vscreenmlscore.py

import pickle
import pandas as pd
import sys
import numpy as np
from configuration import config as cfg
from utilities.featurizer import VscreenMLFeaturizer, VscreenMLAggregateFeatures


#data = np.array([fa_atr, fa_rep, fa_sol, fa_elec, hbond_bb_sc, hbond_sc, Interface_Energy, Total_BSA, Interface_HB, Total_packstats, Interface_unsat, Total_pose_exposed_SASA, interface_hydrophobic_sasa, interface_polar_sasa, C_C, N_C, O_C, S_C, C_N, N_N, O_N, S_N, C_O, N_O, O_O, S_O, C_F, N_F, O_F, S_F, C_P, N_P, O_P, S_P, C_S, N_S, O_S, S_S, C_Cl, N_Cl, O_Cl, S_Cl, C_Br, N_Br, O_Br, S_Br, C_I, N_I, O_I, S_I, SIDE_flex_ALPHA, SIDE_flex_BETA, SIDE_flex_OTHER, BACK_flex_ALPHA, BACK_flex_BETA, BACK_flex_OTHER, TotalElec, TotalHbond, Hphobe_contact, Pi_Pi, Tstack, Cationpi, Salt_Bridge, entropy, pienergy, fsp3, polarsurfacearea, vdwsa]).reshape((1,-1)).astype(float)

#predict vscreenml scrore
if len (sys.argv) != 3 :
    print ("Usage: python vscreenmlscore.py folderectory filetag_base_name ")
    sys.exit (1)

#pass folderectory and filetag
folder =sys.argv[1]
filetag =sys.argv[2]

data = np.array(VscreenMLAggregateFeatures(folder, filetag).vscreenml_featurize()).reshape((1,-1)).astype(float)

predictors = ['fa_atr','fa_rep','fa_sol','fa_elec','hbond_bb_sc','hbond_sc','Interface_Energy','Total_BSA','Interface_HB','Total_packstats','Interface_unsat','Total_pose_exposed_SASA','interface_hydrophobic_sasa','interface_polar_sasa','6.6','7.6','8.6','16.6','6.7','7.7','8.7','16.7','6.8','7.8','8.8','16.8','6.9','7.9','8.9','16.9','6.15','7.15','8.15','16.15','6.16','7.16','8.16','16.16','6.17','7.17','8.17','16.17','6.35','7.35','8.35','16.35','6.53','7.53','8.53','16.53','SIDE_flex_ALPHA','SIDE_flex_BETA','SIDE_flex_OTHER','BACK_flex_ALPHA','BACK_flex_BETA','BACK_flex_OTHER','TotalElec','TotalHbond','Hphobe_contact','Pi_Pi','T-stack','Cation-pi','Salt_Bridge','diff_entropy','pienergy','fsp3','polarsurfacearea','vdwsa']


classes = ['decoy', 'active']

def predict(model, tag, input_arr):
    #print (input_arr)
    #print (input_arr.shape)
    prediction = model.predict(input_arr)
    prediction_prob = model.predict_proba(input_arr)[:,1][0]
    print('{}\t{}\t{}'.format('Name','vscreenml_score','Label'))
    print('{}\t{}\t{}'.format(tag, prediction_prob, classes[int(prediction)]))


with open(cfg.VSCREENML+'/model/XGB_CLASSIFIER_alldata.pickle.dat', 'rb') as f:
    clf = pickle.load(f)


df = pd.DataFrame(data=data, columns=predictors)
predict(clf, filetag, df)