Test Model Added

test_model.py

@@ -0,0 +1,171 @@
+import tensorflow as tf 
+import numpy as np 
+import pandas as pd 
+import requests 
+import sys
+from get_data import get_data_genome
+from process_data import get_nearest_neighbors
+from create_synteny_matrix import create_synteny_matrix_mul
+from ete3 import Tree
+import traceback
+
+arg=sys.argv
+arg=arg[1:]
+
+enable_break=0
+
+if arg[-1]=="-test":
+    enable_break=1
+
+arg=arg[:-1]
+
+if len(arg)!=5:
+    print("No. of arguments more or less. Please check")
+    sys.exit(1)
+
+dir_g="data"
+cmap,cimap,ld,ldg,a,d=get_data_genome(arg,dir_g)
+n=3
+
+server = "https://rest.ensembl.org"
+def get_gene_data(gid):
+    empty={}
+    ext = "/lookup/id/"+gid+"?" 
+    try:
+        r = requests.get(server+ext, headers={ "Content-Type" : "application/json"}) 
+        if not r.ok:
+            r.raise_for_status()
+    except:
+        return empty
+    decoded = r.json()
+    return dict(decoded)
+
+def create_branch_length_padding(bl):
+    maxlen=29
+    for x in bl:
+        for i in range(len(x),maxlen):
+            x.append(0)
+
+def create_tree_data(x,y):
+    t=Tree("species_tree.tree")
+    branch_lengths_s=[]
+    branch_lengths_hs=[]
+    dist=[]
+    ns=[]
+    nhs=[]
+    d=0
+    bl=[]
+    c=0
+    mca=t.get_common_ancestor(x,y)
+    node=t&x
+    while node.up!=mca:
+        d+=node.dist
+        bl.append(node.dist)
+        node=node.up
+        c+=1
+    ns.append(c)
+    c=0
+    branch_lengths_s.append(bl)
+    bl=[]
+    node=t&y
+    while node.up!=mca:
+        d+=node.dist
+        bl.append(node.dist)
+        node=node.up
+        c+=1
+    nhs.append(c)
+    branch_lengths_hs.append(bl)
+    dist.append(d)
+    create_branch_length_padding(branch_lengths_s)
+    create_branch_length_padding(branch_lengths_hs)
+    return np.array(branch_lengths_s),np.array(branch_lengths_hs),np.array(dist),np.array(ns),np.array(nhs)
+
+def filter_gene_details(gid,n,a,d,ld,ldg,cmap,cimap):
+    gd=get_gene_data(gid)
+    if len(gd)==0 or gd["biotype"]!="protein_coding":
+        print("Gene Id is Incorrect!!!. Try again.")
+        return list()
+    sp=gd["species"]
+    print(sp)
+    try:
+        t2=d[sp.capitalize()]
+    except:
+        print("Gene Species does not exist in gene maps. Try another species.")
+        return list(),sp
+    nl,nr=get_nearest_neighbors(gid,sp,n,a,d,ld,ldg,cmap,cimap)
+    if len(nl)==0 or len(nr)==0:
+        print("Gene is not present in gtf file. Please Try another gene.")
+        return list(),sp
+    print("Found nearest neighbors")
+    x=[]
+    for i in range(len(nl)-1,-1,-1):
+        x.append(nl[i])
+    x.append(gid)
+    for k in nr:
+        x.append(k)
+    assert(len(x)==2*n+1)
+    return x,sp
+
+def get_label(l):
+    if l==0:
+        return "Paralogous"
+    if l==1:
+        return "Orthologous"
+    return "Non Homologous"
+
+def softmax(arr):
+    arr=arr-np.amax(arr)
+    arr=np.exp(arr)
+    arr=arr/np.sum(arr)
+    return arr
+
+geneseq={}
+try:
+    model=tf.train.import_meta_graph('saved_models/model.ckpt.meta')
+except:
+    print("Something wrong with the model.")
+    sys.exit(1)
+with tf.Session() as sess:
+    try:
+        model.restore(sess,"saved_models/model.ckpt")
+        graph = tf.get_default_graph()
+        synmgt,synmlt,blst,blhst,dpst,dphst,dist,lrt,yt=graph.get_collection("input_nodes")
+        predictions=graph.get_tensor_by_name("Predictions/BiasAdd:0")
+        print("Model Loaded Successfully :)")
+    except:
+        print(":(")
+        sys.exit()
+    while(1):
+        try:
+            ch=input("Do you want to enter a gene id [y/n]:")
+            if ch=='n':
+                break
+            g1=input("Enter the first gene id:")
+            g1,sp1=filter_gene_details(g1,3,a,d,ld,ldg,cmap,cimap)
+            if len(g1)==0:
+                continue
+            g2=input("Enter the second gene id:")
+            g2,sp2=filter_gene_details(g2,3,a,d,ld,ldg,cmap,cimap)
+            if len(g2)==0:
+                continue
+            smg,sml=create_synteny_matrix_mul(geneseq,g1,g2,2*n+1)
+            bls,blhs,dis,dps,dphs=create_tree_data(sp1,sp2)
+            fd={synmgt:smg.reshape((1,7,7,2)),
+                synmlt:sml.reshape((1,7,7,2)),
+                blst:bls.reshape((1,29)),
+                blhst:blhs.reshape((1,29)),
+                dpst:dps.reshape((1,1)),
+                dist:dis.reshape((1,1)),
+                dphst:dphs.reshape((1,1))}
+            preds=sess.run([predictions],feed_dict=fd)
+            label=np.argmax(preds)
+            print(get_label(label))
+            print(softmax(preds))
+            
+        except Exception as e:
+            print("Some Error Was There Try Again:(",e)
+            traceback.print_exc()
+            continue
+        
+        
+