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voteGenParallel.py
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# coding: utf-8
import pandas as pd
import numpy as np
from copy import deepcopy
import time
from joblib import Parallel, delayed
import multiprocessing
import gc
t0=time.time()
print t0
df = pd.read_stata('/scratch/sv1239/projects/mlcs/raw/Votelevel_stuffjan2013.dta')
#df = pd.read_stata('../raw/Votelevel_stuffjan2013.dta')
# columns to be removed, casenum or codej1... or j2vote... or j2maj... will be removed later
del_cols = ['fileid','cite','vol','beginpg','endopin','endpage','docnum','priorpub','_merge','year',
'circuit','pseatno','decision_date','aatty_first_name','aatty_last_name','afirm_name',
'ratty_first_name','ratty_last_name','rname_of_first_listed_amicus_gro','rfirm_namew','decisiondatenew2',
'j1name','j2name','j3name','quartertoelect','pname','seatno','success','lsuc','ls1','ls2','ls3','lp',
'lp2','lp3','sseatno','congress','congreso','afirst_listed_amicus_group','yearquarter','name','Name','State','j',
'codej4','j4vote1','j4vote2','j4maj1','j4maj2','codej5','j5vote1','j5vote2','j5maj1','j5maj2',
'codej6','j6vote1','j6vote2','j6maj1','j6maj2','codej7','j7vote1','j7vote2','j7maj1','j7maj2',
'codej8','j8vote1','j8vote2','j8maj1','j8maj2','codej9','j9vote1','j9vote2','j9maj1','j9maj2',
'codej10','j10vote1','j10vote2','j10maj1','j10maj2','codej11','j11vote1','j11vote2','j11maj1','j11maj2',
'codej12','j12vote1','j12vote2','j12maj1','j12maj2','codej13','j13vote1','j13vote2','j13maj1','j13maj2',
'codej14','j14vote1','j14vote2','j14maj1','j14maj2','codej15','j15vote1','j15vote2','j15maj1','j15maj2','j16maj1','j16vote1']
#
# to_dummies = ['month','day','method','state','district','origin','source','distjudg',
# 'applfrom','adminrev','opinstat','treat','classact','crossapp','counsel1','counsel2','sanction',
# 'initiate','numappel','appnatpr','appnatpr','appbus','appnonp','appfed','appsubst','appstate',
# 'appfiduc','ap_stid','genapel1','bank_ap1','genapel2','bank_ap2','appel1','appel2',]
print df.shape
df.drop(labels=del_cols,axis=1,inplace=True)
moredropcolumns=df.columns.tolist() # .tolist?
for i in moredropcolumns:
if len(pd.unique(df[i]))==1:
df.drop(labels=i,axis=1,inplace=True)
caseList=pd.unique(df['casenum'])
caseList=caseList[pd.notnull(caseList)].tolist()
print len(caseList)
num_cores = multiprocessing.cpu_count()
print "num_cores is: ",num_cores
def do_to_case(case):
newframe=pd.DataFrame() ## the rearrange of the original data
output=[] ## the corresponding alignment of judge 1 and judge 2, yes =1, no = -1
subtest=df[df.casenum==case].reset_index(drop=True) ## 'subtest' only take the records that have a specific case id
num=subtest.shape[0] ## num will be 3, because usally there are 3 records for each case
for j in range(num):
if pd.notnull(subtest.ix[j].codej1):
j1=subtest.ix[j].codej1
if pd.notnull(subtest.ix[j].codej2):
j2=subtest.ix[j].codej2
if pd.notnull(subtest.ix[j].codej3):
j3=subtest.ix[j].codej3
if subtest.ix[0].majvotes == 3:
j1j2=1
j1j3=1
j2j3=1
else:
if subtest.ix[0].direct1==subtest.ix[0].j2vote1:
j1j2=1
else:
j1j2=-1
if subtest.ix[0].direct1==subtest.ix[0].j3vote1:
j1j3=1
else:
j1j3=-1
if subtest.ix[0].j2vote1==subtest.ix[0].j3vote1:
j2j3=1
else:
j2j3=-1
for j in range(num):
copytest=deepcopy(subtest.ix[j])
if copytest.ids==j1:
copytest.codej1=j1
newframe=newframe.append(copytest)
output.append(j1j2)
copytest.codej2=j3
copytest.codej3=j2
newframe=newframe.append(copytest)
output.append(j1j3)
if copytest.ids==j2:
copytest.codej1=j2
copytest.codej2=j1
newframe=newframe.append(copytest)
output.append(j1j2)
copytest.codej2=j3
copytest.codej3=j1
newframe=newframe.append(copytest)
output.append(j2j3)
if copytest.ids==j3:
copytest.codej1=j3
copytest.codej3=j1
newframe=newframe.append(copytest)
output.append(j2j3)
copytest.codej2=j1
copytest.codej3=j2
newframe=newframe.append(copytest)
output.append(j1j3)
assert newframe.shape[0]==len(output)
print newframe.shape
return newframe, output
print "parallel jobs started"
jobs=Parallel(n_jobs=num_cores)(delayed(do_to_case)(case) for case in caseList)
bigout=[]
bignew=pd.DataFrame()
print "parallel jobs done"
print "concatenating df's and out's"
for x in jobs:
if bignew.empty:
bignew=x[0]
else:
bignew=pd.concat([bignew,x[0]],ignore_index=True)
bigout = bigout + x[1]
gc.collect()
## remove the columns that we are done with
bignew.drop(labels=['casenum','j2vote1','j2vote2','j2maj1','direct1',
'j2maj2','j3vote1','j3vote2','j3maj1','j3maj2','majvotes','ids'],axis=1,inplace=True)
new_cols=bignew.columns
new_cols=new_cols.tolist()
keep_cols=['j1score','j2score','j3score','popularpct','electoralpct','closerd','fartherd','dAds3','dF2Ads3',
'dF1Ads3','dL1Ads3','dL2Ads3','dL3Ads3','dL4Ads3','dL5Ads3','logAds3','logL1Ads3','logL2Ads3','logF1Ads3',
'logF2Ads3','decade2','propneg','likely_elev2','score','d12','d13','d23']
'''
This keep_cols are all float no categories. So null=0 will be fine.
for col in keep_cols:
if len(pd.unique(newframe[col]))<4:
print pd.unique(newframe[col]),col+"\n"
'''
#doubt that sth like dl4ads3 is also categories
for col in keep_cols:
if col in new_cols:
new_cols.remove(col)
print "removing columns"
bignew=pd.get_dummies(bignew,columns=new_cols,dummy_na=True)
bignew=bignew.fillna(value=0)
bignew.to_csv('/scratch/sv1239/projects/mlcs/mlcs16/final_feats.csv')
(pd.DataFrame(bigout)).to_csv('/scratch/sv1239/projects/mlcs/mlcs16/final_outs.csv')
t1=time.time()
print "time in sec:",t1-t0