Merge branch 'dev' into sam

.github/workflows/tests-torch.yml

@@ -24,8 +24,8 @@ jobs:
  pip install ".[arrow]" \
  -r requirements/requirements-test.txt \
  -r requirements/requirements-extras-m-competitions.txt \
- -r requirements/requirements-pytorch.txt \
-   -r requirements/requirements-extras-cpflows.txt
+ -r requirements/requirements-pytorch.txt
+ pip install --no-deps -r requirements/requirements-extras-cpflows.txt
  - name: Test with pytest
  run: |
  pytest -n2 --doctest-modules --ignore test/nursery test

requirements/requirements-pytorch.txt

@@ -1,6 +1,6 @@
 torch>=1.9,<3
-lightning>=2.2.2,<2.4
+lightning>=2.2.2,<2.5
 # Capping `lightning` does not cap `pytorch_lightning`, so we cap manually
-pytorch_lightning>=2.2.2,<2.4
+pytorch_lightning>=2.2.2,<2.5
 scipy~=1.10; python_version > "3.7.0"
 scipy~=1.7.3; python_version <= "3.7.0"

requirements/requirements.txt

@@ -1,4 +1,4 @@
-numpy~=1.16
+numpy>=1.16,<2.2
 pandas>=1.0,<3
 pydantic>=1.7,<3
 tqdm~=4.23

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, cast
+from typing import Any, Iterable, Optional, Type, Union
 
 import numpy as np
 import pandas as pd
@@ -351,4 +351,4 @@ def is_uniform(index: pd.PeriodIndex) -> bool:
  False
  """
 
- return cast(bool, np.all(np.diff(index.asi8) == index.freq.n))
+ return bool(np.all(np.diff(index.asi8) == index.freq.n))

src/gluonts/itertools.py

@@ -240,7 +240,7 @@ def _location_for(self, idx, side="right") -> _SubIndex:
  else:
  local_idx = idx - self._offsets[part_no - 1]
 
- return _SubIndex(part_no, local_idx)
+ return _SubIndex(int(part_no), int(local_idx))
 
  def __getitem__(self, idx):
  if isinstance(idx, slice):

src/gluonts/model/forecast_generator.py

@@ -59,7 +59,7 @@ def _unpack(batched) -> Iterator:
  This assumes that arrays are wrapped in a nested structure of lists and
  tuples, and each array has the same shape::
 
- >>> a = np.arange(5)
+ >>> a = np.arange(5, dtype="O")
  >>> batched = [a, (a, [a, a, a])]
  >>> list(_unpack(batched))
  [[0, (0, [0, 0, 0])],

src/gluonts/mx/trainer/_base.py

@@ -227,7 +227,7 @@ def base_path() -> str:
  best_epoch_info = {
  "params_path": "{}-{}.params".format(base_path(), "init"),
  "epoch_no": -1,
- "score": np.Inf,
+ "score": float("inf"),
  }
 
  optimizer = mx.optimizer.Adam(

src/gluonts/mx/trainer/learning_rate_scheduler.py

@@ -50,15 +50,15 @@ def should_update(self, metric: float) -> bool:
 
 @dataclass
 class Min(Objective):
- best: float = np.Inf
+ best: float = float("inf")
 
  def should_update(self, metric: float) -> bool:
  return metric < self.best
 
 
 @dataclass
 class Max(Objective):
- best: float = -np.Inf
+ best: float = -float("inf")
 
  def should_update(self, metric: float) -> bool:
  return metric > self.best

test/torch/distribution/test_negative_binomial.py

@@ -12,6 +12,7 @@
 # permissions and limitations under the License.
 
 import pytest
+import sys
 
 import numpy as np
 import torch
@@ -49,11 +50,13 @@ def test_custom_neg_bin_cdf(total_count, probs, value):
 @pytest.mark.parametrize("probs", [0.1, 0.5, 0.8])
 @pytest.mark.parametrize("total_count", [3, 7, 100])
 @pytest.mark.parametrize("value", [0.1, 0.5, 0.9])
-def test_custom_studentt_icdf(total_count, probs, value):
+def test_custom_neg_bin_icdf(total_count, probs, value):
  torch_dist = NegativeBinomial(total_count=total_count, probs=probs)
  scipy_dist = torch_dist.scipy_nbinom
 
- torch_icdf = torch_dist.icdf(torch.as_tensor(value)).numpy()
+ torch_icdf = torch_dist.icdf(
+ torch.as_tensor(value, dtype=torch.float64)
+ ).numpy()
  scipy_icdf = scipy_dist.ppf(np.asarray(value))
 
  assert np.allclose(torch_icdf, scipy_icdf)

test/transform/test_transform.py

@@ -155,8 +155,8 @@ def test_AddTimeFeatures(start, target, is_train: bool):
  tmp_idx = pd.period_range(
  start=start, freq=start.freq, periods=expected_length
  )
- assert np.alltrue(mat[0] == time_feature.day_of_week(tmp_idx))
- assert np.alltrue(mat[1] == time_feature.day_of_month(tmp_idx))
+ assert np.all(mat[0] == time_feature.day_of_week(tmp_idx))
+ assert np.all(mat[1] == time_feature.day_of_month(tmp_idx))
 
 
 @pytest.mark.parametrize("is_train", TEST_VALUES["is_train"])
@@ -285,7 +285,7 @@ def test_InstanceSplitter(
 
  # expected_length = len(target) + (0 if is_train else pred_length)
  # assert len(out['age']) == expected_length
- # assert np.alltrue(out['age'] == np.log10(2.0 + np.arange(expected_length)))
+ # assert np.all(out['age'] == np.log10(2.0 + np.arange(expected_length)))
 
 
 @pytest.mark.parametrize("is_train", TEST_VALUES["is_train"])