Merge remote-tracking branch 'origin/precommit' into precommit

docs/getting_started/models.md

@@ -2,6 +2,9 @@
 
 Model + Paper                                                | Local/global | Data layout              | Architecture/method | Implementation
 -------------------------------------------------------------|--------------|--------------------------|---------------------|----------------
+PatchTST<br>[Nie et al., 2023][Nie2023]                      | Global       | Univariate               | MLP, multi-head attention | [Pytorch][PatchTST_torch]
+LagTST<br>                                                   | Global       | Univariate               | MLP, multi-head attention | [Pytorch][LagTST_torch]
+DLinear<br>[Zeng et al., 2023][Zeng2023]                     | Global       | Univariate               | MLP | [Pytorch][DLinear_torch]
 DeepAR<br>[Salinas et al. 2020][Salinas2020]                 | Global       | Univariate               | RNN | [MXNet][DeepAR_mx], [PyTorch][DeepAR_torch]
 DeepState<br>[Rangapuram et al. 2018][Rangapuram2018]        | Global       | Univariate               | RNN, state-space model | [MXNet][DeepState]
 DeepFactor<br>[Wang et al. 2019][Wang2019]                   | Global       | Univariate               | RNN, state-space model, Gaussian process | [MXNet][DeepFactor]
@@ -30,6 +33,8 @@ NPTS                                                         | Local        | Un
 
 <!-- Links to bibliography -->
 
+[Nie2023]: https://arxiv.org/abs/2211.14730
+[Zeng2023]: https://arxiv.org/abs/2205.13504
 [Rangapuram2021]: https://proceedings.mlr.press/v139/rangapuram21a.html
 [Salinas2020]: https://doi.org/10.1016/j.ijforecast.2019.07.001
 [Rangapuram2018]: https://papers.nips.cc/paper/2018/hash/5cf68969fb67aa6082363a6d4e6468e2-Abstract.html
@@ -52,6 +57,9 @@ NPTS                                                         | Local        | Un
 
 <!-- Links to code -->
 
+[PatchTST_torch]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/torch/model/patch_tst/estimator.py
+[LagTST_torch]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/torch/model/lag_tst/estimator.py
+[DLinear_torch]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/torch/model/d_linear/estimator.py
 [DeepAR_mx]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/deepar/_estimator.py
 [DeepAR_torch]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/torch/model/deepar/estimator.py
 [DeepState]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/deepstate/_estimator.py

docs/tutorials/advanced_topics/howto_pytorch_lightning.md.template

@@ -110,10 +110,10 @@ class FeedForwardNetwork(nn.Module):
         torch.nn.init.zeros_(lin.bias)
         return lin
 
-    def forward(self, context):
-        scale = self.scaling(context)
-        scaled_context = context / scale
-        nn_out = self.nn(scaled_context)
+    def forward(self, past_target):
+        scale = self.scaling(past_target)
+        scaled_past_target = past_target / scale
+        nn_out = self.nn(scaled_past_target)
         nn_out_reshaped = nn_out.reshape(-1, self.prediction_length, self.hidden_dimensions[-1])
         distr_args = self.args_proj(nn_out_reshaped)
         return distr_args, torch.zeros_like(scale), scale
@@ -143,15 +143,15 @@ class LightningFeedForwardNetwork(FeedForwardNetwork, pl.LightningModule):
         super().__init__(*args, **kwargs)
 
     def training_step(self, batch, batch_idx):
-        context = batch["past_target"]
-        target = batch["future_target"]
+        past_target = batch["past_target"]
+        future_target = batch["future_target"]
 
-        assert context.shape[-1] == self.context_length
-        assert target.shape[-1] == self.prediction_length
+        assert past_target.shape[-1] == self.context_length
+        assert future_target.shape[-1] == self.prediction_length
 
-        distr_args, loc, scale = self(context)
+        distr_args, loc, scale = self(past_target)
         distr = self.distr_output.distribution(distr_args, loc, scale)
-        loss = -distr.log_prob(target)
+        loss = -distr.log_prob(future_target)
 
         return loss.mean()
 

requirements/requirements-extras-anomaly-evaluation.txt

@@ -1,2 +1,2 @@
 numba~=0.51,<0.54
-scikit-learn~=0.22
+scikit-learn~=1.0

requirements/requirements-extras-sagemaker-sdk.txt

@@ -1,4 +1,4 @@
-sagemaker~=2.0
+sagemaker~=2.0,>2.214.3
 s3fs~=0.6; python_version >= "3.7.0"
 s3fs~=0.5; python_version < "3.7.0"
 fsspec~=0.8,<0.9; python_version < "3.7.0"

requirements/requirements-pytorch.txt

@@ -1,6 +1,6 @@
 torch>=1.9,<3
-lightning>=2.0,<2.2
+lightning>=2.2.2,<2.4
 # Capping `lightning` does not cap `pytorch_lightning`, so we cap manually
-pytorch_lightning>=2.0,<2.2
+pytorch_lightning>=2.2.2,<2.4
 scipy~=1.10; python_version > "3.7.0"
 scipy~=1.7.3; python_version <= "3.7.0"

requirements/requirements-rotbaum.txt

@@ -1,2 +1,2 @@
 xgboost>=0.90,<2
-scikit-learn>=0.22,<2
+scikit-learn~=1.0

src/gluonts/core/serde/_base.py

@@ -287,6 +287,15 @@ def encode_partial(v: partial) -> Any:
     }
 
 
+decode_disallow = [
+    eval,
+    exec,
+    compile,
+    open,
+    input,
+]
+
+
 def decode(r: Any) -> Any:
     """
     Decodes a value from an intermediate representation `r`.
@@ -312,7 +321,10 @@ def decode(r: Any) -> Any:
         kind = r["__kind__"]
         cls = cast(Any, locate(r["class"]))
 
-        assert cls is not None, f"Can not locate {r['class']}."
+        if cls is None:
+            raise ValueError(f"Cannot locate {r['class']}.")
+        if cls in decode_disallow:
+            raise ValueError(f"{r['class']} cannot be run.")
 
         if kind == Kind.Type:
             return cls

src/gluonts/ext/rotbaum/_predictor.py

@@ -13,7 +13,6 @@
 
 import concurrent.futures
 import logging
-import pickle
 from itertools import chain
 from typing import Iterator, List, Optional, Any, Dict
 from toolz import first
@@ -24,6 +23,7 @@
 from itertools import compress
 
 from gluonts.core.component import validated
+from gluonts.core.serde import dump_json, load_json
 from gluonts.dataset.common import Dataset
 from gluonts.dataset.util import forecast_start
 from gluonts.model.forecast import Forecast
@@ -355,8 +355,8 @@ class name, version information and constructor arguments.
         generated when pickling the TreePredictor.
         """
         super().serialize(path)
-        with (path / "predictor.pkl").open("wb") as f:
-            pickle.dump(self.model_list, f)
+        with (path / "model_list.json").open("w") as fp:
+            print(dump_json(self.model_list), file=fp)
 
     @classmethod
     def deserialize(cls, path: Path, **kwargs: Any) -> "TreePredictor":
@@ -369,8 +369,8 @@ def deserialize(cls, path: Path, **kwargs: Any) -> "TreePredictor":
 
         predictor = super().deserialize(path)
         assert isinstance(predictor, cls)
-        with (path / "predictor.pkl").open("rb") as f:
-            predictor.model_list = pickle.load(f)
+        with (path / "model_list.json").open("r") as fp:
+            predictor.model_list = load_json(fp.read())
         return predictor
 
     def explain(

src/gluonts/model/forecast_generator.py

@@ -82,6 +82,18 @@ def make_distribution_forecast(distr, *args, **kwargs) -> Forecast:
     raise NotImplementedError
 
 
+def make_predictions(prediction_net, inputs: dict):
+    try:
+        # Feed inputs as positional arguments for MXNet block predictors
+        import mxnet as mx
+
+        if isinstance(prediction_net, mx.gluon.Block):
+            return prediction_net(*inputs.values())
+    except ImportError:
+        pass
+    return prediction_net(**inputs)
+
+
 class ForecastGenerator:
     """
     Classes used to bring the output of a network into a class.
@@ -115,7 +127,7 @@ def __call__(
     ) -> Iterator[Forecast]:
         for batch in inference_data_loader:
             inputs = select(input_names, batch, ignore_missing=True)
-            (outputs,), loc, scale = prediction_net(*inputs.values())
+            (outputs,), loc, scale = make_predictions(prediction_net, inputs)
             outputs = to_numpy(outputs)
             if scale is not None:
                 outputs = outputs * to_numpy(scale[..., None])
@@ -159,14 +171,16 @@ def __call__(
     ) -> Iterator[Forecast]:
         for batch in inference_data_loader:
             inputs = select(input_names, batch, ignore_missing=True)
-            outputs = to_numpy(prediction_net(*inputs.values()))
+            outputs = to_numpy(make_predictions(prediction_net, inputs))
             if output_transform is not None:
                 outputs = output_transform(batch, outputs)
             if num_samples:
                 num_collected_samples = outputs[0].shape[0]
                 collected_samples = [outputs]
                 while num_collected_samples < num_samples:
-                    outputs = to_numpy(prediction_net(*inputs.values()))
+                    outputs = to_numpy(
+                        make_predictions(prediction_net, inputs)
+                    )
                     if output_transform is not None:
                         outputs = output_transform(batch, outputs)
                     collected_samples.append(outputs)
@@ -209,7 +223,7 @@ def __call__(
     ) -> Iterator[Forecast]:
         for batch in inference_data_loader:
             inputs = select(input_names, batch, ignore_missing=True)
-            outputs = prediction_net(*inputs.values())
+            outputs = make_predictions(prediction_net, inputs)
 
             if output_transform:
                 log_once(OUTPUT_TRANSFORM_NOT_SUPPORTED_MSG)

src/gluonts/nursery/few_shot_prediction/pyproject.toml

@@ -16,7 +16,7 @@ gluonts = {git = "https://github.com/awslabs/gluon-ts.git"}
 pandas = "^1.3.1"
 python = "^3.8,<3.10"
 pytorch-lightning = "^1.4.4"
-sagemaker = "^2.40.0,<2.41.0"
+sagemaker = "^2.218.0"
 scikit-learn = "^1.4.0"
 torch = "^1.9.0"
 sagemaker-training = "^3.9.2"
@@ -27,7 +27,7 @@ catch22 = "^0.2.0"
 seaborn = "^0.11.2"
 
 [tool.poetry.dev-dependencies]
-black = "^21.7b0"
+black = "^24.3.0"
 isort = "^5.9.3"
 jupyter = "^1.0.0"
 pylint = "^2.10.2"

src/gluonts/nursery/robust-mts-attack/pts/modules/distribution_output.py

@@ -44,11 +44,10 @@
     TransformedImplicitQuantile,
 )
 from gluonts.core.component import validated
-from gluonts.torch.modules.distribution_output import (
-    DistributionOutput,
-    LambdaLayer,
-    PtArgProj,
-)
+from gluonts.torch.distributions.distribution_output import DistributionOutput
+from gluonts.torch.modules.lambda_layer import LambdaLayer
+from gluonts.torch.distributions.output import PtArgProj
+
 from pts.modules.iqn_modules import ImplicitQuantileModule
 
 

src/gluonts/nursery/tsbench/pyproject.toml

@@ -25,7 +25,7 @@ plotly = "^5.3.1"
 pyarrow = "^14.0.1"
 pydantic = "^1.8.2"
 pygmo = "^2.16.1"
-pymongo = "^3.12.0"
+pymongo = "^4.6.3"
 pystan = "^2.0.0"
 python = ">=3.8,<3.9"
 pytorch-lightning = "^1.5.0"
@@ -43,7 +43,7 @@ ujson = "^5.1.0"
 xgboost = "^1.4.1"
 
 [tool.poetry.dev-dependencies]
-black = "^21.5b1"
+black = "^24.3.0"
 isort = "^5.8.0"
 jupyter = "^1.0.0"
 mypy = "^0.812"

src/gluonts/time_feature/_base.py

@@ -11,6 +11,7 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
+from packaging.version import Version
 from typing import Any, Callable, Dict, List
 
 import numpy as np
@@ -196,7 +197,10 @@ def norm_freq_str(freq_str: str) -> str:
     # Note: Secondly ("S") frequency exists, where we don't want to remove the
     # "S"!
     if len(base_freq) >= 2 and base_freq.endswith("S"):
-        return base_freq[:-1]
+        base_freq = base_freq[:-1]
+        # In pandas >= 2.2, period end frequencies have been renamed, e.g. "M" -> "ME"
+        if Version(pd.__version__) >= Version("2.2.0"):
+            base_freq += "E"
 
     return base_freq
 
@@ -252,17 +256,13 @@ def time_features_from_frequency_str(freq_str: str) -> List[TimeFeature]:
     Unsupported frequency {freq_str}
 
     The following frequencies are supported:
-
-        Y   - yearly
-            alias: A
-        Q   - quarterly
-        M   - monthly
-        W   - weekly
-        D   - daily
-        B   - business days
-        H   - hourly
-        T   - minutely
-            alias: min
-        S   - secondly
+    
     """
+
+    for offset_cls in features_by_offsets:
+        offset = offset_cls()
+        supported_freq_msg += (
+            f"\t{offset.freqstr.split('-')[0]} - {offset_cls.__name__}"
+        )
+
     raise RuntimeError(supported_freq_msg)

src/gluonts/time_feature/seasonality.py

@@ -33,6 +33,7 @@
     "ME": 12,
     "B": 5,
     "Q": 4,
+    "QE": 4,
 }
 
 

src/gluonts/torch/distributions/distribution_output.py

@@ -90,14 +90,6 @@ def loss(
             nll = nll * (variance.detach() ** self.beta)
         return nll
 
-    @property
-    def event_shape(self) -> Tuple:
-        r"""
-        Shape of each individual event contemplated by the distributions that
-        this object constructs.
-        """
-        raise NotImplementedError()
-
     @property
     def event_dim(self) -> int:
         r"""

src/gluonts/torch/distributions/output.py

@@ -105,6 +105,13 @@ def loss(
         """
         raise NotImplementedError()
 
+    @property
+    def event_shape(self) -> Tuple:
+        r"""
+        Shape of each individual event compatible with the output object.
+        """
+        raise NotImplementedError()
+
     @property
     def forecast_generator(self) -> ForecastGenerator:
         raise NotImplementedError()

src/gluonts/torch/distributions/quantile_output.py

@@ -37,6 +37,10 @@ def __init__(self, quantiles: List[float]) -> None:
     def forecast_generator(self) -> ForecastGenerator:
         return QuantileForecastGenerator(quantiles=self.quantiles)
 
+    @property
+    def event_shape(self) -> Tuple:
+        return ()
+
     def domain_map(self, *args: torch.Tensor) -> Tuple[torch.Tensor, ...]:
         return args