def __make_dataframes__(self):
""" wrapper for dfhandler.DataHandler.make_dataframes
"""
from radd.dfhandler import DataHandler
# initialize dataframe handler
self.handler = DataHandler(self)
# make dataframes
self.handler.make_dataframes()
# Group dataframe (nsubjects*nconds*nlevels x ndatapoints)
self.observedDF = self.handler.observedDF.copy()
# list (nsubjects long) of data arrays (nconds*nlevels x ndatapoints) to fit
self.observed = self.handler.observed
# list of flattened data arrays (averaged across conditions)
self.observed_flat = self.handler.observed_flat
# dataframe with same dim as observeddf for storing model predictions
self.yhatDF = self.handler.yhatDF
# dataframe with same dim as observeddf for storing fit info
self.fitDF = self.handler.fitDF
# dataframe containing cost_function wts (see dfhandler docs)
self.wtsDF = self.handler.wtsDF
# list of arrays containing conditional costfx weights
self.cond_wts = self.handler.cond_wts
# list of arrays containing flat costfx weights
self.flat_wts = self.handler.flat_wts
# define iterables containing fit y & wts for each fit
self.iter_flat = zip(self.observed_flat, self.flat_wts)
self.iter_cond = zip(self.observed, self.cond_wts)
def __make_dataframes__(self):
""" wrapper for dfhandler.DataHandler.make_dataframes
"""
from radd.dfhandler import DataHandler
# initialize dataframe handler
self.handler = DataHandler(self)
# make dataframes
self.handler.make_dataframes()
# Group dataframe (nsubjects*nconds*nlevels x ndatapoints)
self.observedDF = self.handler.observedDF.copy()
self.observedErr = self.handler.observedErr.copy()
# list (nsubjects long) of data arrays (nconds*nlevels x ndatapoints) to fit
self.observed = self.handler.observed
# list of flattened data arrays (averaged across conditions)
self.observed_flat = self.handler.observed_flat
# observed frequency bins dataframe
# self.freqDF = self.handler.make_freq_df()
# dataframe with same dim as observeddf for storing model predictions
self.yhatdf = self.handler.yhatdf
# dataframe with same dim as observeddf for storing fit info (& popt)
self.fitdf = self.handler.fitdf
# dataframe with same dim as observeddf for storing optimized params as matrix
self.poptdf = self.handler.poptdf
# dataframe containing cost_function wts (see dfhandler docs)
self.wtsDF = self.handler.wtsDF
try:
# dataframe containing ssd's per idx (see dfhandler docs)
self.ssdDF = self.handler.ssdDF
except Exception:
pass
# list of arrays containing conditional costfx weights
self.cond_wts = self.handler.cond_wts
# list of arrays containing flat costfx weights
self.flat_wts = self.handler.flat_wts
# define iterables containing fit y & wts for each fit
self.iter_flat = zip(self.observed_flat, self.flat_wts)
self.iter_cond = zip(self.observed, self.cond_wts)
# self.resultsdir = os.self.handler.make_results_dir(custompath=self.custompath, get_path=True)
# get working directory
self.resultsdir = os.path.abspath('./')
class RADDCore(object):
""" Parent class for constructing attributes and methods used by
of Model objects. Not meant to be used directly.
Contains methods for building dataframes, generating observed data vectors
that are entered into cost function during fitting as well as calculating
summary measures and weight matrix for weighting residuals during optimization.
"""
def __init__(self, data=None, kind='xdpm', inits=None, fit_on='average', depends_on={'all':'flat'}, quantiles=np.arange(.1, 1.,.1), ssd_method=None, weighted=True, verbose=False, custompath=None, nested_models=None):
self.kind = kind
self.fit_on = fit_on
self.ssd_method = ssd_method
self.weighted = weighted
self.quantiles = quantiles
self.tb = data[data.response == 1].rt.max()
self.idx = list(data.idx.unique())
self.nidx = len(self.idx)
self.inits = inits
self.data = data
self.set_conditions(depends_on)
self.__prepare_fit__()
self.finished_sampling = False
self.track_subjects = False
self.track_basins = False
self.pbars = None
self.is_nested = False
def __prepare_fit__(self):
""" model setup and initiates dataframes. Automatically run when Model object is initialized
*pc_map is a dict containing parameter names as keys with values
corresponding to the names given to that parameter in Parameters object
(see optmize.Optimizer).
*Parameters (p[pkey]=pval) that are constant across conditions are broadcast as [pval]*n.
Conditional parameters are treated as arrays with distinct values [V1, V2...Vn], one for
each condition.
pc_map (dict): see bound __format_pcmap__ method
"""
from radd.optimize import Optimizer
from radd.models import Simulator
if self.inits is None:
self.__get_default_inits__()
# pc_map (see docstrings)
self.__format_pcmap__()
# create model_id string for naming output
self.generate_model_id()
# initialize DataHandler & generate I/O dataframes
self.__make_dataframes__()
# set fit parameters with default values
self.set_fitparams()
# set basinhopping parameters with default values
self.set_basinparams()
# initialize model simulator, mainly accessed by the model optimizer object
self.simulator = Simulator(fitparams=self.fitparams, kind=self.kind, pc_map=self.pc_map)
# initialize optimizer object for controlling fit routines
# (updated with fitparams/basinparams whenever params are set)
self.optimizer = Optimizer(simulator=self.simulator, basinparams=self.basinparams)
def __make_dataframes__(self):
""" wrapper for dfhandler.DataHandler.make_dataframes
"""
from radd.dfhandler import DataHandler
# initialize dataframe handler
self.handler = DataHandler(self)
# make dataframes
self.handler.make_dataframes()
# Group dataframe (nsubjects*nconds*nlevels x ndatapoints)
self.observedDF = self.handler.observedDF.copy()
# list (nsubjects long) of data arrays (nconds*nlevels x ndatapoints) to fit
self.observed = self.handler.observed
# list of flattened data arrays (averaged across conditions)
self.observed_flat = self.handler.observed_flat
# dataframe with same dim as observeddf for storing model predictions
self.yhatDF = self.handler.yhatDF
# dataframe with same dim as observeddf for storing fit info
self.fitDF = self.handler.fitDF
# dataframe containing cost_function wts (see dfhandler docs)
self.wtsDF = self.handler.wtsDF
# list of arrays containing conditional costfx weights
self.cond_wts = self.handler.cond_wts
# list of arrays containing flat costfx weights
self.flat_wts = self.handler.flat_wts
# define iterables containing fit y & wts for each fit
self.iter_flat = zip(self.observed_flat, self.flat_wts)
self.iter_cond = zip(self.observed, self.cond_wts)
def set_fitparams(self, force_conditional=False, **kwargs):
""" dictionary of fit parameters, passed to Optimizer/Simulator objects
"""
if not hasattr(self, 'fitparams'):
# initialize with default values and first arrays in observed_flat, flat_wts
self.fitparams = {'ix':0, 'ntrials': 20000, 'tol': 1.e-30, 'method': 'nelder',
'maxfev': 3000, 'tb': self.tb, 'nlevels': 1, 'fit_on': self.fit_on,
'kind': self.kind, 'clmap': self.clmap, 'quantiles': self.quantiles,
'model_id': self.model_id, 'depends_on': self.depends_on}
self.fitparams = pd.Series(self.fitparams)
else:
# fill with kwargs (i.e. y, wts, ix, etc) for the upcoming fit
for kw_arg, kw_val in kwargs.items():
self.fitparams[kw_arg] = kw_val
if 'quantiles' in list(kwargs):
self.__update_quantiles__()
if 'depends_on' in list(kwargs):
reformat_dataframes = False
if self.is_flat:
reformat_dataframes=True
self.set_conditions(kwargs['depends_on'])
if reformat_dataframes:
self.__prepare_fit__()
self.update_data(force_conditional)
if hasattr(self, 'ssd'):
self.__set_ssd_info__()
if hasattr(self, 'optimizer'):
self.simulator = self.optimizer.update(fitparams=self.fitparams, pc_map=self.pc_map, get_simulator=True)
def set_basinparams(self, **kwargs):
""" dictionary of global fit parameters, passed to Optimizer/Simulator objects
"""
if not hasattr(self, 'basinparams'):
self.basinparams = {'ninits': 3, 'nsamples': 3000, 'interval': 10, 'T': 1.,
'stepsize': .05, 'niter': 100, 'nsuccess': 60, 'tol': 1.e-20, 'method': 'TNC',
'init_sample_method': 'best', 'progress': False, 'disp': False}
else:
# fill with kwargs for the upcoming fit
for kw_arg, kw_val in kwargs.items():
self.basinparams[kw_arg] = kw_val
if hasattr(self, 'optimizer'):
self.optimizer.update(basinparams=self.basinparams)
def update_data(self, force_conditional=False):
""" called when ix (int) is passed to fitparams as kwarg.
Fills fitparams with y and wts vectors corresponding to ix'th
arrays in observed(_flat) and (flat/cond)_wts lists.
"""
if force_conditional:
self.fitparams['nlevels'] = self.nlevels
nlevels = self.fitparams.nlevels
i = self.fitparams['ix']
if nlevels>1:
self.fitparams['y'] = self.observed[i]
self.fitparams['wts'] = self.cond_wts[i]
else:
self.fitparams['y'] = self.observed_flat[i]
self.fitparams['wts'] = self.flat_wts[i]
def set_conditions(self, depends_on=None):
data = self.data.copy()
self.depends_on = depends_on
self.conds = np.unique(listvalues(self.depends_on)).tolist()
if 'flat' in self.conds:
self.is_flat = True
data['flat'] = 'flat'
self.data = data.copy()
else:
self.is_flat = False
self.nconds = len(self.conds)
self.clmap = {c: np.sort(data[c].unique()) for c in self.conds}
self.nlevels = np.sum([len(lvls) for lvls in listvalues(self.clmap)])
self.groups = np.hstack([['idx'], self.conds]).tolist()
self.__format_pcmap__()
if hasattr(self, 'ssd'):
self.__set_ssd_info__()
if hasattr(self, 'fitparams'):
self.generate_model_id()
self.set_fitparams(nlevels=self.nlevels, clmap=self.clmap)
def __format_pcmap__(self):
""" dict used by Simulator to extract conditional parameter values by name
from lmfit Parameters object
|<--- PARAMETERS OBJECT [LMFIT] <------- [IN]
|---> p = {'v_bsl': V1, 'v_pnl': V2...} --->|
|<--- pc_map = {'v':['v_bsl', 'v_pnl']} <---|
|---> p['v'] = array([V1, V2]) -------> [OUT]
"""
pc_map = {}
if not self.is_flat:
for p, cond in self.depends_on.items():
levels = np.sort(self.data[cond].unique())
pc_map[p] = ['_'.join([p, lvl]) for lvl in levels]
self.pc_map = pc_map
if hasattr(self, 'handler'):
self.handler.pc_map = pc_map
def __set_ssd_info__(self):
""" set ssd_info for upcoming fit and store in fitparams dict
"""
if self.fit_on=='average':
ssd = np.array(self.ssd).mean(axis=0)
else:
# get ssd vector for fit index == ix
ssd = self.ssd[self.fitparams['ix']]
if self.fitparams.nlevels==1:
# single vector (nlevels=1), don't squeeze
ssd = np.mean(ssd, axis=0, keepdims=True)
nssd = ssd.shape[-1]
nss = int((.5 * self.fitparams.ntrials))
nss_per_ssd = int(nss/nssd)
ssd_ix = np.arange(nssd) * np.ones((ssd.shape[0], ssd.shape[-1])).astype(np.int)
# store all ssd_info in fitparams, accessed by Simulator
self.fitparams['ssd_info'] = [ssd, nssd, nss, nss_per_ssd, ssd_ix]
def update_quantiles(self):
""" recalculate observed dataframes w/ passed quantiles array
"""
self.quantiles = self.fitparams.quantiles
self.__make_dataframes__()
self.fitparams['y'] = self.observed_flat[self.fitparams['ix']]
self.fitparams['wts'] = self.flat_wts[self.fitparams['ix']]
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 filter_params(self, p_sets=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 nsamples is None:
nsamples = self.basinparams['nsamples']
if nkeep is None:
nkeep = self.basinparams['ninits']
if p_sets is None:
p_sets = self.param_sets
p_fmins = [self.optimizer.simulator.cost_fx(p, sse=1) for p in p_sets]
method = self.basinparams['init_sample_method']
self.param_sets, self.gmin = theta.filter_params(p_sets, p_fmins, nkeep=nkeep, method=method)
def log_fit_info(self, finfo=None, popt=None, yhat=None):
""" write meta-information about latest fit
to logfile (.txt) in working directory
"""
finfo, popt, yhat =self.set_results(finfo, popt, yhat)
fp = dict(deepcopy(self.fitparams))
fp['yhat'] = self.yhat
# lmfit-structured fit_report to write in log file
param_report = self.optimizer.param_report
# log all fit and meta information in working directory
messages.logger(param_report, finfo=finfo, popt=popt, fitparams=fp, kind=self.kind)
def set_results(self, finfo=None, popt=None, yhat=None):
if finfo is None:
finfo = self.finfo
if popt is None:
popt = self.popt
if yhat is None:
yhat = self.yhat
return finfo, popt, yhat
def generate_model_id(self, appendstr=None):
""" generate an identifying string with model information.
used for reading and writing model output
"""
model_id = list(self.depends_on)
if 'all' in model_id:
model_id = ['flat']
model_id.append(self.fit_on)
model_id.insert(0, self.kind)
if appendstr is not None:
model_id.append(appendstr)
self.model_id = '_'.join(model_id)
if hasattr(self, 'fitparams'):
self.fitparams['model_id'] = self.model_id
def set_testing_params(self, tol=1e-3, nsuccess=10, nsamples=50, ninits=2, maxfev=200, progress=True):
self.set_fitparams(tol=tol, maxfev=maxfev)
self.set_basinparams(tol=tol, ninits=ninits, nsamples=nsamples, nsuccess=nsuccess)
self.optimizer.update(basinparams=self.basinparams, progress=progress)
def __remove_outliers__(self, sd=1.5, verbose=False):
""" remove slow rts (>sd above mean) from main data DF
"""
from radd.tools.analyze import remove_outliers
self.data = analyze.remove_outliers(self.data.copy(), sd=sd, verbose=verbose)
def __get_default_inits__(self):
""" if inits not provided by user, initialize with default values
see tools.theta.get_default_inits
"""
self.inits = theta.get_default_inits(kind=self.kind, depends_on=self.depends_on)
def __check_inits__(self, inits):
""" ensure inits dict is appropriate for Model kind
see tools.theta.check_inits
"""
inits = dict(deepcopy(inits))
checked = theta.check_inits(inits=inits, depends_on=self.depends_on, kind=self.kind)
return checked
class RADDCore(object):
""" Parent class for constructing attributes and methods used by
of Model objects. Not meant to be used directly
Contains methods for building dataframes, generating observed data vectors
that are entered into cost function during fitting as well as calculating
summary measures and weight matrix for weighting residuals during optimization.
"""
def __init__(self, data=None, kind='xdpm', inits=None, fit_on='average', depends_on={'all':'flat'}, ssd_method=None, weighted=True, verbose=False, custompath=None, nested_models=None, learn=False, bwfactors=None, ssdelay=False, gbase=False, quantiles=np.arange(.1, 1.,.1), presample=False, ksfit=False):
self.kind = kind
self.fit_on = fit_on
self.ssd_method = ssd_method
self.weighted = weighted
self.quantiles = quantiles
self.learn = learn
self.ssdelay = ssdelay
self.gbase = gbase
self.custompath = custompath
self.data = data.copy()
# self.data = analyze.remove_outliers(data, 3.5)
self.tb = analyze.estimate_timeboundary(self.data)
self.idx = list(self.data.idx.unique())
self.nidx = len(self.idx)
self.bwfactors = bwfactors
self.inits = inits
self.finished_sampling = False
self.track_subjects = False
self.track_basins = False
self.pbars = None
self.is_nested = False
self.ksfit=ksfit
self.__prepare_fit__(depends_on, presample=presample)
def __prepare_fit__(self, depends_on, presample=False):
""" model setup and initiates dataframes. Automatically run when Model object is initialized
*pcmap is a dict containing parameter names as keys with values
corresponding to the names given to that parameter in Parameters object
(see optmize.Optimizer).
*Parameters (p[pkey]=pval) that are constant across conditions are broadcast as [pval]*n.
Conditional parameters are treated as arrays with distinct values [V1, V2...Vn], one for
each condition.
pcmap (dict): see bound __format_pcmap__ method
"""
# from radd.optimize import Optimizer
# from radd.models import Simulator
from radd import optimize
self.set_conditions(depends_on, self.bwfactors)
if self.inits is None:
self.__get_default_inits__()
# pcmap (see docstrings)
self.__format_pcmap__()
# create model_id string for naming output
self.generate_model_id()
# initialize DataHandler & generate I/O dataframes
self.__make_dataframes__()
# set fit parameters with default values
self.set_fitparams()
# # set ssd info
# self.__set_ssd_info__()
# set basinhopping parameters with default values
self.set_basinparams()
# initialize optimizer object for controlling fit routines
# (updated with fitparams/basinparams whenever params are set)
self.opt = optimize.Optimizer(fitparams=self.fitparams, basinparams=self.basinparams, inits=self.inits, data=self.data)
self.sim = self.opt.sim
if self.learn:
self.simRL = self.opt.simRL
if presample:
# sample init params
self.sample_theta()
def __make_dataframes__(self):
""" wrapper for dfhandler.DataHandler.make_dataframes
"""
from radd.dfhandler import DataHandler
# initialize dataframe handler
self.handler = DataHandler(self)
# make dataframes
self.handler.make_dataframes()
# Group dataframe (nsubjects*nconds*nlevels x ndatapoints)
self.observedDF = self.handler.observedDF.copy()
self.observedErr = self.handler.observedErr.copy()
# list (nsubjects long) of data arrays (nconds*nlevels x ndatapoints) to fit
self.observed = self.handler.observed
# list of flattened data arrays (averaged across conditions)
self.observed_flat = self.handler.observed_flat
# observed frequency bins dataframe
# self.freqDF = self.handler.make_freq_df()
# dataframe with same dim as observeddf for storing model predictions
self.yhatdf = self.handler.yhatdf
# dataframe with same dim as observeddf for storing fit info (& popt)
self.fitdf = self.handler.fitdf
# dataframe with same dim as observeddf for storing optimized params as matrix
self.poptdf = self.handler.poptdf
# dataframe containing cost_function wts (see dfhandler docs)
self.wtsDF = self.handler.wtsDF
try:
# dataframe containing ssd's per idx (see dfhandler docs)
self.ssdDF = self.handler.ssdDF
except Exception:
pass
# list of arrays containing conditional costfx weights
self.cond_wts = self.handler.cond_wts
# list of arrays containing flat costfx weights
self.flat_wts = self.handler.flat_wts
# define iterables containing fit y & wts for each fit
self.iter_flat = zip(self.observed_flat, self.flat_wts)
self.iter_cond = zip(self.observed, self.cond_wts)
# self.resultsdir = os.self.handler.make_results_dir(custompath=self.custompath, get_path=True)
# get working directory
self.resultsdir = os.path.abspath('./')
def set_fitparams(self, force=None, **kwargs):
""" dictionary of fit parameters, passed to Optimizer/Simulator objects
"""
if not hasattr(self, 'fitparams'):
# initialize with default values and first arrays in observed_flat, flat_wts
#local methods = ['nelder', 'powell', 'lbfgsb' 'tnc', 'cobyla']
self.fitparams = {'ix':0,
'ntrials': 20000,
'si': .1,
'dt':.003,
'tol': 1.e-25,
'method': 'nelder',
'maxfev': 800,
'maxiter': 800,
'kind': self.kind,
'clmap': self.clmap,
'pcmap':self.pcmap,
'depends_on': self.depends_on,
'ssd_method': self.handler.ssd_method,
'quantiles': self.quantiles,
'fit_on': self.fit_on,
'model_id': self.model_id,
'learn': self.learn,
'inits': self.inits,
'nlevels': 1,
'nidx': self.nidx,
'idx': self.idx[0],
'tb': self.tb}
self.fitparams = pd.Series(self.fitparams)
else:
# fill with kwargs (i.e. y, wts, ix, etc)
for kw_arg, kw_val in kwargs.items():
self.fitparams[kw_arg] = kw_val
if 'quantiles' in list(kwargs):
self.update_quantiles()
if 'depends_on' in list(kwargs):
self.__prepare_fit__(kwargs['depends_on'])
self.generate_model_id()
self.set_fitparams(nlevels = self.nlevels, clmap=self.clmap, model_id=self.model_id)
self.fitparams['nlevels'] = self.nlevels
if force=='cond':
self.fitparams['nlevels'] = self.nlevels
elif force=='flat':
self.fitparams['nlevels'] = 1
self.update_data(self.fitparams.nlevels)
if hasattr(self, 'ssdDF') and 'ssd' in self.data.columns:
self.__set_ssd_info__()
ksData=None
if self.ksfit:
ksData = self.get_ksdata(nlevels=self.fitparams['nlevels'])
if hasattr(self, 'opt'):
self.opt.update(fitparams=self.fitparams, inits=self.fitparams.inits, ksData=ksData)
self.sim = self.opt.sim
if self.learn:
self.simRL = self.opt.simRL
def set_basinparams(self, **kwargs):
""" dictionary of global fit parameters, passed to Optimizer/Simulator objects
"""
if not hasattr(self, 'basinparams'):
self.basinparams = {'ninits': 4,
'nsamples': 2500,
'interval': 10,
'T': .035,
'mutation': (0.5, 1),
'stepsize': .35,
'niter': 300,
'maxiter': 300,
'nsuccess': 100,
'polish_tol': 1.e-25,
'tol': .001,
'method': 'evolution',
'local_method': 'powell', #'L-BFGS-B', 'TNC'
'sample_method':'random',
'popsize': 15,
'recombination': .65,
'progress': True,
'strategy': 'best1bin',
'disp': False}
else:
# fill with kwargs for the upcoming fit
for kw_arg, kw_val in kwargs.items():
self.basinparams[kw_arg] = kw_val
if self.basinparams['method']=='basin':
self.basinparams['local_method']='TNC'
self.basinparams['tol'] = 1e-20
if hasattr(self, 'opt'):
old_method = self.opt.basinparams['method']
self.opt.update(basinparams=self.basinparams)
self.sim = self.opt.sim
if old_method != self.basinparams['method']:
self.opt.make_progress_bars()
def get_ksdata(self, nlevels=1):
self.ksDataCond = {'corRT':[], 'errRT':[], 'goAcc':[], 'stopAcc':[]}
self.ksDataFlat = deepcopy(self.ksDataCond)
df = self.data.copy()
self.ksDataFlat['corRT'].append(df[(df.ttype=='go')&(df.acc==1)].rt.values)
self.ksDataFlat['errRT'].append(df[(df.ttype=='stop')&(df.acc==0)].rt.values)
self.ksDataFlat['goAcc'].append(df[df.ttype=='go'].acc.mean())
if self.ssd_method=='all':
nssd = self.fitparams.ssd_info[1]
self.ksDataFlat['stopAcc'].append(df.iloc[:, 3:3+nssd].mean())
else:
self.ksDataFlat['stopAcc'].append(df[df.ttype=='stop'].acc.mean())
for col, vals in listitems(self.clmap):
for lvl in vals:
if lvl.isalnum():
try:
lvl = int(lvl)
except ValueError:
lvl = lvl
dfi = self.data[self.data[col]==lvl]
self.ksDataCond['corRT'].append(dfi[(dfi.ttype=='go')&(dfi.acc==1)].rt.values)
self.ksDataCond['errRT'].append(dfi[(dfi.ttype=='stop')&(dfi.acc==0)].rt.values)
self.ksDataCond['goAcc'].append(dfi[dfi.ttype=='go'].acc.mean())
if self.ssd_method=='all':
dfStop = self.observedDF[self.observedDF[col]==lvl]
nssd = self.fitparams.ssd_info[1]
self.ksDataCond['stopAcc'].append(dfStop.iloc[:, 3:3+nssd].mean().values)
else:
self.ksDataCond['stopAcc'].append(dfi[dfi.ttype=='stop'].acc.mean())
#self.ksDataCond['stopAcc'].append(dfi[(dfi.ttype=='stop')&(dfi.probe==1)].acc.mean())
if nlevels==1:
return self.ksDataFlat
return self.ksDataCond
def update_data(self, nlevels=1):
""" called when ix (int) is passed to fitparams as kwarg.
Fills fitparams with y and wts vectors corresponding to ix'th
arrays in observed(_flat) and (flat/cond)_wts lists.
"""
i = self.fitparams['ix']
if nlevels>1:
self.fitparams['y'] = self.observed[i]
self.fitparams['wts'] = self.cond_wts[i]
else:
self.fitparams['y'] = self.observed_flat[i]
self.fitparams['wts'] = self.flat_wts[i]
if self.fit_on=='subjects':
self.fitparams['idx']=str(self.idx[i])
else:
self.fitparams['idx'] = 'avg'
def sample_theta(self):
pkeys = list(self.inits)
nsamples = self.basinparams['nsamples']
method = self.basinparams['sample_method']
kind = self.kind
if self.ssdelay:
kind = '_'.join([kind, 'sso'])
if self.gbase:
kind = '_'.join([kind, 'z'])
datadir = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'inits')
theta_fname = "{}_{}".format(method, kind)
yhat_fname = "{}_{}_yhat".format(method, kind)
thetaAll = pd.read_pickle(os.path.join(datadir, theta_fname), compression='xz')
yhatAll = pd.read_pickle(os.path.join(datadir, yhat_fname), compression='xz')
self.init_params = thetaAll.to_dict('records')
groupedYhat = yhatAll.groupby('pset')
yhats_ = groupedYhat.apply(analyze.rangl_data, self.ssd_method, self.quantiles)
self.init_yhats = pd.DataFrame(np.vstack(yhats_.values))
self.opt.init_params = self.init_params
self.opt.init_yhats = self.init_yhats
self.opt.sample_theta()
def set_conditions(self, depends_on=None, bwfactors=None):
data = self.data.copy()
self.depends_on = depends_on
self.conds = np.unique(np.hstack(listvalues(self.depends_on))).tolist()
self.nconds = len(self.conds)
if 'flat' in self.conds:
self.is_flat = True
data['flat'] = 'flat'
self.data = data.copy()
else:
self.is_flat = False
clevels = [np.sort(data[c].unique()) for c in self.conds]
clevels = [np.array([str(lvl) for lvl in levels]) for levels in clevels]
self.clmap = {c: lvls for c, lvls in zip(self.conds, clevels)}
self.cond_matrix = np.array([lvls.size for lvls in clevels])
self.nlevels = np.cumprod(self.cond_matrix)[-1]
self.groups = np.hstack([['idx'], self.conds]).tolist()
self.__format_pcmap__()
if hasattr(self, 'ssdDF') and not self.is_nested:
self.__set_ssd_info__()
if hasattr(self, 'fitparams') and not self.is_nested:
self.generate_model_id()
self.set_fitparams(nlevels=self.nlevels, clmap=self.clmap, model_id=self.model_id)
def __format_pcmap__(self):
""" dict used by Simulator to extract conditional parameter values by name
from lmfit Parameters object
|<--- PARAMETERS OBJECT [LMFIT] <------- [IN]
|---> p = {'v_bsl': V1, 'v_pnl': V2...} --->|
|<--- pcmap = {'v':['v_bsl', 'v_pnl']} <---|
|---> p['v'] = array([V1, V2]) -------> [OUT]
"""
pcmap = {}
for p, conds in self.depends_on.items():
if isinstance(conds, list):
levels = []
for cond in conds:
levels.append(self.clmap[cond])
level_data = list(product(*levels))
clevels = ['_'.join([str(lvl) for lvl in lvls]) for lvls in level_data]
else:
clevels = [lvl for lvl in self.clmap[conds]]
param_clevels = ['{}_{}'.format(p, clvl) for clvl in clevels]
pcmap[p] = param_clevels
self.pcmap = pcmap
if hasattr(self, 'handler'):
self.handler.pcmap = pcmap
def __set_ssd_info__(self):
""" set ssd_info for upcoming fit and store in fitparams dict
"""
if self.fit_on=='average':
ssdDF = self.ssdDF.copy()
ssdDF = ssdDF.drop('idx', axis=1)
ssd = ssdDF.groupby(self.conds).mean().values
else:
idx = self.idx[self.fitparams['ix']]
# get ssd vector for fit index == ix
ssd = self.ssdDF[self.ssdDF['idx'] == idx].groupby(self.conds).mean().values[:,1:]
# if self.bwfactors is not None and hasattr(self, 'sim'):
# ix = self.conds.index([c for c in self.conds if c!=self.bwfactors][0])
# ssd = ssd[self.sim.pvary_ix[ix]]
self.ssd = ssd
if self.fitparams.nlevels==1:
# single vector (nlevels=1), don't squeeze
ssd = np.mean(ssd, axis=0, keepdims=True)
nssd = ssd.shape[-1]
nss = int((.5 * self.fitparams.ntrials))
# nss = self.fitparams.ntrials
nss_per_ssd = int(nss/nssd)
ssd_ix = np.arange(nssd) * np.ones((ssd.shape[0], ssd.shape[-1])).astype(np.int)
# store all ssd_info in fitparams, accessed by Simulator
self.fitparams['ssd_info'] = [ssd, nssd, nss, nss_per_ssd, ssd_ix]
def update_quantiles(self):
""" recalculate observed dataframes w/ passed quantiles array
"""
self.quantiles = self.fitparams.quantiles
self.__make_dataframes__()
self.fitparams['y'] = self.observed_flat[self.fitparams['ix']]
self.fitparams['wts'] = self.flat_wts[self.fitparams['ix']]
def set_results(self, finfo=None, popt=None, yhat=None):
if finfo is None:
finfo = self.finfo
if popt is None:
popt = self.popt
if yhat is None:
yhat = self.yhat
return finfo, popt, yhat
def generate_model_id(self, appendstr=None):
""" generate an identifying string with model information.
used for reading and writing model output
"""
model_id = list(self.depends_on)
if 'all' in model_id:
model_id = ['flat']
model_id.insert(0, self.kind)
fit_on = 'avg'
if self.fit_on=='subjects':
fit_on = 'idx'
model_id.append(fit_on)
if appendstr is not None:
model_id.append(appendstr)
self.model_id = '_'.join(model_id)
if hasattr(self, 'fitparams'):
self.fitparams['model_id'] = self.model_id
def set_testing_params(self, tol=1e-20, nsuccess=50, nsamples=1000, ninits=2, maxfev=1000, progress=True):
self.set_fitparams(tol=tol, maxfev=maxfev)
self.set_basinparams(tol=tol, ninits=ninits, nsamples=nsamples, nsuccess=nsuccess)
self.opt.update(basinparams=self.basinparams, progress=progress)
def toggle_pbars(self, progress=False, models=None):
self.set_basinparams(progress=progress)
if not progress:
return None
if self.fit_on=='subjects':
status = ''.join(['Subj {}', '/{}'.format(self.nidx)])
self.idxbar = utils.PBinJ(n=self.nidx, color='y', status=status)
if models is not None:
pvary = [list(depends_on) for depends_on in models]
pnames = [vis.parameter_name(p, False) for p in pvary]
self.mbar = utils.PBinJ(n=len(pnames), color='b', status='{}')
return pnames
def __remove_outliers__(self, sd=1.5, verbose=False):
""" remove slow rts (>sd above mean) from main data DF
"""
from radd.tools.analyze import remove_outliers
self.data = analyze.remove_outliers(self.data.copy(), sd=sd, verbose=verbose)
def __get_default_inits__(self):
""" if inits not provided by user, initialize with default values
see tools.theta.get_default_inits
"""
self.inits = theta.get_default_inits(kind=self.kind, depends_on=self.depends_on, learn=self.learn, ssdelay=self.ssdelay, gbase=self.gbase)
def __check_inits__(self, inits):
""" ensure inits dict is appropriate for Model kind
see tools.theta.check_inits
"""
inits = dict(deepcopy(inits))
checked = theta.check_inits(inits=inits, depends_on=self.depends_on, kind=self.kind)
return checked
