def tca(X,
non_neg=True,
R=10,
prefix="",
max_iter=500,
epoc="all",
effect="all"):
# Fit CP tensor decomposition (two times).
U = None
V = None
opti_str = 'ncp_bcd_'
if non_neg:
U = tt.ncp_bcd(X, rank=R, verbose=True, max_iter=max_iter, tol=1e-6)
V = tt.ncp_bcd(X, rank=R, verbose=True, max_iter=max_iter, tol=1e-6)
else:
U = tt.cp_als(X, rank=R, verbose=True, max_iter=max_iter, tol=1e-6)
V = tt.cp_als(X, rank=R, verbose=True, max_iter=max_iter, tol=1e-6)
opti_str = 'cp_als_'
# Compare the low-dimensional factors from the two fits.
# fig, ax, po = tt.plot_factors(U.factors)
# tt.plot_factors(V.factors, fig=fig)
# fig.suptitle("raw models")
# fig.tight_layout()
# Align the two fits and print a similarity score.
sim = tt.kruskal_align(U.factors,
V.factors,
permute_U=True,
permute_V=True)
print(sim)
# Plot the results again to see alignment.
fig, ax, po = tt.plot_factors(U.factors,
plots=["scatter", "scatter", "line"])
tt.plot_factors(V.factors, plots=["scatter", "scatter", "line"], fig=fig)
[x.set_xticks([11.5, 15.5, 27.5, 31.5]) for x in ax[:, 2]]
ax[-1, 0].set_xlabel("SU #")
ax[-1, 1].set_xlabel("Trial #")
ax[-1, 2].set_xlabel("Time (s)")
ax[-1, 2].set_xticklabels(["S", "+1", "T", "+1"])
fig.suptitle("aligned models")
fig.tight_layout()
# Show plots.
plt.show()
fig.set_size_inches(40, 40)
fig.set_dpi(300)
fig.savefig(
opti_str + prefix + "tca_trial_" + epoc + "_" + effect + "_" +
str(X.shape[1]) + "_R" + str(R) + ".png",
dpi=300,
bbox_inches="tight",
)
return (U, V, sim)
def plot_similarity_score(X, ranks=[1, 2, 3, 4, 5, 10, 20, 40, 60], n_runs=5):
print('this may take awhile....')
rank_similarity_scores = []
for rank in ranks:
U = []
for n in range(n_runs):
U_r = tensortools.ncp_bcd(X, rank=rank, verbose=False)
U.append(U_r)
similarity_scores = []
for n in range(n_runs - 1):
similarity = tensortools.kruskal_align(U[n].factors,
U[n + 1].factors,
permute_U=True,
permute_V=True)
similarity_scores.append(similarity)
rank_similarity_scores.append(similarity_scores)
rank_similarity_scores = np.array(rank_similarity_scores)
# plot similarity scores
fig = plt.figure(figsize=(7, 3))
x = np.arange(len(ranks))
sem = rank_similarity_scores.std(axis=1) / np.sqrt(
rank_similarity_scores.shape[1])
plt.errorbar(x, rank_similarity_scores.mean(axis=1), yerr=sem)
plt.xticks(ticks=x, labels=ranks)
plt.xlabel('N factors')
plt.ylabel('similarity mean+/-SEM')
return fig
def plot_fit_error(X, ranks=[1, 2, 3, 4, 5, 10, 15, 20]):
print('this may take awhile....')
fits = []
errs = []
for rank in ranks:
U_r = tensortools.ncp_bcd(X, rank=rank, verbose=False)
fits.append(U_r)
errs.append(U_r.obj)
fig, ax = plt.subplots(1, 2, figsize=(12, 3))
ax[0].plot(ranks, errs, '-o')
ax[0].set_ylabel('Final objective value')
ax[0].set_xlabel('N factors')
cmap = plt.cm.winter
# optimization performance across iterations
for i in range(0, len(ranks)):
ax[1].plot(fits[i].obj_hist, '-', color=cmap(7 / (i + 1)))
ax[1].set_xlabel('Optimization iteration')
ax[1].set_ylabel('Objective (Fit error)')
return fig
def tensor_decomp(self, network_tensor, n_comp):
self.ent_vocab = [self.id2voc[i] for i in range(len(self.id2voc))]
tensors = tt.ncp_bcd(network_tensor, n_comp, verbose=False)
self.factors = tensors.factors.rebalance()
import tensortools as tt
import numpy as np
import matplotlib.pyplot as plt
# Make synthetic dataset.
I, J, K, R = 25, 25, 25, 4 # dimensions and rank
X = tt.randexp_ktensor((I, J, K), rank=R).full()
X += np.random.randn(I, J, K) * .5
X = np.maximum(X, 0.0)
# Fit CP tensor decomposition (two times).
U = tt.ncp_bcd(X, rank=R, verbose=True)
V = tt.ncp_bcd(X, rank=R, verbose=True)
# Compare the low-dimensional factors from the two fits.
fig, ax, po = tt.plot_factors(U.factors)
tt.plot_factors(V.factors, fig=fig)
fig.suptitle("raw models")
fig.tight_layout()
# Align the two fits and print a similarity score.
sim = tt.kruskal_align(U.factors, V.factors, permute_U=True, permute_V=True)
print(sim)
# Plot the results again to see alignment.
fig, ax, po = tt.plot_factors(U.factors)
tt.plot_factors(V.factors, fig=fig)
fig.suptitle("aligned models")
fig.tight_layout()
# Show plots.
def run_pipeline(filename):
data = lm.loadmat(filename)
session_name = os.path.basename(filename)[0:-4]
(good_cells, pos_edges, trial_idx, spikelocations, spike_idx,
location_vec) = prepareData(data)
n_trials = 30
n_cells = len(good_cells)
shape = (n_cells, len(pos_edges) - 1, n_trials)
counts = np.zeros(shape, dtype=float)
_fast_bin(counts, trial_idx, spikelocations, spike_idx)
occupancy = np.zeros((len(pos_edges) - 1, n_trials), dtype=float)
_fast_occ(occupancy, data['trial'] - 1, location_vec)
for iT in range(n_trials):
tmp = occupancy[:, iT]
idx_v = np.flatnonzero(tmp)
idx_n = np.flatnonzero(tmp == 0)
tmp[idx_n] = np.interp(idx_n, idx_v, tmp[idx_v])
occupancy[:, iT] = tmp
spMapN = np.zeros(counts.shape)
for iC in range(n_cells):
spMapN[iC, :, :] = np.divide(counts[iC, :, :], occupancy)
spMapN = spi.gaussian_filter(spMapN, (0, 2, 0))
n_cells = len(good_cells)
n_bins = len(pos_edges) - 1
spFlat = np.zeros((n_cells, n_trials * n_bins))
for iC in range(n_cells):
spFlat[iC, :] = spMapN[iC, :, :].ravel(order='F')
#spFlat = spFlat-spFlat.mean(axis=1)[:,np.newaxis]
spFlat = normalize(spFlat, axis=0, norm='l2')
for iC in range(n_cells):
for iT in range(n_trials):
start = iT * n_bins
stop = (iT + 1) * n_bins
trial_idx = np.arange(start, stop)
tmp = spFlat[iC, trial_idx]
spMapN[iC, :, iT] = tmp
R = 5
# Fit CP tensor decomposition (two times).
U = tt.ncp_bcd(spMapN, rank=R, verbose=False)
V = tt.ncp_bcd(spMapN, rank=R, verbose=False)
# Align the two fits and print a similarity score.
sim = tt.kruskal_align(U.factors,
V.factors,
permute_U=True,
permute_V=True)
#print(sim)
# Plot the results again to see alignment.
fig, ax, po = tt.plot_factors(U.factors)
tt.plot_factors(V.factors, fig=fig)
fig.suptitle("aligned models")
fig.tight_layout()
fig.savefig('C:\\temp\\try3\\' + session_name + '_tca.png')
ff = np.matmul(np.transpose(spFlat), spFlat)
plt.figure()
ax = plt.imshow(ff)
plt.colorbar()
plt.axvline(x=n_bins * 20, color='red', ls='--', linewidth=1)
plt.axvline(x=n_bins * 21, color='green', ls='--', linewidth=1)
plt.axhline(y=n_bins * 20, color='red', ls='--', linewidth=1)
plt.axhline(y=n_bins * 21, color='green', ls='--', linewidth=1)
plt.savefig('C:\\temp\\try3\\' + session_name + '_cov.png')
plt.close('all')