DEBUG + ADD real test

.travis.yml

@@ -7,7 +7,7 @@ notifications:
 
 install:
     - pip install -q cython numpy networkx scipy scikit-learn pandas
-    - python setup.py build_ext --inplace
+    - pip install .
 
 script:
   - pytest test/test.py
\ No newline at end of file

README.md

@@ -28,7 +28,7 @@ cd GMatch4py
 In `GMatch4py`, algorithms manipulate `networkx.Graph`, a complete graph model that 
 comes with a large spectrum of parser to load your graph from various inputs : `*.graphml,*.gexf,..` (check [here](https://networkx.github.io/documentation/stable/reference/readwrite/index.html) to see all the format accepted)
 
-### Use Gmatch4py
+### Use GMatch4py
 If you want to use algorithms like *graph edit distances*, here is an example:
 
 ```python
@@ -44,7 +44,7 @@ g1=nx.complete_bipartite_graph(5,4)
 g2=nx.complete_bipartite_graph(6,4)
 ```
 
-All graph matching algorithms in `Gmatch4py work this way:
+All graph matching algorithms in `Gmatch4py` work this way:
  * Each algorithm is associated with an object, each object having its specific parameters. In this case, the parameters are the edit costs (delete a vertex, add a vertex, ...)
  * Each object is associated with a `compare()` function with two parameters. First parameter is **a list of the graphs** you want to **compare**, i.e. measure the distance/similarity (depends on the algorithm). Then, you can specify a sample of graphs to be compared to all the other graphs. To this end, the second parameter should be **a list containing the indices** of these graphs (based on the first parameter list). If you rather compute the distance/similarity **between all graphs**, just use the `None` value.
 
@@ -68,7 +68,16 @@ ged.similarity(result)
 ged.distance(result)
 ```
 
+## Exploit nodes and edges attributes
 
+In this latest version, we add the possibility to exploit graph attributes ! To do so, the `base.Base` is extended with the `set_attr_graph_used(node_attr,edge_attr)` method.
+
+```python
+import networkx as nx 
+import gmatch4py as gm
+ged = gm.GraphEditDistance(1,1,1,1)
+ged.set_attr_graph_used("theme","color") # Edge colors and node themes attributes will be used.
+```
 
 ## List of algorithms
 
@@ -115,6 +124,6 @@ each code is associated with a reference to the original.**
 
 ## TODO List
 
-  * Debug algorithms --> Random Walk Kernel
+  * Debug algorithms --> Random Walk Kernel, Deltacon
   * Optimize algorithms --> Vertex Ranking
   * Write the documentation :runner:
\ No newline at end of file

gmatch4py/graph.pyx

@@ -22,7 +22,7 @@ cdef class Graph:
                 for id1 in G.adj:
                     for id2 in G.adj[id1]:
                         for id3 in G.adj[id1][id2]:
-                            G._adj[id1][id2][id3]["id"]=i
+                            G._adj[id1][id2][id3]["id"]=str(i)
                             i+=1
             self.is_edge_attr = True
             edge_attr_key = "id"  
@@ -91,7 +91,7 @@ cdef class Graph:
                 if self.is_edge_attr:
                     if self.is_directed:
                         in_edge=list(G.in_edges(n,data=True))
-                        out_edge=list(G.in_edges(n,data=True))
+                        out_edge=list(G.out_edges(n,data=True))
                         for n1,n2,attr_dict in in_edge:
                             self.degree_per_attr[attr_dict[self.edge_attr_key]][n]["in"]+=1
                             self.degree_per_attr_weighted[attr_dict[self.edge_attr_key]][n]["in"]+=1*(attr_dict["weight"] if "weight" in attr_dict else 1 )
@@ -169,10 +169,10 @@ cdef class Graph:
         return "_".join(sorted([n1,attr_value]))
 
     cpdef str hash_edge_attr(self,str n1,str n2, str attr_value):
-        if not self.is_directed:
+        if self.is_directed:
             return "_".join([n1,n2,attr_value])
         ed=sorted([n1,n2])
-        ed.extend(attr_value)
+        ed.extend([attr_value])
         return "_".join(ed)
     
     ## EXIST FUNCTION

setup.py

@@ -58,6 +58,7 @@ this_directory = path.abspath(path.dirname(__file__))
 with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:
     long_description = f.read()
 
+requirements=["numpy","networkx","scipy",'scikit-learn','tqdm','pandas']
 setup(
     name="GMatch4py",
     author="Jacques Fize",
@@ -68,9 +69,9 @@ setup(
     packages=["gmatch4py","gmatch4py.helpers"],
     ext_modules=extensions,
     cmdclass={'build_ext': build_ext},
-    setup_requires=["numpy","networkx","scipy",'scikit-learn'],
-    install_requires=["numpy","networkx","scipy",'scikit-learn'],
-    version="0.2.4.2beta",
+    setup_requires=requirements,
+    install_requires=requirements,
+    version="0.2.4.3beta",
     classifiers=[
             "Programming Language :: Python :: 3",
             "License :: OSI Approved :: MIT License",

test/gmatch4py_performance_test.py

-from gmatch4py import *
-import networkx as nx
-import time
-from tqdm import tqdm
-import pandas as pd
-max_=500
-size_g=10
-graphs_all=[nx.random_tree(size_g) for i in range(max_)]
-result_compiled=[]
-for size_ in tqdm(range(50,max_,50)):
-    graphs=graphs_all[:size_]
-    comparator=None
-    for class_ in [BagOfNodes,WeisfeleirLehmanKernel,GraphEditDistance,  GreedyEditDistance, HED, BP_2  Jaccard, MCS, VertexEdgeOverlap]:
-        deb=time.time()
-        if class_ in (GraphEditDistance, BP_2, GreedyEditDistance, HED):
-            comparator = class_(1, 1, 1, 1)
-        elif class_ == WeisfeleirLehmanKernel:
-            comparator = class_(h=2)
-        else:
-            comparator=class_()
-        matrix = comparator.compare(graphs,None)
-        print([class_.__name__,size_,time.time()-deb])
-        result_compiled.append([class_.__name__,size_,time.time()-deb])
+import os
+os.chdir(os.environ["HOME"])
 
-df=pd.DataFrame(result_compiled,columns="algorithm size_data time_exec_s".split())
-df.to_csv("new_gmatch4py_res_{0}graphs_{1}size.csv".format(max_,size_g))
\ No newline at end of file
+def test_mesure():
+    import gmatch4py as gm
+    import networkx as nx
+    import time
+    from tqdm import tqdm
+    import pandas as pd
+
+
+    max_=100
+    size_g=10
+    graphs_all=[nx.random_tree(size_g) for i in range(max_)]
+    result_compiled=[]
+    for size_ in tqdm(range(50,max_,50)):
+        graphs=graphs_all[:size_]
+        comparator=None
+        for class_ in [gm.BagOfNodes,gm.WeisfeleirLehmanKernel, gm.GraphEditDistance,  gm.GreedyEditDistance, gm.HED, gm.BP_2,  gm.Jaccard, gm.MCS, gm.VertexEdgeOverlap]:
+            deb=time.time()
+            if class_ in (gm.GraphEditDistance, gm.BP_2, gm.GreedyEditDistance, gm.HED):
+                comparator = class_(1, 1, 1, 1)
+            elif class_ == gm.WeisfeleirLehmanKernel:
+                comparator = class_(h=2)
+            else:
+                comparator=class_()
+            matrix = comparator.compare(graphs,None)
+            print([class_.__name__,size_,time.time()-deb])
+            result_compiled.append([class_.__name__,size_,time.time()-deb])
+
+    df = pd.DataFrame(result_compiled,columns="algorithm size_data time_exec_s".split())
+    df.to_csv("new_gmatch4py_res_{0}graphs_{1}size.csv".format(max_,size_g))
\ No newline at end of file

test/real_test.py

-from gmatch4py import *
-import networkx as nx
-
-graphs=[nx.random_tree(10) for i in range(10)]
-comparator=None
-for class_ in [BagOfNodes,WeisfeleirLehmanKernel,GraphEditDistance, BP_2, GreedyEditDistance, HED, Jaccard, MCS, VertexEdgeOverlap]:
-    print(class_)
-    if class_ in (GraphEditDistance, BP_2, GreedyEditDistance, HED):
-        comparator = class_(1, 1, 1, 1)
-    elif class_ == WeisfeleirLehmanKernel:
-        comparator = class_(h=2)
-    else:
-        comparator=class_()
-    matrix = comparator.compare(graphs, [])

test/test.py

 import pytest
-    
+import os
+import networkx as nx
 
-def nimport():
+def __import():
     # Gmatch4py use networkx graph 
     import networkx as nx 
+    import gmatch4py as gm
 
 
 def test_import():
-    nimport()
+    os.chdir(os.environ["HOME"] )
+    __import()
+
+def test_graph():
+    os.chdir(os.environ["HOME"])
+    import networkx as nx 
+    import gmatch4py as gm
+
+    # Simple Graph
+    G1 = nx.Graph()
+    G2 = nx.Graph()
+    G1.add_edge("1","2")
+    G1.add_edge("1","3")
+    
+    gm.graph.Graph(G1)
+
+    # Digraph Graph
+    G1 = nx.DiGraph()
+    G1.add_edge("1","2")
+    G1.add_edge("1","3")
+    assert list(G1.edges()) == gm.graph.Graph(G1).edges()
+
+    G1 = nx.DiGraph()
+    G1.add_edge("1","2",color="blue")
+    G1.add_edge("1","2",color="red")
+    G1.add_edge("1","3",color="green")
+    assert gm.graph.Graph(G1,edge_attr_key="color").density() == 2
+    assert gm.graph.Graph(G1).density() == 2
+
+    # Multi Graph
+    G1 = nx.MultiGraph()
+    G1.add_edge("1","2",color="blue")
+    G1.add_edge("1","3",color="green")
+    assert list(G1.edges()) == gm.graph.Graph(G1).edges()
+    G1 = nx.MultiGraph()
+    G1.add_edge("1","2",color="blue")
+    G1.add_edge("1","3",color="green")
+    assert len(set([gm.graph.Graph(G1).hash_edge_attr(ed[0],ed[1],ed[2]["color"]) for ed in list(G1.edges(data=True))]).intersection(gm.graph.Graph(G1,edge_attr_key="color").get_edges_hash())) == 2
+
+    G1 = nx.MultiGraph()
+    G1.add_edge("1","2",color="blue")
+    G1.add_edge("1","2",color="red")
+    G1.add_edge("1","3",color="green")
+    assert gm.graph.Graph(G1,edge_attr_key="color").density() == len(G1.edges(data=True))
+    assert gm.graph.Graph(G1).density() == len(G1.edges(data=True))
+
+    # Multi DiGraph
+    G1 = nx.MultiDiGraph()
+    G1.add_edge("1","2",color="blue")
+    G1.add_edge("1","2",color="red")
+    G1.add_edge("1","3",color="green")
+    assert gm.graph.Graph(G1,edge_attr_key="color").density() == len(G1.edges(data=True))
+    assert gm.graph.Graph(G1).density() == len(G1.edges(data=True))
+
+def test_hash():
+    os.chdir(os.environ["HOME"])
+    import networkx as nx 
+    import gmatch4py as gm
+
+    # Basic HASH
+    G1 = nx.Graph()
+    G_gm = gm.graph.Graph(G1)
+    assert G_gm.hash_edge("1","2") == "1_2"
+    assert G_gm.hash_edge("2","1") == "1_2"
+
+    # IF directed
+    G1 = nx.DiGraph()
+    G1.add_edge("1","2")
+    G_gm = gm.graph.Graph(G1)
+    assert G_gm.hash_edge("3","2") == "3_2"
+    assert G_gm.hash_edge("2","1") == "2_1"
+
+    # IF color and directed
+    G1 = nx.DiGraph()
+    G1.add_edge("1","2",color="blue")
+    G_gm = gm.graph.Graph(G1,edge_attr_key="color")
+    assert G_gm.hash_edge_attr("3","2","blue") == "3_2_blue"
+    assert G_gm.get_edges_hash() == {"1_2_blue"}
+
+    # if color and not directed
+    G1 = nx.Graph()
+    G1.add_edge("1","2",color="blue")
+    G_gm = gm.graph.Graph(G1,edge_attr_key="color")
+    assert G_gm.hash_edge_attr("3","2","blue") == "2_3_blue"
+
+def test_intersect_union():
+    os.chdir(os.environ["HOME"])
+    import networkx as nx 
+    import gmatch4py as gm
+
+    # Basic 
+    G1 = nx.Graph()
+    G1.add_edge("1","2")
+    G1.add_edge("1","3")
+    G2 = G1.copy()
+    G2.add_edge("3","4")
+    GM1 = gm.graph.Graph(G1)
+    GM2 = gm.graph.Graph(G2)
+
+    assert GM1.size_edge_union(GM2) == 3
+    assert GM1.size_node_union(GM2) == 4
+
+    assert GM1.size_edge_intersect(GM2) == 2
+    assert GM1.size_node_intersect(GM2) == 3
+
+    # BASIC and noised for hash
+    G1 = nx.Graph()
+    G1.add_edge("1","2")
+    G1.add_edge("1","3")
+    G2 = nx.Graph()
+    G2.add_edge("1","2")
+    G2.add_edge("3","1") # Changing the direction (no impact if working)
+    G2.add_edge("3","4")
+    GM1 = gm.graph.Graph(G1)
+    GM2 = gm.graph.Graph(G2)
 
-def ged():
-    import networkx as nx
-    # import the GED using the munkres algorithm
+    assert GM1.size_edge_union(GM2) == 3
+    assert GM1.size_node_union(GM2) == 4
     
+    assert GM1.size_edge_intersect(GM2) == 2
+    assert GM1.size_node_intersect(GM2) == 3
+
+
+    # Directed 
+    G1 = nx.DiGraph()
+    G1.add_edge("1","2")
+    G1.add_edge("1","3")
+    G2 = nx.DiGraph()
+    G2.add_edge("1","2")
+    G2.add_edge("3","1") # Changing the direction (no impact if working)
+    G2.add_edge("3","4")
+    GM1 = gm.graph.Graph(G1)
+    GM2 = gm.graph.Graph(G2)
+
+    assert GM1.size_edge_union(GM2) == 4
+    assert GM1.size_node_union(GM2) == 4
+    
+    assert GM1.size_edge_intersect(GM2) == 1
+    assert GM1.size_node_intersect(GM2) == 3
+
+
+    # IF COLOR
+    G1 = nx.DiGraph(); G1.add_node("1",color="blue")
+    G2 = nx.DiGraph(); G2.add_node("1",color="red")
+
+    GM1,GM2 = gm.graph.Graph(G1),gm.graph.Graph(G2) 
+    assert GM1.size_node_intersect(GM2) == 1
+    GM1,GM2 = gm.graph.Graph(G1,node_attr_key="color"),gm.graph.Graph(G2,node_attr_key="color") 
+    assert GM1.size_node_intersect(GM2) == 0
+    
+
+    G1 = nx.DiGraph(); G1.add_edge("1","2",color="blue")
+    G2 = nx.DiGraph(); G2.add_edge("1","2",color="red")
+
+    GM1,GM2 = gm.graph.Graph(G1),gm.graph.Graph(G2) 
+    assert GM1.size_edge_intersect(GM2) == 1
+    assert GM1.size_edge_union(GM2) == 1
+    GM1,GM2 = gm.graph.Graph(G1,edge_attr_key="color"),gm.graph.Graph(G2,edge_attr_key="color") 
+    assert GM1.size_edge_intersect(GM2) == 0
+    assert GM1.size_edge_union(GM2) == 2
+
+def test_degree():
+    os.chdir(os.environ["HOME"])
+    import networkx as nx 
+    import gmatch4py as gm
+
+    # Not DIRECTED and no attr
+    G1 = nx.Graph()
+    G1.add_edge("1","2")
+    G1.add_edge("1","3")
+    GM1 = gm.graph.Graph(G1)
+    assert GM1.degree('1') == 2
+
+    G1 = nx.DiGraph()
+    G1.add_edge("1","2")
+    G1.add_edge("3","1")
+    GM1 = gm.graph.Graph(G1)
+    assert GM1.degree('1') == 2
+    assert GM1.in_degree('1') == 1
+    assert GM1.out_degree('1') == 1
+
+    G1 = nx.MultiGraph()
+    G1.add_edge("1","2",color="blue")
+    G1.add_edge("1","2",color="red")
+    G1.add_edge("1","3",color="blue")
+    GM1 = gm.graph.Graph(G1,edge_attr_key ="color")
+    
+    assert GM1.degree_attr('1',"blue") == 2
+    assert GM1.degree('1') == 3
+
+    G1 = nx.MultiDiGraph()
+    G1.add_edge("1","2",color="blue")
+    G1.add_edge("1","2",color="red")
+    G1.add_edge("1","3",color="green")
+    GM1 = gm.graph.Graph(G1,edge_attr_key ="color")
+    assert GM1.in_degree_attr('2','red') == 1
+    assert GM1.in_degree('2') == 2
+
+
+
+
+
 
-def test_ged():
-    ged()
\ No newline at end of file
+    
\ No newline at end of file