Backports v0.13.1 (#2886)

* Speedup is_uniform for PandasDataset. (#2878)

* Fix docstrings in torch lightning modules (#2880)

* Fix default scale in torch DeepAR (#2885)

---------

Co-authored-by: Jasper <[email protected]>

src/gluonts/dataset/pandas.py

@@ -15,7 +15,7 @@
 
 import logging
 from dataclasses import dataclass, field, InitVar
-from typing import Any, Iterable, Optional, Type, Union
+from typing import Any, Iterable, Optional, Type, Union, cast
 
 import numpy as np
 import pandas as pd
@@ -350,5 +350,5 @@ def is_uniform(index: pd.PeriodIndex) -> bool:
         >>> is_uniform(pd.DatetimeIndex(ts).to_period("2H"))
         False
     """
-    other = pd.period_range(index[0], periods=len(index), freq=index.freq)
-    return (other == index).all()
+
+    return cast(bool, np.all(np.diff(index.asi8) == index.freq.n))

src/gluonts/torch/model/d_linear/lightning_module.py

@@ -30,15 +30,14 @@ class DLinearLightningModule(pl.LightningModule):
 
     Parameters
     ----------
-    model
-        ``DLinearModel`` to be trained.
+    model_kwargs
+        Keyword arguments to construct the ``DLinearModel`` to be trained.
     loss
-        Loss function to be used for training,
-        default: ``NegativeLogLikelihood()``.
+        Loss function to be used for training.
     lr
-        Learning rate, default: ``1e-3``.
+        Learning rate.
     weight_decay
-        Weight decay regularization parameter, default: ``1e-8``.
+        Weight decay regularization parameter.
     """
 
     @validated()

src/gluonts/torch/model/deepar/estimator.py

@@ -166,7 +166,7 @@ def __init__(
         distr_output: DistributionOutput = StudentTOutput(),
         loss: DistributionLoss = NegativeLogLikelihood(),
         scaling: bool = True,
-        default_scale: float = 0.0,
+        default_scale: Optional[float] = None,
         lags_seq: Optional[List[int]] = None,
         time_features: Optional[List[TimeFeature]] = None,
         num_parallel_samples: int = 100,

src/gluonts/torch/model/deepar/lightning_module.py

@@ -33,17 +33,16 @@ class DeepARLightningModule(pl.LightningModule):
 
     Parameters
     ----------
-    model
-        ``DeepARModel`` to be trained.
+    model_kwargs
+        Keyword arguments to construct the ``DeepARModel`` to be trained.
     loss
-        Loss function to be used for training,
-        default: ``NegativeLogLikelihood()``.
+        Loss function to be used for training.
     lr
-        Learning rate, default: ``1e-3``.
+        Learning rate.
     weight_decay
-        Weight decay regularization parameter, default: ``1e-8``.
+        Weight decay regularization parameter.
     patience
-        Patience parameter for learning rate scheduler, default: ``10``.
+        Patience parameter for learning rate scheduler.
     """
 
     @validated()

src/gluonts/torch/model/deepar/module.py

@@ -105,7 +105,7 @@ def __init__(
         distr_output: DistributionOutput = StudentTOutput(),
         lags_seq: Optional[List[int]] = None,
         scaling: bool = True,
-        default_scale: float = 0.0,
+        default_scale: Optional[float] = None,
         num_parallel_samples: int = 100,
     ) -> None:
         super().__init__()

src/gluonts/torch/model/lag_tst/lightning_module.py

@@ -30,15 +30,14 @@ class LagTSTLightningModule(pl.LightningModule):
 
     Parameters
     ----------
-    model
-        ``LagTSTModel`` to be trained.
+    model_kwargs
+        Keyword arguments to construct the ``LagTSTModel`` to be trained.
     loss
-        Loss function to be used for training,
-        default: ``NegativeLogLikelihood()``.
+        Loss function to be used for training.
     lr
-        Learning rate, default: ``1e-3``.
+        Learning rate.
     weight_decay
-        Weight decay regularization parameter, default: ``1e-8``.
+        Weight decay regularization parameter.
     """
 
     @validated()

src/gluonts/torch/model/mqf2/lightning_module.py

@@ -35,14 +35,14 @@ class MQF2MultiHorizonLightningModule(pl.LightningModule):
 
     Parameters
     ----------
-    model
-        An MQF2MultiHorizonModel instance
+    model_kwargs
+        Keyword arguments to construct the ``MQF2MultiHorizonModel`` to be trained.
     loss
-        Distribution loss
+        Distribution loss.
     lr
-        Learning rate
+        Learning rate.
     weight_decay
-        Weight decay during training
+        Weight decay during training.
     patience
         Patience parameter for learning rate scheduler, default: ``10``.
     """

src/gluonts/torch/model/patch_tst/lightning_module.py

@@ -30,15 +30,14 @@ class PatchTSTLightningModule(pl.LightningModule):
 
     Parameters
     ----------
-    model
-        ``PatchTSTModel`` to be trained.
+    model_kwargs
+        Keyword arguments to construct the ``PatchTSTModel`` to be trained.
     loss
-        Loss function to be used for training,
-        default: ``NegativeLogLikelihood()``.
+        Loss function to be used for training.
     lr
-        Learning rate, default: ``1e-3``.
+        Learning rate.
     weight_decay
-        Weight decay regularization parameter, default: ``1e-8``.
+        Weight decay regularization parameter.
     """
 
     @validated()

src/gluonts/torch/model/simple_feedforward/lightning_module.py

@@ -30,15 +30,14 @@ class SimpleFeedForwardLightningModule(pl.LightningModule):
 
     Parameters
     ----------
-    model
-        ``SimpleFeedForwardModel`` to be trained.
+    model_kwargs
+        Keyword arguments to construct the ``SimpleFeedForwardModel`` to be trained.
     loss
-        Loss function to be used for training,
-        default: ``NegativeLogLikelihood()``.
+        Loss function to be used for training.
     lr
-        Learning rate, default: ``1e-3``.
+        Learning rate.
     weight_decay
-        Weight decay regularization parameter, default: ``1e-8``.
+        Weight decay regularization parameter.
     """
 
     @validated()

src/gluonts/torch/model/tft/lightning_module.py

@@ -31,14 +31,14 @@ class TemporalFusionTransformerLightningModule(pl.LightningModule):
 
     Parameters
     ----------
-    model
-        ``TemporalFusionTransformerModel`` to be trained.
+    model_kwargs
+        Keyword arguments to construct the ``TemporalFusionTransformerModel`` to be trained.
     lr
-        Learning rate, default: ``1e-3``.
+        Learning rate.
     weight_decay
-        Weight decay regularization parameter, default: ``1e-8``.
+        Weight decay regularization parameter.
     patience
-        Patience parameter for learning rate scheduler, default: ``10``.
+        Patience parameter for learning rate scheduler.
     """
 
     @validated()