"latent_features": latent_features,
"learning_rate": learning_rate,
"percent_zeros": percent_zero
}
results = 0
for run in range(params["number_of_runs"]):
T = {}
new_playlist_tracks = {}
for input_playlist_index in range(
params["number_of_playlists_to_test"]):
input_pid = indexed_pids[input_playlist_index]
T[input_pid], new_playlist_tracks[
input_pid] = matrix.split_playlist(
input_pid, playlist_dict)
matrix.update_input_playlist_tracks(
input_playlist_index,
new_playlist_tracks[input_pid],
track_playlist_matrix, unique_track_dict)
factorized_matrix = nn_mf.get_factorized_matrix(
mongo_collection, track_playlist_matrix,
train_params)
for input_playlist_index in range(
params["number_of_playlists_to_test"]):
input_pid = indexed_pids[input_playlist_index]
ranked_tracks = nn_mf.get_ranked_tracks(
factorized_matrix, input_playlist_index,
indexed_tids)
recommended_tracks = helpers.recommend_n_tracks(
N, ranked_tracks,
input_playlist_index = 0
for index, pid in enumerate(indexed_pids):
if pid == input_pid:
input_playlist_index = index
break
avg_ndcg = {}
avg_r = {}
for N in range(1, max_N + 1):
avg_ndcg[N] = 0
avg_r[N] = 0
num_runs = 10
for run in range(num_runs):
T, new_playlist_tracks = matrix.split_playlist(input_pid, playlist_dict)
matrix.update_input_playlist_tracks(input_playlist_index, new_playlist_tracks, track_playlist_matrix, unique_track_dict)
ranked_tracks = []
if rec_system == 'item':
ranked_tracks = item_based.get_ranked_tracks(new_playlist_tracks, indexed_tids, track_playlist_matrix, mongo_collection)
elif rec_system == 'user':
ranked_tracks = user_based.get_ranked_tracks(input_pid, input_playlist_index, playlist_dict, unique_track_dict, track_playlist_matrix, mongo_collection)
elif rec_system == 'mf':
factorized_matrix = matrix_factorization.get_factorized_matrix(mongo_collection, track_playlist_matrix)
ranked_tracks = matrix_factorization.get_ranked_tracks(factorized_matrix, input_playlist_index, indexed_tids)
elif rec_system == 'feature_mf':
feature_matrix = []
for tid in unique_track_dict.keys():
feature_matrix.append([
unique_track_dict[tid]["danceability"],
unique_track_dict[tid]["energy"],
