Contribution page

.github/workflows/test.yml

@@ -7,6 +7,7 @@ on:
     paths:
       - ".github/workflows/*"
       - "pymc_experimental/**"
+      - "tests/**"
       - "setup.py"
       - "pyproject.toml"
       - "buildosx"
@@ -20,7 +21,7 @@ jobs:
         os: [ubuntu-latest]
         python-version: ["3.10"]
         test-subset:
-          - pymc_experimental/tests
+          - tests
       fail-fast: false
     runs-on: ${{ matrix.os }}
     env:
@@ -58,7 +59,7 @@ jobs:
         os: [windows-latest]
         python-version: ["3.12"]
         test-subset:
-          - pymc_experimental/tests
+          - tests
       fail-fast: false
     runs-on: ${{ matrix.os }}
     env:

CONTRIBUTING.md

@@ -1,3 +1,113 @@
 # Contributing guide
 
-Page in construction, for now go to https://github.com/pymc-devs/pymc-experimental#questions.
+Thank you for your interest in contributing to PyMC and PyMC-experimental!
+
+This page outlines the steps to follow if you wish to contribute to the pymc-experimental repo and clone the repo locally.
+
+## Install locally
+**1**: Create a folder `pymc-devs` in your local machine and follow the steps on [cloning PyMC locally](https://www.pymc.io/projects/docs/en/latest/contributing/pr_tutorial.html).
+
+Since PyMC-experimental should integrate with the latest version of PyMC, it is recommended that any development work on PyMC-experimental must also work with the latest version of PyMC.
+
+You should now have a local copy of PyMC under `pymc-devs/pymc`.
+
+**2**: Fork the PyMC-experimental repo and clone it locally:
+
+```
+git clone [email protected]:<your GitHub handle>/pymc-experimental.git
+cd pymc-experimental
+git remote add upstream [email protected]:pymc-devs/pymc-experimental.git
+```
+
+You should now have a local copy of PyMC-experimental under `pymc-devs/pymc-experimental`.
+
+Create a new conda environment by first installing the dependencies in the main PyMC repo:
+```
+conda env create -n pymc-experimental -f /path/to/pymc-devs/pymc/conda-envs/environment-dev.yml
+conda activate pymc-experimental
+pip install -e /path/to/pymc-devs/pymc
+
+# ignores the specific pymc version to install pymc-experimental
+pip install -e /path/to/pymc-devs/pymc-experimental --ignore-installed pymc
+```
+
+**3** Check origin and upstream is correct.
+
+**PyMC**
+```
+cd /path/to/pymc-devs/pymc
+git remote -v
+```
+Output:
+```
+origin  [email protected]:<your GitHub handle>/pymc.git (fetch)
+origin  [email protected]:<your GitHub handle>/pymc.git (push)
+upstream        [email protected]:pymc-devs/pymc.git (fetch)
+upstream        [email protected]:pymc-devs/pymc.git (push)
+```
+
+**PyMC-experimental**
+```
+cd /path/to/pymc-devs/pymc-experimental
+git remote -v
+```
+Output:
+```
+origin  [email protected]:<your GitHub handle>/pymc-experimental.git (fetch)
+origin  [email protected]:<your GitHub handle>/pymc-experimental.git (push)
+upstream        [email protected]:pymc-devs/pymc-experimental.git (fetch)
+upstream        [email protected]:pymc-devs/pymc-experimental.git (push)
+```
+
+
+
+## Git integration [(from PyMC's main page)](https://www.pymc.io/projects/docs/en/latest/contributing/pr_tutorial.html)
+
+**1** Develop the feature on your feature branch:
+```
+git checkout -b my-exp-feature
+```
+
+**2** Before committing, run pre-commit checks:
+```
+pip install pre-commit
+pre-commit run --all      # 👈 to run it manually
+pre-commit install        # 👈 to run it automatically before each commit
+```
+
+**3** Add changed files using git add and then git commit files:
+```
+git add modified_files
+git commit
+```
+to record your changes locally.
+
+**4** After committing, it is a good idea to sync with the base repository in case there have been any changes:
+```
+# pymc
+cd /path/to/pymc-devs/pymc
+git fetch upstream
+git rebase upstream/main
+
+# (pymc-dev team) Please double check this
+pip install -e /path/to/pymc-devs/pymc
+
+# pymc-exp
+cd /path/to/pymc-devs/pymc-experimental
+git fetch upstream
+git rebase upstream/main
+```
+Then push the changes to the fork in your GitHub account with:
+```
+git push -u origin my-exp-feature
+```
+
+**5** Go to the GitHub web page of your fork of the PyMC repo. Click the ‘Pull request’ button to send your changes to the project’s maintainers for review. This will send a notification to the committers.
+
+## Final steps
+
+Review contributing guide in [PyMC's main page](https://www.pymc.io/projects/docs/en/latest/contributing/index.html).
+
+FAQ [page](https://github.com/pymc-devs/pymc-experimental#questions).
+
+Discussions [page](https://github.com/pymc-devs/pymc-experimental/discussions/5).

codecov.yml

@@ -22,7 +22,7 @@ coverage:
         base: auto
 
 ignore:
-  - "pymc_experimental/tests/*"
+  - "tests/*"
 
 comment:
   layout: "reach, diff, flags, files"

pymc_experimental/inference/pathfinder.py

@@ -95,15 +95,18 @@ def fit_pathfinder(
     """
     # Temporarily helper
     if version.parse(blackjax.__version__).major < 1:
+        # test
         raise ImportError("fit_pathfinder requires blackjax 1.0 or above")
-
     model = modelcontext(model)
 
     ip = model.initial_point()
     ip_map = DictToArrayBijection.map(ip)
 
     new_logprob, new_input = pm.pytensorf.join_nonshared_inputs(
-        ip, (model.logp(),), model.value_vars, ()
+        ip,
+        (model.logp(),),
+        model.value_vars,
+        (),
     )
 
     logprob_fn_list = get_jaxified_graph([new_input], new_logprob)

pymc_experimental/model/transforms/autoreparam.py

@@ -246,6 +246,44 @@ def _(
     return vip_rep
 
 
+@_vip_reparam_node.register
+def _(
+    op: pm.Exponential,
+    node: Apply,
+    name: str,
+    dims: List[Variable],
+    transform: Optional[Transform],
+    lam: pt.TensorVariable,
+) -> ModelDeterministic:
+    rng, size, scale = node.inputs
+    scale_centered = scale**lam
+    scale_noncentered = scale ** (1 - lam)
+    vip_rv_ = pm.Exponential.dist(
+        scale=scale_centered,
+        size=size,
+        rng=rng,
+    )
+    vip_rv_value_ = vip_rv_.clone()
+    vip_rv_.name = f"{name}::tau_"
+    if transform is not None:
+        vip_rv_value_.name = f"{vip_rv_.name}_{transform.name}__"
+    else:
+        vip_rv_value_.name = vip_rv_.name
+    vip_rv = model_free_rv(
+        vip_rv_,
+        vip_rv_value_,
+        transform,
+        *dims,
+    )
+
+    vip_rep_ = scale_noncentered * vip_rv
+
+    vip_rep_.name = name
+
+    vip_rep = model_deterministic(vip_rep_, *dims)
+    return vip_rep
+
+
 def vip_reparametrize(
     model: pm.Model,
     var_names: Sequence[str],

pymc_experimental/tests/model/test_marginal_model.py → tests/model/test_marginal_model.py

@@ -22,7 +22,7 @@
     is_conditional_dependent,
     marginalize,
 )
-from pymc_experimental.tests.utils import equal_computations_up_to_root
+from tests.utils import equal_computations_up_to_root
 
 
 @pytest.fixture

pymc_experimental/tests/model/transforms/test_autoreparam.py → tests/model/transforms/test_autoreparam.py

@@ -11,6 +11,7 @@ def model_c():
         m = pm.Normal("m")
         s = pm.LogNormal("s")
         pm.Normal("g", m, s, shape=5)
+        pm.Exponential("e", scale=s, shape=7)
     return mod
 
 
@@ -20,31 +21,34 @@ def model_nc():
         m = pm.Normal("m")
         s = pm.LogNormal("s")
         pm.Deterministic("g", pm.Normal("z", shape=5) * s + m)
+        pm.Deterministic("e", pm.Exponential("z_e", 1, shape=7) * s)
     return mod
 
 
-def test_reparametrize_created(model_c: pm.Model):
-    model_reparam, vip = vip_reparametrize(model_c, ["g"])
-    assert "g" in vip.get_lambda()
-    assert "g::lam_logit__" in model_reparam.named_vars
-    assert "g::tau_" in model_reparam.named_vars
+@pytest.mark.parametrize("var", ["g", "e"])
+def test_reparametrize_created(model_c: pm.Model, var):
+    model_reparam, vip = vip_reparametrize(model_c, [var])
+    assert f"{var}" in vip.get_lambda()
+    assert f"{var}::lam_logit__" in model_reparam.named_vars
+    assert f"{var}::tau_" in model_reparam.named_vars
     vip.set_all_lambda(1)
-    assert ~np.isfinite(model_reparam["g::lam_logit__"].get_value()).any()
+    assert ~np.isfinite(model_reparam[f"{var}::lam_logit__"].get_value()).any()
 
 
-def test_random_draw(model_c: pm.Model, model_nc):
+@pytest.mark.parametrize("var", ["g", "e"])
+def test_random_draw(model_c: pm.Model, model_nc, var):
     model_c = pm.do(model_c, {"m": 3, "s": 2})
     model_nc = pm.do(model_nc, {"m": 3, "s": 2})
-    model_v, vip = vip_reparametrize(model_c, ["g"])
-    assert "g" in [v.name for v in model_v.deterministics]
-    c = pm.draw(model_c["g"], random_seed=42, draws=1000)
-    nc = pm.draw(model_nc["g"], random_seed=42, draws=1000)
+    model_v, vip = vip_reparametrize(model_c, [var])
+    assert var in [v.name for v in model_v.deterministics]
+    c = pm.draw(model_c[var], random_seed=42, draws=1000)
+    nc = pm.draw(model_nc[var], random_seed=42, draws=1000)
     vip.set_all_lambda(1)
-    v_1 = pm.draw(model_v["g"], random_seed=42, draws=1000)
+    v_1 = pm.draw(model_v[var], random_seed=42, draws=1000)
     vip.set_all_lambda(0)
-    v_0 = pm.draw(model_v["g"], random_seed=42, draws=1000)
+    v_0 = pm.draw(model_v[var], random_seed=42, draws=1000)
     vip.set_all_lambda(0.5)
-    v_05 = pm.draw(model_v["g"], random_seed=42, draws=1000)
+    v_05 = pm.draw(model_v[var], random_seed=42, draws=1000)
     np.testing.assert_allclose(c.mean(), nc.mean())
     np.testing.assert_allclose(c.mean(), v_0.mean())
     np.testing.assert_allclose(v_05.mean(), v_1.mean())
@@ -57,10 +61,12 @@ def test_random_draw(model_c: pm.Model, model_nc):
 
 
 def test_reparam_fit(model_c):
-    model_v, vip = vip_reparametrize(model_c, ["g"])
+    vars = ["g", "e"]
+    model_v, vip = vip_reparametrize(model_c, ["g", "e"])
     with model_v:
-        vip.fit(random_seed=42)
-    np.testing.assert_allclose(vip.get_lambda()["g"], 0, atol=0.01)
+        vip.fit(50000, random_seed=42)
+    for var in vars:
+        np.testing.assert_allclose(vip.get_lambda()[var], 0, atol=0.01)
 
 
 def test_multilevel():

pymc_experimental/tests/statespace/test_SARIMAX.py → tests/statespace/test_SARIMAX.py

@@ -17,10 +17,10 @@
     SARIMAX_STATE_STRUCTURES,
     SHORT_NAME_TO_LONG,
 )
-from pymc_experimental.tests.statespace.utilities.shared_fixtures import (  # pylint: disable=unused-import
+from tests.statespace.utilities.shared_fixtures import (  # pylint: disable=unused-import
     rng,
 )
-from pymc_experimental.tests.statespace.utilities.test_helpers import (
+from tests.statespace.utilities.test_helpers import (
     load_nile_test_data,
     make_stationary_params,
     simulate_from_numpy_model,

pymc_experimental/tests/statespace/test_VARMAX.py → tests/statespace/test_VARMAX.py

@@ -11,7 +11,7 @@
 
 from pymc_experimental.statespace import BayesianVARMAX
 from pymc_experimental.statespace.utils.constants import SHORT_NAME_TO_LONG
-from pymc_experimental.tests.statespace.utilities.shared_fixtures import (  # pylint: disable=unused-import
+from tests.statespace.utilities.shared_fixtures import (  # pylint: disable=unused-import
     rng,
 )
 
@@ -25,7 +25,7 @@
 @pytest.fixture(scope="session")
 def data():
     df = pd.read_csv(
-        "pymc_experimental/tests/statespace/test_data/statsmodels_macrodata_processed.csv",
+        "tests/statespace/test_data/statsmodels_macrodata_processed.csv",
         index_col=0,
         parse_dates=True,
     ).astype(floatX)

pymc_experimental/tests/statespace/test_coord_assignment.py → tests/statespace/test_coord_assignment.py

@@ -18,9 +18,7 @@
     NO_FREQ_INFO_WARNING,
     NO_TIME_INDEX_WARNING,
 )
-from pymc_experimental.tests.statespace.utilities.test_helpers import (
-    load_nile_test_data,
-)
+from tests.statespace.utilities.test_helpers import load_nile_test_data
 
 function_names = ["pandas_date_freq", "pandas_date_nofreq", "pandas_nodate", "numpy", "pytensor"]
 expected_warning = [

pymc_experimental/tests/statespace/test_distributions.py → tests/statespace/test_distributions.py

@@ -17,10 +17,10 @@
     OBS_STATE_DIM,
     TIME_DIM,
 )
-from pymc_experimental.tests.statespace.utilities.shared_fixtures import (  # pylint: disable=unused-import
+from tests.statespace.utilities.shared_fixtures import (  # pylint: disable=unused-import
     rng,
 )
-from pymc_experimental.tests.statespace.utilities.test_helpers import (
+from tests.statespace.utilities.test_helpers import (
     delete_rvs_from_model,
     fast_eval,
     load_nile_test_data,

pymc_experimental/tests/statespace/test_kalman_filter.py → tests/statespace/test_kalman_filter.py

@@ -13,10 +13,10 @@
     UnivariateFilter,
 )
 from pymc_experimental.statespace.filters.kalman_filter import BaseFilter
-from pymc_experimental.tests.statespace.utilities.shared_fixtures import (  # pylint: disable=unused-import
+from tests.statespace.utilities.shared_fixtures import (  # pylint: disable=unused-import
     rng,
 )
-from pymc_experimental.tests.statespace.utilities.test_helpers import (
+from tests.statespace.utilities.test_helpers import (
     get_expected_shape,
     get_sm_state_from_output_name,
     initialize_filter,

pymc_experimental/tests/statespace/test_representation.py → tests/statespace/test_representation.py

@@ -6,11 +6,8 @@
 from numpy.testing import assert_allclose
 
 from pymc_experimental.statespace.core.representation import PytensorRepresentation
-from pymc_experimental.tests.statespace.utilities.shared_fixtures import TEST_SEED
-from pymc_experimental.tests.statespace.utilities.test_helpers import (
-    fast_eval,
-    make_test_inputs,
-)
+from tests.statespace.utilities.shared_fixtures import TEST_SEED
+from tests.statespace.utilities.test_helpers import fast_eval, make_test_inputs
 
 floatX = pytensor.config.floatX
 atol = 1e-12 if floatX == "float64" else 1e-6

pymc_experimental/tests/statespace/test_statespace.py → tests/statespace/test_statespace.py

@@ -13,10 +13,10 @@
     MATRIX_NAMES,
     SMOOTHER_OUTPUT_NAMES,
 )
-from pymc_experimental.tests.statespace.utilities.shared_fixtures import (  # pylint: disable=unused-import
+from tests.statespace.utilities.shared_fixtures import (  # pylint: disable=unused-import
     rng,
 )
-from pymc_experimental.tests.statespace.utilities.test_helpers import (
+from tests.statespace.utilities.test_helpers import (
     fast_eval,
     load_nile_test_data,
     make_test_inputs,