class Top_pop_p(object):
def __init__(self):
self.dr_on = Datareader(verbose=False, mode='online', only_load=True)
self.dr_of = Datareader(verbose=False, mode='online', only_load=True)
self.urm_on = self.dr_on.get_urm()
self.urm_of = self.dr_on.get_urm()
self.urm_col = sps.csc_matrix(self.urm_of)
self.top_p = np.zeros(self.urm_of.shape[1])
def album(self):
eurm = sps.lil_matrix(self.urm_of.shape)
pids = self.dr_on.get_test_pids(cat=2)
pids_all = self.dr_of.get_test_pids()
ucm_album = self.dr_of.get_ucm_albums().tocsc()
album_dic = self.dr_of.get_track_to_album_dict()
for row in tqdm(pids):
track_ind = self.urm_on.indices[self.urm_on.indptr[row]:self.urm_on.indptr[row + 1]][0]
album = album_dic[track_ind]
playlists = ucm_album.indices[ucm_album.indptr[album]:ucm_album.indptr[album+1]]
top = self.urm_of[playlists].sum(axis=0).A1.astype(np.int32)
track_ind_rec = top.argsort()[-501:][::-1]
eurm[row, track_ind_rec] = top[track_ind_rec]
eurm = eurm.tocsr()[pids_all]
eurm = eurm_remove_seed(eurm, self.dr_on)
return eurm
def track(self):
eurm = sps.lil_matrix(self.urm_of.shape)
pids = self.dr_on.get_test_pids(cat=2)
pids_all = self.dr_of.get_test_pids()
for row in tqdm(pids):
track_ind = self.urm_on.indices[self.urm_on.indptr[row]:self.urm_on.indptr[row + 1]][0]
playlists = self.urm_col.indices[ self.urm_col.indptr[track_ind]: self.urm_col.indptr[track_ind+1]]
top = self.urm_of[playlists].sum(axis=0).A1.astype(np.int32)
track_ind_rec = top.argsort()[-501:][::-1]
eurm[row, track_ind_rec] = top[track_ind_rec]
eurm = eurm.tocsr()[pids_all]
eurm = eurm_remove_seed(eurm, self.dr_on)
print(eurm)
return eurm.copy()
from scipy.sparse import csr_matrix
from utils.datareader import Datareader
from utils.definitions import ROOT_DIR
import numpy as np
import scipy.sparse as sps
import os
import sys
mode = sys.argv[1] # online or offline
if __name__ == '__main__':
dr = Datareader(mode=mode, only_load=True, verbose=False)
track_to_artist_dict = dr.get_track_to_artist_dict()
track_to_album_dict = dr.get_track_to_album_dict()
test_pids = dr.get_test_pids()
urm = dr.get_urm()
popularity = np.diff(urm.tocsc().indptr)
cluster_1_artist = list()
cluster_2_artist = list()
cluster_3_artist = list()
cluster_4_artist = list()
## clustering the matrices
for prog, pid in enumerate(test_pids):
songs = urm[pid].nonzero()[1]
if len(songs) > 0:
class Top_pop_p(object):
'''
Class that allow the user to get the personalized top pop build following track or album
'''
def __init__(self):
1
def get_top_pop_album(self, mode):
'''
:return: csr_matrix filled with the reccomendation for the cat 2 following album
'''
if mode == "online":
self.dr_on = Datareader(verbose=False,
mode='online',
only_load=True)
self.urm_on = self.dr_on.get_urm()
self.urm_col = sps.csc_matrix(self.urm_on)
self.top_p = np.zeros(self.urm_on.shape[1])
eurm = sps.lil_matrix(self.urm_on.shape)
pids = self.dr_on.get_test_pids(cat=2)
pids_all = self.dr_on.get_test_pids()
ucm_album = self.dr_on.get_ucm_albums().tocsc()
album_dic = self.dr_on.get_track_to_album_dict()
for row in tqdm(pids):
track_ind = self.urm_on.indices[self.urm_on.indptr[row]:self.
urm_on.indptr[row + 1]][0]
album = album_dic[track_ind]
playlists = ucm_album.indices[ucm_album.indptr[album]:ucm_album
.indptr[album + 1]]
top = self.urm_on[playlists].sum(axis=0).A1.astype(np.int32)
track_ind_rec = top.argsort()[-501:][::-1]
eurm[row, track_ind_rec] = top[track_ind_rec]
eurm = eurm.tocsr()[pids_all]
eurm = eurm_remove_seed(eurm, self.dr_on)
elif mode == "offline":
self.dr_of = Datareader(verbose=False,
mode='offline',
only_load=True)
self.urm_of = self.dr_of.get_urm()
self.urm_col = sps.csc_matrix(self.urm_of)
self.top_p = np.zeros(self.urm_of.shape[1])
eurm = sps.lil_matrix(self.urm_of.shape)
pids = self.dr_of.get_test_pids(cat=2)
pids_all = self.dr_of.get_test_pids()
ucm_album = self.dr_of.get_ucm_albums().tocsc()
album_dic = self.dr_of.get_track_to_album_dict()
for row in tqdm(pids):
track_ind = self.urm_of.indices[self.urm_of.indptr[row]:self.
urm_of.indptr[row + 1]][0]
album = album_dic[track_ind]
playlists = ucm_album.indices[ucm_album.indptr[album]:ucm_album
.indptr[album + 1]]
top = self.urm_of[playlists].sum(axis=0).A1.astype(np.int32)
track_ind_rec = top.argsort()[-501:][::-1]
eurm[row, track_ind_rec] = top[track_ind_rec]
eurm = eurm.tocsr()[pids_all]
eurm = eurm_remove_seed(eurm, self.dr_of)
return eurm.copy().tocsr()
def get_top_pop_track(self, mode):
'''
:return: csr_matrix filled with the reccomendation for the cat 2 following track
'''
if mode == "online":
self.dr_on = Datareader(verbose=False,
mode='online',
only_load=True)
self.urm_on = self.dr_on.get_urm()
self.urm_col = sps.csc_matrix(self.urm_on)
self.top_p = np.zeros(self.urm_on.shape[1])
eurm = sps.lil_matrix(self.urm_on.shape)
pids = self.dr_on.get_test_pids(cat=2)
pids_all = self.dr_on.get_test_pids()
for row in tqdm(pids):
track_ind = self.urm_on.indices[self.urm_on.indptr[row]:self.
urm_on.indptr[row + 1]][0]
playlists = self.urm_col.indices[self.urm_col.
indptr[track_ind]:self.
urm_col.indptr[track_ind + 1]]
top = self.urm_on[playlists].sum(axis=0).A1.astype(np.int32)
track_ind_rec = top.argsort()[-501:][::-1]
eurm[row, track_ind_rec] = top[track_ind_rec]
eurm = eurm.tocsr()[pids_all]
eurm = eurm_remove_seed(eurm, self.dr_on)
elif mode == "offline":
self.dr_of = Datareader(verbose=False,
mode='offline',
only_load=True)
self.urm_of = self.dr_of.get_urm()
self.urm_col = sps.csc_matrix(self.urm_of)
self.top_p = np.zeros(self.urm_of.shape[1])
eurm = sps.lil_matrix(self.urm_of.shape)
pids = self.dr_of.get_test_pids(cat=2)
pids_all = self.dr_of.get_test_pids()
for row in tqdm(pids):
track_ind = self.urm_of.indices[self.urm_of.indptr[row]:self.
urm_of.indptr[row + 1]][0]
playlists = self.urm_col.indices[self.urm_col.
indptr[track_ind]:self.
urm_col.indptr[track_ind + 1]]
top = self.urm_of[playlists].sum(axis=0).A1.astype(np.int32)
track_ind_rec = top.argsort()[-501:][::-1]
eurm[row, track_ind_rec] = top[track_ind_rec]
eurm = eurm.tocsr()[pids_all]
eurm = eurm_remove_seed(eurm, self.dr_of)
return eurm.copy().tocsr()