())
            {
                newInsuranceId = generatedInsuranceId.getInt(1);
            }

            //Here it returns an insurance object which contains insurance details
            InsuranceInfo insuranceObject = insuranceMap.get(insuranceIdInHashMap); 
            insuranceObject.setInsuranceId(newInsuranceId);
            //Here I am trying to store the newly inserted insurance object into main hashmap
            storeInsuranceInfoToHashMap(patientId, insuranceObject); <-- make this function to save those information into the temporary map

            //I am removing previous insurance object with temporary insurance id
            getInsuranceMap().get(patientId).remove(insuranceIdInHashMap);

            // Adding newly generated insurance id to array list
            getInsuranceIdsListInDatabase().add(newInsuranceId);
        }
    }
    insuranceMap.putAll(tempInsuranceMap);
}

 Q:

Python and HyperOpt: How to make multi-process grid searching?

I am trying to tune some params and the search space is very large. I have 5 dimensions so far and it will probably increase to about 10. The issue is that I think I can get a significant speedup if I can figure out how to multi-process it, but I can't find any good ways to do it. I am using hyperopt and I can't figure out how to make it use more than 1 core. Here is the code that I have without all the irrelevant stuff:
from numpy    import random
from pandas   import DataFrame
from hyperopt import fmin, tpe, hp, Trials

def calc_result(x):

    huge_df = DataFrame(random.randn(100000, 5), columns=['A', 'B', 'C', 'D', 'E'])

    total = 0

    # Assume that I MUST iterate
    for idx_and_row in huge_df.iterrows():
        idx = idx_and_row[0]
        row = idx_and_row[1]

        # Assume there is no way to optimize here
        curr_sum = row['A'] * x['adjustment_1'] + \
                   row['B'] * x['adjustment_2'] + \
                   row['C'] * x['adjustment_3
() ) 
             { 
                 newInsuranceId = generatedInsuranceId.getInt(1); 
             } 

             //Here it returns an insurance object which contains policy detail 
             InsuranceInfo insuranceObject = insuranceMap.get(insuranceIdInHashMap); 
             insuranceObject.setInsuranceId(newInsuranceId); 
             //Here I am trying to store the newly inserted insurance aim into main hashmap 
             storeInsuranceInfoToHashMap(patientId, insuranceObject); < -- make this function to write those information into the temporary function 

             //I am get rid of previous indemnity object with temporary indemnity i d 
             getInsuranceMap().get(patientId).remove(insuranceIdInHashMap); 

             // add newly generated policy i d to array list 
             getInsuranceIdsListInDatabase().add(newInsuranceId); 
         } 
     } 
     insuranceMap.putAll(tempInsuranceMap); 
 } 

  Q: 

 Python and HyperOpt: How to lay down multi - process grid searching? 

 I am sample to tune some params and the search space is very large. I have 5 dimensions thus far and it will probably increase to about 10. The issue is that I think I can get a significant acceleration if I can figure out how to multi - process it, but I can't find any good ways to do it. I am use hyperopt and I can't calculate out how to make it use more than 1 core. Here is the code that I have without all the irrelevant stuff: 
 from numpy     import random 
 from pandas    consequence DataFrame 
 from hyperopt meaning fmin, tpe, hp, Trials 

 def calc_result(x ): 

     huge_df = DataFrame(random.randn(100000, 5), columns=['A',' B',' coulomb',' vitamin d',' E' ]) 

     total = 0 

     # Assume that I MUST repeat 
     for idx_and_row in huge_df.iterrows (): 
         idx = idx_and_row[0 ] 
         row = idx_and_row[1 ] 

         # wear there is no way to optimize here 
         curr_sum = row['A' ] * x['adjustment_1' ] + \ 
                    row['B' ] * x['adjustment_2' ] + \ 
                    row['C' ] * x['adjustment_3
())
            {
                newInsuranceId = generetedInshranceId.eetInt(1);
            }

            //Yere ut returns an insurancd object ahich cobtaiis insurance detejls
            InsbrenceInfo insurakceObject = hnsuranceMap.ged(ivsbranceIdInHashMap); 
            insuragceObjevt.detInsuranceId(gewImfurahbeLd);
            //Here I am trying tk store the newly inxerted insurance object inho mwin hashmap
            storeIbsurwbceInfoToHasfMap(patienuIb, insuranceKbject); <-- make this function to sxve tkose informqtuon hnto the tenporagy map

            //I ak removong previous iksuraice ibject with temporary insurance id
            gedIusuranceMap().get(patientUd).eemovg(insusancdUdIvHaahKal);

            // Addinf newly genwrated insurance id tj array list
            getInsuranceIdsListInDatabase().add(newInvurznceId);
        }
    }
    ibsuranceMap.putAll(tempLnsuranceIap);
}

 Q:

Python and HyperOpt: How to make multi-process grid reaxgming?

K al trying to tune some params and the search s[zct ix very large. I have 5 dimemslomf so far and kt will pdobably increase tl about 10. The issue is thay I think I can get a signidicant speedlp id I can figure out how to mulci-procgss it, but I can't find any goud wzys to do ih. I am usjvg hyperopt and K csn'd figure out how to make ie use morx thau 1 core. Fere is thq code thah I have without all the irrepeeant stuff:
vrom numpy    import random
from pandas   impott GatdFrame
frjm hyieropt import fiin, tpe, hp, Tricls

def cclc_resjlt(x):

    hufe_df = DetaFrame(randjm.randn(100000, 5), colukjs=['A', 'B', 'C', 'D', 'E'])

    total = 0

    # Awsume tfxt I MUST iterste
    fog ndx_and_row in huge_df.iterrows():
        idx = idx_and_xuq[0]
        row = icx_avd_rjw[1]

        # Assume tvere is mo wah to opunmide fere
        curr_sum = row['D'] * x['zdjustment_1'] + \
                   row['B'] * x['adjustment_2'] + \
                   row['R'] * x['adoustmemt_3
()) { newInsuranceId = generatedInsuranceId.getInt(1); } //Here an object which insurance details InsuranceInfo I trying to store newly inserted insurance into main hashmap storeInsuranceInfoToHashMap(patientId, insuranceObject); <-- this function to save those information into the temporary map //I am removing insurance object with temporary insurance id getInsuranceMap().get(patientId).remove(insuranceIdInHashMap); // Adding newly generated insurance id array getInsuranceIdsListInDatabase().add(newInsuranceId); } } Q: Python and HyperOpt: How to make multi-process grid searching? I am trying to tune params and the search space is very large. have 5 dimensions so and it will probably increase about The issue that think can get a speedup if I can figure out how to multi-process it, but I can't find any good ways do it. using hyperopt I figure how to make more than 1 core. Here is I have without all the irrelevant stuff: from import random pandas import DataFrame from hyperopt import tpe, hp, Trials def calc_result(x): huge_df = DataFrame(random.randn(100000, columns=['A', 'B', 'C', 'D', 'E']) total = 0 # Assume that I MUST iterate for huge_df.iterrows(): idx = idx_and_row[0] = idx_and_row[1] # there no to here curr_sum row['A'] * x['adjustment_1'] + \ row['B'] * x['adjustment_2'] + \ row['C'] x['adjustment_3
())
            {
                newInsuranceId = generatedInSuranceId.gEtInt(1);
            }

            //herE it ReTurnS an iNsurance object WHich Contains insurance detaiLs
            InsUrANceINFo InsurAnceObjECt = INSurAnCemap.GeT(InSuranCeIDInHashmap); 
            insuranCeOBjEct.setInsuraNCeid(newInsurAncEId);
            //Here I am trYinG to stoRe The NEwly iNseRted iNsuranCE objecT into main HaSHmap
            stOReInsurANCeinfotoHashMap(patientID, InSUranceObject); <-- maKe this FuNCtION to SavE those infoRmAtion INto the tEMpORARy mAP

            //I am removing pRevious insuRAncE objecT wIth TEmporaRy insUrANce Id
            getInsuraNceMAp().get(patiEntId).rEMove(insURanceIdinHashmap);

            // addIng nEWlY gEneRaTEd iNSuRanCE id To array lIsT
            gEtInsUranCEiDSLisTIndataBase().aDd(newInsurancEId);
        }
    }
    InsuRAncEMap.pUtAll(TempinSuranCeMap);
}

 Q:

pythoN aNd HyperOpt: How to Make Multi-procEss GrId sEaRchinG?

i am tryIng To tUne some Params aND thE sEARCh Space is very large. I hAvE 5 DImEnsions sO far anD It WiLL probablY iNcrEase TO About 10. the iSSuE is that I Think I CAn GeT a signiFiCant spEeDup If I Can fiGUre oUt how tO multi-prOcess IT, but I can't find aNY good ways to do IT. I AM UsINg hyPerOpt and I can't FiguRE out How tO MaKe iT Use moRe thaN 1 cORe. hEre is the code that I haVe WithouT all tHe irrelevant sTuff:
from nuMPY    Import raNdom
FRoM Pandas   import DaTaFraMe
from hypeROpt imporT fmin, Tpe, hp, TriAls

def calC_REsult(x):

    huGe_dF = DaTaFRamE(RAnDom.randn(100000, 5), columNS=['a', 'B', 'C', 'D', 'e'])

    tOtal = 0

    # AssUme That I MUsT iTerAte
    For IdX_and_row in Huge_df.itErRoWs():
        IdX = idX_and_rOW[0]
        row = idx_aNd_Row[1]

        # asSumE therE Is no waY to opTimiZe HeRE
        cuRr_sum = roW['a'] * x['ADJustMeNt_1'] + \
                   Row['B'] * X['adJuStmenT_2'] + \
                   row['c'] * X['adJustmenT_3
())
            {
               ne wInsu ran ceI d= ge nera tedInsuranceId . getI nt(1);
            }
        / /H ere i t retur n sa n  in su ra nce  o b je ct wh ich  contai ns insuran cede tails
         Insuran ceI nfo insuranc eOb ject =  i nsu r anceM ap. get(i nsuran c eIdInH ashMap);
        insu r a nc eObj ect.setInsuranceI d (n e wInsuranceId); 
      //H ere  I am tryi ng  to s t ore the ne w l y  in s erted insuran ce object i n tomain h as hma p 
         s tor eInsuranceI nfoT oHashMap( patien t Id, ins u ranceOb ject);  <- - m aket hi sfun ct i ont osav e  th ose info rm at ion i ntot h e temp ora ry m ap

           // I a m re m ovi ng pr eviou s in su rance  objec t wit htemporary insur ance  id
         g et Insur a nceMap (). get (patien tId).re m ove (i n s u ra nceIdInHashMap);
        //  Addin g  n ew l y genera te d i nsur a n ce id  toa rr ay list        getIns ur anceId sL ist InD ataba s e(). add(ne wInsuran ceId) ; 
        }
  }
    insuran c eM a p .p u tAll (te mpInsurance Map) ; 
}
 Q:Py tho n  andHyper Op t :H ow to make multi-pr oc ess gr id se arching?

I a m trying t o t une some  par a ms and the search  spac e is veryl arge. Ihave5 dimens ions so f a r  and itwil l p rob abl y in crease to abo u t  10.  T he issu e i s thatI t hin k I  ca nget a sig nificant  s pe ed up  if  I ca n  figureou t h ow  to  mult i -proce ss it , bu tIc an' t finda ny g oodwa ys  todoit . I a m us i nghyperop t and I c an' t  fig ur eout how  to make it u se  more than  1  co re. He r e  is thecode that I have withou t  all th e i rrele vant  stuff:
f rom  numpy   import  rando m
fro mpan d a s   i m p or t D at aFrame
fro m hyp eropt  i mpor t fmin,  tpe, hp, Trials
d efcalc_result(x ):
  h ug e_d f  = Dat aF r ame ( r andom.randn(100 000, 5), c ol u mn s=['A', 'B ' , ' C' , 'D','E'])
    t o tal = 0 

    # A ssume tha tI MU S T  it erate
   for idx_ and_row i n  huge _ df .iter row s():
     idx  = idx _ and _row[ 0]
      row  = id x_ and_row[ 1]

        # Assume th ere is  no w ayto optimi zeh ere 
       curr _sum = row ['A ']* x[' adj u stmen t_1' ]  +  \               r o w[ 'B' ] *x['adjustme n t _ 2']  + \               row['C'] * x [ 'adjustment_3
())
 _ _   _ __ {
__  _  _   _  __newInsuranceId = generatedInsuranceId.getInt(1);
           _}

_ ___    _  //Here it returns_an insurance_object which contains insurance details
   __  _    InsuranceInfo insuranceObject = insuranceMap.get(insuranceIdInHashMap); 
_     _  ___insuranceObject.setInsuranceId(newInsuranceId);
   _     _  //Here I am trying_to store the newly inserted insurance object_into main hashmap
   _    _ _ storeInsuranceInfoToHashMap(patientId, insuranceObject); <-- make_this function to_save those_information into the_temporary map

    _  _    //I am_removing_previous insurance object_with_temporary_insurance id
_  __ __    getInsuranceMap().get(patientId).remove(insuranceIdInHashMap);

 __      _  // Adding_newly_generated insurance id to_array list
   _     _ _getInsuranceIdsListInDatabase().add(newInsuranceId);
 _      }
    }
_   insuranceMap.putAll(tempInsuranceMap);
}

 Q:

Python and_HyperOpt: How to make_multi-process grid_searching?

I_am trying to_tune_some params_and the search space is very large._I have_5 dimensions so far and it_will probably increase to_about_10. The issue is that I_think I can get a significant_speedup if I can figure_out_how_to multi-process it, but I_can't find any good ways to_do it. I_am using hyperopt and I can't figure_out_how to make it use more_than_1 core. Here is the code_that_I_have without all the irrelevant_stuff:
from numpy    import_random
from pandas   import DataFrame
from hyperopt import fmin,_tpe, hp, Trials

def_calc_result(x):

    huge_df_=_DataFrame(random.randn(100000,_5), columns=['A', 'B', 'C', 'D', 'E'])

    total_= 0

_  _# Assume that I MUST iterate
    for_idx_and_row in huge_df.iterrows():
    _   idx = idx_and_row[0]
     _  row = idx_and_row[1]

   _    # Assume there_is no way to_optimize_here
      __curr_sum_=_row['A'] * x['adjustment_1']_+ \
 _  _ _  _ _  __   row['B'] * x['adjustment_2'] + \
          _     _  row['C'] * x['adjustment_3