#################################################
###
### show pipeline effects one by one
###
################################################
print "Performing LASSO.",
behaviors = ['AngleVelocity', 'Eigenworm3', 'Eigenworm2']
fig = plt.figure('Lasso 1',(2*6.8, 1.7*len(behaviors)))
outer_grid = gridspec.GridSpec(2, 1, hspace=0.25, wspace=0.25)
results = {'LASSO':{}}
results['LASSO'] = dr.runLasso(data, pars, testIndices, trainingsIndices, plot=0, behaviors = behaviors)
# calculate how much more neurons contribute
tmpDict = dr.scoreModelProgression(data, results, testIndices, trainingsIndices, pars, fitmethod = 'LASSO', behaviors = behaviors)
for tmpKey in tmpDict.keys():
results['LASSO'][tmpKey].update(tmpDict[tmpKey])
print 'Done with Lasso. Plotting...'
print results
gridloc = outer_grid[0]
plotSingleLinearFit(fig, gridloc , pars, results['LASSO'], data, trainingsIndices, testIndices, behaviors)
plt.show()
#es
#################################################
###
### plot velocity versus cms velocity
###
#
##############################################
if lasso:
print "Performing LASSO.",
for kindex, key in enumerate(keyList):
print key
splits = resultDict[key]['Training']
resultDict[key]['LASSO'] = dr.runLasso(dataSets[key],
pars,
splits,
plot=1,
behaviors=behaviors)
# calculate how much more neurons contribute
tmpDict = dr.scoreModelProgression(dataSets[key],
resultDict[key],
splits,
pars,
fitmethod='LASSO',
behaviors=behaviors)
for tmpKey in tmpDict.keys():
resultDict[key]['LASSO'][tmpKey].update(tmpDict[tmpKey])
tmpDict = dr.reorganizeLinModel(dataSets[key],
resultDict[key],
splits,
pars,
fitmethod='LASSO',
behaviors=behaviors)
for tmpKey in tmpDict.keys():
resultDict[key]['LASSO'][tmpKey] = tmpDict[tmpKey]
mp.plotLinearModelResults(dataSets,