Cleanup

Author: ddomingof

src/diffupy/kernels.py

@@ -37,10 +37,10 @@ def commute_time_kernel(graph: nx.Graph, normalized: bool = False) -> Matrix:
     """
     # Apply pseudo-inverse (moore-penrose) of laplacian matrix
 
-    L = LaplacianMatrix(graph, normalized)
-    L.mat = np.linalg.pinv(L.mat)
+    laplacian = LaplacianMatrix(graph, normalized)
+    laplacian.mat = np.linalg.pinv(laplacian.mat)
 
-    return L
+    return laplacian
 
 
 def diffusion_kernel(graph: nx.Graph, sigma2: float = 1, normalized: bool = True) -> Matrix:
@@ -58,10 +58,10 @@ def diffusion_kernel(graph: nx.Graph, sigma2: float = 1, normalized: bool = True
     :param normalized: Indicates if Laplacian transformation is normalized or not.
     :return: Laplacian representation of the graph
     """
-    L = LaplacianMatrix(graph, normalized)
-    L.mat = sp.linalg.expm(-sigma2 / 2 * L.mat)
+    laplacian = LaplacianMatrix(graph, normalized)
+    laplacian.mat = sp.linalg.expm(-sigma2 / 2 * laplacian.mat)
 
-    return L
+    return laplacian
 
 
 def inverse_cosine_kernel(graph: nx.Graph) -> Matrix:
@@ -77,12 +77,12 @@ def inverse_cosine_kernel(graph: nx.Graph) -> Matrix:
     :return: Laplacian representation of the graph
     """
     # Decompose matrix (Singular Value Decomposition)
-    L = LaplacianMatrix(graph, normalized=True)
+    laplacian = LaplacianMatrix(graph, normalized=True)
     # Decompose matrix (Singular Value Decomposition)
-    U, S, _ = np.linalg.svd(L.mat * (pi / 4))
-    L.mat = np.matmul(np.matmul(U, np.diag(np.cos(S))), np.transpose(U))
+    U, S, _ = np.linalg.svd(laplacian.mat * (pi / 4))
+    laplacian.mat = np.matmul(np.matmul(U, np.diag(np.cos(S))), np.transpose(U))
 
-    return L
+    return laplacian
 
 
 def p_step_kernel(graph: nx.Graph, a: int = 2, p: int = 5) -> Matrix:
@@ -101,8 +101,8 @@ def p_step_kernel(graph: nx.Graph, a: int = 2, p: int = 5) -> Matrix:
     :param p: p-step kernels can be cheaper to compute and have been successful in biological tasks.
     :return: Laplacian repr'esentation of the graph.
     """
-    M = LaplacianMatrix(graph, normalized=True)
-    M.mat = -M.mat
+    laplacian = LaplacianMatrix(graph, normalized=True)
+    laplacian.mat = -laplacian.mat
 
     # Not optimal but keep for clarity
     # here we restrict to the normalised version, as the eigenvalues are
@@ -113,14 +113,14 @@ def p_step_kernel(graph: nx.Graph, a: int = 2, p: int = 5) -> Matrix:
     if p < 0:
         sys.exit('p must be greater than 0')
 
-    M.mat = set_diagonal_matrix(M.mat, [x + a for x in np.diag(M.mat)])
+    laplacian.mat = set_diagonal_matrix(laplacian.mat, [x + a for x in np.diag(laplacian.mat)])
 
     if p == 1:
-        return M
+        return laplacian
 
-    M.mat = np.linalg.matrix_power(M.mat, p)
+    laplacian.mat = np.linalg.matrix_power(laplacian.mat, p)
 
-    return M
+    return laplacian
 
 
 def regularised_laplacian_kernel(

src/diffupy/utils.py

@@ -10,7 +10,6 @@
 import numpy as np
 import pybel
 
-
 log = logging.getLogger(__name__)
 
 
@@ -28,7 +27,7 @@ def get_laplacian(graph: nx.Graph, normalized: bool = False) -> np.ndarray:
 
 
 def set_diagonal_matrix(matrix, d):
-    """  """
+    """Set diagonal matrix."""
     for j, row in enumerate(matrix):
         for i, x in enumerate(row):
             if i == j:
@@ -39,6 +38,7 @@ def set_diagonal_matrix(matrix, d):
 
 
 def get_label_node(node: nx.Graph.node) -> str:
+    """Get label node."""
     if hasattr(node, 'name') and node.name is not None:
         if node.name.lower() == "":
             log.debug(f'Empty attribute name: {node.as_bel()}')
@@ -86,7 +86,7 @@ def get_label_list_graph(graph: nx.Graph, label: str) -> List:
 
 
 def get_repeated_labels(labels):
-
+    """Get duplicate labels."""
     seen = set()
     rep = []
     for x in labels:
@@ -145,16 +145,16 @@ def print_dict_dimensions(entities_db, title):
 
 
 def get_simplegraph_from_multigraph(multigraph):
-
-    G = nx.Graph()
+    """Convert undirected graph from multigraph."""
+    graph = nx.Graph()
     for u, v, data in multigraph.edges(data=True):
         u = get_label_node(u)
         v = get_label_node(v)
 
         w = data['weight'] if 'weight' in data else 1.0
-        if G.has_edge(u, v):
-            G[u][v]['weight'] += w
+        if graph.has_edge(u, v):
+            graph[u][v]['weight'] += w
         else:
-            G.add_edge(u, v, weight=w)
+            graph.add_edge(u, v, weight=w)
 
-    return G
+    return graph