mirror of
https://github.com/Priyatham-sai-chand/compara-deep-learning.git
synced 2026-10-05 08:11:34 -07:00
127 lines
3.6 KiB
Python
127 lines
3.6 KiB
Python
import json
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import gc
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import pandas as pd
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import numpy as np
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import pickle
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import tensorflow as tf
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import sys
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from test_prepare_functions import create_data_homology_ls,read_gene_sequences,synteny_matrix,create_tree_data
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from access_data_rest import update_rest
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from process_data import create_map_list
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df=pd.read_csv("input_real_predictions_random_20K.txt",sep="\t",header=None)
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label_dict=dict(ortholog_one2one="Orthologs",
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other_paralog="Paralogs",
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ortholog_one2many="Orthologs",
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ortholog_many2many="Orthologs",
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within_species_paralog="Paralogs",
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gene_split="Gene Split")
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label_dict_2=dict(ortholog_one2one=1,other_paralog=0,ortholog_one2many=1,ortholog_many2many=1,within_species_paralog=0,gene_split=4)
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label_2=[]
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label_1=[]
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for index,row in df.iterrows():
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label_1.append(label_dict_2[row[7]])
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label_2.append(label_dict[row[7]])
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df=df.assign(label=label_1)
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df[7]=label_2
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data={}
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with open("genome_maps","rb") as file:
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data=pickle.load(file)
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cmap=data["cmap"]
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cimap=data["cimap"]
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ld=data["ld"]
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ldg=data["ldg"]
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a=data["a"]
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d=data["d"]
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lsy=create_data_homology_ls(df,3,a,d,ld,ldg,cmap,cimap)
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print(len(lsy))
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with open("ng","wb")as file:
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pickle.dump(lsy,file)
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gene_sequences=read_gene_sequences(df,lsy,"geneseq","gene_sequences")
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print("Gene Sequences Loaded")
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print("Going to update not found sequences:")
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gene_sequences=update_rest(gene_sequences)
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with open("gs","wb")as file:
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pickle.dump(gene_sequences,file)
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"""with open("ng","rb") as file:
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lsy=pickle.load(file)
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with open("gs","rb") as file:
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gene_sequences=pickle.load(file)"""
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df=df[0:20]
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n=3
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smg,sml,indexes=synteny_matrix(gene_sequences,df,lsy,n,0,list(),list(),list())
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print(len(indexes))
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df_temp=df.loc[indexes]
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bls,blhs,dis,dps,dphs=create_tree_data("species_tree.tree",df_temp)
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assert(len(bls)==len(indexes))
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assert(len(blhs)==len(smg))
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assert(len(df_temp)==len(dps))
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print("Data Prepared.")
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index_dict=create_map_list(indexes)
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try:
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model=tf.train.import_meta_graph('saved_models/model.ckpt.meta')
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except:
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print("Something wrong with the model.")
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sys.exit(1)
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with tf.Session() as sess:
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try:
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model.restore(sess,"saved_models/model.ckpt")
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graph = tf.get_default_graph()
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synmgt,synmlt,blst,blhst,dpst,dphst,dist,lrt,yt=graph.get_collection("input_nodes")
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predictions=graph.get_tensor_by_name("Predictions/BiasAdd:0")
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print("Model Loaded Successfully :)")
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except:
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print(":(")
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sys.exit()
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fd={synmgt:smg,
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synmlt:sml,
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blst:bls,
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blhst:blhs,
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dpst:dps.reshape((len(blhs),1)),
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dist:dis.reshape((len(blhs),1)),
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dphst:dphs.reshape((len(blhs),1))}
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preds=sess.run([predictions],feed_dict=fd)
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preds=np.array(preds)[0]
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preds=np.argmax(preds,axis=1)
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print(preds.shape)
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with open("prediction.txt","w") as file:
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for index,row in df.iterrows():
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file.write(str(row[0]))
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file.write("\t")
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file.write(row[1])
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file.write("\t")
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file.write(row[2])
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file.write("\t")
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file.write(str(row["label"]))
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file.write("\t")
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if index in index_dict:
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file.write(str(preds[index_dict[index]]))
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file.write("\t")
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if preds[index_dict[index]]==row["label"]:
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file.write(str(1))
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else:
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file.write(str(0))
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else:
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file.write("Error")
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file.write("\t")
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file.write("NaN")
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file.write("\n")
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