def get_dic_per_cluster(clust_q, data_cluster, dataq, i, out_q=None, kerPCA=False):
if out_q is not None:
name = mpc.current_process().name
print name, 'Starting'
else:
print 'Starting estimation of dic %i...' % i
# parse the feature vectors for each cluster
for q in clust_q:
data_cluster = np.vstack((data_cluster, dataq[q]))
# remove useless first line
data_cluster = data_cluster[1:, :]
# learn the sparse code for that cluster
if kerPCA is False:
dict_learn = DictionaryLearning(n_jobs=10)
dict_learn.fit(data_cluster)
else:
print 'Doing kernel PCA...'
print data_cluster.shape
dict_learn = KernelPCA(kernel="rbf", gamma=10, n_components=3)
#dict_learn = PCA(n_components=10)
dict_learn.fit(data_cluster)
if out_q is not None:
res = {}
res[i] = dict_learn
out_q.put(res)
print name, 'Exiting'
else:
print 'Finished.'
return dict_learn # dict(i = dict_learn)
def sparse_coding(dimension, input_x, alpha, iteration, tolerance): #dl = DictionaryLearning(dimension) dl = DictionaryLearning(dimension, alpha, iteration, tolerance) dl.fit(input_x) #np.set_printoptions(precision=3, suppress=True) #print code #print dl.components_ print "error:", dl.error_[-1] return dl
def test_dict_learning_lassocd_readonly_data():
n_components = 12
with TempMemmap(X) as X_read_only:
dico = DictionaryLearning(n_components, transform_algorithm='lasso_cd',
transform_alpha=0.001, random_state=0, n_jobs=-1)
code = dico.fit(X_read_only).transform(X_read_only)
assert_array_almost_equal(np.dot(code, dico.components_), X_read_only, decimal=2)
def test_dict_learning_split():
n_atoms = 5
dico = DictionaryLearning(n_atoms, transform_algorithm='threshold')
code = dico.fit(X).transform(X)
dico.split_sign = True
split_code = dico.transform(X)
assert_array_equal(split_code[:, :n_atoms] - split_code[:, n_atoms:], code)
def trainLowDict(buffer):
print('Learning the dictionary...')
t0 = time()
dico = DictionaryLearning(n_components=100, alpha=1, max_iter=100,verbose=1)
V = dico.fit(buffer).components_
E = dico.error_
dt = time() - t0
print('done in %.2fs.' % dt)
return V,E
def test_dict_learning_split():
n_components = 5
dico = DictionaryLearning(n_components, transform_algorithm='threshold',
random_state=0)
code = dico.fit(X).transform(X)
dico.split_sign = True
split_code = dico.transform(X)
assert_array_equal(split_code[:, :n_components] -
split_code[:, n_components:], code)
def test_dict_learning_reconstruction():
n_components = 12
dico = DictionaryLearning(n_components, transform_algorithm='omp',
transform_alpha=0.001, random_state=0)
code = dico.fit(X).transform(X)
assert_array_almost_equal(np.dot(code, dico.components_), X)
dico.set_params(transform_algorithm='lasso_lars')
code = dico.transform(X)
assert_array_almost_equal(np.dot(code, dico.components_), X, decimal=2)
def test_dict_learning_nonzero_coefs():
n_components = 4
dico = DictionaryLearning(n_components, transform_algorithm='lars',
transform_n_nonzero_coefs=3, random_state=0)
code = dico.fit(X).transform(X[np.newaxis, 1])
assert_true(len(np.flatnonzero(code)) == 3)
dico.set_params(transform_algorithm='omp')
code = dico.transform(X[np.newaxis, 1])
assert_equal(len(np.flatnonzero(code)), 3)
def test_dict_learning_reconstruction_parallel():
# regression test that parallel reconstruction works with n_jobs=-1
n_components = 12
dico = DictionaryLearning(n_components, transform_algorithm='omp',
transform_alpha=0.001, random_state=0, n_jobs=-1)
code = dico.fit(X).transform(X)
assert_array_almost_equal(np.dot(code, dico.components_), X)
dico.set_params(transform_algorithm='lasso_lars')
code = dico.transform(X)
assert_array_almost_equal(np.dot(code, dico.components_), X, decimal=2)
def create_dictionary_dl(lmbd, K=100, N=10000, dir_mnist='save_exp/mnist'):
import os.path as osp
fname = osp.join(dir_mnist, "D_mnist_K{}_lmbd{}.npy".format(K, lmbd))
if osp.exists(fname):
D = np.load(fname)
else:
from sklearn.decomposition import DictionaryLearning
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
im = mnist.train.next_batch(N)[0]
im = im.reshape(N, 28, 28)
im = [imresize(a, (17, 17), interp='bilinear', mode='L')-.5
for a in im]
X = np.array(im).reshape(N, -1)
print(X.shape)
dl = DictionaryLearning(K, alpha=lmbd*N, fit_algorithm='cd',
n_jobs=-1, verbose=1)
dl.fit(X)
D = dl.components_.reshape(K, -1)
np.save(fname, D)
return D
def test_dict_learning_nonzero_coefs():
n_components = 4
dico = DictionaryLearning(
n_components,
transform_algorithm="lars",
transform_n_nonzero_coefs=3,
random_state=0,
)
code = dico.fit(X).transform(X[np.newaxis, 1])
assert len(np.flatnonzero(code)) == 3
dico.set_params(transform_algorithm="omp")
code = dico.transform(X[np.newaxis, 1])
assert len(np.flatnonzero(code)) == 3
def test_dict_learning_reconstruction_parallel():
# regression test that parallel reconstruction works with n_jobs>1
n_components = 12
dico = DictionaryLearning(n_components,
transform_algorithm='omp',
transform_alpha=0.001,
random_state=0,
n_jobs=4)
code = dico.fit(X).transform(X)
assert_array_almost_equal(np.dot(code, dico.components_), X)
dico.set_params(transform_algorithm='lasso_lars')
code = dico.transform(X)
assert_array_almost_equal(np.dot(code, dico.components_), X, decimal=2)
def create_dictionary_dl(lmbd, K=100, N=10000, dir_mnist='save_exp/mnist'):
import os.path as osp
fname = osp.join(dir_mnist, "D_mnist_K{}_lmbd{}.npy".format(K, lmbd))
if osp.exists(fname):
D = np.load(fname)
else:
from sklearn.decomposition import DictionaryLearning
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
im = mnist.train.next_batch(N)[0]
im = im.reshape(N, 28, 28)
# im = [imresize(a, (17, 17), interp='bilinear', mode='L')-.5
# for a in im]
X = np.array(im).reshape(N, -1)
# model = ResNet50(weights='imagenet',include_top=False)
# X = feat_extract(model,im)
print(X.shape)
dl = DictionaryLearning(K, alpha=lmbd*N, fit_algorithm='cd',
n_jobs=-1, verbose=1)
dl.fit(X)
D = dl.components_.reshape(K, -1)
np.save(fname, D)
return D
def test_dict_learning_lassocd_readonly_data():
n_components = 12
with TempMemmap(X) as X_read_only:
dico = DictionaryLearning(
n_components,
transform_algorithm="lasso_cd",
transform_alpha=0.001,
random_state=0,
n_jobs=4,
)
with ignore_warnings(category=ConvergenceWarning):
code = dico.fit(X_read_only).transform(X_read_only)
assert_array_almost_equal(np.dot(code, dico.components_),
X_read_only,
decimal=2)
def peakmem_fit(self, params):
estimator = DictionaryLearning(**self.dl_params)
estimator.fit(self.data)
func_filename = source + str('bold.nii.gz')
fmri_masked = nifti_masker.fit_transform(func_filename)
fmri_masked = fmri_masked[condition_mask]
fmri_masked = fmri_masked[:, np.all(fmri_masked != 0, axis=0)]
# DEFINING features and targets
features = fmri_masked
targets = target_int
# Dictionary Learning on Target
dict_sparse = DictionaryLearning(alpha=1,
n_components=sparse_components,
max_iter=3,
verbose=3)
dict_sparse.fit(features)
Dt_0 = dict_sparse.components_
coder = SparseCoder(dictionary=Dt_0)
Rt_0 = coder.transform(features)
target_folder = 'C:\\Users\\Pouya\\Documents\\MATLAB\\transfer\\' + str(
subjects[subs]) + '_brain_sparse.mat'
sio.savemat(target_folder, {'Rt_0': Rt_0, 'targets': targets})
##
target_folder = 'C:\\Users\\Pouya\\Documents\\MATLAB\\transfer\\imagenet_fc7_pca200.mat'
sio.savemat(
target_folder, {
'imagenet_feat': imagenet_features,
'pca_feat': pca_feat,
'imagenet_targets': imagenet_targets
class SparseCoding:
DEFAULT_MODEL_PARAMS = {
'n_components': 10,
'n_features': 64,
'max_iter': 5,
'random_state': 1,
'dict_init': None,
'code_init': None
}
def __init__(self, model_filename=None):
if model_filename is not None:
self.load_model(model_filename)
else:
# default model params
self.n_components = SparseCoding.DEFAULT_MODEL_PARAMS[
'n_components']
self.n_features = SparseCoding.DEFAULT_MODEL_PARAMS['n_features']
self.max_iter = SparseCoding.DEFAULT_MODEL_PARAMS['max_iter']
self.random_state = SparseCoding.DEFAULT_MODEL_PARAMS[
'random_state']
self.dict_init = SparseCoding.DEFAULT_MODEL_PARAMS['dict_init']
self.code_init = SparseCoding.DEFAULT_MODEL_PARAMS['code_init']
# initialize Dictionary Learning object with default params and weights
self.DL_obj = DictionaryLearning(n_components=self.n_components,
alpha=1,
max_iter=self.max_iter,
tol=1e-08,
fit_algorithm='lars',
transform_algorithm='omp',
transform_n_nonzero_coefs=None,
transform_alpha=None,
n_jobs=1,
code_init=self.code_init,
dict_init=self.dict_init,
verbose=False,
split_sign=False,
random_state=self.random_state)
def save_model(self, filename):
# save DL object to file, compress is also to prevent multiple model files.
joblib.dump(self.DL_obj, filename, compress=3)
def load_model(self, filename):
# load DL Object from file
self.DL_obj = joblib.load(filename)
# set certain model params as class attributes. Get values from DL Obj.get_params() or use default values.
DL_params = self.DL_obj.get_params()
for param in SparseCoding.DEFAULT_MODEL_PARAMS:
if param in DL_params:
setattr(self, param, DL_params[param])
else:
setattr(self, param, SparseCoding.DEFAULT_MODEL_PARAMS[param])
def learn_dictionary(self, whitened_patches):
# assert correct dimensionality of input data
if whitened_patches.ndim == 3:
whitened_patches = whitened_patches.reshape(
(whitened_patches.shape[0], -1))
assert whitened_patches.ndim == 2, "Whitened patches ndim is %d instead of 2" % whitened_patches.ndim
# learn dictionary
self.DL_obj.fit(whitened_patches)
def get_dictionary(self):
try:
return self.DL_obj.components_
except AttributeError:
raise AttributeError("Feature extraction dictionary has not yet been learnt for this model. " \
+ "Train the feature extraction model at least once to prevent this error.")
def get_sparse_features(self, whitened_patches):
# assert correct dimensionality of input data
if whitened_patches.ndim == 3:
whitened_patches = whitened_patches.reshape(
(whitened_patches.shape[0], -1))
assert whitened_patches.ndim == 2, "Whitened patches ndim is %d instead of 2" % whitened_patches.ndim
try:
sparse_code = self.DL_obj.transform(whitened_patches)
except NotFittedError:
raise NotFittedError("Feature extraction dictionary has not yet been learnt for this model, " \
+ "therefore Sparse Codes cannot be extracted. Train the feature extraction model " \
+ "at least once to prevent this error.")
return sparse_code
def get_sign_split_features(self, sparse_features):
n_samples, n_components = sparse_features.shape
sign_split_features = np.empty((n_samples, 2 * n_components))
sign_split_features[:, :n_components] = np.maximum(sparse_features, 0)
sign_split_features[:, n_components:] = -np.minimum(sparse_features, 0)
return sign_split_features
def get_pooled_features(self, input_feature_map, filter_size=(19, 19)):
# assuming square filters and images
filter_side = filter_size[0]
# reshaping incoming features from 2d to 3d i.e. (3249,20) to (57,57,20)
input_feature_map_shape = input_feature_map.shape
if input_feature_map.ndim == 2:
input_feature_map_side = int(np.sqrt(input_feature_map.shape[0]))
input_feature_map = input_feature_map.reshape(
(input_feature_map_side, input_feature_map_side,
input_feature_map_shape[-1]))
assert input_feature_map.ndim == 3, "Input features dimension is %d instead of 3" % input_feature_map.ndim
# get windows (57,57,20) to (3,3,1,19,19,20)
input_feature_map_windows = view_as_windows(
input_feature_map,
window_shape=(filter_size[0], filter_size[1],
input_feature_map.shape[-1]),
step=filter_size[0])
# reshape windows (3,3,1,19,19,20) to (3**2, 19**2, 20) == (9, 361, 20)
input_feature_map_windows = input_feature_map_windows.reshape(
(input_feature_map_windows.shape[0]**2, filter_size[0]**2,
input_feature_map.shape[-1]))
# calculate norms (9, 361, 20) to (9,361)
input_feature_map_window_norms = np.linalg.norm(
input_feature_map_windows, ord=2, axis=-1)
# calculate indexes of max norms per window (9,361) to (9,1). One max index per window.
max_norm_indexes = np.argmax(input_feature_map_window_norms, axis=-1)
# max pooled features are the features that have max norm indexes (9, 361, 20) to (9,20). One max index per window.
pooled_features = input_feature_map_windows[
np.arange(input_feature_map_windows.shape[0]), max_norm_indexes]
# return pooled feature map
return pooled_features
# Combined Pipeline
def get_pooled_features_from_whitened_patches(self, whitened_patches):
sparse_features = self.get_sparse_features(whitened_patches)
sign_split_features = self.get_sign_split_features(sparse_features)
pooled_features = self.get_pooled_features(sign_split_features)
return pooled_features
# sklearn utilities
from sklearn.decomposition import DictionaryLearning
from sklearn.preprocessing import normalize
def interface():
args = argparse.ArgumentParser()
# Required
args.add_argument('-i', '--data-matrix', help='Input data matrix', required=True)
# Optional
args.add_argument('-d', '--dict-file', help='Dictionary encoder file (.pkl)', default='dict.pkl')
args.add_argument('-n', '--num-atoms', help='Desired dictionary size', default=1000, type=int)
args.add_argument('-a', '--alpha', help='Alpha (sparsity enforcement)', default=1.0, type=float)
args = args.parse_args()
return args
if __name__=="__main__":
args = interface()
# Load and preprocess the data
sample_ids, matrix = parse_otu_matrix(args.data_matrix)
matrix = normalize(matrix)
# Learn a dictionary
dict_transformer = DictionaryLearning(n_components=args.num_atoms, alpha=args.alpha)
dict_transformer.fit(matrix)
# Save dictionary to file
save_object_to_file(dict_transformer, args.dict_file)
from sklearn.decomposition import SparsePCA
from sklearn.decomposition import MiniBatchSparsePCA
from sklearn.decomposition import MiniBatchDictionaryLearning
##
source_folder = 'C:\\Users\\Pouya\\Documents\\MATLAB\\DECAF\\Analysis\\Movie_Genre_adaptation\\feats.mat'
dict = sio.loadmat(source_folder)
features = dict['features']
MovieFeatures = dict['MovieFeatures']
# Source Domain
dict_sparse = DictionaryLearning(alpha=1,
n_components=4,
max_iter=1000,
verbose=3)
dict_sparse.fit(MovieFeatures)
Ds_0 = dict_sparse.components_
coder = SparseCoder(dictionary=Ds_0)
Rs_0 = coder.transform(MovieFeatures)
# Target Domain
dict_feat = [None] * 30
for subs in range(30):
print(subs)
feat = features[0, subs]
#dict_sparse = DictionaryLearning(alpha=0.1, n_components=105, max_iter=10, transform_n_nonzero_coefs=105, verbose=3)
#dict_sparse = SparsePCA(n_components=105, max_iter=3)
#dict_sparse = MiniBatchDictionaryLearning(alpha=1, n_components=105, batch_size=10, n_iter=100)
#dict_sparse.fit(feat)
if i + 1 < video.shape[2]:
image = np.vstack(
(image, np.abs((video[:, :, i].reshape((1, 75 * 50)) - video[:, :, i + 1].reshape((1, 75 * 50)))))
)
idx = np.random.shuffle([i for i in xrange(image[1:].shape[0])])
image = image[idx][0]
image = (image - np.min(image, axis=0)) / (np.max(image, axis=0) + 0.01)
audio = audio.T[idx, :][0]
print image.shape, audio.shape
fusion = np.hstack((image, audio))
# sparse code
video_learner = DictionaryLearning(n_components=784, alpha=0.5, max_iter=50, fit_algorithm="cd", verbose=1)
audio_learner = DictionaryLearning(n_components=10, alpha=0.5, max_iter=50, fit_algorithm="cd", verbose=1)
fusion_learner = DictionaryLearning(n_components=784, alpha=0.5, max_iter=50, fit_algorithm="cd", verbose=1)
video_learner.fit(image)
"""
# build model
face_rbm = RBM(n_components=100, verbose=2, batch_size=20, n_iter=10)
audio_rbm = RBM(n_components=100, verbose=2, batch_size=20, n_iter=10)
# fit model
face_rbm.fit(image)
audio_rbm.fit(audio)
print face_rbm.components_.shape, audio_rbm.components_.shape
hidden = np.hstack((face_rbm.components_, audio_rbm.components_))
print hidden.shape
fusion_rbm = RBM(n_components=100,verbose=2, batch_size=20, n_iter=10)
nmfHOG = NMF(n_components=components)
nmfHOF = NMF(n_components=components)
nmfHOG.fit(np.array([x['hog'] for x in features]).T)
nmfHOF.fit(np.array([x['hof'] for x in features]).T)
hogComponents = icaHOG.components_.T
hofComponents = icaHOF.components_.T
return hogComponents, hofComponents
if 0:
from sklearn.decomposition import DictionaryLearning
dicHOG = DictionaryLearning(25)
dicHOG.fit(hogs)
def displayComponents(components):
sides = ceil(np.sqrt(len(components)))
for i in range(len(components)):
subplot(sides, sides, i + 1)
imshow(hog2image(components[i], imageSize=[24, 24], orientations=4))
sides = ceil(np.sqrt(components.shape[1]))
for i in range(components.shape[1]):
subplot(sides, sides, i + 1)
imshow(hog2image(components[:, i], imageSize=[24, 24], orientations=4))
x_train = x_train.reshape(-1, img_rows*img_cols*channels) # each image as vector
np.random.shuffle(x_train)
print(x_train.shape)
#dictionary file name
if use_fashion:
file_name = 'dictionary_fashion_mnist_undercomplete'
else:
file_name = 'dictionary_mnist_overcomplete'
#check if dictionary exists
if not path.exists(file_name):
d=DictionaryLearning(n_components=2*784, max_iter=20)
# train dictionary
d.fit(x_train[1:10000, :])
dictionary = d.components_
print(dictionary.shape)
with open(file_name, 'wb') as output:
pickle.dump(d, output, pickle.HIGHEST_PROTOCOL)
print("created new dictionary")
else:
with open(file_name, 'rb') as input:
d = pickle.load(input)
print("loaded dictionary")
sparse_dict = np.transpose(d.components_)
print("analyse pursuit")
num_images_to_pursuit = 10
# Use pretrained model
dico = pickle.load(
open(
f'{cfg.save_path}/all_{n_components}_{n_iter}.sklearnmodel',
'rb'))
print(
f'Use hitted {cfg.save_path}/all_{n_components}_{n_iter}.sklearnmodel'
)
hit = True
else:
# Train a new model
dico = DictionaryLearning(n_components=n_components,
n_jobs=-24,
max_iter=n_iter,
verbose=True)
dico.fit(images)
n_iter_actual = dico.n_iter_
print(f'{n_iter_actual} iters')
timer.stop(start=' ')
# Save the model
if cfg.save:
np.save(f'{cfg.save_path}/all_{n_components}_{n_iter_actual}',
dico.components_)
pickle.dump(
dico,
open(
f'{cfg.save_path}/all_{n_components}_{n_iter_actual}.sklearnmodel',
'wb'))
# For pickling
def save_object(obj, filename):
with open(filename, 'wb') as output: # Overwrites any existing file.
pk.dump(obj, output, pk.HIGHEST_PROTOCOL)
# Load sparse data
sparse_fit1 = pk.load(open("sparse_fit1.pkl", 'rb'))
sparse_fit2 = pk.load(open("sparse_fit2.pkl", 'rb'))
sparse_fit3 = pk.load(open("sparse_fit3.pkl", 'rb'))
sparse_fit4 = pk.load(open("sparse_fit4.pkl", 'rb'))
sparse_fit1 = np.concatenate((sparse_fit1, sparse_fit2))
print(sparse_fit1.shape)
sparse_fit2 = np.concatenate((sparse_fit3, sparse_fit4))
print(sparse_fit2.shape)
sparse_fit = np.concatenate((sparse_fit1, sparse_fit2))
print(sparse_fit.shape)
X = sparse_fit[:59478, :]
print(X.shape)
# Uses the dictionary learning class to transform the data
atoms = DictionaryLearning(100, 1, 1000, 1e-8, 'lars', 'lasso_lars')
#fit and transform data
atoms.fit(X)
print(atoms.components_.shape)
# Pickle atoms
save_object(atoms, 'atoms.pkl')
class SC(object):
"""
Wrapper for sklearn package. Performs sparse coding
Sparse Coding, or Dictionary Learning has 5 methods:
- fit(waveforms)
update class instance with Sparse Coding fit
- fit_transform()
do what fit() does, but additionally return the projection onto new basis space
- inverse_transform(A)
inverses the decomposition, returns waveforms for an input A, using Z^\dagger
- get_basis()
returns the basis vectors Z^\dagger
- get_params()
returns metadata used for fits.
"""
def __init__(self, num_components=10,
catalog_name='unknown',
alpha = 0.001,
transform_alpha = 0.01,
max_iter = 2000,
tol = 1e-9,
n_jobs = 1,
verbose = True,
random_state = None):
self._decomposition = 'Sparse Coding'
self._num_components = num_components
self._catalog_name = catalog_name
self._alpha = alpha
self._transform_alpha = 0.001
self._n_jobs = n_jobs
self._random_state = random_state
self._DL = DictionaryLearning(n_components=self._num_components,
alpha = self._alpha,
transform_alpha = self._transform_alpha,
n_jobs = self._n_jobs,
verbose = verbose,
random_state = self._random_state)
def fit(self,waveforms):
# TODO make sure there are more columns than rows (transpose if not)
# normalize waveforms
self._waveforms = waveforms
self._DL.fit(self._waveforms)
def fit_transform(self,waveforms):
# TODO make sure there are more columns than rows (transpose if not)
# normalize waveforms
self._waveforms = waveforms
self._A = self._DL.fit_transform(self._waveforms)
return self._A
def inverse_transform(self,A):
# convert basis back to waveforms using fit
new_waveforms = self._DL.inverse_transform(A)
return new_waveforms
def get_params(self):
# TODO know what catalog was used! (include waveform metadata)
params = self._DL.get_params()
params['num_components'] = params.pop('n_components')
params['Decompositon'] = self._decomposition
return params
def get_basis(self):
""" Return the SPCA basis vectors (Z^\dagger)"""
return self._DL.components_
class SparseCoding:
DEFAULT_MODEL_PARAMS = {
'n_components' : 10,
'n_features' : 64,
'max_iter' : 5,
'random_state' : 1,
'dict_init' : None,
'code_init' : None
}
def __init__(self, model_filename=None):
if model_filename is not None:
self.load_model(model_filename)
else:
# default model params
self.n_components = SparseCoding.DEFAULT_MODEL_PARAMS['n_components']
self.n_features = SparseCoding.DEFAULT_MODEL_PARAMS['n_features']
self.max_iter = SparseCoding.DEFAULT_MODEL_PARAMS['max_iter']
self.random_state = SparseCoding.DEFAULT_MODEL_PARAMS['random_state']
self.dict_init = SparseCoding.DEFAULT_MODEL_PARAMS['dict_init']
self.code_init = SparseCoding.DEFAULT_MODEL_PARAMS['code_init']
# initialize Dictionary Learning object with default params and weights
self.DL_obj = DictionaryLearning(n_components=self.n_components,
alpha=1,
max_iter=self.max_iter,
tol=1e-08,
fit_algorithm='lars',
transform_algorithm='omp',
transform_n_nonzero_coefs=None,
transform_alpha=None,
n_jobs=1,
code_init=self.code_init,
dict_init=self.dict_init,
verbose=False,
split_sign=False,
random_state=self.random_state)
def save_model(self, filename):
# save DL object to file, compress is also to prevent multiple model files.
joblib.dump(self.DL_obj, filename, compress=3)
def load_model(self, filename):
# load DL Object from file
self.DL_obj = joblib.load(filename)
# set certain model params as class attributes. Get values from DL Obj.get_params() or use default values.
DL_params = self.DL_obj.get_params()
for param in SparseCoding.DEFAULT_MODEL_PARAMS:
if param in DL_params:
setattr(self, param, DL_params[param])
else:
setattr(self, param, SparseCoding.DEFAULT_MODEL_PARAMS[param])
def learn_dictionary(self, whitened_patches):
# assert correct dimensionality of input data
if whitened_patches.ndim == 3:
whitened_patches = whitened_patches.reshape((whitened_patches.shape[0], -1))
assert whitened_patches.ndim == 2, "Whitened patches ndim is %d instead of 2" %whitened_patches.ndim
# learn dictionary
self.DL_obj.fit(whitened_patches)
def get_dictionary(self):
try:
return self.DL_obj.components_
except AttributeError:
raise AttributeError("Feature extraction dictionary has not yet been learnt for this model. " \
+ "Train the feature extraction model at least once to prevent this error.")
def get_sparse_features(self, whitened_patches):
# assert correct dimensionality of input data
if whitened_patches.ndim == 3:
whitened_patches = whitened_patches.reshape((whitened_patches.shape[0], -1))
assert whitened_patches.ndim == 2, "Whitened patches ndim is %d instead of 2" %whitened_patches.ndim
try:
sparse_code = self.DL_obj.transform(whitened_patches)
except NotFittedError:
raise NotFittedError("Feature extraction dictionary has not yet been learnt for this model, " \
+ "therefore Sparse Codes cannot be extracted. Train the feature extraction model " \
+ "at least once to prevent this error.")
return sparse_code
def get_sign_split_features(self, sparse_features):
n_samples, n_components = sparse_features.shape
sign_split_features = np.empty((n_samples, 2 * n_components))
sign_split_features[:, :n_components] = np.maximum(sparse_features, 0)
sign_split_features[:, n_components:] = -np.minimum(sparse_features, 0)
return sign_split_features
def get_pooled_features(self, input_feature_map, filter_size=(19,19)):
# assuming square filters and images
filter_side = filter_size[0]
# reshaping incoming features from 2d to 3d i.e. (3249,20) to (57,57,20)
input_feature_map_shape = input_feature_map.shape
if input_feature_map.ndim == 2:
input_feature_map_side = int(np.sqrt(input_feature_map.shape[0]))
input_feature_map = input_feature_map.reshape((input_feature_map_side, input_feature_map_side, input_feature_map_shape[-1]))
assert input_feature_map.ndim == 3, "Input features dimension is %d instead of 3" %input_feature_map.ndim
# get windows (57,57,20) to (3,3,1,19,19,20)
input_feature_map_windows = view_as_windows(input_feature_map,
window_shape=(filter_size[0], filter_size[1], input_feature_map.shape[-1]),
step=filter_size[0])
# reshape windows (3,3,1,19,19,20) to (3**2, 19**2, 20) == (9, 361, 20)
input_feature_map_windows = input_feature_map_windows.reshape((input_feature_map_windows.shape[0]**2,
filter_size[0]**2,
input_feature_map.shape[-1]))
# calculate norms (9, 361, 20) to (9,361)
input_feature_map_window_norms = np.linalg.norm(input_feature_map_windows, ord=2, axis=-1)
# calculate indexes of max norms per window (9,361) to (9,1). One max index per window.
max_norm_indexes = np.argmax(input_feature_map_window_norms, axis=-1)
# max pooled features are the features that have max norm indexes (9, 361, 20) to (9,20). One max index per window.
pooled_features = input_feature_map_windows[np.arange(input_feature_map_windows.shape[0]), max_norm_indexes]
# return pooled feature map
return pooled_features
# Combined Pipeline
def get_pooled_features_from_whitened_patches(self, whitened_patches):
sparse_features = self.get_sparse_features(whitened_patches)
sign_split_features = self.get_sign_split_features(sparse_features)
pooled_features = self.get_pooled_features(sign_split_features)
return pooled_features
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
def test_Pipeline(data):
X_train,X_test,y_train,y_test = data
steps = [('Linear_SVM',LinearSVC(C=1,penalty='l1',dual=False))]
pipeline = Pipeline(steps)
pipeline.fit(X_train,y_train)
print('name steps : \n',pipeline.named_steps)
print('Pipeline score : \n',pipeline.score(X_test,y_test))
data = load_digits()
X = data.data
y = data.target
test_Pipeline(model_selection.train_test_split(X,y,test_size=0.25,stratify=y))
#字典学习
from sklearn.decomposition import DictionaryLearning
X= [[1,2,3,4,5],
[6,7,8,9,10],
[10,9,8,7,6],
[5,4,3,2,1]]
dct = DictionaryLearning(n_components=3)
dct.fit(X)
dct.transform(X)
class SparseCoding(object):
def __init__(self, n, transform_algorithm='lars'):
self.n = n
self.net = DictionaryLearning(n_components=n, alpha=0.8, max_iter=1000)
self.net.set_params(transform_algorithm=transform_algorithm)
def plot_B(self, B):
plt.figure(figsize=(4.2, 4))
for i, comp in enumerate(B[:self.n]):
plt.subplot(10, 10, i + 1)
plt.imshow(comp, cmap=plt.cm.gray_r, interpolation='nearest')
plt.xticks(())
plt.yticks(())
plt.suptitle('Dictionary learned from time series\n' +
'Train time %.1fs on %d patches' % (dt, len(data)),
fontsize=16)
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
def _init(self):
a = np.random.random((self.n, self.m))
b = np.random.random((self.T, self.n))
b /= sum(b)
return a, b
def init_weights(self, X_mat):
B, A, recon = [], [], []
for app in X_mat:
data = X_mat[app].reshape(1, -1)
B_i = self.net.fit(data).components_
A_i = self.net.transform(data)
X_hat = np.dot(A_i, B_i)
B.append(B_i)
A.append(A_i)
recon.append(X_hat)
print("MSE Error: ", np.mean((data - X_hat)**2))
return A, B, recon
def DiscriminativeDisaggregation(self, appliances, B, A):
x = np.array([appliances[app] for app in appliances])
x = x.T
A_star = np.vstack(A)
B_cat = np.hstack(B)
change = 1
t = 0
print(A_star.shape)
print(B_cat.shape)
while t <= self.steps and self.epsilon <= change:
B_cat_p = B_cat
acts = self.F(x, B_cat, A=A_star)
B_cat = (B_cat - self.alpha *
((x - B_cat.dot(acts)).dot(acts.T) -
(x - B_cat.dot(A_star)).dot(A_star.T)))
B_cat = self._pos_constraint(B_cat)
B_cat /= sum(B_cat)
t += 1
change = np.linalg.norm(B_cat - B_cat_p)
print("Change is {} and step is {} ".format(change, t))
return B_cat
def F(self, x, B, x_train=None, A=None, rp_tep=False, rp_gl=False):
B = np.asarray(B)
A = np.asarray(A)
coder = SparseCoder(dictionary=B.T,
transform_alpha=self.rp,
transform_algorithm='lasso_cd')
comps, acts = librosa.decompose.decompose(x, transformer=coder)
acts = self._pos_constraint(acts)
return acts
def predict(self, A, B):
print(A.shape)
print(B.shape)
return B.dot(A)
return feat
def create_dictionary_dl(lmbd, d=2,m=100,n=20, N=10000,case=0,dir_mnist='/home/dujw/darse/save_exp/synthetic'):
import os.path as osp
fname = osp.join(dir_mnist, "D_synthetic_d{}_m{},n{},case{},lmbd{}.npy".format(d,m,n,case,lmbd))
if osp.exists(fname):
D = np.load(fname)
else:
from sklearn.decomposition import DictionaryLearning
aa = SyntheticProblemGenerator(d=d,m=m,n=n)
X = aa.get_batch(N)[0]
K = m
dl = DictionaryLearning(K, alpha=lmbd*N, fit_algorithm='cd',
n_jobs=-1, verbose=1)
dl.fit(X)
D = dl.components_.reshape(K, -1)
np.save(fname, D)
return D
class synthetic_generate(object):
def __init__(self,d,N=100000,m=100,n=20,case=0,save_exp="/home/dujw/darse/save_exp/synthetic"):
self.N = N
self.case = case
self.m = m
self.n = n
self.d = d
self.save_exp = save_exp
self.phi = self.getPhi()
self.x, self.y = self.getXY()
def getXY(self):
N = self.N
class DictionaryLearningMethod(BaseMethod):
"""Implement the dict learning method of the paper using sklearn."""
def __init__(self, width=24, stride=12, n_components=10, alpha=1,
verbose=1, random_state=0, n_jobs=4, max_iter=1):
self.width = width
self.stride = stride
self.n_components = n_components
self.alpha = alpha
self.verbose = verbose
self.random_state = random_state
self.n_jobs = n_jobs
self.max_iter = max_iter
self.estimator = DictionaryLearning(
n_components=n_components,
alpha=alpha,
verbose=verbose,
random_state=random_state,
n_jobs=n_jobs,
max_iter=max_iter,
)
@staticmethod
def window_split(X, s, w):
"""From a signal, create an array of overlapping windows."""
X = np.array(X).reshape(-1, 1)
if w > X.shape[0]:
raise ValueError(f'Window width bigger than signal size ({w}>{X.shape[0]}).')
n_h = X.shape[0]
c = int((n_h - w)/s + 1)
Xs = []
for k in range(c):
i = w + k*s
x = X[i-w:i]
Xs.append(x)
return np.concatenate(Xs, axis=1)
@staticmethod
def window_merge(X_h, s):
"""From array of overlapping windows, reconstruct the original signal.
Parameters:
-----------
X_h : np.array of shape (w, c)
Array of overlapping windows.
s : int
Stride
Returns:
--------
X : np.array of shape
"""
w, c = X_h.shape
W = np.zeros((c, w+s*(c-1)))
for i in range(c):
W[i, i*s:i*s+w] = X_h[:, i]
N = np.sum(W != 0, axis=0)
x_hat = np.divide(np.sum(W, axis=0), N)
return x_hat
def fit(self, X, y=None):
X_h = self.window_split(X, self.stride, self.width)
self.estimator.fit(X_h.T)
def transform_codes(self, X):
X_h = self.window_split(X, self.stride, self.width)
X_pred_codes = self.estimator.transform(X_h.T).T
return X_pred_codes
def codes_to_signal(self, X_codes):
D = self.estimator.components_.T
X_h = D@X_codes
X = self.window_merge(X_h, self.stride)
return X
def transform(self, X):
X_pred_codes = self.transform_codes(X)
X_pred = self.codes_to_signal(X_pred_codes)
return X_pred
def get_atoms(self):
return self.estimator.components_.T
n_components=n_components)
D = D_fixed
n_nonzero = 3
alpha = None
algo = 'omp'
color_1 = 'red'
title = algo.upper()
di = DictionaryLearning(n_components=n_components,
fit_algorithm='cd',
transform_algorithm='lasso_cd',
positive_code=True,
positive_dict=True)
di.fit(comp_matrix)
d = di.transform(comp_matrix)
coder_1 = SparseCoder(dictionary=d.T,
transform_n_nonzero_coefs=n_nonzero,
transform_alpha=alpha,
transform_algorithm=algo)
comps, acts = librosa.decompose.decompose(comp_matrix, transformer=coder_1)
plt.plot(comp_matrix[0, :],
color='black',
lw=2,
linestyle='--',
label='Original signal',
class SC(object):
"""
Wrapper for sklearn package. Performs sparse coding
Sparse Coding, or Dictionary Learning has 5 methods:
- fit(waveforms)
update class instance with Sparse Coding fit
- fit_transform()
do what fit() does, but additionally return the projection onto new basis space
- inverse_transform(A)
inverses the decomposition, returns waveforms for an input A, using Z^\dagger
- get_basis()
returns the basis vectors Z^\dagger
- get_params()
returns metadata used for fits.
"""
def __init__(self,
num_components=10,
catalog_name='unknown',
alpha=0.001,
transform_alpha=0.01,
max_iter=2000,
tol=1e-9,
n_jobs=1,
verbose=True,
random_state=None):
self._decomposition = 'Sparse Coding'
self._num_components = num_components
self._catalog_name = catalog_name
self._alpha = alpha
self._transform_alpha = 0.001
self._n_jobs = n_jobs
self._random_state = random_state
self._DL = DictionaryLearning(n_components=self._num_components,
alpha=self._alpha,
transform_alpha=self._transform_alpha,
n_jobs=self._n_jobs,
verbose=verbose,
random_state=self._random_state)
def fit(self, waveforms):
# TODO make sure there are more columns than rows (transpose if not)
# normalize waveforms
self._waveforms = waveforms
self._DL.fit(self._waveforms)
def fit_transform(self, waveforms):
# TODO make sure there are more columns than rows (transpose if not)
# normalize waveforms
self._waveforms = waveforms
self._A = self._DL.fit_transform(self._waveforms)
return self._A
def inverse_transform(self, A):
# convert basis back to waveforms using fit
new_waveforms = self._DL.inverse_transform(A)
return new_waveforms
def get_params(self):
# TODO know what catalog was used! (include waveform metadata)
params = self._DL.get_params()
params['num_components'] = params.pop('n_components')
params['Decompositon'] = self._decomposition
return params
def get_basis(self):
""" Return the SPCA basis vectors (Z^\dagger)"""
return self._DL.components_
Ymean = Ynoisy.mean(axis=0)
Ynoisy = Ynoisy - numpy.tile(Ymean, [Ynoisy.shape[0], 1])
# Select sample patches for training
ch = numpy.random.permutation(Ynoisy.shape[1])[:N]
Y = Ynoisy[:, ch].T
print(Y.shape)
# Training dictionary
from sklearn.decomposition import DictionaryLearning
dico = DictionaryLearning(n,
transform_algorithm='omp',
alpha=s,
random_state=0,
verbose=False)
dico.fit(Y)
# Testing the validity of the sparse representation
Xt = dico.transform(Y)
print(Xt.shape)
numpy.testing.assert_array_almost_equal(numpy.dot(Xt, dico.components_),
Y,
decimal=1)
# Generating sparse representation for entire image
Xc = dico.transform(Ynoisy.T)
print(Xc.T.shape)
# D * X
A = numpy.dot(Xc, dico.components_).T
# Inverse centering, image restoration and output
nmfHOG = NMF(n_components=components) nmfHOF = NMF(n_components=components) nmfHOG.fit(np.array([x['hog'] for x in features]).T) nmfHOF.fit(np.array([x['hof'] for x in features]).T) hogComponents = icaHOG.components_.T hofComponents = icaHOF.components_.T return hogComponents, hofComponents if 0: from sklearn.decomposition import DictionaryLearning dicHOG = DictionaryLearning(25) dicHOG.fit(hogs) def displayComponents(components): sides = ceil(np.sqrt(len(components))) for i in range(len(components)): subplot(sides, sides, i+1) imshow(hog2image(components[i], imageSize=[24,24],orientations=4)) sides = ceil(np.sqrt(components.shape[1])) for i in range(components.shape[1]): subplot(sides, sides, i+1) imshow(hog2image(components[:,i], imageSize=[24,24],orientations=4))
def main():
start = time.time()
args = sys.argv
target = args[1]
sub = args[2]
threshold = args[3]
dimention = int(args[4])
shift = int(args[5])
sample = int(args[6])
print('target : {}'.format(target))
print('subject : {}'.format(sub))
print('{} secずらし'.format(shift))
#脳活動データ読み込み
with open(
'../data/Brain/' + target + '/' + sub + '_train_reduced_' +
threshold + '.pickle', 'rb') as f:
brain_data = pickle.load(f)
#意味表象データ読み込み
with open('../data/srm/' + target + '_srm300_train.pickle', 'rb') as f:
semantic_data = pickle.load(f)
#時間差を考慮した意味表象行列取得
brain_data, semantic_data = get_time_shift_data(brain_data, semantic_data,
target, sub, shift)
print('brain sample : {}'.format(len(brain_data)))
print('semantic_data : {}'.format(len(semantic_data)))
#2つを結合した合成行列を作成
brainw2vdata = np.c_[brain_data, semantic_data]
brainw2vdata = np.array(brainw2vdata)
brainw2vdata = brainw2vdata[::sample]
print("次元：")
print(brainw2vdata.shape)
#辞書学習
dict_model = DictionaryLearning(n_components=dimention,
alpha=1.0,
transform_algorithm='lasso_lars',
transform_alpha=1.0,
fit_algorithm='lars',
verbose=True)
dict_model.fit(brainw2vdata)
#辞書
Dict = dict_model.components_
print("辞書：")
print(Dict.shape)
#係数
coef = dict_model.transform(brainw2vdata)
print("係数：")
print(coef.shape)
#辞書保存
f = open(
"../data/Dict/" + target + "/Dict_" + sub + "_pred" + threshold +
"_base" + str(dimention) + "_sec" + str(shift) + "_sample" +
str(sample) + ".pickle", "wb")
pickle.dump(Dict, f)
f.close()
#係数保存
f = open(
"../data/Dict/" + target + "/Coef_" + sub + "_pred" + threshold +
"_base" + str(dimention) + "_sec" + str(shift) + "_sample" +
str(sample) + ".pickle", "wb")
pickle.dump(coef, f)
f.close()
#計算時間出力
elapsed_time = time.time() - start
print(("elapsed_time:{0}".format(elapsed_time)) + "[sec]")
from matplotlib import pyplot as plt
import pandas as pd
def decode_image(image):
decoded_image = np.empty((256, 256))
for i in range(1024):
r = i % 32
c = i // 32
decoded_image[r * 8:r * 8 + 8,
c * 8:c * 8 + 8] = image[i].reshape([8, 8], order="F")
return decoded_image
if __name__ == "__main__":
MatPatchedImage = scipy.io.loadmat(
r"C:\Users\ktmks\Documents\Dic_ler1\ver1_02\mono\PatchData.mat")
PatchData = np.array(MatPatchedImage["PatchData"]).T
decoded_image = decode_image(PatchData[:1024, :])
dico = DictionaryLearning(
n_components=128,
transform_n_nonzero_coefs=8,
verbose=True,
max_iter=1000,
)
Dict = dico.fit(PatchData)
print("Hello")
def test_dict_learning_unknown_fit_algorithm():
n_components = 5
dico = DictionaryLearning(n_components, fit_algorithm='<unknown>')
with pytest.raises(ValueError):
dico.fit(X)
import pandas as pd
import dill
N_COMPONENTS = 500
TRANSFORM_N_NONZERO_COEFS = 10
VERBOSE = True
MAX_ITER = 10
MatBrainImage=scipy.io.loadmat(r"C:\Users\ktmks\Documents\research\tmp_results\for_python_data\brain_f_data.mat")
label=MatBrainImage["label"]
Y=MatBrainImage["data"]
dic=DictionaryLearning(n_components = N_COMPONENTS,
transform_n_nonzero_coefs = TRANSFORM_N_NONZERO_COEFS,
verbose = VERBOSE,
max_iter = MAX_ITER
)
dic.fit(Y)
D=dic.components_
X=dic.transform(Y)
Y_=np.dot(X,D)
filepath = r"C:\Users\ktmks\Documents\research\Python\Brain_DL"+"\\"
filename = "res_"+"AtomN-" + str(N_COMPONENTS)\
+"_SparseDegree-" + str(TRANSFORM_N_NONZERO_COEFS)\
+"_MaxIter-" + str(MAX_ITER)
save_filename=filepath+filename+".pkl"
dill.dump_session(save_filename)
scipy.io.savemat(filename+".mat",{"D":D,"X":X,"Y_":Y_,"label":label})
