Add Graph Embedding (DeepWalk, Graph2Vec), ShortestPathKernel checked, debug...

Add Graph Embedding (DeepWalk, Graph2Vec), ShortestPathKernel checked, debug graph, add joblib in requirement

README.md

@@ -117,7 +117,8 @@ Jacques Fize, *jacques[dot]fize[at]cirad[dot]fr*
 Some algorithms from other projects were integrated to Gmatch4py. **Be assured that
 each code is associated with a reference to the original.**
 
-## CHANGELOG
+## CHANGELOG
+
 ### 25.02.2019
  * Add New Graph Class. Features : Cython Extensions, precomputed values (degrees, neighbor info), hash representation of edges and nodes for a faster comparison
  * Some algorithms are parallelized such as graph edit distances or Jaccard

gmatch4py/__init__.py

@@ -9,8 +9,13 @@ from .ged.hausdorff_edit_distance import *
 # Kernels algorithms import
 from .kernels.weisfeiler_lehman import *
 from .kernels.shortest_path_kernel import *
+
+# Graph Embedding import
+from .embedding.graph2vec import *
+from .embedding.deepwalk import *
 # Helpers import
 from .helpers.reader import *
+from .helpers.general import *
 
 # Basic algorithms import
 from .bag_of_cliques import *

gmatch4py/embedding/deepwalk.pyx

+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import os
+import sys
+import random
+import networkx as nx
+from io import open
+from argparse import ArgumentParser, FileType, ArgumentDefaultsHelpFormatter
+from collections import Counter
+from concurrent.futures import ProcessPoolExecutor
+import logging
+
+import graph as graph2
+import walks as serialized_walks
+from gensim.models import Word2Vec
+from skipgram import Skipgram
+
+from six import text_type as unicode
+from six import iteritems
+from six.moves import range
+
+cimport cython
+from sklearn.metrics.pairwise import cosine_similarity
+from ..base cimport Base
+import numpy as np
+cimport numpy as np
+
+import psutil
+from multiprocessing import cpu_count
+from joblib import Parallel, delayed
+
+
+p = psutil.Process(os.getpid())
+try:
+    p.set_cpu_affinity(list(range(cpu_count())))
+except AttributeError:
+    try:
+        p.cpu_affinity(list(range(cpu_count())))
+    except AttributeError:
+        pass
+
+
+def process(gr, number_walks = 10, walk_length = 40, window_size = 5, vertex_freq_degree = False, workers = 1, representation_size = 64, max_memory_data_size = 1000000000, seed = 0):
+    if len(gr.edges())<1:
+        return np.zeros((1,representation_size))
+    G = graph2.from_networkx(gr.copy(), undirected=gr.is_directed())
+    num_walks = len(G.nodes()) * number_walks
+
+    data_size = num_walks * walk_length
+
+    #print("Data size (walks*length): {}".format(data_size))
+
+    if data_size < max_memory_data_size:
+        #print("Walking...")
+        walks = graph2.build_deepwalk_corpus(G, num_paths=number_walks,
+                                            path_length=walk_length, alpha=0, rand=random.Random(seed))
+        #print("Training...")
+        model = Word2Vec(walks, size=representation_size,
+                            window=window_size, min_count=0, sg=1, hs=1, workers=workers)
+    else:
+        #print("Data size {} is larger than limit (max-memory-data-size: {}).  Dumping walks to disk.".format(
+        #    data_size, max_memory_data_size))
+        #print("Walking...")
+
+        walks_filebase = "temp.walks"
+        walk_files = serialized_walks.write_walks_to_disk(G, walks_filebase, num_paths=number_walks,
+                                                            path_length=walk_length, alpha=0, rand=random.Random(seed),
+                                                            num_workers=workers)
+
+        #print("Counting vertex frequency...")
+        if not vertex_freq_degree:
+            vertex_counts = serialized_walks.count_textfiles(
+                walk_files, workers)
+        else:
+            # use degree distribution for frequency in tree
+            vertex_counts = G.degree(nodes=G.iterkeys())
+
+        #print("Training...")
+        walks_corpus = serialized_walks.WalksCorpus(walk_files)
+        model = Skipgram(sentences=walks_corpus, vocabulary_counts=vertex_counts,
+                            size=representation_size,
+                            window=window_size, min_count=0, trim_rule=None, workers=workers)
+
+    return model.wv.vectors
+
+
+cdef class DeepWalk(Base):
+    """
+    Based on :
+    @inproceedings{Perozzi:2014:DOL:2623330.2623732,
+        author = {Perozzi, Bryan and Al-Rfou, Rami and Skiena, Steven},
+        title = {DeepWalk: Online Learning of Social Representations},
+        booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
+        series = {KDD '14},
+        year = {2014},
+        isbn = {978-1-4503-2956-9},
+        location = {New York, New York, USA},
+        pages = {701--710},
+        numpages = {10},
+        url = {http://doi.acm.org/10.1145/2623330.2623732},
+        doi = {10.1145/2623330.2623732},
+        acmid = {2623732},
+        publisher = {ACM},
+        address = {New York, NY, USA},
+        keywords = {deep learning, latent representations, learning with partial labels, network classification, online learning, social networks},
+    }
+
+    Orignal Code : https://github.com/phanein/deepwalk
+
+    Modified by : Jacques Fize
+    """
+
+    def __init__(self):
+        Base.__init__(self,0,True)
+
+    def extract_embedding(self, listgs):
+        from tqdm import tqdm
+        models =  Parallel(n_jobs = cpu_count())(delayed(process)(nx.Graph(g)) for g in tqdm(listgs,desc="Extracting Embeddings..."))
+        return models
+
+    @cython.boundscheck(False)
+    cpdef np.ndarray compare(self,list listgs, list selected):
+        # Selected is ignored
+        models = self.extract_embedding(listgs)
+        vector_matrix = np.array([mod.mean(axis=0) for mod in models])   # Average nodes representations
+        cs = cosine_similarity(vector_matrix)
+        return cs
+

gmatch4py/embedding/graph.pyx

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""Graph utilities."""
+
+import logging
+import sys
+from io import open
+from os import path
+from time import time
+from glob import glob
+from six.moves import range, zip, zip_longest
+from six import iterkeys
+from collections import defaultdict, Iterable
+import random
+from random import shuffle
+from itertools import product,permutations
+from scipy.io import loadmat
+from scipy.sparse import issparse
+
+logger = logging.getLogger("deepwalk")
+
+
+__author__ = "Bryan Perozzi"
+__email__ = "bperozzi@cs.stonybrook.edu"
+
+LOGFORMAT = "%(asctime).19s %(levelname)s %(filename)s: %(lineno)s %(message)s"
+
+class Graph(defaultdict):
+  """Efficient basic implementation of nx `Graph' – Undirected graphs with self loops"""  
+  def __init__(self):
+    super(Graph, self).__init__(list)
+
+  def nodes(self):
+    return self.keys()
+
+  def adjacency_iter(self):
+    return self.iteritems()
+
+  def subgraph(self, nodes={}):
+    subgraph = Graph()
+    
+    for n in nodes:
+      if n in self:
+        subgraph[n] = [x for x in self[n] if x in nodes]
+        
+    return subgraph
+
+  def make_undirected(self):
+  
+    t0 = time()
+
+    for v in self.keys():
+      for other in self[v]:
+        if v != other:
+          self[other].append(v)
+    
+    t1 = time()
+    logger.info('make_directed: added missing edges {}s'.format(t1-t0))
+
+    self.make_consistent()
+    return self
+
+  def make_consistent(self):
+    t0 = time()
+    for k in iterkeys(self):
+      self[k] = list(sorted(set(self[k])))
+    
+    t1 = time()
+    logger.info('make_consistent: made consistent in {}s'.format(t1-t0))
+
+    self.remove_self_loops()
+
+    return self
+
+  def remove_self_loops(self):
+
+    removed = 0
+    t0 = time()
+
+    for x in self:
+      if x in self[x]: 
+        self[x].remove(x)
+        removed += 1
+    
+    t1 = time()
+
+    logger.info('remove_self_loops: removed {} loops in {}s'.format(removed, (t1-t0)))
+    return self
+
+  def check_self_loops(self):
+    for x in self:
+      for y in self[x]:
+        if x == y:
+          return True
+    
+    return False
+
+  def has_edge(self, v1, v2):
+    if v2 in self[v1] or v1 in self[v2]:
+      return True
+    return False
+
+  def degree(self, nodes=None):
+    if isinstance(nodes, Iterable):
+      return {v:len(self[v]) for v in nodes}
+    else:
+      return len(self[nodes])
+
+  def order(self):
+    "Returns the number of nodes in the graph"
+    return len(self)    
+
+  def number_of_edges(self):
+    "Returns the number of nodes in the graph"
+    return sum([self.degree(x) for x in self.keys()])/2
+
+  def number_of_nodes(self):
+    "Returns the number of nodes in the graph"
+    return self.order()
+
+  def random_walk(self, path_length, alpha=0, rand=random.Random(), start=None):
+    """ Returns a truncated random walk.
+
+        path_length: Length of the random walk.
+        alpha: probability of restarts.
+        start: the start node of the random walk.
+    """
+    G = self
+    if start:
+      path = [start]
+    else:
+      # Sampling is uniform w.r.t V, and not w.r.t E
+      path = [rand.choice(list(G.keys()))]
+
+    while len(path) < path_length:
+      cur = path[-1]
+      if len(G[cur]) > 0:
+        if rand.random() >= alpha:
+          path.append(rand.choice(G[cur]))
+        else:
+          path.append(path[0])
+      else:
+        break
+    return [str(node) for node in path]
+
+# TODO add build_walks in here
+
+def build_deepwalk_corpus(G, num_paths, path_length, alpha=0,
+                      rand=random.Random(0)):
+  walks = []
+
+  nodes = list(G.nodes())
+  
+  for cnt in range(num_paths):
+    rand.shuffle(nodes)
+    for node in nodes:
+      walks.append(G.random_walk(path_length, rand=rand, alpha=alpha, start=node))
+  
+  return walks
+
+def build_deepwalk_corpus_iter(G, num_paths, path_length, alpha=0,
+                      rand=random.Random(0)):
+  walks = []
+
+  nodes = list(G.nodes())
+
+  for cnt in range(num_paths):
+    rand.shuffle(nodes)
+    for node in nodes:
+      yield G.random_walk(path_length, rand=rand, alpha=alpha, start=node)
+
+
+def clique(size):
+    return from_adjlist(permutations(range(1,size+1)))
+
+
+# http://stackoverflow.com/questions/312443/how-do-you-split-a-list-into-evenly-sized-chunks-in-python
+def grouper(n, iterable, padvalue=None):
+    "grouper(3, 'abcdefg', 'x') --> ('a','b','c'), ('d','e','f'), ('g','x','x')"
+    return zip_longest(*[iter(iterable)]*n, fillvalue=padvalue)
+
+def parse_adjacencylist(f):
+  adjlist = []
+  for l in f:
+    if l and l[0] != "#":
+      introw = [int(x) for x in l.strip().split()]
+      row = [introw[0]]
+      row.extend(set(sorted(introw[1:])))
+      adjlist.extend([row])
+  
+  return adjlist
+
+def parse_adjacencylist_unchecked(f):
+  adjlist = []
+  for l in f:
+    if l and l[0] != "#":
+      adjlist.extend([[int(x) for x in l.strip().split()]])
+  
+  return adjlist
+
+def load_adjacencylist(file_, undirected=False, chunksize=10000, unchecked=True):
+
+  if unchecked:
+    parse_func = parse_adjacencylist_unchecked
+    convert_func = from_adjlist_unchecked
+  else:
+    parse_func = parse_adjacencylist
+    convert_func = from_adjlist
+
+  adjlist = []
+
+  t0 = time()
+  
+  total = 0 
+  with open(file_) as f:
+    for idx, adj_chunk in enumerate(map(parse_func, grouper(int(chunksize), f))):
+      adjlist.extend(adj_chunk)
+      total += len(adj_chunk)
+  
+  t1 = time()
+
+  logger.info('Parsed {} edges with {} chunks in {}s'.format(total, idx, t1-t0))
+
+  t0 = time()
+  G = convert_func(adjlist)
+  t1 = time()
+
+  logger.info('Converted edges to graph in {}s'.format(t1-t0))
+
+  if undirected:
+    t0 = time()
+    G = G.make_undirected()
+    t1 = time()
+    logger.info('Made graph undirected in {}s'.format(t1-t0))
+
+  return G 
+
+
+def load_edgelist(file_, undirected=True):
+  G = Graph()
+  with open(file_) as f:
+    for l in f:
+      x, y = l.strip().split()[:2]
+      x = int(x)
+      y = int(y)
+      G[x].append(y)
+      if undirected:
+        G[y].append(x)
+  
+  G.make_consistent()
+  return G
+
+
+def load_matfile(file_, variable_name="network", undirected=True):
+  mat_varables = loadmat(file_)
+  mat_matrix = mat_varables[variable_name]
+
+  return from_numpy(mat_matrix, undirected)
+
+
+def from_networkx(G_input, undirected=True):
+    G = Graph()
+
+    for _, x in enumerate(G_input):
+        for y in iterkeys(G_input[x]):
+            G[x].append(y)
+
+    if undirected:
+        G.make_undirected()
+
+    return G
+
+
+def from_numpy(x, undirected=True):
+    G = Graph()
+
+    if issparse(x):
+        cx = x.tocoo()
+        for i,j,v in zip(cx.row, cx.col, cx.data):
+            G[i].append(j)
+    else:
+      raise Exception("Dense matrices not yet supported.")
+
+    if undirected:
+        G.make_undirected()
+
+    G.make_consistent()
+    return G
+
+
+def from_adjlist(adjlist):
+    G = Graph()
+    
+    for row in adjlist:
+        node = row[0]
+        neighbors = row[1:]
+        G[node] = list(sorted(set(neighbors)))
+
+    return G
+
+
+def from_adjlist_unchecked(adjlist):
+    G = Graph()
+    
+    for row in adjlist:
+        node = row[0]
+        neighbors = row[1:]
+        G[node] = neighbors
+
+    return G
+
+

gmatch4py/embedding/graph2vec.pyx

+import hashlib
+import json
+import glob
+import pandas as pd
+import networkx as nx
+from tqdm import tqdm
+cimport numpy as np
+from joblib import Parallel, delayed
+from gensim.models.doc2vec import Doc2Vec, TaggedDocument
+import numpy.distutils.system_info as sysinfo
+from sklearn.metrics.pairwise import cosine_similarity
+
+from ..base cimport Base
+cimport cython
+
+
+class WeisfeilerLehmanMachine:
+    """
+    Weisfeiler Lehman feature extractor class.
+    """
+    def __init__(self, graph, features, iterations):
+        """
+        Initialization method which executes feature extraction.
+        :param graph: The Nx graph object.
+        :param features: Feature hash table.
+        :param iterations: Number of WL iterations.
+        """
+        self.iterations = iterations
+        self.graph = graph
+        self.features = features
+        self.nodes = self.graph.nodes()
+        self.extracted_features = [str(v) for k,v in features.items()]
+        self.do_recursions()
+
+    def do_a_recursion(self):
+        """
+        The method does a single WL recursion.
+        :return new_features: The hash table with extracted WL features.
+        """
+        new_features = {}
+        for node in self.nodes:
+            nebs = self.graph.neighbors(node)
+            degs = [self.features[neb] for neb in nebs]
+            features = "_".join([str(self.features[node])]+list(set(sorted([str(deg) for deg in degs]))))
+            hash_object = hashlib.md5(features.encode())
+            hashing = hash_object.hexdigest()
+            new_features[node] = hashing
+        self.extracted_features = self.extracted_features + list(new_features.values())
+        return new_features
+
+    def do_recursions(self):
+        """
+        The method does a series of WL recursions.
+        """
+        for iteration in range(self.iterations):
+            self.features = self.do_a_recursion()
+        
+
+def dataset_reader(graph):
+    """
+    Function to read the graph and features from a json file.
+    :param path: The path to the graph json.
+    :return graph: The graph object.
+    :return features: Features hash table.
+    :return name: Name of the graph.
+    """
+
+    features = dict(nx.degree(graph))
+
+    features = {k:v for k,v, in features.items()}
+    return graph, features
+
+def feature_extractor(graph, ix, rounds):
+    """
+    Function to extract WL features from a graph.
+    :param path: The path to the graph json.
+    :param rounds: Number of WL iterations.
+    :return doc: Document collection object.
+    """
+    graph, features = dataset_reader(graph)
+    machine = WeisfeilerLehmanMachine(graph,features,rounds)
+    doc = TaggedDocument(words = machine.extracted_features , tags = ["g_{0}".format(ix)])
+    return doc
+        
+
+
+def generate_model(graphs, iteration = 2, dimensions = 64, min_count = 5, down_sampling =  0.0001, learning_rate = 0.0001, epochs = 10, workers = 4 ):
+    """
+    Main function to read the graph list, extract features, learn the embedding and save it.
+    :param args: Object with the arguments.
+    """
+    document_collections = Parallel(n_jobs = workers)(delayed(feature_extractor)(g, ix,iteration) for ix,g in tqdm(enumerate(graphs),desc="Extracting Features..."))
+    graphs=[nx.relabel_nodes(g,{node:str(node) for node in list(g.nodes)},copy=True) for g in graphs]
+    model = Doc2Vec(document_collections,
+                    vector_size = dimensions,
+                    window = 0,
+                    min_count = min_count,
+                    dm = 0,
+                    sample = down_sampling,
+                    workers = workers,
+                    epochs = epochs,
+                    alpha = learning_rate)
+    return model
+
+cdef class Graph2Vec(Base):
+    """
+    Based on :
+    graph2vec: Learning distributed representations of graphs. 
+    Narayanan, Annamalai and Chandramohan, Mahinthan and Venkatesan, Rajasekar and Chen, Lihui and Liu, Yang 
+    MLG 2017, 13th International Workshop on Mining and Learning with Graphs (MLGWorkshop 2017)
+
+    Orignal Code : https://github.com/benedekrozemberczki/graph2vec
+
+    Modified by : Jacques Fize
+    """
+
+    def __init__(self):
+        Base.__init__(self,0,True)
+
+    @cython.boundscheck(False)
+    cpdef np.ndarray compare(self,list listgs, list selected):
+        # Selected is ignored
+        model  = generate_model(listgs)
+        vector_matrix = model.docvecs.vectors_docs
+        cs = cosine_similarity(vector_matrix)
+        return cs

gmatch4py/embedding/skipgram.pyx

+from collections import Counter, Mapping
+from concurrent.futures import ProcessPoolExecutor
+import logging
+from multiprocessing import cpu_count
+from six import string_types
+
+from gensim.models import Word2Vec
+from gensim.models.word2vec import Vocab
+
+logger = logging.getLogger("deepwalk")
+