Merge branch 'master' into pr/1624

.github/workflows/pypi_release.yml

@@ -19,7 +19,7 @@ jobs:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
         with:
-          python-version: 3.8
+          python-version: 3.11
 
       - name: Install poetry
         shell: bash

.github/workflows/test.yml

@@ -25,18 +25,61 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          python -m pip install ".[dev,github-actions,graph,mqf2]"
+          python -m pip install ".[dev,all_extras,github-actions]"
+
+      - name: Show dependencies
+        run:  python -m pip list
+
       - name: Run example notebooks
         run: build_tools/run_examples.sh
         shell: bash
 
+  pytest-nosoftdeps:
+    name: no-softdeps
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-13, windows-latest]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Setup macOS
+        if: runner.os == 'macOS'
+        run: |
+          brew install libomp  # https://github.com/pytorch/pytorch/issues/20030
+
+      - name: Get full Python version
+        id: full-python-version
+        shell: bash
+        run: echo version=$(python -c "import sys; print('-'.join(str(v) for v in sys.version_info))") >> $GITHUB_OUTPUT
+
+      - name: Install dependencies
+        shell: bash
+        run: |
+          pip install ".[dev,github-actions]"
+
+      - name: Show dependencies
+        run: python -m pip list
+
+      - name: Run pytest
+        shell: bash
+        run: python -m pytest tests
+
   pytest:
     name: Run pytest
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest, macos-13] # add windows-2019 when poetry allows installation with `-f` flag
+        os: [ubuntu-latest, macos-13, windows-latest]
         python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
@@ -60,7 +103,7 @@ jobs:
       - name: Install dependencies
         shell: bash
         run: |
-          pip install ".[dev,github-actions,graph,mqf2]"
+          pip install ".[dev,all_extras,github-actions]"
 
       - name: Show dependencies
         run: python -m pip list
@@ -99,7 +142,7 @@ 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

.gitignore

@@ -104,7 +104,7 @@ celerybeat.pid
 *.sage.py
 
 # Environments
-# .env
+.env
 .venv
 env/
 venv/

.pre-commit-config.yaml

@@ -2,26 +2,26 @@
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.6.0
     hooks:
       - id: trailing-whitespace
       - id: end-of-file-fixer
       - id: check-yaml
       - id: check-ast
   - repo: https://github.com/pycqa/flake8
-    rev: 6.1.0
+    rev: 7.1.1
     hooks:
       - id: flake8
   - repo: https://github.com/pre-commit/mirrors-isort
     rev: v5.10.1
     hooks:
       - id: isort
   - repo: https://github.com/psf/black
-    rev: 23.9.0
+    rev: 24.8.0
     hooks:
       - id: black
   - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.7.0
+    rev: 1.8.7
     hooks:
       - id: nbqa-black
       - id: nbqa-isort

.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

codecov.yml

@@ -6,3 +6,6 @@ coverage:
     project:
       default:
         threshold: 0.2%
+    patch:
+      default:
+        informational: true

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",
+]

docs/source/tutorials/ar.ipynb

@@ -14,12 +14,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
     "import warnings\n",
     "\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "\n",
-    "os.chdir(\"../../..\")"
+    "warnings.filterwarnings(\"ignore\")"
    ]
   },
   {

docs/source/tutorials/building.ipynb

@@ -57,12 +57,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
     "import warnings\n",
     "\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "\n",
-    "os.chdir(\"../../..\")"
+    "warnings.filterwarnings(\"ignore\")"
    ]
   },
   {
@@ -1034,16 +1031,19 @@
   {
    "cell_type": "raw",
    "metadata": {
-    "raw_mimetype": "text/restructuredtext"
+    "raw_mimetype": "text/restructuredtext",
+    "vscode": {
+     "languageId": "raw"
+    }
    },
    "source": [
     "While not required, to give the user transparancy over these additional hyperparameters, it is worth passing them explicitly instead of implicitly in ``**kwargs``\n",
     "\n",
-    "They are described in detail in the :py:class:`~pytorch_forecasting.models.base_model.BaseModel`. \n",
+    "They are described in detail in the :py:class:`~pytorch_forecasting.models.base_model.BaseModel`.\n",
     "\n",
     ".. automethod:: pytorch_forecasting.models.base_model.BaseModel.__init__\n",
     "    :noindex:\n",
-    "    \n",
+    "\n",
     "You can simply copy this docstring into your model implementation:"
    ]
   },
@@ -2238,15 +2238,18 @@
   {
    "cell_type": "raw",
    "metadata": {
-    "raw_mimetype": "text/restructuredtext"
+    "raw_mimetype": "text/restructuredtext",
+    "vscode": {
+     "languageId": "raw"
+    }
    },
    "source": [
     "Now that we have established the basics, we can move on to more advanced use cases, e.g. how can we make use of covariates - static and continuous alike. We can leverage the :py:class:`~pytorch_forecasting.models.base_model.BaseModelWithCovariates` for this. The difference to the :py:class:`~pytorch_forecasting.models.base_model.BaseModel` is a :py:meth:`~pytorch_forecasting.models.base_model.BaseModelWithCovariates.from_dataset` method that pre-defines hyperparameters for architectures with covariates.\n",
     "\n",
     ".. autoclass:: pytorch_forecasting.models.base_model.BaseModelWithCovariates\n",
     "    :noindex:\n",
     "    :members: from_dataset\n",
-    "    \n",
+    "\n",
     "\n",
     "Here is a from the BaseModelWithCovariates docstring to copy:"
    ]

docs/source/tutorials/deepar.ipynb

@@ -14,12 +14,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
     "import warnings\n",
     "\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "\n",
-    "os.chdir(\"../../..\")"
+    "warnings.filterwarnings(\"ignore\")"
    ]
   },
   {

docs/source/tutorials/nhits.ipynb

@@ -14,12 +14,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
     "import warnings\n",
     "\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "\n",
-    "os.chdir(\"../../..\")"
+    "warnings.filterwarnings(\"ignore\")"
    ]
   },
   {

docs/source/tutorials/stallion.ipynb

@@ -39,12 +39,9 @@
    },
    "outputs": [],
    "source": [
-    "import os\n",
     "import warnings\n",
     "\n",
-    "warnings.filterwarnings(\"ignore\")  # avoid printing out absolute paths\n",
-    "\n",
-    "os.chdir(\"../../..\")"
+    "warnings.filterwarnings(\"ignore\")  # avoid printing out absolute paths"
    ]
   },
   {
@@ -1557,16 +1554,19 @@
   {
    "cell_type": "raw",
    "metadata": {
-    "raw_mimetype": "text/restructuredtext"
+    "raw_mimetype": "text/restructuredtext",
+    "vscode": {
+     "languageId": "raw"
+    }
    },
    "source": [
-    "Hyperparamter tuning with [optuna](https://optuna.org/) is directly build into pytorch-forecasting. For example, we can use the \n",
+    "Hyperparamter tuning with [optuna](https://optuna.org/) is directly build into pytorch-forecasting. For example, we can use the\n",
     ":py:func:`~pytorch_forecasting.models.temporal_fusion_transformer.tuning.optimize_hyperparameters` function to optimize the TFT's hyperparameters.\n",
     "\n",
     ".. code-block:: python\n",
     "\n",
     "    import pickle\n",
-    "    \n",
+    "\n",
     "    from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters\n",
     "\n",
     "    # create study\n",
@@ -1917,7 +1917,7 @@
    "source": [
     "# calcualte metric by which to display\n",
     "predictions = best_tft.predict(val_dataloader, return_y=True)\n",
-    "mean_losses = SMAPE(reduction=\"none\")(predictions.output, predictions.y).mean(1)\n",
+    "mean_losses = SMAPE(reduction=\"none\").loss(predictions.output, predictions.y[0]).mean(1)\n",
     "indices = mean_losses.argsort(descending=True)  # sort losses\n",
     "for idx in range(10):  # plot 10 examples\n",
     "    best_tft.plot_prediction(\n",

examples/ar.py

@@ -1,22 +1,16 @@
-from pathlib import Path
-import pickle
 import warnings
 
 import lightning.pytorch as pl
 from lightning.pytorch.callbacks import EarlyStopping, LearningRateMonitor
-from lightning.pytorch.loggers import TensorBoardLogger
-from lightning.pytorch.tuner import Tuner
-import numpy as np
 import pandas as pd
 from pandas.core.common import SettingWithCopyWarning
 import torch
 
-from pytorch_forecasting import EncoderNormalizer, GroupNormalizer, TimeSeriesDataSet
+from pytorch_forecasting import GroupNormalizer, TimeSeriesDataSet
 from pytorch_forecasting.data import NaNLabelEncoder
 from pytorch_forecasting.data.examples import generate_ar_data
 from pytorch_forecasting.metrics import NormalDistributionLoss
 from pytorch_forecasting.models.deepar import DeepAR
-from pytorch_forecasting.utils import profile
 
 warnings.simplefilter("error", category=SettingWithCopyWarning)
 

examples/nbeats.py

@@ -2,9 +2,7 @@
 
 import lightning.pytorch as pl
 from lightning.pytorch.callbacks import EarlyStopping
-from lightning.pytorch.tuner import Tuner
 import pandas as pd
-from sklearn.preprocessing import scale
 
 from pytorch_forecasting import NBeats, TimeSeriesDataSet
 from pytorch_forecasting.data import NaNLabelEncoder

examples/stallion.py

@@ -1,21 +1,16 @@
-from pathlib import Path
 import pickle
 import warnings
 
 import lightning.pytorch as pl
 from lightning.pytorch.callbacks import EarlyStopping, LearningRateMonitor
 from lightning.pytorch.loggers import TensorBoardLogger
-from lightning.pytorch.tuner import Tuner
 import numpy as np
-import pandas as pd
 from pandas.core.common import SettingWithCopyWarning
-import torch
 
 from pytorch_forecasting import GroupNormalizer, TemporalFusionTransformer, TimeSeriesDataSet
 from pytorch_forecasting.data.examples import get_stallion_data
-from pytorch_forecasting.metrics import MAE, RMSE, SMAPE, PoissonLoss, QuantileLoss
+from pytorch_forecasting.metrics import QuantileLoss
 from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters
-from pytorch_forecasting.utils import profile
 
 warnings.simplefilter("error", category=SettingWithCopyWarning)