################################################# ### ### 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,