def plot_AlexNet(data, demonstration, changepoints = None, plotter = None, labels = None, end_frame = 1000000, interactive_mode = False, plot_pca = False, plot_tsne = False): # list_of_layers = ["conv3", "conv4", "pool5"] list_of_layers = ["pool5"] PATH_TO_ANNOTATION = constants.PATH_TO_DATA + constants.ANNOTATIONS_FOLDER + demonstration + "_" + str(constants.CAMERA) + ".p" annotations = pickle.load(open(PATH_TO_ANNOTATION, "rb")) for layer in list_of_layers: [X_pca, X_tsne, X_grp] = data[layer] X_pca = utils.sample_matrix(X_pca, 2) X_tsne = utils.sample_matrix(X_tsne, 2) print "AlexNet", layer, X_tsne.shape if plot_pca: plot_scatter_continous(X_pca, "plot_AlexNet_PCA" + layer+ "_" + str(start)+"_"+str(end), colormap_name = colormap_name, changepoints = changepoints, labels = labels, plotter = plotter, end_frame = end_frame, interactive_mode = interactive_mode) if plot_tsne: if interactive_mode: plot_scatter_continous(X_tsne, "plot_AlexNet_tSNE" + layer + "_" + str(end_frame), colormap_name = colormap_name, changepoints = changepoints, labels = labels, plotter = plotter, end_frame = end_frame, interactive_mode = interactive_mode) else: plot_scatter_continous(X_tsne, "plot_AlexNet_tSNE" + layer + "_" + str(start) + "_" + str(end), colormap_name = colormap_name, changepoints = changepoints, labels = labels, plotter = plotter, end_frame = end_frame, interactive_mode = interactive_mode)
def featurize_1(list_of_demonstrations, kinematics, sr):
"""
Given .p (pickle) files of sift features, this function concatenates
Z with W vectors to produce X vector, dumped as pickle file.
"""
print "FEATURIZATION 1"
data_X_1 = {}
data_X_2 = {}
for demonstration in list_of_demonstrations:
print "SIFT for ", demonstration
start, end = utils.get_start_end_annotations(constants.PATH_TO_DATA + constants.ANNOTATIONS_FOLDER
+ demonstration + "_" + constants.CAMERA +".p")
W = kinematics[demonstration]
W_sampled = utils.sample_matrix(W, sampling_rate = sr)
PATH_TO_SIFT = constants.PATH_TO_DATA + "sift_FCED/SIFT_"+ demonstration
Z = pickle.load(open(PATH_TO_SIFT + "_1.p", "rb"))
Z = Z[start:end + 1]
Z_sampled_1 = utils.sample_matrix(Z, sampling_rate = sr)
Z = pickle.load(open(PATH_TO_SIFT + "_2.p", "rb"))
Z = Z[start:end + 1]
Z_sampled_2 = utils.sample_matrix(Z, sampling_rate = sr)
assert Z_sampled_1.shape[0] == W_sampled.shape[0]
assert Z_sampled_2.shape[0] == W_sampled.shape[0]
data_X_1[demonstration] = np.concatenate((W_sampled, Z_sampled_1), axis = 1)
data_X_2[demonstration] = np.concatenate((W_sampled, Z_sampled_2), axis = 1)
pickle.dump(data_X_1, open(PATH_TO_FEATURES + "SIFT_1.p", "wb"))
pickle.dump(data_X_2, open(PATH_TO_FEATURES + "SIFT_2.p", "wb"))
def construct_features_kinematics(self): """ Independently loads/sets-up the kinematics in self.data_W. """ for demonstration in self.list_of_demonstrations: W = utils.sample_matrix(utils.get_kinematic_features(demonstration), sampling_rate = self.sr) scaler = preprocessing.StandardScaler().fit(W) self.data_W[demonstration] = scaler.transform(W)
def plot_SIFT(data, demonstration, changepoints = None, plotter = None, labels = None, end_frame = 1000000, interactive_mode = False, plot_pca = False, plot_tsne = False): [X_pca, X_tsne] = data X_pca = utils.sample_matrix(X_pca, 6) X_tsne = utils.sample_matrix(X_tsne, 6) print "SIFT", X_tsne.shape PATH_TO_ANNOTATION = constants.PATH_TO_DATA + constants.ANNOTATIONS_FOLDER + demonstration + "_" + str(constants.CAMERA) + ".p" annotations = pickle.load(open(PATH_TO_ANNOTATION, "rb")) # labels = utils.get_chronological_sequences(annotations) if plot_pca: plot_scatter_continous(X_pca, "plot_SIFT_PCA" + "_" + str(start) + "_" + str(end), colormap_name = colormap_name, changepoints = changepoints, labels = labels, plotter = plotter) if plot_tsne: plot_scatter_continous(X_tsne, "plot_SIFT_tSNE" + "_" + str(start) + "_" + str(end), colormap_name = colormap_name, changepoints = changepoints, labels = labels, plotter = plotter, end_frame = end_frame, interactive_mode = interactive_mode)
def construct_features_kinematics(self):
"""
Independently loads/sets-up the kinematics in self.data_W.
"""
for demonstration in self.list_of_demonstrations:
W = utils.sample_matrix(
utils.get_kinematic_features(demonstration),
sampling_rate=self.sr)
scaler = preprocessing.StandardScaler().fit(W)
self.data_W[demonstration] = scaler.transform(W)
def construct_features_kinematics(self): """ Loads kinematic features (saved in text files) and populates self.data_X dictionary """ for demonstration in self.list_of_demonstrations: W = utils.sample_matrix(utils.get_kinematic_features(demonstration), sampling_rate = self.sr) scaler = preprocessing.StandardScaler().fit(W) self.data_X[demonstration] = scaler.transform(W) print "Kinematics ", demonstration, self.data_X[demonstration].shape
def construct_features_kinematics(self): """ Loads kinematic features (saved in text files) and populates self.data_X dictionary """ for demonstration in self.list_of_demonstrations: W = utils.sample_matrix(parser.get_kinematic_features(demonstration), sampling_rate = self.sr) scaler = preprocessing.StandardScaler().fit(W) self.data_X[demonstration] = scaler.transform(W) print "Kinematics ", demonstration, self.data_X[demonstration].shape
def featurize_1(list_of_demonstrations, kinematics, sr):
"""
Given .p (pickle) files of sift features, this function concatenates
Z with W vectors to produce X vector, dumped as pickle file.
"""
print "FEATURIZATION 1"
data_X_1 = {}
data_X_2 = {}
for demonstration in list_of_demonstrations:
print "SIFT for ", demonstration
start, end = utils.get_start_end_annotations(
constants.PATH_TO_DATA + constants.ANNOTATIONS_FOLDER +
demonstration + "_" + constants.CAMERA + ".p")
W = kinematics[demonstration]
W_sampled = utils.sample_matrix(W, sampling_rate=sr)
PATH_TO_SIFT = constants.PATH_TO_DATA + "sift_FCED/SIFT_" + demonstration
Z = pickle.load(open(PATH_TO_SIFT + "_1.p", "rb"))
Z = Z[start:end + 1]
Z_sampled_1 = utils.sample_matrix(Z, sampling_rate=sr)
Z = pickle.load(open(PATH_TO_SIFT + "_2.p", "rb"))
Z = Z[start:end + 1]
Z_sampled_2 = utils.sample_matrix(Z, sampling_rate=sr)
assert Z_sampled_1.shape[0] == W_sampled.shape[0]
assert Z_sampled_2.shape[0] == W_sampled.shape[0]
data_X_1[demonstration] = np.concatenate((W_sampled, Z_sampled_1),
axis=1)
data_X_2[demonstration] = np.concatenate((W_sampled, Z_sampled_2),
axis=1)
pickle.dump(data_X_1, open(PATH_TO_FEATURES + "SIFT_1.p", "wb"))
pickle.dump(data_X_2, open(PATH_TO_FEATURES + "SIFT_2.p", "wb"))
def featurize_cnn_features_no_kinematics(list_of_demonstrations,
layer,
folder,
feature_index,
net,
sr=3,
config=[True, False, True]):
# For config params [x,y,z] refers to perform PCA, CCA and GRP respectively
data_X_PCA = {}
data_X_GRP = {}
big_Z = None
# Initialization
demonstration_size = {}
init_demonstration = list_of_demonstrations[0]
big_Z = utils.sample_matrix(load_cnn_features(init_demonstration, layer,
folder, net),
sampling_rate=sr)
demonstration_size[init_demonstration] = big_Z.shape[0]
for demonstration in list_of_demonstrations[1:]:
print "Loading Visual Features for ", demonstration
Z = load_cnn_features(demonstration, layer, folder, net)
Z_sampled = utils.sample_matrix(Z, sampling_rate=sr)
big_Z = np.concatenate((big_Z, Z_sampled), axis=0)
demonstration_size[demonstration] = Z_sampled.shape[0]
PC = min(100, min(demonstration_size.values()))
if config[0]:
big_Z_pca = utils.pca_incremental(big_Z, PC=PC)
if config[2]:
big_Z_grp = utils.grp(big_Z)
start = 0
end = 0
import matlab
import matlab.engine as mateng
eng = mateng.start_matlab()
for demonstration in list_of_demonstrations:
size = demonstration_size[demonstration]
end = start + size
# ------------- PCA -------------
if config[0]:
Z = big_Z_pca[start:end]
data_X_PCA[demonstration] = Z
# ------------- GRP -------------
if config[2]:
Z = big_Z_grp[start:end]
data_X_GRP[demonstration] = Z
start += size
if config[0]:
pickle.dump(
data_X_PCA,
open(PATH_TO_FEATURES + str(feature_index) + "_PCA.p", "wb"))
if config[2]:
pickle.dump(
data_X_GRP,
open(PATH_TO_FEATURES + str(feature_index) + "_GRP.p", "wb"))
def featurize_cnn_features(list_of_demonstrations,
kinematics,
layer,
folder,
feature_index,
net,
sr=3,
config=[True, False, False],
C=100):
# For config params [x,y,z] refers to perform PCA, CCA and GRP respectively
data_X_PCA = {}
data_X_CCA = {}
data_X_GRP = {}
big_Z = None
# Initialization
demonstration_size = {}
init_demonstration = list_of_demonstrations[0]
big_Z = utils.sample_matrix(load_cnn_features(init_demonstration, layer,
folder, net),
sampling_rate=sr)
demonstration_size[init_demonstration] = big_Z.shape[0]
kinematics_sampled = {}
kinematics_sampled[init_demonstration] = utils.sample_matrix(
kinematics[init_demonstration], sampling_rate=sr)
dimensions_of_PCA = demonstration_size[init_demonstration]
for demonstration in list_of_demonstrations[1:]:
print "Loading Visual Features for ", demonstration
Z = load_cnn_features(demonstration, layer, folder, net)
Z_sampled = utils.sample_matrix(Z, sampling_rate=sr)
print "Sampled visual", Z.shape, Z_sampled.shape
dimensions_of_PCA += Z_sampled.shape[0]
big_Z = np.concatenate((big_Z, Z_sampled), axis=0)
demonstration_size[demonstration] = Z_sampled.shape[0]
kinematics_sampled[demonstration] = utils.sample_matrix(
kinematics[demonstration], sampling_rate=sr)
print "Sampled kinematics", kinematics_sampled[demonstration].shape
print "PCA # of rows: ", dimensions_of_PCA
PC = min(C, min(demonstration_size.values()))
print "XXXX - PC:", str(PC)
# Quick check to see if kinematics and visual features are aligned
for demonstration in list_of_demonstrations:
print demonstration_size[demonstration], kinematics_sampled[
demonstration].shape[0]
assert demonstration_size[demonstration] == kinematics_sampled[
demonstration].shape[0]
if config[0]:
big_Z_pca = utils.pca_incremental(big_Z, PC=PC)
if config[2]:
big_Z_grp = utils.grp(big_Z)
start = 0
end = 0
import matlab
import matlab.engine as mateng
eng = mateng.start_matlab()
for demonstration in list_of_demonstrations:
W = kinematics_sampled[demonstration]
size = demonstration_size[demonstration]
end = start + size
# ------------- PCA -------------
if config[0]:
Z = big_Z_pca[start:end]
X = np.concatenate((W, Z), axis=1)
data_X_PCA[demonstration] = X
# ------------- CCA -------------
if config[1]:
Z = big_Z[start:end]
Z = utils.cca(W, Z)
X = np.concatenate((W, Z), axis=1)
data_X_CCA[demonstration] = X
# ------------- GRP -------------
if config[2]:
Z = big_Z_grp[start:end]
X = np.concatenate((W, Z), axis=1)
data_X_GRP[demonstration] = X
start += size
if config[0]:
pickle.dump(
data_X_PCA,
open(PATH_TO_FEATURES + str(feature_index) + "_PCA.p", "wb"))
if config[1]:
pickle.dump(
data_X_CCA,
open(PATH_TO_FEATURES + str(feature_index) + "_CCA.p", "wb"))
if config[2]:
pickle.dump(
data_X_GRP,
open(PATH_TO_FEATURES + str(feature_index) + "_GRP.p", "wb"))
def featurize_LCD_VLAD(list_of_demonstrations,
kinematics,
layer,
net_name,
folder,
dimensions,
batch_size,
fname,
config=[True, True, True]):
M = dimensions[0]
a = dimensions[1]
print "Featurizing LCD + VLAD: ", layer, net_name, folder, M, a, batch_size
BATCH_SIZE = batch_size
if constants.SIMULATION:
BATCH_SIZE = 5
data_X_PCA = {}
data_X_CCA = {}
data_X_GRP = {}
size_sampled_matrices = [
utils.sample_matrix(kinematics[demo],
sampling_rate=BATCH_SIZE).shape[0]
for demo in list_of_demonstrations
]
PC = min(100, min(size_sampled_matrices))
print "PC: ", PC
for demonstration in list_of_demonstrations:
print demonstration
W = kinematics[demonstration]
Z = load_cnn_features(demonstration, layer, folder, net_name)
W_new = utils.sample_matrix(W, sampling_rate=BATCH_SIZE)
Z_batch = None
W_batch = None
j = 1
Z_new = None
IPython.embed()
PATH_TO_ANNOTATION = constants.PATH_TO_DATA + constants.ANNOTATIONS_FOLDER + demonstration + "_" + str(
constants.CAMERA) + ".p"
start, end = utils.get_start_end_annotations(PATH_TO_ANNOTATION)
Z = []
IPython.embed()
for i in range(Z):
vector_W = W[i]
W_batch = utils.safe_concatenate(W_batch, vector_W)
vector_Z = Z[i]
vector_Z = vector_Z.reshape(M, a, a)
vector_Z = lcd.LCD(vector_Z)
Z_batch = utils.safe_concatenate(Z_batch, vector_Z)
if (j == BATCH_SIZE):
print "NEW BATCH", str(i)
Z_batch_VLAD = encoding.encode_VLAD(Z_batch)
Z_new = utils.safe_concatenate(Z_new, Z_batch_VLAD)
# Re-initialize batch variables
j = 0
Z_batch = None
W_batch = None
j += 1
# tail case
if Z_batch is not None:
print "TAIL CASE"
print "NEW BATCH", str(i)
Z_batch_VLAD = encoding.encode_VLAD(Z_batch)
Z_new = utils.safe_concatenate(Z_new, Z_batch_VLAD)
if config[0]:
Z_new_pca = utils.pca_incremental(Z_new, PC=PC)
print Z_new_pca.shape
assert W_new.shape[0] == Z_new_pca.shape[0]
X_PCA = np.concatenate((W_new, Z_new_pca), axis=1)
data_X_PCA[demonstration] = X_PCA
if config[1]:
Z_new_cca = utils.cca(W_new, Z_new)
print Z_new_cca.shape
assert W_new.shape[0] == Z_new_cca.shape[0]
X_CCA = np.concatenate((W_new, Z_new_cca), axis=1)
data_X_CCA[demonstration] = X_CCA
if config[2]:
Z_new_grp = utils.grp(Z_new)
print Z_new_grp.shape
assert W_new.shape[0] == Z_new_grp.shape[0]
X_GRP = np.concatenate((W_new, Z_new_grp), axis=1)
data_X_GRP[demonstration] = X_GRP
if config[0]:
pickle.dump(data_X_PCA,
open(PATH_TO_FEATURES + fname + "_PCA" + ".p", "wb"))
if config[1]:
pickle.dump(data_X_CCA,
open(PATH_TO_FEATURES + fname + "_CCA" + ".p", "wb"))
if config[2]:
pickle.dump(data_X_GRP,
open(PATH_TO_FEATURES + fname + "_GRP" + ".p", "wb"))
def featurize_cnn_features_no_kinematics(list_of_demonstrations, layer, folder, feature_index, net, sr = 3, config = [True, False, True]):
# For config params [x,y,z] refers to perform PCA, CCA and GRP respectively
data_X_PCA = {}
data_X_GRP = {}
big_Z = None
# Initialization
demonstration_size = {}
init_demonstration = list_of_demonstrations[0]
big_Z = utils.sample_matrix(load_cnn_features(init_demonstration, layer, folder, net), sampling_rate = sr)
demonstration_size[init_demonstration] = big_Z.shape[0]
for demonstration in list_of_demonstrations[1:]:
print "Loading Visual Features for ", demonstration
Z = load_cnn_features(demonstration, layer, folder, net)
Z_sampled = utils.sample_matrix(Z, sampling_rate = sr)
big_Z = np.concatenate((big_Z, Z_sampled), axis = 0)
demonstration_size[demonstration] = Z_sampled.shape[0]
PC = min(100, min(demonstration_size.values()))
if config[0]:
big_Z_pca = utils.pca_incremental(big_Z, PC = PC)
if config[2]:
big_Z_grp = utils.grp(big_Z)
start = 0
end = 0
import matlab
import matlab.engine as mateng
eng = mateng.start_matlab()
for demonstration in list_of_demonstrations:
size = demonstration_size[demonstration]
end = start + size
# ------------- PCA -------------
if config[0]:
Z = big_Z_pca[start:end]
data_X_PCA[demonstration] = Z
# ------------- GRP -------------
if config[2]:
Z = big_Z_grp[start:end]
data_X_GRP[demonstration] = Z
start += size
if config[0]:
pickle.dump(data_X_PCA, open(PATH_TO_FEATURES + str(feature_index) + "_PCA.p", "wb"))
if config[2]:
pickle.dump(data_X_GRP, open(PATH_TO_FEATURES + str(feature_index) + "_GRP.p", "wb"))
def featurize_cnn_features(list_of_demonstrations, kinematics, layer, folder, feature_index, net, sr = 3, config = [True, False, False], C = 100):
# For config params [x,y,z] refers to perform PCA, CCA and GRP respectively
data_X_PCA = {}
data_X_CCA = {}
data_X_GRP = {}
big_Z = None
# Initialization
demonstration_size = {}
init_demonstration = list_of_demonstrations[0]
big_Z = utils.sample_matrix(load_cnn_features(init_demonstration, layer, folder, net), sampling_rate = sr)
demonstration_size[init_demonstration] = big_Z.shape[0]
kinematics_sampled = {}
kinematics_sampled[init_demonstration] = utils.sample_matrix(kinematics[init_demonstration], sampling_rate = sr)
dimensions_of_PCA = demonstration_size[init_demonstration]
for demonstration in list_of_demonstrations[1:]:
print "Loading Visual Features for ", demonstration
Z = load_cnn_features(demonstration, layer, folder, net)
Z_sampled = utils.sample_matrix(Z, sampling_rate = sr)
print "Sampled visual", Z.shape, Z_sampled.shape
dimensions_of_PCA += Z_sampled.shape[0]
big_Z = np.concatenate((big_Z, Z_sampled), axis = 0)
demonstration_size[demonstration] = Z_sampled.shape[0]
kinematics_sampled[demonstration] = utils.sample_matrix(kinematics[demonstration], sampling_rate = sr)
print "Sampled kinematics", kinematics_sampled[demonstration].shape
print "PCA # of rows: ", dimensions_of_PCA
PC = min(C, min(demonstration_size.values()))
print "XXXX - PC:", str(PC)
# Quick check to see if kinematics and visual features are aligned
for demonstration in list_of_demonstrations:
print demonstration_size[demonstration], kinematics_sampled[demonstration].shape[0]
assert demonstration_size[demonstration] == kinematics_sampled[demonstration].shape[0]
if config[0]:
big_Z_pca = utils.pca_incremental(big_Z, PC = PC)
if config[2]:
big_Z_grp = utils.grp(big_Z)
start = 0
end = 0
import matlab
import matlab.engine as mateng
eng = mateng.start_matlab()
for demonstration in list_of_demonstrations:
W = kinematics_sampled[demonstration]
size = demonstration_size[demonstration]
end = start + size
# ------------- PCA -------------
if config[0]:
Z = big_Z_pca[start:end]
X = np.concatenate((W, Z), axis = 1)
data_X_PCA[demonstration] = X
# ------------- CCA -------------
if config[1]:
Z = big_Z[start:end]
Z = utils.cca(W, Z)
X = np.concatenate((W, Z), axis = 1)
data_X_CCA[demonstration] = X
# ------------- GRP -------------
if config[2]:
Z = big_Z_grp[start:end]
X = np.concatenate((W, Z), axis = 1)
data_X_GRP[demonstration] = X
start += size
if config[0]:
pickle.dump(data_X_PCA, open(PATH_TO_FEATURES + str(feature_index) + "_PCA.p", "wb"))
if config[1]:
pickle.dump(data_X_CCA, open(PATH_TO_FEATURES + str(feature_index) + "_CCA.p", "wb"))
if config[2]:
pickle.dump(data_X_GRP, open(PATH_TO_FEATURES + str(feature_index) + "_GRP.p", "wb"))
def featurize_LCD_VLAD(list_of_demonstrations, kinematics, layer, net_name, folder, dimensions, batch_size, fname, config = [True, True, True]):
M = dimensions[0]
a = dimensions[1]
print "Featurizing LCD + VLAD: ", layer, net_name, folder, M, a, batch_size
BATCH_SIZE = batch_size
if constants.SIMULATION:
BATCH_SIZE = 5
data_X_PCA = {}
data_X_CCA = {}
data_X_GRP = {}
size_sampled_matrices = [utils.sample_matrix(kinematics[demo], sampling_rate = BATCH_SIZE).shape[0] for demo in list_of_demonstrations]
PC = min(100, min(size_sampled_matrices))
print "PC: ", PC
for demonstration in list_of_demonstrations:
print demonstration
W = kinematics[demonstration]
Z = load_cnn_features(demonstration, layer, folder, net_name)
W_new = utils.sample_matrix(W, sampling_rate = BATCH_SIZE)
Z_batch = None
W_batch = None
j = 1
Z_new = None
IPython.embed()
PATH_TO_ANNOTATION = constants.PATH_TO_DATA + constants.ANNOTATIONS_FOLDER + demonstration + "_" + str(constants.CAMERA) + ".p"
start, end = utils.get_start_end_annotations(PATH_TO_ANNOTATION)
Z = []
IPython.embed()
for i in range(Z):
vector_W = W[i]
W_batch = utils.safe_concatenate(W_batch, vector_W)
vector_Z = Z[i]
vector_Z = vector_Z.reshape(M, a, a)
vector_Z = lcd.LCD(vector_Z)
Z_batch = utils.safe_concatenate(Z_batch, vector_Z)
if (j == BATCH_SIZE):
print "NEW BATCH", str(i)
Z_batch_VLAD = encoding.encode_VLAD(Z_batch)
Z_new = utils.safe_concatenate(Z_new, Z_batch_VLAD)
# Re-initialize batch variables
j = 0
Z_batch = None
W_batch = None
j += 1
# tail case
if Z_batch is not None:
print "TAIL CASE"
print "NEW BATCH", str(i)
Z_batch_VLAD = encoding.encode_VLAD(Z_batch)
Z_new = utils.safe_concatenate(Z_new, Z_batch_VLAD)
if config[0]:
Z_new_pca = utils.pca_incremental(Z_new, PC = PC)
print Z_new_pca.shape
assert W_new.shape[0] == Z_new_pca.shape[0]
X_PCA = np.concatenate((W_new, Z_new_pca), axis = 1)
data_X_PCA[demonstration] = X_PCA
if config[1]:
Z_new_cca = utils.cca(W_new, Z_new)
print Z_new_cca.shape
assert W_new.shape[0] == Z_new_cca.shape[0]
X_CCA = np.concatenate((W_new, Z_new_cca), axis = 1)
data_X_CCA[demonstration] = X_CCA
if config[2]:
Z_new_grp = utils.grp(Z_new)
print Z_new_grp.shape
assert W_new.shape[0] == Z_new_grp.shape[0]
X_GRP = np.concatenate((W_new, Z_new_grp), axis = 1)
data_X_GRP[demonstration] = X_GRP
if config[0]:
pickle.dump(data_X_PCA, open(PATH_TO_FEATURES + fname + "_PCA" + ".p", "wb"))
if config[1]:
pickle.dump(data_X_CCA, open(PATH_TO_FEATURES + fname + "_CCA" + ".p", "wb"))
if config[2]:
pickle.dump(data_X_GRP, open(PATH_TO_FEATURES + fname + "_GRP" + ".p", "wb"))