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https://github.com/Priyatham-sai-chand/compara-deep-learning.git
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86 lines
No EOL
3 KiB
Python
86 lines
No EOL
3 KiB
Python
import pandas as pd
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import numpy as np
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def create_map_reverse(arr):
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m={}
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rm={}
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for i in range(len(arr)):
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m[arr[i]]=i
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rm[i]=arr[i]
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return m,rm
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def get_data_prop(df,nspmap,sp,prop,nos):
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nos=int(nos*prop)
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sp=[nspmap[x] for x in sp]
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data=df[df["homology_species"].isin(sp)]
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if nos<len(data):
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random_indexes=np.random.permutation(len(data))
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data=data.loc[data.index.values[random_indexes[:nos]]]
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return data
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else:
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return data
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def create_balanced_dataset_paralog(df,matrix,spnmap,nspmap,nos,hom_type,spname):
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df=df[df["homology_type"]==hom_type]
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dist=matrix[spnmap[spname]]
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dist_sort=np.argsort(dist)
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sp_far=dist_sort[-5:]
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sp_near=dist_sort[1:6]
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#get the records for the species which are far away
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df_far=get_data_prop(df,nspmap,sp_far,0.2,nos)
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#get the records for the nearby species
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df_near=get_data_prop(df,nspmap,sp_near,0.2,nos)
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df_dist=pd.concat([df_far,df_near])
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nos=nos-len(df_dist)
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df=df.drop(df_dist.index.values)
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random_ind=np.random.permutation(len(df))
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df_r=df.loc[df.index.values[random_ind[:nos]]]
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df=pd.concat([df_r,df_dist])
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return df
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def select_data_goc(df,prop,nos):
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df=df[df["goc_score"]==0.0]
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nos=int(prop*nos)
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rind=np.random.permutation(len(df))
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if len(df)<nos:
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return df
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else:
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return df.loc[df.index.values[rind[:nos]]]
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def create_balanced_dataset_ortholog(df,matrix,spnmap,nspmap,nos,hom_type,spname):
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df=df[df["homology_type"]==hom_type]
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dist=matrix[spnmap[spname]]
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dist_sort=np.argsort(dist)
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sp_far=dist_sort[-5:]
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sp_near=dist_sort[1:6]
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#get the records for the species which are far away
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df_far=get_data_prop(df,nspmap,sp_far,0.2,nos)
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#get the records for the nearby species
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df_near=get_data_prop(df,nspmap,sp_near,0.2,nos)
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df_dist=pd.concat([df_far,df_near])
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len(df_dist)
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df=df.drop(df_dist.index.values)
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df_goc=select_data_goc(df,0.1,nos)
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len(df_goc)
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df=df.drop(df_goc.index.values)
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nos=nos-len(df_dist)-len(df_goc)
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random_ind=np.random.permutation(len(df))
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df_r=df.loc[df.index.values[random_ind[:nos]]]
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df=pd.concat([df_r,df_goc,df_dist])
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return df
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def select(df,nos,matrix,spnmap,nspmap,sp):
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nos_p=int((0.5*nos)/2)
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nos_o=int((0.5*nos)/3)
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#get the paralogy data
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df_p1=create_balanced_dataset_paralog(df,matrix,spnmap,nspmap,nos_p,"within_species_paralog",sp)
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df_p2=create_balanced_dataset_paralog(df,matrix,spnmap,nspmap,nos_p,"other_paralog",sp)
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#get the orthology data
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df_o1=create_balanced_dataset_ortholog(df,matrix,spnmap,nspmap,nos_o,"ortholog_one2many",sp)
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df_o2=create_balanced_dataset_ortholog(df,matrix,spnmap,nspmap,nos_o,"ortholog_many2many",sp)
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df_o3=create_balanced_dataset_ortholog(df,matrix,spnmap,nspmap,nos_o,"ortholog_one2one",sp)
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#concatenate everything
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df=pd.concat([df_o1,df_o2,df_o3,df_p1,df_p2])
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return df |