def setup_model(self):
"""
Function to setup model.
"""
class DriftCoherence(ddm.models.Drift):
name = "Drift depends linearly on coherence"
# Parameter that should be included in the ddm
required_parameters = ["driftcoherence"]
# Task Parameter, i.e. coherence
required_conditions = ["coherence"]
# Define the get_drift function
def get_drift(self, conditions, **kwargs):
return self.driftcoherence * conditions['coherence']
# Set up Model with Drift depending on Coherence
model = Model(
name='Noise Model - Drift varies with coherence',
drift=DriftCoherence(driftcoherence=Fittable(minval=0, maxval=20)),
noise=NoiseConstant(noise=1),
bound=BoundConstant(B=Fittable(minval=.1, maxval=1.5)),
overlay=OverlayChain(overlays=[
OverlayNonDecision(nondectime=Fittable(minval=0, maxval=.4)),
OverlayPoissonMixture(pmixturecoef=.02, rate=1)
]),
dx=.01,
dt=.01,
T_dur=2)
return model
def DDM_FIT(RT, ANSWER):
df = []
# RT is scalles to seconds, the function takes seconds
df = pd.DataFrame({'RT': RT / 1000, 'correct': ANSWER})
df.head()
sample = Sample.from_pandas_dataframe(df,
rt_column_name="RT",
correct_column_name="correct")
model = Model(
name='Model',
drift=DriftConstant(drift=Fittable(minval=6, maxval=25)),
noise=NoiseConstant(
noise=1.5), #(noise=Fittable(minval=0.5, maxval=2.5)),
bound=BoundConstant(B=2.5),
overlay=OverlayChain(overlays=[
OverlayNonDecision(nondectime=Fittable(minval=0, maxval=.8)),
OverlayPoissonMixture(pmixturecoef=.02, rate=1)
]),
dx=.001,
dt=.01,
T_dur=2)
# Fitting this will also be fast because PyDDM can automatically
# determine that DriftCoherence will allow an analytical solution.
fit_model = fit_adjust_model(sample=sample,
model=model,
fitting_method="differential_evolution",
lossfunction=LossRobustBIC,
verbose=False)
param = fit_model.get_model_parameters()
Drift = np.asarray(param[0])
Delay = np.asarray(param[1])
return Drift, Delay
from ddm.functions import fit_adjust_model, display_model
from ddm.models import NoiseConstant, BoundConstant, OverlayChain, OverlayNonDecision, OverlayPoissonMixture
model_rs = Model(
name='Roitman data, drift varies with coherence',
drift=DriftCoherence(driftcoh=Fittable(minval=0, maxval=20)),
noise=NoiseConstant(noise=1),
bound=BoundConstant(B=Fittable(minval=.1, maxval=1.5)),
# Since we can only have one overlay, we use
# OverlayChain to string together multiple overlays.
# They are applied sequentially in order. OverlayNonDecision
# implements a non-decision time by shifting the
# resulting distribution of response times by
# `nondectime` seconds.
overlay=OverlayChain(overlays=[
OverlayNonDecision(nondectime=Fittable(minval=0, maxval=.4)),
OverlayPoissonMixture(pmixturecoef=.02, rate=1)
]),
dx=.001,
dt=.01,
T_dur=2)
# Fitting this will also be fast because PyDDM can automatically
# determine that DriftCoherence will allow an analytical solution.
fit_model_rs = fit_adjust_model(sample=roitman_sample,
model=model_rs,
verbose=False)
display_model(fit_model_rs)
fit_model_rs.parameters()
# Plot the model fit to the PDFs and save the file.
# Simple demonstration of PyDDM.
# Create a simple model with constant drift, noise, and bounds.
from ddm import Model
from ddm.models import DriftConstant, NoiseConstant, BoundConstant, OverlayNonDecision, ICPointSourceCenter
from ddm.functions import fit_adjust_model, display_model
model = Model(name='Simple model',
drift=DriftConstant(drift=2.2),
noise=NoiseConstant(noise=1.5),
bound=BoundConstant(B=1.1),
overlay=OverlayNonDecision(nondectime=.1),
dx=.001, dt=.01, T_dur=2)
# Solve the model, i.e. simulate the differential equations to
# generate a probability distribution solution.
display_model(model)
sol = model.solve()
# Now, sample from the model solution to create a new generated
# sample.
samp = sol.resample(1000)
# Fit a model identical to the one described above on the newly
# generated data so show that parameters can be recovered.
from ddm import Fittable, Fitted
from ddm.models import LossRobustBIC
from ddm.functions import fit_adjust_model
model_fit = Model(name='Simple model (fitted)',
drift=DriftConstant(drift=Fittable(minval=0, maxval=4)),
noise=NoiseConstant(noise=Fittable(minval=.5, maxval=4)),
# Define a model which uses our new DriftCoherence defined above.
from ddm import Model, Fittable
from ddm.functions import fit_adjust_model, display_model
from ddm.models import NoiseConstant, BoundConstant, OverlayChain, OverlayNonDecision, OverlayPoissonMixture
model_rs = Model(name='Roitman data, drift varies with coherence',
drift=DriftCoherence(driftcoh=Fittable(minval=0, maxval=20)),
noise=NoiseConstant(noise=1),
bound=BoundConstant(B=Fittable(minval=.1, maxval=1.5)),
# Since we can only have one overlay, we use
# OverlayChain to string together multiple overlays.
# They are applied sequentially in order. OverlayNonDecision
# implements a non-decision time by shifting the
# resulting distribution of response times by
# `nondectime` seconds.
overlay=OverlayChain(overlays=[OverlayNonDecision(nondectime=Fittable(minval=0, maxval=.4)),
OverlayPoissonMixture(pmixturecoef=.02,
rate=1)]),
dx=.001, dt=.01, T_dur=2)
# Fitting this will also be fast because PyDDM can automatically
# determine that DriftCoherence will allow an analytical solution.
fit_model_rs = fit_adjust_model(sample=roitman_sample, model=model_rs, verbose=False)
display_model(fit_model_rs)
# Plot the model fit to the PDFs and save the file.
import ddm.plot
import matplotlib.pyplot as plt
ddm.plot.plot_fit_diagnostics(model=fit_model_rs, sample=roitman_sample)
plt.savefig("roitman-fit.png")
def Fit_DDM_Group_JK_ib(domain='whole'):
# create a df to record the output data
column_names = ['domain', 'RS_level', 'sub_omit'] + model_rDDM.get_model_parameter_names() + \
['loss_func_value', 'criterion', 'sample_size', 'on_bound']
rDDM_fitting_jk = pd.DataFrame(columns=column_names)
# decide whether to fit rDDM on the whole dataset,
# the advantageous trials or disadvantageous trials
if domain == 'adv':
data_for_fit = data[data['domain_group'] == 1]
elif domain == 'dis':
data_for_fit = data[data['domain_group'] == 2]
else:
data_for_fit = data
# fit on groups of different RS levels
for group in np.sort(data_for_fit['RS_level'].unique()):
data_subgroup = data_for_fit[data_for_fit['RS_level'] == group]
# omit subjects one at a time
for sub_omit in np.sort(data_subgroup['subject'].unique()):
# find the current fitting parameters in the fit_ref
where = np.where((fit_ref['domain'] == domain)
& (fit_ref['RS_level'] == group)
& (fit_ref['sub_omit'] == str(sub_omit)))
where = int(where[0])
# allow for extra ranges from the best fitting results
extra = 0.05
extra_v_dis = 0.0005
extra_v_els = 0.005
if domain == 'dis':
vg_max = fit_ref.iloc[where]['vg_best'] + extra_v_dis
vg_min = fit_ref.iloc[where]['vg_best'] - extra_v_dis
vl_max = fit_ref.iloc[where]['vl_best'] + extra_v_dis
vl_min = fit_ref.iloc[where]['vl_best'] - extra_v_dis
else:
vg_max = fit_ref.iloc[where]['vg_best'] + extra_v_els
vg_min = fit_ref.iloc[where]['vg_best'] - extra_v_els
vl_max = fit_ref.iloc[where]['vl_best'] + extra_v_els
vl_min = fit_ref.iloc[where]['vl_best'] - extra_v_els
fixed_max = fit_ref.iloc[where]['fixed_best'] + extra
fixed_min = fit_ref.iloc[where]['fixed_best'] - extra
B_max = fit_ref.iloc[where]['B_best'] + extra
B_min = fit_ref.iloc[where]['B_best'] - extra
x0_max = fit_ref.iloc[where]['x0_best'] + extra
x0_min = fit_ref.iloc[where]['x0_best'] - extra
nondectime_max = fit_ref.iloc[where]['nondectime_best'] + extra
nondectime_min = fit_ref.iloc[where]['nondectime_best'] - extra
# create a DDM modell based on the current fitting range
model_rDDM_ib = Model(
name='Risk_DDM_individual_range_fit',
drift=Risk_Drift(vg=Fittable(minval=vg_min, maxval=vg_max),
vl=Fittable(minval=vl_min, maxval=vl_max),
fixed=Fittable(minval=fixed_min,
maxval=fixed_max)),
IC=Biased_Start(x0=Fittable(minval=x0_min, maxval=x0_max)),
noise=NoiseConstant(noise=1),
bound=BoundConstant(B=Fittable(minval=B_min, maxval=B_max)),
# Uniform Mixture Model
overlay=OverlayChain(overlays=[
OverlayNonDecision(nondectime=Fittable(
minval=nondectime_min, maxval=nondectime_max)),
OverlayUniformMixture(umixturecoef=.05)
]),
dx=0.001,
dt=0.001,
T_dur=10)
# dx=0.01, dt=0.01, T_dur=10)
data_jk = data_subgroup[data_subgroup['subject'] != sub_omit]
sample_size = data_jk.shape[0]
# create a sample and start fitting
data_jk_sample = Sample.from_pandas_dataframe(
data_jk, rt_column_name="RT", correct_column_name="accept")
fit_sample = fit_adjust_model(
sample=data_jk_sample,
model=model_rDDM_ib,
fitting_method='differential_evolution')
# sort out results
result_list = [[domain, group, sub_omit] + fit_sample.get_model_parameters() + \
[fit_sample.fitresult.value(), fit_sample.fitresult.loss, sample_size, 0]]
# 0 in the end is the place holder for the counts in the on_buond column
result_df = pd.DataFrame(result_list, columns=column_names)
# check whether the estimated results are on the limits of the fitting ranges
if result_df['vg'][0] == vg_min or result_df['vg'][0] == vg_max:
result_df['on_bound'][0] += 1
if result_df['vl'][0] == vl_min or result_df['vl'][0] == vl_max:
result_df['on_bound'][0] += 1
if result_df['fixed'][0] == fixed_min or result_df['fixed'][
0] == fixed_max:
result_df['on_bound'][0] += 1
if result_df['B'][0] == B_min or result_df['B'][0] == B_max:
result_df['on_bound'][0] += 1
if result_df['x0'][0] == x0_min or result_df['x0'][0] == x0_max:
result_df['on_bound'][0] += 1
if result_df['nondectime'][0] == nondectime_min or result_df[
'nondectime'][0] == nondectime_max:
result_df['on_bound'][0] += 1
# append the results
rDDM_fitting_jk = rDDM_fitting_jk.append(result_df,
ignore_index=True)
return rDDM_fitting_jk