Make the training run again
This commit is contained in:
parent
cfb7d65d6d
commit
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6 changed files with 205 additions and 97 deletions
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|
||||
sha256: a757c94cda0587edb727cbf08a4af819921ad42646b55833713c531ead75742d
|
||||
requires_dist:
|
||||
- numpy>=2
|
||||
- scipy
|
||||
- scikit-learn
|
||||
- pandas
|
||||
- tqdm>=4.27.0
|
||||
- packaging>20.9
|
||||
- slicer==0.0.8
|
||||
- numba==0.63.0b1 ; python_full_version >= '3.14'
|
||||
- llvmlite==0.46.0b1 ; python_full_version >= '3.14'
|
||||
- numba>=0.54 ; python_full_version < '3.14'
|
||||
- cloudpickle
|
||||
- typing-extensions
|
||||
- matplotlib ; extra == 'plots'
|
||||
- ipython ; extra == 'plots'
|
||||
- lime ; extra == 'others'
|
||||
- scikit-learn ; extra == 'download-datasets'
|
||||
- matplotlib ; extra == 'docs'
|
||||
- ipython ; extra == 'docs'
|
||||
- numpydoc ; extra == 'docs'
|
||||
- sphinx-rtd-theme ; extra == 'docs'
|
||||
- sphinx ; extra == 'docs'
|
||||
- nbsphinx ; extra == 'docs'
|
||||
- sphinx-github-changelog ; extra == 'docs'
|
||||
- myst-parser ; extra == 'docs'
|
||||
- requests ; extra == 'docs'
|
||||
- ipywidgets ; extra == 'docs'
|
||||
- pytest ; extra == 'test-core'
|
||||
- pytest-mpl ; extra == 'test-core'
|
||||
- pytest-cov ; extra == 'test-core'
|
||||
- mypy ; extra == 'test-core'
|
||||
- pytest ; extra == 'test'
|
||||
- pytest-mpl ; extra == 'test'
|
||||
- pytest-cov ; extra == 'test'
|
||||
- xgboost ; extra == 'test'
|
||||
- lightgbm ; extra == 'test'
|
||||
- catboost ; python_full_version < '3.14' and extra == 'test'
|
||||
- gpboost==1.6.3.1 ; python_full_version < '3.14' and extra == 'test'
|
||||
- ngboost ; extra == 'test'
|
||||
- pyspark ; extra == 'test'
|
||||
- pyod ; extra == 'test'
|
||||
- transformers<4.54.0 ; extra == 'test'
|
||||
- tf-keras ; extra == 'test'
|
||||
- protobuf ; extra == 'test'
|
||||
- torch ; python_full_version < '3.14' and extra == 'test'
|
||||
- torchvision ; python_full_version < '3.14' and extra == 'test'
|
||||
- tensorflow ; python_full_version < '3.14' and extra == 'test'
|
||||
- sentencepiece ; extra == 'test'
|
||||
- opencv-python ; extra == 'test'
|
||||
- numpy>=2.0 ; extra == 'test'
|
||||
- scikit-learn<=1.6.1 ; extra == 'test'
|
||||
- causalml ; extra == 'test'
|
||||
- selenium ; extra == 'test'
|
||||
- jupyter ; extra == 'test-notebooks'
|
||||
- nbconvert ; extra == 'test-notebooks'
|
||||
- nbformat ; extra == 'test-notebooks'
|
||||
- nlp ; extra == 'test-notebooks'
|
||||
- transformers ; extra == 'test-notebooks'
|
||||
- datasets ; extra == 'test-notebooks'
|
||||
- keras ; extra == 'test-notebooks'
|
||||
- nbtest-plugin ; extra == 'nbtest'
|
||||
- nbtest-gen ; extra == 'nbtest'
|
||||
- nbtest-lab-extension ; extra == 'nbtest'
|
||||
requires_python: '>=3.11'
|
||||
- pypi: https://files.pythonhosted.org/packages/f2/a2/83fc37e2a58090e3d2ff79175a95493c664bcd0b653dd75cb9134645a4e5/shapely-2.1.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
|
||||
name: shapely
|
||||
version: 2.1.2
|
||||
|
|
@ -7979,6 +8050,11 @@ packages:
|
|||
purls: []
|
||||
size: 1951720
|
||||
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
|
||||
name: smart-geocubes
|
||||
version: 0.1.2
|
||||
|
|
@ -8158,10 +8234,10 @@ packages:
|
|||
- pygments ; extra == 'tests'
|
||||
- littleutils ; 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
|
||||
version: 0.50.0
|
||||
sha256: 9e5391843ec9b6e472eed1365a78c8098cfceb7a74bfd4d6b1c0c0095efb3bca
|
||||
version: 0.51.0
|
||||
sha256: fb460a3d6fd3c958d729fdd96aee297f89a51b0181f16401fe8fd4cb6129165d
|
||||
requires_dist:
|
||||
- anyio>=3.6.2,<5
|
||||
- typing-extensions>=4.10.0 ; python_full_version < '3.13'
|
||||
|
|
@ -8285,10 +8361,10 @@ packages:
|
|||
- packaging
|
||||
- protobuf>=3.20
|
||||
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
|
||||
version: 7.0.1
|
||||
sha256: f9b7d16fa9b640bfff2a2008bf31e3f2d4429dc85e07a9583be033840ed15174
|
||||
version: 7.1.0
|
||||
sha256: 9209dd0d1d958316832f7e59328f3911112f8e951abef7c3fbe54effd4e4caed
|
||||
requires_dist:
|
||||
- markdown-it-py[linkify]>=2.1.0
|
||||
- mdit-py-plugins
|
||||
|
|
@ -9012,10 +9088,10 @@ packages:
|
|||
- pkg:pypi/triton?source=hash-mapping
|
||||
size: 223243524
|
||||
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
|
||||
version: 0.0.2
|
||||
sha256: eb514711eed3f56d7a130d4885f4b5d8e490fdcd2adac098e5cf175573a0dda3
|
||||
version: 0.0.11
|
||||
sha256: 25f88e8789072830348cb59b761d5ced70642ed5600673b4bf6a849af71eca8b
|
||||
requires_python: '>=3.8'
|
||||
- pypi: https://files.pythonhosted.org/packages/c8/0a/4aca634faf693e33004796b6cee0ae2e1dba375a800c16ab8d3eff4bb800/typer_slim-0.21.1-py3-none-any.whl
|
||||
name: typer-slim
|
||||
|
|
@ -9185,10 +9261,10 @@ packages:
|
|||
- packaging
|
||||
- narwhals>=1.42
|
||||
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
|
||||
version: 20.36.0
|
||||
sha256: e7ded577f3af534fd0886d4ca03277f5542053bedb98a70a989d3c22cfa5c9ac
|
||||
version: 20.36.1
|
||||
sha256: 575a8d6b124ef88f6f51d56d656132389f961062a9177016a50e4f507bbcc19f
|
||||
requires_dist:
|
||||
- distlib>=0.3.7,<1
|
||||
- filelock>=3.16.1,<4 ; python_full_version < '3.10'
|
||||
|
|
@ -9263,11 +9339,11 @@ packages:
|
|||
- requests>=2.13.0,<3.0.0
|
||||
- pydantic>=1.7.4,!=1.8,!=1.8.1,<3.0.0
|
||||
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
|
||||
version: 15.0.1
|
||||
sha256: 0f3c1e2ab208db911594ae5b4f79addeb3501604a165019dd221c0bdcabe4db8
|
||||
requires_python: '>=3.9'
|
||||
version: '16.0'
|
||||
sha256: 95724e638f0f9c350bb1c2b0a7ad0e83d9cc0c9259f3ea94e40d7b02a2179ae5
|
||||
requires_python: '>=3.10'
|
||||
- pypi: https://files.pythonhosted.org/packages/3f/0e/fa3b193432cfc60c93b42f3be03365f5f909d2b3ea410295cf36df739e31/widgetsnbextension-4.0.15-py3-none-any.whl
|
||||
name: widgetsnbextension
|
||||
version: 4.0.15
|
||||
|
|
@ -9405,13 +9481,13 @@ packages:
|
|||
- pyproj>=3.3
|
||||
- xarray
|
||||
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
|
||||
version: 3.1.2
|
||||
sha256: 24879ac75c0ee21acae0101f791bc43303f072a86d70fdfc89dae10a0008767f
|
||||
version: 3.1.3
|
||||
sha256: 687504d1d76dc797df08b0dbe8b83d58629cdc06df52378f617164d16142bf2c
|
||||
requires_dist:
|
||||
- numpy
|
||||
- nvidia-nccl-cu12 ; platform_machine != 'aarch64' and sys_platform == 'linux'
|
||||
- nvidia-nccl-cu12 ; sys_platform == 'linux'
|
||||
- scipy
|
||||
- dask ; extra == 'dask'
|
||||
- distributed ; extra == 'dask'
|
||||
|
|
|
|||
|
|
@ -63,11 +63,12 @@ dependencies = [
|
|||
"duckdb>=1.4.2,<2",
|
||||
"pydeck>=0.9.1,<0.10",
|
||||
"pypalettes>=0.2.1,<0.3",
|
||||
"ty>=0.0.2,<0.0.3",
|
||||
"ruff>=0.14.9,<0.15",
|
||||
"ty>=0.0.11,<0.0.12",
|
||||
"ruff>=0.14.11,<0.15",
|
||||
"pandas-stubs>=2.3.3.251201,<3",
|
||||
"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]
|
||||
|
|
@ -77,6 +78,7 @@ alpha-earth = "entropice.ingest.alphaearth:main"
|
|||
era5 = "entropice.ingest.era5:cli"
|
||||
arcticdem = "entropice.ingest.arcticdem:cli"
|
||||
train = "entropice.ml.training:cli"
|
||||
autogluon = "entropice.ml.autogluon_training:cli"
|
||||
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
|
|
|
|||
|
|
@ -83,7 +83,7 @@ def _process_rts_grid(
|
|||
values=["geometry", "area_km2"],
|
||||
fill_value=0,
|
||||
aggfunc={"geometry": "count", "area_km2": "sum"}, # type: ignore[arg-type]
|
||||
)
|
||||
).rename(columns={"geometry": "count"})
|
||||
rts: xr.Dataset = xr.Dataset.from_dataframe(rts)
|
||||
darts = xr.merge(
|
||||
[rts, covered_area.rename("covered_area_km2"), coverage.rename("coverage")], join="outer", fill_value=0
|
||||
|
|
|
|||
|
|
@ -229,7 +229,7 @@ class TrainingSet:
|
|||
"""Label-Codes of the target categories, None if not categorical."""
|
||||
binned = self.targets["y"]
|
||||
if binned.dtype.name == "category":
|
||||
return list(binned.cat.codes)
|
||||
return list(range(len(binned.cat.categories)))
|
||||
else:
|
||||
return None
|
||||
|
||||
|
|
@ -279,8 +279,9 @@ class DatasetEnsemble:
|
|||
]
|
||||
)
|
||||
temporal_mode: TemporalMode = "synopsis"
|
||||
dimension_filters: dict[L2SourceDataset, dict[str, list]] = field(default_factory=dict)
|
||||
variable_filters: dict[L2SourceDataset, list[str]] = field(default_factory=dict)
|
||||
# ?: 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):
|
||||
|
|
@ -308,7 +309,6 @@ class DatasetEnsemble:
|
|||
def __hash__(self):
|
||||
return int(self.id(), 16)
|
||||
|
||||
@cache
|
||||
def id(self):
|
||||
"""Return an unique, stable identifier based on the settings of this class."""
|
||||
return hashlib.blake2b(
|
||||
|
|
@ -319,7 +319,7 @@ class DatasetEnsemble:
|
|||
@cached_property
|
||||
def cell_ids(self) -> pd.Series:
|
||||
"""Series of all cell-ids of the grid."""
|
||||
return self.read_grid()["cell_id"]
|
||||
return self.read_grid().index.to_series()
|
||||
|
||||
@cached_property
|
||||
def geometries(self) -> pd.Series:
|
||||
|
|
@ -327,6 +327,7 @@ class DatasetEnsemble:
|
|||
return self.read_grid()["geometry"]
|
||||
|
||||
@stopwatch("Reading grid")
|
||||
@cache
|
||||
def read_grid(self) -> gpd.GeoDataFrame:
|
||||
"""Load the grid dataframe and enrich it with lat-lon information."""
|
||||
grid_gdf = entropice.spatial.grids.open(grid=self.grid, level=self.level)
|
||||
|
|
|
|||
|
|
@ -104,6 +104,7 @@ def get_model_hpo_config(model: str, task: Task, **model_kwargs) -> ModelHPOConf
|
|||
eval_metric="mlogloss" if task != "binary" else "logloss",
|
||||
tree_method="hist",
|
||||
max_depth=10,
|
||||
device="gpu",
|
||||
**model_kwargs,
|
||||
)
|
||||
return ModelHPOConfig(clf, xgboost_hpconfig)
|
||||
|
|
@ -113,6 +114,7 @@ def get_model_hpo_config(model: str, task: Task, **model_kwargs) -> ModelHPOConf
|
|||
eval_metric="rmse",
|
||||
tree_method="hist",
|
||||
max_depth=10,
|
||||
device="gpu",
|
||||
**model_kwargs,
|
||||
)
|
||||
return ModelHPOConfig(reg, xgboost_hpconfig)
|
||||
|
|
|
|||
|
|
@ -11,7 +11,17 @@ import toml
|
|||
import xarray as xr
|
||||
from rich import pretty, traceback
|
||||
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 stopuhr import stopwatch
|
||||
|
||||
|
|
@ -32,9 +42,13 @@ pretty.install()
|
|||
|
||||
cli = cyclopts.App("entropice-training", config=cyclopts.config.Toml("training-config.toml")) # ty:ignore[invalid-argument-type]
|
||||
|
||||
_metrics = {
|
||||
"binary": ["accuracy", "recall", "precision", "f1", "jaccard"],
|
||||
"multiclass": [
|
||||
|
||||
def _get_metrics(task: Task) -> tuple[list[str], str]:
|
||||
"""Get the list of metrics for a given task."""
|
||||
if task == "binary":
|
||||
return ["accuracy", "recall", "precision", "f1", "jaccard"], "f1"
|
||||
elif task in ["count_regimes", "density_regimes"]:
|
||||
return [
|
||||
"accuracy", # equals "f1_micro", "precision_micro", "recall_micro", "recall_weighted"
|
||||
"f1_macro",
|
||||
"f1_weighted",
|
||||
|
|
@ -44,8 +58,13 @@ _metrics = {
|
|||
"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
|
||||
|
|
@ -65,6 +84,9 @@ _metric_functions = {
|
|||
"jaccard_micro": partial(jaccard_score, average="micro"),
|
||||
"jaccard_macro": partial(jaccard_score, average="macro"),
|
||||
"jaccard_weighted": partial(jaccard_score, average="weighted"),
|
||||
"neg_mean_squared_error": mean_squared_error,
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"neg_mean_absolute_error": mean_absolute_error,
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"r2": r2_score,
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}
|
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|
||||
|
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|
|
@ -111,7 +133,7 @@ def random_cv(
|
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model_hpo_config = get_model_hpo_config(settings.model, settings.task)
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print(f"Using model: {settings.model} with parameters: {model_hpo_config.hp_config}")
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cv = KFold(n_splits=5, shuffle=True, random_state=42)
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metrics = _metrics["binary" if settings.task == "binary" else "multiclass"]
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metrics, refit = _get_metrics(settings.task)
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search = RandomizedSearchCV(
|
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model_hpo_config.model,
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model_hpo_config.search_space,
|
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|
|
@ -121,7 +143,7 @@ def random_cv(
|
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random_state=42,
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verbose=10,
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scoring=metrics,
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refit="f1" if settings.task == "binary" else "f1_weighted",
|
||||
refit=refit,
|
||||
)
|
||||
|
||||
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()):
|
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print(f"{param_name}: {best_parameters[param_name]}")
|
||||
|
||||
test_accuracy = search.score(
|
||||
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"Accuracy of the best parameters using the inner CV of the random search: {search.best_score_:.3f}")
|
||||
print(f"Accuracy on test set: {test_accuracy:.3f}")
|
||||
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_cv_results_dir(
|
||||
"random_search",
|
||||
|
|
@ -207,14 +232,14 @@ def random_cv(
|
|||
|
||||
# Make confusion matrices for classification taasks
|
||||
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(
|
||||
{
|
||||
"test": (("true_label", "predicted_label"), confusion_matrix(y.test, y_pred.test, labels=labels)),
|
||||
"train": (("true_label", "predicted_label"), confusion_matrix(y.train, y_pred.train, 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=codes)),
|
||||
"combined": (
|
||||
("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},
|
||||
|
|
@ -248,7 +273,9 @@ def random_cv(
|
|||
preds = predict_proba(dataset_ensemble, model=best_estimator, device=device)
|
||||
if training_data.targets["y"].dtype == "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.")
|
||||
preds_file = results_dir / "predicted_probabilities.parquet"
|
||||
print(f"Storing predicted probabilities to {preds_file}")
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue