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">=0.25.0" xgboost = ">=2.0.0" -darts = { version = ">=0.25.0", optional = true } - -[tool.poetry.extras] # https://python-poetry.org/docs/pyproject/#extras -darts = ["darts"] [tool.poetry.group.test.dependencies] # https://python-poetry.org/docs/master/managing-dependencies/ commitizen = ">=3.21.3" coverage = { extras = ["toml"], version = ">=7.4.4" } lightgbm = ">=4.3.0" mypy = ">=1.9.0" -pandas = ">=2.2.1" poethepoet = ">=0.25.0" pre-commit = ">=3.7.0" pytest = ">=8.1.1" diff --git a/src/conformal_tights/_conformal_coherent_quantile_regressor.py b/src/conformal_tights/_conformal_coherent_quantile_regressor.py index 8c61e15..a006b7d 100644 --- a/src/conformal_tights/_conformal_coherent_quantile_regressor.py +++ b/src/conformal_tights/_conformal_coherent_quantile_regressor.py @@ -1,9 +1,10 @@ """Conformal Coherent Quantile Regressor meta-estimator.""" -from typing import TYPE_CHECKING, Any, Literal, TypeVar, overload +from typing import Any, Literal, TypeVar, overload import numpy as np import numpy.typing as npt +import pandas as pd from sklearn.base import BaseEstimator, MetaEstimatorMixin, RegressorMixin, clone from sklearn.exceptions import NotFittedError from sklearn.model_selection import train_test_split @@ -18,9 +19,6 @@ from conformal_tights._coherent_linear_quantile_regressor import CoherentLinearQuantileRegressor from conformal_tights._typing import FloatMatrix, FloatVector -if TYPE_CHECKING: - import pandas as pd - F = TypeVar("F", np.float32, np.float64) @@ -88,8 +86,8 @@ def __init__( # noqa: PLR0913 def fit( self, - X: "FloatMatrix[F] | pd.DataFrame", - y: "FloatVector[F] | pd.Series", + X: FloatMatrix[F] | pd.DataFrame, + y: FloatVector[F] | pd.Series, *, sample_weight: "FloatVector[F] | pd.Series | None" = None, ) -> "ConformalCoherentQuantileRegressor": @@ -242,19 +240,19 @@ def predict_quantiles( @overload def predict_quantiles( self, - X: "pd.DataFrame", + X: pd.DataFrame, *, quantiles: npt.ArrayLike, priority: Literal["accuracy", "coverage"] = "accuracy", - ) -> "pd.DataFrame": ... + ) -> pd.DataFrame: ... def predict_quantiles( self, - X: "FloatMatrix[F] | pd.DataFrame", + X: FloatMatrix[F] | pd.DataFrame, *, quantiles: npt.ArrayLike, priority: Literal["accuracy", "coverage"] = "accuracy", - ) -> "FloatMatrix[F] | pd.DataFrame": + ) -> FloatMatrix[F] | pd.DataFrame: """Predict conformally calibrated quantiles on a given dataset.""" # Predict the absolute and relative quantiles. quantiles = np.asarray(quantiles) @@ -285,25 +283,20 @@ def predict_quantiles( ŷ_quantiles = ŷ_quantiles.astype(self.y_dtype_) # Convert ŷ_quantiles to a pandas DataFrame if X is a pandas DataFrame. if hasattr(X, "dtypes") and hasattr(X, "index"): - try: - import pandas as pd - except ImportError: - pass - else: - ŷ_quantiles_df = pd.DataFrame(ŷ_quantiles, index=X.index, columns=quantiles) - ŷ_quantiles_df.columns.name = "quantile" - return ŷ_quantiles_df + ŷ_quantiles_df = pd.DataFrame(ŷ_quantiles, index=X.index, columns=quantiles) + ŷ_quantiles_df.columns.name = "quantile" + return ŷ_quantiles_df return ŷ_quantiles @overload def predict_interval(self, X: FloatMatrix[F], *, coverage: float = 0.95) -> FloatMatrix[F]: ... @overload - def predict_interval(self, X: "pd.DataFrame", *, coverage: float = 0.95) -> "pd.DataFrame": ... + def predict_interval(self, X: pd.DataFrame, *, coverage: float = 0.95) -> pd.DataFrame: ... def predict_interval( - self, X: "FloatMatrix[F] | pd.DataFrame", *, coverage: float = 0.95 - ) -> "FloatMatrix[F] | pd.DataFrame": + self, X: FloatMatrix[F] | pd.DataFrame, *, coverage: float = 0.95 + ) -> FloatMatrix[F] | pd.DataFrame: """Predict conformally calibrated intervals on a given dataset.""" # Convert the coverage probability to lower and upper quantiles. lb = (1 - coverage) / 2 @@ -329,26 +322,26 @@ def predict( @overload def predict( - self, X: "pd.DataFrame", *, coverage: None = None, quantiles: None = None - ) -> "pd.Series": ... + self, X: pd.DataFrame, *, coverage: None = None, quantiles: None = None + ) -> pd.Series: ... @overload def predict( - self, X: "pd.DataFrame", *, coverage: float, quantiles: None = None - ) -> "pd.DataFrame": ... + self, X: pd.DataFrame, *, coverage: float, quantiles: None = None + ) -> pd.DataFrame: ... @overload def predict( - self, X: "pd.DataFrame", *, coverage: None = None, quantiles: npt.ArrayLike - ) -> "pd.DataFrame": ... + self, X: pd.DataFrame, *, coverage: None = None, quantiles: npt.ArrayLike + ) -> pd.DataFrame: ... def predict( self, - X: "FloatMatrix[F] | pd.DataFrame", + X: FloatMatrix[F] | pd.DataFrame, *, coverage: float | None = None, quantiles: npt.ArrayLike | None = None, - ) -> "FloatVector[F] | pd.Series | FloatMatrix[F] | pd.DataFrame": + ) -> FloatVector[F] | pd.Series | FloatMatrix[F] | pd.DataFrame: """Predict on a given dataset.""" assert coverage is None or quantiles is None check_is_fitted(self) @@ -363,13 +356,8 @@ def predict( if not np.issubdtype(self.y_dtype_, np.integer): ŷ = ŷ.astype(self.y_dtype_) if hasattr(X, "dtypes") and hasattr(X, "index"): - try: - import pandas as pd - except ImportError: - pass - else: - ŷ_series = pd.Series(ŷ, index=X.index) - return ŷ_series + ŷ_series = pd.Series(ŷ, index=X.index) + return ŷ_series return ŷ def _more_tags(self) -> dict[str, Any]: diff --git a/src/conformal_tights/_darts_forecaster.py b/src/conformal_tights/_darts_forecaster.py index afe2ac0..c41baae 100644 --- a/src/conformal_tights/_darts_forecaster.py +++ b/src/conformal_tights/_darts_forecaster.py @@ -1,52 +1,21 @@ """Darts Forecaster.""" from collections.abc import Sequence -from typing import TYPE_CHECKING, Any, TypeVar +from typing import Any, TypeVar import numpy as np import numpy.typing as npt +import pandas as pd +from darts import TimeSeries +from darts.models.forecasting.regression_model import ( + FUTURE_LAGS_TYPE, + LAGS_TYPE, + RegressionModel, + RegressionModelWithCategoricalCovariates, + _LikelihoodMixin, +) from sklearn.utils import check_random_state -if TYPE_CHECKING: - import pandas as pd -else: - try: - import pandas as pd - except ImportError: - pd = None - -if TYPE_CHECKING: - from darts import TimeSeries - from darts.models.forecasting.regression_model import ( - FUTURE_LAGS_TYPE, - LAGS_TYPE, - RegressionModel, - RegressionModelWithCategoricalCovariates, - _LikelihoodMixin, - ) -else: - try: - from darts import TimeSeries - from darts.models.forecasting.regression_model import ( - FUTURE_LAGS_TYPE, - LAGS_TYPE, - RegressionModel, - RegressionModelWithCategoricalCovariates, - _LikelihoodMixin, - ) - except ImportError: - FUTURE_LAGS_TYPE = tuple[int, int] | list[int] | dict[str, tuple[int, int] | list[int]] - LAGS_TYPE = int | list[int] | dict[str, int | list[int]] - - class TimeSeries: ... - - class RegressionModel: ... - - class RegressionModelWithCategoricalCovariates: ... - - class _LikelihoodMixin: ... - - from conformal_tights._conformal_coherent_quantile_regressor import ( ConformalCoherentQuantileRegressor, ) @@ -63,7 +32,7 @@ def __init__( self.quantile = quantile self.quantiles = np.asarray(quantiles) - def predict(self, x: "pd.DataFrame", **kwargs: Any) -> FloatMatrix[F]: + def predict(self, x: pd.DataFrame, **kwargs: Any) -> FloatMatrix[F]: # Call ConformalCoherentQuantileRegressor's predict_quantiles. q = np.asarray(self.model.predict_quantiles(x, quantiles=self.quantiles)) # Filter out the requested quantile. @@ -92,13 +61,6 @@ def __init__( # noqa: PLR0913 categorical_static_covariates: str | list[str] | None = None, ) -> None: """Initialize a Darts Conformal Coherent Quantile Regressor.""" - # Verify that the required dependencies are installed. - try: - import darts # noqa: F401 - import pandas as pd # noqa: F401 - except ImportError: - required_dependencies = "Please install darts and pandas to use DartsForecaster." - raise ImportError(required_dependencies) from None # Initialise _LikelihoodMixin. self.likelihood = "quantile" self._model_container = self._get_model_container() @@ -138,7 +100,7 @@ def _create_lagged_data( past_covariates: Sequence[TimeSeries], future_covariates: Sequence[TimeSeries], max_samples_per_ts: int, - ) -> tuple["pd.DataFrame", FloatVector[F]]: + ) -> tuple[pd.DataFrame, FloatVector[F]]: """Override training data to add support for categorical covariates.""" # Validate categoricals with RegressionModelWithCategoricalCovariates. We cannot inherit # from RegressionModelWithCategoricalCovariates because it was developed with LightGBM in @@ -162,7 +124,7 @@ def _create_lagged_data( ) # Convert categorical columns to pd.Categorical so that the wrapped regressor can handle # them appropriately. - self.cat_col_categories_: dict[float, "pd.Index"] = {} + self.cat_col_categories_: dict[float, pd.Index] = {} training_samples_df = pd.DataFrame(training_samples) cols = training_samples_df.columns for cat_col_index in cat_col_indices: diff --git a/tests/test_optional_dependencies.py b/tests/test_optional_dependencies.py deleted file mode 100644 index 616a419..0000000 --- a/tests/test_optional_dependencies.py +++ /dev/null @@ -1,30 +0,0 @@ -"""Test this package's optional dependencies.""" - -import sys -from unittest.mock import patch - -import pytest - - -@pytest.mark.parametrize("optional_dependency", ["darts", "pandas"]) -def test_optional_dependencies(optional_dependency: str) -> None: - """Test that we get an expected error when an optional dependency are not available.""" - # Prevent the optional dependency from being loaded. - with patch.dict("sys.modules", {optional_dependency: None}): - # Unload Conformal Tights. - mods_to_unload = [mod for mod in sys.modules if mod.startswith("conformal_tights")] - for mod in mods_to_unload: - del sys.modules[mod] - - # Reload Conformal Tights now that the selected optional dependency is not available. - from conformal_tights import ConformalCoherentQuantileRegressor, DartsForecaster - - # Test that we raise the appropriate error. - conformal_predictor = ConformalCoherentQuantileRegressor() - with pytest.raises(ImportError, match=f".*install.*{optional_dependency}.*"): - _ = DartsForecaster(model=conformal_predictor) - - # Unload Conformal Tights again. - mods_to_unload = [mod for mod in sys.modules if mod.startswith("conformal_tights")] - for mod in mods_to_unload: - del sys.modules[mod]