Adding validation phase

MeLU.py

         self.fast_weights = OrderedDict()
 
     def forward(self, support_set_x, support_set_y, query_set_x, num_local_update):
+
+        # this line added my maheri
+        self.keep_weight = deepcopy(self.model.state_dict())
+
         for idx in range(num_local_update):
             if idx > 0:
                 self.model.load_state_dict(self.fast_weights)
+
+            # weight_for_local_update = list(self.model.state_dict().values())
             weight_for_local_update = list(self.model.state_dict().values())
+
             support_set_y_pred = self.model(support_set_x)
             loss = F.mse_loss(support_set_y_pred, support_set_y.view(-1, 1))
             self.model.zero_grad()
             grad = torch.autograd.grad(loss, self.model.parameters(), create_graph=True)
+
             # local update
             for i in range(self.weight_len):
                 if self.weight_name[i] in self.local_update_target_weight_name:
                     self.fast_weights[self.weight_name[i]] = weight_for_local_update[i] - self.local_lr * grad[i]
                 else:
                     self.fast_weights[self.weight_name[i]] = weight_for_local_update[i]
+
         self.model.load_state_dict(self.fast_weights)
+        # self.fast_weights = OrderedDict()
         query_set_y_pred = self.model(query_set_x)
         self.model.load_state_dict(self.keep_weight)
+
+
         return query_set_y_pred
 
     def global_update(self, support_set_xs, support_set_ys, query_set_xs, query_set_ys, num_local_update):
         losses_q.backward()
         self.meta_optim.step()
         self.store_parameters()
+
         return
 
     def get_weight_avg_norm(self, support_set_x, support_set_y, num_local_update):

main.py

 from MeLU import MeLU
 from options import config
 from model_training import training
+from model_test import test
 from data_generation import generate
 from evidence_candidate import selection
 
 
 if __name__ == "__main__":
+
     # master_path= "./ml"
-    master_path = "/media/external_3TB/3TB/rafie/maheri/melr"
-    # master_path = "/media/external_10TB/10TB/pourmand/ml"
+    master_path = "/media/external_10TB/10TB/maheri/melu_data"
     if not os.path.exists("{}/".format(master_path)):
-        print("inajm")
+        print("generating data phase started")
         os.mkdir("{}/".format(master_path))
         # preparing dataset. It needs about 22GB of your hard disk space.
         generate(master_path)
 
-    # # training model.
-    # melu = MeLU(config)
-    # model_filename = "{}/models.pkl".format(master_path)
-    # if not os.path.exists(model_filename):
-    #     # Load training dataset.
-    #     training_set_size = int(len(os.listdir("{}/warm_state".format(master_path))) / 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(master_path, idx), "rb")))
-    #         supp_ys_s.append(pickle.load(open("{}/warm_state/supp_y_{}.pkl".format(master_path, idx), "rb")))
-    #         query_xs_s.append(pickle.load(open("{}/warm_state/query_x_{}.pkl".format(master_path, idx), "rb")))
-    #         query_ys_s.append(pickle.load(open("{}/warm_state/query_y_{}.pkl".format(master_path, 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)
-    #     training(melu, total_dataset, batch_size=config['batch_size'], num_epoch=config['num_epoch'], model_save=True, model_filename=model_filename)
-    # else:
-    #     trained_state_dict = torch.load(model_filename)
-    #     melu.load_state_dict(trained_state_dict)
-    #
-    # # selecting evidence candidates.
+    # training model.
+    melu = MeLU(config)
+    model_filename = "{}/models2.pkl".format(master_path)
+    if not os.path.exists(model_filename):
+        print("training phase started")
+        # Load training dataset.
+        training_set_size = int(len(os.listdir("{}/warm_state".format(master_path))) / 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(master_path, idx), "rb")))
+            supp_ys_s.append(pickle.load(open("{}/warm_state/supp_y_{}.pkl".format(master_path, idx), "rb")))
+            query_xs_s.append(pickle.load(open("{}/warm_state/query_x_{}.pkl".format(master_path, idx), "rb")))
+            query_ys_s.append(pickle.load(open("{}/warm_state/query_y_{}.pkl".format(master_path, 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)
+        training(melu, total_dataset, batch_size=config['batch_size'], num_epoch=config['num_epoch'], model_save=True, model_filename=model_filename)
+    else:
+        trained_state_dict = torch.load(model_filename)
+        melu.load_state_dict(trained_state_dict)
+
+    print("training finished")
+    # selecting evidence candidates.
     # evidence_candidate_list = selection(melu, master_path, config['num_candidate'])
     # for movie, score in evidence_candidate_list:
     #     print(movie, score)
+
+
+    print("start of test phase")
+    test_dataset = None
+    test_set_size = int(len(os.listdir("{}/user_cold_state".format(master_path))) / 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("{}/user_cold_state/supp_x_{}.pkl".format(master_path, idx), "rb")))
+        supp_ys_s.append(pickle.load(open("{}/user_cold_state/supp_y_{}.pkl".format(master_path, idx), "rb")))
+        query_xs_s.append(pickle.load(open("{}/user_cold_state/query_x_{}.pkl".format(master_path, idx), "rb")))
+        query_ys_s.append(pickle.load(open("{}/user_cold_state/query_y_{}.pkl".format(master_path, 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)
+
+    model_filename = "{}/models_test.pkl".format(master_path)
+    test(melu, test_dataset, batch_size=config['batch_size'], num_epoch=config['num_epoch'])

model_test.py

+import os
+import torch
+import pickle
+import random
+from MeLU import MeLU
+from options import config, states
+from torch.nn import functional as F
+from torch.nn import L1Loss
+# from pytorchltr.evaluation import ndcg
+import matchzoo as mz
+import numpy as np
+
+
+def test(melu, total_dataset, batch_size, num_epoch):
+    if config['use_cuda']:
+        melu.cuda()
+
+    test_set_size = len(total_dataset)
+
+    trained_state_dict = torch.load("/media/external_10TB/10TB/maheri/melu_data/models2.pkl")
+    melu.load_state_dict(trained_state_dict)
+    melu.eval()
+
+    random.shuffle(total_dataset)
+    a, b, c, d = zip(*total_dataset)
+
+    losses_q = []
+    predictions = None
+    predictions_size = None
+
+    # y_true = []
+    # y_pred = []
+    ndcgs1 = []
+    ndcgs3 = []
+
+    for iterator in range(test_set_size):
+        # trained_state_dict = torch.load("/media/external_10TB/10TB/maheri/melu_data/models.pkl")
+        # melu.load_state_dict(trained_state_dict)
+        # melu.eval()
+
+        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
+
+        num_local_update = config['inner']
+        query_set_y_pred = melu.forward(supp_xs, supp_ys, query_xs, num_local_update)
+
+        l1 = L1Loss(reduction='mean')
+        loss_q = l1(query_set_y_pred, query_ys)
+        print("testing - iterator:{} - l1:{} ".format(iterator,loss_q))
+        losses_q.append(loss_q)
+
+        # if predictions is None:
+        #     predictions = query_set_y_pred
+        #     predictions_size = torch.FloatTensor(len(query_set_y_pred))
+        # else:
+        #     predictions = torch.cat((predictions,query_set_y_pred),0)
+        #     predictions_size = torch.cat((predictions_size,torch.FloatTensor(len(query_set_y_pred))),0)
+        # y_true.append(query_ys.cpu().detach().numpy())
+        # y_pred.append(query_set_y_pred.cpu().detach().numpy())
+
+        y_true = query_ys.cpu().detach().numpy()
+        y_pred = query_set_y_pred.cpu().detach().numpy()
+        ndcgs1.append(mz.metrics.NormalizedDiscountedCumulativeGain(k=1)(y_true,y_pred))
+        ndcgs3.append(mz.metrics.NormalizedDiscountedCumulativeGain(k=3)(y_true, y_pred))
+
+
+        del supp_xs, supp_ys, query_xs, query_ys
+
+
+    # calculate metrics
+    print(losses_q)
+    print("======================================")
+    losses_q = torch.stack(losses_q).mean(0)
+    print("mean of mse: ",losses_q)
+    print("======================================")
+
+    # n1 = ndcg(d, predictions.cuda(), predictions_size.cuda(), k=1)
+    # n1 =  mz.metrics.NormalizedDiscountedCumulativeGain(k=1)(np.array(y_true),np.array(y_pred))
+    n1 = np.array(ndcgs1).mean()
+    print("nDCG1: ",n1)
+    n3 = np.array(ndcgs3).mean()
+    print("nDCG3: ", n3)
+

model_training.py

     if config['use_cuda']:
         melu.cuda()
 
+    print("mode: " + str(config['use_cuda']))
+
     training_set_size = len(total_dataset)
     melu.train()
-    for _ in range(num_epoch):
+    for epoch in range(num_epoch):
         random.shuffle(total_dataset)
         num_batch = int(training_set_size / batch_size)
         a,b,c,d = zip(*total_dataset)
         for i in range(num_batch):
+            print("training - epoch:{} - batch:{}".format(epoch,i))
             try:
                 supp_xs = list(a[batch_size*i:batch_size*(i+1)])
                 supp_ys = list(b[batch_size*i:batch_size*(i+1)])
             except IndexError:
                 continue
             melu.global_update(supp_xs, supp_ys, query_xs, query_ys, config['inner'])
+            del supp_xs,supp_ys,query_xs,query_ys
 
     if model_save:
         torch.save(melu.state_dict(), model_filename)