Merge branch 'main' into aryan_valid_pag

.github/workflows/main.yml

@@ -27,12 +27,12 @@ jobs:
       - name: Checkout repository
         uses: actions/checkout@v4
       - name: Setup Python 3.9
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.9"
           architecture: "x64"
       - name: Install Poetry ${{ matrix.poetry-version }}
-        uses: abatilo/actions-poetry@v2.3.0
+        uses: abatilo/actions-poetry@v2.4.0
         with:
           poetry-version: ${{ matrix.poetry-version }}
       - name: Install Poetry Dynamic Versioning Plugin
@@ -69,12 +69,12 @@ jobs:
       - name: Checkout repository
         uses: actions/checkout@v4
       - name: Setup Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
           architecture: "x64"
       - name: Install Poetry ${{ matrix.poetry-version }}
-        uses: abatilo/actions-poetry@v2.3.0
+        uses: abatilo/actions-poetry@v2.4.0
         with:
           poetry-version: ${{ matrix.poetry-version }}
       - name: Install Poetry Dynamic Versioning Plugin
@@ -92,7 +92,7 @@ jobs:
         run: poetry build
       - name: Upload package distribution files
         if: ${{ matrix.os == 'ubuntu' && matrix.python-version == '3.11' }}
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: package
           path: dist
@@ -130,12 +130,12 @@ jobs:
       - name: Checkout repository
         uses: actions/checkout@v4
       - name: Setup Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
           architecture: "x64"
       - name: Install Poetry ${{ matrix.poetry-version }}
-        uses: abatilo/actions-poetry@v2.3.0
+        uses: abatilo/actions-poetry@v2.4.0
         with:
           poetry-version: ${{ matrix.poetry-version }}
       - name: Install Poetry Dynamic Versioning Plugin
@@ -158,6 +158,7 @@ jobs:
         if: "matrix.os == 'ubuntu'"
         shell: bash
         run: |
+          sudo apt-get update
           sudo apt-get install nvidia-cuda-toolkit nvidia-cuda-toolkit-gcc
 
       - name: Run pytest # headless via Xvfb on linux
@@ -180,7 +181,7 @@ jobs:
       - name: Checkout repository
         uses: actions/checkout@v4
       - name: Setup Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.9
           architecture: "x64"
@@ -193,7 +194,7 @@ jobs:
           echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/v}" >> $GITHUB_ENV
           echo "TAG=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
       - name: Download package distribution files
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
           name: package
           path: dist

README.md

@@ -76,7 +76,7 @@ To install the package from github, clone the repository and then `cd` into the
 
 Pywhy-Graphs is always looking for new contributors to help make the package better, whether it is algorithms, documentation, examples of graph usage, and more! Contributing to Pywhy-Graphs will be rewarding because you will contribute to a much needed package for causal inference.
 
-See our [contributing guide](https://github.com/py-why/pywhy-graphs/CONTRIBUTING.md) for more details.
+See our [contributing guide](https://github.com/py-why/pywhy-graphs/blob/main/CONTRIBUTING.md) for more details.
 
 # Citing
 

doc/api.rst

@@ -61,6 +61,28 @@ causal graph operations.
    find_connected_pairs
    add_all_snode_combinations
    compute_invariant_domains_per_node
+   is_semi_directed_path
+   all_semi_directed_paths
+
+:mod:`pywhy_graphs.algorithms`: Algorithms for dealing with CPDAGs
+==================================================================
+With Markov equivalence classes of DAGs in a Markovian SCM setting, we obtain
+a potentially directed acyclic graph (PDAG), which may be completed (CPDAG).
+We may want to generate a consistent DAG extension (i.e. Markov equivalent) of a CPDAG
+then we may use some of the algorithms described here. Or perhaps one may want to 
+convert a DAG to its corresponding CPDAG.
+
+.. currentmodule:: pywhy_graphs.algorithms
+
+.. autosummary::
+   :toctree: generated/
+
+   pdag_to_dag
+   dag_to_cpdag
+   pdag_to_cpdag
+   order_edges
+   label_edges
+
 
 Conversions between other package's causal graphs
 =================================================

doc/installation.md

@@ -1,9 +1,10 @@
 Installation
 ============
 
-**pywhy-graphs** supports Python >= 3.8.
+**pywhy-graphs** closely follows the NetworkX dependencies and thus supports Python >= 3.9.
 
-## Installing with ``pip``
+Installing with ``pip``
+-----------------------
 
 **pywhy-graphs** is available [on PyPI](https://pypi.org/project/pywhy-graphs/). Just run
 
@@ -12,7 +13,8 @@ Installation
     # or if you use poetry which is recommended
     poetry add pywhy-graphs
 
-## Installing from source
+Installing from source
+----------------------
 
 To install **pywhy-graphs** from source, first clone [the repository](https://github.com/py-why/pywhy-graphs):
 

doc/reference/algorithms/index.rst

@@ -63,3 +63,15 @@ Algorithms for handling acyclicity
    :toctree: ../../generated/
 
    acyclification
+
+
+***************************************
+Semi-directed (possibly-directed) Paths
+***************************************
+
+.. automodule:: pywhy_graphs.algorithms.semi_directed_paths
+.. autosummary::
+   :toctree: ../../generated/
+
+   all_semi_directed_paths
+   is_semi_directed_path

doc/references.bib

@@ -17,6 +17,16 @@ @article{bareinboim_causal_2016
   pages    = {7345--7352}
 }
 
+@article{chickering2002learning,
+  title     = {Learning equivalence classes of Bayesian-network structures},
+  author    = {Chickering, David Maxwell},
+  journal   = {The Journal of Machine Learning Research},
+  volume    = {2},
+  pages     = {445--498},
+  year      = {2002},
+  publisher = {JMLR}
+}
+
 @article{Colombo2012,
   author    = {Diego Colombo and Marloes H. Maathuis and Markus Kalisch and Thomas S. Richardson},
   title     = {{Learning high-dimensional directed acyclic graphs with latent and selection variables}},

doc/whats_new/v0.2.rst

@@ -29,6 +29,8 @@ Changelog
 - |Feature| Implement and test functions to convert a DAG to MAG, by `Aryan Roy`_ (:pr:`96`)
 - |Feature| Implement and test functions to convert a PAG to MAG, by `Aryan Roy`_ (:pr:`93`)
 - |API| Remove support for Python 3.8 by `Adam Li`_ (:pr:`99`)
+- |Feature| Implement a suite of functions for finding and checking semi-directed paths on a mixed-edge graph, by `Adam Li`_ (:pr:`101`)
+- |Feature| Implement functions for converting between a DAG and PDAG and CPDAG for generating consistent extensions of a CPDAG for example. These functions are :func:`pywhy_graphs.algorithms.pdag_to_cpdag`, :func:`pywhy_graphs.algorithms.pdag_to_dag` and :func:`pywhy_graphs.algorithms.dag_to_cpdag`, by `Adam Li`_ (:pr:`102`)
 
 Code and Documentation Contributors
 -----------------------------------

examples/mixededge/plot_mixed_edge_graph.py

@@ -34,12 +34,8 @@
 # %%
 # Construct a MixedEdgeGraph
 # --------------------------
-# Using the ``MixedEdgeGraph``, we can represent a causal graph
-# with two different kinds of edges. To create the graph, we
-# use networkx ``nx.DiGraph`` class to represent directed edges,
-# and ``nx.Graph`` class to represent edges without directions (i.e.
-# bidirected edges). The edge types are then specified, so the mixed edge
-# graph object knows which graphs are associated with which types of edges.
+# Here we demonstrate how to construct a mixed edge graph
+# by composing networkx graphs.
 
 directed_G = nx.DiGraph(
     [
@@ -60,7 +56,6 @@
     name="IV Graph",
 )
 
-# Compute the multipartite_layout using the "layer" node attribute
 pos = nx.spring_layout(G)
 
 # we can then visualize the mixed-edge graph

examples/visualization/draw_and_compare_graphs_with_same_layout.py

@@ -34,6 +34,8 @@
 cpdag.orient_uncertain_edge("x", "y")
 cpdag.orient_uncertain_edge("xy", "y")
 cpdag.orient_uncertain_edge("z", "y")
+
+# create a PAG from the CPDAG, with all undirected edges
 pag = PAG()
 pag.add_edges_from(G.edges, cpdag.undirected_edge_name)
 

pyproject.toml

@@ -7,7 +7,7 @@ exclude_dirs = ["tests"]
 
 [tool.black]
 line-length = 100
-target-version = ['py38']
+target-version = ['py39']
 include = '\.pyi?$'
 extend-exclude = '''
 (
@@ -102,10 +102,10 @@ readme = "README.md"
 classifiers = [
   'Development Status :: 4 - Beta',
   'License :: OSI Approved :: MIT License',
-  'Programming Language :: Python :: 3.8',
   'Programming Language :: Python :: 3.9',
   'Programming Language :: Python :: 3.10',
-  'Programming Language :: Python :: 3.11'
+  'Programming Language :: Python :: 3.11',
+  'Programming Language :: Python :: 3.12'
 ]
 keywords = ['causality', 'graphs', 'causal-inference', 'graphical-model']
 

pywhy_graphs/algorithms/__init__.py

@@ -1,4 +1,6 @@
+from .cpdag import *  # noqa: F403
 from .cyclic import *  # noqa: F403
 from .generic import *  # noqa: F403
 from .multidomain import *  # noqa: F403
 from .pag import *  # noqa: F403
+from .semi_directed_paths import *  # noqa: F403