add kdd2020-p3ac (#4238)

* update README

* fix monopoly info

* add kdd2020-p3ac

* add kdd2020-p3ac

PaddleST/README.md

@@ -19,3 +19,4 @@ The full list of frontier industrial projects:
 |应用项目|项目简介|开源地址|
 |----|----|----|
 ||||
+

PaddleST/Research/CIKM2019-MONOPOLY/README.md

@@ -29,7 +29,7 @@ We have conducted extensive experiments with the large-scale urban data of sever
 
 1. paddle安装
 
-    本项目依赖于Paddle Fluid 1.5.1 及以上版本，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装
+    本项目依赖于Paddle Fluid 1.6.1 及以上版本，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装
 
 2. 下载代码
 

PaddleST/Research/CIKM2019-MONOPOLY/conf/house_price/house_price.local.template

@@ -280,7 +280,7 @@ num_in_dimension: ${DEFAULT:num_in_dimension}
 num_out_dimension: ${DEFAULT:num_out_dimension}
 
 # Directory where the results are saved to
-eval_dir: ${Train:train_dir}/epoch<s>
+eval_dir: ${Train:train_dir}/checkpoint_1
 
 # The number of samples in each batch
 batch_size: ${DEFAULT:eval_batch_size}

PaddleST/Research/CIKM2019-MONOPOLY/nets/house_price/house_price.py

@@ -77,8 +77,7 @@ class HousePrice(BaseNet):
             act=act)
         return _fc
  
-    
-    def pred_format(self, result):
+    def pred_format(self, result, **kwargs):
         """
             format pred output
         """
@@ -118,7 +117,7 @@ class HousePrice(BaseNet):
 
         max_house_num = FLAGS.max_house_num
         max_public_num = FLAGS.max_public_num
-       
+        pred_keys = inputs.keys() 
         #step1. get house self feature
         if FLAGS.with_house_attr:
             def _get_house_attr(name, attr_vec_size):
@@ -136,6 +135,10 @@ class HousePrice(BaseNet):
         else:
             #no house attr
             house_vec = fluid.layers.reshape(inputs["house_business"], [-1, self.city_info.business_num])
+            pred_keys.remove('house_wuye')
+            pred_keys.remove('house_kfs')
+            pred_keys.remove('house_age')
+            pred_keys.remove('house_lou')
 
         house_self = self.fc_fn(house_vec, 1, act='sigmoid', layer_name='house_self', FLAGS=FLAGS)
         house_self = fluid.layers.reshape(house_self, [-1, 1])
@@ -192,8 +195,8 @@ class HousePrice(BaseNet):
         net_output = {"debug_output": debug_output, 
                       "model_output": model_output}
 
-        model_output['feeded_var_names'] = inputs.keys()
-        model_output['target_vars'] = [label, pred]
+        model_output['feeded_var_names'] = pred_keys   
+        model_output['fetch_targets'] = [label, pred]
         model_output['loss'] = avg_cost
 
         #debug_output['pred'] = pred 

PaddleST/Research/KDD2020-P3AC/README.md

+# P3AC
+
+## 任务说明(Introduction)
+
+TODO
+
+![](docs/framework.png)
+
+
+## 安装说明(Install Guide)
+
+### 环境准备
+
+1. paddle安装
+
+    本项目依赖于Paddle Fluid 1.6.1 及以上版本，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装
+
+2. 下载代码
+
+    克隆数据集代码库到本地, 本代码依赖[Paddle-EPEP框架](https://github.com/PaddlePaddle/epep)
+    ```
+    git clone https://github.com/PaddlePaddle/epep.git
+    cd epep
+    git clone https://github.com/PaddlePaddle/models.git
+    ln -s models/PaddleST/Research/KDD2020-P3AC/conf/poi_qac_personalized conf/poi_qac_personalized
+    ln -s models/PaddleST/Research/KDD2020-P3AC/datasets/poi_qac_personalized datasets/poi_qac_personalized
+    ln -s models/PaddleST/Research/KDD2020-P3AC/nets/poi_qac_personalized nets/poi_qac_personalized
+    ```
+
+3. 环境依赖
+
+    python版本依赖python 2.7
+
+
+### 实验说明
+
+1. 数据准备
+
+    TODO
+    ```
+    #script to download 
+    ```
+
+2. 模型训练
+
+    ```
+    cp conf/poi_qac_personalized/poi_qac_personalized.local.conf.template conf/poi_qac_personalized/poi_qac_personalized.local.conf
+    sh run.sh -c conf/poi_qac_personalized/poi_qac_personalized.local.conf -m train [ -g 0 ]
+    ```
+
+3. 模型评估
+    ```
+    pred_gpu=$1
+    mode=$2 #query, poi, eval
+
+    if [ $# -lt 2 ];then
+        exit 1
+    fi
+
+    #编辑conf/poi_qac_personalized/poi_qac_personalized.local.conf.template，打开 CUDA_VISIBLE_DEVICES: <pred_gpu>
+    cp conf/poi_qac_personalized/poi_qac_personalized.local.conf.template conf/poi_qac_personalized/poi_qac_personalized.local.conf
+    sed -i "s#<pred_gpu>#$pred_gpu#g" conf/poi_qac_personalized/poi_qac_personalized.local.conf
+    sed -i "s#<mode>#$mode#g" conf/poi_qac_personalized/poi_qac_personalized.local.conf
+
+    sh run.sh -c poi_qac_personalized.local -m predict 1>../tmp/$mode-pred$pred_gpu.out 2>../tmp/$mode-pred$pred_gpu.err
+    ```
+
+## 论文下载(Paper Download)
+
+Please feel free to review our paper :)
+
+TODO
+
+## 引用格式(Paper Citation)
+
+TODO
+
+

PaddleST/Research/KDD2020-P3AC/conf/poi_qac_personalized/poi_qac_personalized.local.conf.template

+[DEFAULT]
+sample_seed: 1234
+# The value in `DEFAULT` section will be referenced by other sections.
+# For convinence, we will put the variables which changes frequently here and 
+# let other section refer them
+debug_mode: False
+#reader: dataset |  pyreader | async | datafeed | sync
+#data_reader: dataset
+dataset_mode: Memory
+#data_reader: datafeed
+data_reader: pyreader
+py_reader_iterable: False
+
+#model_type: lstm_net 
+model_type: cnn_net 
+vocab_size: 93896 
+#emb_dim: 200
+emb_dim: 128
+time_size: 28
+tag_size: 371
+fc_dim: 64
+
+emb_lr: 1.0
+base_lr: 0.001
+margin: 0.35
+window_size: 3
+pooling_type: max 
+#activate: sigmoid
+activate: None
+use_attention: True
+use_personal: True
+max_seq_len: 128
+prefix_word_id: True
+#print_period: 200
+#TODO personal_resident_drive + neg_only_sample
+#query cityid trendency, poi tag/alias
+#local-cpu | local-gpu | pserver-cpu | pserver-gpu | nccl2
+platform: local-gpu
+# Input settings
+dataset_name: PoiQacPersonalized
+
+CUDA_VISIBLE_DEVICES: 0,1,2,3
+#CUDA_VISIBLE_DEVICES: <pred_gpu>
+
+train_batch_size: 128
+#train_batch_size: 2
+eval_batch_size: 2
+#file_list: ../tmp/data/poi/qac/train_data/part-00000
+dataset_dir: ../tmp/data/poi/qac/train_data
+#init_train_params: ../tmp/data/poi/qac/tencent_pretrain.words
+tag_dict_path: None 
+qac_dict_path: None 
+kv_path: None
+#qac_dict_path: ./datasets/poi_qac_personalized/qac_term.dict
+#tag_dict_path: ./datasets/poi_qac_personalized/poi_tag.dict
+#kv_path: ../tmp/data/poi/qac/kv
+
+# Model settings
+model_name: PoiQacPersonalized
+preprocessing_name: None 
+#file_pattern: %s-part-*
+file_pattern: part-
+num_in_dimension: 3
+num_out_dimension: 4
+
+# Learning options
+num_samples_train: 100
+num_samples_eval: 10
+max_number_of_steps: 155000
+
+[Convert]
+# The name of the dataset to convert
+dataset_name: ${DEFAULT:dataset_name}
+
+#dataset_dir: ${DEFAULT:dataset_dir}
+dataset_dir: stream
+
+# The output Records file name prefix.
+dataset_split_name: train
+
+# The number of Records per shard
+num_per_shard: 100000
+
+# The dimensions of net input vectors, it is just used by svm dataset
+# which of input are sparse tensors now
+num_in_dimension: ${DEFAULT:num_in_dimension}
+
+# The output file name pattern with two placeholders ("%s" and "%d"), 
+# it must correspond to the glob `file_pattern' in Train and Evaluate
+# config sections
+#file_pattern: %s-part-%05d
+file_pattern: part-
+
+
+[Train]
+#######################
+#  Dataset Configure  #
+#######################
+# The name of the dataset to load
+dataset_name: ${DEFAULT:dataset_name}
+
+# The directory where the dataset files are stored
+dataset_dir:  ${DEFAULT:dataset_dir}
+
+# dataset_split_name
+dataset_split_name: train
+
+batch_shuffle_size: 128
+#log_exp or hinge
+#loss_func: hinge
+loss_func: log_exp
+neg_sample_num: 5
+reader_batch: True
+drop_last_batch: False
+
+# The glob pattern for data path, `file_pattern' must contain only one "%s" 
+# which is the placeholder for split name (such as 'train', 'validation')
+file_pattern: ${DEFAULT:file_pattern}
+
+# The file type text or record
+file_type: record
+
+# kv path, used in image_sim
+kv_path: ${DEFAULT:kv_path}
+
+# The number of input sample for training
+num_samples: ${DEFAULT:num_samples_train}
+
+# The number of parallel readers that read data from the dataset
+num_readers: 2
+
+# The number of threads used to create the batches
+num_preprocessing_threads: 2
+
+# Number of epochs from dataset source
+num_epochs_input: 10
+
+###########################
+#  Basic Train Configure  #
+###########################
+# Directory where checkpoints and event logs are written to.
+train_dir: ../tmp/model/poi/qac/save_model
+# The max number of ckpt files to store variables
+save_max_to_keep: 40
+
+# The frequency with which the model is saved, in seconds.
+save_model_secs: None
+
+# The frequency with which the model is saved, in steps.
+save_model_steps: 5000
+
+# The name of the architecture to train
+model_name: ${DEFAULT:model_name}
+
+# The dimensions of net input vectors, it is just used by svm dataset
+# which of input are sparse tensors now
+num_in_dimension: ${DEFAULT:num_in_dimension}
+
+# The dimensions of net output vector, it will be num of classes in image classify task 
+num_out_dimension: ${DEFAULT:num_out_dimension}
+
+#####################################
+#  Training Optimization Configure  #
+#####################################
+# The number of samples in each batch
+batch_size: ${DEFAULT:train_batch_size}
+
+# The maximum number of training steps
+max_number_of_steps: ${DEFAULT:max_number_of_steps}
+
+# The weight decay on the model weights
+#weight_decay: 0.00000001
+weight_decay: None
+
+# The decay to use for the moving average. If left as None, then moving averages are not used
+moving_average_decay: None
+
+# ***************** learning rate options ***************** #
+
+# Specifies how the learning rate is decayed. One of "fixed", "exponential" or "polynomial"
+learning_rate_decay_type: fixed 
+
+# Learning rate decay factor
+learning_rate_decay_factor: 0.1
+
+# Proportion of training steps to perform linear learning rate warmup for
+learning_rate_warmup_proportion: 0.1
+
+init_learning_rate: 0
+
+learning_rate_warmup_steps: 10000
+
+# The minimal end learning rate used by a polynomial decay learning rate
+end_learning_rate: 0.0001
+
+# Number of epochs after which learning rate decays
+num_epochs_per_decay: 10
+
+# A boolean, whether or not it should cycle beyond decay_steps
+learning_rate_polynomial_decay_cycle: False
+
+# ******************* optimizer options ******************* #
+# The name of the optimizer, one of the following:
+# "adadelta", "adagrad", "adam", "ftrl", "momentum", "sgd" or "rmsprop"
+#optimizer: weight_decay_adam
+optimizer: adam
+#optimizer: sgd
+# Epsilon term for the optimizer, used for adadelta, adam, rmsprop
+opt_epsilon: 1e-8
+
+# conf for adadelta
+# The decay rate for adadelta
+adadelta_rho: 0.95
+# Starting value for the AdaGrad accumulators
+adagrad_initial_accumulator_value: 0.1
+
+# conf for adam
+# The exponential decay rate for the 1st moment estimates
+adam_beta1: 0.9
+# The exponential decay rate for the 2nd moment estimates
+adam_beta2: 0.997
+
+adam_weight_decay: 0.01
+#adam_exclude_from_weight_decay: LayerNorm,layer_norm,bias
+# conf for ftrl
+# The learning rate power
+ftrl_learning_rate_power: -0.1
+# Starting value for the FTRL accumulators
+ftrl_initial_accumulator_value: 0.1
+# The FTRL l1 regularization strength
+ftrl_l1: 0.0
+# The FTRL l2 regularization strength
+ftrl_l2: 0.01
+
+# conf for momentum
+# The momentum for the MomentumOptimizer and RMSPropOptimizer
+momentum: 0.9
+
+# conf for rmsprop
+# Decay term for RMSProp
+rmsprop_decay: 0.9
+
+
+# Number of model clones to deploy
+num_gpus: 3
+
+#############################
+#  Log and Trace Configure  #
+#############################
+# The frequency with which logs are print
+log_every_n_steps: 100
+
+# The frequency with which logs are trace.
+trace_every_n_steps: 1
+
+
+[Evaluate]
+# process mode: pred, eval or export
+#proc_name: eval
+proc_name: pred
+
+#data_reader: datafeed
+py_reader_iterable: True
+#platform: hadoop
+platform: local-gpu
+qac_dict_path: ./datasets/poi_qac_personalized/qac_term.dict
+tag_dict_path: ./datasets/poi_qac_personalized/poi_tag.dict
+#kv_path: ../tmp/data/poi/qac/kv
+# The directory where the dataset files are stored
+#file_list: ../tmp/x.bug
+file_list: ../tmp/data/poi/qac/recall_data/<mode>/part-0<pred_gpu>
+#file_list: ../tmp/data/poi/qac/ltr_data/<mode>/part-0<pred_gpu>
+#dataset_dir: stream_record
+# The directory where the model was written to or an absolute path to a checkpoint file
+init_pretrain_model: ../tmp/model/poi/qac/save_model_logexp/checkpoint_125000
+#init_pretrain_model: ../tmp/model/poi/qac/save_model_personal_logexp/checkpoint_125000
+#init_pretrain_model: ../tmp/model/poi/qac/save_model_wordid_logexp/checkpoint_125000
+#init_pretrain_model: ../tmp/model/poi/qac/save_model_personal_wordid_logexp/checkpoint_125000
+#init_pretrain_model: ../tmp/model/poi/qac/save_model_attention_logexp/checkpoint_125000
+#init_pretrain_model: ../tmp/model/poi/qac/save_model_attention_personal_logexp/checkpoint_125000
+#init_pretrain_model: ../tmp/model/poi/qac/save_model_attention_wordid_logexp/checkpoint_125000
+#init_pretrain_model: ../tmp/model/poi/qac/save_model_attention_personal_wordid_logexp/checkpoint_125000
+model_type: cnn_net 
+fc_dim: 64
+use_attention: False
+use_personal: False
+prefix_word_id: False
+
+#dump_vec: query
+#dump_vec: <mode>
+dump_vec: eval
+# The number of samples in each batch
+#batch_size: ${DEFAULT:eval_batch_size}
+batch_size: 1
+
+# The file type text or record
+#file_type: record
+file_type: text
+
+reader_batch: False
+
+# only exectute evaluation once
+eval_once: True
+
+#######################
+#  Dataset Configure  #
+#######################
+# The name of the dataset to load
+dataset_name: ${DEFAULT:dataset_name}
+
+# The name of the train/test split
+dataset_split_name: validation
+
+# The glob pattern for data path, `file_pattern' must contain only one "%s" 
+# which is the placeholder for split name (such as 'train', 'validation')
+file_pattern: ${DEFAULT:file_pattern}
+
+# The number of input sample for evaluation
+num_samples: ${DEFAULT:num_samples_eval}
+
+# The number of parallel readers that read data from the dataset
+num_readers: 2
+
+# The number of threads used to create the batches
+num_preprocessing_threads: 1
+
+# Number of epochs from dataset source
+num_epochs_input: 1
+
+# The name of the architecture to evaluate
+model_name: ${DEFAULT:model_name}
+
+# The dimensions of net input vectors, it is just used by svm dataset
+# which of input are sparse tensors now
+num_in_dimension: ${DEFAULT:num_in_dimension}
+
+# The dimensions of net output vector, it will be num of classes in image classify task 
+num_out_dimension: ${DEFAULT:num_out_dimension}
+
+# Directory where the results are saved to
+eval_dir: ${Train:train_dir}/checkpoint_1
+