def test_student(args, student_train_loader, student_labels, student_test_loader, test_size, cat_emb_size, num_conts, device, sensitive_idx): student_model = RegressionModel(emb_szs=cat_emb_size, n_cont=num_conts, emb_drop=0.04, out_sz=1, szs=[1000, 500, 250], drops=[0.001, 0.01, 0.01], y_range=(0, 1)).to(device) criterion = nn.BCELoss() optimizer = optim.SGD(student_model.parameters(), lr=args.lr, momentum=0) steps = 0 running_loss = 0 correct = 0 print("========== Testing Student Model ==========") for epoch in range(args.epochs): student_model.train() train_loader = student_loader(student_train_loader, student_labels) for (cats, conts) , labels in train_loader: #for _batch_idx, (data, target) in enumerate(tqdm(train_loader)): #cats = data[0] #conts = data[1] steps += 1 optimizer.zero_grad() output = student_model(cats, conts).view(-1) labels = labels.to(torch.float32) loss = criterion(output, labels) loss.backward() optimizer.step() running_loss += loss.item() # if steps % 50 == 0: student_model.eval() test_loss = 0 correct = 0 i = 0 avg_recall = 0 avg_precision = 0 overall_results = [] avg_eq_odds = 0 avg_dem_par = 0 avg_tpr = 0 avg_tp = 0 avg_tn = 0 avg_fp = 0 avg_fn = 0 with torch.no_grad(): for batch_idx, (cats, conts, target) in enumerate(student_test_loader): print("target\n", sum(target)) i+=1 output = student_model(cats, conts) loss += criterion(output, target).item() test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss)) pred = (output > 0.5).float() print("pred\n", sum(pred)) correct += pred.eq(target.view_as(pred)).sum().item() curr_datetime = datetime.now() curr_hour = curr_datetime.hour curr_min = curr_datetime.minute pred_df = pd.DataFrame(pred.numpy()) pred_df.to_csv(f"pred_results/{args.run_name}_{curr_hour}-{curr_min}.csv") #print(pred, np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())) #correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy()) #total += cats.size(0) # confusion matrixç tn, fp, fn, tp = confusion_matrix(target, pred).ravel() avg_tn += tn avg_fp += fp avg_fn += fn avg_tp += tp # position of col for sensitive values sensitive = [i[sensitive_idx].item() for i in cats] cat_len = max(sensitive) #exit() sub_cm = [] # print(cat_len) for j in range(cat_len+1): try: idx = list(locate(sensitive, lambda x: x == j)) sub_tar = target[idx] sub_pred = pred[idx] sub_tn, sub_fp, sub_fn, sub_tp = confusion_matrix(sub_tar, sub_pred).ravel() except: # when only one value to predict print("----WHAT?") temp_tar = int(sub_tar.numpy()[0]) temp_pred = int(sub_pred.numpy()[0]) # print(tar, pred) if temp_tar and temp_pred: sub_tn, sub_fp, sub_fn, sub_tp = 0, 0, 0, 1 elif temp_tar and not temp_pred: sub_tn, sub_fp, sub_fn, sub_tp = 0, 0, 1, 0 elif not temp_tar and not temp_pred: sub_tn, sub_fp, sub_fn, sub_tp = 1, 0, 0, 0 elif not temp_tar and temp_pred: sub_tn, sub_fp, sub_fn, sub_tp = 0, 1, 0, 0 else: sub_tn, sub_fp, sub_fn, sub_tp = 0, 0, 0, 0 total = mysum(sub_tn, sub_fp, sub_fn, sub_tp) print("??", total) sub_cm.append((sub_tn / total, sub_fp / total, sub_fn / total, sub_tp / total)) # Fairness metrics group_metrics = MetricFrame({'precision': skm.precision_score, 'recall': skm.recall_score}, target, pred, sensitive_features=sensitive) demographic_parity = flm.demographic_parity_difference(target, pred, sensitive_features=sensitive) eq_odds = flm.equalized_odds_difference(target, pred, sensitive_features=sensitive) # metric_fns = {'true_positive_rate': true_positive_rate} tpr = MetricFrame(true_positive_rate, target, pred, sensitive_features=sensitive) # tpr = flm.true_positive_rate(target, pred,sample_weight=sensitive) sub_results = group_metrics.overall.to_dict() sub_results_by_group = group_metrics.by_group.to_dict() # print("\n", group_metrics.by_group, "\n") avg_precision += sub_results['precision'] avg_recall += sub_results['recall'] print("pre_rec", sub_results) overall_results.append(sub_results_by_group) avg_eq_odds += eq_odds print("eqo", eq_odds) avg_dem_par += demographic_parity print("dempar", demographic_parity) avg_tpr += tpr.difference(method='between_groups') print("tpr", tpr.difference(method='between_groups')) total = mysum(avg_tn, avg_fp, avg_fn, avg_tp) print("!!", total) cm = (avg_tn / total, avg_fp / total, avg_fn / total, avg_tp / total) test_loss /= test_size accuracy = correct / test_size avg_loss = test_loss return accuracy, avg_loss, avg_precision, avg_recall, avg_eq_odds, avg_tpr, avg_dem_par, cm, sub_cm, overall_results