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main_search.py
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main_search.py
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"""
Created on Mon Oct 12 09:09:35 2020
@author: Bing
"""
import maching_learning as machlea
import spectrum_featurization as spefea
import pandas as pd
import caculator as ccl
import joblib
import numpy as np
import warnings
import math
import re
warnings.filterwarnings("ignore")
def formulatolist(formula):
formula=formula.replace("Cl","A")
ptn=re.compile('([A-Z])([\d]*)')
frag=ptn.findall(formula)
list1=[]
list2=[]
list3=[]
for i in range(len(frag)):
list1.append(frag[i][0])
if(frag[i][1]==""):
list2.append(1)
else:
list2.append(int(frag[i][1]))
if("C" in list1):
list3.append(list2[list1.index("C")])
else:
list3.append(0)
if("H" in list1):
list3.append(list2[list1.index("H")])
else:
list3.append(0)
if("A" in list1):
list3.append(list2[list1.index("A")])
else:
list3.append(0)
if("F" in list1):
list3.append(list2[list1.index("F")])
else:
list3.append(0)
if("N" in list1):
list3.append(list2[list1.index("N")])
else:
list3.append(0)
if("O" in list1):
list3.append(list2[list1.index("O")])
else:
list3.append(0)
if("P" in list1):
list3.append(list2[list1.index("P")])
else:
list3.append(0)
if("S" in list1):
list3.append(list2[list1.index("S")])
else:
list3.append(0)
return list3
def listtoformula(list_):
formula=""
list2=["C","H","Cl","F","N","O","P","S"]
for i in range(len(list_)):
if(list_[i]!=0):
formula=formula+list2[i]+str(list_[i])
return formula
def trans_pfas(cac_formula,database_formula,num_thre):
list_cac=formulatolist(cac_formula)
list_data=formulatolist(database_formula)
num=0
kind=0
kind_hf=0
list3=[]
for i in range(len(list_cac)):
list3.append(list_cac[i]-list_data[i])
if(list3[i]!=0):
if(i!=1 and i!=3):
num=num+abs(list3[i])
kind=kind+1
else:
num=num+0.5*abs(list3[i])
kind_hf+=1
if(num>num_thre):
return -1,-1,-1,""
return num,kind,kind_hf,listtoformula(list3)
def transform_pfas1(list_database,df_formula,num_thre=10):
list_for=[]
list_error=[]
list_t=[]
list_da=[]
list_n=[]
list_k=[]
list_f=[]
for i in range(len(df_formula)):
list_data=[]
list_trans=[]
list_num=[]
list_kind=[]
f=[]
for j in range(len(list_database)):
num_,kind_,f_,trans_=trans_pfas(df_formula["Formula_mole"][i],list_database[j],num_thre)
if(num_==-1):
continue
list_data.append(list_database[j])
list_trans.append(trans_)
list_num.append(num_)
list_kind.append(kind_)
f.append(f_)
if(list_data==[]):
continue
df_i=pd.DataFrame({"Formula_":list_data,"trans":list_trans,"num":list_num,"kind":list_kind,"f":f,})
df_i=df_i.sort_values(by=["num","kind","f"],ascending=["True","True","False"])
df_i=df_i.reset_index(drop=True)
list_for.append(df_formula["Formula_mole"][i])
list_error.append(df_formula["Error(ppm)"][i])
list_t.append(df_i["trans"][0])
list_da.append(df_i["Formula_"][0])
list_n.append(df_i["num"][0])
list_k.append(df_i["kind"][0])
list_f.append(df_i["f"][0])
if(list_for==[]):
return pd.DataFrame()
df_=pd.DataFrame({"Formula":list_for,"Error(ppm)":list_error,"Database_formula":list_da,"Trans":list_t,"num":list_n,"kind":list_k,"f":list_f})
df_=df_.sort_values(by=["num","kind","f"],ascending=["True","True","False"])
df_=df_.reset_index(drop=True)
return df_
# rank candidate from offline pubchem database
def rank_candidate(train_test,index,df_svm,mz,df_formula,MS2spe,database_pubchem,MS2_error_Da=0.01,inhouse_database=None):
list_smi=[]
if(type(database_pubchem)==dict and type(inhouse_database)==dict):
for i in range(len(df_formula)):
if(df_formula["Formula_mole"][i] in database_pubchem.keys()):
list_smi=list_smi+database_pubchem[df_formula["Formula_mole"][i]]
if(df_formula["Formula_mole"][i] in inhouse_database.keys()):
list_smi=list_smi+inhouse_database[df_formula["Formula_mole"][i]]
list_smi=list(set(list_smi))
elif(type(database_pubchem)==dict and type(inhouse_database)!=dict):
for i in range(len(df_formula)):
if(df_formula["Formula_mole"][i] in database_pubchem.keys()):
list_smi=list_smi+database_pubchem[df_formula["Formula_mole"][i]]
elif(type(database_pubchem)!=dict and type(inhouse_database)==dict):
for i in range(len(df_formula)):
if(df_formula["Formula_mole"][i] in inhouse_database.keys()):
list_smi=list_smi+inhouse_database[df_formula["Formula_mole"][i]]
else:
return pd.DataFrame(),[]
sim_=np.zeros(shape=(1,len(train_test)))
MS2=spefea.spetostr(MS2spe)
for j in range(len(train_test)):
sim_[0][j]=spefea.FNR(train_test["MSMS spectrum"][j],MS2,train_test["m/z"][j],mz,MS2_error_Da,minmz=50,maxmz=1250)
fp_pre=np.zeros(shape=(1,len(index)))
for k in range(len(index)):
model_name="model\svm"+str(k)+".model"
f1=open(model_name,"rb")
model_svm=joblib.load(f1)
f1.close()
fp_pre[0,k]=model_svm.predict(sim_)
if(list_smi==[]):
return pd.DataFrame(),fp_pre
list_score=[]
for m in range(len(list_smi)):
fp_cac=np.array([machlea.get_cdk_fingerprints(list_smi[m])])
fp_cac=fp_cac[:,index]
score_=machlea.score(fp_pre,fp_cac,df_svm)
list_score.append(score_)
df_fp=pd.DataFrame({"SMILES":list_smi,"score":list_score})
df_fp=df_fp.sort_values(by="score",ascending=False)
df_fp=df_fp.reset_index(drop=True)
return df_fp,fp_pre
# rank along with transformation with offline database
def rank_candidate_2(train_test,index,df_svm,mz,df_formula,MS2spe,database_pubchem,inhouse_database,MS2_error_Da=0.01,thresh=1):
list_smi=[]
if(type(database_pubchem)==dict and type(inhouse_database)==dict):
for i in range(len(df_formula)):
if(df_formula["Formula_mole"][i] in database_pubchem.keys()):
list_smi=list_smi+database_pubchem[df_formula["Formula_mole"][i]]
if(df_formula["Formula_mole"][i] in inhouse_database.keys()):
list_smi=list_smi+inhouse_database[df_formula["Formula_mole"][i]]
list_smi=list(set(list_smi))
else:
return pd.DataFrame(),""
sim_=np.zeros(shape=(1,len(train_test)))
MS2=spefea.spetostr(MS2spe)
for j in range(len(train_test)):
sim_[0][j]=spefea.FNR(train_test["MSMS spectrum"][j],MS2,train_test["m/z"][j],mz,MS2_error_Da,minmz=50,maxmz=1250)
fp_pre=np.zeros(shape=(1,len(index)))
for k in range(len(index)):
model_name="model\svm"+str(k)+".model"
f1=open(model_name,"rb")
model_svm=joblib.load(f1)
f1.close()
fp_pre[0,k]=model_svm.predict(sim_)
smi_t1=[]
sco_t1=[]
trans_t1=[]
error_ppm=[]
formula_0=""
score_for1=0
# caculate transformation score based on inhouse database with threshhold
list_database=list(inhouse_database.keys())
df1=transform_pfas1(list_database,df_formula,num_thre=10)
if(len(df1)==0):
df2=pd.DataFrame()
elif(len(df1)>5):
df2=df1[0:5].copy()
else:
df2=df1.copy()
for i in range(len(df2)):
list_smi1=inhouse_database[df2["Database_formula"][i]]
smi2_=""
sco1=0
for m in range(len(list_smi1)):
fp_cac=np.array([machlea.get_cdk_fingerprints(list_smi1[m])])
fp_cac=fp_cac[:,index]
score_=machlea.score(fp_pre,fp_cac,df_svm)
if(score_>sco1):
smi2_=list_smi1[m]
sco1=score_
smi_t1.append(smi2_)
sco_t1.append(sco1*(1-0.02*(math.pow(df2["kind"][i],2)+df2["num"][i]))*thresh)
trans_t1.append(df2["Trans"][i])
error_ppm.append(str(round(df2["Error(ppm)"][i],2)))
if(i==0):
formula_0=df2["Formula"][i]
score_for1=sco_t1[i]
else:
if(sco_t1[i]>score_for1):
formula_0=df2["Formula"][i]
score_for1=sco_t1[i]
# caculate pubchem smiles score
sco_t0=[]
trans_t0=[]
error_ppm0=[]
for m in range(len(list_smi)):
fp_cac=np.array([machlea.get_cdk_fingerprints(list_smi[m])])
fp_cac=fp_cac[:,index]
score_=machlea.score(fp_pre,fp_cac,df_svm)
sco_t0.append(score_)
trans_t0.append("")
error_ppm0.append(str(round((mz-machlea.SmilestoMW(list_smi[m]))*1000000/mz,2)))
if(sco_t1==[] and sco_t0==[]):
return pd.DataFrame(),""
else:
df_fp=pd.DataFrame({"SMILES":(list_smi+smi_t1),"score":(sco_t0+sco_t1),"trans":(trans_t0+trans_t1),"error":(error_ppm0+error_ppm)})
df_fp=df_fp.sort_values(by="score",ascending=False)
df_fp=df_fp.reset_index(drop=True)
if(df_fp["trans"][0]==""):
return df_fp,machlea.SmilestoFormula(df_fp["SMILES"][0])
else:
return df_fp,formula_0
# rank along with transformation with offline database excluing true formula
def rank_candidate_valid(train_test,index,df_svm,mz,formula,df_formula,MS2spe,database_pubchem,inhouse_database,MS2_error_Da=0.01,thresh=1):
list_smi=[]
if(type(database_pubchem)==dict and type(inhouse_database)==dict):
for i in range(len(df_formula)):
if(df_formula["Formula_mole"][i] in database_pubchem.keys() and df_formula["Formula_mole"][i]!=formula):
list_smi=list_smi+database_pubchem[df_formula["Formula_mole"][i]]
if(df_formula["Formula_mole"][i] in inhouse_database.keys() and df_formula["Formula_mole"][i]!=formula):
list_smi=list_smi+inhouse_database[df_formula["Formula_mole"][i]]
list_smi=list(set(list_smi))
else:
return pd.DataFrame()
sim_=np.zeros(shape=(1,len(train_test)))
MS2=spefea.spetostr(MS2spe)
for j in range(len(train_test)):
sim_[0][j]=spefea.FNR(train_test["MSMS spectrum"][j],MS2,train_test["m/z"][j],mz,MS2_error_Da,minmz=50,maxmz=1250)
fp_pre=np.zeros(shape=(1,len(index)))
for k in range(len(index)):
model_name="model\svm"+str(k)+".model"
f1=open(model_name,"rb")
model_svm=joblib.load(f1)
f1.close()
fp_pre[0,k]=model_svm.predict(sim_)
smi_t1=[]
sco_t1=[]
trans_t1=[]
error_ppm=[]
formula_0=""
score_for1=0
# caculate transformation score based on inhouse database with threshhold
list_database=list(inhouse_database.keys())
df1=transform_pfas1(list_database,df_formula,num_thre=10)
if(len(df1)==0):
df2=pd.DataFrame()
elif(len(df1)>5):
df2=df1[0:5].copy()
else:
df2=df1.copy()
for i in range(len(df2)):
list_smi1=inhouse_database[df2["Database_formula"][i]]
smi2_=""
sco1=0
for m in range(len(list_smi1)):
fp_cac=np.array([machlea.get_cdk_fingerprints(list_smi1[m])])
fp_cac=fp_cac[:,index]
score_=machlea.score(fp_pre,fp_cac,df_svm)
if(score_>sco1):
smi2_=list_smi1[m]
sco1=score_
smi_t1.append(smi2_)
sco_t1.append(sco1*(1-0.02*(math.pow(df2["kind"][i],2)+df2["num"][i]))*thresh)
trans_t1.append(df2["Trans"][i])
error_ppm.append(str(round(df2["Error(ppm)"][i],2)))
if(i==0):
formula_0=df2["Formula"][i]
score_for1=sco_t1[i]
else:
if(sco_t1[i]>score_for1):
formula_0=df2["Formula"][i]
score_for1=sco_t1[i]
# caculate pubchem smiles score
sco_t0=[]
trans_t0=[]
error_ppm0=[]
for m in range(len(list_smi)):
fp_cac=np.array([machlea.get_cdk_fingerprints(list_smi[m])])
fp_cac=fp_cac[:,index]
score_=machlea.score(fp_pre,fp_cac,df_svm)
sco_t0.append(score_)
trans_t0.append("")
error_ppm0.append(str(round((mz-machlea.SmilestoMW(list_smi[m]))*1000000/mz,2)))
if(sco_t1==[] and sco_t0==[]):
return pd.DataFrame(),""
else:
df_fp=pd.DataFrame({"SMILES":(list_smi+smi_t1),"score":(sco_t0+sco_t1),"trans":(trans_t0+trans_t1),"error":(error_ppm0+error_ppm)})
df_fp=df_fp.sort_values(by="score",ascending=False)
df_fp=df_fp.reset_index(drop=True)
if(df_fp["trans"][0]==""):
return df_fp,machlea.SmilestoFormula(df_fp["SMILES"][0])
else:
return df_fp,formula_0