Removed references to IDAD in utils.py

.github/workflows/build.yml

@@ -89,7 +89,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python_version: ["3.7", "3.8", "3.9"]
+        python_version: ["3.8", "3.9", "3.10"]
       fail-fast: false
     steps:
       - uses: actions/checkout@v2

CONTRIBUTING.md

@@ -71,7 +71,7 @@ branch using a virtual environment. Steps:
    right of the page. This creates a copy of the code under your GitHub user account. For more details on how to
    fork a repository see [this guide](https://help.github.com/articles/fork-a-repo/).
 
-2. Clone your fork of the `graspologic` repo from your GitHub account to your local disk:
+2. Clone your fork of the `graspologic` repo from your GitHub account to your local disk. Do this by typing the following into command prompt or the equivelant on your operating system:
 
    ```bash
    git clone git@github.com:YourGithubAccount/graspologic.git
@@ -87,8 +87,7 @@ branch using a virtual environment. Steps:
    Always use a `feature` branch. Pull requests directly to either `dev` or `main` will be rejected
    until you create a feature branch based on `dev`.
 
-4. From the project root, create a [virtual environment](https://docs.python.org/3/library/venv.html) and install all development dependencies. Examples using various terminals are provided below. These examples use Python 3.8 but you may use any Python version supported by graspologic. These commands should install `graspologic` in editable mode, as well as
-all of its dependencies and several tools you need for developing `graspologic`.
+4. From the project root, create a [virtual environment](https://docs.python.org/3/library/venv.html) and install all development dependencies. Examples using various terminals are provided below. These examples use Python 3.8 but you may use any Python version supported by graspologic. If using Python 3.8 does not work feel free to type the same command simply using "Python" instead of "Python 3.8".These commands should install `graspologic` in editable mode, as well as all of its dependencies and several tools you need for developing `graspologic`. These commands assume that your operating system has already activated virtual environments which will allow virtual environments to be created. 
 
    **Bash**
     ```bash
@@ -104,7 +103,7 @@ all of its dependencies and several tools you need for developing `graspologic`.
    ```
    **CMD (Windows)**
     ```cmd
-   rem Create virtual environment.  Depending on your installation you might need "py -3.8 -m venv venv" instead
+   rem Create virtual environment.  Depending on your installation you might need "py -3 -m venv venv" instead
    python3.8 -m venv venv
 
    rem Activate the virtual environment

README.md

@@ -1,9 +1,9 @@
+<!-- omit in toc -->
 # graspologic
 [![Paper shield](https://img.shields.io/badge/JMLR-Paper-red)](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf)
 [![PyPI version](https://img.shields.io/pypi/v/graspologic.svg)](https://pypi.org/project/graspologic/)
 [![Downloads shield](https://pepy.tech/badge/graspologic)](https://pepy.tech/project/graspologic)
 ![graspologic CI](https://github.com/microsoft/graspologic/workflows/graspologic%20CI/badge.svg)
-[![DOI](https://zenodo.org/badge/147768493.svg)](https://zenodo.org/badge/latestdoi/147768493)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
 ## `graspologic` is a package for graph statistical algorithms.
@@ -15,6 +15,7 @@
 - [Contributing](#contributing)
 - [License](#license)
 - [Issues](#issues)
+- [Citing `graspologic`](#citing-graspologic)
 
 # Overview
 A graph, or network, provides a mathematically intuitive representation of data with some sort of relationship between items. For example, a social network can be represented as a graph by considering all participants in the social network as nodes, with connections representing whether each pair of individuals in the network are friends with one another. Naively, one might apply traditional statistical techniques to a graph, which neglects the spatial arrangement of nodes within the network and is not utilizing all of the information present in the graph. In this package, we provide utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms.
@@ -25,31 +26,36 @@ The official documentation with usage is at https://microsoft.github.io/graspolo
 Please visit the [tutorial section](https://microsoft.github.io/graspologic/latest/tutorials/index.html) in the official website for more in depth usage.
 
 # System Requirements
+<!-- omit in toc -->
 ## Hardware requirements
 `graspologic` package requires only a standard computer with enough RAM to support the in-memory operations.
 
+<!-- omit in toc -->
 ## Software requirements
+<!-- omit in toc -->
 ### OS Requirements
 `graspologic` is tested on the following OSes:
 - Linux x64
 - macOS x64
 - Windows 10 x64
 
 And across the following **x86_64** versions of Python:
-- 3.7
 - 3.8
 - 3.9
+- 3.10
 
 If you try to use `graspologic` for a different platform than the ones listed and notice any unexpected behavior,
 please feel free to [raise an issue](https://github.com/microsoft/graspologic/issues/new).  It's better for ourselves and our users
 if we have concrete examples of things not working!
 
 # Installation Guide
+<!-- omit in toc -->
 ## Install from pip
 ```
 pip install graspologic
 ```
 
+<!-- omit in toc -->
 ## Install from Github
 ```
 git clone https://github.com/microsoft/graspologic

docs/reference/release.rst

@@ -3,6 +3,15 @@
 Release Log
 ===========
 
+graspologic 2.0.1
+-----------------
+- Fixed bug with a matplotlib version incompatibility
+  `#996 <https://github.com/microsoft/graspologic/pull/996>`
+- Fixed graph matching with similarity matrix of unequal dimensions
+  `#1002 <https://github.com/microsoft/graspologic/pull/1002>`
+- Fixed bug with missing typing-extensions dependency
+  `#999 <https://github.com/microsoft/graspologic/pull/999>`
+
 graspologic 2.0.0
 -----------------
 - Refactored graph matching code and added many new features

docs/tutorials/embedding/CovariateAssistedEmbed.ipynb

@@ -139,7 +139,7 @@
     "def plot_latents(latent_positions, *, title, labels, ax=None):\n",
     "    if ax is None:\n",
     "        ax = plt.gca()\n",
-    "    plot = sns.scatterplot(latent_positions[:, 0], latent_positions[:, 1], hue=labels, \n",
+    "    plot = sns.scatterplot(x=latent_positions[:, 0], y=latent_positions[:, 1], hue=labels, \n",
     "                           linewidth=0, s=10, ax=ax, palette=\"Set1\")\n",
     "    plot.set_title(title, wrap=True);\n",
     "    ax.axes.xaxis.set_visible(False)\n",

graspologic/align/orthogonal_procrustes.py

@@ -55,7 +55,6 @@ class OrthogonalProcrustes(BaseAlign):
     def __init__(
         self,
     ) -> None:
-
         super().__init__()
 
     def fit(self, X: np.ndarray, Y: np.ndarray) -> "OrthogonalProcrustes":

graspologic/cluster/divisive_cluster.py

@@ -122,7 +122,6 @@ def __init__(
         max_level: int = 4,
         delta_criter: float = 0,
     ):
-
         _check_common_inputs(min_components, max_components, cluster_kws)
 
         if cluster_method not in ["gmm", "kmeans"]:

graspologic/match/solver.py

@@ -169,6 +169,9 @@ def __init__(
         _compare_dimensions(B, AB, "row", "column", "B", "AB")
         _compare_dimensions(A, BA, "row", "column", "A", "BA")
         _compare_dimensions(B, BA, "row", "row", "B", "BA")
+        if S is not None:
+            _compare_dimensions(A, [S], "row", "row", "A", "S")
+            _compare_dimensions(B, [S], "column", "column", "B", "S")
 
         # padding for unequally sized inputs
         if self.n_A != self.n_B:
@@ -189,9 +192,11 @@ def __init__(
         # check for similarity term
         if S is None:
             S = csr_array((self.n, self.n))
+        elif self.padded:
+            S = _adj_pad(S, n_padded=self.n, method="naive")
 
         _compare_dimensions(A, [S], "row", "row", "A", "S")
-        _compare_dimensions(B, [S], "row", "column", "B", "S")
+        _compare_dimensions(B, [S], "column", "column", "B", "S")
 
         self.A = A
         self.B = B
@@ -643,9 +648,7 @@ def _multilayer_adj_pad(
         return new_matrices
 
 
-def _adj_pad(
-    matrix: AdjacencyMatrix, n_padded: Int, method: PaddingType
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+def _adj_pad(matrix: AdjacencyMatrix, n_padded: Int, method: PaddingType) -> np.ndarray:
     if isinstance(matrix, (csr_matrix, csr_array)) and (method == "adopted"):
         msg = (
             "Using adopted padding method with a sparse adjacency representation; this "
@@ -691,7 +694,6 @@ def _compare_dimensions(
 def _check_partial_match(
     partial_match: Optional[Union[np.ndarray, Tuple]], n1: int, n2: int
 ) -> np.ndarray:
-
     _partial_match: np.ndarray
     if partial_match is None:
         _partial_match = np.array([[], []]).T

graspologic/models/edge_swaps.py

@@ -46,7 +46,6 @@ class EdgeSwapper:
 
     @beartype
     def __init__(self, adjacency: AdjacencyMatrix, seed: Optional[int] = None):
-
         weight_check = is_unweighted(adjacency)
         check_argument(weight_check, "adjacency must be unweighted")
 

graspologic/nominate/spectralVN.py

@@ -121,7 +121,6 @@ def _check_y(self, y: np.ndarray) -> None:
             )
 
     def _check_params(self) -> None:
-
         if self.n_neighbors is not None and type(self.n_neighbors) is not int:
             raise TypeError("k must be an integer")
         elif self.n_neighbors is not None and self.n_neighbors <= 0:

graspologic/partition/leiden.py

@@ -138,7 +138,7 @@ def _community_python_to_native(
     if starting_communities is None:
         return None
     native_safe: Dict[str, int] = {}
-    for (node_id, partition) in starting_communities.items():
+    for node_id, partition in starting_communities.items():
         node_id_as_str = identity(node_id)
         native_safe[node_id_as_str] = partition
     return native_safe
@@ -399,7 +399,6 @@ def _from_native(
     native_cluster: gn.HierarchicalCluster,
     identifier: _IdentityMapper,
 ) -> HierarchicalCluster:
-
     if not isinstance(native_cluster, gn.HierarchicalCluster):
         raise TypeError(
             "This class method is only valid for graspologic_native.HierarchicalCluster"
@@ -420,7 +419,7 @@ def hierarchical_leiden(
         List[Tuple[Any, Any, Union[int, float]]],
         nx.Graph,
         np.ndarray,
-        scipy.sparse.csr.csr_matrix,
+        scipy.sparse.csr_matrix,
     ],
     max_cluster_size: int = 1000,
     starting_communities: Optional[Dict[str, int]] = None,

graspologic/pipeline/embed/adjacency_spectral_embedding.py

@@ -180,7 +180,7 @@ def adjacency_spectral_embedding(
 
     node_labels = np.array(list(graph.nodes()))
 
-    graph_as_csr = nx.to_scipy_sparse_matrix(
+    graph_as_csr = nx.to_scipy_sparse_array(
         graph, weight=used_weight_attribute, nodelist=node_labels
     )
 

graspologic/pipeline/embed/laplacian_spectral_embedding.py

@@ -196,7 +196,7 @@ def laplacian_spectral_embedding(
 
     node_labels = np.array(list(graph.nodes()))
 
-    graph_as_csr = nx.to_scipy_sparse_matrix(
+    graph_as_csr = nx.to_scipy_sparse_array(
         graph, weight=used_weight_attribute, nodelist=node_labels
     )
 

graspologic/pipeline/embed/omnibus_embedding.py

@@ -296,7 +296,7 @@ def _augment_graph(
     weight_attribute: Optional[str],
     perform_augment_diagonal: bool = True,
 ) -> np.ndarray:
-    graph_sparse = nx.to_scipy_sparse_matrix(
+    graph_sparse = nx.to_scipy_sparse_array(
         graph, weight=weight_attribute, nodelist=node_ids
     )
 

graspologic/plot/plot.py

@@ -996,7 +996,6 @@ def _distplot(
     xlabel: str = "",
     ylabel: str = "Density",
 ) -> matplotlib.pyplot.Axes:
-
     plt.figure(figsize=figsize)
     ax = plt.gca()
     palette = sns.color_palette(palette)

graspologic/plot/plot_matrix.py

@@ -812,7 +812,6 @@ def matrixplot(  # type: ignore
         # in the opposite order
         rev_group = list(col_group[::-1])
         for i, sc in enumerate(rev_group):
-
             # Add a new axis when needed
             tick_ax = divider.append_axes(
                 "top", size="1%", pad=col_tick_pad[i], sharex=ax

graspologic/utils/utils.py

@@ -286,7 +286,8 @@ def is_almost_symmetric(
 
 
 def symmetrize(
-    graph: Union[np.ndarray, csr_matrix], method: Literal["avg", "triu", "tril"] = "avg"
+    graph: Union[np.ndarray, csr_matrix],
+    method: Literal["w_avg", "avg", "triu", "tril"] = "avg",
 ) -> Union[np.ndarray, csr_matrix]:
     """
     A function for forcing symmetry upon a graph.
@@ -296,13 +297,15 @@ def symmetrize(
     graph: object
         Either array-like, (n_vertices, n_vertices) numpy matrix or csr_matrix
 
-    method: {'avg' (default), 'triu', 'tril',}, optional
+    method: {'w_avg' (default), 'triu', 'tril',}, optional
         An option indicating which half of the edges to
         retain when symmetrizing.
 
-            - 'avg'
+            - 'w_avg'
                 Retain the average weight between the upper and lower
                 right triangle, of the adjacency matrix.
+            - "avg"
+                Performs the same function as "w_avg" but outputs a binearized matrix.
             - 'triu'
                 Retain the upper right triangle.
             - 'tril'
@@ -332,8 +335,10 @@ def symmetrize(
         graph = pac.triu(graph)
     elif method == "tril":
         graph = pac.tril(graph)
-    elif method == "avg":
+    elif method == "w_avg":
         graph = (pac.triu(graph) + pac.tril(graph)) / 2
+    elif method == "avg":
+        graph = binarize((pac.triu(graph) + pac.tril(graph)) / 2)
     else:
         msg = "You have not passed a valid parameter for the method."
         raise ValueError(msg)
@@ -367,7 +372,7 @@ def remove_loops(graph: GraphRepresentation) -> Union[np.ndarray, csr_matrix]:
     return graph
 
 
-LaplacianFormType = Literal["I-DAD", "DAD", "R-DAD"]
+LaplacianFormType = Literal["DAD", "R-DAD"]
 
 
 def to_laplacian(
@@ -378,8 +383,8 @@ def to_laplacian(
     r"""
     A function to convert graph adjacency matrix to graph Laplacian.
 
-    Currently supports I-DAD, DAD, and R-DAD Laplacians, where D is the diagonal matrix
-    of degrees of each node, I is the identity matrix, and A is the adjacency matrix.
+    Currently supports DAD and R-DAD Laplacians, where D is the diagonal matrix
+    of degrees of each node and A is the adjacency matrix.
 
     R-DAD is regularized Laplacian: where :math:`D_t = D + regularizer \times I`.
 
@@ -389,10 +394,8 @@ def to_laplacian(
         Either array-like, (n_vertices, n_vertices) numpy array,
         scipy.sparse.csr_matrix, or an object of type networkx.Graph.
 
-    form: {'I-DAD', 'DAD' (default), 'R-DAD'}, string, optional
+    form: {'DAD' (default), 'R-DAD'}, string, optional
 
-        - 'I-DAD'
-            Computes :math:`L = I - D_i^{-1/2} A D_i^{-1/2}`
         - 'DAD'
             Computes :math:`L = D_o^{-1/2} A D_i^{-1/2}`
         - 'R-DAD'
@@ -432,7 +435,7 @@ def to_laplacian(
 
     """
 
-    valid_inputs = ["I-DAD", "DAD", "R-DAD"]
+    valid_inputs = ["DAD", "R-DAD"]
     if form not in valid_inputs:
         raise TypeError("Unsuported Laplacian normalization")
 
@@ -468,10 +471,7 @@ def to_laplacian(
     in_root = diag(in_root)  # just change to sparse diag for sparse support
     out_root = diag(out_root)
 
-    if form == "I-DAD":
-        L = diag(in_degree) - A
-        L = in_root @ L @ in_root
-    elif form == "DAD" or form == "R-DAD":
+    if form == "DAD" or form == "R-DAD":
         L = out_root @ A @ in_root
     if is_symmetric(A):
         return symmetrize(