initial push

Disney.true

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IDDisneyMapping.csv

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data.py

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+import xmltodict
+import numpy as np
+
+def raw():
+    with open("Disney.graphml","r") as f:
+        return xmltodict.parse(f.read())
+
+def basic_parse():
+    d=raw()
+    d=d["graphml"]
+    d=d["graph"]
+    nodes=d["node"]
+    edges=d["edge"]
+
+    return nodes,edges
+
+def read():
+    nodes,edges=basic_parse()
+    nodes=[{"id":int(zw["@id"]),"data":[float(zx["#text"]) for zx in zw["data"]]} for zw in nodes]
+
+    edges=[[int(zw["@source"]),int(zw["@target"])] for zw in edges]
+
+    nodes.sort(key=lambda x:x["id"])
+    nodes=[zw["data"] for zw in nodes]
+
+    return nodes,edges
+
+def adj():
+    n,e=read()
+    a=np.zeros((len(n),len(n)))
+    for e1,e2 in e:
+        a[e1,e2]=1.0
+        a[e2,e1]=1.0
+    a=np.expand_dims(a,axis=0)
+    n=np.array(n)
+    n=np.expand_dims(n,axis=0)
+    return a,n
+
+def load_true():
+    with open("Disney.true","r") as f:
+        q=f.read().split("\n")
+    return np.array([";1" in zw for zw in q if len(zw.strip())>2])
+
+if __name__ == "__main__":
+    a,n=adj()
+
+    t=load_true()
+
+    #print(t.shape,np.mean(t),np.sum(t))
+
+    np.savez_compressed("data",x=n,a=a,t=t)
+
+

main.py

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+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
+
+import grapa as g
+from grapa.functionals import *
+from grapa.layers import *
+from grapa.constants import *
+
+keras=tf.keras
+K=keras.backend
+
+from data import adj
+
+
+A,X=adj()
+
+data=np.concatenate((X,A),axis=2)
+
+
+def createmodel():
+    i=Input(shape=data.shape[1:])#define your input
+    gs=int(data.shape[1])#find the number of initial nodes
+    param=int(data.shape[2])-gs#find the number of features per node
+    g=grap(state(gs=gs,param=param))#create a grap object for use in the functional api
+    g.X,g.A=gcutparam(gs=gs,param1=param,param2=gs)([i])#set the current feature vector and adjacency matrix of your grap object by cutting the input 
+
+    m=getm()#get the standart constant file
+
+    xx=g.X#save this value, since it is the initial comparison variable
+
+    g=gnl(g,m)#a single graph update step
+
+    oparam=g.s.param#save the number of parameters, to assert that after the ae step this number is still the same
+
+
+    #g=compress(g,m,5,12)#execute one compression step, reducing gs nodes into gs/5 nodes and adding 12 features to each node
+
+    #g=gll(g,m,k=4)#a new graph update step. Here gll means relearning of your graph using a topk (k=4) algorithm
+
+    #m.decompress="paramlike"#choose paramlike decompression
+
+    #g,com,i2=decompress(g,m,5)#decompress by a factor of 5 (gs/5 nodes -> gs nodes)
+
+    g=gnl(g,m)#a final graph update step, using the graph generated in the decompression step
+
+    g=remparam(g,oparam)#remove to many parameters again
+
+    return i,g
+    return handlereturn(i,xx,com,i2,g.X,False)#this function uses compression input, initial comparison, compressed state,decompression input,decompressed version, run as a variational autoencoder
+
+
+def prepare():
+    i,g=createmodel()#returns z1,z2, which are the same since we dont use a vae,
+
+    model=Model(i,g)
+
+    plot_model(model,to_file="model.png",show_shapes=True)#save model plots
+
+    loss=mse(g,K.ones_like(g))#loss is mse
+    loss=K.mean(loss)
+
+    #if shallvae:
+    #    kl_loss=-0.5*K.mean(1+z2-K.square(z1)-K.exp(z2))
+    #    loss+=kl_loss
+
+    model.add_loss(loss)
+    model.compile(Adam(lr=lr))#we use the adam optimizer
+    model.summary()
+    #plot_model(vae,to_file=f"{nam}.png",show_shapes=True)
+
+    return model
+
+if __name__ == '__main__':
+    model=prepare()
+