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run_modelling_analyses.py
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run_modelling_analyses.py
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import os
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
plt.ion()
import arviz as az
mpl.rcParams['axes.linewidth'] = 0.5
mpl.rcParams['xtick.major.width'] = 0.5
mpl.rcParams['ytick.major.width'] = 0.5
# custom modules
import beh_analysis
import plots
import modelling
## set up figures etc.
save_figs = 'paper'
# control whether we should do sampling or not. Note that sampling can take a while, and
# also storing traces takes up quite a bit of space (~3GB).
do_sampling = False
#traces_path = '../traces'
traces_path = '/project/3018045.01/traces-4paper'
try:
os.makedirs(traces_path)
except:
pass
# output dir for summary stats and figures
output_dir = '../results/modelling'
try:
os.makedirs(output_dir)
except:
pass
# this is optional if you want publication-quality fonts
# if save_figs == 'paper':
# plots.set_font(font='Helvetica Neue LT Std', stretch='condensed', size=14)
# else:
# plots.set_font(font='Muli', size=16)
fig_counter = 0
def savefig(fig, title):
global fig_counter
fig_counter += 1
fig.tight_layout()
if save_figs == 'paper':
fig.savefig('{}/{:03d}-{}.pdf'.format(output_dir, fig_counter, title))
elif save_figs == 'png':
fig.savefig('{}/{:03d}-{}.png'.format(output_dir, fig_counter, title), dpi=300)
outfile = open(output_dir + '/statresults-modelling.txt', 'w', buffering=1)
pd.set_option('display.max_columns', 12)
## load data
df_all_cd = pd.read_pickle('../data/exp1-cd.pkl.gz')
df_all_2afc = pd.read_pickle('../data/exp2-2afc.pkl.gz')
## reject outliers
df_all_cd = beh_analysis.reject_outliers(df_all_cd,
depvars=['log_rt', 'log_loc_error', 'log_loc_rt'],
group_columns=['subnum', 'item'])
df_all_2afc = beh_analysis.reject_outliers(df_all_2afc,
depvars=['log_correct_rt', 'correct'],
group_columns=['subnum', 'item'])
## cd: sampling
if do_sampling:
df = df_all_cd.dropna()
for depvar in ('log_rt', 'log_loc_error'):
model = modelling.build_bambi_model(df, depvar,
family='gaussian', factors='1 + con_incon')
results = modelling.sample_and_get_results(model)
az.to_netcdf(results, '{}/cd-{}.az'.format(traces_path, depvar))
## 2afc: sampling
if do_sampling:
for depvar in ('correct', 'log_correct_rt'):
if depvar == 'log_correct_rt':
df = df_all_2afc.dropna()
family = 'gaussian'
else:
df = df_all_2afc
family = 'bernoulli'
model = modelling.build_bambi_model(df, depvar,
family=family, factors='1 + con_incon * probe_key_other')
results = modelling.sample_and_get_results(model)
az.to_netcdf(results, '{}/2afc-{}.az'.format(traces_path, depvar))
## both tasks: define variables and label them
# variables to inspect/plot (mapping from name to label)
variables_2afc = {'Intercept': 'Intercept',
'con_incon': 'Congruency',
'probe_key_other': 'Probe',
'con_incon:probe_key_other': 'Congruency X Probe',
'1|subnum_sigma': '$\sigma_{Subject}$(1)',
'con_incon|subnum_sigma': '$\sigma_{Subject}$(Congruency)',
'probe_key_other|subnum_sigma': '$\sigma_{Subject}$(Probe)',
'con_incon:probe_key_other|subnum_sigma': '$\sigma_{Subject}$(Congruency X Probe)',
'1|item_sigma': '$\sigma_{Item}$(1)',
'con_incon|item_sigma': '$\sigma_{Item}$(Congruency)',
'probe_key_other|item_sigma': '$\sigma_{Item}$(Probe)',
'con_incon:probe_key_other|item_sigma': '$\sigma_{Item}$(Congruency X Probe)'}
variables_cd = {k: v for k, v in variables_2afc.items() if not 'probe' in k}
## cd: stats, plotting
has_refline = ['con_incon']
for depvar in ('log_rt', 'log_loc_error'):
results = az.from_netcdf('{}/cd-{}.az'.format(traces_path, depvar))
fig, allax = plt.subplots(3, 2, figsize=(5, 6))
prop_above = plots.plot_posterior(results, allax.ravel(), variables_cd, has_refline)
savefig(fig, 'model-cd-{}'.format(depvar))
print('CD, {}'.format(depvar), file=outfile)
summary = az.summary(results, kind='stats', var_names=list(variables_cd.keys()),
round_to=6)
print(summary.to_string() + '\n', file=outfile)
print('p(<> 0):\n' + str(prop_above) + '\n', file=outfile)
## 2afc: stats, plotting
# variables to inspect/plot (mapping from name to label)
variables_2afc = {'Intercept': 'Intercept',
'con_incon': 'Congruency',
'probe_key_other': 'Probe',
'con_incon:probe_key_other': 'Congruency X Probe',
'1|subnum_sigma': '$\sigma_{Subject}$(1)',
'con_incon|subnum_sigma': '$\sigma_{Subject}$(Congruency)',
'probe_key_other|subnum_sigma': '$\sigma_{Subject}$(Probe)',
'con_incon:probe_key_other|subnum_sigma': '$\sigma_{Subject}$(Congruency X Probe)',
'1|item_sigma': '$\sigma_{Item}$(1)',
'con_incon|item_sigma': '$\sigma_{Item}$(Congruency)',
'probe_key_other|item_sigma': '$\sigma_{Item}$(Probe)',
'con_incon:probe_key_other|item_sigma': '$\sigma_{Item}$(Congruency X Probe)'}
has_refline = ['con_incon', 'probe_key_other', 'con_incon:probe_key_other']
for depvar in ('correct', 'log_correct_rt'):
results = az.from_netcdf('{}/2afc-{}.az'.format(traces_path, depvar))
fig, allax = plt.subplots(3, 4, figsize=(9, 6))
prop_above = plots.plot_posterior(results, allax.ravel(), variables_2afc, has_refline)
savefig(fig, 'model-2afc-{}'.format(depvar))
print('2AFC, {}'.format(depvar), file=outfile)
summary = az.summary(results, kind='stats', var_names=list(variables_2afc.keys()),
round_to=6)
print(summary.to_string() + '\n', file=outfile)
print('p(<> 0):\n' + str(prop_above) + '\n', file=outfile)
## wrap up
outfile.close()
plt.close('all')