Update things

.github/workflows/run-tox.yml

@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v2

doc/source/conf.py

@@ -92,7 +92,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = 'en'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

hcga/analysis.py

@@ -1,6 +1,7 @@
 """
 Functions for the analysis of extracted feature matrix.
 """
+
 import itertools
 import logging
 import os

hcga/app.py

@@ -18,6 +18,7 @@
 Alternatively these commands can be bundled together into a single bash file,
 see 'run_example.sh' in the examples folder.
 """
+
 import logging
 import os
 from pathlib import Path

hcga/dataset_creation/__init__.py

@@ -1,4 +1,5 @@
 """submodule to create datasets of various types"""
+
 from .benchmark_datasets import make as make_benchmark_dataset
 from .synthetic_datasets import make as make_synthetic
 from .test_dataset import make as make_test_dataset

hcga/dataset_creation/benchmark_datasets.py

@@ -1,10 +1,10 @@
 """make benchmark datasets"""
+
 import os
 import shutil
 import zipfile
 from pathlib import Path
 
-import numpy as np
 import pandas as pd
 import wget
 
@@ -51,7 +51,7 @@ def extract_benchmark_graphs(datadir, dataname):  # pylint: disable=too-many-loc
     prefix = str(Path(datadir) / dataname)
 
     with open(prefix + "_graph_indicator.txt") as f:
-        nodes_df = pd.read_csv(f, dtype=np.int, header=None) - 1
+        nodes_df = pd.read_csv(f, dtype=int, header=None) - 1
     nodes_df.columns = ["graph_id"]
 
     with open(prefix + "_graph_labels.txt") as f:
@@ -60,9 +60,9 @@ def extract_benchmark_graphs(datadir, dataname):  # pylint: disable=too-many-loc
     edges_df = pd.DataFrame()
     with open(prefix + "_A.txt") as f:
         for edges_df_next in pd.read_csv(
-            f, sep=",", delimiter=None, dtype=np.int, header=None, chunksize=1e6
+            f, sep=",", delimiter=None, dtype=int, header=None, chunksize=1e6
         ):
-            edges_df = edges_df.append(edges_df_next - 1)
+            edges_df = pd.concat([edges_df, edges_df_next - 1])
     edges_df.columns = ["start_node", "end_node"]
     edges_df["graph_id"] = nodes_df["graph_id"][edges_df["start_node"].to_list()].to_list()
 

hcga/dataset_creation/synthetic_datasets.py

@@ -1,4 +1,5 @@
 """make synthetic datasets: WIP!!"""
+
 import networkx as nx
 import numpy as np
 

hcga/dataset_creation/test_dataset.py

@@ -1,4 +1,5 @@
 """make test datasets"""
+
 import networkx as nx
 import numpy as np
 import pandas as pd

hcga/extraction.py

@@ -1,4 +1,5 @@
 """Functions necessary for the extraction of graph features."""
+
 import logging
 import time
 from functools import partial

hcga/feature_class.py

@@ -5,6 +5,7 @@
 The functions here are necessary to evaluate each individual feature found inside a feature class.
 
 """
+
 import logging
 import multiprocessing
 import sys

hcga/features/assortativity.py

@@ -1,4 +1,5 @@
 """Assortativity class."""
+
 from functools import partial
 
 import networkx as nx

hcga/features/basal_nodes.py

@@ -1,4 +1,5 @@
 """Basal nodes class."""
+
 from functools import lru_cache
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/basic_stats.py

@@ -1,4 +1,5 @@
 """Basic stats class."""
+
 import networkx as nx
 import numpy as np
 

hcga/features/centralities_basic.py

@@ -1,4 +1,5 @@
 """Centralities class."""
+
 import networkx as nx
 import numpy as np
 from networkx.algorithms import centrality

hcga/features/chemical_theory.py

@@ -1,4 +1,5 @@
 """Chemical theory class."""
+
 from functools import partial
 
 import networkx as nx

hcga/features/cliques.py

@@ -1,4 +1,5 @@
 """Cliques class."""
+
 from functools import lru_cache
 
 import numpy as np
@@ -66,15 +67,15 @@ def compute_features(self):
         # graph clique number
         self.add_feature(
             "graph_clique_number",
-            clique.graph_clique_number,
+            lambda graph: max(len(c) for c in clique.find_cliques(graph)),
             "The clique number of a graph is the size of the largest clique in the graph",
             InterpretabilityScore(3),
         )
 
         # number of maximal cliques
         self.add_feature(
             "num_max_cliques",
-            clique.graph_number_of_cliques,
+            lambda graph: sum(1 for _ in clique.find_cliques(graph)),
             "The number of maximal cliques in the graph",
             InterpretabilityScore(3),
         )

hcga/features/clustering.py

@@ -1,4 +1,5 @@
 """Clustering class."""
+
 import networkx as nx
 import numpy as np
 

hcga/features/communities_asyn.py

@@ -1,4 +1,5 @@
 """Communities Asyn class."""
+
 from collections import Counter
 from functools import lru_cache, partial
 

hcga/features/communities_bisection.py

@@ -1,4 +1,5 @@
 """Communities Bisection class."""
+
 from functools import lru_cache
 
 from networkx.algorithms.community import kernighan_lin_bisection

hcga/features/communities_labelprop.py

@@ -1,4 +1,5 @@
 """Communities Label propagation class."""
+
 from functools import lru_cache
 
 from networkx.algorithms.community import label_propagation_communities

hcga/features/communities_modularity.py

@@ -1,4 +1,5 @@
 """Communities Modularity propagation class."""
+
 from functools import lru_cache, partial
 
 from networkx.algorithms.community import greedy_modularity_communities

hcga/features/components.py

@@ -1,4 +1,5 @@
 """Components class."""
+
 from functools import lru_cache
 
 import networkx as nx

hcga/features/core_number.py

@@ -1,4 +1,5 @@
 """Core number class."""
+
 import networkx as nx
 import numpy as np
 

hcga/features/covering.py

@@ -1,4 +1,5 @@
 """Covering class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/cycle_basis.py

@@ -1,4 +1,5 @@
 """Cycle Basis class."""
+
 from functools import lru_cache
 
 import networkx as nx

hcga/features/distance_measures.py

@@ -1,4 +1,5 @@
 """Distance Measures class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/dominating_sets.py

@@ -1,4 +1,5 @@
 """Dominating sets class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/efficiency.py

@@ -1,4 +1,5 @@
 """Distance Measures class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/eulerian.py

@@ -1,4 +1,5 @@
 """Eulerian Measures class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/flow_hierarchy.py

@@ -1,4 +1,5 @@
 """Flow hierarchy class."""
+
 from functools import partial
 
 import networkx as nx

hcga/features/in_out_degrees.py

@@ -1,4 +1,5 @@
 """In Out degrees class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/independent_sets.py

@@ -1,4 +1,5 @@
 """Independent sets class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/jaccard_similarity.py

@@ -1,4 +1,5 @@
 """Jaccard Similarity class."""
+
 from functools import lru_cache
 
 import networkx as nx

hcga/features/k_components.py

@@ -1,4 +1,5 @@
 """K Components class."""
+
 from functools import lru_cache
 
 import networkx as nx

hcga/features/link_analysis_hits.py

@@ -1,4 +1,5 @@
 """HITS hubs class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/looplessness.py

@@ -1,4 +1,5 @@
 """Looplessness class."""
+
 from functools import lru_cache, partial
 
 import networkx as nx

hcga/features/maximal_matching.py

@@ -1,4 +1,5 @@
 """Maximal matching class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/minimum_cuts.py

@@ -1,4 +1,5 @@
 """Minimum cuts class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/node_clique_number.py

@@ -1,4 +1,5 @@
 """Node clique number class."""
+
 from networkx.algorithms import clique
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/node_connectivity.py

@@ -1,4 +1,5 @@
 """Node connectivity class."""
+
 import networkx as nx
 import numpy as np
 

hcga/features/node_features.py

@@ -1,4 +1,5 @@
 """Node Features class."""
+
 from functools import lru_cache
 
 import networkx as nx

hcga/features/rbc.py

@@ -1,4 +1,5 @@
 """Role-similarity Based Comparison class."""
+
 from functools import lru_cache
 
 import networkx as nx

hcga/features/reciprocity.py

@@ -1,4 +1,5 @@
 """Reciprocity class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/rich_club.py

@@ -1,4 +1,5 @@
 """Rich Club class."""
+
 from functools import lru_cache
 
 import networkx as nx

hcga/features/scale_free.py

@@ -1,4 +1,5 @@
 """Scale Free class."""
+
 from functools import partial
 
 import networkx as nx

hcga/features/shortest_paths.py

@@ -1,4 +1,5 @@
 """Shortest paths class."""
+
 from functools import lru_cache
 
 import networkx as nx

hcga/features/simple_cycles.py

@@ -1,4 +1,5 @@
 """Simple cycles class."""
+
 from functools import lru_cache
 
 import networkx as nx

hcga/features/small_worldness.py

@@ -1,4 +1,5 @@
 """Small worldness class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/spectrum.py

@@ -1,4 +1,5 @@
 """Spectrum class."""
+
 from functools import lru_cache, partial
 
 import networkx as nx

hcga/features/structural_holes.py

@@ -1,4 +1,5 @@
 """Structural Holes class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/features/utils.py

@@ -1,4 +1,5 @@
 """Utils functions for feature classes."""
+
 import networkx as nx
 
 

hcga/features/vitality.py

@@ -1,4 +1,5 @@
 """Vitality class."""
+
 import networkx as nx
 
 from hcga.feature_class import FeatureClass, InterpretabilityScore

hcga/graph.py

@@ -9,6 +9,7 @@
 graph type into a generic hcga Graph object.
 
 """
+
 import logging
 
 import networkx as nx

hcga/hcga.py

@@ -1,4 +1,5 @@
 """Object-oriented API to hcga."""
+
 import logging
 
 import pandas as pd

hcga/io.py

@@ -1,4 +1,5 @@
 """input/output functions."""
+
 import os
 import pickle
 from pathlib import Path