Merge pull request #275 from lnccbrown/fix-hddm-wfpt

Set hddm-wfpt as required, separate dependency

.github/workflows/run_tests.yml

@@ -51,3 +51,6 @@ jobs:
 
       - name: Run tests
         run: poetry run pytest
+
+      - name: build docs
+        run: poetry run mkdocs build

.gitignore

@@ -70,3 +70,5 @@ site/
 
 # folders in the repository
 /notebooks
+
+*.so

README.md

@@ -15,7 +15,6 @@
 ![GitHub Repo stars](https://img.shields.io/github/stars/lnccbrown/HSSM)
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
 
-
 ### Overview
 
 HSSM is a Python toolbox that provides a seamless combination of state-of-the-art likelihood approximation methods with the wider ecosystem of probabilistic programming languages. It facilitates flexible hierarchical model building and inference via modern MCMC samplers. HSSM is user-friendly and provides the ability to rigorously estimate the impact of neural and other trial-by-trial covariates through parameter-wise mixed-effects models for a large variety of cognitive process models. HSSM is a <a href="https://ccbs.carney.brown.edu/brainstorm">BRAINSTORM</a> project in collaboration with the Center for Computation and Visualization and the Center for Computational Brain Science within the Carney Institute at Brown University.
@@ -28,7 +27,7 @@ HSSM is a Python toolbox that provides a seamless combination of state-of-the-ar
 - Native ArviZ support for plotting and other convenience functions to aid the Bayesian workflow.
 - Utilizes the ONNX format for translation of differentiable likelihood approximators across backends.
 
-### Official documentation link can be found [here](https://lnccbrown.github.io/HSSM/).
+### [Official documentation](https://lnccbrown.github.io/HSSM/).
 
 ## Installation
 

docs/changelog.md

@@ -2,6 +2,15 @@
 
 ## 0.1.x
 
+### 0.1.5
+
+We fixed the errors in v0.1.4. Sorry for the convenience! If you have accidentally
+downloaded v0.1.4, please make sure that you update hssm to the current version.
+
+- We made Cython dependencies of this package available via pypi. We have also built
+wheels for (almost) all platforms so there is no need to build these Cython
+dependencies.
+
 ### 0.1.4
 
 - Added support of `blackbox` likelihoods for `ddm` and `ddm_sdv` models.

docs/index.md

@@ -17,8 +17,6 @@ HSSM is a Python toolbox that provides a seamless combination of state-of-the-ar
 
 **License**: HSSM is licensed under [Copyright 2023, Brown University, Providence, RI](../LICENSE)
 
-**Version**: 0.1.4
-
 - Allows approximate hierarchical Bayesian inference via various likelihood approximators.
 - Estimate impact of neural and other trial-by-trial covariates via native hierarchical mixed-regression support.
 - Extensible for users to add novel models with corresponding likelihoods.

mkdocs.yml

@@ -34,6 +34,8 @@ plugins:
         - getting_started/getting_started.ipynb
         - tutorials/main_tutorial.ipynb
         - tutorials/likelihoods.ipynb
+        - .ipynb_checkpoints/*.ipynb
+      allow_errors: false
   - mkdocstrings:
       default_handler: python
       handlers:

pyproject.toml

@@ -1,11 +1,11 @@
 [tool.poetry]
 name = "HSSM"
-version = "0.1.3"
-description = ""
+version = "0.1.5"
+description = "Bayesian inference for hierarchical sequential sampling models."
 authors = [
-    "Aisulu Omar <[email protected]>",
-    "Paul Xu <[email protected]>",
     "Alexander Fengler <[email protected]>",
+    "Paul Xu <[email protected]>",
+    "Aisulu Omar <[email protected]>",
     "Michael Frank <[email protected]>",
 ]
 readme = "README.md"
@@ -16,8 +16,8 @@ keywords = ["HSSM", "sequential sampling models", "bayesian", "bayes", "mcmc"]
 
 [tool.poetry.dependencies]
 python = ">=3.9,<3.12"
+pymc = ">=5.6.0,<5.7.0"
 scipy = "1.10.1"
-pymc = "^5.6.0"
 arviz = "^0.14.0"
 numpy = "^1.23.4"
 onnx = "^1.12.0"
@@ -28,11 +28,11 @@ huggingface-hub = "^0.15.1"
 onnxruntime = "^1.15.0"
 bambi = "^0.12.0"
 numpyro = "^0.12.1"
-hddm-wfpt = {git = "https://github.com/brown-ccv/hddm-wfpt.git"}
+hddm-wfpt = "^0.1.1"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.3.1"
-black = {extras = ["jupyter"], version = "^23.7.0"}
+black = { extras = ["jupyter"], version = "^23.7.0" }
 mypy = "^1.4.1"
 pre-commit = "^2.20.0"
 jupyterlab = "^4.0.2"
@@ -162,7 +162,14 @@ ignore = [
     "TID252",
 ]
 
-exclude = [".github", "docs", "notebook", "tests"]
+exclude = [
+    ".github",
+    "docs",
+    "notebook",
+    "tests",
+    "src/hssm/likelihoods/hddm_wfpt/cdfdif_wrapper.c",
+    "src/hssm/likelihoods/hddm_wfpt/wfpt.cpp",
+]
 
 [tool.ruff.pydocstyle]
 convention = "numpy"
@@ -171,5 +178,5 @@ convention = "numpy"
 ignore_missing_imports = true
 
 [build-system]
-requires = ["poetry-core>=1.4.0"]
+requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"

src/hssm/__init__.py

@@ -1,5 +1,6 @@
 """HSSM - Hierarchical Sequential Sampling Models."""
 
+import importlib.metadata
 import logging
 import sys
 
@@ -17,7 +18,7 @@
 handler = logging.StreamHandler(stream=sys.stdout)
 _logger.addHandler(handler)
 
-__version__ = "0.1.4"
+__version__ = importlib.metadata.version(__package__ or __name__)
 
 __all__ = [
     "HSSM",

src/hssm/likelihoods/blackbox.py

@@ -2,8 +2,8 @@
 
 from __future__ import annotations
 
-import hddm_wfpt
 import numpy as np
+from hddm_wfpt import wfpt
 
 
 def hddm_to_hssm(func):
@@ -30,7 +30,7 @@ def logp_ddm_bbox(data: np.ndarray, v, a, z, t) -> np.ndarray:
     size = len(data)
     zeros = np.zeros(size, dtype=np.float64)
 
-    return hddm_wfpt.wfpt.wiener_logp_array(
+    return wfpt.wiener_logp_array(
         x=data,
         v=v,
         sv=zeros,
@@ -49,7 +49,7 @@ def logp_ddm_sdv_bbox(data: np.ndarray, v, a, z, t, sv) -> np.ndarray:
     size = len(data)
     zeros = np.zeros(size, dtype=np.float64)
 
-    return hddm_wfpt.wfpt.wiener_logp_array(
+    return wfpt.wiener_logp_array(
         x=data,
         v=v,
         sv=sv,
@@ -65,7 +65,7 @@ def logp_ddm_sdv_bbox(data: np.ndarray, v, a, z, t, sv) -> np.ndarray:
 @hddm_to_hssm
 def logp_full_ddm(data: np.ndarray, v, a, z, t, sv, sz, st):
     """Compute blackbox log-likelihoods for full_ddm models."""
-    return hddm_wfpt.wfpt.wiener_logp_array(
+    return wfpt.wiener_logp_array(
         x=data,
         v=v,
         sv=sv,