Source code for pygod.models.done

# -*- coding: utf-8 -*-
"""Deep Outlier Aware Attributed Network Embedding (DONE)"""
# Author: Kay Liu <zliu234@uic.edu>
# License: BSD 2 clause

import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import to_dense_adj
from torch_geometric.loader import NeighborLoader
from sklearn.utils.validation import check_is_fitted

from . import BaseDetector
from .basic_nn import MLP
from ..utils import validate_device
from ..metrics import eval_roc_auc


[docs]class DONE(BaseDetector): """ DONE (Deep Outlier Aware Attributed Network Embedding) consists of an attribute autoencoder and a structure autoencoder. It estimates five losses to optimize the model, including an attribute proximity loss, an attribute homophily loss, a structure proximity loss, a structure homophily loss, and a combination loss. It calculates three outlier scores, and averages them as an overall scores. See :cite:`bandyopadhyay2020outlier` for details. Parameters ---------- hid_dim : int, optional Hidden dimension for both attribute autoencoder and structure autoencoder. Default: ``0``. num_layers : int, optional Total number of layers in model. A half (ceil) of the layers are for the encoder, the other half (floor) of the layers are for decoders. Default: ``4``. dropout : float, optional Dropout rate. Default: ``0.``. weight_decay : float, optional Weight decay (L2 penalty). Default: ``0.``. act : callable activation function or None, optional Activation function if not None. Default: ``torch.nn.functional.relu``. a1 : float, optional Loss balance weight for structure proximity. Default: ``0.2``. a2 : float, optional Loss balance weight for structure homophily. Default: ``0.2``. a3 : float, optional Loss balance weight for attribute proximity. Default: ``0.2``. a4 : float, optional Loss balance weight for attribute proximity. Default: ``0.2``. a5 : float, optional Loss balance weight for combination. Default: ``0.2``. contamination : float, optional Valid in (0., 0.5). The proportion of outliers in the data set. Used when fitting to define the threshold on the decision function. Default: ``0.1``. lr : float, optional Learning rate. Default: ``0.004``. epoch : int, optional Maximum number of training epoch. Default: ``5``. gpu : int GPU Index, -1 for using CPU. Default: ``0``. batch_size : int, optional Minibatch size, 0 for full batch training. Default: ``0``. num_neigh : int, optional Number of neighbors in sampling, -1 for all neighbors. Default: ``-1``. verbose : bool Verbosity mode. Turn on to print out log information. Default: ``False``. Examples -------- >>> from pygod.models import DONE >>> model = DONE() >>> model.fit(data) >>> prediction = model.predict(data) """ def __init__(self, hid_dim=32, num_layers=4, dropout=0., weight_decay=0., act=F.leaky_relu, a1=0.2, a2=0.2, a3=0.2, a4=0.2, a5=0.2, contamination=0.1, lr=5e-3, epoch=5, gpu=0, batch_size=0, num_neigh=-1, verbose=False): super(DONE, self).__init__(contamination=contamination) # model param self.hid_dim = hid_dim self.num_layers = num_layers self.dropout = dropout self.weight_decay = weight_decay self.act = act self.a1 = a1 self.a2 = a2 self.a3 = a3 self.a4 = a4 self.a5 = a5 # training param self.lr = lr self.epoch = epoch self.device = validate_device(gpu) self.batch_size = batch_size self.num_neigh = num_neigh # other param self.verbose = verbose self.model = None
[docs] def fit(self, G, y_true=None): """ Fit detector with input data. Parameters ---------- G : torch_geometric.data.Data The input data. y_true : numpy.ndarray, optional The optional outlier ground truth labels used to monitor the training progress. They are not used to optimize the unsupervised model. Default: ``None``. Returns ------- self : object Fitted estimator. """ G.node_idx = torch.arange(G.x.shape[0]) G.s = to_dense_adj(G.edge_index)[0] if self.batch_size == 0: self.batch_size = G.x.shape[0] loader = NeighborLoader(G, [self.num_neigh], batch_size=self.batch_size) self.model = DONE_Base(x_dim=G.x.shape[1], s_dim=G.s.shape[1], hid_dim=self.hid_dim, num_layers=self.num_layers, dropout=self.dropout, act=self.act).to(self.device) optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr, weight_decay=self.weight_decay) self.model.train() decision_scores = np.zeros(G.x.shape[0]) for epoch in range(self.epoch): epoch_loss = 0 for sampled_data in loader: batch_size = sampled_data.batch_size node_idx = sampled_data.node_idx x, s, edge_index = self.process_graph(sampled_data) x_, s_, h_a, h_s, dna, dns = self.model(x, s, edge_index) score, loss = self.loss_func(x[:batch_size], x_[:batch_size], s[:batch_size], s_[:batch_size], h_a[:batch_size], h_s[:batch_size], dna[:batch_size], dns[:batch_size]) epoch_loss += loss.item() * batch_size decision_scores[node_idx[:batch_size]] = score.detach() \ .cpu().numpy() optimizer.zero_grad() loss.backward() optimizer.step() if self.verbose: print("Epoch {:04d}: Loss {:.4f}" .format(epoch, epoch_loss / G.x.shape[0]), end='') if y_true is not None: auc = eval_roc_auc(y_true, decision_scores) print(" | AUC {:.4f}".format(auc), end='') print() self.decision_scores_ = decision_scores self._process_decision_scores() return self
[docs] def decision_function(self, G): """ Predict raw anomaly score using the fitted detector. Outliers are assigned with larger anomaly scores. Parameters ---------- G : PyTorch Geometric Data instance (torch_geometric.data.Data) The input data. Returns ------- outlier_scores : numpy.ndarray The anomaly score of shape :math:`N`. """ check_is_fitted(self, ['model']) G.node_idx = torch.arange(G.x.shape[0]) G.s = to_dense_adj(G.edge_index)[0] if self.batch_size == 0: self.batch_size = G.x.shape[0] loader = NeighborLoader(G, [self.num_neigh], batch_size=self.batch_size) self.model.eval() outlier_scores = np.zeros(G.x.shape[0]) for sampled_data in loader: batch_size = sampled_data.batch_size node_idx = sampled_data.node_idx x, s, edge_index = self.process_graph(G) x_, s_, h_a, h_s, dna, dns = self.model(x, s, edge_index) score, _ = self.loss_func(x[:batch_size], x_[:batch_size], s[:batch_size], s_[:batch_size], h_a[:batch_size], h_s[:batch_size], dna[:batch_size], dns[:batch_size]) outlier_scores[node_idx[:batch_size]] = score.detach() \ .cpu().numpy() return outlier_scores
def process_graph(self, G): """ Process the raw PyG data object into a tuple of sub data objects needed for the model. Parameters ---------- G : PyTorch Geometric Data instance (torch_geometric.data.Data) The input data. Returns ------- x : torch.Tensor Attribute (feature) of nodes. s : torch.Tensor Adjacency matrix of the graph. edge_index : torch.Tensor Edge list of the graph. """ s = G.s.to(self.device) edge_index = G.edge_index.to(self.device) x = G.x.to(self.device) return x, s, edge_index def loss_func(self, x, x_, s, s_, h_a, h_s, dna, dns): # equation 9 is based on the official implementation, and it # is slightly different from the paper dx = torch.sum(torch.pow(x - x_, 2), 1) tmp = self.a3 * dx + self.a4 * dna oa = tmp / torch.sum(tmp) # equation 8 is based on the official implementation, and it # is slightly different from the paper ds = torch.sum(torch.pow(s - s_, 2), 1) tmp = self.a1 * ds + self.a2 * dns os = tmp / torch.sum(tmp) # equation 10 dc = torch.sum(torch.pow(h_a - h_s, 2), 1) oc = dc / torch.sum(dc) # equation 4 loss_prox_a = torch.mean(torch.log(torch.pow(oa, -1)) * dx) # equation 5 loss_hom_a = torch.mean(torch.log(torch.pow(oa, -1)) * dna) # equation 2 loss_prox_s = torch.mean(torch.log(torch.pow(os, -1)) * ds) # equation 3 loss_hom_s = torch.mean(torch.log(torch.pow(os, -1)) * dns) # equation 6 loss_c = torch.mean(torch.log(torch.pow(oc, -1)) * dc) # equation 7 loss = self.a3 * loss_prox_a + \ self.a4 * loss_hom_a + \ self.a1 * loss_prox_s + \ self.a2 * loss_hom_s + \ self.a5 * loss_c score = (oa + os + oc) / 3 return score, loss
class DONE_Base(nn.Module): def __init__(self, x_dim, s_dim, hid_dim, num_layers, dropout, act): super(DONE_Base, self).__init__() # split the number of layers for the encoder and decoders decoder_layers = int(num_layers / 2) encoder_layers = num_layers - decoder_layers self.attr_encoder = MLP(in_channels=x_dim, hidden_channels=hid_dim, out_channels=hid_dim, num_layers=encoder_layers, dropout=dropout, act=act) self.attr_decoder = MLP(in_channels=hid_dim, hidden_channels=hid_dim, out_channels=x_dim, num_layers=decoder_layers, dropout=dropout, act=act) self.struct_encoder = MLP(in_channels=s_dim, hidden_channels=hid_dim, out_channels=hid_dim, num_layers=encoder_layers, dropout=dropout, act=act) self.struct_decoder = MLP(in_channels=hid_dim, hidden_channels=hid_dim, out_channels=s_dim, num_layers=decoder_layers, dropout=dropout, act=act) self.neigh_diff = NeighDiff() def forward(self, x, s, edge_index): h_a = self.attr_encoder(x) x_ = self.attr_decoder(h_a) dna = self.neigh_diff(h_a, edge_index).squeeze() h_s = self.struct_encoder(s) s_ = self.struct_decoder(h_s) dns = self.neigh_diff(h_s, edge_index).squeeze() return x_, s_, h_a, h_s, dna, dns class NeighDiff(MessagePassing): def __init__(self): super().__init__(aggr='mean') def forward(self, h, edge_index): return self.propagate(edge_index, h=h) def message(self, h_i, h_j, edge_index): return torch.sum(torch.pow(h_i - h_j, 2), dim=1, keepdim=True)