Merge remote-tracking branch 'upstream/main' into opt

Author: fnhirwa

docs/source/conf.py

@@ -94,11 +94,14 @@ def get_by_name(string: str):
     """
     Import by name and return imported module/function/class
 
-    Args:
-        string (str): module/function/class to import, e.g. 'pandas.read_csv' will return read_csv function as
-        defined by pandas
-
-    Returns:
+    Parameters
+    ----------
+    string (str):
+        module/function/class to import, e.g. 'pandas.read_csv'
+        will return read_csv function as defined by pandas
+
+    Returns
+    -------
         imported object
     """
     class_name = string.split(".")[-1]

examples/ar.py

@@ -98,7 +98,7 @@
 # deepar.hparams.log_val_interval = -1
 # trainer.limit_train_batches = 1.0
 # res = Tuner(trainer).lr_find(
-#     deepar, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, min_lr=1e-5, max_lr=1e2
+#     deepar, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, min_lr=1e-5, max_lr=1e2 # noqa: E501
 # )
 
 # print(f"suggested learning rate: {res.suggestion()}")

examples/nbeats.py

@@ -87,7 +87,7 @@
 # trainer.limit_train_batches = 1.0
 # # run learning rate finder
 # res = Tuner(trainer).lr_find(
-#     net, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, min_lr=1e-5, max_lr=1e2
+#     net, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, min_lr=1e-5, max_lr=1e2 # noqa: E501
 # )
 # print(f"suggested learning rate: {res.suggestion()}")
 # fig = res.plot(show=True, suggest=True)

examples/stallion.py

@@ -34,7 +34,7 @@
 data["avg_volume_by_agency"] = data.groupby(
     ["time_idx", "agency"], observed=True
 ).volume.transform("mean")
-# data = data[lambda x: (x.sku == data.iloc[0]["sku"]) & (x.agency == data.iloc[0]["agency"])]
+# data = data[lambda x: (x.sku == data.iloc[0]["sku"]) & (x.agency == data.iloc[0]["agency"])] # noqa: E501
 special_days = [
     "easter_day",
     "good_friday",
@@ -151,7 +151,7 @@
 # trainer.limit_train_batches = 1.0
 # # run learning rate finder
 # res = Tuner(trainer).lr_find(
-#     tft, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, min_lr=1e-5, max_lr=1e2
+#     tft, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, min_lr=1e-5, max_lr=1e2 # noqa: E501
 # )
 # print(f"suggested learning rate: {res.suggestion()}")
 # fig = res.plot(show=True, suggest=True)

pyproject.toml

@@ -136,7 +136,9 @@ exclude = [
   ".venv/",
   ".git/",
   ".history/",
+  "docs/source/tutorials/",
 ]
+target-version = "py39"
 
 [tool.ruff.lint]
 select = ["E", "F", "W", "C4", "S"]
@@ -161,6 +163,11 @@ known-first-party = ["pytorch_forecasting"]
 combine-as-imports = true
 force-sort-within-sections = true
 
+[tool.ruff.lint.per-file-ignores]
+"pytorch_forecasting/data/timeseries.py" = [
+  "E501", # Line too long being fixed in #1746 To be removed after merging
+]
+
 [tool.black]
 line-length = 88
 include = '\.pyi?$'
@@ -188,3 +195,6 @@ exclude = '''
 [tool.nbqa.mutate]
 ruff = 1
 black = 1
+
+[tool.nbqa.exclude]
+ruff = "docs/source/tutorials/" # ToDo: Remove this when fixing notebooks

pytorch_forecasting/data/__init__.py

@@ -1,8 +1,8 @@
 """
 Datasets, etc. for timeseries data.
 
-Handling timeseries data is not trivial. It requires special treatment. This sub-package provides the necessary tools
-to abstracts the necessary work.
+Handling timeseries data is not trivial. It requires special treatment.
+This sub-package provides the necessary tools to abstracts the necessary work.
 """
 
 from pytorch_forecasting.data.encoders import (