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utils.py
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utils.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 2 11:19:34 2017 @author: emin
"""
import numpy as np
import generators, models
from scipy.stats import entropy
import torch
def compute_SI(hidr, entrpy_bins, window_size, r_threshold):
# Compute SI scores for a number of trials
# hidr: activities of recurrent units
# entrpy_bins: number of bins used for estimating peak time entropy (20 in the paper)
# window_size: window size around peak time for calculating ridge-to-background ratio (5 in the paper)
# r_threshold: only consider recurrent units with mean responses above this value (0.1 in the paper)
bs = hidr.shape[0] # number of trials
ts = hidr.shape[1] # number of time points
SI_trial_vec = np.zeros(bs)
for b in range(bs):
hidr_t = hidr[b, :, :]
selected_indx = np.nonzero(np.mean(hidr_t, axis=0) > r_threshold)[0]
hidr_t = hidr_t[:, selected_indx]
peak_times = np.argmax(hidr_t, axis=0)
end_times = np.clip(peak_times + window_size / 2, 0, ts - 1)
start_times = np.clip(peak_times - window_size / 2, 0, ts - 1)
entrpy = entropy(np.histogram(peak_times, entrpy_bins)[0] + 0.1 * np.ones(entrpy_bins))
r2b_ratio = np.zeros(len(selected_indx))
for nind in range(len(selected_indx)):
mask = np.zeros(ts)
mask[start_times[nind]:end_times[nind]] = 1
this_hidr = hidr_t[:, nind]
ridge = np.mean(this_hidr[start_times[nind]:end_times[nind]])
backgr = np.mean(np.ma.MaskedArray(this_hidr, mask))
r2b_ratio[nind] = np.log(ridge) - np.log(backgr)
SI_trial_vec[b] = np.nanmean(r2b_ratio) + entrpy
return np.nanmean(SI_trial_vec)
def build_generators(ExptDict):
# Unpack common variables
task = ExptDict["task"]["task_id"]
n_loc = ExptDict["task"]["n_loc"]
n_out = ExptDict["task"]["n_out"]
tr_cond = ExptDict["tr_cond"]
test_cond = ExptDict["test_cond"]
n_in = ExptDict["n_in"]
batch_size = ExptDict["batch_size"]
stim_dur = ExptDict["stim_dur"]
delay_dur = ExptDict["delay_dur"]
resp_dur = ExptDict["resp_dur"]
kappa = ExptDict["kappa"]
spon_rate = ExptDict["spon_rate"]
tr_max_iter = ExptDict["tr_max_iter"]
test_max_iter = ExptDict["test_max_iter"]
if task == 'DE1':
generator = generators.DelayedEstimationTask(max_iter=tr_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur, kappa=kappa,
spon_rate=spon_rate,
tr_cond=tr_cond)
test_generator = generators.DelayedEstimationTask(max_iter=test_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=test_cond)
elif task == 'DE2':
generator = generators.DelayedEstimationTask(max_iter=tr_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur, kappa=kappa,
spon_rate=spon_rate,
tr_cond=tr_cond)
test_generator = generators.DelayedEstimationTask(max_iter=test_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=test_cond)
elif task == 'CD1':
generator = generators.ChangeDetectionTask(max_iter=tr_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur, kappa=kappa,
spon_rate=spon_rate,
tr_cond=tr_cond)
test_generator = generators.ChangeDetectionTask(max_iter=test_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=test_cond)
elif task == 'COMP':
generator = generators.ComparisonTask(max_iter=tr_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur, sig_tc=10.0,
spon_rate=spon_rate,
tr_cond=tr_cond)
test_generator = generators.ComparisonTask(max_iter=test_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
sig_tc=10.0,
spon_rate=spon_rate,
tr_cond=test_cond)
elif task == 'CD2':
generator = generators.ChangeDetectionTask(max_iter=tr_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur, kappa=kappa,
spon_rate=spon_rate,
tr_cond=tr_cond)
test_generator = generators.ChangeDetectionTask(max_iter=test_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=test_cond)
elif task == 'GDE2':
generator = generators.GatedDelayedEstimationTask(max_iter=tr_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=tr_cond)
test_generator = generators.GatedDelayedEstimationTask(max_iter=test_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=test_cond)
elif task == 'VDE1':
max_delay = ExptDict["task"]["max_delay"]
generator = generators.VarDelayedEstimationTask(max_iter=tr_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out, stim_dur=stim_dur,
max_delay=max_delay,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=tr_cond)
test_generator = generators.VarDelayedEstimationTask(max_iter=test_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
max_delay=max_delay,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=test_cond)
elif task == 'Harvey2012':
sigtc = ExptDict["task"]["sigtc"]
stim_rate = ExptDict["task"]["stim_rate"]
generator = generators.Harvey2012(max_iter=tr_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur, resp_dur=resp_dur,
sigtc=sigtc, stim_rate=stim_rate, spon_rate=spon_rate)
test_generator = generators.Harvey2012(max_iter=test_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur, resp_dur=resp_dur,
sigtc=sigtc, stim_rate=stim_rate, spon_rate=spon_rate)
elif task == 'Harvey2012Dynamic':
sigtc = ExptDict["task"]["sigtc"]
stim_rate = ExptDict["task"]["stim_rate"]
generator = generators.Harvey2012Dynamic(max_iter=tr_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur, resp_dur=resp_dur,
sigtc=sigtc, stim_rate=stim_rate, spon_rate=spon_rate)
test_generator = generators.Harvey2012Dynamic(max_iter=test_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur, resp_dur=resp_dur,
sigtc=sigtc, stim_rate=stim_rate, spon_rate=spon_rate)
elif task == 'Harvey2016':
sigtc = ExptDict["task"]["sigtc"]
stim_rate = ExptDict["task"]["stim_rate"]
epoch_dur = ExptDict["task"]["epoch_dur"]
n_epochs = ExptDict["task"]["n_epochs"]
generator = generators.Harvey2016(max_iter=tr_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, n_epochs=n_epochs,
epoch_dur=epoch_dur, sigtc=sigtc,
stim_rate=stim_rate, spon_rate=spon_rate)
test_generator = generators.Harvey2016(max_iter=test_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, n_epochs=n_epochs,
epoch_dur=epoch_dur, sigtc=sigtc,
stim_rate=stim_rate, spon_rate=spon_rate)
elif task == 'SINE':
alpha = ExptDict["task"]["alpha"]
generator = generators.SineTask(max_iter=tr_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur, resp_dur=resp_dur, alpha=alpha)
test_generator = generators.SineTask(max_iter=test_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur, resp_dur=resp_dur, alpha=alpha)
return generator, test_generator
def build_model(input_var,ExptDict):
# Unpack necessary variables
model = ExptDict["model"]["model_id"]
n_loc = ExptDict["task"]["n_loc"]
n_out = ExptDict["task"]["n_out"]
batch_size = ExptDict["batch_size"]
n_in = ExptDict["n_in"]
n_hid = ExptDict["n_hid"]
out_nonlin = ExptDict["task"]["out_nonlin"]
if model == 'LeInitRecurrent':
diag_val = ExptDict["model"]["diag_val"]
offdiag_val = ExptDict["model"]["offdiag_val"]
model = models.LeInitRecurrent(input_var, batch_size=batch_size,
n_in=n_loc*n_in, n_out=n_out,
n_hid=n_hid, diag_val=diag_val,
offdiag_val=offdiag_val,
out_nlin=out_nonlin)
elif model == 'OrthoInitRecurrent':
init_val = ExptDict["model"]["init_val"]
model = models.OrthoInitRecurrent(input_var, batch_size=batch_size,
n_in=n_loc*n_in, n_out=n_out,
n_hid=n_hid, init_val=init_val,
out_nlin=out_nonlin)
elif model == 'GRURecurrent':
diag_val = ExptDict["model"]["diag_val"]
offdiag_val = ExptDict["model"]["offdiag_val"]
model = models.GRURecurrent(input_var, batch_size=batch_size,
n_in=n_loc*n_in, n_out=n_out, n_hid=n_hid,
diag_val=diag_val, offdiag_val=offdiag_val,
out_nlin=out_nonlin)
return model
def build_loss(pred_var,target_var,ExptDict):
# Unpack necessary variables
task = ExptDict["task"]["task_id"]
resp_dur = ExptDict["resp_dur"]
if task in ['DE1','DE2','GDE2','VDE1']:
loss = torch.fmod(torch.abs(pred_var[:,-resp_dur:,:] - target_var[:,-resp_dur:,:]), np.pi).mean()
elif task in ['CD1','CD2','Harvey2012','Harvey2012Dynamic','Harvey2016','COMP']:
loss = torch.nn.functional.F.binary_cross_entropy(pred_var[:,-resp_dur:,-1], target_var[:,-resp_dur:,-1]).mean()
elif task in ['SINE']:
loss = torch.abs(T.abs_(pred_var[:,-resp_dur:,:] - target_var[:,-resp_dur:,:])).mean()
return loss
def build_performance(s_vec,opt_vec,net_vec,ExptDict):
# Unpack necessary variables
task = ExptDict["task"]["task_id"]
if task in ['DE1','DE2','GDE2','VDE1']:
rmse_opt = np.nanmean(np.mod(np.abs(np.squeeze(s_vec) - np.squeeze(opt_vec)), np.pi))
rmse_net = np.nanmean(np.mod(np.abs(np.squeeze(s_vec) - np.squeeze(net_vec)), np.pi))
infloss = (rmse_net - rmse_opt) / rmse_opt
elif task in ['CD1','CD2','Harvey2012','Harvey2012Dynamic','Harvey2016','COMP']:
infloss = np.nanmean( opt_vec * np.log(opt_vec/net_vec) + (1.0 - opt_vec) * np.log((1.0 - opt_vec)/(1.0 - net_vec)))/np.nanmean(opt_vec * np.log(2.0*opt_vec) + (1.0-opt_vec) * np.log(2.0*(1.0-opt_vec)))
elif task in ['SINE']:
infloss = np.mean(np.abs(np.squeeze(opt_vec) - np.squeeze(net_vec)))
return infloss