black formatting

build_tools/changelog.py

@@ -62,7 +62,9 @@ def fetch_latest_release():  # noqa: D103
     """
     import httpx
 
-    response = httpx.get(f"{GITHUB_REPOS}/{OWNER}/{REPO}/releases/latest", headers=HEADERS)
+    response = httpx.get(
+        f"{GITHUB_REPOS}/{OWNER}/{REPO}/releases/latest", headers=HEADERS
+    )
 
     if response.status_code == 200:
         return response.json()
@@ -91,7 +93,9 @@ def fetch_pull_requests_since_last_release() -> list[dict]:
     all_pulls = []
     while not is_exhausted:
         pulls = fetch_merged_pull_requests(page=page)
-        all_pulls.extend([p for p in pulls if parser.parse(p["merged_at"]) > published_at])
+        all_pulls.extend(
+            [p for p in pulls if parser.parse(p["merged_at"]) > published_at]
+        )
         is_exhausted = any(parser.parse(p["updated_at"]) < published_at for p in pulls)
         page += 1
     return all_pulls
@@ -101,7 +105,9 @@ def github_compare_tags(tag_left: str, tag_right: str = "HEAD"):
     """Compare commit between two tags."""
     import httpx
 
-    response = httpx.get(f"{GITHUB_REPOS}/{OWNER}/{REPO}/compare/{tag_left}...{tag_right}")
+    response = httpx.get(
+        f"{GITHUB_REPOS}/{OWNER}/{REPO}/compare/{tag_left}...{tag_right}"
+    )
     if response.status_code == 200:
         return response.json()
     else:
@@ -135,7 +141,9 @@ def assign_prs(prs, categs: list[dict[str, list[str]]]):
     #             if any(l.startswith("module") for l in pr_labels):
     #                 print(i, pr_labels)
 
-    assigned["Other"] = list(set(range(len(prs))) - {i for _, j in assigned.items() for i in j})
+    assigned["Other"] = list(
+        set(range(len(prs))) - {i for _, j in assigned.items() for i in j}
+    )
 
     return assigned
 

docs/source/conf.py

@@ -145,7 +145,9 @@ def setup(app: Sphinx):
     "navbar_end": ["navbar-icon-links.html", "search-field.html"],
     "show_nav_level": 2,
     "header_links_before_dropdown": 10,
-    "external_links": [{"name": "GitHub", "url": "https://github.com/sktime/pytorch-forecasting"}],
+    "external_links": [
+        {"name": "GitHub", "url": "https://github.com/sktime/pytorch-forecasting"}
+    ],
 }
 
 html_sidebars = {

examples/ar.py

@@ -51,14 +51,20 @@
     stop_randomization=True,
 )
 batch_size = 64
-train_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=0)
-val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=0)
+train_dataloader = training.to_dataloader(
+    train=True, batch_size=batch_size, num_workers=0
+)
+val_dataloader = validation.to_dataloader(
+    train=False, batch_size=batch_size, num_workers=0
+)
 
 # save datasets
 training.save("training.pkl")
 validation.save("validation.pkl")
 
-early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=5, verbose=False, mode="min")
+early_stop_callback = EarlyStopping(
+    monitor="val_loss", min_delta=1e-4, patience=5, verbose=False, mode="min"
+)
 lr_logger = LearningRateMonitor()
 
 trainer = pl.Trainer(

examples/nbeats.py

@@ -42,13 +42,21 @@
     add_target_scales=False,
 )
 
-validation = TimeSeriesDataSet.from_dataset(training, data, min_prediction_idx=training_cutoff)
+validation = TimeSeriesDataSet.from_dataset(
+    training, data, min_prediction_idx=training_cutoff
+)
 batch_size = 128
-train_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=2)
-val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=2)
+train_dataloader = training.to_dataloader(
+    train=True, batch_size=batch_size, num_workers=2
+)
+val_dataloader = validation.to_dataloader(
+    train=False, batch_size=batch_size, num_workers=2
+)
 
 
-early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=10, verbose=False, mode="min")
+early_stop_callback = EarlyStopping(
+    monitor="val_loss", min_delta=1e-4, patience=10, verbose=False, mode="min"
+)
 trainer = pl.Trainer(
     max_epochs=100,
     accelerator="auto",
@@ -63,7 +71,12 @@
 
 
 net = NBeats.from_dataset(
-    training, learning_rate=3e-2, log_interval=10, log_val_interval=1, log_gradient_flow=False, weight_decay=1e-2
+    training,
+    learning_rate=3e-2,
+    log_interval=10,
+    log_val_interval=1,
+    log_gradient_flow=False,
+    weight_decay=1e-2,
 )
 print(f"Number of parameters in network: {net.size() / 1e3:.1f}k")
 

examples/stallion.py

@@ -7,10 +7,16 @@
 import numpy as np
 from pandas.core.common import SettingWithCopyWarning
 
-from pytorch_forecasting import GroupNormalizer, TemporalFusionTransformer, TimeSeriesDataSet
+from pytorch_forecasting import (
+    GroupNormalizer,
+    TemporalFusionTransformer,
+    TimeSeriesDataSet,
+)
 from pytorch_forecasting.data.examples import get_stallion_data
 from pytorch_forecasting.metrics import QuantileLoss
-from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters
+from pytorch_forecasting.models.temporal_fusion_transformer.tuning import (
+    optimize_hyperparameters,
+)
 
 warnings.simplefilter("error", category=SettingWithCopyWarning)
 
@@ -22,8 +28,12 @@
 
 data["time_idx"] = data["date"].dt.year * 12 + data["date"].dt.month
 data["time_idx"] -= data["time_idx"].min()
-data["avg_volume_by_sku"] = data.groupby(["time_idx", "sku"], observed=True).volume.transform("mean")
-data["avg_volume_by_agency"] = data.groupby(["time_idx", "agency"], observed=True).volume.transform("mean")
+data["avg_volume_by_sku"] = data.groupby(
+    ["time_idx", "sku"], observed=True
+).volume.transform("mean")
+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"])]
 special_days = [
     "easter_day",
@@ -39,7 +49,9 @@
     "beer_capital",
     "music_fest",
 ]
-data[special_days] = data[special_days].apply(lambda x: x.map({0: "", 1: x.name})).astype("category")
+data[special_days] = (
+    data[special_days].apply(lambda x: x.map({0: "", 1: x.name})).astype("category")
+)
 
 training_cutoff = data["time_idx"].max() - 6
 max_encoder_length = 36
@@ -50,14 +62,17 @@
     time_idx="time_idx",
     target="volume",
     group_ids=["agency", "sku"],
-    min_encoder_length=max_encoder_length // 2,  # allow encoder lengths from 0 to max_prediction_length
+    min_encoder_length=max_encoder_length
+    // 2,  # allow encoder lengths from 0 to max_prediction_length
     max_encoder_length=max_encoder_length,
     min_prediction_length=1,
     max_prediction_length=max_prediction_length,
     static_categoricals=["agency", "sku"],
     static_reals=["avg_population_2017", "avg_yearly_household_income_2017"],
     time_varying_known_categoricals=["special_days", "month"],
-    variable_groups={"special_days": special_days},  # group of categorical variables can be treated as one variable
+    variable_groups={
+        "special_days": special_days
+    },  # group of categorical variables can be treated as one variable
     time_varying_known_reals=["time_idx", "price_regular", "discount_in_percent"],
     time_varying_unknown_categoricals=[],
     time_varying_unknown_reals=[
@@ -78,17 +93,25 @@
 )
 
 
-validation = TimeSeriesDataSet.from_dataset(training, data, predict=True, stop_randomization=True)
+validation = TimeSeriesDataSet.from_dataset(
+    training, data, predict=True, stop_randomization=True
+)
 batch_size = 64
-train_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=0)
-val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=0)
+train_dataloader = training.to_dataloader(
+    train=True, batch_size=batch_size, num_workers=0
+)
+val_dataloader = validation.to_dataloader(
+    train=False, batch_size=batch_size, num_workers=0
+)
 
 
 # save datasets
 training.save("t raining.pkl")
 validation.save("validation.pkl")
 
-early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=10, verbose=False, mode="min")
+early_stop_callback = EarlyStopping(
+    monitor="val_loss", min_delta=1e-4, patience=10, verbose=False, mode="min"
+)
 lr_logger = LearningRateMonitor()
 logger = TensorBoardLogger(log_graph=True)