"""

to_data_list = list()

users = list()

movies = list()

ratings = list()

unique_users = set()

unique_movies = set()

with open(filename, 'r') as csvfile:

reader = csv.reader(csvfile, delimiter=delimiter)

for count, row in enumerate(reader):

#if count!=0:

to_data_list.append({'in0':[int(row[0])], 'in1':[int(row[1])], 'label':float(row[2])})

users.append(row[0])

movies.append(row[1])

ratings.append(float(row[2]))

unique_users.add(row[0])

unique_movies.add(row[1])

if verbose:

print("In file {}, there are {} ratings".format(filename, len(ratings)))

print("The ratings have mean: {}, median: {}, and variance: {}".format(

round(np.mean(ratings), 2),

round(np.median(ratings), 2),

round(np.var(ratings), 2)))

print("There are {} unique users and {} unique movies".format(len(unique_users), len(unique_movies)))

"""

to_users_dict = dict()

to_movies_dict = dict()

with open(filename, 'r') as csvfile:

reader = csv.reader(csvfile, delimiter=delimiter)

for count, row in enumerate(reader):

#if count!=0:

if row[0] not in to_users_dict:

to_users_dict[row[0]] = [(row[1], row[2])]

else:

to_users_dict[row[0]].append((row[1], row[2]))

if row[1] not in to_movies_dict:

to_movies_dict[row[1]] = list(row[0])

else:

to_movies_dict[row[1]].append(row[0])

# turn user_dict format to data list format (acceptable to the algorithm)

data_list = list()

for user, movie_rating_list in user_dict.items():

for movie, rating in movie_rating_list:

data_list.append({'in0':[int(user)], 'in1':[int(movie)], 'label':float(rating)})

"""

ratios = [val for _, val in sp_ratio_dict.items()]

assert np.sum(ratios) == 1, "the sampling ratios must sum to 1!"

divided_dict = {}

for user, movie_rating_list in user_dict.items():

sub_movies_ptr = 0

sub_movies_list = []

#movie_list, _ = zip(*movie_rating_list)

#print(movie_list)

for i, ratio in enumerate(ratios):

if i < len(ratios)-1:

sub_movies_ptr_end = sub_movies_ptr + int(len(movie_rating_list)*ratio)

sub_movies_list.append(movie_rating_list[sub_movies_ptr:sub_movies_ptr_end])

sub_movies_ptr = sub_movies_ptr_end

else:

sub_movies_list.append(movie_rating_list[sub_movies_ptr:])

for subset_name in sp_ratio_dict.keys():

if subset_name not in divided_dict:

divided_dict[subset_name] = {user: sub_movies_list.pop(0)}

else:

#access sub-dictionary

divided_dict[subset_name][user] = sub_movies_list.pop(0)

"""

with jsonlines.open(jsonl_fname, mode='w') as writer:

with open(csv_fname, 'r') as csvfile:

reader = csv.reader(csvfile, delimiter=csv_delimiter)

for count, row in enumerate(reader):

#print(row)

#if count!=0:

writer.write({'in0':[int(row[0])], 'in1':[int(row[1])], 'label':float(row[2])})

"""

with jsonlines.open(to_fname, mode='w') as writer:

for row in data_list:

#print(row)

writer.write({'in0':row['in0'], 'in1':row['in1'], 'label':row['label']})

"""

data_ = [({"in0":row['in0'], 'in1':row['in1']}, row['label']) for row in data_list]

data, label = zip(*data_)

infer_data = {"instances":data}

if binarize:

label = get_binarized_label(list(label), label_thres)

"""

for i, row in enumerate(data_list):

if type(row) is dict:

#if i < 10:

#print(row['label'])

if row['label'] > thres:

#print(row)

data_list[i]['label'] = 1

else:

data_list[i]['label'] = 0

else:

if row > thres:

data_list[i] = 1

else:

data_list[i] = 0