generic algorithms and parameters

hyper_main.py

@@ -0,0 +1,89 @@
+from hyper_tunning import load_data
+import os
+from ray.tune.schedulers import ASHAScheduler
+from ray.tune import CLIReporter
+from ray import tune
+from functools import partial
+from hyper_tunning import train_melu
+import numpy as np
+
+
+def main(num_samples, max_num_epochs=20, gpus_per_trial=2):
+    data_dir = os.path.abspath("/media/external_10TB/10TB/maheri/melu_data5")
+    load_data(data_dir)
+    config = {
+        # "l1": tune.sample_from(lambda _: 2 ** np.random.randint(2, 9)),
+        # "l2": tune.sample_from(lambda _: 2 ** np.random.randint(2, 9)),
+        # "lr": tune.loguniform(1e-4, 1e-1),
+        # "batch_size": tune.choice([2, 4, 8, 16])
+        "transformer": tune.choice(['kronoker']),
+        "meta_algo":tune.choice(['gbml']),
+        "first_order":tune.choice([False]),
+        "adapt_transform":tune.choice([True,False]),
+        # "local_lr":tune.choice([5e-6,5e-4,5e-3]),
+        # "lr":tune.choice([5e-5,5e-4]),
+        "local_lr":tune.loguniform(5e-6,5e-3),
+        "lr":tune.loguniform(5e-5,5e-3),
+        "batch_size":tune.choice([16,32,64]),
+        "inner":tune.choice([7,5,4,3,1]),
+        "test_state":tune.choice(["user_and_item_cold_state"]),
+        # "epochs":tune.choice([5,10,20,25]),
+    }
+
+    scheduler = ASHAScheduler(
+        metric="loss",
+        mode="min",
+        max_t=30,
+        grace_period=6,
+        reduction_factor=2)
+    reporter = CLIReporter(
+        # parameter_columns=["l1", "l2", "lr", "batch_size"],
+        metric_columns=["loss", "ndcg1","ndcg3", "training_iteration"])
+    result = tune.run(
+        partial(train_melu, data_dir=data_dir),
+        resources_per_trial={"cpu": 4, "gpu": gpus_per_trial},
+        config=config,
+        num_samples=num_samples,
+        scheduler=scheduler,
+        progress_reporter=reporter,
+        log_to_file=True,
+        # resume=True,
+        local_dir="./hyper_tunning_all_cold",
+        name="melu_all_cold",
+
+    )
+
+    best_trial = result.get_best_trial("loss", "min", "last")
+    print("Best trial config: {}".format(best_trial.config))
+    print("Best trial final validation loss: {}".format(
+        best_trial.last_result["loss"]))
+    print("Best trial final validation ndcg1: {}".format(
+        best_trial.last_result["ndcg1"]))
+    print("Best trial final validation ndcg3: {}".format(
+        best_trial.last_result["ndcg3"]))
+
+    #
+    print("=======================================================")
+    print(result.results_df)
+    print("=======================================================\n")
+
+    # best_trained_model = Net(best_trial.config["l1"], best_trial.config["l2"])
+    # device = "cpu"
+    # if torch.cuda.is_available():
+    #     device = "cuda:0"
+    #     if gpus_per_trial > 1:
+    #         best_trained_model = nn.DataParallel(best_trained_model)
+    # best_trained_model.to(device)
+    #
+    # best_checkpoint_dir = best_trial.checkpoint.value
+    # model_state, optimizer_state = torch.load(os.path.join(
+    #     best_checkpoint_dir, "checkpoint"))
+    # best_trained_model.load_state_dict(model_state)
+    #
+    # test_acc = test_accuracy(best_trained_model, device)
+    # print("Best trial test set accuracy: {}".format(test_acc))
+
+
+if __name__ == "__main__":
+    # You can change the number of GPUs per trial here:
+    main(num_samples=150, max_num_epochs=25, gpus_per_trial=1)

hyper_testing.py

@@ -0,0 +1,66 @@
+import random
+from torch.nn import L1Loss
+import numpy as np
+from fast_adapt import fast_adapt
+from sklearn.metrics import ndcg_score
+
+
+def hyper_test(embedding,head, total_dataset, adaptation_step):
+
+    test_set_size = len(total_dataset)
+    random.shuffle(total_dataset)
+    a, b, c, d = zip(*total_dataset)
+    losses_q = []
+    ndcgs11 = []
+    ndcgs33=[]
+
+    for iterator in range(test_set_size):
+
+        try:
+            supp_xs = a[iterator].cuda()
+            supp_ys = b[iterator].cuda()
+            query_xs = c[iterator].cuda()
+            query_ys = d[iterator].cuda()
+        except IndexError:
+            print("index error in test method")
+            continue
+
+        learner = head.clone()
+        temp_sxs = embedding(supp_xs)
+        temp_qxs = embedding(query_xs)
+
+        evaluation_error,predictions = fast_adapt(learner,
+                                      temp_sxs,
+                                      temp_qxs,
+                                      supp_ys,
+                                      query_ys,
+                                      adaptation_step,
+                                      get_predictions=True)
+
+        l1 = L1Loss(reduction='mean')
+        loss_q = l1(predictions.view(-1), query_ys)
+        losses_q.append(float(loss_q))
+        predictions = predictions.view(-1)
+        y_true = query_ys.cpu().detach().numpy()
+        y_pred = predictions.cpu().detach().numpy()
+        ndcgs11.append(float(ndcg_score([y_true], [y_pred], k=1, sample_weight=None, ignore_ties=False)))
+        ndcgs33.append(float(ndcg_score([y_true], [y_pred], k=3, sample_weight=None, ignore_ties=False)))
+
+        del supp_xs, supp_ys, query_xs, query_ys, predictions, y_true, y_pred, loss_q
+
+    # calculate metrics
+    try:
+        losses_q = np.array(losses_q).mean()
+    except:
+        losses_q = 100
+
+    try:
+        ndcg1 = np.array(ndcgs11).mean()
+        ndcg3 = np.array(ndcgs33).mean()
+    except:
+        ndcg1 = 0
+        ndcg3 = 0
+
+    return losses_q,ndcg1,ndcg3
+
+

hyper_tunning.py

@@ -0,0 +1,157 @@
+import os
+import torch
+import torch.nn as nn
+from ray import tune
+import pickle
+from options import config
+from embedding_module import EmbeddingModule
+import learn2learn as l2l
+import random
+from fast_adapt import fast_adapt
+import gc
+from learn2learn.optim.transforms import KroneckerTransform
+from hyper_testing import hyper_test
+
+# Define paths (for data)
+master_path= "/media/external_10TB/10TB/maheri/melu_data5"
+
+def load_data(data_dir=master_path,test_state='warm_state'):
+
+    training_set_size = int(len(os.listdir("{}/warm_state".format(data_dir))) / 4)
+    supp_xs_s = []
+    supp_ys_s = []
+    query_xs_s = []
+    query_ys_s = []
+    for idx in range(training_set_size):
+        supp_xs_s.append(pickle.load(open("{}/warm_state/supp_x_{}.pkl".format(data_dir, idx), "rb")))
+        supp_ys_s.append(pickle.load(open("{}/warm_state/supp_y_{}.pkl".format(data_dir, idx), "rb")))
+        query_xs_s.append(pickle.load(open("{}/warm_state/query_x_{}.pkl".format(data_dir, idx), "rb")))
+        query_ys_s.append(pickle.load(open("{}/warm_state/query_y_{}.pkl".format(data_dir, idx), "rb")))
+    total_dataset = list(zip(supp_xs_s, supp_ys_s, query_xs_s, query_ys_s))
+    del (supp_xs_s, supp_ys_s, query_xs_s, query_ys_s)
+    trainset = total_dataset
+
+    test_set_size = int(len(os.listdir("{}/{}".format(data_dir, test_state))) / 4)
+    supp_xs_s = []
+    supp_ys_s = []
+    query_xs_s = []
+    query_ys_s = []
+    for idx in range(test_set_size):
+        supp_xs_s.append(pickle.load(open("{}/{}/supp_x_{}.pkl".format(data_dir, test_state, idx), "rb")))
+        supp_ys_s.append(pickle.load(open("{}/{}/supp_y_{}.pkl".format(data_dir, test_state, idx), "rb")))
+        query_xs_s.append(pickle.load(open("{}/{}/query_x_{}.pkl".format(data_dir, test_state, idx), "rb")))
+        query_ys_s.append(pickle.load(open("{}/{}/query_y_{}.pkl".format(data_dir, test_state, idx), "rb")))
+    test_dataset = list(zip(supp_xs_s, supp_ys_s, query_xs_s, query_ys_s))
+    del (supp_xs_s, supp_ys_s, query_xs_s, query_ys_s)
+
+    random.shuffle(test_dataset)
+    val_size = int(test_set_size * 0.2)
+    validationset = test_dataset[:val_size]
+    testset = test_dataset[val_size:]
+
+    return trainset, validationset,testset
+
+
+def train_melu(conf, checkpoint_dir=None, data_dir=None):
+    embedding_dim = config['embedding_dim']
+    print("inajm1:",checkpoint_dir)
+    fc1_in_dim = config['embedding_dim'] * 8
+    fc2_in_dim = config['first_fc_hidden_dim']
+    fc2_out_dim = config['second_fc_hidden_dim']
+
+    fc1 = torch.nn.Linear(fc1_in_dim, fc2_in_dim)
+    fc2 = torch.nn.Linear(fc2_in_dim, fc2_out_dim)
+    linear_out = torch.nn.Linear(fc2_out_dim, 1)
+    head = torch.nn.Sequential(fc1, fc2, linear_out)
+
+    emb = EmbeddingModule(config).cuda()
+
+    transform = None
+    if conf['transformer'] == "kronoker":
+        transform = KroneckerTransform(l2l.nn.KroneckerLinear)
+    elif conf['transformer'] == "linear":
+        transform = l2l.optim.ModuleTransform(torch.nn.Linear)
+
+    # define meta algorithm
+    if conf['meta_algo'] == "maml":
+        head = l2l.algorithms.MAML(head, lr=conf['local_lr'], first_order=conf['first_order'])
+    elif conf['meta_algo'] == 'metasgd':
+        head = l2l.algorithms.MetaSGD(head, lr=conf['local_lr'], first_order=conf['first_order'])
+    elif conf['meta_algo'] == 'gbml':
+        head = l2l.algorithms.GBML(head, transform=transform, lr=conf['local_lr'],
+                                   adapt_transform=conf['adapt_transform'], first_order=conf['first_order'])
+    head.cuda()
+    net = nn.Sequential(emb,head)
+
+    criterion = nn.MSELoss()
+    all_parameters =  list(emb.parameters()) + list(head.parameters())
+    optimizer = torch.optim.Adam(all_parameters, lr=conf['lr'])
+
+    if checkpoint_dir:
+        model_state, optimizer_state = torch.load(
+            os.path.join(checkpoint_dir, "checkpoint"))
+        net.load_state_dict(model_state)
+        optimizer.load_state_dict(optimizer_state)
+
+    # loading data
+    train_dataset,validation_dataset,test_dataset = load_data(data_dir,test_state=conf['test_state'])
+    print(conf['test_state'])
+    batch_size = conf['batch_size']
+    num_batch = int(len(train_dataset) / batch_size)
+    a, b, c, d = zip(*train_dataset)
+
+    for epoch in range(config['num_epoch']):  # loop over the dataset multiple times
+        for i in range(num_batch):
+            optimizer.zero_grad()
+            meta_train_error = 0.0
+
+            # print("EPOCH: ", epoch, " BATCH: ", i)
+            supp_xs = list(a[batch_size * i:batch_size * (i + 1)])
+            supp_ys = list(b[batch_size * i:batch_size * (i + 1)])
+            query_xs = list(c[batch_size * i:batch_size * (i + 1)])
+            query_ys = list(d[batch_size * i:batch_size * (i + 1)])
+            batch_sz = len(supp_xs)
+
+            # iterate over all tasks
+            for task in range(batch_sz):
+                sxs = supp_xs[task].cuda()
+                qxs = query_xs[task].cuda()
+                sys = supp_ys[task].cuda()
+                qys = query_ys[task].cuda()
+
+                learner = head.clone()
+                temp_sxs = emb(sxs)
+                temp_qxs = emb(qxs)
+
+                evaluation_error = fast_adapt(learner,
+                                               temp_sxs,
+                                               temp_qxs,
+                                               sys,
+                                               qys,
+                                               conf['inner'])
+
+                evaluation_error.backward()
+                meta_train_error += evaluation_error.item()
+
+                del(sxs,qxs,sys,qys)
+                supp_xs[task].cpu()
+                query_xs[task].cpu()
+                supp_ys[task].cpu()
+                query_ys[task].cpu()
+
+            # Average the accumulated gradients and optimize (After each batch we will update params)
+            for p in all_parameters:
+                p.grad.data.mul_(1.0 / batch_sz)
+            optimizer.step()
+            del (supp_xs, supp_ys, query_xs, query_ys)
+            gc.collect()
+
+        # test results on the validation data
+        val_loss,val_ndcg1,val_ndcg3 = hyper_test(emb,head,validation_dataset,adaptation_step=conf['inner'])
+
+        with tune.checkpoint_dir(epoch) as checkpoint_dir:
+            path = os.path.join(checkpoint_dir, "checkpoint")
+            torch.save((net.state_dict(), optimizer.state_dict()), path)
+
+        tune.report(loss=val_loss, ndcg1=val_ndcg1,ndcg3=val_ndcg3)
+    print("Finished Training")

learnToLearn.py

@@ -43,6 +43,8 @@ def parse_args():
                         help='MAML/MetaSGD/GBML')
     parser.add_argument('--gpu', type=int, default=0,
                         help='number of gpu to run the code')
+    parser.add_argument('--epochs', type=int, default=config['num_epoch'],
+                        help='number of gpu to run the code')
 
 
 
@@ -108,7 +110,6 @@ if __name__ == '__main__':
 
     # META LEARNING
     print("META LEARNING PHASE")
-    # head = l2l.algorithms.MetaSGD(head, lr=config['local_lr'],first_order=True)
 
     # define transformer
     transform = None
@@ -119,11 +120,11 @@ if __name__ == '__main__':
 
     # define meta algorithm
     if args.meta_algo == "maml":
-        head = l2l.algorithms.MAML(head, lr=config['local_lr'],first_order=args.first_order)
+        head = l2l.algorithms.MAML(head, lr=args.lr_inner,first_order=args.first_order)
     elif args.meta_algo == 'metasgd':
-        head = l2l.algorithms.MetaSGD(head, lr=config['local_lr'],first_order=args.first_order)
+        head = l2l.algorithms.MetaSGD(head, lr=args.lr_inner,first_order=args.first_order)
     elif args.meta_algo == 'gbml':
-        head = l2l.algorithms.GBML(head, transform=transform, lr=config['local_lr'],adapt_transform=args.adapt_transform, first_order=args.first_order)
+        head = l2l.algorithms.GBML(head, transform=transform, lr=args.lr_inner,adapt_transform=args.adapt_transform, first_order=args.first_order)
 
     if use_cuda:
         head.cuda()
@@ -131,7 +132,7 @@ if __name__ == '__main__':
     # Setup optimization
     print("SETUP OPTIMIZATION PHASE")
     all_parameters =  list(emb.parameters()) + list(head.parameters())
-    optimizer = torch.optim.Adam(all_parameters, lr=config['lr'])
+    optimizer = torch.optim.Adam(all_parameters, lr=args.lr_meta)
     # loss = torch.nn.MSELoss(reduction='mean')
 
     # Load training dataset.
@@ -157,7 +158,7 @@ if __name__ == '__main__':
     a, b, c, d = zip(*total_dataset)
 
     print("\n\n\n")
-    for iteration in range(config['num_epoch']):
+    for iteration in range(args.epochs):
         for i in range(num_batch):
             optimizer.zero_grad()
             meta_train_error = 0.0
@@ -254,7 +255,7 @@ if __name__ == '__main__':
         del (supp_xs_s, supp_ys_s, query_xs_s, query_ys_s)
 
         print("===================== " + test_state + " =====================")
-        test(emb,head, test_dataset, batch_size=config['batch_size'], num_epoch=config['num_epoch'],adaptation_step=args.inner_eval)
+        test(emb,head, test_dataset, batch_size=config['batch_size'], num_epoch=args.epochs,adaptation_step=args.inner_eval)
         print("===================================================\n\n\n")
     print(args)
 

learnToLearnTest.py

@@ -54,7 +54,8 @@ def test(embedding,head, total_dataset, batch_size, num_epoch,adaptation_step=co
                                       temp_qxs,
                                       supp_ys,
                                       query_ys,
-                                      config['inner'],
+                                      # config['inner'],
+                                      adaptation_step,
                                       get_predictions=True)
 
         l1 = L1Loss(reduction='mean')
@@ -88,4 +89,8 @@ def test(embedding,head, total_dataset, batch_size, num_epoch,adaptation_step=co
     print("nDCG3: ", np.array(ndcgs33).mean())
     # print("nDCG3: ", np.array(ndcgs333).mean())
 
+    print("is there nan?  " + str(np.any(np.isnan(ndcgs11))))
+    print("is there nan?  " + str(np.any(np.isnan(ndcgs33))))
+    print("is there nan?  " + str(np.any(np.isnan(losses_q))))
+