def get_model_nll(params):
"""
Computes the negative log likelihood of the global data set given the
parameters of the aDDM.
Args:
params: list containing the 3 model parameters, in the following order: d,
theta, sigma.
Returns:
The negative log likelihood for the global data set and the given model.
"""
d = params[0]
theta = params[1]
sigma = params[2]
logLikelihood = 0
subjects = choice.keys()
for subject in subjects:
trials = choice[subject].keys()
trialSet = np.random.choice(trials, trialsPerSubject, replace=False)
for trial in trialSet:
try:
likelihood = get_trial_likelihood(
choice[subject][trial], valueLeft[subject][trial],
valueRight[subject][trial], fixItem[subject][trial],
fixTime[subject][trial], d, theta, sigma=sigma)
except:
print("An exception occurred during the likelihood computation "
"for subject " + subject + ", trial " + str(trial) + ".")
raise
if likelihood != 0:
logLikelihood += np.log(likelihood)
print("NLL for " + str(params) + ": " + str(-logLikelihood))
return -logLikelihood
def get_trial_likelihood_wrapper(params):
"""
Wrapper for addm.get_trial_likelihood() which takes a single argument.
Intended for parallel computation using a thread pool.
Args:
params: tuple consisting of all arguments required by
addm.get_trial_likelihood().
Returns:
The output of addm.get_trial_likelihood().
"""
return get_trial_likelihood(*params)
def get_trial_likelihood_wrapper(params):
"""
Wrapper for addm.get_trial_likelihood() which takes a single argument.
Intended for parallel computation using a thread pool.
Args:
params: tuple consisting of all arguments required by
addm.get_trial_likelihood().
Returns:
The output of addm.get_trial_likelihood().
"""
return get_trial_likelihood(*params)
def evaluate(individual):
"""
Computes the negative log likelihood of the global data set given the
parameters of the aDDM.
Args:
individual: list containing the 3 model parameters, in the following
order: d, theta, sigma.
Returns:
A list containing the negative log likelihood for the global data set and
the given model.
"""
d = individual[0]
theta = individual[1]
sigma = individual[2]
logLikelihood = 0
subjects = choice.keys()
for subject in subjects:
trials = choice[subject].keys()
trialSet = np.random.choice(trials, trialsPerSubject, replace=False)
for trial in trialSet:
try:
likelihood = get_trial_likelihood(choice[subject][trial],
valueLeft[subject][trial],
valueRight[subject][trial],
fixItem[subject][trial],
fixTime[subject][trial],
d,
theta,
sigma=sigma,
plotResults=False)
except:
print(
"An exception occurred during the likelihood computation "
"for subject " + subject + ", trial " + str(trial) + ".")
raise
if likelihood != 0:
logLikelihood += np.log(likelihood)
print("NLL for " + str(individual) + ": " + str(-logLikelihood))
return -logLikelihood,
def get_model_nll(choice, valueLeft, valueRight, fixItem, fixTime, d, theta,
sigma, trialsPerSubject=100, useOddTrials=True,
useEvenTrials=True, isCisTrial=None, isTransTrial=None,
useCisTrials=True, useTransTrials=True, verbose=True):
"""
Computes the negative log likelihood of a data set given the parameters of
the aDDM.
Args:
choice: dict of dicts, indexed first by subject then by trial number. Each
entry is an integer corresponding to the decision made in that trial.
valueLeft: dict of dicts with same indexing as choice. Each entry is an
integer corresponding to the value of the left item.
valueRight: dict of dicts with same indexing as choice. Each entry is an
integer corresponding to the value of the right item.
fixItem: dict of dicts with same indexing as choice. Each entry is an
ordered list of fixated items in the trial.
fixTime: dict of dicts with same indexing as choice. Each entry is an
ordered list of fixation durations in the trial.
d: float, parameter of the model which controls the speed of integration
of the signal.
theta: float between 0 and 1, parameter of the model which controls the
attentional bias.
sigma: float, parameter of the model, standard deviation for the normal
distribution.
trialsPerSubject: integer, number of trials to be used from each subject.
If smaller than one, all trials will be used.
useOddTrials: boolean, whether or not to use odd trials in the analysis.
useEvenTrials: boolean, whether or not to use even trials in the analysis.
isCisTrial: dict of dicts, indexed first by subject then by trial number.
Each entry is a boolean indicating if the trial is cis (both bars on
the same side of the target).
isTransTrial: dict of dicts with same indexing as isCisTrial. Each entry
is a boolean indicating if the trial is trans (bars on either side of
the target).
useCisTrials: boolean, whether or not to use cis trials in the analysis.
useTransTrials: boolean, whether or not to use trans trials in the
analysis.
verbose: boolean, whether or not to print updates during computation.
Returns:
The negative log likelihood for the given data set and model.
"""
logLikelihood = 0
subjects = choice.keys()
for subject in subjects:
trials = choice[subject].keys()
if trialsPerSubject < 1:
trialsPerSubject = len(trials)
if useEvenTrials and useOddTrials:
if useCisTrials and useTransTrials:
trialSet = np.random.choice(trials, trialsPerSubject,
replace=False)
elif useCisTrials and not useTransTrials:
trialSet = np.random.choice(
[trial for trial in trials if isCisTrial[subject][trial]],
trialsPerSubject, replace=False)
elif not useCisTrials and useTransTrials:
trialSet = np.random.choice(
[trial for trial in trials if isTransTrial[subject][trial]],
trialsPerSubject, replace=False)
else:
return 0
elif useEvenTrials and not useOddTrials:
if useCisTrials and useTransTrials:
trialSet = np.random.choice(
[trial for trial in trials if not trial % 2],
trialsPerSubject, replace=False)
elif useCisTrials and not useTransTrials:
trialSet = np.random.choice(
[trial for trial in trials if not trial % 2 and
isCisTrial[subject][trial]],
trialsPerSubject, replace=False)
elif not useCisTrials and useTransTrials:
trialSet = np.random.choice(
[trial for trial in trials if not trial % 2 and
isTransTrial[subject][trial]],
trialsPerSubject, replace=False)
else:
return 0
elif not useEvenTrials and useOddTrials:
if useCisTrials and useTransTrials:
trialSet = np.random.choice(
[trial for trial in trials if trial % 2],
trialsPerSubject, replace=False)
elif useCisTrials and not useTransTrials:
trialSet = np.random.choice(
[trial for trial in trials if trial % 2 and
isCisTrial[subject][trial]],
trialsPerSubject, replace=False)
elif not useCisTrials and useTransTrials:
trialSet = np.random.choice(
[trial for trial in trials if trial % 2 and
isTransTrial[subject][trial]],
trialsPerSubject, replace=False)
else:
return 0
else:
return 0
for trial in trialSet:
try:
likelihood = get_trial_likelihood(
choice[subject][trial], valueLeft[subject][trial],
valueRight[subject][trial], fixItem[subject][trial],
fixTime[subject][trial], d, theta, sigma=sigma)
except:
print("An exception occurred during the likelihood computation "
"for subject " + subject + ", trial " + str(trial) + ".")
raise
if likelihood != 0:
logLikelihood += np.log(likelihood)
if verbose:
print("NLL for " + str(d) + ", " + str(theta) + ", " + str(sigma) +
": " + str(-logLikelihood))
return -logLikelihood