compara-deep-learning/selector.py

import pandas as pd
import numpy as np

def create_map_reverse(arr):
    m={}
    rm={}
    for i in range(len(arr)):
        m[arr[i]]=i
        rm[i]=arr[i]
    return m,rm
        
def get_data_prop(df,nspmap,sp,prop,nos):
    nos=int(nos*prop)
    sp=[nspmap[x] for x in sp]
    data=df[df["homology_species"].isin(sp)]
    if nos<len(data):
        random_indexes=np.random.permutation(len(data))
        data=data.loc[data.index.values[random_indexes[:nos]]]
        return data
    else:
        return data

def create_balanced_dataset_paralog(df,matrix,spnmap,nspmap,nos,hom_type,spname):
    df=df[df["homology_type"]==hom_type]
    dist=matrix[spnmap[spname]]
    dist_sort=np.argsort(dist)
    sp_far=dist_sort[-5:]
    sp_near=dist_sort[1:6]    
    #get the records for the species which are far away
    df_far=get_data_prop(df,nspmap,sp_far,0.2,nos)
    #get the records for the nearby species
    df_near=get_data_prop(df,nspmap,sp_near,0.2,nos)
    df_dist=pd.concat([df_far,df_near])
    nos=nos-len(df_dist)
    df=df.drop(df_dist.index.values)
    random_ind=np.random.permutation(len(df))
    df_r=df.loc[df.index.values[random_ind[:nos]]]
    df=pd.concat([df_r,df_dist])
    return df

def select_data_goc(df,prop,nos):
    df=df[df["goc_score"]==0.0]
    nos=int(prop*nos)
    rind=np.random.permutation(len(df))
    if len(df)<nos:
        return df
    else:
        return df.loc[df.index.values[rind[:nos]]]
    
def create_balanced_dataset_ortholog(df,matrix,spnmap,nspmap,nos,hom_type,spname):
    df=df[df["homology_type"]==hom_type]
    dist=matrix[spnmap[spname]]
    dist_sort=np.argsort(dist)
    sp_far=dist_sort[-5:]
    sp_near=dist_sort[1:6]    
    #get the records for the species which are far away
    df_far=get_data_prop(df,nspmap,sp_far,0.2,nos)
    #get the records for the nearby species
    df_near=get_data_prop(df,nspmap,sp_near,0.2,nos)
    df_dist=pd.concat([df_far,df_near])
    len(df_dist)
    df=df.drop(df_dist.index.values)
    df_goc=select_data_goc(df,0.1,nos)
    len(df_goc)
    df=df.drop(df_goc.index.values)
    nos=nos-len(df_dist)-len(df_goc)
    random_ind=np.random.permutation(len(df))
    df_r=df.loc[df.index.values[random_ind[:nos]]]
    df=pd.concat([df_r,df_goc,df_dist])
    return df

def select(df,nos,matrix,spnmap,nspmap,sp):
    nos_p=int((0.5*nos)/2)
    nos_o=int((0.5*nos)/3)

    #get the paralogy data
    df_p1=create_balanced_dataset_paralog(df,matrix,spnmap,nspmap,nos_p,"within_species_paralog",sp)
    df_p2=create_balanced_dataset_paralog(df,matrix,spnmap,nspmap,nos_p,"other_paralog",sp)
    #get the orthology data
    df_o1=create_balanced_dataset_ortholog(df,matrix,spnmap,nspmap,nos_o,"ortholog_one2many",sp)
    df_o2=create_balanced_dataset_ortholog(df,matrix,spnmap,nspmap,nos_o,"ortholog_many2many",sp)
    df_o3=create_balanced_dataset_ortholog(df,matrix,spnmap,nspmap,nos_o,"ortholog_one2one",sp)

    #concatenate everything
    df=pd.concat([df_o1,df_o2,df_o3,df_p1,df_p2])
    return df