Bump minimum PyMC dependency

.github/workflows/pypi.yml

@@ -17,7 +17,7 @@ jobs:
           fetch-depth: 0
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: "3.10"
       - name: Build the sdist and the wheel
         run: |
           pip install build
@@ -63,7 +63,7 @@ jobs:
           repository_url: https://test.pypi.org/legacy/
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: "3.10"
       - name: Test pip install from test.pypi
         run: |
           python -m venv venv-test-pypi

.github/workflows/test.yml

@@ -19,7 +19,7 @@ jobs:
       matrix:
         os: [ubuntu-latest]
         floatx: [float64]
-        python-version: ["3.8"]
+        python-version: ["3.9"]
         test-subset:
           - pymc_experimental/tests
       fail-fast: false
@@ -84,7 +84,7 @@ jobs:
       matrix:
         os: [windows-latest]
         floatx: [float32]
-        python-version: ["3.10"]
+        python-version: ["3.11"]
         test-subset:
           - pymc_experimental/tests
       fail-fast: false

conda-envs/environment-test.yml

@@ -11,6 +11,6 @@ dependencies:
 - xhistogram
 - statsmodels
 - pip:
-  - pymc>=5.6.0  # CI was failing to resolve
+  - pymc>=5.8.1  # CI was failing to resolve
   - blackjax
   - scikit-learn

conda-envs/windows-environment-test.yml

@@ -10,5 +10,5 @@ dependencies:
 - xhistogram
 - statsmodels
 - pip:
-  - pymc>=5.6.0  # CI was failing to resolve
+  - pymc>=5.8.1  # CI was failing to resolve
   - scikit-learn

docs/api_reference.rst

@@ -35,31 +35,16 @@ Distributions
    histogram_approximation
 
 
-Model Transformations
-=====================
-
-.. currentmodule:: pymc_experimental.model_transform
-.. autosummary::
-   :toctree: generated/
-
-   conditioning.do
-   conditioning.observe
-   conditioning.change_value_transforms
-   conditioning.remove_value_transforms
-
-
 Utils
 =====
 
 .. currentmodule:: pymc_experimental.utils
 .. autosummary::
    :toctree: generated/
 
-   clone_model
    spline.bspline_interpolation
    prior.prior_from_idata
-   model_fgraph.fgraph_from_model
-   model_fgraph.model_from_fgraph
+
 
 Statespace Models
 =================

pymc_experimental/distributions/continuous.py

@@ -28,7 +28,7 @@
 from pymc.distributions.shape_utils import rv_size_is_none
 from pymc.pytensorf import floatX
 from pytensor.tensor.random.op import RandomVariable
-from pytensor.tensor.var import TensorVariable
+from pytensor.tensor.variable import TensorVariable
 from scipy import stats
 
 

pymc_experimental/distributions/histogram_utils.py

@@ -100,15 +100,15 @@ def histogram_approximation(name, dist, *, observed, **h_kwargs):
     ----------
     name : str
         Name for the Potential
-    dist : pytensor.tensor.var.TensorVariable
+    dist : TensorVariable
         The output of pm.Distribution.dist()
     observed : ArrayLike
         Observed value to construct a histogram. Histogram is computed over 0th axis.
         Dask is supported.
 
     Returns
     -------
-    pytensor.tensor.var.TensorVariable
+    TensorVariable
         Potential
 
     Examples

pymc_experimental/gp/latent_approx.py

@@ -11,12 +11,16 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
-
+from functools import partial
 
 import numpy as np
 import pymc as pm
 import pytensor.tensor as pt
-from pymc.gp.util import JITTER_DEFAULT, cholesky, solve_lower, solve_upper, stabilize
+from pymc.gp.util import JITTER_DEFAULT, stabilize
+from pytensor.tensor.linalg import cholesky, solve_triangular
+
+solve_lower = partial(solve_triangular, lower=True)
+solve_upper = partial(solve_triangular, lower=False)
 
 
 class LatentApprox(pm.gp.Latent):