Fix style and docs

Signed-off-by: Adam Li <[email protected]>

Author: adam2392

docs/conf.py

@@ -313,6 +313,7 @@
     ("py:obj", "nx.MixedEdgeGraph"),
     ("py:obj", "networkx.MixedEdgeGraph"),
     ("py:obj", "pywhy_graphs.networkx.MixedEdgeGraph"),
+    ("py:obj", "pywhy_nx.MixedEdgeGraph"),
     ("py:class", "networkx.classes.mixededge.MixedEdgeGraph"),
     ("py:class", "numpy._typing._array_like._SupportsArray"),
     ("py:class", "numpy._typing._nested_sequence._NestedSequence"),

docs/index.rst

@@ -20,7 +20,7 @@ Contents
 
    installation
    api
-   use
+   Usage<use>
    whats_new
 
 Team

docs/use.rst

@@ -1,14 +1,8 @@
-:orphan:
-
-Examples using pywhy-graphs
-===========================
+How to use pywhy-graphs with examples and tutorials
+===================================================
 
 To be able to effectively use pywhy-graphs, look at some of the examples here
 to learn everything you need! We give an overview of concepts in causal graphs.
 
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-   Examples<../auto_examples/index>
+.. include:: auto_examples/index.rst
+    :start-after: :orphan:

examples/README.txt

@@ -1,6 +1,4 @@
-.. _examples_gallery:
-
 Examples Gallery
 ----------------
 
-Examples demonstrating how to use causal graphs.
+Simple examples demonstrating how to use causal graphs.

examples/intro/intro_causal_graphs.py

@@ -17,6 +17,7 @@
 from scipy import stats
 
 import pywhy_graphs
+import pywhy_graphs.networkx as pywhy_nx
 from pywhy_graphs import CPDAG, PAG
 from pywhy_graphs.viz import draw
 
@@ -163,15 +164,15 @@ def clone(self):
 
 # We can also look at m-separation statements similarly to a DAG.
 # For example, 'z' is still m-separated from 'x' because of the collider at 'y'
-print(f"'z' is d-separated from 'x': {nx.m_separated(admg, {'z'}, {'x'}, set())}")
+print(f"'z' is d-separated from 'x': {pywhy_nx.m_separated(admg, {'z'}, {'x'}, set())}")
 
 # Conditioning on the collider, opens up the path
-print(f"'z' is d-separated from 'x' given 'y': {nx.m_separated(admg, {'z'}, {'x'}, {'y'})}")
+print(f"'z' is d-separated from 'x' given 'y': {pywhy_nx.m_separated(admg, {'z'}, {'x'}, {'y'})}")
 
 # Say we add a bidirected edge between 'z' and 'x', then they are no longer
 # d-separated.
 admg.add_edge("z", "x", admg.bidirected_edge_name)
-print(f"'z' is d-separated from 'x': {nx.m_separated(admg, {'z'}, {'x'}, set())}")
+print(f"'z' is d-separated from 'x': {pywhy_nx.m_separated(admg, {'z'}, {'x'}, set())}")
 
 # Markov Equivalence Classes
 # --------------------------

pywhy_graphs/algorithms/generic.py

@@ -106,7 +106,7 @@ def set_nodes_as_latent_confounders(G: Union[nx.DiGraph, ADMG], nodes: List[Node
     return new_graph
 
 
-def is_valid_mec_graph(G: Union[PAG, CPDAG], on_error: str = "raise") -> bool:
+def is_valid_mec_graph(G, on_error: str = "raise") -> bool:
     """Check G is a valid PAG.
 
     A valid CPDAG/PAG is one where each pair of nodes have
@@ -129,7 +129,7 @@ def is_valid_mec_graph(G: Union[PAG, CPDAG], on_error: str = "raise") -> bool:
     if isinstance(G, CPDAG):
         check_func = _check_adding_cpdag_edge
     elif isinstance(G, PAG):
-        check_func = _check_adding_pag_edge
+        check_func = _check_adding_pag_edge  # type: ignore
 
     for edge_type, edgeview in G.edges().items():
         for u, v in edgeview:

pywhy_graphs/array/export.py

@@ -4,13 +4,12 @@
 from numpy.typing import ArrayLike
 
 import pywhy_graphs
-import pywhy_graphs.networkx as pywhy_nx
 from pywhy_graphs.classes.functions import edge_types
 from pywhy_graphs.config import CLearnEndpoint, EdgeType
 from pywhy_graphs.typing import Node
 
 
-def _graph_to_clearn_arr(G: pywhy_nx.MixedEdgeGraph) -> Tuple[ArrayLike, List[Node]]:
+def _graph_to_clearn_arr(G) -> Tuple[ArrayLike, List[Node]]:
     # define the array
     arr = np.zeros((G.number_of_nodes(), G.number_of_nodes()), dtype=int)
 
@@ -125,9 +124,7 @@ def _graph_to_clearn_arr(G: pywhy_nx.MixedEdgeGraph) -> Tuple[ArrayLike, List[No
     return arr, arr_idx
 
 
-def clearn_arr_to_graph(
-    arr: ArrayLike, arr_idx: List[Node], graph_type: str
-) -> pywhy_nx.MixedEdgeGraph:
+def clearn_arr_to_graph(arr: ArrayLike, arr_idx: List[Node], graph_type: str):
     """Convert causal-learn array to a graph object.
 
     Parameters
@@ -170,7 +167,7 @@ def clearn_arr_to_graph(
     elif graph_type == "admg":
         graph = pywhy_graphs.ADMG()
     elif graph_type == "cpdag":
-        graph = pywhy_graphs.CPDAG()
+        graph = pywhy_graphs.CPDAG()  # type: ignore
     elif graph_type == "pag":
         graph = pywhy_graphs.PAG()
     else:
@@ -248,18 +245,21 @@ def clearn_arr_to_graph(
                 elif (endpoint_v == CLearnEndpoint.TAIL) and (endpoint_u == CLearnEndpoint.TAIL):
                     graph.add_edge(u, v, edge_type=graph.undirected_edge_name)
             else:
+                if not hasattr(graph, "circle_edge_name"):
+                    raise RuntimeError(f"Graph {graph} is adding circular end points...")
+
                 # Endpoints contain a circle...
                 # u o- v
                 if endpoint_u == CLearnEndpoint.CIRCLE:
-                    graph.add_edge(v, u, edge_type=graph.circle_edge_name)
+                    graph.add_edge(v, u, edge_type=graph.circle_edge_name)  # type: ignore
                 elif endpoint_u == CLearnEndpoint.ARROW:
                     graph.add_edge(v, u, edge_type=graph.directed_edge_name)
                 elif endpoint_u == CLearnEndpoint.TAIL:
                     graph.add_edge(v, u, edge_type=graph.undirected_edge_name)
 
                 # u -o v
                 if endpoint_v == CLearnEndpoint.CIRCLE:
-                    graph.add_edge(u, v, edge_type=graph.circle_edge_name)
+                    graph.add_edge(u, v, edge_type=graph.circle_edge_name)  # type: ignore
                 elif endpoint_v == CLearnEndpoint.ARROW:
                     graph.add_edge(u, v, edge_type=graph.directed_edge_name)
                 elif endpoint_v == CLearnEndpoint.TAIL:
@@ -271,7 +271,7 @@ def clearn_arr_to_graph(
 
 
 def graph_to_arr(
-    G: pywhy_nx.MixedEdgeGraph,
+    G,
     format: str = "causal-learn",
     node_order: Optional[ArrayLike] = None,
 ) -> Tuple[ArrayLike, List[Node]]:

pywhy_graphs/viz/draw.py

@@ -1,9 +1,7 @@
 from typing import Optional
 
-import pywhy_graphs.networkx as pywhy_nx
 
-
-def draw(G: pywhy_nx.MixedEdgeGraph, direction: Optional[str] = None, pos: Optional[dict] = None):
+def draw(G, direction: Optional[str] = None, pos: Optional[dict] = None):
     """Visualize the graph.
 
     Parameters