def x(self, in_spikes):
""" converts data into a dependent variable time series, ie. it chooses the cell that was configured and extracts only this timeseries. """
if isinstance(in_spikes, Data) or isinstance(in_spikes, ni.data.data.Data):
data = in_spikes
else:
return in_spikes
if data.nr_cells == 1:
x = data.getFlattend()
else:
x = data.cell(self.configuration.cell).getFlattend()
return x.squeeze()
def x(self, in_spikes):
""" converts data into a dependent variable time series, ie. it chooses the cell that was configured and extracts only this timeseries. """
if isinstance(in_spikes, Data) or isinstance(in_spikes, ni.data.data.Data):
data = in_spikes
else:
return in_spikes
if data.nr_cells == 1:
x = data.getFlattend()
else:
x = data.cell(self.configuration.cell).getFlattend()
return x.squeeze()
def fit(self, data=None,beta=None,x= None,dm = None, nr_trials=None):
"""
Fits the model
**in_spikes** `ni.data.data.Data` instance
example::
from scipy.ndimage import gaussian_filter
import ni
model = ni.model.ip.Model(ni.model.ip.Configuration({'crosshistory':False}))
data = ni.data.monkey.Data()
data = data.condition(0).trial(range(int(data.nr_trials/2)))
dm = model.dm(data)
x = model.x(data)
from sklearn import linear_model
betas = []
fm = model.fit(data)
betas.append(fm.beta)
print "fitted."
for clf in [linear_model.LinearRegression(), linear_model.RidgeCV(alphas=[0.1, 1.0, 10.0])]:
clf.fit(dm,x)
betas.append(clf.coef_)
figure()
plot(clf.coef_.transpose(),'.')
title('coefficients')
prediction = np.dot(dm,clf.coef_.transpose())
figure()
plot(prediction)
title('prediction')
ll = x * log(prediction) + (len(x)-x)*log(1-prediction)
figure()
plot(ll)
title('ll')
print np.sum(ll)
"""
fittedmodel = FittedModel(self)
fittedmodel.configuration = copy(self.configuration)
fittedmodel.history_kernel = self.history_kernel
if fittedmodel.history_kernel == "log kernel":
fittedmodel.history_kernel = cs.create_splines_logspace(self.configuration.history_length, self.configuration.knot_number, 0)
spike_train_all_trial = []
if data is not None:
spike_train_all_trial = data.getFlattend()
firing_rate = np.mean(spike_train_all_trial)
fittedmodel.firing_rate = firing_rate
fittedmodel.trial_length = data.time_bins
fittedmodel.design = self.generateDesignMatrix(data,data.time_bins)
if dm is None:
dm = self.dm(data,fittedmodel.design)
if x is None:
x = self.x(data)
w = np.where(dm.transpose())[0]
cnt = [np.sum(w== i) for i in range(dm.shape[1])]
if sum(np.array(cnt) >= dm.shape[0]) > 0:
log("!! "+str(sum(np.array(cnt) == dm.shape[0]))+ " Components are only 0. \n"+str(sum(np.array(cnt) <= dm.shape[0]*0.1))+" are mostly 0. "+str(sum(np.array(cnt) <= dm.shape[0]*0.5))+" are half 0.")
else:
log(""+str(sum(np.array(cnt) == dm.shape[0]))+ " Components are only 0. \n"+str(sum(np.array(cnt) <= dm.shape[0]*0.1))+" are mostly 0. "+str(sum(np.array(cnt) <= dm.shape[0]*0.5))+" are half 0.")
zeroed_components = [i for i in range(len(cnt)) if cnt[i] == 0]
backend_config = self.configuration.backend_config
fittedmodel.backend_model = self.backend.Model(backend_config)
if beta is None:
fit = fittedmodel.backend_model.fit(x, dm) # NOTE frequently produces LinAlgError: SVD did not converge
if False:
# This might be introduced at a later data:
# Components that are 0 can be excluded from the fitting process and then virtually reinserted in the beta and params attributes.
beta = fit.params
i_z = 0
for i in range(len(cnt)):
if i in zeroed_components:
beta.append(0)
else:
beta.append(fit.params[i_z])
i_z = i_z + 1
fit.params = beta
else:
for z in zeroed_components:
fit.params[z] = 0
fittedmodel.beta = fit.params
else:
fit = self.backend.Fit(f=None,m=fittedmodel.backend_model)
fit.params = beta
fittedmodel.beta = beta
fittedmodel.fit = fit
if "llf" in fit.statistics:
fit.statistics["llf_all"] = fit.statistics["llf"]
if hasattr(data,'nr_trials'):
fit.statistics["llf"] = fit.statistics["llf"]/data.nr_trials
elif nr_trials is not None:
fit.statistics["llf"] = fit.statistics["llf"]/nr_trials
fittedmodel.statistics = fit.statistics
#log("done fitting.")
return fittedmodel
def fit(self, data=None,beta=None,x= None,dm = None, nr_trials=None):
"""
Fits the model
**in_spikes** `ni.data.data.Data` instance
example::
from scipy.ndimage import gaussian_filter
import ni
model = ni.model.ip.Model(ni.model.ip.Configuration({'crosshistory':False}))
data = ni.data.monkey.Data()
data = data.condition(0).trial(range(int(data.nr_trials/2)))
dm = model.dm(data)
x = model.x(data)
from sklearn import linear_model
betas = []
fm = model.fit(data)
betas.append(fm.beta)
print "fitted."
for clf in [linear_model.LinearRegression(), linear_model.RidgeCV(alphas=[0.1, 1.0, 10.0])]:
clf.fit(dm,x)
betas.append(clf.coef_)
figure()
plot(clf.coef_.transpose(),'.')
title('coefficients')
prediction = np.dot(dm,clf.coef_.transpose())
figure()
plot(prediction)
title('prediction')
ll = x * log(prediction) + (len(x)-x)*log(1-prediction)
figure()
plot(ll)
title('ll')
print np.sum(ll)
"""
fittedmodel = FittedModel(self)
fittedmodel.configuration = copy(self.configuration)
fittedmodel.history_kernel = self.history_kernel
if fittedmodel.history_kernel == "log kernel":
fittedmodel.history_kernel = cs.create_splines_logspace(self.configuration.history_length, self.configuration.knot_number, 0)
spike_train_all_trial = []
if data is not None:
spike_train_all_trial = data.getFlattend()
firing_rate = np.mean(spike_train_all_trial)
fittedmodel.firing_rate = firing_rate
fittedmodel.trial_length = data.time_bins
fittedmodel.design = self.generateDesignMatrix(data,data.time_bins)
if dm is None:
dm = self.dm(data,fittedmodel.design)
if x is None:
x = self.x(data)
w = np.where(dm.transpose())[0]
cnt = [np.sum(w== i) for i in range(dm.shape[1])]
if sum(np.array(cnt) >= dm.shape[0]) > 0:
log("!! "+str(sum(np.array(cnt) == dm.shape[0]))+ " Components are only 0. \n"+str(sum(np.array(cnt) <= dm.shape[0]*0.1))+" are mostly 0. "+str(sum(np.array(cnt) <= dm.shape[0]*0.5))+" are half 0.")
else:
log(""+str(sum(np.array(cnt) == dm.shape[0]))+ " Components are only 0. \n"+str(sum(np.array(cnt) <= dm.shape[0]*0.1))+" are mostly 0. "+str(sum(np.array(cnt) <= dm.shape[0]*0.5))+" are half 0.")
zeroed_components = [i for i in range(len(cnt)) if cnt[i] == 0]
backend_config = self.configuration.backend_config
fittedmodel.backend_model = self.backend.Model(backend_config)
if beta is None:
fit = fittedmodel.backend_model.fit(x, dm) # NOTE frequently produces LinAlgError: SVD did not converge
if False:
# This might be introduced at a later data:
# Components that are 0 can be excluded from the fitting process and then virtually reinserted in the beta and params attributes.
beta = fit.params
i_z = 0
for i in range(len(cnt)):
if i in zeroed_components:
beta.append(0)
else:
beta.append(fit.params[i_z])
i_z = i_z + 1
fit.params = beta
else:
for z in zeroed_components:
fit.params[z] = 0
fittedmodel.beta = fit.params
else:
fit = self.backend.Fit(f=None,m=fittedmodel.backend_model)
fit.params = beta
fittedmodel.beta = beta
fittedmodel.fit = fit
if "llf" in fit.statistics:
fit.statistics["llf_all"] = fit.statistics["llf"]
if hasattr(data,'nr_trials'):
fit.statistics["llf"] = fit.statistics["llf"]/data.nr_trials
elif nr_trials is not None:
fit.statistics["llf"] = fit.statistics["llf"]/nr_trials
fittedmodel.statistics = fit.statistics
#log("done fitting.")
return fittedmodel
