fix some bugs

PaddleRec/ctr/wide_deep/args.py

@@ -27,7 +27,7 @@ def parse_args():
     parser.add_argument("--epochs", type=int, default=40, help="epochs")
     parser.add_argument("--batch_size", type=int, default=40, help="batch_size")
     parser.add_argument('--use_gpu', type=int, default=0, help='whether using gpu')
-    parser.add_argument('--test_epoch', type=str, default='1',help='test_epoch')
+    parser.add_argument('--test_epoch', type=str, default='39',help='test_epoch')
     parser.add_argument('--train_path', type=str, default='data/adult.data', help='train_path')
     parser.add_argument('--test_path', type=str, default='data/adult.test', help='test_path')
     parser.add_argument('--train_data_path', type=str, default='train_data/train_data.csv', help='train_data_path')

PaddleRec/ctr/wide_deep/infer.py

@@ -27,10 +27,8 @@ def set_zero(var_name,scope=fluid.global_scope(), place=fluid.CPUPlace(),param_t
 
 def run_infer(args,test_data_path):
     wide_deep_model = wide_deep()
-
-    test_data_generator = utils.CriteoDataset()
-    test_reader = paddle.batch(test_data_generator.test(test_data_path), batch_size=args.batch_size)
-    
+    test_data_generator = utils.Dataset()
+    test_reader = fluid.io.batch(test_data_generator.test(test_data_path), batch_size=args.batch_size)
     inference_scope = fluid.Scope()
     startup_program = fluid.framework.Program()
     test_program = fluid.framework.Program()
@@ -43,20 +41,20 @@ def run_infer(args,test_data_path):
             place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
             loss, acc, auc, batch_auc, auc_states = wide_deep_model.model(inputs, args.hidden1_units, args.hidden2_units, args.hidden3_units)
             exe = fluid.Executor(place)
-            exe.run(startup_program)
 
             fluid.load(fluid.default_main_program(), cur_model_path,exe)
-            feeder = fluid.DataFeeder(feed_list=inputs, place=place)
+            loader = fluid.io.DataLoader.from_generator(feed_list=inputs, capacity=args.batch_size, iterable=True)
+            loader.set_sample_list_generator(test_reader, places=place)
             
             for var in auc_states:  # reset auc states
                 set_zero(var.name, scope=inference_scope, place=place)
 
             mean_acc = []
             mean_auc = []
-            for batch_id, data in enumerate(test_reader()):
+            for batch_id, data in enumerate(loader()):
                 begin = time.time()
                 acc_val,auc_val = exe.run(program=test_program,
-                                        feed=feeder.feed(data),
+                                        feed=data,
                                         fetch_list=[acc.name, auc.name],
                                         return_numpy=True
                                         )

PaddleRec/ctr/wide_deep/net.py

@@ -5,6 +5,7 @@ import paddle.fluid as fluid
 
 class wide_deep(object):
     def wide_part(self, data):
+
         out = fluid.layers.fc(input=data,
                             size=1, 
                             param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0 / math.sqrt(data.shape[1])),
@@ -14,6 +15,7 @@ class wide_deep(object):
         return out
         
     def fc(self, data, hidden_units, active, tag):
+        
         output = fluid.layers.fc(input=data,
                             size=hidden_units, 
                             param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0 / math.sqrt(data.shape[1]))),
@@ -23,6 +25,7 @@ class wide_deep(object):
         return output
         
     def deep_part(self, data, hidden1_units, hidden2_units, hidden3_units):
+        
         l1 = self.fc(data, hidden1_units, 'relu', 'l1')
         l2 = self.fc(l1, hidden2_units, 'relu', 'l2')
         l3 = self.fc(l2, hidden3_units, 'relu', 'l3')
@@ -38,6 +41,7 @@ class wide_deep(object):
         return inputs
         
     def model(self, inputs, hidden1_units, hidden2_units, hidden3_units):
+
         wide_output = self.wide_part(inputs[0])
         deep_output = self.deep_part(inputs[1], hidden1_units, hidden2_units, hidden3_units)
         

PaddleRec/ctr/wide_deep/train.py

@@ -15,8 +15,8 @@ logger.setLevel(logging.INFO)
 def train(args, train_data_path):
     wide_deep_model = wide_deep()
     inputs = wide_deep_model.input_data()
-    train_data_generator = utils.CriteoDataset()
-    train_reader = paddle.batch(train_data_generator.train(train_data_path), batch_size=args.batch_size)
+    train_data_generator = utils.Dataset()
+    train_reader = fluid.io.batch(train_data_generator.train(train_data_path), batch_size=args.batch_size)
     
     loss, acc, auc, batch_auc, auc_states  = wide_deep_model.model(inputs, args.hidden1_units, args.hidden2_units, args.hidden3_units)
     optimizer = fluid.optimizer.AdagradOptimizer(learning_rate=0.01)
@@ -25,13 +25,16 @@ def train(args, train_data_path):
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
-    feeder = fluid.DataFeeder(feed_list=inputs, place=place)
+
+    loader = fluid.io.DataLoader.from_generator(
+        feed_list=inputs, capacity=args.batch_size, iterable=True)
+    loader.set_sample_list_generator(train_reader, places=place)
     
     for epoch in range(args.epochs):
-        for batch_id, data in enumerate(train_reader()):
+        for batch_id, data in enumerate(loader()):
             begin = time.time()
             loss_val, acc_val, auc_val = exe.run(program=fluid.default_main_program(),
-                    feed=feeder.feed(data),
+                    feed=data,
                     fetch_list=[loss.name, acc.name, auc.name],
                     return_numpy=True)
             end = time.time()

PaddleRec/ctr/wide_deep/utils.py

@@ -2,7 +2,7 @@ import numpy as np
 import os
 import paddle.fluid as fluid
 
-class CriteoDataset(object):
+class Dataset(object):
 
     def _reader_creator(self, file):
         def reader():

PaddleRec/multi_task/esmm/README.md

@@ -108,7 +108,7 @@ python infer.py --use_gpu True\  #是否使用gpu
 
 CPU环境
 
-在cpu_train.sh脚本文件中设置好数据路径、参数。
+在cpu_infer.sh脚本文件中设置好数据路径、参数。
 
 ```shell
 python infer.py --use_gpu False\  #是否使用gpu
@@ -118,6 +118,14 @@ python infer.py --use_gpu False\  #是否使用gpu
                 --vocab_path ./vocab/vocab_size.txt #embedding词汇表大小路径
 ```
 
+修改脚本的可执行权限并运行
+
+```
+./cpu_infer.sh
+```
+
+
+
 ## 模型效果
 
 目前只抽取部分数据验证模型正确性。模型预测结果实例如下：

PaddleRec/multi_task/esmm/dataset_generator.py

@@ -15,8 +15,6 @@ class CriteoDataset(dg.MultiSlotStringDataGenerator):
         
         def reader():
             features = line.strip().split(',')
-            #ctr = list(map(int, features[1]))
-            #cvr = list(map(int, features[2]))
             ctr = features[1]
             cvr = features[2]
             
@@ -29,7 +27,6 @@ class CriteoDataset(dg.MultiSlotStringDataGenerator):
                     continue
                 all_field_id_dict[field_id][0] = True
                 index = all_field_id_dict[field_id][1]   
-                #feat_id = list(map(int, feat_id))    
                 output[index][1].append(feat_id) 
                 
             for field_id in all_field_id_dict:

PaddleRec/multi_task/esmm/infer.py

@@ -25,11 +25,6 @@ def run_infer(args,model_path,test_data_path,vocab_size):
     place = fluid.CPUPlace()
     esmm_model = ESMM()
     
-    
-    test_data_generator = utils.CriteoDataset()
-    test_reader = paddle.batch(test_data_generator.test(test_data_path),batch_size=args.batch_size)
-    
-    
     startup_program = fluid.framework.Program()
     test_program = fluid.framework.Program()
 
@@ -41,7 +36,6 @@ def run_infer(args,model_path,test_data_path,vocab_size):
             dataset, file_list = utils.get_dataset(inputs, test_data_path,args.batch_size,args.cpu_num)
             
             exe = fluid.Executor(place)
-            #加载模型
             fluid.load(fluid.default_main_program(),os.path.join(model_path, "checkpoint"), exe)
             
             set_zero(place)
@@ -71,13 +65,3 @@ if __name__ == "__main__":
         run_infer(args, model,args.test_data_path)
                 
              
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-                
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-                
-
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\ No newline at end of file

PaddleRec/multi_task/esmm/net.py

@@ -5,7 +5,6 @@ import paddle
 import utils
 import args
 
-
 class ESMM(object):
  
     def fc(self,tag, data, out_dim, active='prelu'):
@@ -47,14 +46,12 @@ class ESMM(object):
                                                             initializer=fluid.initializer.Xavier(fan_in=embed_size,fan_out=embed_size)
                                                             ),
                                 is_sparse=True)
-            #fluid.layers.Print(feat_emb, message="feat_emb")
+
             field_emb = fluid.layers.sequence_pool(input=feat_emb,pool_type='sum')
             emb.append(field_emb)
         concat_emb = fluid.layers.concat(emb, axis=1)
          
-        
         # ctr
-        
         active = 'relu'
         ctr_fc1 = self.fc('ctr_fc1', concat_emb, 200, active)
         ctr_fc2 = self.fc('ctr_fc2', ctr_fc1, 80, active)
@@ -67,15 +64,10 @@ class ESMM(object):
     
         ctr_clk = inputs[-2]
         ctcvr_buy = inputs[-1]
-        #ctr_label = fluid.layers.concat(input=[ctr_clk,1-ctr_clk],axis=1)
-        #ctcvr_label = fluid.layers.concat(input=[ctcvr_buy,1-ctcvr_buy],axis=1)
-        #ctr_label = fluid.layers.cast(x=ctr_label, dtype='float32')
-        #ctcvr_label = fluid.layers.cast(x=ctcvr_label, dtype='float32')
 
         ctr_prop_one = fluid.layers.slice(ctr_out, axes=[1], starts=[1], ends=[2])
         cvr_prop_one = fluid.layers.slice(cvr_out, axes=[1], starts=[1], ends=[2])
         
-        
         ctcvr_prop_one = fluid.layers.elementwise_mul(ctr_prop_one, cvr_prop_one)
         ctcvr_prop = fluid.layers.concat(input=[1-ctcvr_prop_one,ctcvr_prop_one], axis = 1)
     
@@ -83,26 +75,9 @@ class ESMM(object):
         loss_ctcvr = paddle.fluid.layers.cross_entropy(input=ctcvr_prop, label=ctcvr_buy)
         cost = loss_ctr + loss_ctcvr
         avg_cost = fluid.layers.mean(cost)
-        #fluid.layers.Print(ctr_clk, message="ctr_clk")
+
         auc_ctr, batch_auc_ctr, auc_states_ctr = fluid.layers.auc(input=ctr_out, label=ctr_clk)
         auc_ctcvr, batch_auc_ctcvr, auc_states_ctcvr = fluid.layers.auc(input=ctcvr_prop, label=ctcvr_buy)
     
         return avg_cost,auc_ctr,auc_ctcvr
   
\ No newline at end of file
-    
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\ No newline at end of file

PaddleRec/multi_task/esmm/reader.py

@@ -7,7 +7,7 @@ import os
 def join_data(file1,file2,write_file,sample_size):
     sample_list = []
     common_logs = defaultdict(lambda: '')
-    file = open(write_file, 'w',encoding='utf-8')  
+    file = open(write_file, 'w')  
     
     print("begin push sample_list!")
     with open(file1,'r') as f:
@@ -45,7 +45,7 @@ def join_data(file1,file2,write_file,sample_size):
 
 
 def read_data(file_name,write_file):
-    file = open(write_file, 'w',encoding='utf-8')  
+    file = open(write_file, 'w')  
     print("begin to write!")
     with open(file_name,'r') as f:
         for i, line in enumerate(f):
@@ -65,7 +65,7 @@ def read_data(file_name,write_file):
                 
                 #sample_id|y|z|common_feature_index|feat_num|feat_list
                 elif(feat_len == 6):
-                    # y=0 & z=1过滤
+                    # y=0 & z=1 filter
                     if(line[1] == '0' and line[2] == '1'):
                         continue
                     feat_strs = line[5]
@@ -80,15 +80,13 @@ def read_data(file_name,write_file):
            
     file.close()
 
-
-##重新编码
 def recode(file_path,writh_file,vocab_path):
     all_feat_id_dict = defaultdict(int)
-    file1 = open(writh_file[0], 'w',encoding='utf-8') 
-    file2 = open(writh_file[1], 'w',encoding='utf-8')
-    vocab_file = open(vocab_path, 'w',encoding='utf-8') 
+    file1 = open(writh_file[0], 'w') 
+    file2 = open(writh_file[1], 'w')
+    vocab_file = open(vocab_path, 'w') 
     id = 0
-    with open(file_path[0], "r", encoding='utf-8') as f:
+    with open(file_path[0], "r") as f:
         for i, line in enumerate(f):
             line = line.strip().split(',')
             feat_lists = []
@@ -100,7 +98,7 @@ def recode(file_path,writh_file,vocab_path):
                 feat_lists.append('%s:%s' % (field_id,all_feat_id_dict[feat_id]))
             sample = "{0},{1},{2},{3}".format(line[0], line[1], line[2], ','.join(feat_lists)) + "\n"
             file1.write(sample)
-    with open(file_path[1], "r", encoding='utf-8') as f:
+    with open(file_path[1], "r") as f:
         for i, line in enumerate(f):
             line = line.strip().split(',')
             feat_lists = []
@@ -131,7 +129,7 @@ if __name__ == "__main__":
     
     write_file = args.train_data_path + '/train_data.csv'
     join_data(skeleton_train_path,features_train_path,write_file,args.train_sample_size)
-    ##删除产生的中间文件
+
     os.system('rm -rf ' + skeleton_train_path)
     os.system('rm -rf ' + features_train_path)
     
@@ -146,7 +144,7 @@ if __name__ == "__main__":
     
     write_file = args.test_data_path + '/test_data.csv'
     join_data(skeleton_test_path,features_test_path,write_file,args.test_sample_size)
-    ##删除产生的中间文件
+
     os.system('rm -rf ' + skeleton_test_path)
     os.system('rm -rf ' + features_test_path)
     
@@ -154,13 +152,6 @@ if __name__ == "__main__":
     file_path = [args.train_data_path + '/train_data.csv', args.test_data_path + '/test_data.csv']
     write_file = [args.train_data_path + '/traindata.csv',args.test_data_path + '/testdata.csv']
     recode(file_path,write_file,args.vocab_path)
-    ##删除产生的中间文件
+
     for file in file_path:
         os.system('rm -rf ' + file_path)
-
-
-
-
-
-
-

PaddleRec/multi_task/esmm/train.py

@@ -6,9 +6,6 @@ import paddle
 import utils
 import args
 
-
-
-
 def train(args, vocab_size, train_data_path):
     esmm_model = ESMM()
     inputs = esmm_model.input_data()
@@ -16,8 +13,6 @@ def train(args, vocab_size, train_data_path):
     dataset, file_list = utils.get_dataset(inputs, train_data_path,args.batch_size,args.cpu_num)
     
     avg_cost,auc_ctr,auc_ctcvr= esmm_model.net(inputs, vocab_size, args.embed_size)
-    
-    # 选择反向更新优化策略
     optimizer = fluid.optimizer.Adam()
     optimizer.minimize(avg_cost)
     
@@ -42,12 +37,7 @@ def train(args, vocab_size, train_data_path):
         main_program = fluid.default_main_program()
         fluid.io.save(main_program,model_dir)
 
-
-    
 if __name__ == "__main__":
     args = args.parse_args()
     vocab_size =utils.get_vocab_size(args.vocab_path)
     train(args, vocab_size, args.train_data_path)
-
-
-

PaddleRec/multi_task/esmm/utils.py

@@ -31,47 +31,3 @@ def get_vocab_size(vocab_path):
         line = rf.readline()
         return int(line.strip()) + 1
 
-
-class CriteoDataset(object):
-
-    def _reader_creator(self, file):
-        def reader():
-            with open(file, 'r') as f:
-                for line in f:
-                    features = line.strip().split(',')
-                    ctr = features[1]
-                    cvr = features[2]
-                    
-                    padding = '0'
-                    output = [(field_id,[]) for field_id in all_field_id_dict]
-        
-                    for elem in features[4:]:
-                        field_id,feat_id = elem.strip().split(':')
-                        if field_id not in all_field_id_dict:
-                            continue
-                        all_field_id_dict[field_id][0] = True
-                        index = all_field_id_dict[field_id][1]
-                        output[index][1].append(feat_id) 
-                        
-                    for field_id in all_field_id_dict:
-                        visited,index = all_field_id_dict[field_id]
-                        if visited:
-                            all_field_id_dict[field_id][0] = False
-                        else:
-                            output[index][1].append(padding) 
-                    output.append(('ctr',ctr))
-                    output.append(('cvr',cvr))
-                    yield output
-
-        return reader
-
-    def train(self, file):
-        return self._reader_creator(file)
-
-    def test(self, file):
-        return self._reader_creator(file)
-
-
-
-
-            
\ No newline at end of file

PaddleRec/ncf/Dataset.py

@@ -33,6 +33,3 @@ class Dataset(object):
                 negativeList.append(negatives)
                 line = f.readline()
         return negativeList
\ No newline at end of file
-    
-
-

PaddleRec/ncf/README.md

@@ -3,6 +3,7 @@
  以下是本例的简要目录结构及说明： 
 
 ```
+├── Data/ #原始数据集目录
 ├── README.md # 文档
 ├── requirements.txt   # 需要的安装包
 ├── gmf.py # gmf网络文件

PaddleRec/ncf/evaluate.py

@@ -14,7 +14,6 @@ import paddle
 import args
 import utils
 import time
-#from numba import jit, autojit
 
 # Global variables that are shared across processes
 _model = None

PaddleRec/ncf/gmf.py

@@ -28,23 +28,4 @@ class GMF(object):
         
         return avg_cost, prediction
 
-        
-   
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PaddleRec/ncf/infer.py

@@ -23,26 +23,10 @@ if __name__ == "__main__":
     topK = 10
    
     begin = time.time()
-    model_path = args.model_dir + "/epoch_" + str(args.epochs - 1)
+    model_path = args.model_dir + "/epoch_" + str(12)
     (hits, ndcgs) = evaluate_model(args, testRatings, testNegatives, topK, model_path)
     hr, ndcg = np.array(hits).mean(), np.array(ndcgs).mean()
     end = time.time()
     
     logger.info("epoch: {}, epoch_time: {:.5f}s, HR: {:.5f}, NDCG: {:.5f}".format(args.epochs, end - begin, hr, ndcg))
         
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PaddleRec/ncf/mlp.py

@@ -30,7 +30,6 @@ class MLP(object):
                                        name='layer_' + str(i))
                                        
         # Final prediction layer
-        
         prediction = fluid.layers.fc(input=vector,
                                     size=1,
                                     act='sigmoid',
@@ -42,23 +41,4 @@ class MLP(object):
         
         return avg_cost, prediction
 
-        
-   
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PaddleRec/ncf/neumf.py

@@ -31,14 +31,12 @@ class NeuMF(object):
         mf_user_latent = fluid.layers.flatten(x=MF_Embedding_User, axis=1)
         mf_item_latent = fluid.layers.flatten(x=MF_Embedding_Item, axis=1)
         mf_vector = fluid.layers.elementwise_mul(mf_user_latent, mf_item_latent)
-        #fluid.layers.Print(mf_vector, message="mf_vector")
         
         # MLP part 
         # The 0-th layer is the concatenation of embedding layers
         mlp_user_latent = fluid.layers.flatten(x=MLP_Embedding_User, axis=1)
         mlp_item_latent = fluid.layers.flatten(x=MLP_Embedding_Item, axis=1)
         mlp_vector = fluid.layers.concat(input=[mlp_user_latent, mlp_item_latent], axis=-1)
-        #fluid.layers.Print(mlp_vector, message="mlp_vector")
         
         for i in range(1, num_layer):
             mlp_vector = fluid.layers.fc(input=mlp_vector,
@@ -63,23 +61,4 @@ class NeuMF(object):
         
         return avg_cost, prediction
         
-        
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PaddleRec/ncf/train.py

@@ -22,8 +22,8 @@ def train(args, train_data_path):
     dataset = Dataset(args.path + args.dataset)
     testRatings, testNegatives = dataset.testRatings, dataset.testNegatives
 
-    train_data_generator = utils.CriteoDataset()
-    train_reader = paddle.batch(train_data_generator.train(train_data_path, True), batch_size=args.batch_size)
+    train_data_generator = utils.Dataset()
+    train_reader = fluid.io.batch(train_data_generator.train(train_data_path, True), batch_size=args.batch_size)
     
     inputs = utils.input_data(True)
     if args.GMF:
@@ -42,14 +42,17 @@ def train(args, train_data_path):
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
-    feeder = fluid.DataFeeder(feed_list=inputs, place=place)
+
+    loader = fluid.io.DataLoader.from_generator(
+        feed_list=inputs, capacity=args.batch_size, iterable=True)
+    loader.set_sample_list_generator(train_reader, places=place)
     
     for epoch in range(args.epochs):
 
-        for batch_id, data in enumerate(train_reader()):
+        for batch_id, data in enumerate(loader()):
             begin = time.time()
             loss_val = exe.run(program=fluid.default_main_program(),
-                    feed=feeder.feed(data),
+                    feed=data,
                     fetch_list=[loss.name],
                     return_numpy=True)
             end = time.time()
@@ -60,8 +63,6 @@ def train(args, train_data_path):
         fetch_vars = [pred]
         fluid.io.save_inference_model(save_dir, feed_var_names, fetch_vars, exe)
  
-            
-  
 if __name__ == "__main__":
     args = args.parse_args()
     train(args, args.train_data_path)

PaddleRec/ncf/train_gpu.sh

 CUDA_VISIBLE_DEVICES=0 python train.py --use_gpu 1 \
-                                        --NeuMF 1 \
+                                        --MLP 1 \
                                         --epochs 20 \
                                         --batch_size 256 \
                                         --num_factors 8 \
                                         --num_neg 4 \
                                         --lr 0.001 \
-                                        --model_dir 'model_dir' 
\ No newline at end of file
+                                        --model_dir 'mlp_model_dir' 
\ No newline at end of file

PaddleRec/ncf/utils.py

@@ -2,7 +2,7 @@ import numpy as np
 import os
 import paddle.fluid as fluid
 
-class CriteoDataset(object):
+class Dataset(object):
 
     def _reader_creator(self, file, is_train):
         def reader():