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 <abhibera@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
>
>
>
>
>
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