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learning.py
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learning.py
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from scipy.stats import spearmanr
import scipy.sparse.linalg
from scipy import io
import numpy as np
from matplotlib import pyplot as plt
import os
from scipy.stats import zscore
from scipy.interpolate import interp1d
from scipy.ndimage import gaussian_filter1d
from sklearn.manifold import isomap
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import Lasso, Lars
from sklearn.linear_model import orthogonal_mp
from scipy.stats import linregress
import utils, decoders
def weak_learning(data, istim, itrain, itest, dcdtype='best_neuron'):
y = np.sign(istim)
NN = data.shape[0]
ntot = y.shape[0]
X = data[:,itrain]
mu = np.mean(X, axis=1)
sd = np.std(X, axis=1)
X -= mu[:,np.newaxis]
X /=sd[:,np.newaxis]
xlist = []
if dcdtype is 'best_neuron':
A = np.zeros(NN,)
cc = X @ zscore(y[itrain])/itrain.size
ix = np.argmax(np.abs(cc))
A[ix] = np.sign(cc[ix])
xlist.append(cc)
elif dcdtype is 'one_shot':
ipos = itrain[y[itrain]>4*np.pi/36]
ineg = itrain[y[itrain]<-4*np.pi/36]
ipos = np.random.choice(ipos, (1,))
ineg = np.random.choice(ineg, (1,))
A = data[:, ipos] - data[:, ineg]
A = np.squeeze(A)
xlist.append(data[:,ipos])
xlist.append(data[:,ineg])
elif dcdtype is 'random_projection':
A = np.random.randn(NN, 100)
xproj = A.T @ X
xproj = zscore(xproj, axis=1)
cc = np.sum(xproj * y[itrain], axis=1)
imax = np.argmax(np.abs(cc))
A = A[:, imax] * np.sign(cc[imax])
A = np.squeeze(A)
zdata = (data[:, itest] - mu[:,np.newaxis])/sd[:,np.newaxis]
ypred = A.T @ zdata
return ypred, xlist
def perceptron_learning(sresp, istim, itrain, itest, Ltype = 'sign', lam = 0, eta = 4e-5):
X = sresp[:,itrain]
y = istim[itrain,np.newaxis]
y = zscore(y, axis=0)
y = np.squeeze(y)
ylabel = np.sign(y)
NN = X.shape[0]
w = 1e-6 * np.random.randn(NN,)
Pcorrect = []
nstim = 2**np.linspace(4, np.log2(X.shape[1]-1), 21)
nstim = np.concatenate(([0, 1, 2, 3, 4, 6, 10], nstim))
nstim = np.array(nstim)
nstim = nstim.astype('int')
D = istim[itest]
for j in range(X.shape[1]):
if Ltype is 'regression':
if np.isin(j, nstim):
w = decoders.fast_ridge(X[:, :j+1], ylabel[:j+1], lam=lam)
if Ltype is not 'regression':
ypred = w.T @ X[:,j]
#ypred = 1./(1+np.exp(-ypred))
#dsigm = ypred * (1-ypred)
if Ltype is 'Hebb':
err = ylabel[j]
elif Ltype is 'full':
err = ylabel[j] - ypred
else:
err = (ylabel[j] - np.sign(ypred))/2
dw = err * X[:,j] - lam * w
w = w + eta * dw
if np.isin(j, nstim):
dy = w.T @ sresp[:, itest]
pc = (np.mean(dy[D>0]>0) + np.mean(dy[D<0]<0))/2
Pcorrect.append(pc)
Pcorrect = np.array(Pcorrect)
return nstim, Pcorrect
def train_perceptrons(fs, task_type='hard'):
thmax = np.pi/6
if task_type=='hard':
theta_pref = np.pi/4
all_thetas = [theta_pref]
else:
all_thetas = np.linspace(0, 2*np.pi, 33)[:-1]
nstim = np.zeros((len(fs),len(all_thetas), 28))
perf = np.zeros((4, len(fs), len(all_thetas), 28))
for t,f in enumerate(fs):
print(os.path.basename(f))
dat = np.load(f, allow_pickle=True).item()
sresp, istim, itrain, itest = utils.compile_resp(dat)
for j, theta_pref in enumerate(all_thetas):
dy = istim - theta_pref
itrain0=itrain
itest0=itest
if task_type!='hard':
dy = dy%(2*np.pi)
dy[dy>np.pi] = dy[dy>np.pi] - 2*np.pi
ix = np.logical_and(np.abs(dy) < thmax, np.abs(dy) > thmax/6)
itrain0 = itrain[ix[itrain]]
itest0 = itest[ix[itest]]
nstim[t,j], perf[0,t,j] = perceptron_learning(sresp, dy, itrain0, itest0, Ltype='regression', lam = 1)
_,perf[1,t,j] = perceptron_learning(sresp, dy, itrain0, itest0, Ltype='basic')
_,perf[2,t,j] = perceptron_learning(sresp, dy, itrain0, itest0, Ltype='full')
_,perf[3,t,j] = perceptron_learning(sresp, dy, itrain0, itest0, Ltype='Hebb')
return nstim,perf
def train_weak_learners(fs):
nstim = np.zeros((len(fs), 32))
perf = np.zeros((3, len(fs), 32))
theta_pref = np.pi/4
thmax = np.pi/6
all_thetas = np.linspace(0, 2*np.pi, 33)[:-1]
D = np.zeros((0,))
dy = np.zeros((0,3))
for t,f in enumerate(fs):
print(os.path.basename(f))
dat = np.load(f, allow_pickle=True).item()
sresp, istim, itrain, itest = utils.compile_resp(dat)
for j, theta_pref in enumerate(all_thetas):
ds = istim - theta_pref
ds = ds%(2*np.pi)
ds[ds>np.pi] = ds[ds>np.pi] - 2*np.pi
ix = np.logical_and(np.abs(ds) < thmax, np.abs(ds) > thmax/6)
itrain0 = itrain[ix[itrain]]
itest0 = itest[ix[itest]]
ypred1,xlist = weak_learning(sresp, ds, itrain0, itest0, dcdtype = 'best_neuron')
ypred2,_ = weak_learning(sresp, ds, itrain0, itest0, dcdtype = 'one_shot')
ypred3,_ = weak_learning(sresp, ds, itrain0, itest0, dcdtype = 'random_projection')
D = np.concatenate((D, ds[itest0]), axis=0)
dy = np.concatenate((dy, np.vstack((ypred1,ypred2, ypred3)).T ), axis=0)
drange = np.concatenate((np.arange(-29, -4), np.arange(5, 30)))
P = np.zeros((len(drange),3))
dd = .5
for j,deg in enumerate(drange):
ix = np.logical_and(D>np.pi/180 * (deg-dd), D<np.pi/180 * (deg+dd))
P[j, :] = np.mean(dy[ix, :]>0, axis=0)
return P, drange, xlist