view ABSapprox.py @ 2:a98abf25c5c9

ABSapprox.py: Fixed: number of processors in Pool() is default, not fixed to 4
ABSapprox.py: Fixed: imported matplotlib.colors

# -*- coding: utf-8 -*-
"""
Created on Sat Nov 05 18:08:40 2011

@author: Nic
"""

import numpy as np
import scipy.io
import math
import os
import time

import stdparams
import pyCSalgos.Analysis

#==========================
# Pool initializer function (multiprocessing)
# Needed to pass the shared variable to the worker processes
# The variables must be global in the module in order to be seen later in run_once_tuple()
# see http://stackoverflow.com/questions/1675766/how-to-combine-pool-map-with-array-shared-memory-in-python-multiprocessing
#==========================
def initProcess(share, njobs):
    import sys
    currmodule = sys.modules[__name__]
    currmodule.proccount = share
    currmodule.njobs = njobs
          
#==========================
# Interface run functions
#==========================
def run_mp(std=stdparams.std2,ncpus=None):
  
  algosN,algosL,d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata,savedataname,doshowplot,dosaveplot,saveplotbase,saveplotexts = std()
  run_multi(algosN, algosL, d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata=dosavedata,savedataname=savedataname,\
            doparallel=True, ncpus=ncpus,\
            doshowplot=doshowplot,dosaveplot=dosaveplot,saveplotbase=saveplotbase,saveplotexts=saveplotexts)

def run(std=stdparams.std2):
  algosN,algosL,d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata,savedataname,doshowplot,dosaveplot,saveplotbase,saveplotexts = std()
  run_multi(algosN, algosL, d,sigma,deltas,rhos,lambdas,numvects,SNRdb,dosavedata=dosavedata,savedataname=savedataname,\
            doparallel=False,\
            doshowplot=doshowplot,dosaveplot=dosaveplot,saveplotbase=saveplotbase,saveplotexts=saveplotexts)  
#==========================
# Main functions  
#==========================
def run_multi(algosN, algosL, d, sigma, deltas, rhos, lambdas, numvects, SNRdb,
            doparallel=False, ncpus=None,\
            doshowplot=False, dosaveplot=False, saveplotbase=None, saveplotexts=None,\
            dosavedata=False, savedataname=None):

  print "This is analysis recovery ABS approximation script by Nic"
  print "Running phase transition ( run_multi() )"

  # Not only for parallel  
  #if doparallel:
  import multiprocessing
  # Shared value holding the number of finished processes
  # Add it as global of the module
  import sys
  currmodule = sys.modules[__name__]
  currmodule.proccount = multiprocessing.Value('I', 0) # 'I' = unsigned int, see docs (multiprocessing, array)
    
  if dosaveplot or doshowplot:
    try:
      import matplotlib
      if doshowplot or os.name == 'nt':
        print "Importing matplotlib with default (GUI) backend... ",
      else:
        print "Importing matplotlib with \"Cairo\" backend... ",
        matplotlib.use('Cairo')
      import matplotlib.pyplot as plt
      import matplotlib.cm as cm
      import matplotlib.colors as mcolors
      print "OK"        
    except:
      print "FAIL"
      print "Importing matplotlib.pyplot failed. No figures at all"
      print "Try selecting a different backend"
      doshowplot = False
      dosaveplot = False
  
  # Print summary of parameters
  print "Parameters:"
  if doparallel:
    if ncpus is None:
      print "  Running in parallel with default threads using \"multiprocessing\" package"
    else:
      print "  Running in parallel with",ncpus,"threads using \"multiprocessing\" package"
  else:
    print "Running single thread"
  if doshowplot:
    print "  Showing figures"
  else:
    print "  Not showing figures"
  if dosaveplot:
    print "  Saving figures as "+saveplotbase+"* with extensions ",saveplotexts
  else:
    print "  Not saving figures"
  print "  Running algorithms",[algotuple[1] for algotuple in algosN],[algotuple[1] for algotuple in algosL]
  
  nalgosN = len(algosN)  
  nalgosL = len(algosL)
  
  meanmatrix = dict()
  elapsed = dict()
  for i,algo in zip(np.arange(nalgosN),algosN):
    meanmatrix[algo[1]]   = np.zeros((rhos.size, deltas.size))
    elapsed[algo[1]] = 0
  for i,algo in zip(np.arange(nalgosL),algosL):
    meanmatrix[algo[1]]   = np.zeros((lambdas.size, rhos.size, deltas.size))
    elapsed[algo[1]] = np.zeros(lambdas.size)
  
  # Prepare parameters
  jobparams = []
  print "  (delta, rho) pairs to be run:"
  for idelta,delta in zip(np.arange(deltas.size),deltas):
    for irho,rho in zip(np.arange(rhos.size),rhos):
      
      # Generate data and operator
      Omega,x0,y,M,realnoise = generateData(d,sigma,delta,rho,numvects,SNRdb)
      
      #Save the parameters, and run after
      print "    delta = ",delta," rho = ",rho
      jobparams.append((algosN,algosL, Omega,y,lambdas,realnoise,M,x0))
  
  # Not only for parallel
  #if doparallel:
  currmodule.njobs = deltas.size * rhos.size  
  print "End of parameters"
  
  # Run
  jobresults = []
  
  if doparallel:
    pool = multiprocessing.Pool(None,initializer=initProcess,initargs=(currmodule.proccount,currmodule.njobs))
    jobresults = pool.map(run_once_tuple, jobparams)
  else:
    for jobparam in jobparams:
      jobresults.append(run_once_tuple(jobparam))

  # Read results
  idx = 0
  for idelta,delta in zip(np.arange(deltas.size),deltas):
    for irho,rho in zip(np.arange(rhos.size),rhos):
      mrelerrN,mrelerrL,addelapsed = jobresults[idx]
      idx = idx+1
      for algotuple in algosN: 
        meanmatrix[algotuple[1]][irho,idelta] = 1 - mrelerrN[algotuple[1]]
        if meanmatrix[algotuple[1]][irho,idelta] < 0 or math.isnan(meanmatrix[algotuple[1]][irho,idelta]):
          meanmatrix[algotuple[1]][irho,idelta] = 0
        elapsed[algotuple[1]] = elapsed[algotuple[1]] + addelapsed[algotuple[1]]
      for algotuple in algosL:
        for ilbd in np.arange(lambdas.size):
          meanmatrix[algotuple[1]][ilbd,irho,idelta] = 1 - mrelerrL[algotuple[1]][ilbd]
          if meanmatrix[algotuple[1]][ilbd,irho,idelta] < 0 or math.isnan(meanmatrix[algotuple[1]][ilbd,irho,idelta]):
            meanmatrix[algotuple[1]][ilbd,irho,idelta] = 0
          elapsed[algotuple[1]][ilbd] = elapsed[algotuple[1]][ilbd] + addelapsed[algotuple[1]][ilbd]

  # Save
  if dosavedata:
    tosave = dict()
    tosave['meanmatrix'] = meanmatrix
    tosave['elapsed'] = elapsed
    tosave['d'] = d
    tosave['sigma'] = sigma
    tosave['deltas'] = deltas
    tosave['rhos'] = rhos
    tosave['numvects'] = numvects
    tosave['SNRdb'] = SNRdb
    tosave['lambdas'] = lambdas
    # Save algo names as cell array
    obj_arr = np.zeros((len(algosN)+len(algosL),), dtype=np.object)
    idx = 0
    for algotuple in algosN+algosL:
      obj_arr[idx] = algotuple[1]
      idx = idx+1
    tosave['algonames'] = obj_arr
    try:
      scipy.io.savemat(savedataname, tosave)
    except:
      print "Save error"
  # Show
  if doshowplot or dosaveplot:
    for algotuple in algosN:
      algoname = algotuple[1]
      plt.figure()
      plt.imshow(meanmatrix[algoname], cmap=cm.gray, norm=mcolors.Normalize(0,1), interpolation='nearest',origin='lower')
      if dosaveplot:
        for ext in saveplotexts:
          plt.savefig(saveplotbase + algoname + '.' + ext, bbox_inches='tight')
    for algotuple in algosL:
      algoname = algotuple[1]
      for ilbd in np.arange(lambdas.size):
        plt.figure()
        plt.imshow(meanmatrix[algoname][ilbd], cmap=cm.gray, norm=mcolors.Normalize(0,1), interpolation='nearest',origin='lower')
        if dosaveplot:
          for ext in saveplotexts:
            plt.savefig(saveplotbase + algoname + ('_lbd%.0e' % lambdas[ilbd]) + '.' + ext, bbox_inches='tight')
    if doshowplot:
      plt.show()
    
  print "Finished."
  
def run_once_tuple(t):
  results = run_once(*t)
  import sys
  currmodule = sys.modules[__name__]  
  currmodule.proccount.value = currmodule.proccount.value + 1
  print "================================"
  print "Finished job",currmodule.proccount.value,"of",currmodule.njobs
  print "================================"
  return results

def run_once(algosN,algosL,Omega,y,lambdas,realnoise,M,x0):
  
  d = Omega.shape[1]  
  
  nalgosN = len(algosN)  
  nalgosL = len(algosL)
  
  
  xrec = dict()
  err = dict()
  relerr = dict()
  elapsed = dict()

  # Prepare storage variables for algorithms non-Lambda
  for i,algo in zip(np.arange(nalgosN),algosN):
    xrec[algo[1]]    = np.zeros((d, y.shape[1]))
    err[algo[1]]     = np.zeros(y.shape[1])
    relerr[algo[1]]  = np.zeros(y.shape[1])
    elapsed[algo[1]] = 0
  # Prepare storage variables for algorithms with Lambda    
  for i,algo in zip(np.arange(nalgosL),algosL):
    xrec[algo[1]]    = np.zeros((lambdas.size, d, y.shape[1]))
    err[algo[1]]     = np.zeros((lambdas.size, y.shape[1]))
    relerr[algo[1]]  = np.zeros((lambdas.size, y.shape[1]))
    elapsed[algo[1]] = np.zeros(lambdas.size)
  
  # Run algorithms non-Lambda
  for iy in np.arange(y.shape[1]):
    for algofunc,strname in algosN:
      epsilon = 1.1 * np.linalg.norm(realnoise[:,iy])
      try:
        timestart = time.time()
        xrec[strname][:,iy] = algofunc(y[:,iy],M,Omega,epsilon)
        elapsed[strname] = elapsed[strname] + (time.time() - timestart)
      except pyCSalgos.BP.l1qec.l1qecInputValueError as e:
        print "Caught exception when running algorithm",strname," :",e.message
      except pyCSalgos.NESTA.NESTA.NestaError as e:
        print "Caught exception when running algorithm",strname," :",e.message
      err[strname][iy]    = np.linalg.norm(x0[:,iy] - xrec[strname][:,iy])
      relerr[strname][iy] = err[strname][iy] / np.linalg.norm(x0[:,iy])
  for algofunc,strname in algosN:
    print strname,' : avg relative error = ',np.mean(relerr[strname])  

  # Run algorithms with Lambda
  for ilbd,lbd in zip(np.arange(lambdas.size),lambdas):
    for iy in np.arange(y.shape[1]):
      D = np.linalg.pinv(Omega)
      U,S,Vt = np.linalg.svd(D)
      for algofunc,strname in algosL:
        epsilon = 1.1 * np.linalg.norm(realnoise[:,iy])
        try:
          timestart = time.time()
          gamma = algofunc(y[:,iy],M,Omega,D,U,S,Vt,epsilon,lbd)
          elapsed[strname][ilbd] = elapsed[strname][ilbd] + (time.time() - timestart)
        except pyCSalgos.BP.l1qc.l1qcInputValueError as e:
          print "Caught exception when running algorithm",strname," :",e.message
        xrec[strname][ilbd,:,iy] = np.dot(D,gamma)
        err[strname][ilbd,iy]    = np.linalg.norm(x0[:,iy] - xrec[strname][ilbd,:,iy])
        relerr[strname][ilbd,iy] = err[strname][ilbd,iy] / np.linalg.norm(x0[:,iy])
    print 'Lambda = ',lbd,' :'
    for algofunc,strname in algosL:
      print '   ',strname,' : avg relative error = ',np.mean(relerr[strname][ilbd,:])

  # Prepare results
  mrelerrN = dict()
  for algotuple in algosN:
    mrelerrN[algotuple[1]] = np.mean(relerr[algotuple[1]])
  mrelerrL = dict()
  for algotuple in algosL:
    mrelerrL[algotuple[1]] = np.mean(relerr[algotuple[1]],1)
  
  return mrelerrN,mrelerrL,elapsed



def generateData(d,sigma,delta,rho,numvects,SNRdb):

  # Process parameters
  noiselevel = 1.0 / (10.0**(SNRdb/10.0));
  p = round(sigma*d);
  m = round(delta*d);
  l = round(d - rho*m);
  
  # Generate Omega and data based on parameters
  Omega = pyCSalgos.Analysis.Generate_Analysis_Operator(d, p);
  # Optionally make Omega more coherent
  U,S,Vt = np.linalg.svd(Omega);
  Sdnew = S * (1+np.arange(S.size)) # Make D coherent, not Omega!
  Snew = np.vstack((np.diag(Sdnew), np.zeros((Omega.shape[0] - Omega.shape[1], Omega.shape[1]))))
  Omega = np.dot(U , np.dot(Snew,Vt))

  # Generate data  
  x0,y,M,Lambda,realnoise = pyCSalgos.Analysis.Generate_Data_Known_Omega(Omega, d,p,m,l,noiselevel, numvects,'l0');
  
  return Omega,x0,y,M,realnoise


def runsingleexampledebug():
  d = 50.0;
  sigma = 2.0
  delta = 0.9
  rho = 0.05
  numvects = 20; # Number of vectors to generate
  SNRdb = 7.;    # This is norm(signal)/norm(noise), so power, not energy
  lbd = 10000
  
  Omega,x0,y,M,realnoise = generateData(d,sigma,delta,rho,numvects,SNRdb)
  D = np.linalg.pinv(Omega)
  U,S,Vt = np.linalg.svd(D)
 
  xrec   = np.zeros((d, y.shape[1]))
  err    = np.zeros((y.shape[1]))
  relerr = np.zeros((y.shape[1]))  
 
  for iy in np.arange(y.shape[1]):
    epsilon = 1.1 * np.linalg.norm(realnoise[:,iy])
    gamma = run_sl0(y[:,iy],M,Omega,D,U,S,Vt,epsilon,lbd)
    xrec[:,iy] = np.dot(D,gamma)
    err[iy]    = np.linalg.norm(x0[:,iy] - xrec[:,iy])
    relerr[iy] = err[iy] / np.linalg.norm(x0[:,iy])    
  
  print "Finished runsingleexampledebug()"
  
# Script main
if __name__ == "__main__":
  #import cProfile
  #cProfile.run('mainrun()', 'profile')    
  run(stdparams.stdtest)
  #runsingleexampledebug()