def sslim_train(A, B, l1_reg=0.001, l2_reg=0.0001):
"""
Computes W matrix of SLIM
This link is useful to understand the parameters used:
http://web.stanford.edu/~hastie/glmnet_matlab/intro.html
Basically, we are using this:
Sum( yi - B0 - xTB) + ...
As:
Sum( aj - 0 - ATwj) + ...
Remember that we are wanting to learn wj. If you don't undestand this
mathematical notation, I suggest you to read section III of:
http://glaros.dtc.umn.edu/gkhome/slim/overview
"""
alpha = l1_reg+l2_reg
l1_ratio = l1_reg/alpha
model = SGDRegressor(
penalty='elasticnet',
fit_intercept=False,
alpha=alpha,
l1_ratio=l1_ratio
)
# Following cSLIM proposal on creating an M' matrix = [ M, FT]
# * alpha is used to control relative importance of the side information
#Balpha = np.sqrt(alpha) * B
Balpha = B
Mline = vstack((A, Balpha), format='lil')
# Fit each column of W separately. We put something in each positions of W
# to allow us direct indexing of each position in parallel
total_columns = Mline.shape[1]
ranges = generate_slices(total_columns)
separated_tasks = []
for from_j, to_j in ranges:
separated_tasks.append([from_j, to_j, Mline, model])
pool = multiprocessing.Pool()
pool.map(work, separated_tasks)
pool.close()
pool.join()
return shared_array
def sslim_train(A, B, l1_reg=0.001, l2_reg=0.0001):
"""
Computes W matrix of SLIM
This link is useful to understand the parameters used:
http://web.stanford.edu/~hastie/glmnet_matlab/intro.html
Basically, we are using this:
Sum( yi - B0 - xTB) + ...
As:
Sum( aj - 0 - ATwj) + ...
Remember that we are wanting to learn wj. If you don't undestand this
mathematical notation, I suggest you to read section III of:
http://glaros.dtc.umn.edu/gkhome/slim/overview
"""
alpha = l1_reg + l2_reg
l1_ratio = l1_reg / alpha
model = SGDRegressor(penalty='elasticnet',
fit_intercept=False,
alpha=alpha,
l1_ratio=l1_ratio)
# Following cSLIM proposal on creating an M' matrix = [ M, FT]
# * alpha is used to control relative importance of the side information
#Balpha = np.sqrt(alpha) * B
Balpha = B
Mline = vstack((A, Balpha), format='lil')
# Fit each column of W separately. We put something in each positions of W
# to allow us direct indexing of each position in parallel
total_columns = Mline.shape[1]
ranges = generate_slices(total_columns)
separated_tasks = []
for from_j, to_j in ranges:
separated_tasks.append([from_j, to_j, Mline, model])
pool = multiprocessing.Pool()
pool.map(work, separated_tasks)
pool.close()
pool.join()
return shared_array
def slim_train(A, l1_reg=0.001, l2_reg=0.0001):
"""
Computes W matrix of SLIM
This link is useful to understand the parameters used:
http://web.stanford.edu/~hastie/glmnet_matlab/intro.html
Basically, we are using this:
Sum( yi - B0 - xTB) + ...
As:
Sum( aj - 0 - ATwj) + ...
Remember that we are wanting to learn wj. If you don't undestand this
mathematical notation, I suggest you to read section III of:
http://glaros.dtc.umn.edu/gkhome/slim/overview
"""
alpha = l1_reg+l2_reg
l1_ratio = l1_reg/alpha
model = SGDRegressor(
penalty='elasticnet',
fit_intercept=False,
alpha=alpha,
l1_ratio=l1_ratio,
)
total_columns = A.shape[1]
ranges = generate_slices(total_columns)
separated_tasks = []
for from_j, to_j in ranges:
separated_tasks.append([from_j, to_j, A, model])
pool = multiprocessing.Pool()
pool.map(work, separated_tasks)
pool.close()
pool.join()
return shared_array
