Ignore cpflows dependencies in test workflows, fix numpy 2 incompatibilities.

.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

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"])