[BUG] Fix pyproject package structure and failures in tutorial notebo…

…oks (#1615) * add nbconvert * Show environment * test which python in sh file * remove py_modules * add [tool.setuptools].packages * packages to package-dir * package discovery by exclude * clean up build_tools/run_examples.sh * remove warning in ar.ipynb
sktime · Aug 26, 2024 · 119fa89 · 119fa89
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commit 119fa89
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Showing 8 changed files with 31 additions and 32 deletions.
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
@@ -26,6 +26,10 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           python -m pip install ".[dev,github-actions,graph,mqf2]"
+
+      - name: Show dependencies
+        run:  python -m pip list
+
       - name: Run example notebooks
         run: build_tools/run_examples.sh
         shell: bash

diff --git a/docs/source/tutorials/ar.ipynb b/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\")"
    ]
   },
   {

diff --git a/docs/source/tutorials/building.ipynb b/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:"
    ]

diff --git a/docs/source/tutorials/deepar.ipynb b/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\")"
    ]
   },
   {

diff --git a/docs/source/tutorials/nhits.ipynb b/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\")"
    ]
   },
   {

diff --git a/docs/source/tutorials/stallion.ipynb b/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",

diff --git a/pyproject.toml b/pyproject.toml
@@ -87,6 +87,7 @@ dev = [
   "pyarrow",
   # jupyter notebook
   "ipykernel",
+  "nbconvert",
   "black[extras]",
   # documentatation
   "sphinx",
@@ -103,8 +104,8 @@ github-actions = ["pytest-github-actions-annotate-failures"]
 graph = ["networkx"]
 mqf2 = ["cpflows"]
 
-[tool.setuptools]
-py-modules = ["pytorch_forecasting"]
+[tool.setuptools.packages.find]
+exclude = ["build_tools"]
 
 [build-system]
 build-backend = "setuptools.build_meta"

diff --git a/pytorch_forecasting/__init__.py b/pytorch_forecasting/__init__.py
@@ -112,4 +112,4 @@
     "unpack_sequence",
 ]
 
-__version__ = "0.0.0"
+__version__ = "1.0.0"