def learnModel(self, X):
learner = WRMFRecommender(self.k, self.alpha, self.lmbda, self.maxIterations)
learner.fit(X)
self.U = learner.U
self.V = learner.V
return self.U, self.V
def learnModel(self, X):
learner = WRMFRecommender(self.k, self.alpha, self.lmbda,
self.maxIterations)
learner.fit(X)
self.U = learner.U
self.V = learner.V
return self.U, self.V
def main():
import sys
from mrec import load_sparse_matrix, save_recommender
from mrec.mf.wrmf import WRMFRecommender
file_format = sys.argv[1]
filepath = sys.argv[2]
outfile = sys.argv[3]
# load training set as scipy sparse matrix
train = load_sparse_matrix(file_format, filepath)
model = WRMFRecommender(d=5)
model.fit(train)
save_recommender(model, outfile)
def run_mrec(d=10,num_iters=4,reg=0.02):
#d is dimension of subspace, i.e. groups
import sys
from mrec import load_sparse_matrix, save_recommender
from mrec.sparse import fast_sparse_matrix
from mrec.mf.wrmf import WRMFRecommender
alpha=1.0
start=time.time()
file_format = "csv"
#file shoule be csv, with: row,col,data
#data may just be ones
filepath = PARS['data_dir']+"/reduced_row_col_num_cutoff_1.5.csv"
#filepath = PARS['data_dir']+"test_10_mill.csv"
outfile = make_mrec_outfile(filepath,d,num_iters,reg)
print outfile
print 'reading file: %s'%filepath
# load training set as scipy sparse matrix
print "loading file"
train = load_sparse_matrix(file_format,filepath)
print "loaded file"
print (time.time()-start),"seconds"
print "size:",train.shape
print "creating recommender"
model = WRMFRecommender(d=d,num_iters=num_iters,alpha=alpha,lbda=reg)
print "training on data"
print time.time()-start
model.fit(train)
print "done training"
print time.time()-start
print "saving model"
save_recommender(model,outfile)
print "wrote model to: %s"%outfile
print time.time()-start
return
print "validating"
data,U,V=read_mrec(mrec_file=outfile)
plot_file=outfile.replace('.npz','.png')
multi_thresh(data,model,thresh_list=None,plot_file=plot_file)
run_time=(time.time()-start)/60.0
print "runtime: %0.3f minutes"%run_time
print 'done'
def main():
import sys
from mrec import load_sparse_matrix, save_recommender
from mrec.sparse import fast_sparse_matrix
from mrec.mf.wrmf import WRMFRecommender
file_format = sys.argv[1]
filepath = sys.argv[2]
outfile = sys.argv[3]
# load training set as scipy sparse matrix
train = load_sparse_matrix(file_format,filepath)
model = WRMFRecommender(d=5)
model.fit(train)
save_recommender(model,outfile)
def run_mrec(d=10, num_iters=4, reg=0.02):
#d is dimension of subspace, i.e. groups
import sys
from mrec import load_sparse_matrix, save_recommender
from mrec.sparse import fast_sparse_matrix
from mrec.mf.wrmf import WRMFRecommender
alpha = 1.0
start = time.time()
file_format = "csv"
#file shoule be csv, with: row,col,data
#data may just be ones
filepath = PARS['data_dir'] + "/reduced_row_col_num_cutoff_1.5.csv"
#filepath = PARS['data_dir']+"test_10_mill.csv"
outfile = make_mrec_outfile(filepath, d, num_iters, reg)
print outfile
print 'reading file: %s' % filepath
# load training set as scipy sparse matrix
print "loading file"
train = load_sparse_matrix(file_format, filepath)
print "loaded file"
print(time.time() - start), "seconds"
print "size:", train.shape
print "creating recommender"
model = WRMFRecommender(d=d, num_iters=num_iters, alpha=alpha, lbda=reg)
print "training on data"
print time.time() - start
model.fit(train)
print "done training"
print time.time() - start
print "saving model"
save_recommender(model, outfile)
print "wrote model to: %s" % outfile
print time.time() - start
return
print "validating"
data, U, V = read_mrec(mrec_file=outfile)
plot_file = outfile.replace('.npz', '.png')
multi_thresh(data, model, thresh_list=None, plot_file=plot_file)
run_time = (time.time() - start) / 60.0
print "runtime: %0.3f minutes" % run_time
print 'done'
