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"pandas-stubs>=2.3.3.251201,<3", "pytest>=9.0.2,<10", + "pandas-stubs>=2.3.3.251201,<3", + "pytest>=9.0.2,<10", + "autogluon-tabular[all]>=1.5.0", ] [project.scripts] @@ -90,15 +91,15 @@ url = "https://pypi.nvidia.com" explicit = true [tool.uv.sources] -entropyc = { git = "ssh://git@github.com/AlbertEMC2Stein/entropyc", branch = "refactor/tobi" } +# entropyc = { git = "ssh://git@github.com/AlbertEMC2Stein/entropyc", branch = "refactor/tobi" } entropy = { git = "ssh://git@forgejo.tobiashoelzer.de:22222/tobias/entropy.git" } xanimate = { git = "https://github.com/davbyr/xAnimate" } xdem = { git = "https://github.com/GlacioHack/xdem" } xdggs = { git = "https://github.com/relativityhd/xdggs", branch = "feature/make-plotting-useful" } xarray-spatial = { git = "https://github.com/relativityhd/xarray-spatial" } -cudf-cu12 = { index = "nvidia" } -cuml-cu12 = { index = "nvidia" } -cuspatial-cu12 = { index = "nvidia" } +# cudf-cu12 = { index = "nvidia" } +# cuml-cu12 = { index = "nvidia" } +# cuspatial-cu12 = { index = "nvidia" } [tool.ruff] line-length = 120 @@ -148,5 +149,10 @@ nccl = ">=2.27.7.1,<3" cudnn = ">=9.13.1.26,<10" cusparselt = ">=0.8.1.1,<0.9" cuda-version = "12.9.*" -rapids = ">=25.10.0,<26" +# cudf = ">=25.10.0,<26" +# cuml = ">=25.10.0,<26" healpix-geo = ">=0.0.6" +scikit-learn = ">=1.4.0,<1.8.0" +pyarrow = ">=7.0.0,<21.0.0" +cudf = ">=25.12.0,<26" +cuml = ">=25.12.0,<26" diff --git a/scripts/rechunk_zarr.py b/scripts/rechunk_zarr.py index 664ccc9..22621b4 100644 --- a/scripts/rechunk_zarr.py +++ b/scripts/rechunk_zarr.py @@ -1,12 +1,12 @@ -import xarray as xr -import zarr -from rich import print import dask.distributed as dd +import xarray as xr +from rich import print -from entropice.utils.paths import get_era5_stores import entropice.utils.codecs +from entropice.utils.paths import get_era5_stores -def print_info(daily_raw = None, show_vars: bool = True): + +def print_info(daily_raw=None, show_vars: bool = True): if daily_raw is None: daily_store = get_era5_stores("daily") daily_raw = xr.open_zarr(daily_store, consolidated=False) @@ -14,12 +14,12 @@ def print_info(daily_raw = None, show_vars: bool = True): print(f" Dims: {daily_raw.sizes}") numchunks = 1 chunksizes = {} - approxchunksize = 4 # 4 Bytes = float32 + approxchunksize = 4 # 4 Bytes = float32 for d, cs in daily_raw.chunksizes.items(): numchunks *= len(cs) chunksizes[d] = max(cs) approxchunksize *= max(cs) - approxchunksize /= 10e6 # MB + approxchunksize /= 10e6 # MB print(f" Chunks: {chunksizes} (~{approxchunksize:.2f}MB) => {numchunks} total") print(f" Encoding: {daily_raw.encoding}") if show_vars: @@ -30,29 +30,109 @@ def print_info(daily_raw = None, show_vars: bool = True): print(da.encoding) print("") -def rechunk(): + +def rechunk(use_shards: bool = False): + if use_shards: + # ! MEEEP: https://github.com/pydata/xarray/issues/10831 + print("WARNING! Rechunking with shards. This is known to be broken in xarray/dask!") + with ( + dd.LocalCluster(n_workers=8, threads_per_worker=5, memory_limit="20GB") as cluster, + dd.Client(cluster) as client, + ): + print(f"Dashboard: {client.dashboard_link}") + daily_store = get_era5_stores("daily") + daily_store_rechunked = daily_store.with_stem(f"{daily_store.stem}_rechunked_sharded") + daily_raw = xr.open_zarr(daily_store, consolidated=False) + daily_raw = daily_raw.chunk( + { + "time": 120, + "latitude": -1, # Should be 337, + "longitude": -1, # Should be 3600 + } + ) + encoding = entropice.utils.codecs.from_ds(daily_raw, filter_existing=False) + for var in daily_raw.data_vars: + encoding[var]["chunks"] = (120, 337, 3600) + if use_shards: + encoding[var]["shards"] = (1200, 337, 3600) + print(encoding) + daily_raw.to_zarr(daily_store_rechunked, mode="w", consolidated=False, encoding=encoding) + + +def validate(): daily_store = get_era5_stores("daily") daily_raw = xr.open_zarr(daily_store, consolidated=False) - print_info(daily_raw, False) - daily_raw = daily_raw.chunk({ - "time": 120, - "latitude": -1, # Should be 337, - "longitude": -1 # Should be 3600 - }) - print_info(daily_raw, False) - encoding = entropice.utils.codecs.from_ds(daily_raw) daily_store_rechunked = daily_store.with_stem(f"{daily_store.stem}_rechunked") - daily_raw.to_zarr(daily_store_rechunked, mode="w", encoding=encoding, consolidated=False) + daily_rechunked = xr.open_zarr(daily_store_rechunked, consolidated=False) + print("\n=== Comparing Datasets ===") + + # Compare dimensions + if daily_raw.sizes != daily_rechunked.sizes: + print("❌ Dimensions differ:") + print(f" Original: {daily_raw.sizes}") + print(f" Rechunked: {daily_rechunked.sizes}") + else: + print("✅ Dimensions match") + + # Compare variables + raw_vars = set(daily_raw.data_vars) + rechunked_vars = set(daily_rechunked.data_vars) + if raw_vars != rechunked_vars: + print("❌ Variables differ:") + print(f" Only in original: {raw_vars - rechunked_vars}") + print(f" Only in rechunked: {rechunked_vars - raw_vars}") + else: + print("✅ Variables match") + + # Compare each variable + print("\n=== Variable Comparison ===") + all_equal = True + for var in raw_vars & rechunked_vars: + raw_var = daily_raw[var] + rechunked_var = daily_rechunked[var] + + if raw_var.equals(rechunked_var): + print(f"✅ {var}: Equal") + else: + all_equal = False + print(f"❌ {var}: NOT Equal") + + # Check if values are equal + try: + values_equal = raw_var.values.shape == rechunked_var.values.shape + if values_equal: + import numpy as np + + values_equal = np.allclose(raw_var.values, rechunked_var.values, equal_nan=True) + + if values_equal: + print(" → Values are numerically equal (likely metadata/encoding difference)") + else: + print(" → Values differ!") + print(f" Original shape: {raw_var.values.shape}") + print(f" Rechunked shape: {rechunked_var.values.shape}") + except Exception as e: + print(f" → Error comparing values: {e}") + + # Check attributes + if raw_var.attrs != rechunked_var.attrs: + print(" → Attributes differ:") + print(f" Original: {raw_var.attrs}") + print(f" Rechunked: {rechunked_var.attrs}") + + # Check encoding + if raw_var.encoding != rechunked_var.encoding: + print(" → Encoding differs:") + print(f" Original: {raw_var.encoding}") + print(f" Rechunked: {rechunked_var.encoding}") + + if all_equal: + print("\n✅ Validation successful: All datasets are equal.") + else: + print("\n❌ Validation failed: Datasets have differences (see above).") if __name__ == "__main__": - with ( - dd.LocalCluster(n_workers=1, threads_per_worker=10, memory_limit="100GB") as cluster, - dd.Client(cluster) as client, - ): - print(client) - print(client.dashboard_link) - rechunk() - print("Done.") \ No newline at end of file + validate() diff --git a/src/entropice/dashboard/plots/hyperparameter_analysis.py b/src/entropice/dashboard/plots/hyperparameter_analysis.py index 76ba038..9ada355 100644 --- a/src/entropice/dashboard/plots/hyperparameter_analysis.py +++ b/src/entropice/dashboard/plots/hyperparameter_analysis.py @@ -9,11 +9,11 @@ import numpy as np import pandas as pd import pydeck as pdk import streamlit as st +import xarray as xr from entropice.dashboard.utils.class_ordering import get_ordered_classes from entropice.dashboard.utils.colors import get_cmap, get_palette from entropice.dashboard.utils.geometry import fix_hex_geometry -from entropice.ml.dataset import DatasetEnsemble from entropice.ml.training import TrainingSettings @@ -1154,73 +1154,36 @@ def render_top_configurations(results: pd.DataFrame, metric: str, top_n: int = 1 @st.fragment -def render_confusion_matrix_map(result_path: Path, settings: TrainingSettings): - """Render 3D pydeck map showing prediction results. +def render_confusion_matrix_map( + result_path: Path, settings: TrainingSettings, merged_predictions: gpd.GeoDataFrame | None = None +): + """Render 3D pydeck map showing model performance on training data. - Uses true labels for elevation (height) and different shades of red for incorrect predictions - based on the predicted class. + Displays cells from the training dataset with predictions, colored by correctness. + Uses true labels for elevation (height) and different shades of red for incorrect predictions. Args: - result_path: Path to the training result directory. + result_path: Path to the training result directory (not used, kept for compatibility). settings: Settings dictionary containing grid, level, task, and target information. + merged_predictions: GeoDataFrame with predictions, true labels, and split info. """ - st.subheader("🗺️ Prediction Results Map") - - # Load predicted probabilities - preds_file = result_path / "predicted_probabilities.parquet" - if not preds_file.exists(): - st.warning("No predicted probabilities found for this training run.") + if merged_predictions is None: + st.warning("Prediction data not available. Cannot display map.") return - preds_gdf = gpd.read_parquet(preds_file) - - # Get task and target information from settings - task = settings.task - target = settings.target + # Get grid type and task from settings grid = settings.grid - level = settings.level + task = settings.task - # Create dataset ensemble to get true labels - # We need to load the target data to get true labels - try: - ensemble = DatasetEnsemble( - grid=grid, - level=level, - target=target, - members=[], # We don't need feature data, just target - ) - training_data = ensemble.create_cat_training_dataset(task=task, device="cpu") - except Exception as e: - st.error(f"Error loading training data: {e}") - return - - # Get all cells from the complete dataset (not just test split) - # Use the full dataset which includes both train and test splits - all_cells = training_data.dataset.copy() - - # Merge predictions with true labels - # Reset index to avoid ambiguity between index and column - labeled_gdf = all_cells.reset_index().rename(columns={"index": "cell_id"}) - labeled_gdf["true_class"] = training_data.y.binned.loc[all_cells.index].to_numpy() - - # Merge with predictions - use left join to keep all cells - merged_df = labeled_gdf.merge(preds_gdf[["cell_id", "predicted_class"]], on="cell_id", how="left") - merged = gpd.GeoDataFrame(merged_df, geometry="geometry", crs=labeled_gdf.crs) - - # Mark which cells have predictions (test split) vs not (training split) - merged["in_test_split"] = merged["predicted_class"].notna() - - # For cells without predictions (training split), use true class as predicted class for visualization - merged["predicted_class"] = merged["predicted_class"].fillna(merged["true_class"]) + # Use the merged predictions which already have true labels, predictions, and split info + merged = merged_predictions.copy() + merged["is_correct"] = merged["true_class"] == merged["predicted_class"] if len(merged) == 0: - st.warning("No matching predictions found for labeled cells.") + st.warning("No predictions found for labeled cells.") return - # Mark correct vs incorrect predictions (only meaningful for test split) - merged["is_correct"] = merged["true_class"] == merged["predicted_class"] - # Get ordered class labels for the task ordered_classes = get_ordered_classes(task) @@ -1228,51 +1191,55 @@ def render_confusion_matrix_map(result_path: Path, settings: TrainingSettings): col1, col2, col3 = st.columns([2, 1, 1]) with col1: - # Filter by prediction correctness and split - categories = ["All", "Test Split Only", "Training Split Only", "Correct (Test)", "Incorrect (Test)"] - - selected_category = st.selectbox( - "Filter by Category", - options=categories, - key="confusion_map_category", + # Split selector (similar to confusion matrix) + split_type = st.selectbox( + "Select Data Split", + options=["test", "train", "all"], + format_func=lambda x: {"test": "Test Set", "train": "Training Set (CV)", "all": "All Data"}[x], + help="Choose which data split to display on the map", + key="prediction_map_split_select", ) with col2: + # Color scheme selector + show_only_incorrect = st.checkbox( + "Highlight Errors Only", + value=False, + help="Show only incorrect predictions in red, hide correct ones", + key="prediction_map_errors_only", + ) + + with col3: opacity = st.slider( "Opacity", min_value=0.1, max_value=1.0, value=0.7, step=0.1, - key="confusion_map_opacity", + key="prediction_map_opacity", ) - with col3: - line_width = st.slider( - "Line Width", - min_value=0.5, - max_value=3.0, - value=1.0, - step=0.5, - key="confusion_map_line_width", - ) - - # Filter data if needed - if selected_category == "Test Split Only": - display_gdf = merged[merged["in_test_split"]].copy() - elif selected_category == "Training Split Only": - display_gdf = merged[~merged["in_test_split"]].copy() - elif selected_category == "Correct (Test)": - display_gdf = merged[merged["is_correct"] & merged["in_test_split"]].copy() - elif selected_category == "Incorrect (Test)": - display_gdf = merged[~merged["is_correct"] & merged["in_test_split"]].copy() - else: # "All" + # Filter data by split + if split_type == "test": + display_gdf = merged[merged["split"] == "test"].copy() + split_caption = "Test Set (held-out data)" + elif split_type == "train": + display_gdf = merged[merged["split"] == "train"].copy() + split_caption = "Training Set (CV data)" + else: # "all" display_gdf = merged.copy() + split_caption = "All Available Data" + + # Optionally filter to show only incorrect predictions + if show_only_incorrect: + display_gdf = display_gdf[~display_gdf["is_correct"]].copy() if len(display_gdf) == 0: - st.warning(f"No cells found for category: {selected_category}") + st.warning(f"No cells found for {split_caption}.") return + st.caption(f"📍 Showing {len(display_gdf)} cells from {split_caption}") + # Convert to WGS84 for pydeck display_gdf_wgs84 = display_gdf.to_crs("EPSG:4326") @@ -1303,12 +1270,12 @@ def render_confusion_matrix_map(result_path: Path, settings: TrainingSettings): display_gdf_wgs84["fill_color"] = display_gdf_wgs84.apply(get_color, axis=1) - # Add line color based on split: blue for test split, orange for training split + # Add line color based on split: blue for test, orange for train def get_line_color(row): - if row["in_test_split"]: + if row["split"] == "test": return [52, 152, 219] # Blue for test split else: - return [230, 126, 34] # Orange for training split + return [230, 126, 34] # Orange for train split display_gdf_wgs84["line_color"] = display_gdf_wgs84.apply(get_line_color, axis=1) @@ -1329,18 +1296,15 @@ def render_confusion_matrix_map(result_path: Path, settings: TrainingSettings): geojson_data = [] for _, row in display_gdf_wgs84.iterrows(): # Determine split and status for tooltip - split_name = "Test Split" if row["in_test_split"] else "Training Split" - if row["in_test_split"]: - status = "✓ Correct" if row["is_correct"] else "✗ Incorrect" - else: - status = "(No prediction - training data)" + split_name = "Test" if row["split"] == "test" else "Training (CV)" + status = "✓ Correct" if row["is_correct"] else "✗ Incorrect" feature = { "type": "Feature", "geometry": row["geometry"].__geo_interface__, "properties": { - "true_class": str(row["true_class"]), - "predicted_class": str(row["predicted_class"]) if row["in_test_split"] else "N/A", + "true_label": str(row["true_class"]), + "predicted_label": str(row["predicted_class"]), "is_correct": bool(row["is_correct"]), "split": split_name, "status": status, @@ -1362,7 +1326,7 @@ def render_confusion_matrix_map(result_path: Path, settings: TrainingSettings): wireframe=False, get_fill_color="properties.fill_color", get_line_color="properties.line_color", - line_width_min_pixels=line_width, + line_width_min_pixels=2, get_elevation="properties.elevation", elevation_scale=500000, pickable=True, @@ -1376,10 +1340,10 @@ def render_confusion_matrix_map(result_path: Path, settings: TrainingSettings): layers=[layer], initial_view_state=view_state, tooltip={ - "html": "Split: {split}
" - "True Label: {true_class}
" - "Predicted Label: {predicted_class}
" - "Status: {status}", + "html": "Status: {status}
" + "True Label: {true_label}
" + "Predicted Label: {predicted_label}
" + "Split: {split}", "style": {"backgroundColor": "steelblue", "color": "white"}, }, map_style="https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json", @@ -1388,102 +1352,91 @@ def render_confusion_matrix_map(result_path: Path, settings: TrainingSettings): # Render the map st.pydeck_chart(deck) - # Show statistics - col1, col2, col3, col4 = st.columns(4) + # Show statistics for displayed data + col1, col2, col3 = st.columns(3) with col1: - st.metric("Total Labeled Cells", len(merged)) + st.metric("Cells Displayed", len(display_gdf)) with col2: - test_count = len(merged[merged["in_test_split"]]) - st.metric("Test Split", test_count) + correct = len(display_gdf[display_gdf["is_correct"]]) + st.metric("Correct Predictions", correct) with col3: - train_count = len(merged[~merged["in_test_split"]]) - st.metric("Training Split", train_count) - - with col4: - test_cells = merged[merged["in_test_split"]] - if len(test_cells) > 0: - correct = len(test_cells[test_cells["is_correct"]]) - accuracy = correct / len(test_cells) - st.metric("Test Accuracy", f"{accuracy:.2%}") + if len(display_gdf) > 0: + accuracy = correct / len(display_gdf) + st.metric("Accuracy", f"{accuracy:.2%}") else: - st.metric("Test Accuracy", "N/A") + st.metric("Accuracy", "N/A") # Add legend with st.expander("Legend", expanded=True): - # Split indicators (border colors) - st.markdown("**Data Split (Border Color):**") + st.markdown("**Fill Color (Prediction Correctness):**") - test_count = len(merged[merged["in_test_split"]]) - train_count = len(merged[~merged["in_test_split"]]) - - st.markdown( - f'
' - f'
' - f"Test Split ({test_count} cells, {test_count / len(merged) * 100:.1f}%)
", - unsafe_allow_html=True, - ) - - st.markdown( - f'
' - f'
' - f"Training Split ({train_count} cells, {train_count / len(merged) * 100:.1f}%)
", - unsafe_allow_html=True, - ) - - st.markdown("---") - st.markdown("**Fill Color (Prediction Results):**") - - # Correct predictions (test split only) - test_cells = merged[merged["in_test_split"]] - correct = len(test_cells[test_cells["is_correct"]]) if len(test_cells) > 0 else 0 - incorrect = len(test_cells[~test_cells["is_correct"]]) if len(test_cells) > 0 else 0 + # Correct predictions + correct_count = len(display_gdf[display_gdf["is_correct"]]) + incorrect_count = len(display_gdf[~display_gdf["is_correct"]]) st.markdown( f'
' f'
' - f"Correct Predictions (Test) ({correct} cells, {correct / len(test_cells) * 100 if len(test_cells) > 0 else 0:.1f}%)
", + f"Correct Predictions ({correct_count} cells, " + f"{correct_count / len(display_gdf) * 100 if len(display_gdf) > 0 else 0:.1f}%)", unsafe_allow_html=True, ) # Incorrect predictions by predicted class (shades of red) - st.markdown( - f"Incorrect Predictions by Predicted Class (Test) ({incorrect} cells):", unsafe_allow_html=True - ) + if incorrect_count > 0: + st.markdown( + f"Incorrect Predictions by Predicted Class ({incorrect_count} cells):", unsafe_allow_html=True + ) - for class_idx, class_label in enumerate(ordered_classes): - # Get count of incorrect predictions for this predicted class (test split only) - count = len(test_cells[(~test_cells["is_correct"]) & (test_cells["predicted_class"] == class_label)]) - if count > 0: - # Get color for this predicted class - color_value = red_cmap(class_idx / max(n_classes - 1, 1)) - rgb = [int(color_value[0] * 255), int(color_value[1] * 255), int(color_value[2] * 255)] + for class_idx, class_label in enumerate(ordered_classes): + # Get count of incorrect predictions for this predicted class + count = len(display_gdf[(~display_gdf["is_correct"]) & (display_gdf["predicted_class"] == class_label)]) + if count > 0: + # Get color for this predicted class + color_value = red_cmap(class_idx / max(n_classes - 1, 1)) + rgb = [int(color_value[0] * 255), int(color_value[1] * 255), int(color_value[2] * 255)] - percentage = count / incorrect * 100 if incorrect > 0 else 0 + percentage = count / incorrect_count * 100 - st.markdown( - f'
' - f'
' - f"Predicted as {class_label}: {count} ({percentage:.1f}%)
", - unsafe_allow_html=True, - ) - - # Note about training split - st.markdown( - f'
' - f"Note: Training split cells ({train_count}) are shown with their true labels (green fill) " - f"since predictions are only available for the test split.
", - unsafe_allow_html=True, - ) + st.markdown( + f'
' + f'
' + f"Predicted as {class_label}: {count} ({percentage:.1f}%)
", + unsafe_allow_html=True, + ) st.markdown("---") - st.markdown("**Elevation (3D):**") + st.markdown("**Border Color (Data Split):**") + + # Count by split in displayed data + test_in_display = len(display_gdf[display_gdf["split"] == "test"]) + train_in_display = len(display_gdf[display_gdf["split"] == "train"]) + + if test_in_display > 0: + st.markdown( + f'
' + f'
' + f"Test Split ({test_in_display} cells)
", + unsafe_allow_html=True, + ) + + if train_in_display > 0: + st.markdown( + f'
' + f'
' + f"Training Split ({train_in_display} cells)
", + unsafe_allow_html=True, + ) + + st.markdown("---") + st.markdown("**Elevation (3D Height):**") # Show elevation mapping for each true class st.markdown("Height represents the true label:", unsafe_allow_html=True) @@ -1514,13 +1467,23 @@ def render_confusion_matrix_heatmap(confusion_matrix: "xr.DataArray", task: str) true_labels = confusion_matrix.coords["true_label"].values pred_labels = confusion_matrix.coords["predicted_label"].values - # For binary classification, map 0/1 to No-RTS/RTS - if task == "binary": + # Check if labels are already strings (from predictions) or numeric (from stored confusion matrices) + first_true_label = true_labels[0] + is_string_labels = isinstance(first_true_label, str) or ( + hasattr(first_true_label, "dtype") and first_true_label.dtype.kind in ("U", "O") + ) + + if is_string_labels: + # Labels are already string labels, use them directly + true_labels_str = [str(label) for label in true_labels] + pred_labels_str = [str(label) for label in pred_labels] + elif task == "binary": + # Numeric binary labels - map 0/1 to No-RTS/RTS label_map = {0: "No-RTS", 1: "RTS"} true_labels_str = [label_map.get(int(label), str(label)) for label in true_labels] pred_labels_str = [label_map.get(int(label), str(label)) for label in pred_labels] else: - # For multiclass, use numeric labels as is + # Numeric multiclass labels - use as is true_labels_str = [str(label) for label in true_labels] pred_labels_str = [str(label) for label in pred_labels] diff --git a/src/entropice/dashboard/views/model_state_page.py b/src/entropice/dashboard/views/model_state_page.py index c536d99..f589fd6 100644 --- a/src/entropice/dashboard/views/model_state_page.py +++ b/src/entropice/dashboard/views/model_state_page.py @@ -41,7 +41,6 @@ def get_members_from_settings(settings) -> list[L2SourceDataset]: return settings.members -@st.fragment def render_sidebar_selection(training_results: list[TrainingResult]) -> TrainingResult: """Render sidebar for training run selection. diff --git a/src/entropice/dashboard/views/training_analysis_page.py b/src/entropice/dashboard/views/training_analysis_page.py index e73dbd8..b1ad430 100644 --- a/src/entropice/dashboard/views/training_analysis_page.py +++ b/src/entropice/dashboard/views/training_analysis_page.py @@ -2,7 +2,9 @@ from typing import cast +import geopandas as gpd import streamlit as st +import xarray as xr from stopuhr import stopwatch from entropice.dashboard.plots.hyperparameter_analysis import ( @@ -22,6 +24,127 @@ from entropice.dashboard.utils.stats import CVResultsStatistics from entropice.utils.types import GridConfig +def load_predictions_with_labels(selected_result: TrainingResult) -> gpd.GeoDataFrame | None: + """Load predictions and merge with training data to get true labels and split info. + + Args: + selected_result: The selected TrainingResult object. + + Returns: + GeoDataFrame with predictions, true labels, and split information, or None if unavailable. + + """ + from sklearn.model_selection import train_test_split + + from entropice.ml.dataset import DatasetEnsemble, bin_values, taskcol + + # Load predictions + preds_gdf = selected_result.load_predictions() + if preds_gdf is None: + return None + + # Create a minimal dataset ensemble to access target data + settings = selected_result.settings + dataset_ensemble = DatasetEnsemble( + grid=settings.grid, + level=settings.level, + target=settings.target, + members=[], # No feature data needed, just targets + ) + + # Load target dataset (just labels, no features) + with st.spinner("Loading target labels..."): + targets = dataset_ensemble._read_target() + + # Get coverage and task columns + task_col = taskcol[settings.task][settings.target] + + # Filter for valid labels (same as in _cat_and_split) + valid_labels = targets[task_col].notna() + filtered_targets = targets.loc[valid_labels].copy() + + # Apply binning to get class labels (same logic as _cat_and_split) + if settings.task == "binary": + binned = filtered_targets[task_col].map({False: "No RTS", True: "RTS"}).astype("category") + elif settings.task == "count": + binned = bin_values(filtered_targets[task_col].astype(int), task=settings.task) + elif settings.task == "density": + binned = bin_values(filtered_targets[task_col], task=settings.task) + else: + raise ValueError(f"Invalid task: {settings.task}") + + filtered_targets["true_class"] = binned.to_numpy() + + # Recreate the train/test split deterministically (same random_state=42 as in _cat_and_split) + _train_idx, test_idx = train_test_split( + filtered_targets.index.to_numpy(), test_size=0.2, random_state=42, shuffle=True + ) + filtered_targets["split"] = "train" + filtered_targets.loc[test_idx, "split"] = "test" + filtered_targets["split"] = filtered_targets["split"].astype("category") + + # Ensure cell_id is available as a column for merging + # Check if cell_id already exists, otherwise use the index + if "cell_id" not in filtered_targets.columns: + filtered_targets = filtered_targets.reset_index().rename(columns={"index": "cell_id"}) + + # Merge predictions with labels (inner join to keep only cells with predictions) + merged = filtered_targets.merge(preds_gdf[["cell_id", "predicted_class"]], on="cell_id", how="inner") + merged_gdf = gpd.GeoDataFrame(merged, geometry="geometry", crs=targets.crs) + + return merged_gdf + + +def compute_confusion_matrix_from_merged_data( + merged_data: gpd.GeoDataFrame, + split_type: str, + label_names: list[str], +) -> xr.DataArray | None: + """Compute confusion matrix from merged predictions and labels. + + Args: + merged_data: GeoDataFrame with 'true_class', 'predicted_class', and 'split' columns. + split_type: One of 'test', 'train', or 'all'. + label_names: List of class label names in order. + + Returns: + xarray.DataArray with confusion matrix or None if data unavailable. + + """ + from sklearn.metrics import confusion_matrix + + # Filter by split type + if split_type == "train": + data = merged_data[merged_data["split"] == "train"] + elif split_type == "test": + data = merged_data[merged_data["split"] == "test"] + elif split_type == "all": + data = merged_data + else: + raise ValueError(f"Invalid split_type: {split_type}") + + if len(data) == 0: + st.warning(f"No data available for {split_type} split.") + return None + + # Get true and predicted labels + y_true = data["true_class"].to_numpy() + y_pred = data["predicted_class"].to_numpy() + + # Compute confusion matrix + cm = confusion_matrix(y_true, y_pred, labels=label_names) + + # Create xarray DataArray + cm_xr = xr.DataArray( + cm, + dims=["true_label", "predicted_label"], + coords={"true_label": label_names, "predicted_label": label_names}, + name="confusion_matrix", + ) + + return cm_xr + + def render_analysis_settings_sidebar(training_results: list[TrainingResult]) -> tuple[TrainingResult, str, str, int]: """Render sidebar for training run and analysis settings selection. @@ -233,21 +356,56 @@ def render_cv_statistics_section(selected_result, selected_metric): ) -def render_confusion_matrix_section(selected_result: TrainingResult): +@st.fragment +def render_confusion_matrix_section(selected_result: TrainingResult, merged_predictions: gpd.GeoDataFrame | None): """Render confusion matrix visualization and analysis. Args: selected_result: The selected TrainingResult object. + merged_predictions: GeoDataFrame with predictions merged with true labels and split info. """ st.header("🎲 Confusion Matrix") - st.caption("Detailed breakdown of predictions on the test set") + st.caption("Detailed breakdown of predictions") - if selected_result.confusion_matrix is None: - st.warning("No confusion matrix available for this training run.") - return + # Add selector for confusion matrix type + cm_type = st.selectbox( + "Select Data Split", + options=["test", "train", "all"], + format_func=lambda x: {"test": "Test Set", "train": "CV Set (Train Split)", "all": "All Available Data"}[x], + help="Choose which data split to display the confusion matrix for", + key="cm_split_select", + ) - render_confusion_matrix_heatmap(selected_result.confusion_matrix, selected_result.settings.task) + # Get label names from settings + label_names = selected_result.settings.classes + + # Compute or load confusion matrix based on selection + if cm_type == "test": + if selected_result.confusion_matrix is None: + st.warning("No confusion matrix available for the test set.") + return + cm = selected_result.confusion_matrix + st.info("📊 Showing confusion matrix for the **Test Set** (held-out data, never used during training)") + else: + if merged_predictions is None: + st.warning("Predictions data not available. Cannot compute confusion matrix.") + return + + with st.spinner(f"Computing confusion matrix for {cm_type} split..."): + cm = compute_confusion_matrix_from_merged_data(merged_predictions, cm_type, label_names) + if cm is None: + return + + if cm_type == "train": + st.info( + "📊 Showing confusion matrix for the **CV Set (Train Split)** " + "(data used during hyperparameter search cross-validation)" + ) + else: # all + st.info("📊 Showing confusion matrix for **All Available Data** (combined train and test splits)") + + render_confusion_matrix_heatmap(cm, selected_result.settings.task) def render_parameter_space_section(selected_result, selected_metric): @@ -374,6 +532,9 @@ def render_training_analysis_page(): return selected_result, selected_metric, refit_metric, top_n = selection_result + # Load predictions with labels once (used by confusion matrix and map) + merged_predictions = load_predictions_with_labels(selected_result) + # Main content area results = selected_result.results settings = selected_result.settings @@ -389,7 +550,7 @@ def render_training_analysis_page(): st.divider() # Confusion Matrix Section - render_confusion_matrix_section(selected_result) + render_confusion_matrix_section(selected_result, merged_predictions) st.divider() @@ -400,9 +561,10 @@ def render_training_analysis_page(): st.divider() - # Confusion Matrix Map Section - st.header("🗺️ Prediction Results Map") - render_confusion_matrix_map(selected_result.path, settings) + # Prediction Analysis Map Section + st.header("🗺️ Model Performance Map") + st.caption("Interactive 3D map showing prediction correctness across the training dataset") + render_confusion_matrix_map(selected_result.path, settings, merged_predictions) st.divider() diff --git a/src/entropice/ingest/era5.py b/src/entropice/ingest/era5.py index 667e43e..9b865b3 100644 --- a/src/entropice/ingest/era5.py +++ b/src/entropice/ingest/era5.py @@ -734,14 +734,14 @@ def spatial_agg( 3: _Aggregations.common(), 4: _Aggregations.common(), 5: _Aggregations(mean=True), - 6: "interpolate", + 6: "interpolate", # nearest neighbor interpolation }, "healpix": { 6: _Aggregations.common(), 7: _Aggregations.common(), 8: _Aggregations(mean=True), 9: _Aggregations(mean=True), - 10: "interpolate", + 10: "interpolate", # nearest neighbor interpolation }, } aggregations = aggregations_by_gridlevel[grid][level] diff --git a/src/entropice/ml/training.py b/src/entropice/ml/training.py index 8a10d44..b8317ec 100644 --- a/src/entropice/ml/training.py +++ b/src/entropice/ml/training.py @@ -8,8 +8,8 @@ import cupy as cp import cyclopts import pandas as pd import toml +import torch import xarray as xr -from array_api_compat import get_namespace from cuml.ensemble import RandomForestClassifier from cuml.neighbors import KNeighborsClassifier from entropy import ESPAClassifier @@ -233,10 +233,9 @@ def random_cv( # Compute predictions on the test set y_pred = best_estimator.predict(training_data.X.test) labels = list(range(len(training_data.y.labels))) - xp = get_namespace(y_test) - y_test = xp.as_array(y_test) - y_pred = xp.as_array(y_pred) - labels = xp.as_array(labels) + y_test = torch.asarray(y_test, device="cuda") + y_pred = torch.asarray(y_pred, device="cuda") + labels = torch.asarray(labels, device="cuda") test_metrics = {metric: _metric_functions[metric](y_test, y_pred) for metric in metrics} @@ -244,7 +243,7 @@ def random_cv( cm = confusion_matrix(y_test, y_pred, labels=labels) label_names = [training_data.y.labels[i] for i in range(len(training_data.y.labels))] cm = xr.DataArray( - xp.as_numpy(cm), + cm.cpu().numpy(), dims=["true_label", "predicted_label"], coords={"true_label": label_names, "predicted_label": label_names}, name="confusion_matrix", diff --git a/src/entropice/spatial/aggregators.py b/src/entropice/spatial/aggregators.py index f6e6ba0..bffed3e 100644 --- a/src/entropice/spatial/aggregators.py +++ b/src/entropice/spatial/aggregators.py @@ -368,7 +368,7 @@ def _init_worker(r: xr.Dataset | None): def _align_partition( grid_partition_gdf: gpd.GeoDataFrame, raster: xr.Dataset | Callable[[], xr.Dataset] | None, - aggregations: _Aggregations | None, # None -> Interpolation + aggregations: _Aggregations | str, # str -> Interpolation method pxbuffer: int, ): # ? This function is expected to run inside a worker process @@ -441,7 +441,7 @@ def _align_partition( ) memprof.log_memory("After reading partial raster", log=False) - if aggregations is None: + if isinstance(aggregations, str): cell_ids = grids.convert_cell_ids(grid_partition_gdf) if grid_partition_gdf.crs.to_epsg() == 4326: centroids = grid_partition_gdf.geometry.apply(antimeridian.fix_shape).apply(antimeridian.centroid) @@ -453,14 +453,26 @@ def _align_partition( cy = centroids.y interp_x = xr.DataArray(cx, dims=["cell_ids"], coords={"cell_ids": cell_ids}) interp_y = xr.DataArray(cy, dims=["cell_ids"], coords={"cell_ids": cell_ids}) - interp_coords = ( - {"latitude": interp_y, "longitude": interp_x} - if "latitude" in raster.dims and "longitude" in raster.dims - else {"y": interp_y, "x": interp_x} - ) + ydim = "latitude" if "latitude" in raster.dims else "y" + xdim = "longitude" if "longitude" in raster.dims else "x" + interp_coords = {ydim: interp_y, xdim: interp_x} + # ?: Cubic does not work with NaNs in xarray interp with stopwatch("Interpolating data to grid centroids", log=False): - ongrid = partial_raster.interp(interp_coords, method="linear", kwargs={"fill_value": np.nan}) + # Fill the nan + y_is_rev = partial_raster.indexes[ydim].is_monotonic_decreasing + if y_is_rev: + partial_raster = partial_raster.sortby(ydim) + partial_raster = partial_raster.interpolate_na( + dim=ydim, + method=aggregations, + ).interpolate_na( + dim=xdim, + method=aggregations, + ) + if y_is_rev: + partial_raster = partial_raster.sortby(ydim) + ongrid = partial_raster.interp(interp_coords, method=aggregations, kwargs={"fill_value": np.nan}) memprof.log_memory("After interpolating data", log=False) else: others_shape = tuple( @@ -519,7 +531,7 @@ def _align_partition( def _align_data( grid_gdf: gpd.GeoDataFrame | list[gpd.GeoDataFrame], raster: xr.Dataset | Callable[[], xr.Dataset], - aggregations: _Aggregations | None, + aggregations: _Aggregations | str, n_partitions: int | None, concurrent_partitions: int, pxbuffer: int, @@ -620,7 +632,7 @@ def _align_data( def aggregate_raster_into_grid( raster: xr.Dataset | Callable[[], xr.Dataset], grid_gdf: gpd.GeoDataFrame | list[gpd.GeoDataFrame], - aggregations: _Aggregations | Literal["interpolate"], + aggregations: _Aggregations | Literal["nearest", "linear", "cubic", "interpolate"], grid: Grid, level: int, n_partitions: int | None = 20, @@ -634,7 +646,11 @@ def aggregate_raster_into_grid( grid_gdf (gpd.GeoDataFrame | list[gpd.GeoDataFrame]): The grid to aggregate into. If a list of GeoDataFrames is provided, each will be processed as a separate partition. No further partitioning will be done and the n_partitions argument will be ignored. - aggregations (_Aggregations | Literal["interpolate"]): The aggregations to perform. + aggregations (_Aggregations | Literal[["nearest", "linear", "cubic", "interpolate"]): + The aggregations to perform. + If a string is provided, interpolation will be used with the specified method. + If "interpolate" is provided, the nearest neighbor interpolation will be used. + Supported methods are "nearest", "linear", and "cubic". grid (Grid): The type of grid to use. level (int): The level of the grid. n_partitions (int | None, optional): Number of partitions to divide the grid into. Defaults to 20. @@ -649,7 +665,7 @@ def aggregate_raster_into_grid( ongrid = _align_data( grid_gdf, raster, - aggregations if aggregations != "interpolate" else None, + aggregations if aggregations != "interpolate" else "nearest", n_partitions=n_partitions, concurrent_partitions=concurrent_partitions, pxbuffer=pxbuffer,