def sample_param_sets(self, pkeys=None, nsamples=None, nkeep=None):
""" sample *nsamples* (default=5000, see set_fitparams) different
parameter sets (param_sets) and get model yhat for each set (param_yhats)
"""
if pkeys is None:
pkeys = np.sort(list(self.inits))
if nsamples is None:
nsamples = self.basinparams['nsamples']
if nkeep is None:
nkeep = self.basinparams['ninits']
unfiltered = theta.random_inits(pkeys, ninits=nsamples, kind=self.kind, as_list=True)
self.filter_params(unfiltered, nsamples=nsamples, nkeep=nkeep)
self.finished_sampling = True
def sample_theta(self):
""" sample *nsamples* (default=5000, see set_fitparams) different
parameter sets (param_sets) and get model yhat for each set (param_yhats)
"""
pkeys = self.sim.theta.keys().tolist()
nsamples = self.basinparams['nsamples']
if not hasattr(self, 'init_params'):
init_params = theta.random_inits(pkeys, ninits=nsamples, kind=self.kind, as_list=True, method=self.basinparams['sample_method'])
init_yhats = pd.DataFrame(np.vstack([self.sim.simulate_model(p) for p in init_params]))
self.init_params = init_params
self.init_yhats = init_yhats.copy()
init_params = self.init_params
init_yhats = self.init_yhats.copy()
psets = self.filter_params(init_params, init_yhats)
self.param_sets = psets
def plot_param_distributions(p, nsamples=1000):
from radd import theta
pkeys = np.sort(list(p))
nparams = pkeys.size
p_dists = theta.random_inits(pkeys=pkeys, ninits=nsamples)
clrs = colors.param_color_map()
lbls = {pk: parameter_name(pk,True) for pk in pkeys}
ncols = np.ceil(nparams/2.).astype(int)
fig, axes = plt.subplots(2, ncols, figsize=(10,5))
axes = axes.flatten()
for i, pk in enumerate(pkeys):
sns.distplot(p_dists[pk], label=lbls[pk], color=clrs[pk], ax=axes[i])
for ax in axes:
ax.legend(loc=0, fontsize=16)
plt.tight_layout()
sns.despine()
def sample_rl_theta(self, fitDynamics=True):
simfx = self.simRL.sim_rt_sacc_blocks
nsamples = self.basinparams['nsamples']
nkeep = self.basinparams['ninits']
if hasattr(self, 'popt'):
idxParams = pd.DataFrame([pd.Series(self.inits)]*nsamples)
idxParams = pd.DataFrame([pd.Series(self.popt)]*nsamples)
if not hasattr(self, 'rlParams'):
rlParams = theta.random_inits(['AX', 'BX', 'PX'], ninits=nsamples, kind=self.kind, as_list=True, method=self.basinparams['sample_method'])
rlParamsDF = pd.concat([idxParams,pd.DataFrame(rlParams.tolist())], axis=1)
self.rlParams = np.array(rlParamsDF.to_dict('records'))
self.rlYhat = pd.DataFrame(np.vstack([simfx(p) for p in self.rlParams]))
rlPsets = self.filter_rlParams(self.rlParams, self.rlYhat)
self.param_sets = rlPsets
