def SCCA_r(X,Y, n_components, pen):
df_X = pd.DataFrame(X)
df_Y = pd.DataFrame(Y)
rmat_X = com.convert_to_r_matrix(df_X)
rmat_Y = com.convert_to_r_matrix(df_Y)
ri.globalenv['X'] = rmat_X
ri.globalenv['Y'] = rmat_Y
com.r(
"""
out <- CCA(x = X, z = Y, K = %i, niter = 100, standardize = FALSE,
penaltyx = %f, penaltyz = %f)
""" % (n_components, pen[0], pen[1]))
# convert the results back to dataframes and then to numpy arrays
df_u = com.convert_robj(com.r('out[1]'))['u']
df_v = com.convert_robj(com.r('out[2]'))['v']
cors = com.convert_robj(com.r('out[16]'))['cors']
x_loadings = df_u.as_matrix()
y_loadings = df_v.as_matrix()
cors = np.array(cors)
loadings = (x_loadings, y_loadings)
return loadings, cors
def bench_xts(n=100000, pct_overlap=0.20):
from pandas.rpy.common import r
r('a <- 5')
xrng = '1:%d' % n
start = n * pct_overlap + 1
end = n + start - 1
yrng = '%d:%d' % (start, end)
r('library(xts)')
iterations = 500
kinds = ['left', 'outer', 'inner']
result = {}
for kind in kinds:
r('x <- xts(rnorm(%d), as.POSIXct(Sys.Date()) + %s)' % (n, xrng))
r('y <- xts(rnorm(%d), as.POSIXct(Sys.Date()) + %s)' % (n, yrng))
stmt = 'for (i in 1:%d) merge(x, y, join="%s")' % (iterations, kind)
elapsed = r('as.list(system.time(%s, gcFirst=F))$elapsed' % stmt)[0]
result[kind] = (elapsed / iterations) * 1000
return Series(result)
def bench_xts(n=100000, pct_overlap=0.20):
from pandas.rpy.common import r
r('a <- 5')
xrng = '1:%d' % n
start = n * pct_overlap + 1
end = n + start - 1
yrng = '%d:%d' % (start, end)
r('library(xts)')
iterations = 500
kinds = ['left', 'outer', 'inner']
result = {}
for kind in kinds:
r('x <- xts(rnorm(%d), as.POSIXct(Sys.Date()) + %s)' % (n, xrng))
r('y <- xts(rnorm(%d), as.POSIXct(Sys.Date()) + %s)' % (n, yrng))
stmt = 'for (i in 1:%d) merge(x, y, join="%s")' % (iterations, kind)
elapsed = r('as.list(system.time(%s, gcFirst=F))$elapsed' % stmt)[0]
result[kind] = (elapsed / iterations) * 1000
return Series(result)
iters = [1000, 1000, 100, 1]
fancy_2d = []
take_2d = []
cython_2d = []
n = 1000
def _timeit(stmt, size, k=5, iters=1000):
timer = timeit.Timer(stmt=stmt, setup=setup % (sz, k))
return timer.timeit(n) / n
for sz, its in zip(sizes, iters):
print(sz)
fancy_2d.append(_timeit('arr[indexer]', sz, iters=its))
take_2d.append(_timeit('arr.take(indexer, axis=0)', sz, iters=its))
cython_2d.append(_timeit('lib.take_axis0(arr, indexer)', sz, iters=its))
df = DataFrame({'fancy': fancy_2d,
'take': take_2d,
'cython': cython_2d})
print(df)
from pandas.rpy.common import r
r('mat <- matrix(rnorm(50000), nrow=10000, ncol=5)')
r('set.seed(12345')
r('indexer <- sample(1:10000)')
r('mat[indexer,]')
sizes = [100, 1000, 10000, 100000]
iters = [1000, 1000, 100, 1]
fancy_2d = []
take_2d = []
cython_2d = []
n = 1000
def _timeit(stmt, size, k=5, iters=1000):
timer = timeit.Timer(stmt=stmt, setup=setup % (sz, k))
return timer.timeit(n) / n
for sz, its in zip(sizes, iters):
print(sz)
fancy_2d.append(_timeit('arr[indexer]', sz, iters=its))
take_2d.append(_timeit('arr.take(indexer, axis=0)', sz, iters=its))
cython_2d.append(_timeit('lib.take_axis0(arr, indexer)', sz, iters=its))
df = DataFrame({'fancy': fancy_2d, 'take': take_2d, 'cython': cython_2d})
print(df)
from pandas.rpy.common import r
r('mat <- matrix(rnorm(50000), nrow=10000, ncol=5)')
r('set.seed(12345')
r('indexer <- sample(1:10000)')
r('mat[indexer,]')
penalty = (pen_brain,pen_behave) data = (X, Y) ''' define CCA ''' import readline import rpy2.rinterface as ri import pandas as pd import pandas.rpy.common as com # load the Stanford package for SCCA, it should load dependent packages as well # please change the path. com.r( ''' library(plyr) library(impute) library(Rcpp) library(PMA) ''') def SCCA_r(X,Y, n_components, pen): df_X = pd.DataFrame(X) df_Y = pd.DataFrame(Y) rmat_X = com.convert_to_r_matrix(df_X) rmat_Y = com.convert_to_r_matrix(df_Y) ri.globalenv['X'] = rmat_X ri.globalenv['Y'] = rmat_Y
pen_behave = 0.5
n_areas = 14
WD = 'U:\\PhDProjects\\Project_CCA'
behavefn = 'select_data_MWQ_sessionMean.npy'
beh_keysfn = 'select_keys_MWQ.npy'
rscorrfn = 'cs_cross_corr_Bzdok_DMN14.npy'
exp_var_fn = 'BzdokDMN14_MWQ_exp_var_penBrain%1.1f_penBehav%1.1f.pdf' %(pen_brain, pen_behave)
import pandas.rpy.common as com
# load the Stanford package for SCCA, it should load dependent packages as well
# please change the path.
com.r(
'''
library(plyr, lib.loc='U:/My Documents/R/win-library/3.2')
library(impute, lib.loc='U:/My Documents/R/win-library/3.2')
library(Rcpp, lib.loc='U:/My Documents/R/win-library/3.2')
library(PMA, lib.loc='U:/My Documents/R/win-library/3.2')
''')
###################################################################################################
from os.path import expanduser
from joblib import load
import numpy as np
import matplotlib.pyplot as plt
import os
import pandas as pd
#set cd here
pen_behave = 0.5
n_areas = 14
WD = 'U:\\PhDProjects\\Project_CCA'
behavefn = 'select_data_MWQ_sessionMean.npy'
beh_keysfn = 'select_keys_MWQ.npy'
rscorrfn = 'cs_cross_corr_Bzdok_DMN14.npy'
exp_var_fn = 'BzdokDMN14_MWQ_exp_var_penBrain%1.1f_penBehav%1.1f.pdf' % (
pen_brain, pen_behave)
import pandas.rpy.common as com
# load the Stanford package for SCCA, it should load dependent packages as well
# please change the path.
com.r('''
library(plyr, lib.loc='U:/My Documents/R/win-library/3.2')
library(impute, lib.loc='U:/My Documents/R/win-library/3.2')
library(Rcpp, lib.loc='U:/My Documents/R/win-library/3.2')
library(PMA, lib.loc='U:/My Documents/R/win-library/3.2')
''')
###################################################################################################
from os.path import expanduser
from joblib import load
import numpy as np
import matplotlib.pyplot as plt
import os
import pandas as pd
#set cd here
os.chdir(WD)