[MNT] deduplicating utilities with scikit-base

pyproject.toml

@@ -33,6 +33,7 @@ dependencies = [
   "scipy >=1.8,<2.0",
   "pandas >=1.3.0,<3.0.0",
   "scikit-learn >=1.2,<2.0",
+  "scikit-base <0.13.0",
 ]
 
 [project.optional-dependencies]
@@ -102,7 +103,6 @@ dev = [
   "pytest-dotenv>=0.5.2,<1.0.0",
   "tensorboard>=2.12.1,<3.0.0",
   "pandoc>=2.3,<3.0.0",
-  "scikit-base",
 ]
 
 # docs - dependencies for building the documentation

pytorch_forecasting/models/base/_base_model.py

@@ -20,6 +20,7 @@
 from numpy import iterable
 import pandas as pd
 import scipy.stats
+from skbase.utils.dependencies._dependencies import _check_soft_dependencies
 import torch
 import torch.nn as nn
 from torch.nn.utils import rnn
@@ -61,10 +62,7 @@
     to_list,
 )
 from pytorch_forecasting.utils._classproperty import classproperty
-from pytorch_forecasting.utils._dependencies import (
-    _check_matplotlib,
-    _get_installed_packages,
-)
+from pytorch_forecasting.utils._dependencies import _check_matplotlib
 
 # todo: compile models
 
@@ -1355,7 +1353,7 @@ def configure_optimizers(self):
         Returns:
             Tuple[List]: first entry is list of optimizers and second is list of schedulers
         """  # noqa: E501
-        ptopt_in_env = "pytorch_optimizer" in _get_installed_packages()
+        ptopt_in_env = _check_soft_dependencies("pytorch_optimizer", severity="none")
         # either set a schedule of lrs or find it dynamically
         if self.hparams.optimizer_params is None:
             optimizer_params = {}

pytorch_forecasting/models/temporal_fusion_transformer/tuning.py

@@ -13,12 +13,12 @@
 from lightning.pytorch.tuner import Tuner
 import numpy as np
 import scipy._lib._util
+from skbase.utils.dependencies._dependencies import _check_soft_dependencies
 from torch.utils.data import DataLoader
 
 from pytorch_forecasting import TemporalFusionTransformer
 from pytorch_forecasting.data import TimeSeriesDataSet
 from pytorch_forecasting.metrics import QuantileLoss
-from pytorch_forecasting.utils._dependencies import _get_installed_packages
 
 optuna_logger = logging.getLogger("optuna")
 
@@ -108,9 +108,7 @@ def optimize_hyperparameters(
     Returns:
         optuna.Study: optuna study results
     """  # noqa : E501
-    pkgs = _get_installed_packages()
-
-    if "optuna" not in pkgs or "statsmodels" not in pkgs:
+    if not _check_soft_dependencies(["optuna", "statsmodels"], severity="none"):
         raise ImportError(
             "optimize_hyperparameters requires optuna and statsmodels. "
             "Please install these packages with `pip install optuna statsmodels`. "

pytorch_forecasting/utils/_dependencies/__init__.py

@@ -1,13 +1,9 @@
 """Utilities for managing dependencies."""
 
-from pytorch_forecasting.utils._dependencies._dependencies import (
-    _check_matplotlib,
-    _get_installed_packages,
-)
+from pytorch_forecasting.utils._dependencies._dependencies import _check_matplotlib
 from pytorch_forecasting.utils._dependencies._safe_import import _safe_import
 
 __all__ = [
-    "_get_installed_packages",
     "_check_matplotlib",
     "_safe_import",
 ]

pytorch_forecasting/utils/_dependencies/_dependencies.py

@@ -3,45 +3,9 @@
 Copied from sktime/skbase.
 """
 
-from functools import lru_cache
+from skbase.utils.dependencies._dependencies import _check_soft_dependencies
 
-
-@lru_cache
-def _get_installed_packages_private():
-    """Get a dictionary of installed packages and their versions.
-
-    Same as _get_installed_packages, but internal to avoid mutating the lru_cache
-    by accident.
-    """
-    from importlib.metadata import distributions, version
-
-    dists = distributions()
-    package_names = {
-        dist.metadata["Name"]
-        for dist in dists
-        if dist.metadata and "Name" in dist.metadata
-    }
-    package_versions = {pkg_name: version(pkg_name) for pkg_name in package_names}
-    # developer note:
-    # we cannot just use distributions naively,
-    # because the same top level package name may appear *twice*,
-    # e.g., in a situation where a virtual env overrides a base env,
-    # such as in deployment environments like databricks.
-    # the "version" contract ensures we always get the version that corresponds
-    # to the importable distribution, i.e., the top one in the sys.path.
-    return package_versions
-
-
-def _get_installed_packages():
-    """Get a dictionary of installed packages and their versions.
-
-    Returns
-    -------
-    dict : dictionary of installed packages and their versions
-        keys are PEP 440 compatible package names, values are package versions
-        MAJOR.MINOR.PATCH version format is used for versions, e.g., "1.2.3"
-    """
-    return _get_installed_packages_private().copy()
+__all__ = ["_check_soft_dependencies", "_check_matplotlib"]
 
 
 def _check_matplotlib(ref="This feature", raise_error=True):
@@ -58,12 +22,11 @@ def _check_matplotlib(ref="This feature", raise_error=True):
     -------
     bool : whether matplotlib is installed
     """
-    pkgs = _get_installed_packages()
-
-    if raise_error and "matplotlib" not in pkgs:
+    matplotlib_present = _check_soft_dependencies("matplotlib", severity="none")
+    if raise_error and not matplotlib_present:
         raise ImportError(
             f"{ref} requires matplotlib."
             " Please install matplotlib with `pip install matplotlib`."
         )
 
-    return "matplotlib" in pkgs
+    return matplotlib_present

pytorch_forecasting/utils/_dependencies/_safe_import.py

@@ -8,7 +8,7 @@
 import importlib
 from unittest.mock import MagicMock
 
-from pytorch_forecasting.utils._dependencies import _get_installed_packages
+from skbase.utils.dependencies._dependencies import _get_installed_packages
 
 
 def _safe_import(import_path, pkg_name=None):

pytorch_forecasting/utils/_dependencies/tests/test_safe_import.py

@@ -1,16 +1,6 @@
 __author__ = ["jgyasu", "fkiraly"]
 
-from pytorch_forecasting.utils._dependencies import (
-    _get_installed_packages,
-    _safe_import,
-)
-
-
-def test_import_present_module():
-    """Test importing a dependency that is installed."""
-    result = _safe_import("pandas")
-    assert result is not None
-    assert "pandas" in _get_installed_packages()
+from pytorch_forecasting.utils._dependencies import _safe_import
 
 
 def test_import_missing_module():

pytorch_forecasting/utils/_maint/_show_versions.py

@@ -82,7 +82,7 @@ def _get_deps_info(deps=None, source="distributions"):
         deps = ["pytorch-forecasting"]
 
     if source == "distributions":
-        from pytorch_forecasting.utils._dependencies import _get_installed_packages
+        from skbase.utils.dependencies._dependencies import _get_installed_packages
 
         KEY_ALIAS = {"sklearn": "scikit-learn", "skbase": "scikit-base"}
 

tests/test_models/test_nbeats.py

@@ -5,9 +5,9 @@
 from lightning.pytorch.callbacks import EarlyStopping
 from lightning.pytorch.loggers import TensorBoardLogger
 import pytest
+from skbase.utils.dependencies import _check_soft_dependencies
 
 from pytorch_forecasting.models import NBeats
-from pytorch_forecasting.utils._dependencies import _get_installed_packages
 
 
 def test_integration(dataloaders_fixed_window_without_covariates, tmp_path):
@@ -90,7 +90,7 @@ def test_pickle(model):
 
 
 @pytest.mark.skipif(
-    "matplotlib" not in _get_installed_packages(),
+    not _check_soft_dependencies("matplotlib", severity="none"),
     reason="skip test if required package matplotlib not installed",
 )
 def test_interpretation(model, dataloaders_fixed_window_without_covariates):

tests/test_models/test_nhits.py

@@ -7,14 +7,14 @@
 import numpy as np
 import pandas as pd
 import pytest
+from skbase.utils.dependencies import _check_soft_dependencies
 
 from pytorch_forecasting.data.timeseries import TimeSeriesDataSet
 from pytorch_forecasting.metrics import MQF2DistributionLoss, QuantileLoss
 from pytorch_forecasting.metrics.distributions import (
     ImplicitQuantileNetworkDistributionLoss,
 )
 from pytorch_forecasting.models import NHiTS
-from pytorch_forecasting.utils._dependencies import _get_installed_packages
 
 
 def _integration(dataloader, tmp_path, trainer_kwargs=None, **kwargs):
@@ -96,7 +96,7 @@ def _integration(dataloader, tmp_path, trainer_kwargs=None, **kwargs):
     "implicit-quantiles",
 ]
 
-if "cpflows" in _get_installed_packages():
+if _check_soft_dependencies("cpflows", severity="none"):
     LOADERS += ["multivariate-quantiles"]
 
 
@@ -158,7 +158,7 @@ def test_pickle(model):
 
 
 @pytest.mark.skipif(
-    "matplotlib" not in _get_installed_packages(),
+    not _check_soft_dependencies("matplotlib", severity="none"),
     reason="skip test if required package matplotlib not installed",
 )
 def test_interpretation(model, dataloaders_with_covariates):

tests/test_models/test_temporal_fusion_transformer.py

@@ -9,6 +9,7 @@
 import numpy as np
 import pandas as pd
 import pytest
+from skbase.utils.dependencies import _check_soft_dependencies
 from test_models.conftest import make_dataloaders
 import torch
 
@@ -29,7 +30,6 @@
 from pytorch_forecasting.models.temporal_fusion_transformer.tuning import (
     optimize_hyperparameters,
 )
-from pytorch_forecasting.utils._dependencies import _get_installed_packages
 
 
 def test_integration(multiple_dataloaders_with_covariates, tmp_path):
@@ -71,7 +71,7 @@ def test_distribution_loss(data_with_covariates, tmp_path):
 
 
 @pytest.mark.skipif(
-    "cpflows" not in _get_installed_packages(),
+    not _check_soft_dependencies("cpflows", severity="none"),
     reason="Test skipped if required package cpflows not available",
 )
 def test_mqf2_loss(data_with_covariates, tmp_path):
@@ -331,7 +331,7 @@ def test_predict_dependency(
 
 
 @pytest.mark.skipif(
-    "matplotlib" not in _get_installed_packages(),
+    not _check_soft_dependencies("matplotlib", severity="none"),
     reason="skip test if required package matplotlib not installed",
 )
 def test_actual_vs_predicted_plot(model, dataloaders_with_covariates):
@@ -424,8 +424,7 @@ def test_prediction_with_dataframe(model, data_with_covariates):
 SKIP_HYPEPARAM_TEST = (
     sys.platform.startswith("win")
     # Test skipped on Windows OS due to issues with ddp, see #1632"
-    or "optuna" not in _get_installed_packages()
-    or "statsmodels" not in _get_installed_packages()
+    or not _check_soft_dependencies(["optuna", "statsmodels"], severity="none")
     # Test skipped if required package optuna or statsmodels not available
 )
 

tests/test_models/test_tide.py

@@ -7,12 +7,12 @@
 import numpy as np
 import pandas as pd
 import pytest
+from skbase.utils.dependencies import _check_soft_dependencies
 
 from pytorch_forecasting.data.timeseries import TimeSeriesDataSet
 from pytorch_forecasting.metrics import SMAPE
 from pytorch_forecasting.models import TiDEModel
 from pytorch_forecasting.tests._conftest import make_dataloaders
-from pytorch_forecasting.utils._dependencies import _get_installed_packages
 
 
 def _integration(
@@ -192,7 +192,7 @@ def test_pickle(model):
 
 
 @pytest.mark.skipif(
-    "matplotlib" not in _get_installed_packages(),
+    not _check_soft_dependencies("matplotlib", severity="none"),
     reason="skip test if required package matplotlib not installed",
 )
 def test_prediction_visualization(model, dataloaders_with_covariates):