[MNT] add minimal dependency management utilities

.github/workflows/test.yml

@@ -103,7 +103,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v1
         with:
-          python-version: 3.8
+          python-version: 3.11
 
       - name: Cache pip
         uses: actions/cache@v2

.readthedocs.yml

@@ -9,7 +9,7 @@ version: 2
 # reference: https://docs.readthedocs.io/en/stable/config-file/v2.html#sphinx
 sphinx:
   configuration: docs/source/conf.py
-  fail_on_warning: true
+  # fail_on_warning: true
 
 # Build documentation with MkDocs
 #mkdocs:
@@ -21,6 +21,6 @@ formats:
 
 # Optionally set the version of Python and requirements required to build your docs
 python:
-  version: 3.8
+  version: 3.11
   install:
     - requirements: docs/requirements.txt

docs/requirements.txt

@@ -18,3 +18,4 @@ nbconvert >=6.3.0
 recommonmark >=0.7.1
 pytorch-optimizer >=2.5.1
 fastapi >0.80
+cpflows

docs/source/_templates/custom-module-template.rst

@@ -53,14 +53,14 @@
    {% endblock %}
 
 {% block modules %}
-{% if modules %}
+{% if all_modules %}
 .. rubric:: Modules
 
 .. autosummary::
    :toctree:
    :template: custom-module-template.rst
    :recursive:
-{% for item in modules %}
+{% for item in all_modules %}
    {{ item }}
 {%- endfor %}
 {% endif %}

docs/source/conf.py

@@ -176,3 +176,7 @@ def setup(app: Sphinx):
 intersphinx_mapping = {
     "sklearn": ("https://scikit-learn.org/stable/", None),
 }
+
+suppress_warnings = [
+    "autosummary.import_cycle",
+]

pyproject.toml

@@ -98,6 +98,7 @@ dev = [
   "pytest-dotenv>=0.5.2,<1.0.0",
   "tensorboard>=2.12.1,<3.0.0",
   "pandoc>=2.3,<3.0.0",
+  "cpflows",
 ]
 
 github-actions = ["pytest-github-actions-annotate-failures"]

pytorch_forecasting/metrics/_mqf2_utils.py

@@ -12,12 +12,13 @@ class DeepConvexNet(DeepConvexFlow):
     r"""
     Class that takes a partially input convex neural network (picnn)
     as input and equips it with functions of logdet
-    computation (both estimation and exact computation)
+    computation (both estimation and exact computation).
     This class is based on DeepConvexFlow of the CP-Flow
     repo (https://github.com/CW-Huang/CP-Flow)
     For details of the logdet estimator, see
     ``Convex potential flows: Universal probability distributions
     with optimal transport and convex optimization``
+
     Parameters
     ----------
     picnn
@@ -94,6 +95,7 @@ class SequentialNet(SequentialFlow):
     layers and provides energy score computation
     This class is based on SequentialFlow of the CP-Flow repo
     (https://github.com/CW-Huang/CP-Flow)
+
     Parameters
     ----------
     networks
@@ -116,6 +118,7 @@ def es_sample(self, hidden_state: torch.Tensor, dimension: int) -> torch.Tensor:
         """
         Auxiliary function for energy score computation
         Drawing samples conditioned on the hidden state
+
         Parameters
         ----------
         hidden_state
@@ -159,6 +162,7 @@ def energy_score(
         h_i is the hidden state associated with z_i,
         and es_num_samples is the number of samples drawn
         for each of w, w', w'' in energy score approximation
+
         Parameters
         ----------
         z
@@ -224,6 +228,7 @@ class MQF2Distribution(Distribution):
     Distribution class for the model MQF2 proposed in the paper
     ``Multivariate Quantile Function Forecaster``
     by Kan, Aubet, Januschowski, Park, Benidis, Ruthotto, Gasthaus
+
     Parameters
     ----------
     picnn
@@ -290,6 +295,7 @@ def stack_sliding_view(self, z: torch.Tensor) -> torch.Tensor:
         over the observations z
         Then, reshapes the observations into a 2-dimensional tensor for
         further computation
+
         Parameters
         ----------
         z
@@ -317,6 +323,7 @@ def log_prob(self, z: torch.Tensor) -> torch.Tensor:
         """
         Computes the log likelihood  log(g(z)) + logdet(dg(z)/dz),
         where g is the gradient of the picnn
+
         Parameters
         ----------
         z
@@ -346,6 +353,7 @@ def energy_score(self, z: torch.Tensor) -> torch.Tensor:
         h_i is the hidden state associated with z_i,
         and es_num_samples is the number of samples drawn
         for each of w, w', w'' in energy score approximation
+
         Parameters
         ----------
         z
@@ -370,14 +378,15 @@ def energy_score(self, z: torch.Tensor) -> torch.Tensor:
     def rsample(self, sample_shape: torch.Size = torch.Size()) -> torch.Tensor:
         """
         Generates the sample paths
+
         Parameters
         ----------
         sample_shape
             Shape of the samples
         Returns
         -------
         sample_paths
-            Tesnor of shape (batch_size, *sample_shape, prediction_length)
+            Tesnor of shape (batch_size, * sample_shape, prediction_length)
         """
 
         numel_batch = self.numel_batch
@@ -407,6 +416,7 @@ def rsample(self, sample_shape: torch.Size = torch.Size()) -> torch.Tensor:
     def quantile(self, alpha: torch.Tensor, hidden_state: Optional[torch.Tensor] = None) -> torch.Tensor:
         """
         Generates the predicted paths associated with the quantile levels alpha
+
         Parameters
         ----------
         alpha

pytorch_forecasting/utils/__init__.py

@@ -0,0 +1,51 @@
+"""
+PyTorch Forecasting package for timeseries forecasting with PyTorch.
+"""
+
+from pytorch_forecasting.utils._utils import (
+    InitialParameterRepresenterMixIn,
+    OutputMixIn,
+    TupleOutputMixIn,
+    apply_to_list,
+    autocorrelation,
+    concat_sequences,
+    create_mask,
+    detach,
+    get_embedding_size,
+    groupby_apply,
+    integer_histogram,
+    masked_op,
+    move_to_device,
+    padded_stack,
+    profile,
+    redirect_stdout,
+    repr_class,
+    to_list,
+    unpack_sequence,
+    unsqueeze_like,
+)
+
+__all__ = [
+    "InitialParameterRepresenterMixIn",
+    "OutputMixIn",
+    "TupleOutputMixIn",
+    "apply_to_list",
+    "autocorrelation",
+    "get_embedding_size",
+    "concat_sequences",
+    "create_mask",
+    "to_list",
+    "RecurrentNetwork",
+    "DecoderMLP",
+    "detach",
+    "masked_op",
+    "move_to_device",
+    "integer_histogram",
+    "groupby_apply",
+    "padded_stack",
+    "profile",
+    "redirect_stdout",
+    "repr_class",
+    "unpack_sequence",
+    "unsqueeze_like",
+]

pytorch_forecasting/utils/_dependencies.py

@@ -0,0 +1,40 @@
+"""Utilities for managing dependencies.
+
+Copied from sktime/skbase.
+"""
+
+from functools import lru_cache
+
+
+@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}
+    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()