def load_official_trainvaltest_split(dataset, testing=False):
"""
Loads official train/test split and uses 10% of training samples for validaiton
For each split computes 1-of-num_classes labels. Also computes training
adjacency matrix. Assumes flattening happens everywhere in row-major fashion.
"""
sep = '\t'
# Check if files exist and download otherwise
files = ['/u1.base', '/u1.test', '/u.item', '/u.user']
fname = dataset
data_dir = 'data/' + fname
download_dataset(fname, files, data_dir) #liy : 自己下数据，啥也不干
dtypes = {
'u_nodes': np.int32,
'v_nodes': np.int32,
'ratings': np.float32,
'timestamp': np.float64
}
filename_train = 'data/' + dataset + '/u1.base'
filename_test = 'data/' + dataset + '/u1.test'
#liy 他这里读取列的时候指定了列的类型dtype
data_train = pd.read_csv(
filename_train,
sep=sep,
header=None,
names=['u_nodes', 'v_nodes', 'ratings', 'timestamp'],
dtype=dtypes)
data_test = pd.read_csv(
filename_test,
sep=sep,
header=None,
names=['u_nodes', 'v_nodes', 'ratings', 'timestamp'],
dtype=dtypes)
# .values 不可以吗
data_array_train = data_train.as_matrix().tolist()
data_array_train = np.array(data_array_train)
data_array_test = data_test.as_matrix().tolist()
data_array_test = np.array(data_array_test)
data_array = np.concatenate([data_array_train, data_array_test], axis=0)
#liy 这里还用astype吗...
u_nodes_ratings = data_array[:, 0].astype(dtypes['u_nodes']) #user id
v_nodes_ratings = data_array[:, 1].astype(dtypes['v_nodes']) #item id
ratings = data_array[:, 2].astype(dtypes['ratings'])
u_nodes_ratings, u_dict, num_users = map_data(u_nodes_ratings)
v_nodes_ratings, v_dict, num_items = map_data(v_nodes_ratings)
#liy 为啥一个用int32 一个用int64呢？
u_nodes_ratings, v_nodes_ratings = u_nodes_ratings.astype(
np.int64), v_nodes_ratings.astype(np.int32)
ratings = ratings.astype(np.float64)
u_nodes = u_nodes_ratings
v_nodes = v_nodes_ratings
neutral_rating = -1 # int(np.ceil(np.float(num_classes)/2.)) - 1
# assumes that ratings_train contains at least one example of every rating type
rating_dict = {
r: i
for i, r in enumerate(np.sort(np.unique(ratings)).tolist())
}
labels = np.full((num_users, num_items), neutral_rating, dtype=np.int32)
#将rating离散化
labels[u_nodes, v_nodes] = np.array([rating_dict[r] for r in ratings])
for i in range(len(u_nodes)):
assert (labels[u_nodes[i], v_nodes[i]] == rating_dict[ratings[i]])
labels = labels.reshape([-1]) #liy - 拍扁？
# number of test and validation edges, see cf-nade code
num_train = data_array_train.shape[0]
num_test = data_array_test.shape[0]
num_val = int(np.ceil(num_train * 0.2))
num_train = num_train - num_val
pairs_nonzero = np.array([[u, v] for u, v in zip(u_nodes, v_nodes)])
#liy - nonzero 元素在训练矩阵中的idx(矩阵拍扁状态)
idx_nonzero = np.array([u * num_items + v for u, v in pairs_nonzero])
for i in range(len(ratings)):
assert (labels[idx_nonzero[i]] == rating_dict[ratings[i]])
idx_nonzero_train = idx_nonzero[0:num_train + num_val]
idx_nonzero_test = idx_nonzero[num_train + num_val:]
pairs_nonzero_train = pairs_nonzero[0:num_train + num_val]
pairs_nonzero_test = pairs_nonzero[num_train + num_val:]
# Internally shuffle training set (before splitting off validation set)
rand_idx = list(range(len(idx_nonzero_train)))
np.random.seed(42)
np.random.shuffle(rand_idx)
idx_nonzero_train = idx_nonzero_train[rand_idx]
pairs_nonzero_train = pairs_nonzero_train[rand_idx]
idx_nonzero = np.concatenate([idx_nonzero_train, idx_nonzero_test], axis=0)
pairs_nonzero = np.concatenate([pairs_nonzero_train, pairs_nonzero_test],
axis=0)
val_idx = idx_nonzero[0:num_val]
train_idx = idx_nonzero[num_val:num_train + num_val]
test_idx = idx_nonzero[num_train + num_val:]
assert (len(test_idx) == num_test)
val_pairs_idx = pairs_nonzero[0:num_val]
train_pairs_idx = pairs_nonzero[num_val:num_train + num_val]
test_pairs_idx = pairs_nonzero[num_train + num_val:]
u_test_idx, v_test_idx = test_pairs_idx.transpose()
u_val_idx, v_val_idx = val_pairs_idx.transpose()
u_train_idx, v_train_idx = train_pairs_idx.transpose()
# create labels
train_labels = labels[train_idx]
val_labels = labels[val_idx]
test_labels = labels[test_idx]
if testing:
u_train_idx = np.hstack([u_train_idx, u_val_idx])
v_train_idx = np.hstack([v_train_idx, v_val_idx])
train_labels = np.hstack([train_labels, val_labels])
# for adjacency matrix construction
train_idx = np.hstack([train_idx, val_idx])
# make training adjacency matrix
rating_mx_train = np.zeros(num_users * num_items, dtype=np.float32)
rating_mx_train[train_idx] = labels[train_idx].astype(np.float32) + 1.
rating_mx_train = sp.csr_matrix(
rating_mx_train.reshape(num_users, num_items))
class_values = np.sort(np.unique(ratings))
if dataset == 'ml_100k':
# movie features (genres)
sep = r'|'
movie_file = 'data/' + dataset + '/u.item'
movie_headers = [
'movie id', 'movie title', 'release date', 'video release date',
'IMDb URL', 'unknown', 'Action', 'Adventure', 'Animation',
'Childrens', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy',
'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi',
'Thriller', 'War', 'Western'
]
movie_df = pd.read_csv(movie_file,
sep=sep,
header=None,
names=movie_headers,
engine='python')
genre_headers = movie_df.columns.values[6:]
num_genres = genre_headers.shape[0]
v_features = np.zeros((num_items, num_genres), dtype=np.float32)
for movie_id, g_vec in zip(movie_df['movie id'].values.tolist(),
movie_df[genre_headers].values.tolist()):
# check if movie_id was listed in ratings file and therefore in mapping dictionary
if movie_id in v_dict.keys():
v_features[v_dict[movie_id], :] = g_vec
# user features
sep = r'|'
users_file = 'data/' + dataset + '/u.user'
users_headers = ['user id', 'age', 'gender', 'occupation', 'zip code']
users_df = pd.read_csv(users_file,
sep=sep,
header=None,
names=users_headers,
engine='python')
occupation = set(users_df['occupation'].values.tolist())
age = users_df['age'].values
age_max = age.max()
gender_dict = {'M': 0., 'F': 1.}
occupation_dict = {f: i for i, f in enumerate(occupation, start=2)}
num_feats = 2 + len(occupation_dict)
u_features = np.zeros((num_users, num_feats), dtype=np.float32)
for _, row in users_df.iterrows():
u_id = row['user id']
if u_id in u_dict.keys():
# age
u_features[u_dict[u_id], 0] = row['age'] / np.float(age_max)
# gender
u_features[u_dict[u_id], 1] = gender_dict[row['gender']]
# occupation
u_features[u_dict[u_id],
occupation_dict[row['occupation']]] = 1.
elif dataset == 'ml_1m':
# load movie features
movies_file = 'data/' + dataset + '/movies.dat'
movies_headers = ['movie_id', 'title', 'genre']
movies_df = pd.read_csv(movies_file,
sep=sep,
header=None,
names=movies_headers,
engine='python')
# extracting all genres
genres = []
for s in movies_df['genre'].values:
genres.extend(s.split('|'))
genres = list(set(genres))
num_genres = len(genres)
genres_dict = {g: idx for idx, g in enumerate(genres)}
# creating 0 or 1 valued features for all genres
v_features = np.zeros((num_items, num_genres), dtype=np.float32)
for movie_id, s in zip(movies_df['movie_id'].values.tolist(),
movies_df['genre'].values.tolist()):
# check if movie_id was listed in ratings file and therefore in mapping dictionary
if movie_id in v_dict.keys():
gen = s.split('|')
for g in gen:
v_features[v_dict[movie_id], genres_dict[g]] = 1.
# load user features
users_file = 'data/' + dataset + '/users.dat'
users_headers = ['user_id', 'gender', 'age', 'occupation', 'zip-code']
users_df = pd.read_csv(users_file,
sep=sep,
header=None,
names=users_headers,
engine='python')
# extracting all features
cols = users_df.columns.values[1:]
cntr = 0
feat_dicts = []
for header in cols:
d = dict()
feats = np.unique(users_df[header].values).tolist()
d.update({f: i for i, f in enumerate(feats, start=cntr)})
feat_dicts.append(d)
cntr += len(d)
num_feats = sum(len(d) for d in feat_dicts)
u_features = np.zeros((num_users, num_feats), dtype=np.float32)
for _, row in users_df.iterrows():
u_id = row['user_id']
if u_id in u_dict.keys():
for k, header in enumerate(cols):
u_features[u_dict[u_id], feat_dicts[k][row[header]]] = 1.
else:
raise ValueError('Invalid dataset option %s' % dataset)
u_features = sp.csr_matrix(u_features)
v_features = sp.csr_matrix(v_features)
print("User features shape: " + str(u_features.shape))
print("Item features shape: " + str(v_features.shape))
return u_features, v_features, rating_mx_train, train_labels, u_train_idx, v_train_idx, \
val_labels, u_val_idx, v_val_idx, test_labels, u_test_idx, v_test_idx, class_values
def load_wsdream_official_trainvaltest_split(dataset, testing=False):
"""
Loads official train/test split and uses 10% of training samples for validaiton
For each split computes 1-of-num_classes labels. Also computes training
adjacency matrix. Assumes flattening happens everywhere in row-major fashion.
"""
sep = '\t'
# Check if files exist and download otherwise
files = ['/train.csv', '/test.csv', '/userlist.csv', '/wslist.csv']
fname = dataset
data_dir = 'data/' + fname
dtypes = {
'u_nodes': np.int32, 'v_nodes': np.int32,
'rt': np.float32}
filename_train = 'data/' + dataset + '/train.csv'
filename_test = 'data/' + dataset + '/test.csv'
data_train = pd.read_csv(
filename_train, sep=sep, header=None,
names=['u_nodes', 'v_nodes', 'rt'], dtype=dtypes)
data_test = pd.read_csv(
filename_test, sep=sep, header=None,
names=['u_nodes', 'v_nodes', 'rt'], dtype=dtypes)
data_array_train = data_train.values.tolist()
data_array_train = np.array(data_array_train)
data_array_test = data_test.values.tolist()
data_array_test = np.array(data_array_test)
data_array = np.concatenate([data_array_train, data_array_test], axis=0)
u_nodes_ratings = data_array[:, 0].astype(dtypes['u_nodes'])
v_nodes_ratings = data_array[:, 1].astype(dtypes['v_nodes'])
ratings = data_array[:, 2].astype(dtypes['rt'])
u_nodes_ratings, u_dict, num_users = map_data(u_nodes_ratings)
v_nodes_ratings, v_dict, num_items = map_data(v_nodes_ratings)
u_nodes_ratings, v_nodes_ratings = u_nodes_ratings.astype(np.int64), v_nodes_ratings.astype(np.int32)
ratings = ratings.astype(np.float64)
u_nodes = u_nodes_ratings
v_nodes = v_nodes_ratings
neutral_rating = -1 # int(np.ceil(np.float(num_classes)/2.)) - 1
# assumes that ratings_train contains at least one example of every rating type
rating_dict = {r: i for i, r in enumerate(np.sort(np.unique(ratings)).tolist())}
labels = np.full((num_users, num_items), neutral_rating, dtype=np.float32)
labels[u_nodes, v_nodes] = np.array([r for r in ratings])
for i in range(len(u_nodes)):
assert(labels[u_nodes[i], v_nodes[i]] == ratings[i])
labels = labels.reshape([-1])
# number of test and validation edges, see cf-nade code
num_train = data_array_train.shape[0]
num_test = data_array_test.shape[0]
num_val = int(np.ceil(num_train * 0.2))
num_train = num_train - num_val
pairs_nonzero = np.array([[u, v] for u, v in zip(u_nodes, v_nodes)])
idx_nonzero = np.array([u * num_items + v for u, v in pairs_nonzero])
for i in range(len(ratings)):
assert(labels[idx_nonzero[i]] == ratings[i])
idx_nonzero_train = idx_nonzero[0:num_train+num_val]
idx_nonzero_test = idx_nonzero[num_train+num_val:]
pairs_nonzero_train = pairs_nonzero[0:num_train+num_val]
pairs_nonzero_test = pairs_nonzero[num_train+num_val:]
# Internally shuffle training set (before splitting off validation set)
rand_idx = list(range(len(idx_nonzero_train)))
np.random.seed(42)
np.random.shuffle(rand_idx)
idx_nonzero_train = idx_nonzero_train[rand_idx]
pairs_nonzero_train = pairs_nonzero_train[rand_idx]
idx_nonzero = np.concatenate([idx_nonzero_train, idx_nonzero_test], axis=0)
pairs_nonzero = np.concatenate([pairs_nonzero_train, pairs_nonzero_test], axis=0)
val_idx = idx_nonzero[0:num_val]
train_idx = idx_nonzero[num_val:num_train + num_val]
test_idx = idx_nonzero[num_train + num_val:]
assert(len(test_idx) == num_test)
val_pairs_idx = pairs_nonzero[0:num_val]
train_pairs_idx = pairs_nonzero[num_val:num_train + num_val]
test_pairs_idx = pairs_nonzero[num_train + num_val:]
u_test_idx, v_test_idx = test_pairs_idx.transpose()
u_val_idx, v_val_idx = val_pairs_idx.transpose()
u_train_idx, v_train_idx = train_pairs_idx.transpose()
# create labels
train_labels = labels[train_idx]
val_labels = labels[val_idx]
test_labels = labels[test_idx]
if testing:
u_train_idx = np.hstack([u_train_idx, u_val_idx])
v_train_idx = np.hstack([v_train_idx, v_val_idx])
train_labels = np.hstack([train_labels, val_labels])
# for adjacency matrix construction
train_idx = np.hstack([train_idx, val_idx])
# make training adjacency matrix
rating_mx_train = np.zeros(num_users * num_items, dtype=np.float32)
rating_mx_train[train_idx] = labels[train_idx].astype(np.float32) + 1.
rating_mx_train = sp.csr_matrix(rating_mx_train.reshape(num_users, num_items))
# class_values = np.sort(np.unique(ratings))
class_values = np.array([1])
u_features = []
v_features = []
u_features = sp.csr_matrix(u_features)
v_features = sp.csr_matrix(v_features)
print("User features shape: "+str(u_features.shape))
print("Item features shape: "+str(v_features.shape))
return u_features, v_features, rating_mx_train, train_labels, u_train_idx, v_train_idx, \
val_labels, u_val_idx, v_val_idx, test_labels, u_test_idx, v_test_idx, class_values
