x夏鹏宁12012608

train.py

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+import pandas as pd
+import numpy as np
+from gmf import GMFEngine
+from mlp import MLPEngine
+from neumf import NeuMFEngine
+from data import SampleGenerator
+import os
+
+gmf_config = {'alias': 'gmf_factor8neg4-implict',
+              'num_epoch': 50,
+              'batch_size': 1024,
+              # 'optimizer': 'sgd',
+              # 'sgd_lr': 1e-3,
+              # 'sgd_momentum': 0.9,
+              # 'optimizer': 'rmsprop',
+              # 'rmsprop_lr': 1e-3,
+              # 'rmsprop_alpha': 0.99,
+              # 'rmsprop_momentum': 0,
+              'optimizer': 'adam',
+              'adam_lr': 1e-3,
+              'num_users': 6040,
+              'num_items': 3706,
+              'latent_dim': 8,
+              'num_negative': 4,
+              'l2_regularization': 0,  # 0.01
+              'weight_init_gaussian': True,
+              'use_cuda': False,
+              'device_id': 0,
+              'model_dir': 'checkpoints/{}_Epoch{}_HR{:.4f}_NDCG{:.4f}.model'}
+
+mlp_config = {'alias': 'mlp_factor8neg4_bz256_166432168_pretrain_reg_0.0000001',
+              'num_epoch': 50,
+              'batch_size': 256,  # 1024,
+              'optimizer': 'adam',
+              'adam_lr': 1e-3,
+              'num_users': 6040,
+              'num_items': 3706,
+              'latent_dim': 8,
+              'num_negative': 4,
+              'layers': [16, 64, 32, 16, 8],  # layers[0] is the concat of latent user vector & latent item vector
+              'l2_regularization': 0.0000001,  # MLP model is sensitive to hyper params
+              'weight_init_gaussian': True,
+              'use_cuda': False,
+              'device_id': 0,
+              'pretrain': False,
+              'pretrain_mf': 'checkpoints/{}'.format('gmf_factor8neg4_Epoch100_HR0.6391_NDCG0.2852.model'),
+              'model_dir': 'checkpoints/{}_Epoch{}_HR{:.4f}_NDCG{:.4f}.model'}
+
+neumf_config = {'alias': 'neumf_factor8neg4',
+                'num_epoch': 50,
+                'batch_size': 1024,
+                'optimizer': 'adam',
+                'adam_lr': 1e-3,
+                'num_users': 6040,
+                'num_items': 3706,
+                'latent_dim_mf': 8,
+                'latent_dim_mlp': 8,
+                'num_negative': 4,
+                'layers': [16, 64, 32, 16, 8],  # layers[0] is the concat of latent user vector & latent item vector
+                'l2_regularization': 0.0000001,
+                'weight_init_gaussian': True,
+                'use_cuda': False,
+                'device_id': 0,
+                'pretrain': False,
+                'pretrain_mf': 'checkpoints/{}'.format('gmf_factor8neg4_Epoch100_HR0.6391_NDCG0.2852.model'),
+                'pretrain_mlp': 'checkpoints/{}'.format('mlp_factor8neg4_Epoch100_HR0.5606_NDCG0.2463.model'),
+                'model_dir': 'checkpoints/{}_Epoch{}_HR{:.4f}_NDCG{:.4f}.model'
+                }
+
+
+if not os.path.exists('checkpoints'):
+    os.makedirs('checkpoints')
+
+# Load Data
+ml1m_dir = "C:/Users/xpn/Desktop/SAS/neural-collaborative-filtering-master/neural-collaborative-filtering-master/src/data/ml-1m/ratings.dat"
+ml1m_rating = pd.read_csv(ml1m_dir, sep='::', header=None, names=['uid', 'mid', 'rating', 'timestamp'], engine='python')
+# Reindex
+user_id = ml1m_rating[['uid']].drop_duplicates().reindex()
+user_id['userId'] = np.arange(len(user_id))
+ml1m_rating = pd.merge(ml1m_rating, user_id, on=['uid'], how='left')
+item_id = ml1m_rating[['mid']].drop_duplicates()
+item_id['itemId'] = np.arange(len(item_id))
+ml1m_rating = pd.merge(ml1m_rating, item_id, on=['mid'], how='left')
+ml1m_rating = ml1m_rating[['userId', 'itemId', 'rating', 'timestamp']]
+print('Range of userId is [{}, {}]'.format(ml1m_rating.userId.min(), ml1m_rating.userId.max()))
+print('Range of itemId is [{}, {}]'.format(ml1m_rating.itemId.min(), ml1m_rating.itemId.max()))
+# DataLoader for training
+sample_generator = SampleGenerator(ratings=ml1m_rating)
+evaluate_data = sample_generator.evaluate_data
+# Specify the exact model
+# config = gmf_config
+# engine = GMFEngine(config)
+# config = mlp_config
+# engine = MLPEngine(config)
+config = neumf_config
+engine = NeuMFEngine(config)
+for epoch in range(config['num_epoch']):
+    print('Epoch {} starts !'.format(epoch))
+    print('-' * 80)
+    train_loader = sample_generator.instance_a_train_loader(config['num_negative'], config['batch_size'])
+    engine.train_an_epoch(train_loader, epoch_id=epoch)
+    hit_ratio, ndcg = engine.evaluate(evaluate_data, epoch_id=epoch)
+    engine.save(config['alias'], epoch, hit_ratio, ndcg)