Source code for pygod.models.dominant

# -*- coding: utf-8 -*-
"""Deep Anomaly Detection on Attributed Networks (DOMINANT)"""
# Author: Kay Liu <zliu234@uic.edu>
# License: BSD 2 clause

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
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 GCN
from ..utils import validate_device
from ..metrics import eval_roc_auc


[docs]class DOMINANT(BaseDetector): """ DOMINANT (Deep Anomaly Detection on Attributed Networks) is an anomaly detector consisting of a shared graph convolutional encoder, a structure reconstruction decoder, and an attribute reconstruction decoder. The reconstruction mean square error of the decoders are defined as structure anomaly score and attribute anomaly score, respectively. See :cite:`ding2019deep` for details. Parameters ---------- hid_dim : int, optional Hidden dimension of model. 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``. alpha : float, optional Loss balance weight for attribute and structure. ``None`` for balancing by standard deviation. Default: ``None``. 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 DOMINANT >>> model = DOMINANT() >>> model.fit(data) # PyG graph data object >>> prediction = model.predict(data) """ def __init__(self, hid_dim=64, num_layers=4, dropout=0.3, weight_decay=0., act=F.relu, alpha=None, contamination=0.1, lr=5e-3, epoch=5, gpu=0, batch_size=0, num_neigh=-1, verbose=False): super(DOMINANT, 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.alpha = alpha # 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] # automated balancing by std if self.alpha is None: self.alpha = torch.std(G.s).detach() / \ (torch.std(G.x).detach() + torch.std(G.s).detach()) if self.batch_size == 0: self.batch_size = G.x.shape[0] loader = NeighborLoader(G, [self.num_neigh] * self.num_layers, batch_size=self.batch_size) self.model = DOMINANT_Base(in_dim=G.x.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_ = self.model(x, edge_index) score = self.loss_func(x[:batch_size], x_[:batch_size], s[:batch_size, node_idx], s_[:batch_size]) decision_scores[node_idx[:batch_size]] = score.detach() \ .cpu().numpy() loss = torch.mean(score) epoch_loss += loss.item() * batch_size 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] loader = NeighborLoader(G, [self.num_neigh] * self.num_layers, 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(sampled_data) x_, s_ = self.model(x, edge_index) score = self.loss_func(x[:batch_size], x_[:batch_size], s[:batch_size, node_idx], s_[: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_): # attribute reconstruction loss diff_attribute = torch.pow(x - x_, 2) attribute_errors = torch.sqrt(torch.sum(diff_attribute, 1)) # structure reconstruction loss diff_structure = torch.pow(s - s_, 2) structure_errors = torch.sqrt(torch.sum(diff_structure, 1)) score = self.alpha * attribute_errors \ + (1 - self.alpha) * structure_errors return score
class DOMINANT_Base(nn.Module): def __init__(self, in_dim, hid_dim, num_layers, dropout, act): super(DOMINANT_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.shared_encoder = GCN(in_channels=in_dim, hidden_channels=hid_dim, num_layers=encoder_layers, out_channels=hid_dim, dropout=dropout, act=act) self.attr_decoder = GCN(in_channels=hid_dim, hidden_channels=hid_dim, num_layers=decoder_layers, out_channels=in_dim, dropout=dropout, act=act) self.struct_decoder = GCN(in_channels=hid_dim, hidden_channels=hid_dim, num_layers=decoder_layers - 1, out_channels=in_dim, dropout=dropout, act=act) def forward(self, x, edge_index): # encode h = self.shared_encoder(x, edge_index) # decode feature matrix x_ = self.attr_decoder(h, edge_index) # decode adjacency matrix h_ = self.struct_decoder(h, edge_index) s_ = h_ @ h_.T # return reconstructed matrices return x_, s_