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data.py
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import os
import pandas as pd
from scipy import sparse
import numpy as np
import pickle
class DataLoader():
'''
Load Movielens-20m dataset
'''
def __init__(self, path):
self.pro_dir = os.path.join(path, 'pro_sg')
assert os.path.exists(self.pro_dir), "Preprocessed files does not exist. Run data.py"
self.n_items = self.load_n_items()
def load_data(self, datatype='train'):
if datatype == 'train':
return self._load_train_data()
elif datatype == 'validation':
return self._load_tr_te_data(datatype)
elif datatype == 'test':
return self._load_tr_te_data(datatype)
else:
raise ValueError("datatype should be in [train, validation, test]")
def load_n_items(self):
unique_sid = list()
with open(os.path.join(self.pro_dir, 'unique_sid.txt'), 'r') as f:
for line in f:
unique_sid.append(line.strip())
n_items = len(unique_sid)
return n_items
def _load_train_data(self):
path = os.path.join(self.pro_dir, 'train.csv')
tp = pd.read_csv(path)
n_users = tp['uid'].max() + 1
rows, cols = tp['uid'], tp['sid']
data = sparse.csr_matrix((np.ones_like(rows),
(rows, cols)), dtype='float64',
shape=(n_users, self.n_items))
df = tp.drop_duplicates(keep='first')
res = dict()
for i, row in df.iterrows():
if row['uid'] in res:
res[row['uid']].append(row['sid'])
else:
res[row['uid']] = [row['sid']]
return data, res
def _load_tr_te_data(self, datatype='test'):
tr_path = os.path.join(self.pro_dir, '{}_tr.csv'.format(datatype))
te_path = os.path.join(self.pro_dir, '{}_te.csv'.format(datatype))
tp_tr = pd.read_csv(tr_path)
tp_te = pd.read_csv(te_path)
start_idx = min(tp_tr['uid'].min(), tp_te['uid'].min())
end_idx = max(tp_tr['uid'].max(), tp_te['uid'].max())
rows_tr, cols_tr = tp_tr['uid'] - start_idx, tp_tr['sid']
rows_te, cols_te = tp_te['uid'] - start_idx, tp_te['sid']
data_tr = sparse.csr_matrix((np.ones_like(rows_tr),
(rows_tr, cols_tr)), dtype='float64', shape=(end_idx - start_idx + 1, self.n_items))
data_te = sparse.csr_matrix((np.ones_like(rows_te),
(rows_te, cols_te)), dtype='float64', shape=(end_idx - start_idx + 1, self.n_items))
df = tp_tr.drop_duplicates(keep='first')
res = dict()
for i, row in df.iterrows():
if (row['uid']-start_idx) in res:
res[row['uid']-start_idx].append(row['sid'])
else:
res[row['uid']-start_idx] = [row['sid']]
return data_tr, data_te, res
def get_count(tp, id):
playcount_groupbyid = tp[[id]].groupby(id, as_index=False)
count = playcount_groupbyid.size()
return count
def filter_triplets(tp, min_uc=5, min_sc=0):
if min_sc > 0:
itemcount = get_count(tp, 'movieId')
tp = tp[tp['movieId'].isin(itemcount.index[itemcount >= min_sc])]
if min_uc > 0:
usercount = get_count(tp, 'userId')
tp = tp[tp['userId'].isin(usercount.index[usercount >= min_uc])]
usercount, itemcount = get_count(tp, 'userId'), get_count(tp, 'movieId')
return tp, usercount, itemcount
def split_train_test_proportion(data, test_prop=0.2):
data_grouped_by_user = data.groupby('userId')
tr_list, te_list = list(), list()
np.random.seed(98765)
for _, group in data_grouped_by_user:
n_items_u = len(group)
if n_items_u >= 5:
idx = np.zeros(n_items_u, dtype='bool')
idx[np.random.choice(n_items_u, size=int(test_prop * n_items_u), replace=False).astype('int64')] = True
tr_list.append(group[np.logical_not(idx)])
te_list.append(group[idx])
else:
tr_list.append(group)
data_tr = pd.concat(tr_list)
data_te = pd.concat(te_list)
return data_tr, data_te
def numerize(tp, profile2id, show2id):
uid = tp['userId'].apply(lambda x: profile2id[x])
sid = tp['movieId'].apply(lambda x: show2id[x])
return pd.DataFrame(data={'uid': uid, 'sid': sid}, columns=['uid', 'sid'])
if __name__ == '__main__':
which_dataset_to_use = 4 # in {0, 1, 2, 3}, see below.
dataset = {0: 'ml-latest-small', 1: 'ml-1m', 2: 'ml-20m', 3: 'netflix', 4: 'amazon'}
n_heldout_users = (50, 500, 10000, 40000, 900)[which_dataset_to_use]
DATA_DIR = '%s/' % dataset[which_dataset_to_use]
print("Load and Preprocess %s dataset" % dataset[which_dataset_to_use])
# Load Data
if 'ml-1m' in DATA_DIR:
raw_data = pd.read_csv(os.path.join(DATA_DIR, 'ratings.dat'),
header=None, delimiter='::',
names=['userId', 'movieId', 'rating', 'timestamp'])
else:
raw_data = pd.read_csv(os.path.join(DATA_DIR, 'ratings.csv'), header=0)
raw_data = raw_data[raw_data['rating'] > 3.5]
# Filter Data
raw_data, user_activity, item_popularity = filter_triplets(raw_data)
# Shuffle User Indices
unique_uid = user_activity.index
np.random.seed(98765)
idx_perm = np.random.permutation(unique_uid.size)
unique_uid = unique_uid[idx_perm]
n_users = unique_uid.size
# n_heldout_users = 10000
# Split Train/Validation/Test User Indices
tr_users = unique_uid[:(n_users - n_heldout_users * 2)]
vd_users = unique_uid[(n_users - n_heldout_users * 2): (n_users - n_heldout_users)]
te_users = unique_uid[(n_users - n_heldout_users):]
train_plays = raw_data.loc[raw_data['userId'].isin(tr_users)]
unique_sid = pd.unique(train_plays['movieId'])
show2id = dict((sid, i) for (i, sid) in enumerate(unique_sid))
profile2id = dict((pid, i) for (i, pid) in enumerate(unique_uid))
pro_dir = os.path.join(DATA_DIR, 'pro_sg')
if not os.path.exists(pro_dir):
os.makedirs(pro_dir)
with open(os.path.join(pro_dir, 'unique_sid.txt'), 'w') as f:
for sid in unique_sid:
f.write('%s\n' % sid)
mapping = {'item2id': show2id, 'user2id': profile2id}
with open(os.path.join(pro_dir, 'mapping.pickle'), 'wb') as f:
pickle.dump(mapping, f, protocol=pickle.HIGHEST_PROTOCOL)
vad_plays = raw_data.loc[raw_data['userId'].isin(vd_users)]
vad_plays = vad_plays.loc[vad_plays['movieId'].isin(unique_sid)]
vad_plays_tr, vad_plays_te = split_train_test_proportion(vad_plays)
test_plays = raw_data.loc[raw_data['userId'].isin(te_users)]
test_plays = test_plays.loc[test_plays['movieId'].isin(unique_sid)]
test_plays_tr, test_plays_te = split_train_test_proportion(test_plays)
train_data = numerize(train_plays, profile2id, show2id)
train_data.to_csv(os.path.join(pro_dir, 'train.csv'), index=False)
vad_data_tr = numerize(vad_plays_tr, profile2id, show2id)
vad_data_tr.to_csv(os.path.join(pro_dir, 'validation_tr.csv'), index=False)
vad_data_te = numerize(vad_plays_te, profile2id, show2id)
vad_data_te.to_csv(os.path.join(pro_dir, 'validation_te.csv'), index=False)
test_data_tr = numerize(test_plays_tr, profile2id, show2id)
test_data_tr.to_csv(os.path.join(pro_dir, 'test_tr.csv'), index=False)
test_data_te = numerize(test_plays_te, profile2id, show2id)
test_data_te.to_csv(os.path.join(pro_dir, 'test_te.csv'), index=False)
print("Done!")