Update jar3d.py

bayespairing/src/jar3d.py

@@ -5,6 +5,7 @@ import statistics
 import sys
 import ast
 from Bio import SeqIO
+import json
 #mod_seqs,full_seqs,indexes = pickle.load(open("../models/RECOMB_sequences.pickle","rb"))
 
 tr = {"A":0,"C":1,"G":2,"U":3}
@@ -30,13 +31,14 @@ def gen_seq(weights,size):
 
 def direct_score(modules,file,dataset="RECOMB",is_list=False,retrain=False,THRESHOLD=3.5,pretrained=False):
     
-    
+    mod_data=json.load(open("../models/"+dataset+".json"))
     if pretrained:
         BNs = pickle.load(open("../models/" + dataset + "_models.pickle", "rb"))
     else:
         BNs = {}
         for module in modules:
-            BN = makeBN.call_makeBN(module, dataset,"NONE",False,"", retrain=retrain)
+            #BN = makeBN.call_makeBN(module, dataset,"NONE",False,"", retrain=retrain)
+            BN = makeBN.call_makeBN(module, dataset,mod_data[str(module)],False,"", retrain=retrain)
             BNs[module] = BN
         print("TRAINED BAYES NETS",BNs)
     if is_list:
@@ -90,4 +92,4 @@ if __name__ == "__main__":
         a = direct_score(mod,file,dataset,pretrained=True)
     else:
         a = direct_score(mod,file,dataset,pretrained=False)
-    print(a[1])
\ No newline at end of file
+    print(a[1])

bayespairing/src/make_BN_from_carnaval.py

@@ -409,6 +409,31 @@ def call_makeBN(mod, dataset, module_data, Lambda=1, Theta=1,retrain=False):
     '''
     
 
+                       
+
+
+
+# Temporary placeholder function for CV for building a Bayesian Network
+def make_BN(module, dataset, graphs, motif_sequences, Lambda=0.35, Theta=1):
+
+    # Test sequences should be tuple of seq and positions, seems like it is outdated/not used
+    test_seqs = []
+
+    # Build the BN with the motif sequences
+    g = graphs[module][0]
+    aln = {}
+    for n in sorted(list(g.nodes())):
+        aln[n] = n
+    alignment = get_alignment(g, aln, motif_sequences)
+
+    motif = make_graph_from_carnaval(g, alignment)
+    pwm = BN_builder.build_pwm(sorted(list(motif.nodes())), alignment)
+
+    BN = BN_builder.build_BN(motif, pwm, alignment)
+
+    # PDBs array is also empty, outdated?
+    BN.from_alignment_dataset(dataset, module, [g], alignment, test_seqs, [], Lambda, Theta)
+    return BN
 
 
 if __name__ == "__main__":