"""
simple code to evaluate a recommender, you need a sparse matrix of shape (10k,2,2kk)
"""
from utils.datareader import Datareader
from utils.evaluator import Evaluator
import utils.post_processing as post
import scipy.sparse as sps
filename = "file.npz"
output_name = "matrix_factorization"
if __name__ == '__main__':
dr = Datareader(mode="offline", only_load=True)
ev = Evaluator(dr)
pids = dr.get_test_playlists().transpose()[0]
algorithm_eurm_full = sps.load_npz(filename)
algorithm_eurm_small = algorithm_eurm_full[pids]
ev.evaluate(post.eurm_to_recommendation_list(algorithm_eurm_small),
name=output_name,
verbose=True,
show_plot=True,
save=True)
import warnings
warnings.warn(
'This function still use the old version of the remove seed, it should be replaced soon by the one in post_processing class'
)
self.urm = sps.csr_matrix(self.urm[self.pid])
tmp = self.urm.tocoo()
row = tmp.row
col = tmp.col
min = self.eurm.tocoo().min()
self.eurm = sps.lil_matrix(self.eurm)
self.eurm[row, col] = -1
self.eurm = sps.csr_matrix(self.eurm)
return self.eurm
if __name__ == '__main__':
from utils.datareader import Datareader
dr = Datareader(verbose=False, mode="offline", only_load="False")
rec = Top_pop()
rec.fit(dr.get_urm(), dr.get_test_playlists().transpose()[0])
eurm = rec.compute_rating().tocsr()
sps.save_npz("top_pop online.npz", eurm.tocsr())
exit()
import utils.evaluator as ev
from utils.post_processing import eurm_to_recommendation_list
eva = ev.Evaluator(dr)
eva.evaluate(eurm_to_recommendation_list(eurm), "cacca TOPTOP")
