSet  # TODO: Store Training Set to improve loading time (?)
     model: Any
     model_type: Model
     metrics: pd.DataFrame
     feature_importance: pd.DataFrame
-    shap_explanation: Explanation
+    shap_explanation: Explanation | None
     predictions: gpd.GeoDataFrame
     confusion_matrix: xr.Dataset | None  # only for classification tasks
     cv_results: pd.DataFrame | None  # only for HPOCV
@@ -115,6 +116,31 @@ class Training:
         """Get the list of metric names from the metrics DataFrame."""
         return self.metrics["metric"].unique().tolist()
 
+    @cached_property
+    def training_set(self) -> TrainingSet:
+        """Get the training set for this training run."""
+        return self.dataset.create_training_set(self.task, self.target, device="cpu")
+
+    @property
+    def method_type(self) -> Literal["HPOCV", "AutoML"]:
+        """Get the type of method used in this training run."""
+        if isinstance(self.method, HPOCV):
+            return "HPOCV"
+        elif isinstance(self.method, AutoML):
+            return "AutoML"
+        else:
+            raise ValueError(f"Unknown method type: {type(self.method)}")
+
+    @property
+    def n_trials(self) -> int | None:
+        """Get the number of trials in the hyperparameter search, if applicable."""
+        if self.method_type == "HPOCV" and self.cv_results is not None:
+            return len(self.cv_results)
+        elif self.method_type == "AutoML" and self.leaderboard is not None:
+            return len(self.leaderboard)
+        else:
+            return None
+
     @property
     def get_state(self) -> xr.Dataset | pd.DataFrame | None:
         """Get the inner state of the trained model, if available."""
@@ -123,6 +149,10 @@ class Training:
         else:
             return None
 
+    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]
+
     def save(self):
         """Save the training results to the specified path."""
         self.path.mkdir(parents=True, exist_ok=True)
@@ -130,7 +160,6 @@ class Training:
         model_file = self.path / "model.pkl"
         metrics_file = self.path / "metrics.parquet"
         feature_importance_file = self.path / "feature_importance.parquet"
-        explanations_file = self.path / "shap_explanation.pkl"
         predictions_file = self.path / "predictions.parquet"
         # Save config
         with open(config_file, "w") as f:
@@ -150,9 +179,13 @@ class Training:
         model_file.write_bytes(pickle.dumps(self.model))
         self.metrics.to_parquet(metrics_file)
         self.feature_importance.to_parquet(feature_importance_file)
-        explanations_file.write_bytes(pickle.dumps(self.shap_explanation))
         self.predictions.to_parquet(predictions_file)
 
+        # Save SHAP explanation if it exists
+        if self.shap_explanation is not None:
+            explanations_file = self.path / "shap_explanation.pkl"
+            explanations_file.write_bytes(pickle.dumps(self.shap_explanation))
+
         # Save the confusion matrix if it exists
         if self.confusion_matrix is not None:
             cm_file = self.path / "confusion_matrix.nc"
@@ -168,13 +201,14 @@ class Training:
             self.leaderboard.to_parquet(leaderboard_file)
 
     @classmethod
-    def load(cls, path: Path, device: Literal["cpu", "cuda"] = "cpu") -> "Training":
+    def load(cls, path: Path) -> "Training":
         """Load a training run from the specified path."""
         config_file = path / "training_config.toml"
         model_file = path / "model.pkl"
         metrics_file = path / "metrics.parquet"
         feature_importance_file = path / "feature_importance.parquet"
         predictions_file = path / "predictions.parquet"
+        explanations_file = path / "shap_explanation.pkl"
         cm_file = path / "confusion_matrix.nc"
         cv_results_file = path / "search_results.parquet"
         leaderboard_file = path / "leaderboard.parquet"
@@ -188,7 +222,6 @@ class Training:
         model_type = config["model_type"]
 
         dataset = DatasetEnsemble(**config["dataset"])
-        training_set = dataset.create_training_set(task, target, device)
 
         method_type = config["method_type"]
         if method_type == "HPOCV":
@@ -202,9 +235,13 @@ class Training:
         model = pickle.loads(model_file.read_bytes())
         metrics = pd.read_parquet(metrics_file)
         feature_importance = pd.read_parquet(feature_importance_file)
-        shap_explanation = pickle.loads((path / "shap_explanation.pkl").read_bytes())
         predictions = gpd.read_parquet(predictions_file)
 
+        # Load SHAP explanation if it exists
+        shap_explanation = None
+        if explanations_file.exists():
+            shap_explanation = pickle.loads(explanations_file.read_bytes())
+
         # Load confusion matrix if it exists
         confusion_matrix = None
         if cm_file.exists():
@@ -225,7 +262,6 @@ class Training:
             method=method,
             task=task,
             target=target,
-            training_set=training_set,
             model=model,
             model_type=model_type,
             metrics=metrics,
diff --git a/tests/test_dataset.py b/tests/test_dataset.py
index c1959f1..ecb253b 100644
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -18,7 +18,6 @@ def sample_ensemble() -> Generator[DatasetEnsemble]:
         grid="hex",
         level=3,  # Use level 3 for much faster tests
         members=["AlphaEarth"],  # Use only one member for faster tests
-        add_lonlat=True,
     )
 
 
@@ -29,7 +28,6 @@ def sample_ensemble_v2() -> Generator[DatasetEnsemble]:
         grid="hex",
         level=3,  # Use level 3 for much faster tests
         members=["AlphaEarth"],  # Use only one member for faster tests
-        add_lonlat=True,
     )
 
 
@@ -41,7 +39,6 @@ class TestDatasetEnsemble:
         assert sample_ensemble.grid == "hex"
         assert sample_ensemble.level == 3
         assert "AlphaEarth" in sample_ensemble.members
-        assert sample_ensemble.add_lonlat is True
 
     def test_get_targets_returns_geodataframe(self, sample_ensemble: DatasetEnsemble) -> None:
         """Test that get_targets() returns a GeoDataFrame."""
@@ -106,10 +103,9 @@ class TestDatasetEnsemble:
         # Should NOT have geometry column
         assert "geometry" not in features.columns
 
-        # Should have location columns if add_lonlat is True
-        if sample_ensemble.add_lonlat:
-            assert "x" in features.columns
-            assert "y" in features.columns
+        # Should have location columns
+        assert "x" in features.columns
+        assert "y" in features.columns
 
         # Should have grid property columns
         assert "cell_area" in features.columns
diff --git a/tests/test_training.py b/tests/test_training.py
deleted file mode 100644
index 1d2fc2b..0000000
--- a/tests/test_training.py
+++ /dev/null
@@ -1,222 +0,0 @@
-"""Tests for training.py module, specifically random_cv function.
-
-This test suite validates the random_cv training function across all model-task
-combinations using a minimal hex level 3 grid with synopsis temporal mode.
-
-Test Coverage:
-- All 12 model-task combinations (4 models x 3 tasks): espa, xgboost, rf, knn
-- Device handling for each model type (torch/CUDA/cuML compatibility)
-- Multi-label target dataset support
-- Temporal mode configuration (synopsis)
-- Output file creation and validation
-
-Running Tests:
-    # Run all training tests (18 tests total, ~3 iterations each)
-    pixi run pytest tests/test_training.py -v
-
-    # Run only device handling tests
-    pixi run pytest tests/test_training.py::TestRandomCV::test_device_handling -v
-
-    # Run a specific model-task combination
-    pixi run pytest tests/test_training.py::TestRandomCV::test_random_cv_all_combinations[binary-espa] -v
-
-Note: Tests use minimal iterations (3) and level 3 grid for speed.
-Full production runs use higher iteration counts (100-2000).
-"""
-
-import shutil
-
-import pytest
-
-from entropice.ml.dataset import DatasetEnsemble
-from entropice.ml.randomsearch import RunSettings, random_cv
-from entropice.utils.types import Model, Task
-
-
-@pytest.fixture(scope="module")
-def test_ensemble():
-    """Create a minimal DatasetEnsemble for testing.
-
-    Uses hex level 3 grid with synopsis temporal mode for fast testing.
-    """
-    return DatasetEnsemble(
-        grid="hex",
-        level=3,
-        temporal_mode="synopsis",
-        members=["AlphaEarth"],  # Use only one member for faster tests
-        add_lonlat=True,
-    )
-
-
-@pytest.fixture
-def cleanup_results():
-    """Clean up results directory after each test.
-
-    This fixture collects the actual result directories created during tests
-    and removes them after the test completes.
-    """
-    created_dirs = []
-
-    def register_dir(results_dir):
-        """Register a directory to be cleaned up."""
-        created_dirs.append(results_dir)
-        return results_dir
-
-    yield register_dir
-
-    # Clean up only the directories created during this test
-    for results_dir in created_dirs:
-        if results_dir.exists():
-            shutil.rmtree(results_dir)
-
-
-# Model-task combinations to test
-# Note: Not all combinations make sense, but we test all to ensure robustness
-MODELS: list[Model] = ["espa", "xgboost", "rf", "knn"]
-TASKS: list[Task] = ["binary", "count", "density"]
-
-
-class TestRandomCV:
-    """Test suite for random_cv function."""
-
-    @pytest.mark.parametrize("model", MODELS)
-    @pytest.mark.parametrize("task", TASKS)
-    def test_random_cv_all_combinations(self, test_ensemble, model: Model, task: Task, cleanup_results):
-        """Test random_cv with all model-task combinations.
-
-        This test runs 3 iterations for each combination to verify:
-        - The function completes without errors
-        - Device handling works correctly for each model type
-        - All output files are created
-        """
-        # Use darts_v1 as the primary target for all tests
-        settings = RunSettings(
-            n_iter=3,
-            task=task,
-            target="darts_v1",
-            model=model,
-        )
-
-        # Run the cross-validation and get the results directory
-        results_dir = random_cv(
-            dataset_ensemble=test_ensemble,
-            settings=settings,
-            experiment="test_training",
-        )
-        cleanup_results(results_dir)
-
-        # Verify results directory was created
-        assert results_dir.exists(), f"Results directory not created for {model=}, {task=}"
-
-        # Verify all expected output files exist
-        expected_files = [
-            "search_settings.toml",
-            "best_estimator_model.pkl",
-            "search_results.parquet",
-            "metrics.toml",
-            "predicted_probabilities.parquet",
-        ]
-
-        # Add task-specific files
-        if task in ["binary", "count", "density"]:
-            # All tasks that use classification (including count/density when binned)
-            # Note: count and density without _regimes suffix might be regression
-            if task == "binary" or "_regimes" in task:
-                expected_files.append("confusion_matrix.nc")
-
-        # Add model-specific files
-        if model in ["espa", "xgboost", "rf"]:
-            expected_files.append("best_estimator_state.nc")
-
-        for filename in expected_files:
-            filepath = results_dir / filename
-            assert filepath.exists(), f"Expected file {filename} not found for {model=}, {task=}"
-
-    @pytest.mark.parametrize("model", MODELS)
-    def test_device_handling(self, test_ensemble, model: Model, cleanup_results):
-        """Test that device handling works correctly for each model type.
-
-        Different models require different device configurations:
-        - espa: Uses torch with array API dispatch
-        - xgboost: Uses CUDA without array API dispatch
-        - rf/knn: GPU-accelerated via cuML
-        """
-        settings = RunSettings(
-            n_iter=3,
-            task="binary",  # Simple binary task for device testing
-            target="darts_v1",
-            model=model,
-        )
-
-        # This should complete without device-related errors
-        try:
-            results_dir = random_cv(
-                dataset_ensemble=test_ensemble,
-                settings=settings,
-                experiment="test_training",
-            )
-            cleanup_results(results_dir)
-        except RuntimeError as e:
-            # Check if error is device-related
-            error_msg = str(e).lower()
-            device_keywords = ["cuda", "gpu", "device", "cpu", "torch", "cupy"]
-            if any(keyword in error_msg for keyword in device_keywords):
-                pytest.fail(f"Device handling error for {model=}: {e}")
-            else:
-                # Re-raise non-device errors
-                raise
-
-    def test_random_cv_with_mllabels(self, test_ensemble, cleanup_results):
-        """Test random_cv with multi-label target dataset."""
-        settings = RunSettings(
-            n_iter=3,
-            task="binary",
-            target="darts_mllabels",
-            model="espa",
-        )
-
-        # Run the cross-validation and get the results directory
-        results_dir = random_cv(
-            dataset_ensemble=test_ensemble,
-            settings=settings,
-            experiment="test_training",
-        )
-        cleanup_results(results_dir)
-
-        # Verify results were created
-        assert results_dir.exists(), "Results directory not created"
-        assert (results_dir / "search_settings.toml").exists()
-
-    def test_temporal_mode_synopsis(self, cleanup_results):
-        """Test that temporal_mode='synopsis' is correctly used."""
-        import toml
-
-        ensemble = DatasetEnsemble(
-            grid="hex",
-            level=3,
-            temporal_mode="synopsis",
-            members=["AlphaEarth"],
-            add_lonlat=True,
-        )
-
-        settings = RunSettings(
-            n_iter=3,
-            task="binary",
-            target="darts_v1",
-            model="espa",
-        )
-
-        # This should use synopsis mode (all years aggregated)
-        results_dir = random_cv(
-            dataset_ensemble=ensemble,
-            settings=settings,
-            experiment="test_training",
-        )
-        cleanup_results(results_dir)
-
-        # Verify the settings were stored correctly
-        assert results_dir.exists(), "Results directory not created"
-        with open(results_dir / "search_settings.toml") as f:
-            stored_settings = toml.load(f)
-
-        assert stored_settings["settings"]["temporal_mode"] == "synopsis"