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8304f29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | import numpy as np
import random
import scipy.sparse as sp
class GraphAugmentor(object):
def __init__(self):
pass
@staticmethod
def node_dropout(sp_adj, drop_rate):
"""Input: a sparse adjacency matrix and a dropout rate."""
adj_shape = sp_adj.get_shape()
row_idx, col_idx = sp_adj.nonzero()
drop_user_idx = random.sample(range(adj_shape[0]), int(adj_shape[0] * drop_rate))
drop_item_idx = random.sample(range(adj_shape[1]), int(adj_shape[1] * drop_rate))
indicator_user = np.ones(adj_shape[0], dtype=np.float32)
indicator_item = np.ones(adj_shape[1], dtype=np.float32)
indicator_user[drop_user_idx] = 0.
indicator_item[drop_item_idx] = 0.
diag_indicator_user = sp.diags(indicator_user)
diag_indicator_item = sp.diags(indicator_item)
mat = sp.csr_matrix(
(np.ones_like(row_idx, dtype=np.float32), (row_idx, col_idx)),
shape=(adj_shape[0], adj_shape[1]))
mat_prime = diag_indicator_user.dot(mat).dot(diag_indicator_item)
return mat_prime
@staticmethod
def edge_dropout(sp_adj, drop_rate):
"""Input: a sparse user-item adjacency matrix and a dropout rate."""
adj_shape = sp_adj.get_shape()
edge_count = sp_adj.count_nonzero()
row_idx, col_idx = sp_adj.nonzero()
keep_idx = random.sample(range(edge_count), int(edge_count * (1 - drop_rate)))
user_np = np.array(row_idx)[keep_idx]
item_np = np.array(col_idx)[keep_idx]
edges = np.ones_like(user_np, dtype=np.float32)
dropped_adj = sp.csr_matrix((edges, (user_np, item_np)), shape=adj_shape)
return dropped_adj
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