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