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) 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' 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) 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) 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['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) 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) 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]) # 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]] == 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 = 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_official_trainvaltest_split( dataset, testing=False, rating_map=None, post_rating_map=None, ratio=1.0, is_cmf=False, is_debug=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 = "raw_data/" + fname download_dataset(fname, files, data_dir) dtypes = { "u_nodes": np.int32, "v_nodes": np.int32, "ratings": np.float32, "timestamp": np.float64, } filename_train = "raw_data/" + dataset + "/u1.base" filename_test = "raw_data/" + dataset + "/u1.test" 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, ) 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) if ratio < 1.0: data_array_train = data_array_train[ data_array_train[:, -1].argsort()[: int(ratio * len(data_array_train))] ] 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["ratings"]) if rating_map is not None: for i, x in enumerate(ratings): ratings[i] = rating_map[x] 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.int32) 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]) # 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]] == 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]) class_values = np.sort(np.unique(ratings)) # make training adjacency matrix rating_mx_train = np.zeros(num_users * num_items, dtype=np.float32) if post_rating_map is None: rating_mx_train[train_idx] = labels[train_idx].astype(np.float32) + 1.0 else: rating_mx_train[train_idx] = ( np.array([post_rating_map[r] for r in class_values[labels[train_idx]]]) + 1.0 ) rating_mx_train = sp.csr_matrix(rating_mx_train.reshape(num_users, num_items)) if dataset == "ml_100k": # movie features (genres) sep = r"|" movie_file = "raw_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[5:] 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 = "raw_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": 0.0, "F": 1.0} 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"]]] = 1.0 elif dataset == "ml_1m": # load movie features movies_file = "raw_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]] = 1.0 # load user features users_file = "raw_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]]] = 1.0 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, )