def drift_diffusion_hddm(data, samples=10000, n_jobs=6, run=True, parallel=True, model_name='model', model_dir='.', accuracy_coding=False):
import hddm
import os
# run the model:
if run:
if parallel:
job_server = pp.Server(ppservers=(), ncpus=n_jobs)
start_time = time.time()
jobs = [(trace_id, job_server.submit(run_model,(trace_id, data, model_dir, model_name, samples, accuracy_coding), (), ('hddm',))) for trace_id in range(n_jobs)]
results = []
for s, job in jobs:
results.append(job())
print "Time elapsed: ", time.time() - start_time, "s"
job_server.print_stats()
# save:
for i in range(n_jobs):
model = results[i]
model.save(os.path.join(model_dir, '{}_{}'.format(model_name,i)))
else:
model = run_model(1, data, model_dir, model_name, samples, accuracy_coding)
model.save(os.path.join(model_dir, model_name))
# load the models:
else:
print 'loading existing model(s)'
if parallel:
model = []
for i in range(n_jobs):
model.append(hddm.load(os.path.join(model_dir, '{}_{}'.format(model_name,i))))
else:
model = hddm.load(os.path.join(model_dir, model_name))
return model
def load_model(empty_model, dbfile):
loadfile = sorted(glob(dbfile))
if len(loadfile) > 1:
models = []
for l in loadfile:
m = hddm.load(empty_model)
m.load_db(l, db='pickle')
models.append(m)
m = load_concat_models(models)
return m, models
else:
m = hddm.load(empty_model)
m.load_db(loadfile[0], db='pickle')
return m
def concat_models(mypath, model_name):
import os, hddm, time, kabuki
# ============================================ #
# APPEND MODELS
# ============================================ #
allmodels = []
print "appending models"
for trace_id in range(60): # 15 models were run
model_filename = os.path.join(mypath, model_name, 'modelfit-md%d.model'%trace_id)
modelExists = os.path.isfile(model_filename)
assert modelExists == True # if not, this model has to be rerun
starttime = time.time()
print model_filename
thism = hddm.load(model_filename)
# now append
allmodels.append(thism)
elapsed = time.time() - starttime
print( "Elapsed time: %f seconds." %elapsed )
# ============================================ #
# MANUALLY APPEND CHAINS
# only
# ============================================ #
1. construct the model object with the original data and parameters
def PPC(samples, n=None):
import hddm
import pandas as pd
import pickle
import os
#set filepath
filepath = './models/'
#import model
m = hddm.load(filepath + 'm_all')
print('model loaded')
#set savepath
savepath = './ppc/'
if not os.path.exists(savepath):
os.makedirs(savepath)
#start PP sampling
print('starting...')
ppc = hddm.utils.post_pred_gen(m, samples=samples)
ppc.reset_index(inplace=True)
print('\nsaving...')
ppc.to_csv('%s/ppc_%d.csv' % (savepath, n), sep=',', encoding='utf-8')
print('PP samples saved')
def test_init_sample_save_load_regressor(self):
for model in self.models:
if len(hddm.model_config.model_config[model]["choices"]) == 2:
# Define Link Function
def id_link(x):
return x
# Define Regression Model
v_reg = {"model": "v ~ 1 + theta", "link_func": id_link}
# Initialize HDDM Model (using cavanagh data)
model_ = hddm.HDDMnnRegressor(
self.cav_data,
[v_reg],
include=hddm.model_config.model_config[model]
["hddm_include"],
model=model,
group_only_regressors=True,
)
# Sample
model_.sample(
self.nmcmc,
burn=self.nburn,
dbname=self.filepath + "test_" + model + ".db",
db="pickle",
)
# Save Model
print("Saving Model: ")
model_.save(self.filepath + "test_" + model + ".pickle")
# Load Model
print("Loading Model: ")
model__ = hddm.load(self.filepath + "test_" + model +
".pickle")
self.assertTrue(model__.nn == True)
# Check if id_link is preserved correctly
print("Checking if link func is correctly recovered")
self.assertTrue(
model__.model_descrs[0]["model"]["link_func"] == id_link)
del model_
del model__
else:
print("Skipping n > 2 choice models for this test for now !")
pass
def test_init_sample_save_load_stimcoding(self):
for model in self.models:
if len(hddm.model_config.model_config[model]["choices"]) == 2:
# Generate Data
data, gt = hddm.simulators.hddm_dataset_generators.simulator_stimcoding(
model=model,
n_samples_by_condition=self.n_samples_per_trial,
split_by="v",
)
# Initialize HDDM Model
model_ = hddm.HDDMnnStimCoding(
data,
model=model,
split_param="v",
drift_criterion=True,
stim_col="stim",
)
# Sample
# Sample
model_.sample(
self.nmcmc,
burn=self.nburn,
dbname=self.filepath + "test_" + model + ".db",
db="pickle",
)
# Save Model
print("Saving Model: ")
model_.save(self.filepath + "test_" + model + ".pickle")
# Load Model
print("Loading Model: ")
model__ = hddm.load(self.filepath + "test_" + model +
".pickle")
self.assertTrue(model__.nn == True)
del model_
del model__
else:
print("Skipping n > 2 choice models for this test for now !")
pass
pass
def test_HDDM_load_save(self):
include = ['z', 'sz', 'st', 'sv']
dbs = ['pickle', 'sqlite']
params = hddm.generate.gen_rand_params(include=include)
data, params_true = hddm.generate.gen_rand_data(params, size=10, subjs=2)
data = pd.DataFrame(data)
data['cov'] = 1.
for db, model_class in itertools.product(dbs, self.model_classes):
if model_class is hddm.models.HDDMRegressor:
model = model_class(data, 'v ~ cov', include=include, is_group_model=True)
else:
model = model_class(data, include=include, is_group_model=True)
model.sample(100, dbname='test.db', db=db)
model.save('test.model')
m_load = hddm.load('test.model')
os.remove('test.db')
os.remove('test.model')
def test_HDDM_load_save(self, assert_=False):
include = ['z', 'sz','st','sv']
dbs = ['pickle', 'sqlite']
model_classes = [hddm.models.HDDMTruncated, hddm.models.HDDM, hddm.models.HDDMRegressor]
reg_func = lambda args, cols: args[0] + args[1]*cols[:,0]
reg = {'func': reg_func, 'args':['v_slope','v_inter'], 'covariates': 'cov', 'outcome':'v'}
params = hddm.generate.gen_rand_params(include=include)
data, params_true = hddm.generate.gen_rand_data(params, size=10, subjs=2)
data = pd.DataFrame(data)
data['cov'] = 1.
for db, model_class in itertools.product(dbs, model_classes):
if model_class is hddm.models.HDDMRegressor:
model = model_class(data, regressor=reg, include=include, is_group_model=True)
else:
model = model_class(data, include=include, is_group_model=True)
model.sample(20, dbname='test.db', db=db)
model.save('test.model')
m_load = hddm.load('test.model')
os.remove('test.db')
os.remove('test.model')
def get_stats():
"""
Chop up the stats data frame for running a repeated-measures ANOVA.
"""
data = pd.read_csv('data.csv', index_col=None)
model = load('model.pickle')
results = model.gen_stats()
print results.index.tolist()
params = 'avtz'
isis = sorted(data.isi.unique().tolist())
deltas = sorted(data.delta.unique().tolist())
subjects = sorted(data.subj_idx.unique().tolist())
df = pd.DataFrame()
for x in product(params, deltas, isis):
y = '%s_subj(%s.%s).' % x
data = []
for s in subjects:
z = y + s
data.append(results.ix[z, 'mean'])
df['%s_%s_%s' % x] = data
df.to_csv('for_anovas.csv', index=False)
def test_HDDM_load_save(self):
include = ['z', 'sz', 'st', 'sv']
dbs = ['pickle', 'sqlite']
params = hddm.generate.gen_rand_params(include=include)
data, params_true = hddm.generate.gen_rand_data(params,
size=10,
subjs=2)
data = pd.DataFrame(data)
data['cov'] = 1.
for db, model_class in itertools.product(dbs, self.model_classes):
if model_class is hddm.models.HDDMRegressor:
model = model_class(data,
'v ~ cov',
include=include,
is_group_model=True)
else:
model = model_class(data, include=include, is_group_model=True)
model.sample(100, dbname='test.db', db=db)
model.save('test.model')
m_load = hddm.load('test.model')
os.remove('test.db')
os.remove('test.model')
def test_HDDM_load_save(self):
include = ["z", "sz", "st", "sv"]
dbs = ["pickle", "sqlite"]
params = hddm.generate.gen_rand_params(include=include)
data, params_true = hddm.generate.gen_rand_data(params,
size=10,
subjs=2)
data = pd.DataFrame(data)
data["cov"] = 1.0
for db, model_class in itertools.product(dbs, self.model_classes):
if model_class is hddm.models.HDDMRegressor:
model = model_class(data,
"v ~ cov",
include=include,
is_group_model=True)
else:
model = model_class(data, include=include, is_group_model=True)
model.sample(100, dbname="test.db", db=db)
model.save("test.model")
m_load = hddm.load("test.model")
os.remove("test.db")
os.remove("test.model")
def test_init_sample_save_load_single_subj(self):
for model in self.models:
# Get simulations
data = self.get_data_single_subj(model=model)
# Initialize HDDMnn Model
print("Loading Model: " + model)
model_ = hddm.HDDMnn(
data,
model=model,
informative=False,
include=hddm.model_config.model_config[model]["hddm_include"],
is_group_model=False,
depends_on={},
p_outlier=0.00,
)
# Sample
print("Sampling: ")
model_.sample(
self.nmcmc,
burn=self.nburn,
dbname=self.filepath + "test_" + model + ".db",
db="pickle",
)
# Save Model
print("Saving Model: ")
model_.save(self.filepath + "test_" + model + ".pickle")
# Load Model
print("Loading Model: ")
model__ = hddm.load(self.filepath + "test_" + model + ".pickle")
self.assertTrue(model__.nn == True)
del model_
del model__
if len(modelformula.split('a')) == 2:
aformula.append(modelformula.split('a')[-1])
modelformula = modelformula.split('a')[0]
else:
aformula.append(None)
if len(modelformula.split('v')) == 2:
vformula.append(modelformula.split('v')[-1])
modelformula = modelformula.split('v')[0]
else:
vformula.append(None)
for i in range(5):
m = hddm.load(path + x + '/' + x + '_' + str(i))
models.append(m)
m_comb = concat_models(models)
print(modelname)
print("****DIC: %f" %m_comb.dic)
print("****BPIC: %f" %(m_comb.dic_info['pD'] + m_comb.dic))
dic_dict[modelname] = m_comb.dic
bdic_dict[modelname] = m_comb.dic_info['pD'] + m_comb.dic
dic.append(m_comb.dic)
bdic.append(m_comb.dic_info['pD'] + m_comb.dic)
# Transform into data frame and store in dic.csv
"""
start_time = time.time() # the start time of the processing
#### model 1, free v,t,z
C_Id_vtz = hddm.HDDM(dat_C_Id,
depends_on={
'v': ['val', 'id'],
'z': ['val', 'id'],
't': ['val', 'id']
},
include=['v', 'z', 't'],
p_outlier=.05)
C_Id_vtz.find_starting_values()
C_Id_vtz.sample(10000, burn=1000, dbname='traces_id_vtz.db', db='pickle')
# save the model
C_Id_vtz.save('C_Id_vtz')
C_Id_vtz = hddm.load('C_Id_vtz')
# check convergence of MCMC #### out put of gelman_rubin ######
models_vtz = list()
for i in range(5):
m = hddm.HDDM(dat_C_Id,
depends_on={
'v': ['val', 'id'],
'z': ['val', 'id'],
't': ['val', 'id']
},
include=['v', 'z', 't'],
p_outlier=.05)
m.find_starting_values()
m.sample(10000, burn=1000)
models_vtz.append(m)
a_reg = {'model': 'a ~ 1 + stimulus + prevpe_bin_noern + postpe_bin_noern', 'link_func': lambda x: x}
v_reg = {'model': 'v ~ 1 + stimulus + prevpe_bin_noern + postpe_bin_noern', 'link_func': v_link_func}
reg_descr = [a_reg, v_reg]
m = hddm.HDDMRegressor(data, reg_descr, group_only_regressors=False, p_outlier=.05)
m.find_starting_values()
m.sample(samples, burn=samples/10, thin=2, dbname=os.path.join(model_dir, 'ERPall_binnedPE_traces_1'), db='pickle')
m.save(os.path.join(model_dir, 'ERPall_binnedPE_1'))
goOn = False
if goOn == True:
import kabuki
import seaborn as sns
import matplotlib.pyplot as plt
models = []
for i in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]: #this assumes you ran 10 different models above and changed the index when saving
models.append(hddm.load('ERPall_binnedPE_%s' %i))
m = kabuki.utils.concat_models(models) #contact these 10 models
#Run the same sanity checks as reported in hddm_fit.py, these are not reiterated here
#Extract the data and save these
results = m.gen_stats()
results.to_csv(os.path.join(model_dir, 'eeg_binnedPe_HDDMestimates.csv'))
a2, a3, a4, a5 = m.nodes_db.node[['a_prevpe_bin_noern[T.B_bin]', 'a_prevpe_bin_noern[T.C_bin]', 'a_prevpe_bin_noern[T.D_bin]', 'a_prevpe_bin_noern[T.E_bin]']]
p2, p3, p4, p5 = m.nodes_db.node[['a_postpe_bin_noern[T.B_bin]', 'a_postpe_bin_noern[T.C_bin]', 'a_postpe_bin_noern[T.D_bin]', 'a_postpe_bin_noern[T.E_bin]']]
v2, v3, v4, v5 = m.nodes_db.node[['v_prevpe_bin_noern[T.B_bin]', 'v_prevpe_bin_noern[T.C_bin]', 'v_prevpe_bin_noern[T.D_bin]', 'v_prevpe_bin_noern[T.E_bin]']]
vp2, vp3, vp4, vp5 = m.nodes_db.node[['v_postpe_bin_noern[T.B_bin]', 'v_postpe_bin_noern[T.C_bin]', 'v_postpe_bin_noern[T.D_bin]', 'v_postpe_bin_noern[T.E_bin]']]
numpy.savetxt("PEonly_noern_a_prevcj_bin2.csv", a2.trace(), delimiter=",")
numpy.savetxt("PEonly_noern_a_prevcj_bin3.csv", a3.trace(), delimiter=",")
pdf[i] = np.exp(node.logp) * 10
#plot shit
plt.plot(xlim, pdf)
plt.xlabel(name)
sns.despine(offset=2, trim=True)
# # Hide the right and top spines
# ax.spines['right'].set_visible(False)
# ax.spines['top'].set_visible(False)
#
# # Only show ticks on the left and bottom spines
# ax.yaxis.set_ticks_position('left')
# ax.xaxis.set_ticks_position('bottom')
#add suptitle
plt.suptitle('HDDM priors')
# save the figure
plt.savefig(os.path.join(mypath, 'priorPlot.pdf'))
## LOAD MODEL WITH THE MOST PARAMETERS WE HAVE
mypath = os.path.realpath(os.path.expanduser('/nfs/aeurai/HDDM/JW_PNAS'))
m = hddm.load(
os.path.join(mypath, 'stimcoding_dc_z_prevresp_st',
'modelfit-combined.model'))
#print(m)
#shell()
plot_all_priors(m)
start_time = time.time() # the start time of the processing
#### model 1, free v,t,z
M_match_vtz = hddm.HDDM(dat_M_match,
depends_on={
'v': ['val', 'id'],
'z': ['val', 'id'],
't': ['val', 'id']
},
include=['v', 'z', 't'],
p_outlier=.05)
M_match_vtz.find_starting_values()
M_match_vtz.sample(10000, burn=1000, dbname='traces_m_vtz.db', db='pickle')
# save the model
M_match_vtz.save('exp7_rep_match_vtz')
M_match_vtz = hddm.load('exp7_rep_match_vtz')
# check convergence of MCMC #### out put of gelman_rubin ######
models_vtz = list()
for i in range(5):
m = hddm.HDDM(dat_M_match,
depends_on={
'v': ['val', 'id'],
'z': ['val', 'id'],
't': ['val', 'id']
},
include=['v', 'z', 't'],
p_outlier=.05)
m.find_starting_values()
m.sample(10000, burn=1000)
models_vtz.append(m)
import hddm
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pickle
try:
import IPython
shell = IPython.get_ipython()
shell.enable_matplotlib(gui='inline')
except:
pass
i = int(input('Load which model? '))
model = hddm.load('Z://Work//UW//projects//RR_TMS//hddm//models//by_cond//fullsplit%i'%i)
model.plot_posterior_predictive(figsize=(14,10))
print("Model DIC: %f"%model.dic)
model.plot_posterior_predictive()
plt.show()
plt.savefig('foo.pdf')
def plot_model(mypath, model_name, trace_id):
# load in the model that was ran
m = hddm.load(os.path.join(mypath, model_name, 'modelfit-md%d.model'%trace_id))
# ============================================ #
# save plots
# ============================================ #
# plot some output stuff in figures subfolder
figpath = os.path.join(mypath, model_name, 'figures-md%d'%trace_id)
if not os.path.exists(figpath):
os.mkdir(figpath)
# 1. plot the traces and posteriors for each parameter
m.plot_posteriors(save=True, path=figpath, format='pdf')
# 2. plot posterior predictive
print('plotting posterior predictive')
plot_posterior_predictive_anne(m, path=figpath)
# 3. plot the actual posteriors and the way they depend on the variables we specified
if model_name in ['prevresp_prevrt_dc', 'prevresp_prevpupil_dc']:
print('plotting the posteriors by previous response and rt')
dc_prevresp0_prevRTlow, dc_prevresp0_prevRTmed, dc_prevresp0_prevRThigh, \
dc_prevresp1_prevRTlow, dc_prevresp1_prevRTmed, dc_prevresp1_prevRThigh \
= m.nodes_db.node[['dc(-1.0.1.0)', 'dc(-1.0.2.0)', 'dc(-1.0.3.0)', 'dc(1.0.1.0)','dc(1.0.2.0)','dc(1.0.3.0)']]
# plot these myself
plot_posterior_nodes_anne([dc_prevresp0_prevRTlow, dc_prevresp0_prevRTmed, dc_prevresp0_prevRThigh, \
dc_prevresp1_prevRTlow, dc_prevresp1_prevRTmed, dc_prevresp1_prevRThigh])
plt.xlabel('Drift criterion')
plt.ylabel('Posterior probability')
plt.title('Posterior of drift-rate group means')
plt.savefig(os.path.join(figpath, 'driftcriterion_posteriors.pdf'))
elif model_name in ['prevresp_prevrt_z', 'prevresp_prevpupil_z']:
print('plotting the posteriors by previous response and rt')
dc_prevresp0_prevRTlow, dc_prevresp0_prevRTmed, dc_prevresp0_prevRThigh, \
dc_prevresp1_prevRTlow, dc_prevresp1_prevRTmed, dc_prevresp1_prevRThigh \
= m.nodes_db.node[['z(-1.0.1.0)', 'z(-1.0.2.0)', 'z(-1.0.3.0)', 'z(1.0.1.0)','z(1.0.2.0)','z(1.0.3.0)']]
# plot these myself
plot_posterior_nodes_anne([dc_prevresp0_prevRTlow, dc_prevresp0_prevRTmed, dc_prevresp0_prevRThigh, \
dc_prevresp1_prevRTlow, dc_prevresp1_prevRTmed, dc_prevresp1_prevRThigh])
plt.xlabel('Starting point')
plt.ylabel('Posterior probability')
plt.title('Posterior of drift-rate group means')
plt.savefig(os.path.join(figpath, 'startingpoint_posteriors.pdf'))
elif model_name in ['prevresp_z']:
print('plotting the posteriors by previous response')
dc_prevresp0, dc_prevresp1 \
= m.nodes_db.node[['z(-1.0)', 'z(1.0)']]
# plot these myself
plot_posterior_nodes_anne([dc_prevresp0, dc_prevresp1])
plt.xlabel('Starting point')
plt.ylabel('Posterior probability')
plt.title('Posterior of drift-rate group means')
plt.savefig(os.path.join(figpath, 'startingpoint_posteriors.pdf'))
elif model_name in ['prevresp_dc']:
print('plotting the posteriors by previous response')
dc_prevresp0, dc_prevresp1 \
= m.nodes_db.node[['dc(-1.0)', 'dc(1.0)']]
# plot these myself
plot_posterior_nodes_anne([dc_prevresp0, dc_prevresp1])
plt.xlabel('Drift criterion')
plt.ylabel('Posterior probability')
plt.title('Posterior of drift-rate group means')
plt.savefig(os.path.join(figpath, 'driftcriterion_posteriors.pdf'))
# written by Liangying, 20/2/2020
import hddm
import pandas as pd
import matplotlib.pyplot as plt
model = hddm.load('model_avtz2_all_StimCoding_2')
#model.plot_posterior_predictive(figsize=(40,40),save = True, path = "D:\\brainbnu\\haiyang\\hddm\\result\\all_StimCoding\\split_z\\avz")
#model.plot_posteriors(['a', 't', 'v', 'z'],save = True, path = "D:\\brainbnu\\haiyang\\hddm\\result\\all_StimCoding\\avtz2")
# Between group drift rate comparisons
data = hddm.load_csv('D://brainbnu//haiyang//hddm//hddm_all_StimCoding.csv')
#Posterior predictive check
ppc_data = hddm.utils.post_pred_gen(model)
ppc_compare = hddm.utils.post_pred_stats(data, ppc_data)
ppc_stats = hddm.utils.post_pred_stats(data, ppc_data, call_compare=False)
'''
# 0 back
Stress_v_0back, Control_v_0back = model.nodes_db.node[['v(0.stress)', 'v(0.control)']]
print "P_v(Stress_v_0back > Control_v_0back) =", (Stress_v_0back.trace()> Control_v_0back.trace()).mean()
print "P_v(Control_v_0back > Stress_v_0back) =", (Control_v_0back.trace() > Stress_v_0back.trace()).mean()
hddm.analyze.plot_posterior_nodes([Stress_v_0back, Control_v_0back],10)
plt.xlabel('drift-rate')
plt.ylabel('Posterior probability')
plt.title('Posterior of drift-rate group means')
plt.show()
# 2 back
Stress_v_2back, Control_v_2back = model.nodes_db.node[['v(2.stress)', 'v(2.control)']]
print "P_v(Stress_v_2back > Control_v_2back) =", (Stress_v_2back.trace()> Control_v_2back.trace()).mean()
burn=samples / 10,
thin=2,
dbname=os.path.join(model_dir, 'Experiment1_traces_1'),
db='pickle')
m.save(os.path.join(model_dir, 'Experiment1_1'))
goOn = False
if goOn == True:
import kabuki
import seaborn as sns
import matplotlib.pyplot as plt
models = []
for i in [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10
]: #this assumes you ran 10 different models above and changed the index when saving
models.append(hddm.load('Experiment1_%s' % i))
m = kabuki.utils.concat_models(models) #contact these 10 models
gelman_rubin(models) #check R hat
#diagnostics
m.plot_posteriors()
#Simulate data and compare to actual data
data['response'] = data['cor']
ppc_data = post_pred_gen(m, append_data=True)
ppc_data['resp_sampled'] = 1
ppc_data['resp_sampled'][ppc_data.rt_sampled > 0] = 0
hddm.utils.post_pred_stats(data, ppc_data)
ppc_data.rt = abs(ppc_data.rt)
ppc_data.rt[ppc_data.cor == 1] = -ppc_data.rt[ppc_data.cor == 1]
import hddm
import pandas as pd
import pickle
models = []
for i in range(5):
m = hddm.load(
'Z://Work//UW//projects//RR_TMS//hddm//models//by_cond//testmodel')
models.append(m)
hddm.analyze.gelman_rubin(models)
def drift_diffusion_hddm(data,
samples=10000,
n_jobs=6,
run=True,
parallel=True,
model_name='model',
model_dir='.',
accuracy_coding=False):
import hddm
import os
# run the model:
if run:
if parallel:
job_server = pp.Server(ppservers=(), ncpus=n_jobs)
start_time = time.time()
jobs = [(trace_id,
job_server.submit(run_model,
(trace_id, data, model_dir, model_name,
samples, accuracy_coding), (),
('hddm', )))
for trace_id in range(n_jobs)]
results = []
shell()
for s, job in jobs:
results.append(job())
print "Time elapsed: ", time.time() - start_time, "s"
job_server.print_stats()
# save:
for i in range(n_jobs):
model = results[i]
model.save(
os.path.join(model_dir, '{}_{}'.format(model_name, i)))
else:
start_time = time.time()
model = run_model(3, data, model_dir, model_name, samples,
accuracy_coding)
model.save(os.path.join(model_dir, '{}_md{}'.format(model_name,
3)))
# print point estimates
results = model.gen_stats()
results.to_csv(os.path.join(fig_dir, 'diagnostics',
'results3.csv'))
# dic:
text_file = open(os.path.join(fig_dir, 'diagnostics', 'DIC3.txt'),
'w')
text_file.write("Model {}: {}\n".format(m, model.dic))
text_file.close()
print "Time elapsed: ", time.time() - start_time, "s"
# load the models:
else:
print 'loading existing model(s)'
if parallel:
model = []
for i in range(n_jobs):
model.append(
hddm.load(
os.path.join(model_dir, '{}_{}'.format(model_name,
i))))
else:
model = hddm.load(
os.path.join(model_dir, '{}_md{}'.format(model_name, 1)))
return model
import hddm
import pickle
model = hddm.load(
'Z://Work//UW//projects//RR_TMS//hddm//models//by_cond//va_stim0')
# init just a couple of vars
# conditions in DRI_TMS task
# inf/ins - X/Y
# sym/fin - S/F
# early/late/no stim - E/L/S
# PMd/Vertex stim - P/V
v_XFEP, v_XFEV, v_XFLP, v_XFLV, v_XFNP, v_XFNV, \
v_XSEP, v_XSEV, v_XSLP, v_XSLV, v_XSNP, v_XSNV, \
v_YFEP, v_YFEV, v_YFLP, v_YFLV, v_YFNP, v_YFNV, \
v_YSEP, v_YSEV, v_YSLP, v_YSLV, v_YSNP, v_YSNV = \
model.nodes_db.node[['v( XFEP)', 'v( XFEV)', 'v( XFLP)', 'v( XFLV)', 'v( XFNP)', 'v( XFNV)',
'v( XSEP)', 'v( XSEV)', 'v( XSLP)', 'v( XSLV)', 'v( XSNP)', 'v( XSNV)',
'v( YFEP)', 'v( YFEV)', 'v( YFLP)', 'v( YFLV)', 'v( YFNP)', 'v( YFNV)',
'v( YSEP)', 'v( YSEV)', 'v( YSLP)', 'v( YSLV)', 'v( YSNP)', 'v( YSNV)']]
a_XFEP, a_XFEV, a_XFLP, a_XFLV, a_XFNP, a_XFNV, \
a_XSEP, a_XSEV, a_XSLP, a_XSLV, a_XSNP, a_XSNV, \
a_YFEP, a_YFEV, a_YFLP, a_YFLV, a_YFNP, a_YFNV, \
a_YSEP, a_YSEV, a_YSLP, a_YSLV, a_YSNP, a_YSNV = \
model.nodes_db.node[['a( XFEP)', 'a( XFEV)', 'a( XFLP)', 'a( XFLV)', 'a( XFNP)', 'a( XFNV)',
'a( XSEP)', 'a( XSEV)', 'a( XSLP)', 'a( XSLV)', 'a( XSNP)', 'a( XSNV)',
'a( YFEP)', 'a( YFEV)', 'a( YFLP)', 'a( YFLV)', 'a( YFNP)', 'a( YFNV)',
'a( YSEP)', 'a( YSEV)', 'a( YSLP)', 'a( YSLV)', 'a( YSNP)', 'a( YSNV)']]
Anne Urai, 2016
adapted from JW de Gee
"""
import hddm, os
nr_traces = 3
model_name = 'basic_stimcoding'
# find path depending on local/klimag
usr = os.environ.get('USER')
if usr in ['anne']:
mypath = '/Users/anne/Data/projects/0/neurodec/Data/MEG-PL/Data/HDDM'
if usr in ['aurai']:
mypath = '/home/aurai/Data/MEG-PL/Data/HDDM'
thispath = os.path.join(mypath, model_name)
print "appending models"
models = []
for t in range(nr_traces): # run the models serially
models.append(hddm.load(os.path.join(thispath, 'modelfit-md%d' % t)))
print "computing gelman-rubin convergence statistics"
# compute gelman rubic
gr = hddm.analyze.gelman_rubin(models)
text_file = open(os.path.join(thispath, 'gelman_rubic.txt'), 'w')
for p in gr.items():
text_file.write("%s:%s\n" % p)
text_file.close()
print "DONE!"
a_reg = {'model': 'a ~ 1 + z_x', 'link_func': lambda x: x}
# a_reg_within = {'model': 'a ~ 1+x + C(condition)', 'link_func': lambda x: x}
# for including and estimating within subject effects of condition
v_reg = {'model': 'v ~ 1 + z_x', 'link_func': lambda x: x}
reg_comb = [a_reg, v_reg]
# m_reg = hddm.HDDMRegressor(data_group, reg_comb, group_only_regressors=['true'])
m_reg = hddm.HDDMRegressor(data_group, a_reg, group_only_regressors=['true'])
m_reg.find_starting_values()
m_reg.sample(3000, burn=500, dbname='a_bwsubs_t200.db', db='pickle')
m_reg.save('a_bwsubs_model_t200')
m_reg.print_stats() # check values of reg coefficients against the generated ones
m_reg = hddm.load('a_bwsubs_model')
data_group = pd.read_csv('data_group.csv')
#look at correlation of recovered parameter with original
subjdf = data_group.groupby('subj_idx').first().reset_index()
## check for residual correlation with x
a_int_recovered =[]
pp=[]
from scipy import stats
for i in range(0,(1+max(x_range))*subjs_per_bin):
a='a_Intercept_subj.'
a+=str(i)
a+='.0'
xx=i//subjs_per_bin
import multiprocessing
# File paths
model_name = sys.argv[1]
model_path = '../output/Study1/ae_only/' + sys.argv[1] + '/' + sys.argv[1]
datafile_name = '../data/Study1/' + sys.argv[2]
df = pd.read_csv(datafile_name, low_memory=False)
data = hddm.utils.flip_errors(df)
model_list = []
for model_index in range(5):
sub_model_name = model_path + '_' + str(model_index)
sub_model = hddm.load(sub_model_name)
model_list.append(sub_model)
m_comb = concat_models(model_list)
print("DIC: %f" % m_comb.dic)
print("BPIC: %f" % (m_comb.dic_info['pD'] + m_comb.dic))
def _parents_to_random_posterior_sample(bottom_node, pos=None):
"""Walks through parents and sets them to pos sample."""
for i, parent in enumerate(bottom_node.extended_parents):
if not isinstance(parent, pm.Node): # Skip non-stochastic nodes
continue
time.sleep(60)
# concatenate the different chains, will save disk space
concat_models(mypath, models[vx])
elif runMe == 2:
# ============================================ #
# POSTERIOR PREDICTIVES TO ASSESS MODEL FIT
# ============================================ #
starttime = time.time()
print "computing ppc"
# specify how many samples are needed
m = hddm.load(
os.path.join(mypath, models[vx], 'modelfit-combined.model'))
print os.path.join(mypath, models[vx], 'modelfit-combined.model')
if 'MEG' in datasets[dx]:
nsmp = 50
else:
nsmp = 100
ppc = hddm.utils.post_pred_gen(m, append_data=True, samples=nsmp)
# make the csv smaller, save disk space
savecols = list(
set(ppc.columns) & set([
'rt', 'rt_sampled', 'response_sampled', 'index',
'stimulus', 'response', 'prevresp', 'subj_idx',
'transitionprob', 'coherence', 'prevcorrect'
]))
# ============================================ #
# post-processing
# ============================================ #
import hddm
import matplotlib.pyplot as plt
print "HDDM imported, starting post-processing"
models = []
for trace_id in range(nr_traces): # run the models serially
thism = hddm.load(
os.path.join(mypath, model_name, 'modelfit-md%d.model' % trace_id))
print os.path.join(mypath, model_name, 'modelfit-md%d.model' % trace_id)
# plot some output stuff in figures subfolder
figpath = os.path.join(mypath, model_name, 'figures-md%d' % trace_id)
if not os.path.exists(figpath):
os.mkdir(figpath)
thism.plot_posteriors(save=True, path=figpath, format='pdf')
plt.close(
'all') # this will leave figures open, make sure to close them all
models.append(thism)
# gelman rubic on the list of models
gr = hddm.analyze.gelman_rubin(models)
text_file = open(os.path.join(mypath, model_name, 'gelman_rubic.txt'), 'w')
for p in gr.items():
text_file.write("%s:%s\n" % p)
text_file.close()
# ============================================ #
def concat_models(mypath, model_name):
nchains = 30
# CHECK IF COMBINED MODEL EXISTS
if os.path.isfile(
os.path.join(mypath, model_name, 'modelfit-combined.model')):
print os.path.join(mypath, model_name, 'modelfit-combined.model')
else:
# ============================================ #
# APPEND MODELS
# ============================================ #
allmodels = []
print("appending models for %s" % model_name)
for trace_id in range(nchains): # how many chains were run?
model_filename = os.path.join(mypath, model_name,
'modelfit-md%d.model' % trace_id)
modelExists = os.path.isfile(model_filename)
if modelExists == True: # if not, this model has to be rerun
print model_filename
thism = hddm.load(model_filename)
allmodels.append(thism) # now append into a list
# ============================================ #
# CHECK CONVERGENCE
# ============================================ #
if len(allmodels) == 0:
return allmodels
try:
gr = hddm.analyze.gelman_rubin(allmodels)
# save
text_file = open(
os.path.join(mypath, model_name, 'gelman_rubin.txt'), 'w')
for p in gr.items():
text_file.write("%s,%s\n" % p)
# print a warning when non-convergence is detected
# Values should be close to 1 and not larger than 1.02 which would indicate convergence problems.
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3731670/
if abs(p[1] - 1) > 0.02:
print "non-convergence found, %s:%s" % p
text_file.close()
print "written gelman rubin stats to file"
except:
pass
# now actually concatenate them, see email Gilles
m = kabuki.utils.concat_models(allmodels)
# ============================================ #
# SAVE THE FULL MODEL
# ============================================ #
print "concatenated models"
m.save(
os.path.join(mypath, model_name,
'modelfit-combined.model')) # save the model to disk
# DELETE FILES to save space
print "deleting separate chains..."
for fl in glob.glob(
os.path.join(mypath, models[vx], 'modelfit-md*.model')):
print(fl)
os.remove(fl)
for fl in glob.glob(os.path.join(mypath, models[vx],
'modelfit-md*.db')):
if not '-md0.db' in fl:
print(fl)
os.remove(fl)
# ============================================ #
# SAVE POINT ESTIMATES
# ============================================ #
print "saving stats"
results = m.gen_stats(
) # point estimate for each parameter and subject
results.to_csv(os.path.join(mypath, model_name,
'results-combined.csv'))
# save the DIC for this model
text_file = open(os.path.join(mypath, model_name, 'DIC-combined.txt'),
'w')
text_file.write("Combined model: {}\n".format(m.dic))
text_file.close()
# ============================================ #
# SAVE TRACES
# ============================================ #
print "saving traces"
# get the names for all nodes that are available here
group_traces = m.get_group_traces()
group_traces.to_csv(
os.path.join(mypath, model_name, 'group_traces.csv'))
all_traces = m.get_traces()
all_traces.to_csv(os.path.join(mypath, model_name, 'all_traces.csv'))
# ============================================ #
# CONCATENATE MODEL COMPARISON
# ============================================ #
# average model comparison values across chains
print('concatenating model comparison')
nchains = 30
for trace_id in range(nchains): # how many chains were run?
filename = os.path.join(mypath, models[vx],
'model_comparison_md%d.csv' % trace_id)
df = pd.read_csv(filename)
if trace_id == 0:
df2 = df
else:
df2 = df2.append(df, ignore_index=True)
# average over chains
df3 = df2.mean()
df3 = df2.describe().loc[['mean']]
df3.to_csv(os.path.join(mypath, models[vx], 'model_comparison.csv'))
for fl in glob.glob(
os.path.join(mypath, models[vx], 'model_comparison_md*.csv')):
print(fl)
os.remove(fl)
#workdir = '/home/mikkel/PM-volition/Dataanalysis' #outdir = '/home/mikkel/PM-volition/Datafiles' workdir = 'C:\\Users\\Mikkel\\Documents\\PM-volition\\Dataanalysis' outdir = 'C:\\Users\\Mikkel\\Documents\\PM-volition\\Datafiles' sys.path.append(workdir) import PM_volition_utilfun as pm # plot_posterior_diff, plot_posterior_nodes2, get_posteriorP # %% Plot options dpi=600 # %% Load model chdir(outdir) #Must be in folder to load databases mod = hddm.load(op.join(outdir, 'ddm_model31')) #f = open(op.join(outdir,"ddm_model22"),"rb") #mod = pickle.load(f) #%% Generate posteriors v_fixPM, v_freePM, v_fixFil, v_freeFil = mod.nodes_db.node[['v(pm.fix)', 'v(pm.free)','v(filler.fix)','v(filler.free)']] a_fix, a_free = mod.nodes_db.node[['a(fix)', 'a(free)']] t_int = mod.nodes_db.node['t'] ## Difference between posteriors: PM _, v_PMdiff = pm.get_posteriorP(v_fixPM, v_freePM, plot=0) _, v_Fildiff = pm.get_posteriorP(v_fixFil, v_freeFil, plot=0) _, a_diff = pm.get_posteriorP(a_fix, a_free, plot=0) #_, z_PMdiff = pm.get_posteriorP(z_fixPM, z_freePM, plot=0)
fig = plt.figure()
ax = fig.add_subplot(111, xlabel='RT', ylabel='count', title='RT distributions')
for i, subj_data in dat_M_Categ_id.groupby('subj_idx'):
subj_data.rt.hist(bins=20, histtype='step', ax=ax)
plt.savefig('plot_MS_Categ_id_flipped.pdf')
start_time = time.time() # the start time of the processing
#### model 1 for valence based categorization, free v,t,z
M_Categ_val_vtz = hddm.HDDM(dat_M_Categ_val,depends_on = {'v':['val','id'],'z':['val','id'],'t':['val','id']}, include=['v', 'z', 't'],p_outlier=.05)
M_Categ_val_vtz.find_starting_values()
M_Categ_val_vtz.sample(10000,burn = 1000, dbname='traces_val_vtz.db', db='pickle')
# save the model
M_Categ_val_vtz.save('M_Categ_val_vtz')
M_Categ_val_vtz = hddm.load('M_Categ_val_vtz')
# doing Gelman-Rubin statistic
models_categ_val = []
for i in range(5):
m_stim = hddm.HDDM(dat_M_Categ_val,depends_on = {'v':['val','id'],'z':['val','id'],'t':['val','id']}, include=['v', 'z', 't'],p_outlier=.05)
m_stim.find_starting_values()
m_stim.sample(10000,burn = 1000)
models_categ_val.append(m_stim)
Categ_val_R_hat_vtz = hddm.analyze.gelman_rubin(models_categ_val)
# save Categ_R_hat_vtz
with open('Categ_val_R_hat_vtz.csv','w') as f:
w = csv.writer(f)
w.writerows(Categ_val_R_hat_vtz.items())