def rdc1(x, y, k=10, s=0.2):
if len(x.shape) == 1: x = x.reshape((-1, 1))
if len(y.shape) == 1: y = y.reshape((-1, 1))
cx = np.column_stack([rankdata(xc, method='ordinal')
for xc in x.T]) / float(x.size)
cy = np.column_stack([rankdata(yc, method='ordinal')
for yc in y.T]) / float(y.size)
# Add a vector of ones so that w.x + b is just a dot product
O = np.ones(cx.shape[0])
X = np.column_stack([cx, O])
Y = np.column_stack([cy, O])
Rx = (s / X.shape[1]) * np.random.randn(X.shape[1], k)
Ry = (s / Y.shape[1]) * np.random.randn(Y.shape[1], k)
X = np.dot(X, Rx)
Y = np.dot(Y, Ry)
X = np.sin()
"""print rcancor(np.sin(X),np.sin(Y))
return 0"
"""
cca = CCA(n_components=1)
xc, yc = cca.fit_transform(X, Y)
result = np.corrcoef(xc.T, yc.T)[0, 1]
print(result)
def get_cca(X, Y, n_comp=10):
cca = CCA(n_components=n_comp)
print("X.shape", X.shape)
print("Y.shape", Y.shape)
x_scores, y_scores = cca.fit_transform(X, Y)
# Manual Transform
X -= cca.x_mean_
X /= cca.x_std_
Y -= cca.y_mean_
Y /= cca.y_std_
calc_scores_x = np.dot(X, cca.x_rotations_)
calc_scores_y = np.dot(Y, cca.y_rotations_)
# id_x = cca.x_rotations_ @ linalg.pinv2(cca.x_rotations_)
# id_y = cca.y_rotations_ @ linalg.pinv2(cca.y_rotations_)
print("x_scores.shape", x_scores.shape)
print("y_scores.shape", y_scores.shape)
correlations = np.diag(
np.corrcoef(x_scores, y_scores, rowvar=False)[:n_comp, n_comp:])
calc_correlations = np.diag(
np.corrcoef(calc_scores_x, calc_scores_y, rowvar=False)[:n_comp,
n_comp:])
print(correlations)
print(calc_correlations)
return x_scores, y_scores
def fbcca(eeg, list_freqs, fs, num_harms=3, num_fbs=5):
fb_coefs = np.power(np.arange(1,num_fbs+1),(-1.25)) + 0.25
num_targs, _, num_smpls = eeg.shape #40 taget (means 40 fre-phase combination that we want to predict)
y_ref = cca_reference(list_freqs, fs, num_smpls, num_harms)
cca = CCA(n_components=1) #initilize CCA
# result matrix
r = np.zeros((num_fbs,num_targs))
results = np.zeros(num_targs)
for targ_i in range(num_targs):
test_tmp = np.squeeze(eeg[targ_i, :, :]) #deal with one target a time
for fb_i in range(num_fbs): #filter bank number, deal with different filter bank
testdata = filterbank(test_tmp, fs, fb_i) #data after filtering
for class_i in range(num_targs):
refdata = np.squeeze(y_ref[class_i, :, :]) #pick corresponding freq target reference signal
test_C, ref_C = cca.fit_transform(testdata.T, refdata.T)
# len(row) = len(observation), len(column) = variables of each observation
# number of rows should be the same, so need transpose here
# output is the highest correlation linear combination of two sets
r_tmp, _ = pearsonr(np.squeeze(test_C), np.squeeze(ref_C)) #return r and p_value, use np.squeeze to adapt the API
r[fb_i, class_i] = r_tmp
rho = np.dot(fb_coefs, r) #weighted sum of r from all different filter banks' result
tau = np.argmax(rho) #get maximum from the target as the final predict (get the index)
results[targ_i] = tau #index indicate the maximum(most possible) target
return results
def test_cca():
"""Test CCA."""
# Compare results with Matlab
# x = np.random.randn(1000, 11)
# y = np.random.randn(1000, 9)
# x = demean(x).squeeze()
# y = demean(y).squeeze()
mat = loadmat('./tests/data/ccadata.mat')
x = mat['x']
y = mat['y']
A2 = mat['A2']
B2 = mat['B2']
A1, B1, R = nt_cca(x, y) # if mean(A1(:).*A2(:))<0; A2=-A2; end
X1 = np.dot(x, A1)
Y1 = np.dot(y, B1)
C1 = tscov(np.hstack((X1, Y1)))[0]
# Sklearn CCA
cca = CCA(n_components=9, scale=False, max_iter=1e6)
X2, Y2 = cca.fit_transform(x, y)
# C2 = tscov(np.hstack((X2, Y2)).T)[0]
# import matplotlib.pyplot as plt
# f, (ax1, ax2) = plt.subplots(2, 1)
# ax1.imshow(C1)
# ax2.imshow(C2)
# plt.show()
# assert_almost_equal(C1, C2, decimal=4)
# Compare with matlab
X2 = np.dot(x, A2)
Y2 = np.dot(y, B2)
C2 = tscov(np.hstack((X2, Y2)))[0]
assert_almost_equal(C1, C2)
def compute_SVCCA(activation1, activation2):
'''
activation1 - Activation array 1 as a numpy array of size n X m1
activation2 - Activation array 2 as a numpy array of size n X m2
'''
pca_r = 40 # value from Shi et al NeurIPS 2019
n = activation1.shape[0]
assert n == activation2.shape[
0], "Size of activation arrays are different!!"
if pca_r > activation1.shape[1]:
print(
"Activation 1 array has less neurons.. changing number of PCs to ",
activation1.shape[1])
pca_r = activation1.shape[1]
if pca_r > activation2.shape[1]:
print(
"Activation 2 array has less neurons.. changing number of PCs to ",
activation2.shape[1])
pca_r = activation2.shape[1]
pca1 = PCA(n_components=pca_r)
red_activation1 = pca1.fit_transform(activation1)
pca2 = PCA(n_components=pca_r)
red_activation2 = pca2.fit_transform(activation2)
cca = CCA(n_components=pca_r)
red_activation1_c, red_activation2_c = cca.fit_transform(
red_activation1, red_activation2)
corr_values = np.zeros(pca_r)
for idx in range(pca_r):
corr_values[idx] = np.corrcoef(
red_activation1_c[:, idx],
red_activation2_c[:, idx])[0, 1] # get the off-diagonal element
return np.mean(corr_values)
def main(args):
(training_file, label_file, test_file, u_file, e, c, output_file, components) = args
X_training = load_feat(training_file)
n = len(X_training)
U = load_feat(u_file)
y_training = [float(line.strip()) for line in open(label_file)]
U = np.asarray(U)
X_training = np.asarray(X_training)
#X = preprocessing.normalize(X, norm='l2')
y_training = np.asarray(y_training)
X_test = load_feat(test_file)
y_test = [float(line.strip()) for line in open(test_label)]
X_test = np.asarray(X_test)
X_test[np.isnan(X_test)] = 0.0
#test_X = preprocessing.normalize(test_X, norm='l2')
y_test = np.asarray(y_test)
s = min(len(X_training), len(U))
cca = CCA(n_components=components, max_iter=50)
(X_cca, U_cca) = cca.fit_transform(X_training[:s], U[:s])
X_test_cca = cca.transform(X_test)
svr = SVR(C=c, epsilon=e, kernel='rbf')
svr.fit(X_cca, y_training[:s])
pred = svr.predict(X_test_cca)
with open(output_file, 'w') as output:
for p in pred:
print >>output, p
return
def rdc_cca(indexes):
i, j, rdc_features = indexes
cca = CCA(n_components=1, max_iter=CCA_MAX_ITER)
X_cca, Y_cca = cca.fit_transform(rdc_features[i], rdc_features[j])
rdc = np.corrcoef(X_cca.T, Y_cca.T)[0, 1]
# logger.info(i, j, rdc)
return rdc
def main(args):
(training_file, label_file, test_file, u_file, e, c, output_file,
components) = args
X_training = load_feat(training_file)
n = len(X_training)
U = load_feat(u_file)
y_training = [float(line.strip()) for line in open(label_file)]
U = np.asarray(U)
X_training = np.asarray(X_training)
#X = preprocessing.normalize(X, norm='l2')
y_training = np.asarray(y_training)
X_test = load_feat(test_file)
y_test = [float(line.strip()) for line in open(test_label)]
X_test = np.asarray(X_test)
X_test[np.isnan(X_test)] = 0.0
#test_X = preprocessing.normalize(test_X, norm='l2')
y_test = np.asarray(y_test)
s = min(len(X_training), len(U))
cca = CCA(n_components=components, max_iter=50)
(X_cca, U_cca) = cca.fit_transform(X_training[:s], U[:s])
X_test_cca = cca.transform(X_test)
svr = SVR(C=c, epsilon=e, kernel='rbf')
svr.fit(X_cca, y_training[:s])
pred = svr.predict(X_test_cca)
with open(output_file, 'w') as output:
for p in pred:
print >> output, p
return
def fbcca_realtime(data, list_freqs, fs, num_harms=3, num_fbs=5):
fb_coefs = np.power(np.arange(1,num_fbs+1),(-1.25)) + 0.25
num_targs = len(list_freqs)
_, num_smpls = data.shape
y_ref = cca_reference(list_freqs, fs, num_smpls, num_harms)
cca = CCA(n_components=1) #initialize CCA
# result matrix
r = np.zeros((num_fbs,num_targs))
for fb_i in range(num_fbs): #filter bank number, deal with different filter bank
testdata = filterbank(data, fs, fb_i) #data after filtering
for class_i in range(num_targs):
refdata = np.squeeze(y_ref[class_i, :, :]) #pick corresponding freq target reference signal
test_C, ref_C = cca.fit_transform(testdata.T, refdata.T)
r_tmp, _ = pearsonr(np.squeeze(test_C), np.squeeze(ref_C)) #return r and p_value
if r_tmp == np.nan:
r_tmp=0
r[fb_i, class_i] = r_tmp
rho = np.dot(fb_coefs, r) #weighted sum of r from all different filter banks' result
print(rho) #print out the correlation
result = np.argmax(rho) #get maximum from the target as the final predict (get the index), and index indicates the maximum entry(most possible target)
''' Threshold '''
THRESHOLD = 2.1
if abs(rho[result])<THRESHOLD: #2.587=np.sum(fb_coefs*0.8) #2.91=np.sum(fb_coefs*0.9) #1.941=np.sum(fb_coefs*0.6)
return 999 #if the correlation isn't big enough, do not return any command
else:
return result
def rdc_cca(indexes):
i, j, cca = indexes
cca = CCA(n_components=1)
X_cca, Y_cca = cca.fit_transform(GLOBAL_RDC_FEATURES[i],
GLOBAL_RDC_FEATURES[j])
# rdc = 1
rdc = numpy.corrcoef(X_cca.T, Y_cca.T)[0, 1]
print('ij', i, j)
return rdc
def cca(X,Y,K):
'''
Perform CCA on two views X, Y and reduce dimension to K
return pro
'''
cca = CCA(n_components = K,scale=False,max_iter = 1000)
X_c, Y_c = cca.fit_transform(X, Y)
return X_c,Y_c, cca
def CCA_corrcoeff(X, Y, n_components):
n_components = 3
cca = CCA(n_components)
U, V = cca.fit_transform(X, Y)
X_mean = np.subtract(X, X.mean(axis=0))
Y_mean = np.subtract(Y, Y.mean(axis=0))
A = np.linalg.solve(X_mean.T.dot(X_mean), X_mean.T.dot(U))
B = np.linalg.solve(Y_mean.T.dot(Y_mean), Y_mean.T.dot(V))
return A, B, U, V
def _extract_corr(data, reference):
"""Correlation extractor. Takes as an input signal and reference,
then calculates canonical correlation between them. After that
it aquires cross-correlation between cca coefficients and returns
asolute value of it."""
data = data.reshape(data.shape[1], 1)
reference = reference.reshape(reference.shape[0], 1)
cancor = CCA(n_components=1)
u, v = cancor.fit_transform(data, reference)
coef = np.corrcoef(u.T, v.T)
return np.abs(coef[0, 1])
def compute_corr(self, X_test, method="cca"):
if self.Y is None:
raise ValueError(
"Reference matrix Y must be computed using `fit` before computing corr"
)
if method == "eig":
rho = CCA.cca_eig(X_test.T, self.Y.T)[0]
else: # use sklearn implementation
cca = CCA_sklearn(n_components=1)
Xc, Yc = cca.fit_transform(X_test.T, self.Y.T)
rho = pearsonr(Xc[:, 0], Yc[:, 0])[0]
return rho
def rdc(x, y, k=20, s=1 / 6., f=np.sin):
"""
Compute the randomized dependence coefficient
This algorithm is able to detect linear and non-linear correlations in the
data vectors x and y.
This is based on the paper titled "The Randomized Dependence Coefficient"
located here https://arxiv.org/abs/1304.7717.
Parameters
----------
x : 1D numpy array with shape (N,)
data coordinates
y : 1D numpy array with shape (N,)
data coordinates
k,s : float
tuning parameters - do not alter unless you really know what you're
doing
f : non-linear basis function
Returns
-------
randomized dependence coefficient
"""
import scipy.stats as stat
from sklearn.cross_decomposition import CCA
# the original was written in R (just 5 lines!), this is my translation
# to numpy/scipy/scikit-learn (the original code is in the comments)
# x <- cbind(apply(as.matrix(x),2,function(u)rank(u)/length(u)),1)
# y <- cbind(apply(as.matrix(y),2,function(u)rank(u)/length(u)),1)
x = stat.rankdata(x) / x.size
y = stat.rankdata(y) / y.size
x = np.insert(x[:, np.newaxis], 1, 1, axis=1)
y = np.insert(y[:, np.newaxis], 1, 1, axis=1)
# x <- s/ncol(x)*x%*%matrix(rnorm(ncol(x)*k),ncol(x))
# y <- s/ncol(y)*y%*%matrix(rnorm(ncol(y)*k),ncol(y))
x = np.dot(s / x.shape[1] * x,
np.random.normal(size=x.shape[1] * k).reshape((x.shape[1], -1)))
y = np.dot(s / y.shape[1] * y,
np.random.normal(size=y.shape[1] * k).reshape((y.shape[1], -1)))
# cancor(cbind(f(x),1),cbind(f(y),1))$cor[1]
x = np.insert(f(x), x.shape[1], 1, axis=1)
y = np.insert(f(y), y.shape[1], 1, axis=1)
# the following is taken from:
# http://stackoverflow.com/questions/37398856/
# how-to-get-the-first-canonical-correlation-from-sklearns-cca-module
cca = CCA(n_components=1)
x_c, y_c = cca.fit_transform(x, y)
return np.corrcoef(x_c.T, y_c.T)[0, 1]
def rdc(X, Y, k=None, s=1. / 6., f=numpy.sin, rand_gen=None, rnorm_X=None, rnorm_Y=None):
if X.ndim == 1:
X = X[:, numpy.newaxis]
if Y.ndim == 1:
Y = Y[:, numpy.newaxis]
#
# heuristic assumption
if k is None:
k = max(X.shape[1], Y.shape[1]) + 1
# print(k)
n_instances = X.shape[0]
assert Y.shape[0] == n_instances, (Y.shape[0], n_instances)
if rand_gen is None:
rand_gen = numpy.random.RandomState(RAND_STATE)
#
# empirical copula transformation
ones_column = numpy.ones((n_instances, 1))
X_c = numpy.concatenate((numpy.apply_along_axis(ecdf, 0, X),
ones_column), axis=1)
Y_c = numpy.concatenate((numpy.apply_along_axis(ecdf, 0, Y),
ones_column), axis=1)
#
# linear projection through a random gaussian
if rnorm_X is None:
rnorm_X = rand_gen.normal(size=(X_c.shape[1], k))
if rnorm_Y is None:
rnorm_Y = rand_gen.normal(size=(Y_c.shape[1], k))
X_proj = s / X_c.shape[1] * numpy.dot(X_c, rnorm_X)
Y_proj = s / Y_c.shape[1] * numpy.dot(Y_c, rnorm_Y)
#
# non-linear projection
# print(f(X_proj), f(X_proj).shape, X_proj.shape)
X_proj = numpy.concatenate((f(X_proj), ones_column), axis=1)
Y_proj = numpy.concatenate((f(Y_proj), ones_column), axis=1)
#
# canonical correlation analysis
cca = CCA(n_components=1)
X_cca, Y_cca = cca.fit_transform(X_proj, Y_proj)
rdc = numpy.corrcoef(X_cca.T, Y_cca.T)
# print(rdc)
return rdc[0, 1]
def cca_correlation(X, Y, n_comp=50):
"""
:param X, Y: should be N-by-p, N-by-q matrices,
:param n_comp: a integer, how many components we want to create and compare.
:return: cca_corr, n_comp-by-n_comp matrix
X_c, Y_c will be the linear mapped version of X, Y with shape N-by-n_comp, N-by-n_comp shape
cc_mat is the
"""
cca = CCA(n_components=n_comp)
X_c, Y_c = cca.fit_transform(X, Y)
ccmat = np.corrcoef(X_c, Y_c, rowvar=False)
cca_corr = np.diag(
ccmat[n_comp:, :n_comp]) # slice out the cross corr part
return cca_corr
def cca_subspace(X, Y, n_comp=50, **kwargs):
"""
:param X, Y: should be N-by-p, N-by-q matrices, N is the dimension for the whole space, p, q are number of basis
vectors (Note p, q functions as number of features to be recombined, while N functions as number of
sampled). CCA will maximize
:param n_comp: a integer, how many components we want to create and compare.
:return: cca_corr, n_comp-by-n_comp matrix
X_c, Y_c will be the linear mapped version of X, Y with shape N-by-n_comp, N-by-n_comp shape
cc_mat is the
"""
cca = CCA(n_components=n_comp, **kwargs)
X_c, Y_c = cca.fit_transform(X, Y)
ccmat = np.corrcoef(X_c, Y_c, rowvar=False)
cca_corr = np.diag(ccmat[n_comp:, :n_comp]) # slice out the cross corr part
return cca_corr, cca
def getCoeff(id,sample,framePeriod,currentTimeMillis): T = framePeriod[0]/60 tau = framePeriod[1]/60 t = currentTimeMillis/1000 x = list(map(lambda x: x - tau/T,sample.copy())) y = [] for n in range(1,N+1): y.append((2/(n*pi)) * sin(pi*n*tau/T) * cos(2*pi*n*(t - tau/2)/T)) del X[id][0] del Y[id][0] X[id].append(x.copy()) Y[id].append(y.copy()) cca = CCA(n_components=1) X_c, Y_c = cca.fit_transform(X[id],Y[id]) result = np.corrcoef(X_c.T, Y_c.T)[0,1] return result
def canonical_correlation_analysis(list_a, list_b, list_y):
X = []
Y = []
if len(list_a) != len(list_b) or len(list_b) != len(list_y):
return None
for i in range(len(list_a)):
X.append([list_a[i], list_b[i]])
Y.append(list_y[i])
cca = CCA(n_components=1)
X_c, Y_c = cca.fit_transform(X, Y)
result = np.corrcoef(X_c.T, Y_c.T)[0, 1]
print(np.corrcoef(X_c.T, Y_c.T))
return result
def fbcca_realtime(eeg, list_freqs, fs, num_harms=3, num_fbs=5):
print("EEG shape: ", eeg.shape)
fb_coefs = np.power(np.arange(1, num_fbs + 1), (-1.25)) + 0.25
num_targs = len(list_freqs)
events, _, num_smpls = eeg.shape # 40 taget (means 40 fre-phase combination that we want to predict)
y_ref = cca_reference(list_freqs, fs, num_smpls, num_harms)
cca = CCA(n_components=1) # initilize CCA
# result matrix
r = np.zeros((num_fbs, num_targs))
results = np.zeros(num_targs)
r_tmp_mode = []
r_tmp_corr_avg = []
for event in range(eeg.shape[0]):
test_tmp = np.squeeze(eeg[event, :, :]) # deal with one event a time
for fb_i in range(num_fbs): # filter bank number, deal with different filter bank
for class_i in range(num_targs):
testdata = filterbank(test_tmp, fs, fb_i) # data after filtering
refdata = np.squeeze(y_ref[class_i, :, :]) # pick corresponding freq target reference signal
test_C, ref_C = cca.fit_transform(testdata.T, refdata.T)
# len(row) = len(observation), len(column) = variables of each observation
# number of rows should be the same, so need transpose here
# output is the highest correlation linear combination of two sets
r_tmp, _ = pearsonr(np.squeeze(test_C),
np.squeeze(ref_C)) # return r and p_value, use np.squeeze to adapt the API
if r_tmp == np.nan:
r_tmp = 0
r[fb_i, class_i] = r_tmp
rho = np.dot(fb_coefs, r) # weighted sum of r from all different filter banks' result
print("rho: ", rho)
result = np.argmax(rho) # get maximum from the target as the final predict (get the index), and index indicates the maximum entry(most possible target)
print("result: ", result)
r_tmp_mode.append(result)
print("correlation: ", abs(rho[result]))
r_tmp_corr_avg.append(abs(rho[result]))
r_mode = mode(r_tmp_mode)[0][0]
r_corr_avg = np.mean(r_tmp_corr_avg)
print("====Most recurrent class: ====", r_mode)
print("====Average correlation: =====", r_corr_avg)
THRESHOLD = 0.3
if r_corr_avg >= THRESHOLD: # 2.749=np.sum(fb_coefs*0.85)
return r_mode # if the correlation isn't big enough, do not return any command
def load_word_bank_dataset():
"""
This function loads the World Bank Data and return it as NxD numpy arrays
"""
fert_dataset_path = './demo/WorldBankData/fertility_rate.csv'
life_exp_dataset_path = './demo/WorldBankData/life_expectancy.csv'
years_str_list = [str(year) for year in range(1960, 2017)]
if os.path.exists(fert_dataset_path) & os.path.exists(
life_exp_dataset_path):
# If files exists, load from files
# Load and drop rows with missing values
fert_rate = pd.read_csv(fert_dataset_path).dropna()
life_exp = pd.read_csv(life_exp_dataset_path).dropna()
country_field_name = 'Country Code'
else:
# If files don't exist, download data with wbdata instead
# Get life expectancy and fertility rate data
life_exp = wbdata.get_dataframe(indicators={
"SP.DYN.LE00.IN": 'value'
}).unstack(level=0).transpose().reset_index()
fert_rate = wbdata.get_dataframe(indicators={
"SP.DYN.TFRT.IN": 'value'
}).unstack(level=0).transpose().reset_index()
# Keep only country name and years columns, filter row with N/A's
life_exp = life_exp[['country'] + years_str_list].dropna()
fert_rate = fert_rate[['country'] + years_str_list].dropna()
country_field_name = 'country'
# Keep only countries which appear on both dataframes
valid_countries = list(
set(life_exp[country_field_name]) & set(fert_rate[country_field_name]))
life_exp = life_exp[life_exp[country_field_name].isin(valid_countries)]
fert_rate = fert_rate[fert_rate[country_field_name].isin(valid_countries)]
# Convert to numpy
life_exp = life_exp[years_str_list].to_numpy()
fert_rate = fert_rate[years_str_list].to_numpy()
# Apply CCA
cca_transformer = CCA(n_components=2)
life_exp_cca, fert_rate_cca = cca_transformer.fit_transform(
fert_rate, life_exp)
return life_exp_cca, fert_rate_cca
def CanonCoff(self, X):
Y = [i for i in range(len(self.cca_frequency))]
for i in range(len(self.cca_frequency)):
ref = 2 * np.pi * self.t * self.cca_frequency[i]
Y[i] = [np.sin(ref), np.cos(ref), np.sin(2 * ref), np.cos(2 * ref)]
print(len(X))
cca = CCA(n_components=4)
result = np.zeros((len(self.cca_frequency), 4))
for i in range(len(self.cca_frequency)):
Z = np.array([Y[i]])
X_c, Y_c = cca.fit_transform(X, Z[0].T)
cca_value = np.corrcoef(X_c.T, Y_c.T)
for k in range(4):
result[i][k] = cca_value[0 + k, 4 + k]
result[i] = np.max(result[i])
return result[:, 0]
def cca(self, X1, X2, n_components=2):
cca = CCA(n_components=n_components)
X1, X2 = cca.fit_transform(X1, X2)
'''
from scipy.stats import pearsonr
print("Correlation Coefficient")
for i in range(n_components):
print("{0}:{1:.3f}".format(i, pearsonr(cca.x_scores_[:,i], cca.y_scores_[:,i])[0]))
print("")
print("")
np.set_printoptions(formatter={'float': '{: 0.3f}'.format})
print("X1 loadings")
print(cca.x_loadings_.T)
print("")
print("X2 loadings")
print(cca.y_loadings_.T)
'''
return pd.DataFrame(X1)
class CCAAnalysis:
"""Canonical Correlation Analysis for SSVEP paradigm"""
def __init__(self, freqs, win_len, s_rate, n_harmonics=1):
"""
Args:
freqs (list): List of target frequencies
win_len (float): Window length
s_rate (int): Sampling rate of EEG signal
n_harmonics (int): Number of harmonics to be considered
"""
self.freqs = freqs
self.win_len = win_len
self.s_rate = s_rate
self.n_harmonics = n_harmonics
self.train_data = self._init_train_data()
self.cca = CCA(n_components=1)
def _init_train_data(self):
t_vec = np.linspace(0, self.win_len, int(self.s_rate * self.win_len))
targets = {}
for freq in self.freqs:
sig_sin, sig_cos = [], []
for harmonics in range(self.n_harmonics):
sig_sin.append(np.sin(2 * np.pi * harmonics * freq * t_vec))
sig_cos.append(np.cos(2 * np.pi * harmonics * freq * t_vec))
targets[freq] = np.array(sig_sin + sig_cos).T
return targets
def apply_cca(self, eeg):
"""Apply CCA analysis to EEG data and return scores for each target frequency
Args:
eeg (np.array): EEG array [n_samples, n_chan]
Returns:
list of scores for target frequencies
"""
scores = []
for key in self.train_data:
sig_c, t_c = self.cca.fit_transform(eeg, self.train_data[key])
scores.append(np.corrcoef(sig_c.T, t_c.T)[0, 1])
return scores
def transform_cca(self,
abs_cols=None,
cols=None,
clusters=None,
kwargs=None):
# process arg: cols, clusters
df = self.select_data(cols=cols, clusters=clusters)
# process arg: abs_cols
if abs_cols is not None:
df = df[abs_cols]
# process arg: kwargs
kwargs = self.process_kwargs('cca', kwargs)
o_cca = CCA(**kwargs)
arr = o_cca.fit_transform(df)
nrows, ncols = arr.shape
cca_cols = ["cca_{}".format(i) for i in range(ncols)]
self.pca_names = cca_cols
cca_df = pd.DataFrame(data=arr, columns=cca_cols)
self.df = pd.concat([self.df, cca_df], axis=1)
def main(args):
(training_file, label_file, test_file, test_label, u_file) = args
X_training = load_feat(training_file)
n = len(X_training)
U = load_feat(u_file)
y_training = [int(line.strip()) for line in open(label_file)]
U = np.asarray(U)
X_training = np.asarray(X_training)
#X = preprocessing.normalize(X, norm='l2')
y_training = np.asarray(y_training)
X_test = load_feat(test_file)
y_test = [int(line.strip()) for line in open(test_label)]
X_test = np.asarray(X_test)
#test_X = preprocessing.normalize(test_X, norm='l2')
y_test = np.asarray(y_test)
cca = CCA(n_components=100)
(X_cca, U_cca) = cca.fit_transform(X_training, U[:n])
X_test_cca = cca.predict(X_test)
svr = SVC()
svr.fit(X_cca, y_training)
pred = svr.predict(X_test_cca)
print pred
print test_y
print accuracy_score(y_test, pred)
with open(test_file + '.cca.2.pred', 'w') as output:
for p in pred:
print >>output, p
#svm_model.fit(X, y)
#pickle.dump(lr, open(model_file, "wb"))
return
return
def fbcca_feature(self,eeg, parameter_list, num_harms=3, num_fbs=10):
fs = parameter_list[2] / parameter_list[3]
fb_coefs = np.power(np.arange(1, num_fbs + 1), (-1.25)) + 0.25
num_targs = len(parameter_list[1])
y_ref = self.cca_reference(parameter_list[1], fs, parameter_list[2], num_harms)
cca = CCA(n_components=1) # initilize CCA
# result matrix
r = np.zeros((num_fbs, num_targs))
for fb_i in range(num_fbs): # filter bank number, deal with different filter bank
testdata = self.filter_bank(eeg, fs, fb_i) # data after filtering
for class_i in range(num_targs):
refdata = np.squeeze(y_ref[class_i, :, :]) # pick corresponding freq target reference signal
test_C, ref_C = cca.fit_transform(testdata.T, refdata.T)
# len(row) = len(observation), len(column) = variables of each observation
# number of rows should be the same, so need transpose here
# output is the highest correlation linear combination of two sets
r_tmp, _ = pearsonr(np.squeeze(test_C),
np.squeeze(ref_C)) # return r and p_value, use np.squeeze to adapt the API
r[fb_i, class_i] = r_tmp
results = np.dot(fb_coefs, r) # weighted sum of r from all different filter banks' result
print("fb_cca:",results)
return results
def main(args):
(training_file, label_file, test_file, test_label, u_file) = args
X_training = load_feat(training_file)
n = len(X_training)
U = load_feat(u_file)
y_training = [int(line.strip()) for line in open(label_file)]
U = np.asarray(U)
X_training = np.asarray(X_training)
#X = preprocessing.normalize(X, norm='l2')
y_training = np.asarray(y_training)
X_test = load_feat(test_file)
y_test = [int(line.strip()) for line in open(test_label)]
X_test = np.asarray(X_test)
#test_X = preprocessing.normalize(test_X, norm='l2')
y_test = np.asarray(y_test)
cca = CCA(n_components=100)
(X_cca, U_cca) = cca.fit_transform(X_training, U[:n])
X_test_cca = cca.predict(X_test)
svr = SVC()
svr.fit(X_cca, y_training)
pred = svr.predict(X_test_cca)
print pred
print test_y
print accuracy_score(y_test, pred)
with open(test_file + '.cca.2.pred', 'w') as output:
for p in pred:
print >> output, p
#svm_model.fit(X, y)
#pickle.dump(lr, open(model_file, "wb"))
return
return
def qvec_cca(**kwargs):
embeddings = load_embeddings(**kwargs).T
lg = kwargs["lg"]
features = load_features(lg).T
common_phonemes = embeddings.columns.intersection(features.columns)
S = features[common_phonemes]
X = embeddings[common_phonemes]
cca = CCA(n_components=1)
a, b = cca.fit_transform(X.T, S.T)
a, b = a.reshape(-1), b.reshape(-1)
r, p = pearsonr(a, b)
# Write results to disk
level, lg, name = kwargs["level"], kwargs["lg"], kwargs["name"]
if "hidden" in kwargs:
hyperparams = f"{kwargs['size']}-{kwargs['hidden']}"
else:
hyperparams = f"{kwargs['size']}-{kwargs['window']}"
path = f"results/{level}/qvec/{lg}/{name}/{hyperparams}"
ensure_dir(path)
epoch = kwargs["epoch"]
filename = os.path.join(path, f"{epoch}.txt")
with open(filename, "w") as file:
file.write(str((r, p)))
return r, p
def ccr_median(U, V):
cca = CCA(n_components=5)
U_c, V_c = cca.fit_transform(U, V)
coef = np.abs(np.corrcoef(U_c.T, V_c.T).diagonal(offset=5))
return (np.median(coef))
def getVLADDescriptors(path, pathVD, pathCNNGT, pathColf):
with open(pathVD, 'rb') as f:
visualDictionary = pickle.load(f)
# load cnn features
with open(pathCNNGT, 'rb') as f:
pkl = pickle.load(f)
# fcs = pkl[2]
scenefs = pkl[1]
img_names = pkl[0]
# update 3/22 VLAD on column feature
with open(pathColf, 'rb') as f:
vd_colf = pickle.load(f)
descriptors = list()
idImage = list()
for imagePath in glob.glob(path + "/*.jpg"):
print(imagePath)
img = cv2.imread(imagePath)
print(img_names.index(imagePath.split("/")[-1]))
scenef = scenefs[img_names.index(imagePath.split("/")[-1])]
print("scenef.shape = ")
print(scenef.shape)
# fc = fcs[img_names.index(imagePath.split("/")[-1])][0]
# print("fc.shape = ")
# print(fc.shape)
# if scenef.shape[1]>2:
# scenef = scenef[:,:2,:,:]
# if scenef.shape[1]<2:
# npad = ((0, 0), (0, 2-scenef.shape[1]), (0, 0), (0, 0))
# scenef = np.pad(scenef, pad_width=npad, mode='constant', constant_values=0)
# if scenef.shape[2]>7:
# scenef = scenef[:,:,:7,:]
# if scenef.shape[2]<7:
# npad = ((0, 0), (0, 0), (0, 7-scenef.shape[2]), (0, 0))
# scenef = np.pad(scenef, pad_width=npad, mode='constant', constant_values=0)
colf = []
scenef = scenef[0]
rows = scenef.shape[0]
columns = scenef.shape[1]
for i in range(rows):
for j in range(columns):
colf.append(scenef[i,j])
colf = np.asarray(colf)
print(colf.shape)
sift = cv2.xfeatures2d.SIFT_create()
kp, des = sift.detectAndCompute(img, None)
if np.any(des) != None: # and np.any(colf) != None
v = VLAD(des, visualDictionary)
vlad_colf = VLAD(colf, vd_colf)
# mergedf = scenef.flatten()
# mergedf = np.concatenate([v, scenef.flatten()])
# mergedf = np.concatenate([v, vlad_colf])
# mergedf = np.concatenate([v, fc])
# print("mergedf.shape = ")
# print(mergedf.shape)
print("==========Performing CCA==========")
cca = CCA(n_components=1)
v_c, vlad_colf_c = cca.fit_transform(v, vlad_colf)
# print(v_c)
print(v_c.shape)
# print(vlad_colf_c)
print(vlad_colf_c.shape)
mergedf = np.concatenate([v_c, vlad_colf_c])
mergedf = mergedf.reshape(1, -1)[0]
print("mergedf.shape = ")
print(mergedf.shape)
print("==================================")
# descriptors.append(fc)
# if '127696' in imagePath:
# print(fc)
descriptors.append(mergedf)
idImage.append(imagePath)
descriptors = np.asarray(descriptors)
print(descriptors.shape)
return descriptors, idImage
if not os.path.isdir(dir_name):
os.makedirs(dir_name)
OutputLog().set_path(dir_name)
OutputLog().set_verbosity(configuration.output_parameters['verbosity'])
data_config = ConfigParser.ConfigParser()
data_config.read(data_set_config)
data_parameters = ConfigSectionMap("dataset_parameters", data_config)
# construct data set
data_set = Container().create(data_parameters['name'], data_parameters)
cca_model = CCA(n_components=top, scale=True, copy=False)
train_transformed_x, train_transformed_y = cca_model.fit_transform(data_set.trainset[0], data_set.trainset[1])
test_transformed_x, test_transformed_y = cca_model.transform(data_set.testset[0], data_set.testset[1])
OutputLog().write('test results:')
correlations, trace_correlation, var, x_test, y_test, test_best_layer = TraceCorrelationTester(
data_set.testset[0],
data_set.testset[1], top).test(IdentityTransformer(), configuration.hyper_parameters)
OutputLog().write('train results:')
correlations, train_trace_correlation, var, x_train, y_train, train_best_layer = TraceCorrelationTester(
data_set.trainset[0],
data_set.trainset[1], top).test(IdentityTransformer(), configuration.hyper_parameters)
OutputLog().write('\nTest results : \n')
configuration.hyper_parameters.print_parameters(OutputLog())
X, good_idx = remove_outliers(X, 6.0)
y = y.ix[y.index[good_idx]]
# sanity check
# idx = np.random.permutation(len(y))[0]
# idx = np.where(y.index == 119384)[0][0]
# image_sanity_check(y.index[idx], X[idx])
# only keep unique values
unique_cols = ['Class1.1', 'Class1.2', 'Class2.1', 'Class3.1', 'Class4.1', 'Class5.1', 'Class5.2', 'Class5.3',
'Class6.1', 'Class7.1', 'Class7.2', 'Class8.1', 'Class8.2', 'Class8.3', 'Class8.4', 'Class8.5',
'Class8.6', 'Class9.1', 'Class9.2', 'Class10.1', 'Class10.2', 'Class11.1', 'Class11.2',
'Class11.3', 'Class11.4', 'Class11.5']
# do CCA
if verbose:
print 'Doing CCA...'
cca = CCA(n_components=len(unique_cols), copy=False)
X_cca, y_cca = cca.fit_transform(X, y[unique_cols].values.astype(np.float32))
cPickle.dump(cca, open(base_dir + 'data/CCA_DCT.pickle', 'wb'))
# make plots
make_cca_images(cca, (100, 100), dct_idx=dct_idx)
fig = plot_cca_projections(X_cca)
fig.savefig(plot_dir + 'CCA_dist_no_outliers.png')
if doshow:
plt.show()
print 'Saving the transformed values...'
np.save(base_dir + 'data/CCA_training_transform', X_cca)
