def check_interpolation_and_subgraphs():
ft = 'raw'
minp = 0.5
minc = 0.6
seed = 2222
standardize_method = "cz"
is_cz = True
# standardize_method = "z"
# is_cz = False
freq_list = [
'001', '002', '003', '004', '005', '006', '008', '009', '01', '011'
]
freq_to_trainFreq_map = {
'001': '001',
'002': '002',
'003': '004',
'004': '005',
'005': '007',
'006': '008',
'008': '01',
'009': '011',
'01': '013',
'011': '014'
}
nel_graph_length = 13
fout = '../observer/check_interpolation_and_subgraphs/seed%s_%s_mice_mp%s_mc%s_%s' % (
seed, ft, minp, minc, standardize_method)
cu.checkAndCreate(fout)
cu.checkAndCreate('../data/seed%s/%s/mice/mp%s_mc%s/%s' %
(seed, ft, minp, minc, standardize_method))
e = run.Experiment(
'../data/seed%s/%s/mice/mp%s_mc%s/%s' %
(seed, ft, minp, minc, standardize_method),
'../data/seed%s/%s/mice/mp%s_mc%s/dataset' % (seed, ft, minp, minc),
seed, is_cz, standardize_method, freq_list, freq_to_trainFreq_map,
nel_graph_length)
foldi = 2
train = e.ftrain % (e.dataset_folder, foldi, e.standardize_method)
test = e.ftest % (e.dataset_folder, foldi, e.standardize_method)
print train
print test
ftrnel = "%s/mimic_train_fold%d.nel" % (fout, foldi)
ftrnode = "%s/mimic_train_fold%d.node" % (fout, foldi)
fnel = "%s/mimic_fold%d.nel" % (fout, foldi)
fnode = "%s/mimic_fold%d.node" % (fout, foldi)
# e.interpolation(trcsv=train, tecsv=test, ftrnel=ftrnel, ftrnode=ftrnode, fnel=fnel, fnode=fnode)
e.subgraph_mining(tr_nel=ftrnel,
tr_te_nel=fnel,
freq_t='011',
foldi=foldi,
cfolder=fout)
def split_test_by_patient(cdn, out_folder, suffix=''):
'''
This is a help function for MICE imputation.
Given folder, extract data for each patient to be used in MICE
'''
for i in range(5):
test = pd.read_csv('%s/test_fold%d%s.csv' % (cdn, i, suffix))
gp = test.groupby('sid')
cu.checkAndCreate('%s/test_fold%d' % (out_folder, i))
fn = open('%s/test_fold%d/sid_list.txt' % (out_folder, i), 'w')
for sid, group in gp:
group.to_csv('%s/test_fold%d/%d.csv' % (out_folder, i, sid),
index=False)
fn.write('%d\n' % sid)
fn.close()
def split_by_feature_type(cdn, fn_prefix):
'''
split data into two sets, one contains raw features + medical features,
another contains standardized features + medical features.
'''
print 'split_by_feature_type'
for i in range(5):
training = pd.read_csv('%s_train_fold%d.csv' % (fn_prefix, i))
testing = pd.read_csv('%s_test_fold%d.csv' % (fn_prefix, i))
raw_train = training[raw_features_for_classify]
raw_test = testing[raw_features_for_classify]
# z_train = training[standardized_features_for_classify]
# z_test = testing[standardized_features_for_classify]
cu.checkAndCreate('%s/raw/' % cdn)
# cu.checkAndCreate('%s/z/'%cdn)
raw_train.to_csv('%s/raw/train_fold%d.csv' % (cdn, i), index=False)
raw_test.to_csv('%s/raw/test_fold%d.csv' % (cdn, i), index=False)
def split_by_feature_type(cdn, fn_prefix, raw_colname, z_colname):
'''
split data into two sets, one contains raw features + medical features,
another contains standardized features + medical features.
'''
for i in range(5):
training = pd.read_csv('%s_train_fold%d.csv' % (fn_prefix, i))
testing = pd.read_csv('%s_test_fold%d.csv' % (fn_prefix, i))
raw_train = training[raw_colname]
raw_test = testing[raw_colname]
z_train = training[z_colname]
z_test = testing[z_colname]
cu.checkAndCreate('%s/raw/' % cdn)
cu.checkAndCreate('%s/z/' % cdn)
raw_train.to_csv('%s/raw/train_fold%d.csv' % (cdn, i), index=False)
raw_test.to_csv('%s/raw/test_fold%d.csv' % (cdn, i), index=False)
z_train.to_csv('%s/z/train_fold%d.csv' % (cdn, i), index=False)
z_test.to_csv('%s/z/test_fold%d.csv' % (cdn, i), index=False)
def nmfClassfyExperiments(l):
minp = l[0]
minc = l[1]
print minp, minc
output = ''
for isg in [0, 3]:
output += 'isg %d:\n' % isg
cu.checkAndCreate('%s/isg%d/nmf_piks' % (self.cdn, isg))
for freq_t in [
'001', '002', '003', '004', '005', '006', '008', '009',
'01', '011'
]:
output += 'freq_t %s:\n' % freq_t
bauc = 0.
htauc = 0.
bnc = 0
for nc in [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120]:
for foldi in range(5):
nmfclassify(isg,
freq_t,
2,
foldi,
nc=nc,
minp=minp,
minc=minc)
best_auc, highest_tr_auc = mean_auc(isg,
freq_t,
2,
nc=nc,
nmf=True)
if best_auc > bauc:
bnc = nc
bauc = max(bauc, best_auc)
htauc = max(htauc, highest_tr_auc)
output += 'nc %s: %s\n%s\n' % (bnc, bauc, htauc)
# output += '\n'
output += '\n'
fn = open('../data/mice/nmfresult_mp%s_mc%s.txt' % (minp, minc), 'w')
fn.write(output)
fn.close()
def tuneSGParamForClassification(nmf=False):
output = ''
for isg in [0, 3]:
output += 'isg %d: ' % isg
if nmf:
cu.checkAndCreate('%s/isg%d/nmf_piks' % (self.cdn, isg))
for freq_t in [
'001', '002', '003', '004', '005', '006', '008', '009',
'01', '011'
]:
prediction_matrics = read_prediction_matrics(isg, freq_t)
if nmf:
bauc = 0.
tbtauc = 0.
bparams = ''
for nc in [
10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120
]:
(tbauc, tbparams,
tbtauc) = tuneCLFParamForClassification(
l, prediction_matrics, nmf=True, nc=nc)
if tbauc > bauc:
bauc = tbauc
bparams = tbparams
btauc = tbtauc
else:
(bauc, bparams,
btauc) = tuneCLFParamForClassification(l,
prediction_matrics,
nmf=False)
output += '%f (%s)\n' % (bauc, bparams)
if nmf:
fn = open('%s/nmfResult.txt' % (cdn), 'w')
else:
fn = open('%s/dirResult.txt' % (cdn), 'w')
fn.write(output)
fn.close()
def run(feature_type, minp, minc):
# self.cdn = '../data/mean_last12h'
# self.cdn = '../data/seed2222/%s/mice/mp%s_mc%s'%(feature_type,minp,minc)
# print self.cdn
cu.checkAndCreate(self.cdn)
for isg in [0, 3]:
cu.checkAndCreate('%s/isg%d' % (self.cdn, isg))
cu.checkAndCreate('%s/isg%d/pt_sg_w' % (self.cdn, isg))
cu.checkAndCreate('%s/isg%d/res' % (self.cdn, isg))
for foldi in range(5):
train = self.ftrain % (self.cdn, foldi)
test = self.ftest % (self.cdn, foldi)
ftrnel = "%s/mimic_train_fold%d.nel" % (self.cdn, foldi)
ftrnode = "%s/mimic_train_fold%d.node" % (self.cdn, foldi)
fnel = "%s/mimic_fold%d.nel" % (self.cdn, foldi)
fnode = "%s/mimic_fold%d.node" % (self.cdn, foldi)
interpolation(trcsv=train,
tecsv=test,
ftrnel=ftrnel,
ftrnode=ftrnode,
fnel=fnel,
fnode=fnode)
get_freq_to_trainFreq_map(foldi)
for freq_t in [
'001', '002', '003', '004', '005', '006', '008', '009',
'01', '011'
]:
subgraph_mining(tr_nel=ftrnel,
tr_te_nel=fnel,
freq_t=freq_t,
foldi=foldi)
for isg in [0, 3]:
gen_pt_sg_files(isg, freq_t, foldi)
def run_best_model(cdn):
ft = 'raw'
seed = 2222
standardize_method = 'z'
is_cz = False
cu.checkAndCreate('%s/seed%d' % (cdn, seed))
pp.split_nfolds('%s/alldata_readmit.csv' % cdn,
'%s/seed%d/alldata_readmit' % (cdn, seed),
shuffle=True,
seed=seed)
pp.split_by_feature_type(cdn='%s/seed%d' % (cdn, seed),
fn_prefix='%s/seed%d/alldata_readmit' %
(cdn, seed))
cu.checkAndCreate('%s/seed%d/raw/interp' % (cdn, seed))
cu.checkAndCreate('%s/seed%d/raw/interp/mean/dataset' % (cdn, seed))
for i in range(5):
pp.impute_by_interpolation_on_last12h(
'%s/seed%d/raw/test_fold%d.csv' % (cdn, seed, i),
'%s/seed%d/raw/interp/test_fold%d.csv' % (cdn, seed, i),
'%s/seed%d/raw/interp/extrapolation_log_test_fold%d.txt' %
(cdn, seed, i))
pp.impute_by_interpolation_on_last12h(
'%s/seed%d/raw/train_fold%d.csv' % (cdn, seed, i),
'%s/seed%d/raw/interp/train_fold%d.csv' % (cdn, seed, i),
'%s/seed%d/raw/interp/extrapolation_log_train_fold%d.txt' %
(cdn, seed, i))
pp.impute_by_mean(
'%s/seed%d/raw/interp/train_fold%d.csv' % (cdn, seed, i),
'%s/seed%d/raw/interp/test_fold%d.csv' % (cdn, seed, i),
'%s/seed%d/raw/interp/mean/dataset/train_fold%d.csv' %
(cdn, seed, i),
'%s/seed%d/raw/interp/mean/dataset/test_fold%d.csv' %
(cdn, seed, i))
pp.standardize_data(
'%s/seed%d/raw/interp/mean/dataset/train_fold%d.csv' %
(cdn, seed, i),
'%s/seed%d/raw/interp/mean/dataset/test_fold%d.csv' %
(cdn, seed, i),
'%s/seed%d/raw/interp/mean/dataset/train_fold%d_%s.csv' %
(cdn, seed, i, standardize_method),
'%s/seed%d/raw/interp/mean/dataset/test_fold%d_%s.csv' %
(cdn, seed, i, standardize_method))
# run temporal model
freq_list = ['011']
freq_to_trainFreq_map = {'011': '014'}
nel_graph_length = 13
cu.checkAndCreate('%s/seed%d/%s/interp/mean/%s' %
(cdn, seed, ft, standardize_method))
e = rn.Experiment(
'%s/seed%d/%s/interp/mean/%s' % (cdn, seed, ft, standardize_method),
'%s/seed%d/%s/interp/mean/dataset' % (cdn, seed, ft), seed, is_cz,
standardize_method, freq_list, freq_to_trainFreq_map, nel_graph_length)
isg = 0
freq_t = '011'
nc = 110
c = 2
pl = 'l1'
cw = 'balanced'
ntestth = 2
cu.checkAndCreate('%s/isg%d' % (e.cdn, isg))
cu.checkAndCreate('%s/isg%d/pt_sg_w' % (e.cdn, isg))
cu.checkAndCreate('%s/isg%d/res' % (e.cdn, isg))
cu.checkAndCreate('%s/isg%d/nmf_piks' % (e.cdn, isg))
for foldi in range(5):
train = e.ftrain % (e.dataset_folder, foldi, e.standardize_method)
test = e.ftest % (e.dataset_folder, foldi, e.standardize_method)
print train
print test
ftrnel = "%s/mimic_train_fold%d.nel" % (e.cdn, foldi)
ftrnode = "%s/mimic_train_fold%d.node" % (e.cdn, foldi)
fnel = "%s/mimic_fold%d.nel" % (e.cdn, foldi)
fnode = "%s/mimic_fold%d.node" % (e.cdn, foldi)
e.interpolation(trcsv=train,
tecsv=test,
ftrnel=ftrnel,
ftrnode=ftrnode,
fnel=fnel,
fnode=fnode)
e.get_freq_to_trainFreq_map(foldi)
for freq_t in e.moss_freq_threshold_list:
e.subgraph_mining(tr_nel=ftrnel,
tr_te_nel=fnel,
freq_t=freq_t,
foldi=foldi)
e.gen_pt_sg_files(isg, freq_t, foldi)
cu.checkAndCreate('%s/seed%d/raw/interp/mean/last_measures/dataset' %
(cdn, seed))
# run baseline model
for i in range(5):
pp.get_last_measurements(
'%s/seed%d/raw/interp/mean/dataset/train_fold%d_%s.csv' %
(cdn, seed, i, standardize_method),
'%s/seed%d/raw/interp/mean/last_measures/dataset/train_fold%d_%s.csv'
% (cdn, seed, i, standardize_method))
pp.get_last_measurements(
'%s/seed%d/raw/interp/mean/dataset/test_fold%d_%s.csv' %
(cdn, seed, i, standardize_method),
'%s/seed%d/raw/interp/mean/last_measures/dataset/test_fold%d_%s.csv'
% (cdn, seed, i, standardize_method))
best_features = rfe(
'%s/seed%d/raw/interp/mean/last_measures' % (cdn, seed), 50,
standardize_method, 5, 'l1', 'balanced')
print best_features
# best_features = ['urineByHrByWeight', 'HCT', 'INR', 'Platelets', 'RBC',
# 'DeliveredTidalVolume', 'PlateauPres', 'RAW', 'RSBI', 'mDBP', 'CV_HR',
# 'Art_BE', 'Art_CO2', 'Art_PaCO2', 'Art_pH', 'Cl', 'Mg', 'Anticoagulant',
# 'beta.Blocking_agent', 'Somatostatin_preparation', 'Vasodilating_agent',
# 'AIDS', 'MetCarcinoma']
baseline_auc = lr('%s/seed%d/raw/interp/mean/last_measures' % (cdn, seed),
standardize_method, 5, 'l1', 'balanced', 50)
print 'baseline AUC: %s' % baseline_auc
res_list = []
for foldi in range(5):
fnaddtr = '../data/seed2222/raw/interp/mean/last_measures/dataset/train_fold%d_%s.csv' % (
foldi, standardize_method)
fnaddte = '../data/seed2222/raw/interp/mean/last_measures/dataset/test_fold%d_%s.csv' % (
foldi, standardize_method)
prediction_matrics = e.read_prediction_matrics(isg, freq_t)
(res, gt_te, pt_te, res_baseline) = e.nmfClassify_ob(
prediction_matrics['ptsg'][foldi],
prediction_matrics['ptwd'][foldi],
prediction_matrics['sgs'][foldi], prediction_matrics['pt'][foldi],
prediction_matrics['gt'][foldi],
'%s/isg%d/nmf_piks/nmf_%s_fold%d_%d.pik' %
(e.cdn, isg, freq_t, foldi, nc), ntestth, foldi, nc, c, pl, cw,
fnaddtr, fnaddte, best_features)
res_list.append(res)
(auc, tr_auc) = e.get_mean_auc(res_list)
print auc, tr_auc
for i in range(len(res_list)):
with open(
'../data/seed2222/raw/interp/mean/z/isg0/res/c_pre_te_fold%d' %
i, 'wb') as f:
pickle.dump(res_list[i]['c_pre_te'], f)
with open('../data/seed2222/raw/interp/mean/z/isg0/res/res_fold%d' % i,
'wb') as f:
pickle.dump(res_list[i], f)
