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run_inference_hddm.py
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run_inference_hddm.py
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import hddm
from copy import deepcopy
import pickle
import pandas as pd
import numpy as np
import pathlib
import psutil
import ssms
import arviz as az
from time import time
import matplotlib
import matplotlib.pyplot as plt
import jax
from jax import numpy as jnp
import math
import argparse
from time import time
import pathlib
from utils import save_traces
def traces_to_arviz_single_subject(traces = None, model = 'ddm'):
traces_dict = {}
for key_ in traces.keys():
tmp_trace = traces[key_].values
if '_trans' in key_:
key_tmp = key_[:key_.find('_trans')]
idx = hddm.model_config.model_config[model]['params'].index(key_tmp)
a = hddm.model_config.model_config[model]['param_bounds'][0][idx]
b = hddm.model_config.model_config[model]['param_bounds'][1][idx] #[exp(traces['z_trans'])
traces_dict[key_tmp] = ((b - a) * np.exp(traces[key_].values) / (1 + np.exp(traces[key_].values))) + a
else:
traces_dict[key_] = traces[key_].values
return az.from_dict(traces_dict), traces_dict
def traces_to_arviz_hierarchical(traces = None, model = 'ddm', n_subjects = 20):
traces_dict = {}
# clean up trans
trans_columns = []
for key_ in traces.keys():
tmp_trace = traces[key_].values
if '_trans' in key_:
trans_columns.append(key_)
if 'subj' in key_:
key_tmp = key_[:key_.find('_trans')] + key_[(key_.find('_trans') + 6):]
else:
key_tmp = key_[:key_.find('_trans')]
key_param_isolated = key_[:key_.find('_')]
idx = hddm.model_config.model_config[model]['params'].index(key_param_isolated)
a = hddm.model_config.model_config[model]['param_bounds'][0][idx]
b = hddm.model_config.model_config[model]['param_bounds'][1][idx] #[exp(traces['z_trans'])
traces[key_tmp] = ((b - a) * np.exp(traces[key_].values) / (1 + np.exp(traces[key_].values))) + a
traces = traces.drop(trans_columns, axis = 1)
# Deal with subject wise variables
traces = traces.rename(columns = {param: param + '_mu_mu' for param in ssms.config.model_config[model]['params']},
inplace = False)
traces = traces.rename(columns = {param + '_std': param + '_mu_std' for param in ssms.config.model_config[model]['params']},
inplace = False)
for param in ssms.config.model_config[model]['params']:
traces_dict[param + '_subj'] = traces[[param + '_subj.' + str(i) for i in range(n_subjects)]].values #.swapaxes(0,1)
traces_dict[param + '_mu_mu'] = traces[param + '_mu_mu'].values
traces_dict[param + '_mu_std'] = traces[param + '_mu_std'].values
for key_ in traces_dict.keys():
if traces_dict[key_].ndim > 1:
traces_dict[key_] = np.expand_dims(traces_dict[key_], axis = 0)
# Now turn to arviz inference data
traces_inf = az.from_dict(traces_dict,
coords = {param + \
'_subj_dim_0': np.arange(n_subjects) for param in ssms.config.model_config[model]['params']},
dims = {param + \
'_subj': [param + '_subj_dim_0'] for param in ssms.config.model_config[model]['params']},
)
traces_inf.add_groups({"posterior_predictive": traces_dict},
coords = {param + \
'_subj_dim_0': np.arange(n_subjects) for param in ssms.config.model_config[model]['params']},
dims = {param + \
'_subj': [param + '_subj_dim_0'] for param in ssms.config.model_config[model]['params']},
)
return traces_inf, traces_dict
def stack_traces_dicts(traces_dicts = []):
traces_dicts_stacked = {}
for key_ in traces_dicts[0].keys():
traces_dicts_stacked[key_] = np.stack([traces_dicts[i][key_] for i in range(len(traces_dicts))], axis = 0)
return traces_dicts_stacked
if __name__ == "__main__":
# Command line interface arguments
CLI = argparse.ArgumentParser()
CLI.add_argument("--model",
type = str,
default = "ddm")
CLI.add_argument("--modeltype",
type = str,
default = "singlesubject")
CLI.add_argument("--machine",
type = str,
default = 'cpu')
CLI.add_argument("--nchains",
type = int,
default = 2)
CLI.add_argument("--nwarmup",
type = int,
default = 2000)
CLI.add_argument("--nmcmc",
type = int,
default = 3000)
CLI.add_argument("--idmin",
type = int,
default = 0)
CLI.add_argument("--idmax",
type = int,
default = 100)
CLI.add_argument("--progressbar",
type = int,
default = 0)
args = CLI.parse_args()
print(args)
if args.modeltype == 'singlesubject':
# Model
model = args.model # for now only DDM (once we have choice probability models --> all models applicable)
model_config = ssms.config.model_config[model].copy() # convenience
central_data_single_subject = pickle.load(open('data/single_subject/' + model + \
'_nsamples_1000_nparams_200_stdfracdenom_6.pickle', 'rb'))
for data_idx in range(args.idmin, args.idmax, 1):
data = central_data_single_subject['data'][data_idx]['hddm']
gt_params = central_data_single_subject['data'][data_idx]['gt_params']
n_samples = central_data_single_subject['data'][data_idx]['hddm'].shape[0]
# Make folder for arviz data if it doesn't already exist
arviz_path = 'data/single_subject/' + \
model + '_nsamples_1000_nparams_200_stdfracdenom_6'
pathlib.Path(arviz_path).mkdir(parents = True,
exist_ok = True)
model_list = []
start_t = time()
for i in range(args.nchains):
model_list.append(hddm.HDDMnn(data,
model = model,
include = hddm.model_config.model_config[model]['hddm_include']
))
model_list[i].sample(args.nmcmc + args.nwarmup, burn = args.nwarmup)
end_t = time()
print('Inference took: ', end_t - start_t, ' seconds...')
run_time = (end_t - start_t) / args.nchains
traces_list = []
traces_dict_list = []
for i in range(2):
traces_list.append(traces_to_arviz_single_subject(traces = model_list[i].get_traces(),
model = model)[0])
traces_dict_list.append(traces_to_arviz_single_subject(traces = model_list[i].get_traces(),
model = model)[1])
mcmc_az = az.concat(traces_list, dim='chain')
mcmc_az.posterior.attrs['runtime'] = run_time
mcmc_az.posterior.attrs['machine_info'] = psutil.subprocess.run(['lscpu'],
capture_output=True,
text=True).stdout.split('\n')
traces_dicts_stacked = stack_traces_dicts(traces_dicts = traces_dict_list)
print('saving files...')
save_traces(file_path = arviz_path,
arviz_trace = mcmc_az,
dict_trace = traces_dicts_stacked,
model = args.model,
idx = data_idx,
backend = 'hddm',
infer_type = 'mcmc',
machine = args.machine,
nparticles = args.nchains)
elif args.modeltype == 'hierarchical':
model = args.model
model_config = ssms.config.model_config[args.model]
central_data_hierarchical = pickle.load(open('data/hierarchical/' + model + \
'_nsamples_1000_nsubjects_20_nparams_200' + \
'_stdfracdenom_6.pickle', 'rb'))
for data_idx in range(args.idmin, args.idmax, 1):
data = central_data_hierarchical['data'][data_idx]['hddm']
gt_params = central_data_hierarchical['data'][data_idx]['gt_params']
n_samples = central_data_hierarchical['data'][data_idx]['hddm'].shape[0]
n_subjects = central_data_hierarchical['data'][data_idx]['hddm'].shape[1]
# Make folder for arviz data if it doesn't already exist
arviz_path = 'data/hierarchical/' + \
model + '_nsamples_1000_nsubjects_20_nparams_200_stdfracdenom_6'
pathlib.Path(arviz_path).mkdir(parents = True,
exist_ok = True)
model_list = []
start_t = time()
for i in range(args.nchains):
model_list.append(hddm.HDDMnn(data,
model = model,
include = hddm.model_config.model_config[model]['hddm_include'],
is_group_model = True,
p_outlier = 0.0
))
model_list[i].sample(args.nmcmc + args.nwarmup, burn = args.nwarmup)
end_t = time()
print('Inference took: ', end_t - start_t, ' seconds...')
run_time = (end_t - start_t) / args.nchains
traces_list = []
traces_dict_list = []
for i in range(2):
traces_list.append(traces_to_arviz_hierarchical(traces = model_list[i].get_traces(),
model = model,
n_subjects = n_subjects)[0])
traces_dict_list.append(traces_to_arviz_hierarchical(traces = model_list[i].get_traces(),
model = model,
n_subjects = n_subjects)[1])
mcmc_az = az.concat(traces_list, dim = 'chain')
mcmc_az.posterior.attrs['runtime'] = run_time
mcmc_az.posterior.attrs['machine_info'] = psutil.subprocess.run(['lscpu'],
capture_output=True,
text=True).stdout.split('\n')
traces_dicts_stacked = stack_traces_dicts(traces_dicts=traces_dict_list)
print('saving files...')
save_traces(file_path = arviz_path,
arviz_trace = mcmc_az,
dict_trace = traces_dicts_stacked,
model = args.model,
idx = data_idx,
backend = 'hddm',
infer_type = 'mcmc',
machine = args.machine,
nparticles = args.nchains)