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import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm
import numpy as np
import logging
import time
import argparse
import os
import json
from models.EdgeBank import edge_bank_link_prediction
from utils.metrics import get_link_prediction_metrics, get_node_classification_metrics
from utils.utils import set_random_seed
from utils.utils import NegativeEdgeSampler, NeighborSampler
from utils.DataLoader import Data
def evaluate_model_link_prediction(model_name: str, model: nn.Module, final_trained_positional_encoding: torch.Tensor,
neighbor_sampler: NeighborSampler, evaluate_idx_data_loader: DataLoader,
evaluate_neg_edge_sampler: NegativeEdgeSampler, evaluate_data: Data, loss_func: nn.Module, num_fft_batches: int = 100,
num_neighbors: int = 20, time_gap: int = 2000, ablation = 'none', testing = False):
# Ensures the random sampler uses a fixed seed for evaluation (i.e. we always sample the same negatives for validation / test set)
assert evaluate_neg_edge_sampler.seed is not None
evaluate_neg_edge_sampler.reset_random_state()
model[0].set_neighbor_sampler(neighbor_sampler)
model.eval()
with torch.no_grad():
# store evaluate losses and metrics
evaluate_losses, evaluate_metrics = [], []
evaluate_idx_data_loader_tqdm = tqdm(evaluate_idx_data_loader, ncols=120)
positional_encoding = final_trained_positional_encoding
for batch_idx, evaluate_data_indices in enumerate(evaluate_idx_data_loader_tqdm):
evaluate_data_indices = evaluate_data_indices.numpy()
batch_src_node_ids, batch_dst_node_ids, batch_node_interact_times, batch_edge_ids = \
evaluate_data.src_node_ids[evaluate_data_indices], evaluate_data.dst_node_ids[evaluate_data_indices], \
evaluate_data.node_interact_times[evaluate_data_indices], evaluate_data.edge_ids[evaluate_data_indices]
if evaluate_neg_edge_sampler.negative_sample_strategy != 'random':
batch_neg_src_node_ids, batch_neg_dst_node_ids = evaluate_neg_edge_sampler.sample(size=len(batch_src_node_ids),
batch_src_node_ids=batch_src_node_ids,
batch_dst_node_ids=batch_dst_node_ids,
current_batch_start_time=batch_node_interact_times[0],
current_batch_end_time=batch_node_interact_times[-1])
else:
_, batch_neg_dst_node_ids = evaluate_neg_edge_sampler.sample(size=len(batch_src_node_ids))
batch_neg_src_node_ids = batch_src_node_ids
batch_node_ids = batch_src_node_ids.tolist() + batch_dst_node_ids.tolist()
batch_node_ids = torch.from_numpy(np.array(batch_node_ids)).unique().numpy()
if positional_encoding.shape[1] > num_fft_batches:
positional_encoding = torch.clone(positional_encoding[:,-num_fft_batches:, :])
# (batch_size, pe_dim)
fft_current_positional_encoding = model[0].fourier_transform_pe(batch_node_ids, positional_encoding, batch_idx)
current_positional_encoding = torch.clone(positional_encoding[:, -1, :])
current_positional_encoding[torch.from_numpy(batch_node_ids)] = fft_current_positional_encoding
if ablation == 'no_pe':
pos_src = model[0].aggregated_node_embeddings(node_ids = batch_src_node_ids,
node_interact_times = batch_node_interact_times,
num_neighbors = num_neighbors,
time_gap = time_gap)
pos_dst = model[0].aggregated_node_embeddings(node_ids = batch_dst_node_ids,
node_interact_times = batch_node_interact_times,
num_neighbors = num_neighbors,
time_gap = time_gap)
neg_src = model[0].aggregated_node_embeddings(node_ids = batch_neg_src_node_ids,
node_interact_times = batch_node_interact_times,
num_neighbors = num_neighbors,
time_gap = time_gap)
neg_dst = model[0].aggregated_node_embeddings(node_ids = batch_neg_dst_node_ids,
node_interact_times = batch_node_interact_times,
num_neighbors = num_neighbors,
time_gap = time_gap)
else:
pos_src = model[0].combining_pe_raw_feat(pe = current_positional_encoding,
node_ids = batch_src_node_ids,
node_interact_times = batch_node_interact_times,
num_neighbors = num_neighbors,
time_gap = time_gap,)
pos_dst = model[0].combining_pe_raw_feat(pe = current_positional_encoding,
node_ids = batch_dst_node_ids,
node_interact_times = batch_node_interact_times,
num_neighbors = num_neighbors,
time_gap = time_gap)
neg_src = model[0].combining_pe_raw_feat(pe = current_positional_encoding,
node_ids = batch_neg_src_node_ids,
node_interact_times = batch_node_interact_times,
num_neighbors = num_neighbors,
time_gap = time_gap)
neg_dst = model[0].combining_pe_raw_feat(pe = current_positional_encoding,
node_ids = batch_neg_dst_node_ids,
node_interact_times = batch_node_interact_times,
num_neighbors = num_neighbors,
time_gap = time_gap)
positive_probabilities = model[1](input_1=pos_src, input_2=pos_dst).squeeze(dim=-1).sigmoid().clamp(0, 1)
negative_probabilities = model[1](input_1=neg_src, input_2=neg_dst).squeeze(dim=-1).sigmoid().clamp(0, 1)
predicts = torch.cat([positive_probabilities, negative_probabilities], dim=0)
labels = torch.cat([torch.ones_like(positive_probabilities), torch.zeros_like(negative_probabilities)], dim=0)
batch_node_ids = batch_src_node_ids.tolist() + batch_dst_node_ids.tolist()
batch_node_ids = torch.from_numpy(np.array(batch_node_ids)).unique().numpy()
new_node_pe = model[0].update_pe(
pe = current_positional_encoding,
node_ids = batch_node_ids,
edge_ids = batch_edge_ids,
batch_src_node_ids = batch_src_node_ids,
batch_dst_node_ids = batch_dst_node_ids,
node_interact_times = batch_node_interact_times,
current_time = batch_node_interact_times.max(),
num_neighbors = num_neighbors,
time_gap = time_gap)
current_positional_encoding = new_node_pe
# current_positional_encoding[torch.from_numpy(batch_node_ids)] = new_node_pe
# spatial_node_embeddings[torch.from_numpy(batch_node_ids)] = new_spatial_node_embeddings
positional_encoding = torch.cat([positional_encoding, current_positional_encoding.unsqueeze(1)], dim = 1)
loss = loss_func(input=predicts, target=labels)
evaluate_losses.append(loss.item())
evaluate_metrics.append(get_link_prediction_metrics(predicts=predicts, labels=labels))
evaluate_idx_data_loader_tqdm.set_description(f'evaluate for the {batch_idx + 1}-th batch, evaluate loss: {loss.item()}')
return evaluate_losses, evaluate_metrics