Merge branch 'main' into iTransformer

Author: PranavBhatP

.github/workflows/pypi_release.yml

@@ -48,7 +48,7 @@ jobs:
           python -m build --wheel --sdist --outdir wheelhouse
 
       - name: Store wheels
-        uses: actions/upload-artifact@v5
+        uses: actions/upload-artifact@v6
         with:
           name: wheels
           path: wheelhouse/*
@@ -61,7 +61,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
 
     steps:
       - uses: actions/checkout@v6
@@ -102,7 +102,7 @@ jobs:
       id-token: write
 
     steps:
-      - uses: actions/download-artifact@v6
+      - uses: actions/download-artifact@v7
         with:
           name: wheels
           path: wheelhouse

.github/workflows/test.yml

@@ -82,7 +82,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
 
     steps:
       - uses: actions/checkout@v6
@@ -122,7 +122,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
 
     steps:
       - uses: actions/checkout@v6

CODE_OF_CONDUCT.md

@@ -0,0 +1,4 @@
+# Code of Conduct
+
+All contributors, maintainers, and participants are expected to follow the
+`sktime` Code of Conduct found on our [website](https://www.sktime.net/en/latest/get_involved/code_of_conduct.html).

GOVERNANCE.md

@@ -0,0 +1,5 @@
+# Governance
+
+`pytorch-forecasting` is governed by the `sktime` community.
+
+You can find our governance guidelines on our [website](https://www.sktime.net/en/latest/get_involved/governance.html).

build_tools/changelog.py

@@ -10,7 +10,7 @@
 if os.getenv("GITHUB_TOKEN") is not None:
     HEADERS["Authorization"] = f"token {os.getenv('GITHUB_TOKEN')}"
 
-OWNER = "jdb78"
+OWNER = "sktime"
 REPO = "pytorch-forecasting"
 GITHUB_REPOS = "https://api.github.com/repos"
 
@@ -118,7 +118,7 @@ def render_contributors(prs: list, fmt: str = "rst"):
     """Find unique authors and print a list in  given format."""
     authors = sorted({pr["user"]["login"] for pr in prs}, key=lambda x: x.lower())
 
-    header = "Contributors"
+    header = "All Contributors"
     if fmt == "github":
         print(f"### {header}")
         print(", ".join(f"@{user}" for user in authors))
@@ -152,9 +152,9 @@ def render_row(pr):
     """Render a single row with PR in restructuredText format."""
     print(
         "*",
-        pr["title"].replace("`", "``"),
-        f"(:pr:`{pr['number']}`)",
-        f":user:`{pr['user']['login']}`",
+        pr["title"],
+        f"(#{pr['number']})",
+        f"@{pr['user']['login']}",
     )
 
 
@@ -166,8 +166,7 @@ def render_changelog(prs, assigned):
     for title, _ in assigned.items():
         pr_group = [prs[i] for i in assigned[title]]
         if pr_group:
-            print(f"\n{title}")
-            print("~" * len(title), end="\n\n")
+            print(f"\n### {title}\n")
 
             for pr in sorted(pr_group, key=lambda x: parser.parse(x["merged_at"])):
                 render_row(pr)
@@ -187,7 +186,7 @@ def render_changelog(prs, assigned):
     assigned = assign_prs(pulls, categories)
     render_changelog(pulls, assigned)
     print()
-    render_contributors(pulls)
+    render_contributors(pulls, fmt="github")
 
     release = fetch_latest_release()
     diff = github_compare_tags(release["tag_name"])

docs/source/installation.rst

@@ -3,7 +3,7 @@ Installation
 
 ``pytorch-forecasting`` currently supports:
 
-* Python versions 3.10, 3.11, 3.12 and 3.13.
+* Python versions 3.10, 3.11, 3.12, 3.13 and 3.14.
 * Operating systems : Linux, macOS, and Windows
 
 Installing pytorch-forecasting

docs/source/tutorials/ar.ipynb

@@ -187,10 +187,16 @@
     "    max_prediction_length=prediction_length,\n",
     ")\n",
     "\n",
-    "validation = TimeSeriesDataSet.from_dataset(training, data, min_prediction_idx=training_cutoff + 1)\n",
+    "validation = TimeSeriesDataSet.from_dataset(\n",
+    "    training, data, min_prediction_idx=training_cutoff + 1\n",
+    ")\n",
     "batch_size = 128\n",
-    "train_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=0)\n",
-    "val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=0)"
+    "train_dataloader = training.to_dataloader(\n",
+    "    train=True, batch_size=batch_size, num_workers=0\n",
+    ")\n",
+    "val_dataloader = validation.to_dataloader(\n",
+    "    train=False, batch_size=batch_size, num_workers=0\n",
+    ")"
    ]
   },
   {
@@ -251,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -269,12 +275,18 @@
    "source": [
     "pl.seed_everything(42)\n",
     "trainer = pl.Trainer(accelerator=\"auto\", gradient_clip_val=0.1)\n",
-    "net = NBeats.from_dataset(training, learning_rate=3e-2, weight_decay=1e-2, widths=[32, 512], backcast_loss_ratio=0.1)"
+    "net = NBeats.from_dataset(\n",
+    "    training,\n",
+    "    learning_rate=3e-2,\n",
+    "    weight_decay=1e-2,\n",
+    "    widths=[32, 512],\n",
+    "    backcast_loss_ratio=0.1,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -321,9 +333,14 @@
    ],
    "source": [
     "# find optimal learning rate\n",
-    "from lightning.pytorch.tuner import Tuner\n",
+    "from pytorch_forecasting.tuning import Tuner\n",
     "\n",
-    "res = Tuner(trainer).lr_find(net, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, min_lr=1e-5)\n",
+    "res = Tuner(trainer).lr_find(\n",
+    "    net,\n",
+    "    train_dataloaders=train_dataloader,\n",
+    "    val_dataloaders=val_dataloader,\n",
+    "    min_lr=1e-5,\n",
+    ")\n",
     "print(f\"suggested learning rate: {res.suggestion()}\")\n",
     "fig = res.plot(show=True, suggest=True)\n",
     "fig.show()\n",
@@ -340,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -443,7 +460,9 @@
     }
    ],
    "source": [
-    "early_stop_callback = EarlyStopping(monitor=\"val_loss\", min_delta=1e-4, patience=10, verbose=False, mode=\"min\")\n",
+    "early_stop_callback = EarlyStopping(\n",
+    "    monitor=\"val_loss\", min_delta=1e-4, patience=10, verbose=False, mode=\"min\"\n",
+    ")\n",
     "trainer = pl.Trainer(\n",
     "    max_epochs=3,\n",
     "    accelerator=\"auto\",\n",
@@ -481,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -645,7 +664,9 @@
    ],
    "source": [
     "for idx in range(10):  # plot 10 examples\n",
-    "    best_model.plot_prediction(raw_predictions.x, raw_predictions.output, idx=idx, add_loss_to_title=True)"
+    "    best_model.plot_prediction(\n",
+    "        raw_predictions.x, raw_predictions.output, idx=idx, add_loss_to_title=True\n",
+    "    )"
    ]
   },
   {

docs/source/tutorials/deepar.ipynb

@@ -301,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -348,7 +348,7 @@
    ],
    "source": [
     "# find optimal learning rate\n",
-    "from lightning.pytorch.tuner import Tuner\n",
+    "from pytorch_forecasting.tuning import Tuner\n",
     "\n",
     "res = Tuner(trainer).lr_find(\n",
     "    net,\n",
@@ -883,7 +883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1268,7 +1268,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": ".venv (3.12.3)",
    "language": "python",
    "name": "python3"
   },
@@ -1282,7 +1282,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/source/tutorials/nhits.ipynb

@@ -306,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -353,7 +353,7 @@
    ],
    "source": [
     "# find optimal learning rate\n",
-    "from lightning.pytorch.tuner import Tuner\n",
+    "from pytorch_forecasting.tuning import Tuner\n",
     "\n",
     "res = Tuner(trainer).lr_find(\n",
     "    net,\n",
@@ -553,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [