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getcs.py
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# purpose: read in category separation for each run, save for all runs
import os
import glob
import argparse
import numpy as np # type: ignore
import sys
# Add current working dir so main can be run from the top level rtAttenPenn directory
sys.path.append(os.getcwd())
import rtfMRI.utils as utils
import rtfMRI.ValidationUtils as vutils
from rtfMRI.RtfMRIClient import loadConfigFile
from rtfMRI.Errors import ValidationError
from rtAtten.RtAttenModel import getSubjectDayDir
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from rtfMRI.StructDict import StructDict, MatlabStructDict
from sklearn.metrics import roc_auc_score
import matplotlib.pyplot as plt
import scipy
from rtAtten.Test_L2_RLR_realtime import Test_L2_RLR_realtime
from sklearn import metrics
# run for each subject after subject completes all days
def getCategSepData(subjectNum):
rtAttenPath = '/data/jag/cnds/amennen/rtAttenPenn/fmridata/behavdata/gonogo'
subjectDir = rtAttenPath + '/' + 'subject' + str(subjectNum)
# this will be less annoying but will just have to average and remember padding zeros
all_matlab_evidence = np.zeros((9,100,3))
all_python_evidence = np.zeros((9,100,3))
for d in np.arange(3):
subjectDay = d + 1
matDataDir= rtAttenPath + '/' + 'subject' + str(subjectNum) + '/' + 'day' + str(subjectDay)
pyDataDir= rtAttenPath + '/' + 'subject' + str(subjectNum) + '/' + 'day' + str(subjectDay)
if subjectDay==1:
# then we have 7 runs
nRuns=7
if subjectNum == 106:
nRuns=6
elif subjectDay==2:
nRuns=9
elif subjectDay==3:
nRuns=8
#n_feedback_runs = nRuns - 1 # no feedback in first run
print(np.arange(2,nRuns+1))
#def getCategSepData(matDataDir, pyDataDir, runId):
for RUN in np.arange(2,nRuns+1):
runId = RUN # because 0 index, skip first run
runDir = 'run'+str(runId)+'/'
# Check how well raw_sm_filt_z values match
matPatternsFn = utils.findNewestFile(matDataDir, runDir+'patternsdata_'+str(runId)+'*.mat')
pyBlkGrp1Fn = utils.findNewestFile(pyDataDir, 'blkGroup_r'+str(runId)+'_p1_*_py.mat')
pyBlkGrp2Fn = utils.findNewestFile(pyDataDir, 'blkGroup_r'+str(runId)+'_p2_*_py.mat')
print("Getting rt classifer data from: Matlab {}, Python {} {}".format(matPatternsFn, pyBlkGrp1Fn, pyBlkGrp2Fn))
matPatterns = utils.loadMatFile(matPatternsFn)
pyBlkGrp1 = utils.loadMatFile(pyBlkGrp1Fn)
pyBlkGrp2 = utils.loadMatFile(pyBlkGrp2Fn)
mat_nTRs = matPatterns.raw.shape[0]
pyp1_nTRs = pyBlkGrp1.raw.shape[0]
pyp2_nTRs = pyBlkGrp2.raw.shape[0]
py_nTRs = pyp1_nTRs + pyp2_nTRs
mat_nVoxels = matPatterns.raw.shape[1]
py_nVoxels = pyBlkGrp1.raw.shape[1]
if mat_nTRs != py_nTRs or mat_nVoxels != py_nVoxels:
raise ValidationError("Number of TRs or Voxels don't match: nTRs m{} p{}, nVoxels m{} p{}".
format(mat_nTRs, py_nTRs, mat_nVoxels, py_nVoxels))
matPatterns.categoryseparation
relevant_TR = np.argwhere(np.sum(matPatterns.regressor,0))[:,0]
RT_TR = relevant_TR[int(len(relevant_TR)/2):]
mat_RT_CS = matPatterns.categoryseparation[:,RT_TR][0,:]
pyCombined_categoryseparation = np.full((py_nTRs,), np.nan)
pyCombined_categoryseparation[0:pyp1_nTRs] = pyBlkGrp1.categoryseparation
pyCombined_categoryseparation[pyp1_nTRs:] = pyBlkGrp2.categoryseparation
py_RT_CS = pyCombined_categoryseparation[RT_TR]
all_matlab_evidence[RUN-2,:,d] = mat_RT_CS
all_python_evidence[RUN-2,:,d] = py_RT_CS
# now you have 2 (100,) arrays with the category separation from that feedback run
outfile = subjectDir + '/' 'realtimeevidence'
np.savez(outfile,mat=all_matlab_evidence,py=all_python_evidence)
def train_test_python_classifier(subjectNum):
ndays = 3
auc_score = np.zeros((8,ndays)) # save larger to fit all days in
RT_cs = np.zeros((8,ndays))
nTRTest = 100
RT_cs_timecourse = np.zeros((8,nTRTest,ndays))
dataPath = '/data/jag/cnds/amennen/rtAttenPenn/fmridata/behavdata/gonogo/'
subjectDir = dataPath + '/' + 'subject' + str(subjectNum)
print(subjectNum)
for d in np.arange(ndays):
print(d)
subjectDay = d + 1
configFile = dataPath + 'subject' + str(subjectNum) + '/usedscripts/PennCfg_Day' + str(subjectDay) + '.toml'
cfg = loadConfigFile(configFile)
subjectDayDir = '/data/jag/cnds/amennen/rtAttenPenn/fmridata/behavdata/gonogo/subject' + str(cfg.session.subjectNum) + '/day' + str(cfg.session.subjectDay)
pyDataDir = subjectDayDir
if subjectDay == 1:
nRuns = 7
if subjectNum == 106:
nRuns = 6
elif subjectDay == 2:
nRuns = 9
elif subjectDay == 3:
nRuns = 8
#nruns = len(cfg.session.Runs) - 1
for runId in np.arange(1,nRuns):
print(runId)
runDir = 'run'+str(runId)+'/'
pyModelFn = utils.findNewestFile(pyDataDir, 'trainedModel_r'+str(runId)+'*_py.mat')
# to find what matModel includes use matModel.keys() --> trainedModel, trainPats, trainLabels
# for each model we have W [ nVoxel x 2 classes], biases [ 1 x 2 classes]
# we can't apply this model to any of the examples in this run, but let's apply it to the first 4 blocks of the next run
# now load testing data from the next run to test it on
pyModel_train = utils.loadMatFile(pyModelFn)
# INSTEAD MAKE NEW MODEL
lrc1 = LogisticRegression(penalty='l2', solver='saga',max_iter=300)
lrc2 = LogisticRegression(penalty='l2', solver='saga',max_iter=300)
lrc1.fit(pyModel_train.trainPats, pyModel_train.trainLabels[:, 0])
lrc2.fit(pyModel_train.trainPats, pyModel_train.trainLabels[:, 1])
newTrainedModel = utils.MatlabStructDict({}, 'trainedModel')
newTrainedModel.trainedModel = StructDict({})
newTrainedModel.trainedModel.weights = np.concatenate((lrc1.coef_.T, lrc2.coef_.T), axis=1)
newTrainedModel.trainedModel.biases = np.concatenate((lrc1.intercept_, lrc2.intercept_)).reshape(1, 2)
newTrainedModel.trainPats = pyModel_train.trainPats
newTrainedModel.trainLabels = pyModel_train.trainLabels
# now load testing data for CV
pyModelFn = utils.findNewestFile(pyDataDir, 'trainedModel_r'+str(runId + 1)+'*_py.mat')
pyModel_test = utils.loadMatFile(pyModelFn)
py_test_data = pyModel_test.trainPats[nTRTest:,:]
test_labels = pyModel_test.trainLabels[nTRTest:,:]
py_cs = np.zeros((nTRTest, 1))
activations = np.zeros((nTRTest,2))
for t in np.arange(nTRTest):
_, _, _, activations_py = Test_L2_RLR_realtime(newTrainedModel,py_test_data[t,:],test_labels[t,:])
activations[t,:] = activations_py
fpr2, tpr2, thresholds2 = metrics.roc_curve(test_labels[:,1],activations[:,1] - activations[:,0],pos_label=1)
auc_score[runId-1,d] = metrics.auc(fpr2,tpr2) # auc of this data applied to the first half of the next run
# now apply to block data-- realtime values
pyDataFn = utils.findNewestFile(pyDataDir, 'blkGroup_r' + str(runId + 1) + '_p2_*_py.mat')
pyData_test = utils.loadMatFile(pyDataFn)
regressor = pyData_test.regressor
TRs_to_test = np.argwhere(np.sum(regressor,axis=0))
RT_data = pyData_test.raw_sm_filt_z[TRs_to_test,:]
RT_regressor = regressor[:,TRs_to_test].T.reshape(nTRTest,2)
# now do the same thing and test for every TR --> get category separation
cs = np.zeros((nTRTest,))
for t in np.arange(nTRTest):
categ = np.flatnonzero(RT_regressor[t,:])
otherCateg = (categ + 1) % 2
_, _, _, activations_py = Test_L2_RLR_realtime(newTrainedModel,RT_data[t,:].flatten(),RT_regressor[t,:])
cs[t] = activations_py[categ] - activations_py[otherCateg]
RT_cs_timecourse[runId-1,:,d] = cs
# take average for this run
RT_cs[runId-1, d] = np.mean(cs)
outfile = subjectDir + '/' 'offlineAUC_RTCS'
np.savez(outfile,auc=auc_score,cs=RT_cs,csOverTime=RT_cs_timecourse)