def factor_strf_fit(site='TAR010c16',
factorN=0,
batch=271,
modelname="fchan100_wc02_fir15_fit01"):
#site='zee015h05'
doval = 1
cellid = "{0}-F{1}".format(site, factorN)
# load the stimulus
stack = ns.nems_stack(cellid=cellid, batch=batch, modelname=modelname)
stack.meta['resp_channels'] = [factorN]
stack.meta['site'] = site
stack.keyfuns = 0
stack.valmode = False
print(modelname)
# evaluate the stack of keywords
if 'nested' in stack.keywords[-1]:
# special case for nested keywords. Stick with this design?
print('Using nested cross-validation, fitting will take longer!')
k = stack.keywords[-1]
keyword_registry[k](stack)
else:
print('Using standard est/val conditions')
for k in stack.keywords:
print(k)
keyword_registry[k](stack)
if doval:
# validation stuff
stack.valmode = True
stack.evaluate(1)
stack.append(nm.metrics.correlation)
#print("mse_est={0}, mse_val={1}, r_est={2}, r_val={3}".format(stack.meta['mse_est'],
# stack.meta['mse_val'],stack.meta['r_est'],stack.meta['r_val']))
valdata = [i for i, d in enumerate(stack.data[-1]) if not d['est']]
if valdata:
stack.plot_dataidx = valdata[0]
else:
stack.plot_dataidx = 0
stack.quick_plot()
savefile = nu.io.get_file_name(cellid, batch, modelname)
nu.io.save_model(stack, savefile)
return stack
def fit_single_cell(cellid, batch, modelname):
stack = ns.nems_stack()
stack.meta['batch'] = batch
stack.meta['cellid'] = cellid
stack.meta['modelname'] = modelname
stack.valmode = False
stack.keywords = modelname.split("_")
print('Evaluating stack')
for k in stack.keywords:
nk.keyfuns[k](stack)
stack.append(nmet.correlation)
return stack
def build_stack_from_signals_and_keywords(signals, modelname):
""" This is a hacked version of fit_single_model """
stack = ns.nems_stack()
stack.append(nm.loaders.load_signals, signals=signals)
stack.append(nm.est_val.crossval, valfrac=0.2)
# I don't think a model should really know or care about its batch / cellid
stack.meta['batch'] = '9999' # Batch numbers are not strictly needed
stack.meta['cellid'] = 'dummy-cellid' # Cellids are also unnecessary
stack.meta['modelname'] = modelname
stack.valmode = False
stack.keywords = modelname.split("_")
# evaluate the stack of keywords
if 'nested' in stack.keywords[-1]:
# special case for nested keywords. Stick with this design?
print('Using nested cross-validation, fitting will take longer!')
k = stack.keywords[-1]
keyword_registry[k](stack)
else:
print('Using standard est/val conditions')
for k in stack.keywords:
print(k)
keyword_registry[k](stack)
# measure performance on both estimation and validation data
stack.valmode = True
stack.evaluate(1)
stack.append(nm.metrics.correlation)
print("mse_est={0}, mse_val={1}, r_est={2}, r_val={3}"
.format(stack.meta['mse_est'],
stack.meta['mse_val'],
stack.meta['r_est'],
stack.meta['r_val']))
valdata = [i for i, d in enumerate(stack.data[-1]) if not d['est']]
if valdata:
stack.plot_dataidx = valdata[0]
else:
stack.plot_dataidx = 0
return stack
def newfunc():
batch = 296
cellid = 'gus030d-b1' # first good example
modelname = "env100e_stp1pc_fir20_fit01_ssa"
stack = ns.nems_stack()
stack.meta['batch'] = batch
stack.meta['cellid'] = cellid
stack.meta['modelname'] = modelname
file = ut.baphy.get_celldb_file(stack.meta['batch'],
stack.meta['cellid'],
fs=100,
stimfmt='envelope')
stack.append(nm.loaders.load_mat, est_files=[file], fs=100, avg_resp=True)
stack.append(nm.metrics.ssa_index)
return stack
imp.reload(nk)
imp.reload(nu)
imp.reload(ns)
batch = 296
# cellid='gus018d-d1'
#cellid = 'gus023e-c2'
cellid = 'gus016c-c2'
#modelname = "env100e_fir20_fit01_ssa"
modelname="env100e_stp1pc_fir20_fit01_ssa"
#modelname="env50e_stp1pc_fir10_fit01_ssa"
#if 0:
# stack = main.fit_single_model(cellid, batch, modelname, autoplot=False)
#else:
stack = ns.nems_stack()
stack.meta['batch'] = batch
stack.meta['cellid'] = cellid
stack.meta['modelname'] = modelname
stack.valmode = False
# extract keywords from modelname, look up relevant functions in nk and save
# so they don't have to be found again.
stack.keywords = modelname.split("_")
# evaluate the stack of keywords
if 'nested' in stack.keywords[-1]:
# special case if last keyword contains "nested". TODO: better imp!
print('Evaluating stack using nested cross validation. May be slow!')
k = stack.keywords[-1]
def fit_single_model(cellid, batch, modelname,
autoplot=True, saveInDB=True, **xvals):
"""
Fits a single NEMS model. With the exception of the autoplot feature,
all the details of modelfitting are taken care of by the model keywords.
fit_single_model functions by iterating through each of the keywords in the
modelname, and perfroming the actions specified by each keyword, usually
appending a nems module. Nested crossval is implemented as a special keyword,
which is placed last in a modelname.
fit_single_model returns the evaluated stack, which contains both the estimation
and validation datasets. In the caste of nested crossvalidation, the validation
dataset contains all the data, while the estimation dataset is just the estimation
data that was fitted last (i.e. on the last nest)
"""
log.info("Creating empty stack object")
stack = ns.nems_stack()
stack.meta['batch'] = batch
stack.meta['cellid'] = cellid
stack.meta['modelname'] = modelname
log.info("Stack.meta information added: {0}".format(stack.meta))
stack.valmode = False
stack.keywords = modelname.split("_")
# evaluate the stack of keywords
if 'nested' in stack.keywords[-1]:
# special case for nested keywords. Stick with this design?
log.info('Using nested cross-validation, fitting will take longer!')
k = stack.keywords[-1]
keyword_registry[k](stack)
else:
log.info('Using standard est/val conditions')
for k in stack.keywords:
log.info("Adding keyword: {0}".format(k))
keyword_registry[k](stack)
# measure performance on both estimation and validation data
stack.valmode = True
stack.evaluate(1)
stack.append(nm.metrics.correlation)
log.info("mse_est={0}, mse_val={1}, r_est={2}, r_val={3}".format(stack.meta['mse_est'],
stack.meta['mse_val'], stack.meta['r_est'], stack.meta['r_val']))
valdata = [i for i, d in enumerate(stack.data[-1]) if not d['est']]
if valdata:
stack.plot_dataidx = valdata[0]
else:
stack.plot_dataidx = 0
#phi = stack.fitter.fit_to_phi()
#stack.meta['n_parms'] = len(phi)
# edit: added autoplot kwarg for option to disable auto plotting
# -jacob, 6/20/17
if autoplot:
stack.quick_plot()
# save in data base
if saveInDB == True:
filename = nems.utilities.io.get_file_name(cellid, batch, modelname)
nems.utilities.io.save_model(stack, filename)
return(stack)
imp.reload(main)
imp.reload(nf)
imp.reload(nk)
imp.reload(nu)
imp.reload(ns)
site = 'TAR010c16'
factorN = 3
#site='zee015h05'
doval = 1
cellid = "{0}-F{1}".format(site, factorN)
# load the stimulus
batch = 271 #A1
modelname = "fchan100_wc02_fir15_fit01"
stack = ns.nems_stack(cellid=cellid, batch=batch, modelname=modelname)
stack.meta['resp_channels'] = [factorN]
stack.meta['site'] = site
stack.keyfuns = 0
stack.valmode = False
# evaluate the stack of keywords
if 'nested' in stack.keywords[-1]:
# special case for nested keywords. Stick with this design?
print('Using nested cross-validation, fitting will take longer!')
k = stack.keywords[-1]
keyword_registry[k](stack)
else:
print('Using standard est/val conditions')
for k in stack.keywords:
def model_name_to_stack(self, model_name):
stack = nems_stack()
for key in model_name.split('_'):
self[key](stack)
return stack
def pop_factor_strf_init(site='TAR010c16',
factorCount=4,
batch=271,
fmodelname="fchan100_wc02_fir15_fit01",
modelname=None):
# find all cells in site that meet iso criterion
d = ndb.get_batch_cells(batch=batch, cellid=site[:-2])
d = d.loc[d['min_isolation'] >= 75]
d = d.loc[d['cellid'] != 'TAR010c-21-2']
d.reset_index(inplace=True)
cellcount = len(d['cellid'])
# modelname should be compatible with fmodelname
if modelname is None:
modelname = fmodelname.replace("fchan100", "ssfb18ch100")
modelname = modelname.replace("_fit01", "")
stack = ns.nems_stack(cellid=site, batch=batch, modelname=modelname)
stack.meta['site'] = site
stack.meta['d'] = d
stack.meta['factorCount'] = factorCount
stack.meta['mini_fit'] = False
stack.keyfuns = 0
stack.valmode = False
# evaluate the stack of keywords
if 'nested' in stack.keywords[-1]:
# special case for nested keywords. Stick with this design?
print('Using nested cross-validation, fitting will take longer!')
k = stack.keywords[-1]
keyword_registry[k](stack)
else:
print('Using standard est/val conditions')
for k in stack.keywords:
print(k)
keyword_registry[k](stack)
wc0 = nu.utils.find_modules(stack, 'filters.weight_channels')[0]
try:
stp0 = nu.utils.find_modules(stack, 'filters.stp')[0]
except:
stp0 = 0
fir0 = nu.utils.find_modules(stack, 'filters.fir')[0]
try:
gn0 = nu.utils.find_modules(stack, 'nonlin.gain')[-1]
except:
gn0 = 0
stack.modules[fir0].baseline[0, 0] = 0
stack.modules[fir0].fit_fields = ['coefs']
# load strfs fit to factors
factorN = 0
cellid = "{0}-F{1}".format(site, factorN)
savefile = nu.io.get_file_name(cellid, batch, fmodelname)
tstack = nu.io.load_model(savefile)
try:
tstp0 = nu.utils.find_modules(tstack, 'filters.stp')[0]
except:
tstp0 = 0
twc0 = nu.utils.find_modules(tstack, 'filters.weight_channels')[0]
tfir0 = nu.utils.find_modules(tstack, 'filters.fir')[0]
try:
tgn0 = nu.utils.find_modules(tstack, 'nonlin.gain')[-1]
except:
tgn0 = 0
stack.modules[wc0].phi = tstack.modules[twc0].phi
stack.modules[wc0].coefs = tstack.modules[twc0].coefs
stack.modules[wc0].baseline = tstack.modules[twc0].baseline
stack.modules[wc0].num_chans = stack.modules[twc0].coefs.shape[0]
chans_per_subspace = stack.modules[wc0].num_chans
if tstp0:
stack.modules[stp0].u = tstack.modules[tstp0].u
stack.modules[stp0].tau = tstack.modules[stp0].tau
stack.modules[fir0].coefs = tstack.modules[tfir0].coefs
stack.modules[fir0].num_dims = stack.modules[fir0].coefs.shape[0]
stack.modules[fir0].bank_count = 1
if tgn0:
stack.modules[gn0].nltype = tstack.modules[tgn0].nltype
stack.modules[gn0].phi = tstack.modules[tgn0].phi
for factorN in range(1, factorCount):
cellid = "{0}-F{1}".format(site, factorN)
savefile = nu.io.get_file_name(cellid, batch, fmodelname)
tstack = nu.io.load_model(savefile)
stack.modules[wc0].phi = np.concatenate(
(stack.modules[wc0].phi, tstack.modules[twc0].phi), axis=0)
stack.modules[wc0].coefs = np.concatenate(
(stack.modules[wc0].coefs, tstack.modules[twc0].coefs), axis=0)
stack.modules[wc0].baseline = np.concatenate(
(stack.modules[wc0].baseline, tstack.modules[twc0].baseline),
axis=0)
stack.modules[wc0].num_chans = stack.modules[wc0].coefs.shape[0]
if tstp0:
stack.modules[stp0].u = np.concatenate(
(stack.modules[stp0].u, tstack.modules[tstp0].u), axis=0)
stack.modules[stp0].tau = np.concatenate(
(stack.modules[stp0].tau, tstack.modules[stp0].tau), axis=0)
elif stp0:
#stack.modules[stp0].num_channels=stack.modules[wc0].num_chans
stack.modules[stp0].u = np.concatenate(
(stack.modules[stp0].u,
stack.modules[stp0].u[0:chans_per_subspace, :]),
axis=0)
stack.modules[stp0].tau = np.concatenate(
(stack.modules[stp0].tau,
stack.modules[stp0].tau[0:chans_per_subspace, :]),
axis=0)
stack.modules[fir0].coefs = np.concatenate(
(stack.modules[fir0].coefs, tstack.modules[tfir0].coefs), axis=0)
stack.modules[fir0].num_dims = stack.modules[fir0].coefs.shape[0]
stack.modules[fir0].bank_count += 1
if tgn0:
stack.modules[gn0].phi = np.concatenate(
(stack.modules[gn0].phi, tstack.modules[tgn0].phi), axis=0)
stack.evaluate(1)
stack.append(nm.filters.WeightChannels, num_chans=cellcount)
stack.modules[-1].fit_fields = ['coefs', 'baseline']
stack.modules[-1].coefs[:, :] = 0
stack.append(nm.metrics.mean_square_error)
stack.error = stack.modules[-1].error
stack.evaluate(2)
return stack
