Dear Abhijit, If you think that table.CAPM is the culprit, you could run the call to such function in R on both platforms using Rprof to check which part of the function is producing the bottleneck.
Best regards, Carlos J. Gil Bellosta http://www.datanalytics.com 2010/5/19 Abhijit Bera <abhib...@gmail.com>: > Update: it appears that the time taken isn't so much on the Data conversion. > The maximum time taken is in CAPM calculation. :( Anyone know why the CAPM > calculation would be faster on Windows? > > On Wed, May 19, 2010 at 5:51 PM, Abhijit Bera <abhib...@gmail.com> wrote: > >> Hi >> >> This is my function. It serves an HTML page after the calculations. I'm >> connecting to a MSSQL DB using pyodbc. >> >> def CAPM(self,client): >> >> r=self.r >> >> cds="1590" >> bm="20559" >> >> d1 = [] >> v1 = [] >> v2 = [] >> >> >> print"Parsing GET Params" >> >> params=client.g[1].split("&") >> >> for items in params: >> item=items.split("=") >> >> if(item[0]=="cds"): >> cds=unquote(item[1]) >> elif(item[0]=="bm"): >> bm=unquote(item[1]) >> >> print "cds: %s bm: %s" % (cds,bm) >> >> print "Fetching data" >> >> t3=datetime.now() >> >> for row in self.cursor.execute("select * from (select * from ( >> select co_code,dlyprice_date,dlyprice_close from feed_dlyprice P where >> co_code in (%s,%s) ) DataTable PIVOT ( max(dlyprice_close) FOR co_code IN >> ([%s],[%s]) )PivotTable ) a order by dlyprice_date" %(cds,bm,cds,bm)): >> d1.append(str(row[0])) >> v1.append(row[1]) >> v2.append(row[2]) >> >> t4=datetime.now() >> >> t1=datetime.now() >> >> print "Calculating" >> >> d1.pop(0) >> d1vec = robjects.StrVector(d1) >> v1vec = robjects.FloatVector(v1) >> v2vec = robjects.FloatVector(v2) >> >> r1 = r('Return.calculate(%s)' %v1vec.r_repr()) >> r2 = r('Return.calculate(%s)' %v2vec.r_repr()) >> >> tl = robjects.rlc.TaggedList([r1,r2],tags=('Geo','Nifty')) >> df = robjects.DataFrame(tl) >> >> ts2 = r.timeSeries(df,d1vec) >> tsa = r.timeSeries(r1,d1vec) >> tsb = r.timeSeries(r2,d1vec) >> >> robjects.globalenv["ta"] = tsa >> robjects.globalenv["tb"] = tsb >> robjects.globalenv["t2"] = ts2 >> a = r('table.CAPM(ta,tb)') >> >> t2=datetime.now() >> >> >> page="<html><title>CAPM</title><body>Result:<br>%s<br>Time taken by >> DB:%s<br>Time taken by R:%s<br>Total time elapsed:%s<br></body></html>" >> %(str(a),str(t4-t3),str(t2-t1),str(t2-t3)) >> print "Serving page:" >> #print page >> >> self.serveResource(page,"text",client) >> >> >> >> On Linux >> Time taken by DB:0:00:00.024165 >> Time taken by R:0:00:05.572084 >> Total time elapsed:0:00:05.596288 >> >> On Windows >> Time taken by DB:0:00:00.112000 >> Time taken by R:0:00:02.355000 >> Total time elapsed:0:00:02.467000 >> >> Why is there such a huge difference in the time taken by R on the two >> platforms? Am I doing something wrong? It's my first Rpy2 code so I guess >> it's badly written. >> >> I'm loading the following libraries: >> 'PerformanceAnalytics','timeSeries','fPortfolio','fPortfolioBacktest' >> >> I'm using Rpy2 2.1.0 and R 2.11 >> >> Regards >> >> Abhijit Bera >> >> >> >> >> > > [[alternative HTML version deleted]] > > ______________________________________________ > R-devel@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel > ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
