Migrate to prior module from pymc-extras

.github/workflows/pypi.yml

@@ -17,7 +17,7 @@ jobs:
           fetch-depth: 0
       - uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.11"
       - name: Build the sdist and the wheel
         run: |
           pip install build
@@ -62,7 +62,7 @@ jobs:
           repository-url: https://test.pypi.org/legacy/
       - uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.11"
       - name: Test pip install from test.pypi
         run: |
           python -m venv venv-test-pypi

.github/workflows/test.yml

@@ -30,7 +30,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        config: [ {python-version: "3.10", oldest-pymc: false}, {python-version: "3.12", oldest-pymc: true}]
+        config: [ {python-version: "3.11", oldest-pymc: false}, {python-version: "3.13", oldest-pymc: true}]
         split:
           - "--ignore tests/mmm --ignore tests/clv"
           - "tests/mmm --ignore tests/mmm/test_tvp.py --ignore tests/mmm/test_budget_optimizer.py --ignore tests/mmm/test_hsgp.py --ignore tests/mmm/test_transformers.py"
@@ -82,7 +82,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.11"
           cache: "pip"
           cache-dependency-path: "pyproject.toml"
       - name: Run tests

.github/workflows/uml.yml

@@ -18,7 +18,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.13"
 
       - name: Configure Git Identity
         run: |

.readthedocs.yml

@@ -6,9 +6,9 @@ sphinx:
   configuration: docs/source/conf.py
 
 build:
-  os: ubuntu-22.04
+  os: ubuntu-24.04
   tools:
-    python: "3.10"
+    python: "3.11"
   jobs:
     pre_build:
       # Install core dependencies first

docs/source/guide/benefits/model_deployment.ipynb

@@ -46,9 +46,9 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "from pymc_extras.prior import Prior\n",
     "\n",
     "from pymc_marketing.mmm import MMM, GeometricAdstock, LogisticSaturation\n",
-    "from pymc_marketing.prior import Prior\n",
     "\n",
     "az.style.use(\"arviz-darkgrid\")\n",
     "plt.rcParams[\"figure.figsize\"] = [12, 7]\n",

docs/source/notebooks/bass/bass_example.ipynb

@@ -215,10 +215,10 @@
     "import pandas as pd\n",
     "import pymc as pm\n",
     "import xarray as xr\n",
+    "from pymc_extras.prior import Prior, Scaled\n",
     "\n",
     "from pymc_marketing.bass.model import create_bass_model\n",
     "from pymc_marketing.plot import plot_curve\n",
-    "from pymc_marketing.prior import Prior, Scaled\n",
     "\n",
     "az.style.use(\"arviz-darkgrid\")\n",
     "plt.rcParams[\"figure.figsize\"] = [12, 7]\n",

docs/source/notebooks/clv/clv_quickstart.ipynb

@@ -48,9 +48,9 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "from arviz.labels import MapLabeller\n",
+    "from pymc_extras.prior import Prior\n",
     "\n",
-    "from pymc_marketing import clv\n",
-    "from pymc_marketing.prior import Prior"
+    "from pymc_marketing import clv"
    ]
   },
   {

docs/source/notebooks/clv/dev/beta_geo_beta_binom.ipynb

@@ -14,7 +14,7 @@
     "\n",
     "import pymc as pm\n",
     "from pymc_marketing.clv import BetaGeoBetaBinomModel\n",
-    "from pymc_marketing.prior import Prior"
+    "from pymc_extras.prior import Prior"
    ]
   },
   {

docs/source/notebooks/clv/dev/bg_nbg_covariates_test_issues.ipynb

@@ -38,7 +38,7 @@
     "from fastprogress.fastprogress import progress_bar\n",
     "\n",
     "from pymc_marketing import clv\n",
-    "from pymc_marketing.prior import Prior\n",
+    "from pymc_extras.prior import Prior\n",
     "\n",
     "from tests.conftest import set_model_fit\n",
     "\n",

docs/source/notebooks/clv/dev/utilities_plotting.ipynb

@@ -15,7 +15,7 @@
     "\n",
     "from pymc_marketing.clv import utils\n",
     "from pymc_marketing.clv import ParetoNBDModel\n",
-    "from pymc_marketing.prior import Prior\n",
+    "from pymc_extras.prior import Prior\n",
     "\n",
     "from pymc_marketing.clv import utils, plotting"
    ]

docs/source/notebooks/clv/pareto_nbd.ipynb

@@ -36,9 +36,9 @@
     "import xarray as xr\n",
     "from fastprogress.fastprogress import progress_bar\n",
     "from lifetimes import ParetoNBDFitter\n",
+    "from pymc_extras.prior import Prior\n",
     "\n",
     "from pymc_marketing import clv\n",
-    "from pymc_marketing.prior import Prior\n",
     "\n",
     "# Plotting configuration\n",
     "az.style.use(\"arviz-darkgrid\")\n",

docs/source/notebooks/general/model_configuration.ipynb

@@ -31,9 +31,9 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import pymc as pm\n",
+    "from pymc_extras.prior import Prior\n",
     "\n",
     "from pymc_marketing.model_config import ModelConfigError, parse_model_config\n",
-    "from pymc_marketing.prior import Prior\n",
     "\n",
     "az.style.use(\"arviz-darkgrid\")\n",
     "plt.rcParams[\"figure.figsize\"] = [12, 7]\n",
@@ -62,7 +62,7 @@
    "source": [
     "## Prior Distributions\n",
     "\n",
-    "The {class}`~pymc_marketing.prior.Prior` class is our way to expression distributions and relationships between them.\n",
+    "The {class}`~pymc_extras.prior.Prior` class is our way to expression distributions and relationships between them.\n",
     "\n",
     "### Basic Usage\n",
     "\n",

docs/source/notebooks/general/prior_predictive.ipynb

@@ -7,7 +7,7 @@
     "(prior_predictive)=\n",
     "# Prior Predictive Modeling\n",
     "\n",
-    "This guide provides an introduction to prior predictive modeling using PyMC (and PyMC-Marketing) and the {class}`Prior <pymc_marketing.prior.Prior>` class from PyMC-Marketing.  We start by looking into a simpler example and then we will see how to apply to real case scenarios with marketing mix models in PyMC-Marketing.\n",
+    "This guide provides an introduction to prior predictive modeling using PyMC (and PyMC-Marketing) and the {class}`Prior <pymc_extras.prior.Prior>` class from PyMC-Marketing.  We start by looking into a simpler example and then we will see how to apply to real case scenarios with marketing mix models in PyMC-Marketing.\n",
     "\n",
     "\n",
     "Before diving into the technical details, let's understand why priors are crucial in Bayesian analysis and their practical importance in industry applications.\n",
@@ -135,7 +135,7 @@
     "3. **Model Sensitivity**: \n",
     "   The relationship between $\\theta^{(s)}$ and $y^{(s)}$ shows how parameters influence predictions\n",
     "\n",
-    "Let's explore these concepts through practical examples using the {class}`Prior <pymc_marketing.prior.Prior>` class from PyMC-Marketing.\n"
+    "Let's explore these concepts through practical examples using the {class}`Prior <pymc_extras.prior.Prior>` class from PyMC-Marketing.\n"
    ]
   },
   {
@@ -161,11 +161,11 @@
     "import pymc as pm\n",
     "import seaborn as sns\n",
     "from numpy.typing import NDArray\n",
+    "from pymc_extras.prior import Prior\n",
     "\n",
     "from pymc_marketing.hsgp_kwargs import HSGPKwargs\n",
     "from pymc_marketing.mmm import MMM, GeometricAdstock, LogisticSaturation\n",
     "from pymc_marketing.paths import data_dir\n",
-    "from pymc_marketing.prior import Prior\n",
     "\n",
     "seed: int = sum(map(ord, \"prior\"))\n",
     "rng: np.random.Generator = np.random.default_rng(seed=seed)\n",
@@ -1975,7 +1975,7 @@
     "## Key Takeaways\n",
     "\n",
     "1. Prior predictive modeling helps us validate our model assumptions before using real data\n",
-    "2. The {class}`Prior <pymc_marketing.prior.prior.Prior>` class provides a convenient interface for:\n",
+    "2. The {class}`Prior <pymc_extras.prior.prior.Prior>` class provides a convenient interface for:\n",
     "   - Creating and visualizing prior distributions\n",
     "   - Sampling from prior predictive distributions\n",
     "3. Always visualize your prior predictive distributions to ensure they align with your domain knowledge\n",