import sys
from scipy import sparse
import numpy as np
import utils.pre_processing as pre
from utils.definitions import *
from utils.datareader import Datareader
from utils.evaluator import Evaluator
from utils.pre_processing import *
from utils.post_processing import *
dr = Datareader(mode='offline', only_load=True, verbose=False)
ev = Evaluator(dr)
urm = dr.get_urm(binary=True)
pos_matrix = dr.get_position_matrix(position_type='last')
rows = []
cols = []
data = []
for p in tqdm(range(pos_matrix.shape[0])):
start = pos_matrix.indptr[p]
end = pos_matrix.indptr[p + 1]
tracks = pos_matrix.indices[start:end]
positions = pos_matrix.indices[start:end]
for idx in range(len(tracks)):
if positions[idx] <= 250:
rows.append(p)
cols.append((tracks[idx] * positions[idx]) + tracks[idx])
test_known_tracks = build_test_dict(dr)
test_pids_cat2 = dr.get_test_pids(cat=2)
rec_list = np.zeros(shape=(10000,500))
pred = np.zeros(shape=(10000, 2262292))
for i in tqdm(range(1000,2000)):
# print("prima target")
# print(test_pids_cat2[0])
# print(test_known_tracks[test_pids_cat2[0]])
# print([x[1] for x in test_known_tracks[test_pids_cat2[0]]])
#
# print("start")
sequences = urm_to_sequences(urm_pos=dr.get_position_matrix(position_type='last'),
target_list=[x[1] for x in test_known_tracks[test_pids_cat2[0]]],
min_common=1)
# for s in sequences: print(s)
# for s in sequences[0:2]:
# print("seuences:", s)
# print("maximal")
seq = fim(sequences[0:2], target='maximal', supp=-2, zmin=2, report='a')
# for s in seq:
# print("max>", s)
# print("normale")
# seq = fim(sequences[0:10], supp=-2, zmin=2, report='a')
