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Make the training run again

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Tobias Hölzer 2026-01-11 22:17:15 +01:00
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version: 2.1.2 version: 2.1.2
@ -7979,6 +8050,11 @@ packages:
purls: [] purls: []
size: 1951720 size: 1951720
timestamp: 1756274576844 timestamp: 1756274576844
- pypi: https://files.pythonhosted.org/packages/63/81/9ef641ff4e12cbcca30e54e72fb0951a2ba195d0cda0ba4100e532d929db/slicer-0.0.8-py3-none-any.whl
name: slicer
version: 0.0.8
sha256: 6c206258543aecd010d497dc2eca9d2805860a0b3758673903456b7df7934dc3
requires_python: '>=3.6'
- pypi: https://files.pythonhosted.org/packages/ab/6c/1d4db72c5dbbb9ea2fbc323a40986917cca84ca098f6fcf80624370979e7/smart_geocubes-0.1.2-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/ab/6c/1d4db72c5dbbb9ea2fbc323a40986917cca84ca098f6fcf80624370979e7/smart_geocubes-0.1.2-py3-none-any.whl
name: smart-geocubes name: smart-geocubes
version: 0.1.2 version: 0.1.2
@ -8158,10 +8234,10 @@ packages:
- pygments ; extra == 'tests' - pygments ; extra == 'tests'
- littleutils ; extra == 'tests' - littleutils ; extra == 'tests'
- cython ; extra == 'tests' - cython ; extra == 'tests'
- pypi: https://files.pythonhosted.org/packages/d9/52/1064f510b141bd54025f9b55105e26d1fa970b9be67ad766380a3c9b74b0/starlette-0.50.0-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/18/c4/09985a03dba389d4fe16a9014147a7b02fa76ef3519bf5846462a485876d/starlette-0.51.0-py3-none-any.whl
name: starlette name: starlette
version: 0.50.0 version: 0.51.0
sha256: 9e5391843ec9b6e472eed1365a78c8098cfceb7a74bfd4d6b1c0c0095efb3bca sha256: fb460a3d6fd3c958d729fdd96aee297f89a51b0181f16401fe8fd4cb6129165d
requires_dist: requires_dist:
- anyio>=3.6.2,<5 - anyio>=3.6.2,<5
- typing-extensions>=4.10.0 ; python_full_version < '3.13' - typing-extensions>=4.10.0 ; python_full_version < '3.13'
@ -8285,10 +8361,10 @@ packages:
- packaging - packaging
- protobuf>=3.20 - protobuf>=3.20
requires_python: '>=3.9' requires_python: '>=3.9'
- pypi: https://files.pythonhosted.org/packages/84/38/47fab2a5fad163ca4851f7a20eb2442491cc63bf2756ec4ef161bc1461dd/textual-7.0.1-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/a9/f4/48e4a4c77ab7eea48d3b0a77f8dea0be101c83421abc64da0888c77c47cf/textual-7.1.0-py3-none-any.whl
name: textual name: textual
version: 7.0.1 version: 7.1.0
sha256: f9b7d16fa9b640bfff2a2008bf31e3f2d4429dc85e07a9583be033840ed15174 sha256: 9209dd0d1d958316832f7e59328f3911112f8e951abef7c3fbe54effd4e4caed
requires_dist: requires_dist:
- markdown-it-py[linkify]>=2.1.0 - markdown-it-py[linkify]>=2.1.0
- mdit-py-plugins - mdit-py-plugins
@ -9012,10 +9088,10 @@ packages:
- pkg:pypi/triton?source=hash-mapping - pkg:pypi/triton?source=hash-mapping
size: 223243524 size: 223243524
timestamp: 1763015352210 timestamp: 1763015352210
- pypi: https://files.pythonhosted.org/packages/94/fc/1d34ec891900d9337169ff9f8252fcaa633ae5c4d36b67effd849ed4f9ac/ty-0.0.2-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl - pypi: https://files.pythonhosted.org/packages/74/18/8dd4fe6df1fd66f3e83b4798eddb1d8482d9d9b105f25099b76703402ebb/ty-0.0.11-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
name: ty name: ty
version: 0.0.2 version: 0.0.11
sha256: eb514711eed3f56d7a130d4885f4b5d8e490fdcd2adac098e5cf175573a0dda3 sha256: 25f88e8789072830348cb59b761d5ced70642ed5600673b4bf6a849af71eca8b
requires_python: '>=3.8' requires_python: '>=3.8'
- pypi: https://files.pythonhosted.org/packages/c8/0a/4aca634faf693e33004796b6cee0ae2e1dba375a800c16ab8d3eff4bb800/typer_slim-0.21.1-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/c8/0a/4aca634faf693e33004796b6cee0ae2e1dba375a800c16ab8d3eff4bb800/typer_slim-0.21.1-py3-none-any.whl
name: typer-slim name: typer-slim
@ -9185,10 +9261,10 @@ packages:
- packaging - packaging
- narwhals>=1.42 - narwhals>=1.42
requires_python: '>=3.9' requires_python: '>=3.9'
- pypi: https://files.pythonhosted.org/packages/eb/6a/0af36875e0023a1f2d0b66b4051721fc26740e947696922df1665b75e5d3/virtualenv-20.36.0-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/6a/2a/dc2228b2888f51192c7dc766106cd475f1b768c10caaf9727659726f7391/virtualenv-20.36.1-py3-none-any.whl
name: virtualenv name: virtualenv
version: 20.36.0 version: 20.36.1
sha256: e7ded577f3af534fd0886d4ca03277f5542053bedb98a70a989d3c22cfa5c9ac sha256: 575a8d6b124ef88f6f51d56d656132389f961062a9177016a50e4f507bbcc19f
requires_dist: requires_dist:
- distlib>=0.3.7,<1 - distlib>=0.3.7,<1
- filelock>=3.16.1,<4 ; python_full_version < '3.10' - filelock>=3.16.1,<4 ; python_full_version < '3.10'
@ -9263,11 +9339,11 @@ packages:
- requests>=2.13.0,<3.0.0 - requests>=2.13.0,<3.0.0
- pydantic>=1.7.4,!=1.8,!=1.8.1,<3.0.0 - pydantic>=1.7.4,!=1.8,!=1.8.1,<3.0.0
requires_python: '>=3.7' requires_python: '>=3.7'
- pypi: https://files.pythonhosted.org/packages/ff/b2/83a6ddf56cdcbad4e3d841fcc55d6ba7d19aeb89c50f24dd7e859ec0805f/websockets-15.0.1-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl - pypi: https://files.pythonhosted.org/packages/bd/28/0a25ee5342eb5d5f297d992a77e56892ecb65e7854c7898fb7d35e9b33bd/websockets-16.0-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl
name: websockets name: websockets
version: 15.0.1 version: '16.0'
sha256: 0f3c1e2ab208db911594ae5b4f79addeb3501604a165019dd221c0bdcabe4db8 sha256: 95724e638f0f9c350bb1c2b0a7ad0e83d9cc0c9259f3ea94e40d7b02a2179ae5
requires_python: '>=3.9' requires_python: '>=3.10'
- pypi: https://files.pythonhosted.org/packages/3f/0e/fa3b193432cfc60c93b42f3be03365f5f909d2b3ea410295cf36df739e31/widgetsnbextension-4.0.15-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/3f/0e/fa3b193432cfc60c93b42f3be03365f5f909d2b3ea410295cf36df739e31/widgetsnbextension-4.0.15-py3-none-any.whl
name: widgetsnbextension name: widgetsnbextension
version: 4.0.15 version: 4.0.15
@ -9405,13 +9481,13 @@ packages:
- pyproj>=3.3 - pyproj>=3.3
- xarray - xarray
requires_python: '>=3.11' requires_python: '>=3.11'
- pypi: https://files.pythonhosted.org/packages/5e/ab/c60fcc137fa685533bb31e721de3ecc88959d393830d59d0204c5cbd2c85/xgboost-3.1.2-py3-none-manylinux_2_28_x86_64.whl - pypi: https://files.pythonhosted.org/packages/6c/9c/9d4ad7f586698bad52a570d2bf81138e500a5d9f32723c2b4ed1dd9252d8/xgboost-3.1.3-py3-none-manylinux_2_28_x86_64.whl
name: xgboost name: xgboost
version: 3.1.2 version: 3.1.3
sha256: 24879ac75c0ee21acae0101f791bc43303f072a86d70fdfc89dae10a0008767f sha256: 687504d1d76dc797df08b0dbe8b83d58629cdc06df52378f617164d16142bf2c
requires_dist: requires_dist:
- numpy - numpy
- nvidia-nccl-cu12 ; platform_machine != 'aarch64' and sys_platform == 'linux' - nvidia-nccl-cu12 ; sys_platform == 'linux'
- scipy - scipy
- dask ; extra == 'dask' - dask ; extra == 'dask'
- distributed ; extra == 'dask' - distributed ; extra == 'dask'

8
pyproject.toml
View file

@ -63,11 +63,12 @@ dependencies = [
"duckdb>=1.4.2,<2", "duckdb>=1.4.2,<2",
"pydeck>=0.9.1,<0.10", "pydeck>=0.9.1,<0.10",
"pypalettes>=0.2.1,<0.3", "pypalettes>=0.2.1,<0.3",
"ty>=0.0.2,<0.0.3", "ty>=0.0.11,<0.0.12",
"ruff>=0.14.9,<0.15", "ruff>=0.14.11,<0.15",
"pandas-stubs>=2.3.3.251201,<3", "pandas-stubs>=2.3.3.251201,<3",
"pytest>=9.0.2,<10", "pytest>=9.0.2,<10",
"autogluon-tabular[all]>=1.5.0", "autogluon-tabular[all,mitra]>=1.5.0",
"shap>=0.50.0,<0.51",
] ]
[project.scripts] [project.scripts]
@ -77,6 +78,7 @@ alpha-earth = "entropice.ingest.alphaearth:main"
era5 = "entropice.ingest.era5:cli" era5 = "entropice.ingest.era5:cli"
arcticdem = "entropice.ingest.arcticdem:cli" arcticdem = "entropice.ingest.arcticdem:cli"
train = "entropice.ml.training:cli" train = "entropice.ml.training:cli"
autogluon = "entropice.ml.autogluon_training:cli"
[build-system] [build-system]
requires = ["hatchling"] requires = ["hatchling"]

2
src/entropice/ingest/darts.py
View file

@ -83,7 +83,7 @@ def _process_rts_grid(
values=["geometry", "area_km2"], values=["geometry", "area_km2"],
fill_value=0, fill_value=0,
aggfunc={"geometry": "count", "area_km2": "sum"}, # type: ignore[arg-type] aggfunc={"geometry": "count", "area_km2": "sum"}, # type: ignore[arg-type]
) ).rename(columns={"geometry": "count"})
rts: xr.Dataset = xr.Dataset.from_dataframe(rts) rts: xr.Dataset = xr.Dataset.from_dataframe(rts)
darts = xr.merge( darts = xr.merge(
[rts, covered_area.rename("covered_area_km2"), coverage.rename("coverage")], join="outer", fill_value=0 [rts, covered_area.rename("covered_area_km2"), coverage.rename("coverage")], join="outer", fill_value=0

11
src/entropice/ml/dataset.py
View file

@ -229,7 +229,7 @@ class TrainingSet:
"""Label-Codes of the target categories, None if not categorical.""" """Label-Codes of the target categories, None if not categorical."""
binned = self.targets["y"] binned = self.targets["y"]
if binned.dtype.name == "category": if binned.dtype.name == "category":
return list(binned.cat.codes) return list(range(len(binned.cat.categories)))
else: else:
return None return None
@ -279,8 +279,9 @@ class DatasetEnsemble:
] ]
) )
temporal_mode: TemporalMode = "synopsis" temporal_mode: TemporalMode = "synopsis"
dimension_filters: dict[L2SourceDataset, dict[str, list]] = field(default_factory=dict) # ?: We can't use L2SourceDataset as types here because cyclopts can't handle Literals as dict keys
variable_filters: dict[L2SourceDataset, list[str]] = field(default_factory=dict) 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 add_lonlat: bool = True
def __post_init__(self): def __post_init__(self):
@ -308,7 +309,6 @@ class DatasetEnsemble:
def __hash__(self): def __hash__(self):
return int(self.id(), 16) return int(self.id(), 16)
@cache
def id(self): def id(self):
"""Return an unique, stable identifier based on the settings of this class.""" """Return an unique, stable identifier based on the settings of this class."""
return hashlib.blake2b( return hashlib.blake2b(
@ -319,7 +319,7 @@ class DatasetEnsemble:
@cached_property @cached_property
def cell_ids(self) -> pd.Series: def cell_ids(self) -> pd.Series:
"""Series of all cell-ids of the grid.""" """Series of all cell-ids of the grid."""
return self.read_grid()["cell_id"] return self.read_grid().index.to_series()
@cached_property @cached_property
def geometries(self) -> pd.Series: def geometries(self) -> pd.Series:
@ -327,6 +327,7 @@ class DatasetEnsemble:
return self.read_grid()["geometry"] return self.read_grid()["geometry"]
@stopwatch("Reading grid") @stopwatch("Reading grid")
@cache
def read_grid(self) -> gpd.GeoDataFrame: def read_grid(self) -> gpd.GeoDataFrame:
"""Load the grid dataframe and enrich it with lat-lon information.""" """Load the grid dataframe and enrich it with lat-lon information."""
grid_gdf = entropice.spatial.grids.open(grid=self.grid, level=self.level) grid_gdf = entropice.spatial.grids.open(grid=self.grid, level=self.level)

2
src/entropice/ml/models.py
View file

@ -104,6 +104,7 @@ def get_model_hpo_config(model: str, task: Task, **model_kwargs) -> ModelHPOConf
eval_metric="mlogloss" if task != "binary" else "logloss", eval_metric="mlogloss" if task != "binary" else "logloss",
tree_method="hist", tree_method="hist",
max_depth=10, max_depth=10,
device="gpu",
**model_kwargs, **model_kwargs,
) )
return ModelHPOConfig(clf, xgboost_hpconfig) return ModelHPOConfig(clf, xgboost_hpconfig)
@ -113,6 +114,7 @@ def get_model_hpo_config(model: str, task: Task, **model_kwargs) -> ModelHPOConf
eval_metric="rmse", eval_metric="rmse",
tree_method="hist", tree_method="hist",
max_depth=10, max_depth=10,
device="gpu",
**model_kwargs, **model_kwargs,
) )
return ModelHPOConfig(reg, xgboost_hpconfig) return ModelHPOConfig(reg, xgboost_hpconfig)

77
src/entropice/ml/training.py
View file

@ -11,7 +11,17 @@ import toml
import xarray as xr import xarray as xr
from rich import pretty, traceback from rich import pretty, traceback
from sklearn import set_config from sklearn import set_config
from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, jaccard_score, precision_score, recall_score from sklearn.metrics import (
accuracy_score,
confusion_matrix,
f1_score,
jaccard_score,
mean_absolute_error,
mean_squared_error,
precision_score,
r2_score,
recall_score,
)
from sklearn.model_selection import KFold, RandomizedSearchCV from sklearn.model_selection import KFold, RandomizedSearchCV
from stopuhr import stopwatch from stopuhr import stopwatch
@ -32,20 +42,29 @@ pretty.install()
cli = cyclopts.App("entropice-training", config=cyclopts.config.Toml("training-config.toml")) # ty:ignore[invalid-argument-type] cli = cyclopts.App("entropice-training", config=cyclopts.config.Toml("training-config.toml")) # ty:ignore[invalid-argument-type]
_metrics = {
"binary": ["accuracy", "recall", "precision", "f1", "jaccard"], def _get_metrics(task: Task) -> tuple[list[str], str]:
"multiclass": [ """Get the list of metrics for a given task."""
"accuracy", # equals "f1_micro", "precision_micro", "recall_micro", "recall_weighted" if task == "binary":
"f1_macro", return ["accuracy", "recall", "precision", "f1", "jaccard"], "f1"
"f1_weighted", elif task in ["count_regimes", "density_regimes"]:
"precision_macro", return [
"precision_weighted", "accuracy", # equals "f1_micro", "precision_micro", "recall_micro", "recall_weighted"
"recall_macro", "f1_macro",
"jaccard_micro", "f1_weighted",
"jaccard_macro", "precision_macro",
"jaccard_weighted", "precision_weighted",
], "recall_macro",
} "jaccard_micro",
"jaccard_macro",
"jaccard_weighted",
], "f1_weighted"
else:
return [
"neg_mean_squared_error",
"neg_mean_absolute_error",
"r2",
], "r2"
# Compute other metrics - using predictions directly instead of re-predicting for each metric # Compute other metrics - using predictions directly instead of re-predicting for each metric
@ -65,6 +84,9 @@ _metric_functions = {
"jaccard_micro": partial(jaccard_score, average="micro"), "jaccard_micro": partial(jaccard_score, average="micro"),
"jaccard_macro": partial(jaccard_score, average="macro"), "jaccard_macro": partial(jaccard_score, average="macro"),
"jaccard_weighted": partial(jaccard_score, average="weighted"), "jaccard_weighted": partial(jaccard_score, average="weighted"),
"neg_mean_squared_error": mean_squared_error,
"neg_mean_absolute_error": mean_absolute_error,
"r2": r2_score,
} }
@ -111,7 +133,7 @@ def random_cv(
model_hpo_config = get_model_hpo_config(settings.model, settings.task) model_hpo_config = get_model_hpo_config(settings.model, settings.task)
print(f"Using model: {settings.model} with parameters: {model_hpo_config.hp_config}") print(f"Using model: {settings.model} with parameters: {model_hpo_config.hp_config}")
cv = KFold(n_splits=5, shuffle=True, random_state=42) cv = KFold(n_splits=5, shuffle=True, random_state=42)
metrics = _metrics["binary" if settings.task == "binary" else "multiclass"] metrics, refit = _get_metrics(settings.task)
search = RandomizedSearchCV( search = RandomizedSearchCV(
model_hpo_config.model, model_hpo_config.model,
model_hpo_config.search_space, model_hpo_config.search_space,
@ -121,7 +143,7 @@ def random_cv(
random_state=42, random_state=42,
verbose=10, verbose=10,
scoring=metrics, scoring=metrics,
refit="f1" if settings.task == "binary" else "f1_weighted", refit=refit,
) )
print(f"Starting RandomizedSearchCV with {search.n_iter} candidates...") print(f"Starting RandomizedSearchCV with {search.n_iter} candidates...")
@ -140,12 +162,15 @@ def random_cv(
for param_name in sorted(model_hpo_config.hp_config.keys()): for param_name in sorted(model_hpo_config.hp_config.keys()):
print(f"{param_name}: {best_parameters[param_name]}") print(f"{param_name}: {best_parameters[param_name]}")
test_accuracy = search.score( test_score = search.score(
training_data.X.test, training_data.X.test,
training_data.y.as_numpy().test if settings.model == "xgboost" else training_data.y.test, training_data.y.as_numpy().test if settings.model == "xgboost" else training_data.y.test,
) )
print(f"Accuracy of the best parameters using the inner CV of the random search: {search.best_score_:.3f}") print(
print(f"Accuracy on test set: {test_accuracy:.3f}") 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_cv_results_dir( results_dir = get_cv_results_dir(
"random_search", "random_search",
@ -207,14 +232,14 @@ def random_cv(
# Make confusion matrices for classification taasks # Make confusion matrices for classification taasks
if settings.task in ["binary", "count_regimes", "density_regimes"]: if settings.task in ["binary", "count_regimes", "density_regimes"]:
labels = np.array(training_data.target_codes) codes = np.array(training_data.target_codes)
cm = xr.Dataset( cm = xr.Dataset(
{ {
"test": (("true_label", "predicted_label"), confusion_matrix(y.test, y_pred.test, labels=labels)), "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=labels)), "train": (("true_label", "predicted_label"), confusion_matrix(y.train, y_pred.train, labels=codes)),
"combined": ( "combined": (
("true_label", "predicted_label"), ("true_label", "predicted_label"),
confusion_matrix(y.combined, y_pred.combined, labels=labels), confusion_matrix(y.combined, y_pred.combined, labels=codes),
), ),
}, },
coords={"true_label": training_data.target_labels, "predicted_label": training_data.target_labels}, coords={"true_label": training_data.target_labels, "predicted_label": training_data.target_labels},
@ -248,7 +273,9 @@ def random_cv(
preds = predict_proba(dataset_ensemble, model=best_estimator, device=device) preds = predict_proba(dataset_ensemble, model=best_estimator, device=device)
if training_data.targets["y"].dtype == "category": if training_data.targets["y"].dtype == "category":
preds["predicted"] = preds["predicted"].astype("category") preds["predicted"] = preds["predicted"].astype("category")
preds["predicted"].cat.categories = training_data.targets["y"].cat.categories preds["predicted"] = preds["predicted"].cat.set_categories(
training_data.targets["y"].cat.categories, ordered=True
)
print(f"Predicted probabilities DataFrame with {len(preds)} entries.") print(f"Predicted probabilities DataFrame with {len(preds)} entries.")
preds_file = results_dir / "predicted_probabilities.parquet" preds_file = results_dir / "predicted_probabilities.parquet"
print(f"Storing predicted probabilities to {preds_file}") print(f"Storing predicted probabilities to {preds_file}")